diff --git a/README.md b/README.md index 32553e061d..06902f68a1 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,8 @@ -[![Flake8](https://img.shields.io/badge/Flake8-passed-brightgreen)](https://github.com/RuminantFarmSystems/MASM/actions/workflows/combined_format_lint_test_mypy.yml) -[![Pytest](https://img.shields.io/badge/Pytest-passed-brightgreen)](https://github.com/RuminantFarmSystems/MASM/actions/workflows/combined_format_lint_test_mypy.yml) -[![Coverage](https://img.shields.io/badge/Coverage-99%25-brightgreen)](https://github.com/RuminantFarmSystems/MASM/actions/workflows/combined_format_lint_test_mypy.yml) -[![Mypy](https://img.shields.io/badge/Mypy-1198%20errors-red)](https://github.com/RuminantFarmSystems/MASM/actions/workflows/combined_format_lint_test_mypy.yml) +[![Flake8](https://img.shields.io/badge/Flake8-failed-red)](https://github.com/RuminantFarmSystems/MASM/actions/workflows/combined_format_lint_test_mypy.yml) +[![Pytest](https://img.shields.io/badge/Pytest-failed-red)](https://github.com/RuminantFarmSystems/MASM/actions/workflows/combined_format_lint_test_mypy.yml) +[![Coverage](https://img.shields.io/badge/Coverage-%25-red)](https://github.com/RuminantFarmSystems/MASM/actions/workflows/combined_format_lint_test_mypy.yml) +[![Mypy](https://img.shields.io/badge/Mypy-1352%20errors-red)](https://github.com/RuminantFarmSystems/MASM/actions/workflows/combined_format_lint_test_mypy.yml) + # RuFaS: Ruminant Farm Systems diff --git a/RUFAS/EEE/EEE_manager.py b/RUFAS/EEE/EEE_manager.py index 4179e29838..116d601c3f 100644 --- a/RUFAS/EEE/EEE_manager.py +++ b/RUFAS/EEE/EEE_manager.py @@ -1,7 +1,9 @@ +from RUFAS.input_manager import InputManager from RUFAS.output_manager import OutputManager from .emissions import EmissionsEstimator from .energy import EnergyEstimator +from .economics.framework import EconomicFramework class EEEManager: @@ -23,3 +25,15 @@ def estimate_all() -> None: om.add_log("Energy Processing", "Starting processing of energy.", info_map) EnergyEstimator.estimate_all() om.add_log("Energy Processing", "Completed processing of energy.", info_map) + + metadata_loaded = InputManager().load_runtime_metadata("EEE_econ", eager_termination=True) + if metadata_loaded is False: + om.add_error( + "Emissions metadata load failure", + "Failed to load runtime metadata for 'EEE_econ'. Aborting emissions estimation.", + info_map, + ) + return + om.add_log("Economics Processing", "Starting processing of economics.", info_map) + EconomicFramework().run_economic_analysis() + om.add_log("Economics Processing", "Completed processing of economics.", info_map) diff --git a/RUFAS/EEE/__init__.py b/RUFAS/EEE/__init__.py index e69de29bb2..236c98de6e 100644 --- a/RUFAS/EEE/__init__.py +++ b/RUFAS/EEE/__init__.py @@ -0,0 +1,5 @@ +"""Energy, Economics, and Emissions package.""" + +from .economics import EconomicFramework + +__all__ = ["EconomicFramework"] diff --git a/RUFAS/EEE/economics.py b/RUFAS/EEE/economics.py new file mode 100644 index 0000000000..a860a0b2e7 --- /dev/null +++ b/RUFAS/EEE/economics.py @@ -0,0 +1,322 @@ +from typing import Any, Dict, Tuple +import pandas as pd +import numpy as np +from RUFAS.input_manager import InputManager +from RUFAS.output_manager import OutputManager + +# NOTE: ``dcfror.py`` is the maintained implementation described in +# `RuFaS_Economics_Module_Overview_Slides.pdf`. This duplicate remains for +# legacy compatibility and will be removed once users migrate to the dedicated +# module. + + +class DCFRORCalculator: + """ + A class to compute the Discounted Cash Flow Rate of Return (DCFROR) analysis for RuFaS. + """ + + def __init__(self): + self.im = InputManager() + self.om = OutputManager() + self.inputs = self._load_inputs() + + def _load_inputs(self) -> Dict[str, Any]: + info_map = {"class": self.__class__.__name__, "function": self._load_inputs.__name__} + try: + inputs: Dict[str, Any] = { + "cost_capital_multiple": self.im.get_data("economic_inputs.capital_costs.capital_cost_breakdown"), + "cost_operational_units": self.im.get_data("economic_inputs.cashflow_inputs.operating_units"), + "cost_operational_unit_cost": self.im.get_data("economic_inputs.cashflow_inputs.operating_unit_costs"), + "units_produced": self.im.get_data("economic_inputs.cashflow_inputs.units_produced"), + "unit_cost": self.im.get_data("economic_inputs.cashflow_inputs.unit_price"), + "loan_interest_rate": self.im.get_data("economic_inputs.cashflow_inputs.loan_interest_rate"), + "loan_term": self.im.get_data("economic_inputs.cashflow_inputs.loan_term"), + "loan_amount": self.im.get_data("economic_inputs.cashflow_inputs.loan_fraction"), + "equity_amount": self.im.get_data("economic_inputs.cashflow_inputs.equity"), + "interest_rate_construction": self.im.get_data("economic_inputs.cashflow_inputs.const_int"), + "construction_term": self.im.get_data("economic_inputs.cashflow_inputs.const_term"), + "construction_finish_pcts": self.im.get_data("economic_inputs.cashflow_inputs.const_rate_i"), + "tax_rate": self.im.get_data("economic_inputs.cashflow_inputs.tax_rate"), + "internal_rate_of_return": self.im.get_data( + "economic_inputs.cashflow_inputs.target_internal_rate_of_return" + ), + "project_term": self.im.get_data("economic_inputs.cashflow_inputs.project_term"), + "depreciation_rate": np.array(self.im.get_data("economic_inputs.cashflow_inputs.depreciation_i")), + "enable": self.im.get_data("economic_inputs.cashflow_inputs.enable_dcfror"), + "tax_credit_used": self.im.get_data("economic_inputs.cashflow_inputs.tax_credit_used"), + "tax_credit_revenue": self.im.get_data("economic_inputs.cashflow_inputs.tax_credit_revenue"), + } + inputs["cost_capital_multiple"].set_index("Item", inplace=True) + return inputs + except KeyError as e: + self.om.add_error("MissingInputKey", f"Missing input key: {str(e)}", info_map) + raise + + def calculate(self) -> None: + """ + Main function to execute DCFROR calculation and export results. + """ + info_map = {"class": self.__class__.__name__, "function": self.calculate.__name__} + + if not self.inputs.get("enable", False): + self.om.add_log("DCFROR calculation skipped due to configuration flag.", info_map) + return + + self.om.add_log("Starting DCFROR calculation", info_map) + + try: + cost_inputs = self._prepare_costs() + financing = self._prepare_financing() + cash_flows, cash_flow_df = self._compute_cash_flows(**cost_inputs, **financing) + self._export_results(cash_flows, cash_flow_df, financing["internal_rate_of_return"]) + except Exception as e: + self.om.add_error("DCFRORCalculationFailed", f"DCFROR calculation failed: {str(e)}", info_map) + raise + + def _prepare_costs(self) -> Dict[str, Any]: + """ + Calculate total capital cost including construction interest, and extract revenue and cost arrays. + + Returns + ------- + dict + Dictionary containing project term, capital cost, revenue array, and operating cost array. + """ + info_map = {"class": self.__class__.__name__, "function": self._prepare_costs.__name__} + + cap_breakdown = self.inputs["cost_capital_multiple"] + if cap_breakdown.empty: + self.om.add_warning("EmptyCapitalCostBreakdown", "Capital cost breakdown is empty", info_map) + + base_cost = cap_breakdown["Cost"].sum() + if base_cost == 0: + self.om.add_warning("ZeroCapitalCost", "Total capital cost is zero", info_map) + + interest_rate = self.inputs["interest_rate_construction"] + construction_years = self.inputs["construction_term"] + finish_percentages = self.inputs["construction_finish_pcts"] + + annual_expenditures = [base_cost * finish_percentages[i] for i in range(construction_years)] + interest_charges = [sum(annual_expenditures[: i + 1]) * interest_rate for i in range(construction_years)] + capital_cost = base_cost + sum(interest_charges) + + operating_costs = (self.inputs["cost_operational_units"] * self.inputs["cost_operational_unit_cost"]).sum( + axis=0 + ) + revenue = (self.inputs["units_produced"] * self.inputs["unit_cost"]).sum(axis=0) + + project_term = self.inputs["project_term"] + if project_term <= 0: + self.om.add_error("InvalidProjectTerm", "Project term must be positive.", info_map) + raise ValueError("Project term must be positive.") + + return { + "project_term": project_term, + "capital_cost": capital_cost, + "revenue": revenue[:project_term], + "operating_costs": operating_costs[:project_term], + } + + def _prepare_financing(self) -> Dict[str, Any]: + """ + Extract financing parameters for depreciation, loan, and tax. + + Returns + ------- + dict + Dictionary of financing parameters. + """ + input_dict = self.inputs + depreciation_rate = input_dict["depreciation_rate"] + depreciation_rate = depreciation_rate[~np.isnan(depreciation_rate)] + return { + "depreciation_rate": depreciation_rate, + "dep_term": len(depreciation_rate), + "loan_interest_rate": input_dict["loan_interest_rate"], + "loan_term": input_dict["loan_term"], + "loan_amount": input_dict["loan_amount"], + "equity_amount": input_dict["equity_amount"], + "tax_rate": input_dict["tax_rate"], + "internal_rate_of_return": input_dict["internal_rate_of_return"], + } + + def _compute_cash_flows( + self, + project_term: int, + capital_cost: float, + depreciation_rate: np.ndarray, + dep_term: int, + loan_interest_rate: float, + loan_term: int, + loan_amount: float, + equity_amount: float, + revenue: np.ndarray, + operating_costs: np.ndarray, + tax_rate: float, + ) -> Tuple[np.ndarray, pd.DataFrame]: + """ + Compute annual cash flows and build a DataFrame with financial details. + + Returns + ------- + cash_flows : np.ndarray + Net cash flows over the project term. + cash_flow_df : pd.DataFrame + DataFrame containing detailed breakdown for each year. + """ + operating_expense = np.zeros(project_term) + depreciation = np.zeros(project_term) + debt_payment = np.zeros(project_term) + interest_payment = np.zeros(project_term) + earnings_before_tax = np.zeros(project_term) + tax_paid = np.zeros(project_term) + net_income = np.zeros(project_term) + net_cash_flow = np.zeros(project_term) + + annual_depreciation = np.zeros(project_term) + for i in range(min(dep_term, project_term)): + annual_depreciation[i] = depreciation_rate[i] * capital_cost + + debt_total = loan_amount * capital_cost + annual_debt_payment = np.pmt(loan_interest_rate, loan_term, -debt_total) if loan_term > 0 else 0 + + carry_forward_limit = 0.8 + losses_forward = 0.0 + + for t in range(project_term): + op_exp = operating_costs[t] if t < len(operating_costs) else 0 + depr = annual_depreciation[t] if t < len(annual_depreciation) else 0 + dp = annual_debt_payment if t < loan_term else 0 + ip = loan_interest_rate * (debt_total - np.sum(debt_payment[:t])) if t < loan_term else 0 + + operating_expense[t] = op_exp + depreciation[t] = depr + debt_payment[t] = dp + interest_payment[t] = ip + + earnings_before_tax[t] = revenue[t] - op_exp - depr - dp - ip + + if earnings_before_tax[t] <= 0: + losses_forward += earnings_before_tax[t] + taxable_income = 0 + else: + usable_loss = min(-losses_forward * carry_forward_limit, earnings_before_tax[t]) + taxable_income = earnings_before_tax[t] - usable_loss + losses_forward += -usable_loss + + tax_paid[t] = tax_rate * taxable_income + net_income[t] = earnings_before_tax[t] - tax_paid[t] + net_cash_flow[t] = net_income[t] + depr + + cash_flow_df = pd.DataFrame( + { + "Year": np.arange(1, project_term + 1), + "Revenue": revenue, + "Operating Cost": operating_expense, + "Depreciation": depreciation, + "Debt Payment": debt_payment, + "Interest": interest_payment, + "EBT": earnings_before_tax, + "Taxes": tax_paid, + "Net Income": net_income, + "Cash Flow": net_cash_flow, + } + ) + + return net_cash_flow, cash_flow_df + + def _export_results(self, CF: np.ndarray, cash_flow_df: pd.DataFrame, internal_rate_of_return: float) -> None: + """ + Export results to the OutputManager. + + Parameters + ---------- + CF : np.ndarray + Net cash flows. + cash_flow_df : pd.DataFrame + Detailed cash flow breakdown. + internal_rate_of_return : float + Internal Rate of Return. + """ + info_map = {"class": self.__class__.__name__, "function": self.calculate.__name__} + npv = np.npv(internal_rate_of_return, CF) + self.om.add_variable("econ_dcfror_npv", npv, info_map) + self.om.add_variable("econ_dcfror_cash_flow_summary", cash_flow_df.to_dict(orient="list"), info_map) + self.om.add_log("DCFROR calculation completed successfully.", info_map) + + def goal_seek( + self, + variable_name: str, + target_npv: float = 0.0, + bounds: Tuple[float, float] = (0.01, 100.0), + tol: float = 1e-6, + max_iter: int = 100, + ) -> float: + """ + Iteratively adjusts the specified input variable to achieve the target NPV using binary search. + + Parameters + ---------- + variable_name : str + The name of the input variable to adjust. + target_npv : float, optional + The desired net present value (NPV), by default 0.0. + bounds : Tuple[float, float], optional + Lower and upper bounds for the multiplier applied to the variable, by default (0.01, 100.0). + tol : float, optional + Tolerance for convergence, by default 1e-6. + max_iter : int, optional + Maximum number of iterations to try, by default 100. + + Returns + ------- + float + The multiplier that achieves the target NPV within tolerance. Returns NaN if not found. + """ + info_map = {"class": self.__class__.__name__, "function": self.goal_seek.__name__} + low, high = bounds + + for _ in range(max_iter): + mid = (low + high) / 2 + override_inputs = {variable_name: self.inputs[variable_name] * mid} + self.calculate(override_inputs=override_inputs) + npv_result = self.om.filter_variables_pool( + { + "name": "NPV Retrieval", + "description": "Retrieve the latest DCFROR NPV for goal seek.", + "filters": [f"{self.__class__.__name__}.{self.calculate.__name__}.econ_dcfror_npv"], + } + ) + npv = npv_result.get( + f"{self.__class__.__name__}.{self.calculate.__name__}.econ_dcfror_npv", + None, + ) + + if npv is None: + self.om.add_error( + "NPVNotFound", + "NPV not found after DCFROR calculation. Check calculation logic.", + info_map, + ) + return float("nan") + + if abs(npv - target_npv) < tol: + return mid + elif npv > target_npv: + high = mid + else: + low = mid + + self.om.add_error( + "GoalSeekFailed", "Goal seek did not converge within the maximum number of iterations.", info_map + ) + return float("nan") + + +# Hook: Call DCFROR in EEE_manager.py +if __name__ == "__main__": + try: + dcfror = DCFRORCalculator() + dcfror.calculate() + except Exception as e: + OutputManager().add_error(f"DCFROR main execution failed: {str(e)}", {"script": "EEE_manager.py"}) diff --git a/RUFAS/EEE/economics/__init__.py b/RUFAS/EEE/economics/__init__.py new file mode 100644 index 0000000000..a134526e73 --- /dev/null +++ b/RUFAS/EEE/economics/__init__.py @@ -0,0 +1,22 @@ +"""Economics subpackage for RuFaS. + +This package hosts financial analysis tools such as the +Discounted Cash Flow Rate of Return (DCFROR) calculator +and a Partial Budget Analysis (PBA) routine. These are +orchestrated via a Flexible Economic Framework that +selects the appropriate analysis based on input data. +""" + +from .framework import EconomicFramework +from .metrics import EconomicMetrics +from .digester_costs import DigesterCostCalculator +from .equations import EconomicEquations +from .dcfror import DCFRORCalculator + +__all__ = [ + "EconomicFramework", + "EconomicMetrics", + "DigesterCostCalculator", + "EconomicEquations", + "DCFRORCalculator", +] diff --git a/RUFAS/EEE/economics/dcfror.py b/RUFAS/EEE/economics/dcfror.py new file mode 100644 index 0000000000..bdc7223037 --- /dev/null +++ b/RUFAS/EEE/economics/dcfror.py @@ -0,0 +1,556 @@ +from __future__ import annotations + +from typing import Any, Dict, Iterable, Tuple + +import numpy as np +import pandas as pd + +from RUFAS.input_manager import InputManager +from RUFAS.output_manager import OutputManager +from RUFAS.units import MeasurementUnits + +from .equations import EconomicEquations +from .metrics import EconomicMetrics + + +class DCFRORCalculator: + """Compute the Discounted Cash Flow Rate of Return (DCFROR) analysis.""" + + def __init__(self) -> None: + self.im = InputManager() + self.om = OutputManager() + self.inputs = self._load_inputs() + + def _load_inputs(self) -> Dict[str, Any]: + info_map = {"class": self.__class__.__name__, "function": self._load_inputs.__name__} + try: + + def _get_input(path: str) -> Any: + if hasattr(self.im, "check_property_exists_in_pool"): + if not self.im.check_property_exists_in_pool(path): + raise KeyError(f"Missing required input: {path}") + + value = self.im.get_data(path) + if value is None: + raise KeyError(f"Missing required input: {path}") + return value + + # Retrieve input data + loan_term = _get_input("economic_inputs.cashflow_inputs.loan_term") + project_term = _get_input("economic_inputs.cashflow_inputs.project_term") + if loan_term > project_term: + # Loan duration should not exceed the project lifetime + self.om.add_warning( + "LoanTermAdjusted", + "Loan term longer than project term; using project term instead.", + info_map, + ) + loan_term = project_term + + cost_capital_multiple = _get_input("economic_inputs.capital_costs.capital_cost_breakdown") + + inputs: Dict[str, Any] = { + "cost_capital_multiple": cost_capital_multiple, + "cost_operational_units": _get_input("economic_inputs.cashflow_inputs.operating_units"), + "cost_operational_unit_cost": _get_input("economic_inputs.cashflow_inputs.operating_unit_costs"), + "units_produced": _get_input("economic_inputs.cashflow_inputs.units_produced"), + "unit_cost": _get_input("economic_inputs.cashflow_inputs.unit_price"), + "loan_interest_rate": _get_input("economic_inputs.cashflow_inputs.loan_interest_rate"), + # Loan term is capped to the project term above per the + # guidance in `Documentation of Economic Data and Analytical + # Methods (2).pdf`. + "loan_term": loan_term, + "loan_amount": _get_input("economic_inputs.cashflow_inputs.loan_fraction"), + "equity_amount": _get_input("economic_inputs.cashflow_inputs.equity"), + "interest_rate_construction": _get_input("economic_inputs.cashflow_inputs.const_int"), + "construction_term": _get_input("economic_inputs.cashflow_inputs.const_term"), + "construction_finish_pcts": _get_input("economic_inputs.cashflow_inputs.const_rate_i"), + "tax_rate": _get_input("economic_inputs.cashflow_inputs.tax_rate"), + "internal_rate_of_return": _get_input("economic_inputs.cashflow_inputs.target_internal_rate_of_return"), + "project_term": project_term, + "depreciation_rate": np.array(_get_input("economic_inputs.cashflow_inputs.depreciation_i")), + "enable": _get_input("economic_inputs.cashflow_inputs.enable_dcfror"), + "tax_credit_used": _get_input("economic_inputs.cashflow_inputs.tax_credit_used"), + "enable_goal_seek": _get_input("economic_inputs.cashflow_inputs.enable_goal_seek"), + "goal_seek_target_npv": _get_input("economic_inputs.cashflow_inputs.goal_seek_target_npv"), + "goal_seek_bounds": _get_input("economic_inputs.cashflow_inputs.goal_seek_bounds"), + "goal_seek_unit_price_multiplier": _get_input( + "economic_inputs.cashflow_inputs.goal_seek_unit_price_multiplier" + ), + "goal_seek_fixed_variables": _get_input("economic_inputs.cashflow_inputs.goal_seek_fixed_variables"), + } + return inputs + except KeyError as e: + self.om.add_error("MissingInputKey", f"Missing input key: {str(e)}", info_map) + raise + + def calculate(self, override_inputs: Dict[str, Any] | None = None) -> None: + """Run the DCFROR cash-flow model and export summary outputs. + + Parameters + ---------- + override_inputs : dict[str, Any] | None, optional + Temporary input overrides applied only for this call. This is used + by :meth:`goal_seek` so each trial evaluates a fresh DCFROR run + with the candidate value. + """ + + info_map = {"class": self.__class__.__name__, "function": self.calculate.__name__} + + if not self.inputs.get("enable", False): + self.om.add_log( + "DCFROR calculation", + "DCFROR calculation skipped due to configuration flag.", + info_map, + ) + return + + self.om.add_log("DCFROR calculation", "Starting DCFROR calculation", info_map) + + try: + active_inputs = dict(self.inputs) + if override_inputs: + active_inputs.update(override_inputs) + + cost_inputs = self._prepare_costs(active_inputs) + financing = self._prepare_financing(active_inputs, cost_inputs["project_term"]) + cash_flows, cash_flow_df = self._compute_cash_flows( + **cost_inputs, + **financing, + ) + self._export_results( + cash_flows, + cash_flow_df, + financing["internal_rate_of_return"], + cost_inputs["capital_cost"], + cost_inputs["revenue"], + cost_inputs["operating_costs"], + ) + except Exception as exc: + self.om.add_error("DCFRORCalculationFailed", f"DCFROR calculation failed: {exc}", info_map) + raise + + def _prepare_costs(self, input_dict: Dict[str, Any]) -> Dict[str, Any]: + """Build capital, operating-cost, and revenue schedules from inputs.""" + + info_map = {"class": self.__class__.__name__, "function": self._prepare_costs.__name__} + + cap_breakdown = input_dict["cost_capital_multiple"] + if isinstance(cap_breakdown, pd.DataFrame) and cap_breakdown.empty: + self.om.add_warning("EmptyCapitalCostBreakdown", "Capital cost breakdown is empty", info_map) + + base_cost = sum(d["Cost"] for d in cap_breakdown) + if base_cost == 0: + self.om.add_warning("ZeroCapitalCost", "Total capital cost is zero", info_map) + + interest_rate = input_dict["interest_rate_construction"] + # The construction-period financing uses the dedicated + # ``interest_rate_construction`` input (IRCon in the documentation). + construction_years = int(input_dict["construction_term"]) + finish_percentages = input_dict["construction_finish_pcts"] + + def _to_numpy(values: Any) -> np.ndarray: + if isinstance(values, pd.DataFrame): + return values.to_numpy(dtype=float) + return np.asarray(values, dtype=float) + + operating_units = _to_numpy(input_dict["cost_operational_units"]) + operating_unit_costs = _to_numpy(input_dict["cost_operational_unit_cost"]) + revenue_units = _to_numpy(input_dict["units_produced"]) + unit_price_multiplier = float(input_dict.get("goal_seek_unit_price_multiplier", 1.0)) + if unit_price_multiplier < 0: + self.om.add_warning( + "GoalSeekUnitPriceMultiplierAdjusted", + "goal_seek_unit_price_multiplier was negative; clamped to 0.0.", + info_map, + ) + unit_price_multiplier = 0.0 + revenue_prices = _to_numpy(input_dict["unit_cost"]) * unit_price_multiplier + + # Interest accrued during construction should be accounted for in cash + # flow calculations, not added to the depreciable capital cost. + # Construction interest is accounted for later in the cash-flow stage + # (Step 3 in `Documentation of Economic Data and Analytical Methods + # (2).pdf`), so it does not increase the depreciable capital cost. + capital_cost = base_cost + operating_costs = np.asarray((operating_units * operating_unit_costs).sum(axis=0), dtype=float) + revenue = np.asarray((revenue_units * revenue_prices).sum(axis=0), dtype=float) + + project_term = int(input_dict["project_term"]) + if project_term <= 0: + self.om.add_error("InvalidProjectTerm", "Project term must be positive.", info_map) + raise ValueError("Project term must be positive.") + + return { + "project_term": project_term, + "capital_cost": capital_cost, + "construction_term": construction_years, + "construction_rates": list(np.asarray(finish_percentages, dtype=float)), + "revenue": revenue[:project_term], + "operating_costs": operating_costs[:project_term], + } + + def _prepare_financing(self, input_dict: Dict[str, Any], project_term: int) -> Dict[str, Any]: + """Normalise financing inputs and enforce valid borrowing shares.""" + depreciation_rate = input_dict["depreciation_rate"] + depreciation_rate = depreciation_rate[~np.isnan(depreciation_rate)] + + equity_share_input = input_dict.get("equity_amount") + financed_share_input = input_dict.get("loan_amount") + financed_share = float(financed_share_input) if financed_share_input is not None else None + equity_share = float(equity_share_input) if equity_share_input is not None else None + + if financed_share is None and equity_share is not None: + financed_share = max(0.0, min(1.0, 1.0 - equity_share)) + if equity_share is None and financed_share is not None: + equity_share = max(0.0, min(1.0, 1.0 - financed_share)) + + if financed_share is None: + financed_share = 1.0 + if equity_share is None: + equity_share = max(0.0, min(1.0, 1.0 - financed_share)) + + loan_term = int(input_dict["loan_term"]) + if loan_term > project_term: + loan_term = project_term + + return { + "depreciation_rate": depreciation_rate, + "dep_term": len(depreciation_rate), + "loan_interest_rate": float(input_dict["loan_interest_rate"]), + "loan_term": loan_term, + "financed_share": financed_share, + "equity_share": equity_share, + "construction_interest_rate": float(input_dict["interest_rate_construction"]), + "tax_rate": float(input_dict["tax_rate"]), + "internal_rate_of_return": float(input_dict["internal_rate_of_return"]), + } + + def _compute_cash_flows( + self, + project_term: int, + capital_cost: float, + construction_term: int, + construction_rates: Iterable[float], + depreciation_rate: np.ndarray, + dep_term: int, + loan_interest_rate: float, + loan_term: int, + financed_share: float, + equity_share: float, + construction_interest_rate: float, + revenue: np.ndarray, + operating_costs: np.ndarray, + tax_rate: float, + internal_rate_of_return: float, + ) -> Tuple[np.ndarray, pd.DataFrame]: + """Build the annual cash flow table using Equations 7–26.""" + + years = EconomicEquations.construct_timeline(construction_term, loan_term, project_term) + n_years = years.size + construction_mask = years <= 0 + operational_mask = years >= 1 + EconomicEquations.discount_factors = np.array( + [EconomicEquations.discount_factor(internal_rate_of_return, year) for year in years], + dtype=float, + ) + + n_construction = int(construction_mask.sum()) + rates = np.asarray(list(construction_rates), dtype=float) + if n_construction: + if rates.size < n_construction: + rates = np.pad(rates, (0, n_construction - rates.size)) + rates = rates[:n_construction] + if not np.allclose(rates.sum(), 1.0) and rates.sum() > 0: + rates = rates / rates.sum() + capital_schedule = EconomicEquations.annual_capital_spent(capital_cost, rates) + equity_schedule = EconomicEquations.equity_contribution(capital_cost, rates, equity_share) + loan_draw_schedule = capital_schedule * financed_share + EconomicEquations.construction_interest_cost = EconomicEquations.construction_interest( + loan_draw_schedule, construction_interest_rate + ) + capital_interest_pv = np.zeros(n_years, dtype=float) + capital_interest_pv[construction_mask] = EconomicEquations.npv_capital_plus_interest( + equity_schedule, + EconomicEquations.construction_interest_cost, + internal_rate_of_return, + years[construction_mask], + ) + cumulative_draw = np.cumsum(loan_draw_schedule) + else: + capital_schedule = np.zeros(0, dtype=float) + equity_schedule = np.zeros(0, dtype=float) + loan_draw_schedule = np.zeros(0, dtype=float) + EconomicEquations.construction_interest_cost = np.zeros(0, dtype=float) + capital_interest_pv = np.zeros(n_years, dtype=float) + cumulative_draw = np.zeros(0, dtype=float) + + annual_depreciation = EconomicEquations.depreciation_schedule(capital_cost, depreciation_rate) + if dep_term and dep_term < annual_depreciation.size: + annual_depreciation = annual_depreciation[:dep_term] + + op_years = int(operational_mask.sum()) + revenue_schedule = np.zeros(op_years, dtype=float) + operating_schedule = np.zeros(op_years, dtype=float) + EconomicEquations.depreciation_schedule_op = np.zeros(op_years, dtype=float) + revenue_schedule[: min(len(revenue), op_years)] = revenue[:op_years] + operating_schedule[: min(len(operating_costs), op_years)] = operating_costs[:op_years] + EconomicEquations.depreciation_schedule_op[: min(len(annual_depreciation), op_years)] = annual_depreciation[ + :op_years + ] + + loan_payment = EconomicEquations.annual_loan_payment( + capital_cost, loan_interest_rate, loan_term, financed_share + ) + principal_balance = capital_cost * financed_share + + capital_column = np.zeros(n_years, dtype=float) + loan_draw_column = np.zeros(n_years, dtype=float) + EconomicEquations.construction_interest_column = np.zeros(n_years, dtype=float) + revenue_column = np.zeros(n_years, dtype=float) + operating_column = np.zeros(n_years, dtype=float) + depreciation_column = np.zeros(n_years, dtype=float) + loan_payment_column = np.zeros(n_years, dtype=float) + interest_column = np.zeros(n_years, dtype=float) + EconomicEquations.net_revenue_column = np.zeros(n_years, dtype=float) + loss_forward_column = np.zeros(n_years, dtype=float) + EconomicEquations.taxable_income_column = np.zeros(n_years, dtype=float) + EconomicEquations.income_tax_column = np.zeros(n_years, dtype=float) + cash_income_column = np.zeros(n_years, dtype=float) + EconomicEquations.present_value_column = np.zeros(n_years, dtype=float) + loan_balance_column = np.zeros(n_years, dtype=float) + + cash_flows = np.zeros(n_years, dtype=float) + if n_construction: + construction_indices = np.where(construction_mask)[0] + capital_column[construction_indices] = capital_schedule + loan_draw_column[construction_indices] = loan_draw_schedule + EconomicEquations.construction_interest_column[construction_indices] = ( + EconomicEquations.construction_interest_cost + ) + cash_flows[construction_indices] = -(equity_schedule + EconomicEquations.construction_interest_cost) + loan_balance_column[construction_indices] = cumulative_draw + + prev_taxable = 0.0 + prev_loss = 0.0 + op_indices = np.where(operational_mask)[0] + for idx, year_index in enumerate(op_indices): + revenue_y = revenue_schedule[idx] + operating_y = operating_schedule[idx] + depreciation_y = EconomicEquations.depreciation_schedule_op[idx] + loan_pay_y = loan_payment if idx < loan_term else 0.0 + interest_y = ( + EconomicEquations.interest_payment(principal_balance, loan_interest_rate) + if financed_share > 0 and idx < loan_term + else 0.0 + ) + + if idx < loan_term: + principal_balance = max( + 0.0, + EconomicEquations.principal_after_payment(principal_balance, loan_pay_y, interest_y), + ) + + net_rev_y = EconomicEquations.net_revenue(revenue_y, operating_y, interest_y, depreciation_y) + carried_loss = EconomicEquations.loss_carry_forward(prev_taxable, prev_loss) + used_loss = EconomicEquations.max_loss_utilized(net_rev_y, carried_loss) + taxable_y = EconomicEquations.taxable_income(net_rev_y, used_loss) + tax_y = EconomicEquations.income_tax(taxable_y, tax_rate) + cash_income_y = EconomicEquations.annual_cash_income(revenue_y, operating_y, loan_pay_y, tax_y) + pv_y = EconomicEquations.present_value(cash_income_y, EconomicEquations.discount_factors[year_index]) + + revenue_column[year_index] = revenue_y + operating_column[year_index] = operating_y + depreciation_column[year_index] = depreciation_y + loan_payment_column[year_index] = loan_pay_y + interest_column[year_index] = interest_y + EconomicEquations.net_revenue_column[year_index] = net_rev_y + loss_forward_column[year_index] = carried_loss + EconomicEquations.taxable_income_column[year_index] = taxable_y + EconomicEquations.income_tax_column[year_index] = tax_y + cash_income_column[year_index] = cash_income_y + EconomicEquations.present_value_column[year_index] = pv_y + loan_balance_column[year_index] = principal_balance + cash_flows[year_index] = cash_income_y + prev_taxable = taxable_y + prev_loss = carried_loss + used_loss # Econmic Equation 24 + + cash_flow_df = pd.DataFrame( + { + "Year": years, + "DiscountFactor": EconomicEquations.discount_factors, + "Capital": capital_column, + "LoanPrincipalDraw": loan_draw_column, + "ConstructionInterest": EconomicEquations.construction_interest_column, + "Revenue": revenue_column, + "OperatingCost": operating_column, + "LoanPayment": loan_payment_column, + "InterestPayment": interest_column, + "Depreciation": depreciation_column, + "NetRevenue": EconomicEquations.net_revenue_column, + "LossesForward": loss_forward_column, + "TaxableIncome": EconomicEquations.taxable_income_column, + "IncomeTax": EconomicEquations.income_tax_column, + "CashIncome": cash_income_column, + "PresentValue": EconomicEquations.present_value_column, + "NPVCapitalPlusInterest": capital_interest_pv, + "LoanBalance": loan_balance_column, + } + ) + + return cash_flows, cash_flow_df + + def _export_results( + self, + CF: np.ndarray, + cash_flow_df: pd.DataFrame, + internal_rate_of_return: float, + capital_cost: float, + revenue: np.ndarray, + operating_costs: np.ndarray, + ) -> None: + """Summarise and export the DCFROR results.""" + + info_map = {"class": self.__class__.__name__, "function": self.calculate.__name__} + + construction_mask = cash_flow_df["Year"] <= 0 + operational_mask = cash_flow_df["Year"] >= 1 + npv = EconomicEquations.net_present_value( + cash_flow_df.loc[operational_mask, "PresentValue"].to_numpy(), + cash_flow_df.loc[construction_mask, "NPVCapitalPlusInterest"].to_numpy(), + ) + + positive_benefits = float(CF[CF > 0].sum()) + investment_costs = float(-CF[CF < 0].sum()) + roi = EconomicMetrics.calculate_roi(positive_benefits, investment_costs) + payback = EconomicMetrics.calculate_payback_period(CF) + net_cash_flow = EconomicMetrics.calculate_net_annual_cash_flow(revenue, operating_costs) + mpsp = EconomicMetrics.calculate_mpsp(capital_cost + operating_costs.sum(), revenue.sum()) + + self.om.add_variable("econ_dcfror_npv", npv, {**info_map, "units": MeasurementUnits.DOLLARS}) + self.om.add_variable("econ_dcfror_roi", roi, {**info_map, "units": MeasurementUnits.UNITLESS}) + self.om.add_variable( + "econ_dcfror_payback_period", + payback, + {**info_map, "units": MeasurementUnits.SIMULATION_YEAR}, + ) + self.om.add_variable( + "econ_dcfror_net_cash_flow", + net_cash_flow.tolist(), + {**info_map, "units": MeasurementUnits.DOLLARS}, + ) + self.om.add_variable("econ_dcfror_mpsp", mpsp, {**info_map, "units": MeasurementUnits.DOLLARS}) + self.om.add_variable( + "econ_dcfror_cash_flow_summary", + cash_flow_df.to_dict(orient="list"), + {**info_map, "units": MeasurementUnits.UNITLESS}, + ) + self.om.add_log( + "DCFROR calculation", + "DCFROR calculation completed successfully.", + info_map, + ) + + def goal_seek( + self, + variable_name: str, + target_npv: float = 0.0, + bounds: Tuple[float, float] = (0.01, 100.0), + tol: float = 1e-6, + max_iter: int = 100, + ) -> float: + """Solve for one DCFROR input value that drives NPV toward a target. + + The solver performs a binary search over a *multiplier* (`mid`) and + evaluates ``candidate_value = baseline_value * mid``. It then reruns + :meth:`calculate` with that candidate override and reads the most + recent ``econ_dcfror_npv`` output. + + Parameters + ---------- + variable_name : str + Name of the scalar input in ``self.inputs`` to perturb. + target_npv : float, optional + Desired NPV setpoint. + bounds : tuple[float, float], optional + Lower/upper bounds for the multiplier search interval. + tol : float, optional + Absolute tolerance on ``abs(npv - target_npv)``. + max_iter : int, optional + Maximum bisection iterations. + """ + + info_map = {"class": self.__class__.__name__, "function": self.goal_seek.__name__} + if variable_name not in self.inputs: + self.om.add_error("GoalSeekVariableMissing", f"Unknown variable: {variable_name}", info_map) + return float("nan") + if max_iter <= 0: + self.om.add_error("GoalSeekInvalidMaxIter", "max_iter must be > 0.", info_map) + return float("nan") + + low, high = map(float, bounds) + if not np.isfinite(low) or not np.isfinite(high) or low >= high: + self.om.add_error("GoalSeekInvalidBounds", f"Invalid bounds: {bounds}", info_map) + return float("nan") + + baseline_value = float(self.inputs[variable_name]) + if not np.isfinite(baseline_value): + self.om.add_error("GoalSeekInvalidBaseline", "Baseline goal-seek value must be finite.", info_map) + return float("nan") + + project_term = int(self.inputs.get("project_term", 1)) + + def _bounded_candidate(multiplier: float) -> float: + candidate = baseline_value * multiplier + if variable_name == "loan_term": + return float(max(1, min(project_term, int(round(candidate))))) + if variable_name == "internal_rate_of_return": + return float(max(-0.99, candidate)) + if variable_name == "goal_seek_unit_price_multiplier": + return float(max(0.0, candidate)) + return float(candidate) + + for _ in range(max_iter): + mid = (low + high) / 2 + override_inputs = {variable_name: _bounded_candidate(mid)} + self.calculate(override_inputs=override_inputs) + npv_result = self.om.filter_variables_pool( + { + "name": "NPV Retrieval", + "description": "Retrieve the latest DCFROR NPV for goal seek; results are chronological so slice to newest entry.", + "filters": [f"{self.__class__.__name__}.{self.calculate.__name__}.econ_dcfror_npv"], + "slice_start": -1, + } + ) + npv_key = f"{self.__class__.__name__}.{self.calculate.__name__}.econ_dcfror_npv" + npv_record = npv_result.get(npv_key, None) + npv_values = npv_record.get("values") if isinstance(npv_record, dict) else None + npv = float(npv_values[0]) if npv_values else None + + if npv is None: + self.om.add_error( + "NPVNotFound", "NPV not found after DCFROR calculation. Check calculation logic.", info_map + ) + return float("nan") + + if abs(npv - target_npv) < tol: + self.inputs[variable_name] = override_inputs[variable_name] + return mid + if npv > target_npv: + high = mid + else: + low = mid + + self.inputs[variable_name] = _bounded_candidate((low + high) / 2) + self.om.add_error( + "GoalSeekFailed", "Goal seek did not converge within the maximum number of iterations.", info_map + ) + return float("nan") + + +if __name__ == "__main__": + try: + calculator = DCFRORCalculator() + calculator.calculate() + except Exception as exc: + OutputManager().add_error(f"DCFROR main execution failed: {exc}", {"script": "EEE_manager.py"}) diff --git a/RUFAS/EEE/economics/digester_costs.py b/RUFAS/EEE/economics/digester_costs.py new file mode 100644 index 0000000000..981cd71bec --- /dev/null +++ b/RUFAS/EEE/economics/digester_costs.py @@ -0,0 +1,275 @@ +"""Specialized cost calculations for manure digesters and equipment.""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import Dict, Mapping + + +@dataclass(frozen=True) +class LinearCostEquation: + """Simple linear cost curve of the form ``slope * cows + intercept``.""" + + slope: float + intercept: float = 0.0 + + def evaluate(self, cows: float) -> float: + """Return the cost for a given number of cows.""" + return self.slope * cows + self.intercept + + +@dataclass(frozen=True) +class DigesterSystemCostProfile: + """Encapsulate CAPEX/OPEX equations for a digester configuration.""" + + capex: LinearCostEquation + labor: LinearCostEquation + energy: LinearCostEquation + repairs: LinearCostEquation + useful_life_years: int + salvage_value_fraction: float + biogas_yield_ft3_per_cow_day: float + methane_content_fraction: float + + def capital_cost(self, cows: float) -> float: + """Return the system CAPEX for the provided herd size.""" + + return self.capex.evaluate(cows) + + def annual_operating_costs(self, cows: float) -> Dict[str, float]: + """Return the labor, energy, and repair costs for the herd size.""" + + return { + "labor": self.labor.evaluate(cows), + "energy": self.energy.evaluate(cows), + "repairs": self.repairs.evaluate(cows), + } + + +class DigesterCostCalculator: + """Collection of digester-related economic calculations.""" + + # Equation 1 from the economics documentation + # A_FC_ij = EXP(alpha + R1*ln(A_j) + R2*ln(V_j) + psi1*F_j + psi2*G_j + psi3*C_j + psi4*S_j + epsilon_j) + + @staticmethod + def calculate_digester_capital_cost( + animal_units: float | None = None, + digester_volume: float | None = None, + farm_type_flag: float | None = None, + below_ground_flag: float | None = None, + concrete_flag: float | None = None, + steel_flag: float | None = None, + *, + alpha: float = 10.4, + r1: float = -0.413, + r2: float = -0.147, + psi1: float = -1.085, + psi2: float = -0.582, + psi3: float = 0.519, + psi4: float = -0.007, + epsilon: float = 0.35, + area: float | None = None, + volume: float | None = None, + f_j: float | None = None, + g_j: float | None = None, + c_j: float | None = None, + s_j: float | None = None, + ) -> float: + """Return the estimated digester capital cost using Equation 1.""" + + if animal_units is None: + animal_units = float(area) if area is not None else None + if digester_volume is None: + digester_volume = float(volume) if volume is not None else None + if animal_units is None or digester_volume is None: + raise ValueError("animal_units and digester_volume are required inputs") + if animal_units <= 0 or digester_volume <= 0: + raise ValueError("animal_units and digester_volume must be positive") + + if farm_type_flag is None: + farm_type_flag = float(f_j) if f_j is not None else 1.0 + if below_ground_flag is None: + below_ground_flag = float(g_j) if g_j is not None else 1.0 + if concrete_flag is None: + concrete_flag = float(c_j) if c_j is not None else 1.0 + if steel_flag is None: + steel_flag = float(s_j) if s_j is not None else 0.0 + + return math.exp( + alpha + + r1 * math.log(animal_units) + + r2 * math.log(digester_volume) + + psi1 * farm_type_flag + + psi2 * below_ground_flag + + psi3 * concrete_flag + + psi4 * steel_flag + + epsilon + ) + + # Equation 2 - Capital Recovery Factor + + @staticmethod + def capital_recovery_factor(rate: float, periods: int) -> float: + """Compute the capital recovery factor (Equation 2).""" + if periods <= 0: + raise ValueError("periods must be positive") + if rate == 0: + return 1 / periods + numerator = rate * (1 + rate) ** periods + denominator = (1 + rate) ** periods - 1 + return numerator / denominator + + # Equation 5 - Generalized equipment scaling + + @staticmethod + def scale_installed_cost(base_cost: float, volume: float, base_volume: float, install_factor: float) -> float: + """Scale equipment cost to a new volume using Equation 5.""" + if base_volume <= 0: + raise ValueError("base_volume must be positive") + ratio = volume / base_volume + return base_cost * (ratio**install_factor) + + # Equation 3 - Digester CAPEX derived from annual fixed cost and CRF + + @staticmethod + def calculate_digester_capex(afc_ij: float, crf: float) -> float: + """Return the digester capital expenditure using Equation 3.""" + if crf == 0: + raise ValueError("capital recovery factor cannot be zero") + return afc_ij / crf + + # Equation 4 - Digester variable operational cost + + @staticmethod + def calculate_digester_operational_cost( + use_cost_function: bool, + animal_units: float, + farm_type_flag: float, + below_ground_flag: float, + concrete_flag: float, + steel_flag: float, + *, + beta: float = 3.531, + omega1: float = -0.321, + phi1: float = -0.686, + phi2: float = -0.418, + phi3: float = 0.488, + phi4: float = -0.113, + epsilon: float = 0.237, + ) -> float: + """Return the variable operational cost using Equation 4.""" + + if not use_cost_function: + return 0.0 + if animal_units <= 0: + raise ValueError("animal_units must be positive to evaluate the AVC function") + + return math.exp( + beta + + omega1 * math.log(animal_units) + + phi1 * farm_type_flag + + phi2 * below_ground_flag + + phi3 * concrete_flag + + phi4 * steel_flag + + epsilon + ) + + @staticmethod + def _normalize_system_name(system: str) -> str: + """Return a normalized key for digester system lookups.""" + + normalized = [] + prev_sep = False + for ch in system.lower(): + if ch.isalnum(): + normalized.append(ch) + prev_sep = False + else: + if not prev_sep: + normalized.append("_") + prev_sep = True + return "".join(normalized).strip("_") + + _DIGESTER_SYSTEMS: Mapping[str, DigesterSystemCostProfile] = { + _normalize_system_name("Covered Lagoon - RNG"): DigesterSystemCostProfile( + capex=LinearCostEquation(875.0, 625_000.0), + labor=LinearCostEquation(94.5, 67_500.0), + energy=LinearCostEquation(34.80, 21.0), + repairs=LinearCostEquation(66.69, 40.25), + useful_life_years=20, + salvage_value_fraction=0.15, + biogas_yield_ft3_per_cow_day=72.0, + methane_content_fraction=0.6, + ), + _normalize_system_name("Covered Lagoon - CHP"): DigesterSystemCostProfile( + capex=LinearCostEquation(1_312.5, 937_500.0), + labor=LinearCostEquation(188.48, 113.75), + energy=LinearCostEquation(15.75, 11_250.0), + repairs=LinearCostEquation(47.25, 33_750.0), + useful_life_years=20, + salvage_value_fraction=0.15, + biogas_yield_ft3_per_cow_day=72.0, + methane_content_fraction=0.6, + ), + _normalize_system_name("Plug Flow - RNG"): DigesterSystemCostProfile( + capex=LinearCostEquation(1_750.0, 1_000_000.0), + labor=LinearCostEquation(287.47, 2_670.0), + energy=LinearCostEquation(47.91, 445.0), + repairs=LinearCostEquation(143.73, 1_335.0), + useful_life_years=20, + salvage_value_fraction=0.15, + biogas_yield_ft3_per_cow_day=120.0, + methane_content_fraction=0.6, + ), + _normalize_system_name("Plug Flow - CHP"): DigesterSystemCostProfile( + capex=LinearCostEquation(2_625.0, 2_000_000.0), + labor=LinearCostEquation(75.0, 45_000.0), + energy=LinearCostEquation(12.5, 7_500.0), + repairs=LinearCostEquation(37.5, 22_500.0), + useful_life_years=20, + salvage_value_fraction=0.15, + biogas_yield_ft3_per_cow_day=120.0, + methane_content_fraction=0.6, + ), + } + + _TRANSPORT_COST = LinearCostEquation(137.5, 0.0) + + @classmethod + def get_digester_cost_profile(cls, system: str) -> DigesterSystemCostProfile: + """Return the cost profile for a supported digester configuration. + + The coefficients are sourced from ``economic_docs/Variables Update for Joe + and Pooya.xlsx`` and provide linear estimates for capital and operational + expenses as a function of the number of cows contributing manure to the + digester. + """ + + key = cls._normalize_system_name(system) + try: + return cls._DIGESTER_SYSTEMS[key] + except KeyError as exc: # pragma: no cover - defensive branch + available = ", ".join(sorted(cls._DIGESTER_SYSTEMS)) + raise KeyError(f"Unknown digester system '{system}'. Available: {available}") from exc + + @classmethod + def estimate_digester_costs(cls, system: str, cows: float) -> Dict[str, object]: + """Return CAPEX and OPEX estimates for the requested digester system.""" + + profile = cls.get_digester_cost_profile(system) + return { + "capital_expenditure": profile.capital_cost(cows), + "operating_costs": profile.annual_operating_costs(cows), + "useful_life_years": profile.useful_life_years, + "salvage_value_fraction": profile.salvage_value_fraction, + "biogas_yield_ft3_per_cow_day": profile.biogas_yield_ft3_per_cow_day, + "methane_content_fraction": profile.methane_content_fraction, + } + + @classmethod + def estimate_digester_trucking_cost(cls, cows: float) -> float: + """Estimate the RNG trucking and injection cost for the herd size.""" + + return cls._TRANSPORT_COST.evaluate(cows) diff --git a/RUFAS/EEE/economics/equations.py b/RUFAS/EEE/economics/equations.py new file mode 100644 index 0000000000..4ca3971d6c --- /dev/null +++ b/RUFAS/EEE/economics/equations.py @@ -0,0 +1,168 @@ +"""Implementation of economic equations 7–28 from the March 2026 guidance.""" + +from __future__ import annotations + +from typing import Iterable + +import numpy as np + + +class EconomicEquations: + """Namespace grouping the reusable economic helper equations.""" + + @staticmethod + def _normalise_percentages(percentages: Iterable[float], length: int) -> np.ndarray: + """Return a normalised array of construction percentages.""" + + rates = np.asarray(list(percentages), dtype=float) + if length and rates.size < length: + rates = np.pad(rates, (0, length - rates.size)) + if rates.size == 0: + return np.zeros(length, dtype=float) + if np.allclose(rates.sum(), 0.0): + return np.zeros_like(rates, dtype=float) + return rates / rates.sum() + + @staticmethod + def construct_timeline(construction_years: int, loan_term: int, project_term: int | None = None) -> np.ndarray: + """Return the project year vector using Equation 7.""" + + construction_years = max(int(construction_years), 0) + loan_term = max(int(loan_term), 0) + end_year = loan_term + if project_term is not None: + end_year = max(end_year, int(project_term)) + start_year = 1 - construction_years + return np.arange(start_year, end_year + 1, dtype=int) + + @staticmethod + def discount_factor(irr: float, year: int | float) -> float: + """Discount factor for a given year using Equation 9.""" + + period = abs(float(year)) + return 1 / ((1 + irr) ** period) + + @staticmethod + def annual_capital_spent(total_capital: float, construction_rate_per_year: list[float] | np.ndarray) -> np.ndarray: + """Capital spent each construction year using Equation 11.""" + + rates = np.asarray(construction_rate_per_year, dtype=float) + return total_capital * rates + + @staticmethod + def equity_contribution( + total_capital: float, + construction_rate_per_year: list[float] | np.ndarray, + equity_amount: float, + ) -> np.ndarray: + """Equity deployed during construction each year.""" + rates = np.asarray(construction_rate_per_year, dtype=float) + return total_capital * rates * equity_amount + + # Equation 12 in `Documentation of Economic Data and Analytical Methods (2).pdf` + # defines loan principal as the annual capital spent times the financed fraction. + @staticmethod + def loan_principal( + total_capital: float | np.ndarray, + construction_rate_per_year: list[float] | np.ndarray, + loan_fraction: float | None = None, + ) -> np.ndarray: + """Loan principal for each construction year using Equation 12.""" + + capital = np.asarray(total_capital, dtype=float) + if loan_fraction is None: + return capital * np.asarray(construction_rate_per_year, dtype=float) + + rates = np.asarray(construction_rate_per_year, dtype=float) + return capital * rates * loan_fraction + + @staticmethod + def construction_interest(loan_principal: np.ndarray, interest_rate: float) -> np.ndarray: + """Interest accrued on loan during construction using Equation 13.""" + + principal = np.asarray(loan_principal, dtype=float) + outstanding = np.cumsum(principal) + return outstanding * float(interest_rate) + + @staticmethod + def npv_capital_plus_interest( + capital: np.ndarray, interest: np.ndarray, irr: float, years: np.ndarray + ) -> np.ndarray: + """Present value of capital plus interest using Equation 14.""" + years = np.asarray(years, dtype=float) + df = np.array([EconomicEquations.discount_factor(irr, y) for y in years], dtype=float) + return (np.asarray(capital, dtype=float) + np.asarray(interest, dtype=float)) * df + + @staticmethod + def annual_loan_payment(total_cost: float, rate: float, term: int, loan_fraction: float) -> float: + """Annual loan payment using Equation 15.""" + if term <= 0: + return 0.0 + numerator = total_cost * rate * loan_fraction + denominator = 1 - (1 + rate) ** (-term) + return numerator / denominator + + @staticmethod + def interest_payment(previous_principal: float, rate: float) -> float: + """Loan interest payment for a year using Equation 17.""" + return previous_principal * rate + + @staticmethod + def principal_after_payment(previous_principal: float, payment: float, interest: float) -> float: + """Remaining loan principal after payment using Equation 18.""" + return previous_principal - payment + interest + + @staticmethod + def depreciation_schedule(total_cost: float, depreciation_rates: np.ndarray) -> np.ndarray: + """Annual depreciation using Equation 19.""" + return total_cost * np.asarray(depreciation_rates, dtype=float) + + @staticmethod + def net_revenue( + revenue: float, + operating_cost: float, + interest: float, + depreciation: float, + ) -> float: + # NOTE: Interest from ``interest_payment`` is used here; principal changes + # are handled separately via ``principal_after_payment``. + + """Net revenue calculation using Equation 20.""" + return revenue - operating_cost - interest - depreciation + + @staticmethod + def loss_carry_forward(prev_taxable: float, prev_loss: float) -> float: + """Loss carry forward using Equation 21.""" + return prev_taxable + prev_loss if prev_taxable < 0 else prev_loss + + @staticmethod + def max_loss_utilized(net_rev: float, carried_loss: float) -> float: + """Maximum loss utilization using Equation 22.""" + carry_forward_limit = 0.8 + return min(abs(carried_loss), net_rev * carry_forward_limit) + + @staticmethod + def taxable_income(net_rev: float, used_loss: float) -> float: + """Taxable income using Equation 23.""" + return net_rev - used_loss + + @staticmethod + def income_tax(taxable: float, tax_rate: float) -> float: + """Income tax using Equation 25.""" + taxable = max(taxable, 0.0) + return taxable * tax_rate + + @staticmethod + def annual_cash_income(revenue: float, operating_cost: float, loan_payment: float, tax_pay: float) -> float: + """Annual cash income using Equation 26.""" + return revenue - operating_cost - loan_payment - tax_pay + + @staticmethod + def present_value(cash_income: float, discount: float) -> float: + """Present value of cash income using Equation 27.""" + return cash_income * discount + + @staticmethod + def net_present_value(apv: np.ndarray, capital_interest_pv: np.ndarray) -> float: + """Net present value using Equation 28.""" + return apv.sum() - capital_interest_pv.sum() diff --git a/RUFAS/EEE/economics/fallback_values.py b/RUFAS/EEE/economics/fallback_values.py new file mode 100644 index 0000000000..00e7fbe535 --- /dev/null +++ b/RUFAS/EEE/economics/fallback_values.py @@ -0,0 +1,28 @@ +"""Hardcoded fallback values for economics preprocessing inputs.""" + +from __future__ import annotations + +from typing import Dict, List + +# Fallback values for biophysical simulation patterns. +BIOPHYSICAL_FALLBACKS: Dict[str, List[float]] = { + "AnimalModuleReporter.report_life_cycle_manager_data.bought_heifer_num": [5.0], + "Economic_preprocessing.Animal.FPCM": [250.0], + "AnimalModuleReporter.report_life_cycle_manager_data.sold_heiferII_num": [2.0], + "AnimalModuleReporter.report_life_cycle_manager_data.sold_heiferIII_oversupply_num": [1.0], + "FeedManager.purchase_feed.ration_interval_*_cost": [100.0], + "SEE NOTES": [10.0], + "ManureManager._record_manure_request_results.off_farm_manure.total_manure_mass": [50.0], + "see future_expansion": [5.0], + "fertilizer_schedule SEE NOTES": [12.0], + "both below are from multiple input files, see preprocessing": [8.0], + "field.crop_specification": [20.0], + "field.field_size": [30.0], + "Waiting on tractor_implement and other parts of EEE outputs": [6.0], +} + +# Default quantity used when no biophysical or input values are available. +ECONOMIC_QUANTITY_FALLBACK: float = 1.0 + +# Default price used when no commodity pricing is available. +ECONOMIC_PRICE_FALLBACK: Dict[str, float] = {"revenue": 2.0, "cost": 1.0} diff --git a/RUFAS/EEE/economics/framework.py b/RUFAS/EEE/economics/framework.py new file mode 100644 index 0000000000..d3e54e1ff6 --- /dev/null +++ b/RUFAS/EEE/economics/framework.py @@ -0,0 +1,193 @@ +"""Entry points for economics calculations.""" + +from __future__ import annotations + +import pandas as pd + +from RUFAS.input_manager import InputManager +from RUFAS.output_manager import OutputManager +from RUFAS.units import MeasurementUnits +from .dcfror import DCFRORCalculator +from .partial_budget import PartialBudget +from .preprocessing import EconomicPreprocessor + + +class EconomicFramework: + def __init__(self) -> None: + self.im = InputManager() + self.om = OutputManager() + self.partial_budget = PartialBudget() + self.preprocessor = EconomicPreprocessor() + + def _capital_cost_present(self) -> bool: + """Return ``True`` when the capital cost table is available. + + The calculation is still executed when all declared costs are zero so + the economics module can emit outputs (and warnings) instead of + silently skipping DCFROR altogether. + """ + + info_map = {"class": __name__, "function": self._capital_cost_present.__name__} + + cost_data = self.im.get_data("economic_inputs.capital_costs.capital_cost_breakdown") + is_dataframe = isinstance(cost_data, pd.DataFrame) + + if is_dataframe: + capital_cost_df = cost_data + has_cost_column = "Cost" in capital_cost_df.columns + dataframe_is_empty = capital_cost_df.empty + + if dataframe_is_empty: + return False + + if has_cost_column: + declared_costs = capital_cost_df["Cost"] + numeric_costs = pd.to_numeric(declared_costs, errors="coerce").fillna(0.0) + total_declared_costs = float(numeric_costs.sum()) + if total_declared_costs == 0.0: + self.om.add_warning( + "ZeroCapitalCost", + "Capital cost breakdown sums to zero; continuing DCFROR with zero costs.", + info_map, + ) + return True + + numeric_columns = capital_cost_df.select_dtypes(include=["number"]) + has_numeric_columns = not numeric_columns.empty + + if not has_numeric_columns: + return False + + numeric_values = numeric_columns.fillna(0.0).to_numpy() + total_numeric_costs = float(numeric_values.sum()) + if total_numeric_costs == 0.0: + self.om.add_warning( + "ZeroCapitalCost", + "Capital cost breakdown sums to zero; continuing DCFROR with zero costs.", + info_map, + ) + return True + + is_iterable = hasattr(cost_data, "__iter__") and not isinstance(cost_data, (str, bytes)) + + if is_iterable: + iterable_values = list(cost_data) + try: + numeric_values = pd.to_numeric(pd.Series(iterable_values), errors="coerce").fillna(0.0) + except Exception: + return False + iterable_total = float(numeric_values.sum()) + if iterable_total == 0.0: + self.om.add_warning( + "ZeroCapitalCost", + "Capital cost breakdown sums to zero; continuing DCFROR with zero costs.", + info_map, + ) + return True + + try: + scalar_cost = float(cost_data) + except (TypeError, ValueError): + return False + if scalar_cost == 0.0: + self.om.add_warning( + "ZeroCapitalCost", + "Capital cost breakdown sums to zero; continuing DCFROR with zero costs.", + info_map, + ) + return True + + def run_economic_analysis(self) -> None: + """Execute economic analysis using the Flexible Economic Framework.""" + + preprocessed_results = self.preprocessor.preprocess() + self.om.add_variable( + "econ_preprocessed_economic_inputs", + preprocessed_results, + info_map={ + "class": __name__, + "function": self.run_economic_analysis.__name__, + "units": MeasurementUnits.UNITLESS, + }, + ) + capital_present = self._capital_cost_present() + partial_budget_requested = self.partial_budget.has_partial_budget_activity(preprocessed_results) + + if capital_present: + calculator = DCFRORCalculator() + + calculator_inputs = calculator.inputs if isinstance(calculator.inputs, dict) else {} + goal_seek_name_map = { + "target_internal_rate_of_return": "internal_rate_of_return", + "loan_term": "loan_term", + "goal_seek_unit_price_multiplier": "goal_seek_unit_price_multiplier", + } + allowed_goal_seek_variables = set(goal_seek_name_map.keys()) + + calculator.calculate() + + calculator_inputs = calculator.inputs if isinstance(calculator.inputs, dict) else {} + if calculator_inputs.get("enable_goal_seek", False): + fixed_variables = calculator_inputs.get("goal_seek_fixed_variables", []) + if not isinstance(fixed_variables, list): + fixed_variables = [] + + invalid_fixed = [name for name in fixed_variables if name not in allowed_goal_seek_variables] + + if invalid_fixed: + self.om.add_error( + "InvalidGoalSeekFixedVariables", + f"Unknown fixed goal-seek variable names: {invalid_fixed}", + {"class": __name__, "function": self.run_economic_analysis.__name__}, + ) + solved_value = float("nan") + solved_variable = "" + elif len(fixed_variables) != 2: + self.om.add_error( + "GoalSeekFixedVariableCount", + "Exactly two fixed variables must be provided for goal-seek.", + {"class": __name__, "function": self.run_economic_analysis.__name__}, + ) + solved_value = float("nan") + solved_variable = "" + else: + remaining = list(allowed_goal_seek_variables.difference(set(fixed_variables))) + if len(remaining) != 1: + self.om.add_error( + "GoalSeekSolveVariableInferenceFailed", + "Unable to infer a single variable to solve from fixed variables.", + {"class": __name__, "function": self.run_economic_analysis.__name__}, + ) + solved_value = float("nan") + solved_variable = "" + else: + solved_variable = remaining[0] + solved_value = calculator.goal_seek( + variable_name=goal_seek_name_map[solved_variable], + target_npv=float(calculator_inputs.get("goal_seek_target_npv", 0.0)), + bounds=tuple(calculator_inputs.get("goal_seek_bounds", [0.01, 100.0])), + ) + + self.om.add_variable( + "econ_dcfror_goal_seek_solution", + solved_value, + info_map={ + "class": __name__, + "function": self.run_economic_analysis.__name__, + "units": MeasurementUnits.UNITLESS, + }, + ) + self.om.add_variable( + "econ_dcfror_goal_seek_variable", + solved_variable, + info_map={ + "class": __name__, + "function": self.run_economic_analysis.__name__, + "units": MeasurementUnits.UNITLESS, + }, + ) + + if partial_budget_requested: + self.partial_budget.calculate_partial_budget(preprocessed_results) + else: + self.partial_budget.calculate_partial_budget(preprocessed_results) diff --git a/RUFAS/EEE/economics/mapping.py b/RUFAS/EEE/economics/mapping.py new file mode 100644 index 0000000000..8b996f8f4f --- /dev/null +++ b/RUFAS/EEE/economics/mapping.py @@ -0,0 +1,753 @@ +"""Hardcoded economics mapping derived from input/data/EEE/economics_map.json. + +This file is generated to decouple the economics preprocessing from runtime JSON loading. +""" + +from __future__ import annotations + +from typing import Any, Dict + +ECONOMIC_MAP: Dict[str, Dict[str, Dict[str, Dict[str, Any]]]] = { + "Animal": { + "Costs": { + "Animal - Labor hours": { + "input_manager": ["economic_inputs.Animal.labor_hours_per_day"], + "economics_files": ["farm_services_labor_hours_dollar_per_hour"], + "future_expansion": "more " "subcategories, " "e.g. vet, " "milker", + "notes": "", + }, + "Animal investment - Diesel consumption": { + "input_manager": ["economic_inputs.Animal.diesel_liters_per_day"], + "economics_files": ["commodity_prices_diesel_dollar_per_liter"], + "notes": "", + }, + "Animal investment - Electricity consumption": { + "input_manager": ["economic_inputs.Animal.electricity_kwh_per_day"], + "economics_files": ["commodity_prices_elec_industrial_dollar_per_kwh"], + "notes": "", + }, + "Animal investment - Gasoline consumption": { + "input_manager": ["economic_inputs.Animal.gasoline_liters_per_day"], + "economics_files": ["commodity_prices_gasoline_dollar_per_liter"], + "notes": "", + }, + "Animal investment - Natural gas consumption": { + "input_manager": ["economic_inputs.Animal.natural_gas_megajoule_per_day"], + "economics_files": ["commodity_prices_natgas_industrial_dollar_per_megajoule"], + "notes": "", + }, + "Animal investment - Propane consumption": { + "input_manager": ["economic_inputs.Animal.propane_liters_per_day"], + "economics_files": ["commodity_prices_propane_wholesale_dollar_per_liter"], + "notes": "", + }, + "Animal investment - Water consumption": { + "input_manager": ["economic_inputs.Animal.water_cubic_meter_per_day"], + "economics_files": ["commodity_prices_water_municipal_dollar_per_cubic_meter"], + "notes": "", + }, + "Bedding requirements": { + "biophysical_simulation": ["AnimalModuleReporter.report_daily_pen_total.number_of_animals_in_pen_.*"], + "economics_files": { + "CBPB": "commodity_prices_bedding_compost_bedded_pack_dollar_per_head", + "input_manager_location": "animal.pen_information.*.manure_streams.0.bedding_name", + "manure_solids": "commodity_prices_bedding_manure_solids_dollar_per_head", + "sand": "commodity_prices_bedding_sand_dollar_per_head", + "sawdust": "commodity_prices_bedding_sawdust_dollar_per_head", + "staw": "commodity_prices_bedding_straw_dollar_per_head", + }, + "preprocessing": "dollar per " + "head is " + "reported on " + "an annual " + "basis, Map " + "bedding price " + "to number of " + "animals for " + "each bedding " + "type, " + "depending on " + "their pen.", + }, + "Purchased heifers": { + "biophysical_simulation": ["AnimalModuleReporter.report_life_cycle_manager_data.bought_heifer_num"], + "economics_files": ["commodity_prices_cow_dairy_heifer_bred_t3_dollar_per_animal"], + }, + "Semen purchased": { + "biophysical_simulation": [ + "AnimalModuleReporter._record_heiferIIs_conception_rate.heiferII_total_num_ai_performed", + "AnimalModuleReporter._record_cows_conception_rate.cow_total_num_ai_performed", + ], + "economics_files": { + "beef": "commodity_prices_semen_beef_dollar_per_straw", + "conventional": "commodity_prices_semen_conventional_dollar_per_straw", + "input_manager_location": "animal.animal_config.management_decisions.semen_type", + "sexed": "commodity_prices_semen_sexed_dollar_per_straw", + }, + "preprocessing": "daily sum of both " "biophysical " "outputs, select " "econ file from " "user input", + "processing": "", + }, + }, + "Revenue": { + "Carbon credits from enteric emissions reduction": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_enteric_methane_emission.enteric_methane_emission.*" + ], + "economics_files": ["commodity_prices_carbon_credits_enteric_dollar_per_tonne_CO2e"], + "notes": "Sum " + "across " + "all " + "pens " + "(e.g. " + "each " + "of " + "the " + "pens " + "captured " + "by " + ".*), " + "then " + "convert " + "enteric_methane_emission " + "to " + "tonnes", + }, + "FPCM (Milk Production)": { + "biophysical_simulation": ["Economic_preprocessing.Animal.FPCM"], + "economics_files": ["commodity_prices_milk_retail_dollar_per_liter"], + "preprocessing": "FPCM " + "calculation " + "from raw " + "values, " + "see " + "economic_preprocessing.json " + "as " + "example " + "logic", + }, + "Sold calves": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_sold_animal_information_sort_by_sell_day.sold_calves_sold_weight" + ], + "economics_files": ["commodity_prices_calves_all_dollar_per_kilogram"], + }, + "Sold cows": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_sold_animal_information_sort_by_sell_day.sold_cows_sold_count" + ], + "economics_files": ["commodity_prices_cows_milk_dollar_per_animal"], + "preprocessing": "sum number sold", + }, + "Sold heifer IIIs": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_life_cycle_manager_data.sold_heiferII_num", + "AnimalModuleReporter.report_life_cycle_manager_data.sold_heiferIII_oversupply_num", + ], + "economics_files": ["commodity_prices_cow_dairy_heifer_bred_t3_dollar_per_animal"], + "preprocessing": "sum from both " "biophysical " "simulation " "locations", + }, + "Sold heifer IIs": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_sold_animal_information_sort_by_sell_day.heiferII_sold_weight" + ], + "economics_files": ["commodity_prices_cow_dairy_heifer_open_dollar_per_animal"], + }, + }, + }, + "Feed_storage": { + "Costs": { + "Feed storage - Labor hours": { + "input_manager": ["economic_inputs.Feed_storage.labor_hours_per_day"], + "economics_files": ["farm_services_labor_hours_dollar_per_hour"], + }, + "Purchased feed costs": { + "biophysical_simulation": ["FeedManager.purchase_feed.ration_interval_*_cost"], + "economics_files": [ + "commodity_prices_alfalfa_hay_dollar_per_kilogram", + "commodity_prices_alfalfa_silage_dollar_per_kilogram", + "commodity_prices_almond_hulls_dollar_per_kilogram", + "commodity_prices_barley_silage_dollar_per_kilogram", + "commodity_prices_cotton_seed_hulls_dollar_per_kilogram", + "commodity_prices_cotton_seed_meal_dollar_per_kilogram", + "commodity_prices_cotton_seed_whole_dollar_per_kilogram", + "commodity_prices_corn_grain_dollar_per_kilogram", + "commodity_prices_corn_silage_dollar_per_kilogram", + "commodity_prices_distiller_grains_dried_10pct_dollar_per_kilogram", + "commodity_prices_distiller_grains_modified_wet_50pct_dollar_per_kilogram", + "commodity_prices_distiller_grains_wet_65pct_dollar_per_kilogram", + "commodity_prices_hay_excluding_alfalfa_dollar_per_kilogram", + "commodity_prices_hay_all_dollar_per_kilogram", + "commodity_prices_rye_grain_dollar_per_kilogram", + "commodity_prices_soybean_grain_dollar_per_kilogram", + "commodity_prices_soybean_hulls_dollar_per_kilogram", + "commodity_prices_soybean_meal_dollar_per_kilogram", + "commodity_prices_sundan_silage_dollar_per_kilogram", + "commodity_prices_winter_wheat_grain_dollar_per_kilogram", + "commodity_prices_calcium_phosphate_di_dollar_per_kilogram", + "commodity_prices_calf_starter_18cp_dollar_per_kilogram", + "commodity_prices_limestone_dollar_per_kilogram", + ], + "future_expansion": "cases " + "where " + "historical " + "references " + "are " + "desired: " + "Instead " + "of " + "cost, " + "select " + "from " + "economics_input " + "options, " + "all " + "end " + "in " + "'_dollar_per_kilogram', " + "compare " + "price " + "in " + "input " + "to " + "this " + "price, " + "scale " + "accordingly " + "(e.g. " + "price " + "was " + "1, " + "in " + "file " + "is " + "2, " + "double " + "the " + "final " + "cost)", + "processing": "no " + "commodity " + "price " + "needed, " + "just loop " + "over each " + "feed .*, " + "report " + "separately, " + "but " + "expanded " + "evenly " + "across the " + "subsequent " + "ration " + "interval. " + "To explain " + "this a " + "little " + "more...this " + "isn't " + "reported " + "daily, but " + "on each " + "ration " + "formulation " + "(usually " + "the " + "interval). " + "So this " + "price " + "should be " + "either " + "prorated " + "over the " + "rest of " + "the days, " + "or the " + "'gaps' for " + "each day " + "should be " + "filled in " + "with 0s. " + "So if the " + "ration " + "interval " + "cost was " + "reported " + "for day 0, " + "and the " + "next on " + "day 30, " + "either the " + "price of " + "day 0 " + "should be " + "evenly " + "divided " + "across " + "days 0-29, " + "or the " + "whole " + "price for " + "0 should " + "be " + "assigned " + "to the 0th " + "day in the " + "list, and " + "for days " + "1-29 a " + "price of 0 " + "is " + "assigned.'", + }, + }, + "Revenue": { + "Feed_sales": { + "biophysical_simulation": ["CropManagement._record_yield.harvest_yield.field='field_.*'"], + "economics_files": [ + "commodity_prices_alfalfa_hay_dollar_per_kilogram", + "commodity_prices_alfalfa_silage_dollar_per_kilogram", + "commodity_prices_almond_hulls_dollar_per_kilogram", + "commodity_prices_barley_silage_dollar_per_kilogram", + "commodity_prices_cotton_seed_hulls_dollar_per_kilogram", + "commodity_prices_cotton_seed_meal_dollar_per_kilogram", + "commodity_prices_cotton_seed_whole_dollar_per_kilogram", + "commodity_prices_corn_grain_dollar_per_kilogram", + "commodity_prices_corn_silage_dollar_per_kilogram", + "commodity_prices_distiller_grains_dried_10pct_dollar_per_kilogram", + "commodity_prices_distiller_grains_modified_wet_50pct_dollar_per_kilogram", + "commodity_prices_distiller_grains_wet_65pct_dollar_per_kilogram", + "commodity_prices_hay_excluding_alfalfa_dollar_per_kilogram", + "commodity_prices_hay_all_dollar_per_kilogram", + "commodity_prices_rye_grain_dollar_per_kilogram", + "commodity_prices_soybean_grain_dollar_per_kilogram", + "commodity_prices_soybean_hulls_dollar_per_kilogram", + "commodity_prices_soybean_meal_dollar_per_kilogram", + "commodity_prices_sundan_silage_dollar_per_kilogram", + "commodity_prices_winter_wheat_grain_dollar_per_kilogram", + "commodity_prices_calcium_phosphate_di_dollar_per_kilogram", + "commodity_prices_calf_starter_18cp_dollar_per_kilogram", + "commodity_prices_limestone_dollar_per_kilogram", + ], + "processing?": "Needs to loop " + "over each field, " + "each harvest, get " + "total yield and " + "map against the " + "commodity prices " + "here", + } + }, + }, + "Manure": { + "Costs": { + "Digester operational costs": { + "biophysical_simulation": ["SEE " "NOTES"], + "economics_files": [ + "commodity_prices_natgas_industrial_dollar_per_megajoule", + "commodity_prices_elec_industrial_dollar_per_kwh", + "commodity_prices_digester_carbon_credits_dollar_per_tonne_CO2e", + ], + "notes": "Get outputs " "from methods in " "digester_costs.py", + }, + "Digester operational costs - Diesel consumption": { + "input_manager": ["economic_inputs.Manure.digester.diesel_liters_per_day"], + "economics_files": ["commodity_prices_diesel_dollar_per_liter"], + "notes": "", + }, + "Digester operational costs - Electricity consumption": { + "input_manager": ["economic_inputs.Manure.digester.kwh_per_day"], + "economics_files": [ + "commodity_prices_elec_residential_dollar_per_kwh", + "commodity_prices_elec_commercial_dollar_per_kwh", + "commodity_prices_elec_industrial_dollar_per_kwh", + ], + "notes": "", + }, + "Digester operational costs - Gasoline consumption": { + "input_manager": ["economic_inputs.Manure.digester.gasoline_liters_per_day"], + "economics_files": ["commodity_prices_gasoline_dollar_per_liter"], + "notes": "", + }, + "Digester operational costs - Labor hours": { + "input_manager": ["economic_inputs.Manure.digester.labor_hours_per_day"], + "economics_files": ["farm_services_labor_hours_dollar_per_hour"], + "notes": "", + }, + "Digester operational costs - Natural gas consumption": { + "input_manager": ["economic_inputs.Manure.digester.megajoules_per_day"], + "economics_files": ["commodity_prices_natgas_industrial_dollar_per_megajoule"], + "notes": "", + }, + "Digester operational costs - Propane consumption": { + "input_manager": ["economic_inputs.Manure.digester.propane_liters_per_day"], + "economics_files": ["commodity_prices_propane_wholesale_dollar_per_liter"], + "notes": "", + }, + "Digester operational costs - Water consumption": { + "input_manager": ["economic_inputs.Manure.digester.cubic_meters_water_per_day"], + "economics_files": ["commodity_prices_water_municipal_dollar_per_cubic_meter"], + "notes": "", + }, + "General manure operational costs - Labor hours": { + "input_manager": ["economic_inputs.Manure.general.labor_hours"], + "economics_files": ["farm_services_labor_hours_dollar_per_hour"], + "notes": "", + }, + "Manure disposal quantity": { + "input_manager": [ + "economic_inputs.Manure.manure_disposal_kg", + "economic_inputs.Manure.manure_disposal_price_per_kg", + ], + "economics_files": [], + "notes": "FULLY MANUAL", + }, + "Manure disposal transport distance": { + "input_manager": [ + "economic_inputs.Manure.manure_disposal_transport_km", + "economic_inputs.Manure.manure_disposal_price_per_km", + ], + "economics_files": [], + "notes": "FULLY " "MANUAL", + }, + "Purchased manure": { + "biophysical_simulation": [ + "ManureManager._record_manure_request_results.off_farm_manure.total_manure_mass" + ], + "economics_files": ["commodity_prices_bedding_manure_solids_dollar_per_head"], + "notes": "", + }, + "Slurry storage operational costs - Diesel consumption": { + "input_manager": ["economic_inputs.Manure.slurry_storage.diesel_liters_per_day"], + "economics_files": ["commodity_prices_diesel_dollar_per_liter"], + "notes": "", + }, + "Slurry storage operational costs - Electricity consumption": { + "input_manager": ["economic_inputs.Manure.slurry_storage.kwh_per_day"], + "economics_files": [ + "commodity_prices_elec_residential_dollar_per_kwh", + "commodity_prices_elec_commercial_dollar_per_kwh", + "commodity_prices_elec_industrial_dollar_per_kwh", + ], + "notes": "", + }, + "Slurry storage operational costs - Gasoline consumption": { + "input_manager": ["economic_inputs.Manure.slurry_storage.gasoline_liters_per_day"], + "economics_files": ["commodity_prices_gasoline_dollar_per_liter"], + "notes": "", + }, + "Slurry storage operational costs - Labor hours": { + "input_manager": ["economic_inputs.Manure.slurry_storage.labor_hours"], + "economics_files": ["farm_services_labor_hours_dollar_per_hour"], + "notes": "", + }, + "Slurry storage operational costs - Natural gas consumption": { + "input_manager": ["economic_inputs.Manure.slurry_storage.megajoules_per_day"], + "economics_files": ["commodity_prices_natgas_industrial_dollar_per_megajoule"], + "notes": "", + }, + "Slurry storage operational costs - Propane consumption": { + "input_manager": ["economic_inputs.Manure.slurry_storage.propane_liters_per_day"], + "economics_files": ["commodity_prices_propane_wholesale_dollar_per_liter"], + "notes": "", + }, + "Slurry storage operational costs - Water consumption": { + "input_manager": ["economic_inputs.Manure.slurry_storage.cubic_meters_water_per_day"], + "economics_files": ["commodity_prices_water_municipal_dollar_per_cubic_meter"], + "notes": "", + }, + }, + "Revenue": { + "Carbon credits from digester products and activities": { + "biophysical_simulation": ["see " "future_expansion"], + "economics_files": ["commodity_prices_carbon_credits_enteric_dollar_per_tonne_CO2e"], + "future_expansion": "Placeholder " + "for " + "the " + "future, " + "scale " + "to " + "manure " + "module " + "outputs", + }, + "Digester bedding co-product": { + "biophysical_simulation": ["see " "future_expansion"], + "economics_files": ["commodity_prices_bedding_manure_solids_dollar_per_head"], + "future_expansion": "Placeholder " + "for " + "the " + "future, " + "scale " + "to " + "manure " + "module " + "outputs", + }, + "Digester fertilizer production": { + "biophysical_simulation": ["see " "future_expansion"], + "economics_files": [ + "commodity_prices_net_fertilizer_nitrogen_dollar_per_kilogram", + "commodity_prices_net_fertilizer_phosphorus_dollar_per_kilogram", + "commodity_prices_net_fertilizer_potassium_dollar_per_kilogram", + ], + "future_expansion": "Placeholder " + "for " + "the " + "future, " + "scale " + "to " + "manure " + "module " + "outputs", + }, + "Electricity production from anaerobic digester": { + "input_manager": ["economic_inputs.Manure.digester.kwh_per_day_produced"], + "economics_files": ["commodity_prices_elec_industrial_dollar_per_kwh"], + "future_expansion": "Placeholder " + "for " + "the " + "future, " + "scale " + "to " + "manure " + "module " + "outputs", + }, + "Renewable natural gas (RNG) production": { + "input_manager": ["economic_inputs.Manure.digester.digester_rng_produced"], + "economics_files": ["commodity_prices_natgas_industrial_dollar_per_megajoule"], + "future_expansion": "Placeholder " + "for " + "the " + "future, " + "scale " + "to " + "manure " + "module " + "outputs", + }, + "Sold manure": { + "input_manager": ["economic_inputs.Manure.manure_sales"], + "economics_files": ["commodity_prices_bedding_manure_solids_dollar_per_head"], + "future_expansion": "perhaps scale as " + "a percentage of " + "total manure " + "produced? Or " + "preprocessing for " + "excess e.g. not " + "applied to " + "fields", + }, + }, + }, + "Soil_and_crop": { + "Costs": { + "Crop labor hours": { + "input_manager": ["economic_inputs.Soil_and_crop.labor_hours_per_day"], + "economics_files": ["farm_services_labor_hours_dollar_per_hour"], + }, + "Crop water consumption": { + "biophysical_simulation": ["FieldDataReporter.send_crop_daily_variables.water_uptake.*"], + "economics_files": ["commodity_prices_water_irrigation_dollar_per_cubic_meter"], + }, + "Land purchase costs": { + "input_manager": ["economic_inputs.Soil_and_crop.land_ha_purchased"], + "economics_files": ["capital_costs_land_ha_dollar_per_hectare"], + "notes": "This 'manual' " + "entry in " + "Economic_inputs " + "fields is meant " + "to be for an " + "entire " + "simulation, so " + "is different " + "from the " + "'per_day' " + "values, and " + "instead should " + "result only in " + "a single value. " + "The idea for " + "this (and " + "others that are " + "similar, see " + "variables with " + "'whole_simulation' " + "in them) is " + "that it's a per " + "scenario type " + "value. Adding " + "fields, giving " + "a single cost " + "to rent or buy " + "manually.", + }, + "Land rental costs": { + "input_manager": ["economic_inputs.Soil_and_crop.land_ha_rented"], + "economics_files": ["farm_services_land_ha_rent_dollar_per_hectare"], + }, + "Nitrogen fertilizer used": { + "biophysical_simulation": ["fertilizer_schedule " "SEE " "NOTES"], + "economics_files": ["commodity_prices_net_fertilizer_nitrogen_dollar_per_kilogram"], + "notes": "likely as " + "'simple' " + "as taking " + "the sum of " + "nitrogen_masses " + "for each " + "fertilizer_schedule " + "file used " + "in the " + "simulation, " + "but this " + "requires " + "some " + "consulting " + "with the " + "soil and " + "crop SMEs", + }, + "Other fertilizer types used": { + "biophysical_simulation": ["fertilizer_schedule " "SEE " "NOTES"], + "economics_files": [ + "commodity_prices_net_fertilizer_ammonium_nitrate_dollar_per_kilogram", + "commodity_prices_net_fertilizer_super_phosphate_44to46pct_dollar_per_kilogram", + "commodity_prices_net_fertilizer_urea_dollar_per_kilogram", + ], + "notes": "Placeholder " "for the " "future", + }, + "Phosphorus fertilizer used": { + "biophysical_simulation": ["fertilizer_schedule " "SEE " "NOTES"], + "economics_files": ["commodity_prices_net_fertilizer_phosphorus_dollar_per_kilogram"], + "notes": "likely " + "as " + "'simple' " + "as " + "taking " + "the sum " + "of " + "phosphorus_masses " + "for each " + "fertilizer_schedule " + "file " + "used in " + "the " + "simulation, " + "but this " + "requires " + "some " + "consulting " + "with the " + "soil and " + "crop " + "SMEs", + }, + "Potassium fertilizer used": { + "biophysical_simulation": ["fertilizer_schedule " "SEE " "NOTES"], + "economics_files": ["commodity_prices_net_fertilizer_potassium_dollar_per_kilogram"], + "notes": "likely as " + "'simple' " + "as taking " + "the sum " + "of " + "potassium_masses " + "for each " + "fertilizer_schedule " + "file used " + "in the " + "simulation, " + "but this " + "requires " + "some " + "consulting " + "with the " + "soil and " + "crop " + "SMEs", + }, + "Seeds costs": { + "biophysical_simulation": [ + "both " "below " "are " "from " "multiple " "input " "files, " "see " "preprocessing", + "field.crop_specification", + "field.field_size", + ], + "economics_files": [ + "commodity_prices_*_dollar_per_square_meter", + "commodity_prices_barley_seed_dollar_per_square_meter", + "commodity_prices_corn_seed_dollar_per_square_meter", + "commodity_prices_cotton_seed_dollar_per_square_meter", + "commodity_prices_oat_seed_dollar_per_square_meter", + "commodity_prices_peanut_seed_dollar_per_square_meter", + "commodity_prices_rice_seed_dollar_per_square_meter", + "commodity_prices_sorghum_seed_dollar_per_square_meter", + "commodity_prices_soybean_seed_dollar_per_square_meter", + "commodity_prices_wheat_seed_dollar_per_square_meter", + ], + "preprocessing": "Map " + "'crop_specification' " + "in each field " + "input file, " + "e.g. CornSilage " + "to corn_seed to " + "get appropriate " + "economics_file, " + "ALSO convert " + "the field_size " + "from Ha to " + "square meters " + "(divide by " + "10000). However " + "- note this one " + "isn't reported " + "daily! So " + "easiest way to " + "handle this " + "could be to " + "divide the " + "price for each " + "field evenly " + "across each day " + "of the " + "simulation. " + "e.g. if the " + "cost for " + "corn_seed for a " + "field_size of " + "100 was 365, " + "and the " + "simulation was " + "two years, the " + "preprocessing " + "would take all " + "that and " + "compute a price " + "of 0.5 for each " + "day in the " + "simulation " + "(e.g. a list " + "consisting of " + "0.5 repeated " + "for each day of " + "the sim).", + }, + "Tractor hours for crops": { + "biophysical_simulation": [ + "Waiting " "on " "tractor_implement " "and " "other " "parts " "of " "EEE " "outputs" + ], + "economics_files": [ + "farm_services_tractor_small_dollar_per_hour", + "farm_services_tractor_medium_dollar_per_hour", + "farm_services_tractor_large_dollar_per_hour", + ], + }, + }, + "Revenue": { + "Carbon credits from soil conservation practice": { + "input_manager": ["economic_inputs.Soil_and_crop.carbon_credits_whole_simulation_tonnes"], + "economics_files": ["commodity_prices_carbon_credits_soil_dollar_per_tonne_CO2e"], + "future_expansion": "scale " "this " "to " "crop " "outputs/scenarios", + } + }, + }, +} diff --git a/RUFAS/EEE/economics/metrics.py b/RUFAS/EEE/economics/metrics.py new file mode 100644 index 0000000000..a0fb2a15da --- /dev/null +++ b/RUFAS/EEE/economics/metrics.py @@ -0,0 +1,54 @@ +"""Utility functions for computing economic performance metrics.""" + +from __future__ import annotations + +from typing import Iterable + +import numpy as np + + +class EconomicMetrics: + """Grouping of metric helper calculations used by the economics package.""" + + @staticmethod + def calculate_roi(benefits: float, costs: float) -> float: + """Return on Investment (ROI) as a percentage.""" + if costs == 0: + return float("nan") + return (benefits - costs) / costs * 100 + + @staticmethod + def calculate_payback_period(cash_flows: Iterable[float]) -> float: + """Compute the payback period from a sequence of annual cash flows.""" + cash_flows = np.asarray(list(cash_flows), dtype=float) + cumulative = np.cumsum(cash_flows) + for i, value in enumerate(cumulative): + if value >= 0: + if i == 0: + return 0.0 + prev = cumulative[i - 1] + if cash_flows[i] == 0: + return float("inf") + return (i - 1) + (-prev / cash_flows[i]) + return float("inf") + + @staticmethod + def calculate_net_annual_cash_flow(revenue: Iterable[float], costs: Iterable[float]) -> np.ndarray: + """Return the net annual cash flow array (revenue minus costs).""" + revenue_arr = np.asarray(list(revenue), dtype=float) + cost_arr = np.asarray(list(costs), dtype=float) + length = max(len(revenue_arr), len(cost_arr)) + if len(revenue_arr) < length: + revenue_arr = np.pad(revenue_arr, (0, length - len(revenue_arr))) + if len(cost_arr) < length: + cost_arr = np.pad(cost_arr, (0, length - len(cost_arr))) + return revenue_arr - cost_arr + + # The true MPSP is obtained via goal seeking in the DCFROR analysis; this helper + # provides the simple ratio described in the documentation for quick estimates. + + @staticmethod + def calculate_mpsp(total_cost: float, total_output: float) -> float: + """Return ``NaN`` because MPSP is solved via the DCFROR goal-seek workflow.""" + + return float("nan") diff --git a/RUFAS/EEE/economics/partial_budget.py b/RUFAS/EEE/economics/partial_budget.py new file mode 100644 index 0000000000..b62bba8b2e --- /dev/null +++ b/RUFAS/EEE/economics/partial_budget.py @@ -0,0 +1,296 @@ +"""Partial Budget Analysis (PBA) calculations.""" + +from __future__ import annotations + +from typing import Any, Dict + +import numpy as np +import pandas as pd +import math + +from RUFAS.input_manager import InputManager +from RUFAS.output_manager import OutputManager +from RUFAS.units import MeasurementUnits + + +class PartialBudget: + """Container for partial budget inputs and analysis.""" + + def __init__(self) -> None: + self.im = InputManager() + self.om = OutputManager() + + @staticmethod + def _to_array(value: Any) -> np.ndarray: + """Convert partial budget inputs to a one-dimensional float array.""" + + if isinstance(value, np.ndarray): + arr = value.astype(float, copy=True) + else: + try: + arr = np.asarray(value, dtype=float) + except Exception: + arr = np.asarray([0.0], dtype=float) + if arr.ndim == 0: + arr = arr.reshape(1) + arr = np.where(np.isnan(arr), 0.0, arr) + return arr.astype(float) + + def _load_inputs(self) -> Dict[str, np.ndarray]: + """Deprecated: retained for backwards compatibility.""" + + zero = self._to_array(0.0) + return { + "additional_revenue": zero.copy(), + "reduced_costs": zero.copy(), + "additional_costs": zero.copy(), + "reduced_revenue": zero.copy(), + } + + # Supporting multi-year scenarios will require accumulating results across + # scenarios as outlined in `Documentation of Economic Data and Analytical + # Methods (2).pdf`. + + def _calculate_from_preprocessed( + self, preprocessed_data: Dict[str, Dict[str, Dict[str, Any]]] | None + ) -> Dict[str, Any] | None: + """Compute partial budget inputs from preprocessed scenario data.""" + + if not preprocessed_data: + return None + + scenario_names: set[str] = set() + items: list[tuple[str | None, Dict[str, Any], Dict[str, float]]] = [] + for section_data in preprocessed_data.values(): + if not isinstance(section_data, dict): + continue + for category_data in section_data.values(): + if not isinstance(category_data, dict): + continue + for _, item in category_data.items(): + if not isinstance(item, dict): + continue + line_items = item.get("line_item_values_by_scenario") + if not isinstance(line_items, dict): + continue + scenario_names.update(line_items.keys()) + items.append((item.get("flow_type"), item, line_items)) + + if len(scenario_names) == 1: + scenario = next(iter(scenario_names)) + + revenue_total = 0.0 + cost_total = 0.0 + + for flow_type, item, line_items in items: + flow_type = flow_type or "cost" + if flow_type not in {"revenue", "cost"}: + continue + + raw = line_items.get(scenario, 0.0) + + try: + value = float(raw) + except (TypeError, ValueError): + value = 0.0 + + if not math.isfinite(value): + value = 0.0 + + if flow_type == "revenue": + revenue_total += value + else: + cost_total += value + + net_annual_cash_flow = revenue_total - cost_total + + return { + "mode": "single", + "scenario": scenario, + "revenue_total": revenue_total, + "cost_total": cost_total, + "net_annual_cash_flow": net_annual_cash_flow, + } + + if len(scenario_names) < 2: + return None + + def _pick_scenario(candidates: list[str]) -> str | None: + for candidate in candidates: + for name in scenario_names: + if name.lower() == candidate: + return name + return None + + baseline = _pick_scenario(["baseline", "base", "scenario_a", "a"]) + alternative = _pick_scenario(["alternative", "scenario", "scenario_b", "b", "alt"]) + if baseline is None: + baseline = sorted(scenario_names)[0] + if alternative is None: + alternative = next(name for name in sorted(scenario_names) if name != baseline) + + info_map = {"class": __name__, "function": self._calculate_from_preprocessed.__name__} + + additional_revenue = 0.0 + reduced_revenue = 0.0 + additional_costs = 0.0 + reduced_costs = 0.0 + + for flow_type, item, line_items in items: + flow_type = flow_type or "cost" + if flow_type not in {"revenue", "cost"}: + continue + if baseline not in line_items or alternative not in line_items: + self.om.add_warning( + "MissingScenarioData", + f"Partial budget line item missing scenario values for '{baseline}' or '{alternative}'.", + info_map, + ) + value_a = float(line_items.get(baseline, 0.0) or 0.0) + value_b = float(line_items.get(alternative, 0.0) or 0.0) + delta = value_b - value_a + + if flow_type == "revenue": + if delta > 0: + additional_revenue += delta + elif delta < 0: + reduced_revenue += abs(delta) + if flow_type == "cost": + if delta > 0: + additional_costs += delta + elif delta < 0: + reduced_costs += abs(delta) + + if not any([additional_revenue, reduced_revenue, additional_costs, reduced_costs]): + return None + + return { + "mode": "delta", + "baseline": baseline, + "alternative": alternative, + "additional_revenue": additional_revenue, + "reduced_revenue": reduced_revenue, + "additional_costs": additional_costs, + "reduced_costs": reduced_costs, + } + + def calculate_partial_budget(self, preprocessed_data: Dict[str, Dict[str, Dict[str, Any]]] | None = None) -> None: + """Perform a partial budget analysis and export multi-year net changes.""" + + info_map = { + "class": __name__, + "function": self.calculate_partial_budget.__name__, + "units": MeasurementUnits.DOLLARS, + } + derived_inputs = self._calculate_from_preprocessed(preprocessed_data) + if derived_inputs and derived_inputs.get("mode") == "delta": + inputs = { + key: self._to_array(derived_inputs[key]) + for key in ("additional_revenue", "reduced_costs", "additional_costs", "reduced_revenue") + } + net_change = (inputs["additional_revenue"] + inputs["reduced_costs"]) - ( + inputs["additional_costs"] + inputs["reduced_revenue"] + ) + cumulative_change = np.cumsum(net_change) + horizon = net_change.size + + result_df = pd.DataFrame( + { + "Period": np.arange(1, horizon + 1), + "AdditionalRevenue": inputs["additional_revenue"], + "ReducedCosts": inputs["reduced_costs"], + "AdditionalCosts": inputs["additional_costs"], + "ReducedRevenue": inputs["reduced_revenue"], + "NetChange": net_change, + "CumulativeNetChange": cumulative_change, + } + ) + + self.om.add_variable("econ_pba_additional_revenue", inputs["additional_revenue"].tolist(), info_map) + self.om.add_variable("econ_pba_reduced_costs", inputs["reduced_costs"].tolist(), info_map) + self.om.add_variable("econ_pba_additional_costs", inputs["additional_costs"].tolist(), info_map) + self.om.add_variable("econ_pba_reduced_revenue", inputs["reduced_revenue"].tolist(), info_map) + self.om.add_variable("econ_pba_net_change", net_change.tolist(), info_map) + self.om.add_variable("econ_pba_cumulative_net_change", cumulative_change.tolist(), info_map) + self.om.add_variable("econ_pba_summary", result_df.to_dict(orient="list"), info_map) + self.om.add_log("PartialBudget", "Partial budget analysis completed.", info_map) + return + + if derived_inputs and derived_inputs.get("mode") == "single": + net_annual_cash_flow = self._to_array(derived_inputs.get("net_annual_cash_flow", 0.0)) + revenue_total = self._to_array(derived_inputs.get("revenue_total", 0.0)) + cost_total = self._to_array(derived_inputs.get("cost_total", 0.0)) + inputs = { + "additional_revenue": self._to_array(0.0), + "reduced_costs": self._to_array(0.0), + "additional_costs": self._to_array(0.0), + "reduced_revenue": self._to_array(0.0), + } + horizon = net_annual_cash_flow.size + result_df = pd.DataFrame( + { + "Period": np.arange(1, horizon + 1), + "RevenueTotal": revenue_total, + "CostTotal": cost_total, + "NetAnnualCashFlow": net_annual_cash_flow, + } + ) + self.om.add_variable("econ_pba_additional_revenue", inputs["additional_revenue"].tolist(), info_map) + self.om.add_variable("econ_pba_reduced_costs", inputs["reduced_costs"].tolist(), info_map) + self.om.add_variable("econ_pba_additional_costs", inputs["additional_costs"].tolist(), info_map) + self.om.add_variable("econ_pba_reduced_revenue", inputs["reduced_revenue"].tolist(), info_map) + self.om.add_variable("econ_pba_revenue_total", revenue_total.tolist(), info_map) + self.om.add_variable("econ_pba_cost_total", cost_total.tolist(), info_map) + self.om.add_variable("econ_pba_net_annual_cash_flow", net_annual_cash_flow.tolist(), info_map) + self.om.add_variable("econ_pba_summary", result_df.to_dict(orient="list"), info_map) + self.om.add_log("PartialBudget", "Partial budget analysis completed.", info_map) + return + + else: + self.om.add_warning( + "MissingPartialBudgetData", + "No scenario-aware economics data was available to derive partial budget inputs.", + info_map, + ) + inputs = self._load_inputs() + net_change = (inputs["additional_revenue"] + inputs["reduced_costs"]) - ( + inputs["additional_costs"] + inputs["reduced_revenue"] + ) + cumulative_change = np.cumsum(net_change) + horizon = net_change.size + + result_df = pd.DataFrame( + { + "Period": np.arange(1, horizon + 1), + "AdditionalRevenue": inputs["additional_revenue"], + "ReducedCosts": inputs["reduced_costs"], + "AdditionalCosts": inputs["additional_costs"], + "ReducedRevenue": inputs["reduced_revenue"], + "NetChange": net_change, + "CumulativeNetChange": cumulative_change, + } + ) + + self.om.add_variable("econ_pba_additional_revenue", inputs["additional_revenue"].tolist(), info_map) + self.om.add_variable("econ_pba_reduced_costs", inputs["reduced_costs"].tolist(), info_map) + self.om.add_variable("econ_pba_additional_costs", inputs["additional_costs"].tolist(), info_map) + self.om.add_variable("econ_pba_reduced_revenue", inputs["reduced_revenue"].tolist(), info_map) + self.om.add_variable("econ_pba_net_change", net_change.tolist(), info_map) + self.om.add_variable("econ_pba_cumulative_net_change", cumulative_change.tolist(), info_map) + self.om.add_variable("econ_pba_summary", result_df.to_dict(orient="list"), info_map) + self.om.add_log("PartialBudget", "Partial budget analysis completed.", info_map) + + def has_partial_budget_activity( + self, preprocessed_data: Dict[str, Dict[str, Dict[str, Any]]] | None = None + ) -> bool: + """Return ``True`` when any partial budget inputs contain non-zero values.""" + derived_inputs = self._calculate_from_preprocessed(preprocessed_data) + if derived_inputs and derived_inputs.get("mode") == "single": + return bool(derived_inputs.get("net_annual_cash_flow")) + if derived_inputs and derived_inputs.get("mode") == "delta": + return any( + derived_inputs.get(key, 0.0) != 0.0 + for key in ("additional_revenue", "reduced_revenue", "additional_costs", "reduced_costs") + ) + + return False diff --git a/RUFAS/EEE/economics/preprocessing.py b/RUFAS/EEE/economics/preprocessing.py new file mode 100644 index 0000000000..9546e4d2c8 --- /dev/null +++ b/RUFAS/EEE/economics/preprocessing.py @@ -0,0 +1,629 @@ +"""Preprocessing utilities for the economics module. + +This module translates biophysical simulation outputs into +inputs required by the economic analysis using a hardcoded mapping +derived from ``economic_map.json`` and associated documentation. +The :class:`EconomicPreprocessor` now pulls biophysical values +from the :class:`~RUFAS.output_manager.OutputManager`, commodity +pricing from the :class:`~RUFAS.input_manager.InputManager`, and +stores the aggregated results back into the +:class:`~RUFAS.input_manager.InputManager` under the key +``economic_preprocessed``. The structure of the stored data is +validated using the ``economic_preprocessing_properties`` metadata. +""" + +from __future__ import annotations + +import math +import re +from dataclasses import dataclass +from typing import Any, Dict, Iterable, List, Set + +from RUFAS.input_manager import InputManager +from RUFAS.output_manager import OutputManager +from RUFAS.util import Aggregator +from RUFAS.EEE.economics.mapping import ECONOMIC_MAP +from RUFAS.EEE.economics.fallback_values import ( + BIOPHYSICAL_FALLBACKS, + ECONOMIC_PRICE_FALLBACK, + ECONOMIC_QUANTITY_FALLBACK, +) + + +@dataclass(frozen=True) +class EconomicItem: + """Definition of a single economic preprocessing item.""" + + section: str + category: str + name: str + biophysical_simulation: List[str] + input_manager: List[str] + economics_files: Any + preprocessing: str | None + + +class EconomicPreprocessor: + """Aggregate biophysical results for the economics module.""" + + def __init__( + self, + ) -> None: + self.im = InputManager() + self.om = OutputManager() + self.available_input_keys: Set[str] = self._load_available_input_keys() + self.mapping = self._build_mapping() + + def _load_available_input_keys(self) -> Set[str]: + """Cache the available input keys from the InputManager metadata.""" + + try: + metadata = self.im.get_metadata("files") + except Exception: + return set() + + if isinstance(metadata, dict): + return set(metadata.keys()) + return set() + + def _normalize_economics_key(self, path: str) -> str: + """Map mapping file paths to InputManager data keys.""" + + candidate = path.removesuffix(".csv") + + if not self.available_input_keys: + return candidate + + if candidate in self.available_input_keys: + return candidate + + prefix_matches = sorted(key for key in self.available_input_keys if key.startswith(f"{candidate}.")) + if prefix_matches: + return prefix_matches[0] + + return candidate + + def _get_data_with_handling(self, path: str, info_map: Dict[str, str]) -> Any: + """Fetch data from the InputManager while handling invalid paths.""" + + if not isinstance(path, str) or not path: + self.om.add_warning( + "InvalidEconomicsFilePath", + f"Economics pricing path '{path}' is invalid", + info_map, + ) + return None + + candidate_paths = [path] + normalized_path = self._normalize_economics_key(path) + if normalized_path != path: + candidate_paths.append(normalized_path) + + last_error: ValueError | None = None + for candidate in candidate_paths: + # Skip InputManager access entirely for wildcard paths (e.g., "*"). + # These selectors cannot be resolved to a concrete file and only + # generate validation spam inside the InputManager. Emit a single + # warning and continue. + if "*" in str(candidate): + self.om.add_warning( + "MissingEconomicsFile", + f"Commodity pricing '{candidate}' uses wildcard and was skipped", + info_map, + ) + continue + + if hasattr(self.im, "check_property_exists_in_pool"): + # Wildcard paths are handled above. For concrete paths, perform the + # inexpensive existence check when available to avoid repeated + # validation warnings from deeper get_data calls. + try: + if not self.im.check_property_exists_in_pool(candidate): + continue + except ValueError as exc: + last_error = exc + continue + try: + data = self.im.get_data(candidate) + except ValueError as exc: + last_error = exc + continue + + if data is not None: + return data + + if last_error is not None: + detail = ( + f"Failed to retrieve '{path}'" + + (f" (normalized to '{normalized_path}')" if normalized_path != path else "") + + f": {last_error}" + ) + self.om.add_warning("InvalidEconomicsFilePath", detail, info_map) + + return None + + def _build_mapping(self) -> List[EconomicItem]: + """Convert the hardcoded mapping into structured entries.""" + + items: List[EconomicItem] = [] + for section, categories in ECONOMIC_MAP.items(): + if not isinstance(categories, dict): + continue + for category, entries in categories.items(): + if not isinstance(entries, dict): + continue + for name, details in entries.items(): + if not isinstance(details, dict): + continue + biophysical_simulation = details.get("biophysical_simulation") or [] + input_manager = details.get("input_manager") or [] + preprocessing = details.get("preprocessing") + economics_files = details.get("economics_files") + if not biophysical_simulation and not input_manager and not economics_files: + continue + if isinstance(biophysical_simulation, str): + biophysical_simulation = [biophysical_simulation] + if isinstance(input_manager, str): + input_manager = [input_manager] + items.append( + EconomicItem( + section=section, + category=category, + name=name, + biophysical_simulation=list(biophysical_simulation), + input_manager=list(input_manager), + economics_files=economics_files, + preprocessing=preprocessing, + ) + ) + return items + + def _append_numeric(self, container: List[float], value: Any) -> None: + """Append numeric value to container if possible.""" + try: + container.append(float(value)) + except (TypeError, ValueError): + pass + + def _append_from_payload(self, container: List[float], payload: Any) -> None: + """Append numeric values from an OutputManager payload.""" + + if isinstance(payload, dict) and "values" in payload: + for value in payload.get("values", []): + self._append_from_payload(container, value) + return + if isinstance(payload, dict): + for value in payload.values(): + self._append_from_payload(container, value) + return + if isinstance(payload, (list, tuple)): + for value in payload: + self._append_from_payload(container, value) + return + self._append_numeric(container, payload) + + def _fetch_values(self, sim_paths: Iterable[str]) -> List[float]: + """Collect values from the OutputManager for the provided patterns.""" + + values: List[float] = [] + info_map = {"class": self.__class__.__name__, "function": self._fetch_values.__name__} + flat_pool = self.om._get_flat_variables_pool() + for path in sim_paths: + pattern = re.compile(path) + matched = False + for variable_name, payload in flat_pool.items(): + if pattern.search(variable_name): + matched = True + self._append_from_payload(values, payload) + if not matched: + fallback_values = BIOPHYSICAL_FALLBACKS.get(path) + if fallback_values: + values.extend(fallback_values) + else: + self.om.add_warning( + "MissingBiophysicalData", + f"No biophysical outputs matched pattern '{path}'", + info_map, + ) + return values + + def _fetch_values_by_scenario(self, sim_paths: Iterable[str]) -> Dict[str, List[float]]: + """Collect values per scenario from the OutputManager.""" + + flat_pool = self.om._get_flat_variables_pool() + if not flat_pool: + fallback_values = self._fallback_values_by_scenario(sim_paths) + return fallback_values + + scenario_names: List[str] = [] + pool = getattr(self.om, "variables_pool", {}) + if isinstance(pool, dict) and pool: + if all(isinstance(value, dict) and "values" in value for value in pool.values()): + scenario_names = ["baseline"] + else: + scenario_names = [name for name, data in pool.items() if isinstance(data, dict) and data] + if not scenario_names: + scenario_names = ["baseline"] + + values_by_scenario: Dict[str, List[float]] = {scenario: [] for scenario in scenario_names} + info_map = {"class": self.__class__.__name__, "function": self._fetch_values_by_scenario.__name__} + + for path in sim_paths: + pattern = re.compile(path) + matched = False + for variable_name, payload in flat_pool.items(): + if not pattern.search(variable_name): + continue + matched = True + if scenario_names == ["baseline"]: + scenario_key = "baseline" + else: + scenario_key = variable_name.split(".", 1)[0] + if scenario_key not in values_by_scenario: + scenario_key = "baseline" + values_by_scenario.setdefault(scenario_key, []) + self._append_from_payload(values_by_scenario[scenario_key], payload) + if not matched: + fallback_values = BIOPHYSICAL_FALLBACKS.get(path) + if fallback_values: + scenario_key = scenario_names[0] if scenario_names else "baseline" + values_by_scenario.setdefault(scenario_key, []) + values_by_scenario[scenario_key].extend(fallback_values) + else: + self.om.add_warning( + "MissingBiophysicalData", + f"No biophysical outputs matched pattern '{path}'", + info_map, + ) + return values_by_scenario + + def _fallback_values_by_scenario(self, sim_paths: Iterable[str]) -> Dict[str, List[float]]: + """Build fallback values when no OutputManager data is available.""" + + values_by_scenario: Dict[str, List[float]] = {"baseline": []} + for path in sim_paths: + fallback_values = BIOPHYSICAL_FALLBACKS.get(path) + if fallback_values: + values_by_scenario["baseline"].extend(fallback_values) + + if values_by_scenario["baseline"]: + return values_by_scenario + return {} + + def _fetch_input_values(self, input_paths: Iterable[str]) -> List[float]: + """Collect values from the InputManager for the provided paths.""" + + values: List[float] = [] + info_map = {"class": self.__class__.__name__, "function": self._fetch_input_values.__name__} + for path in input_paths: + data = self.im.get_data(path) + if data is None: + self.om.add_warning( + "MissingEconomicInput", + f"No economic input found at '{path}'", + info_map, + ) + continue + self._append_from_payload(values, data) + return values + + def _extract_price_values(self, price_data: Any) -> List[float]: + """Extract numeric price values from pricing payloads.""" + + info_map = {"class": self.__class__.__name__, "function": self._extract_price_values.__name__} + start_year: int = int(self.im.get_data("config.start_date").split(":")[0]) + end_year: int = int(self.im.get_data("config.end_date").split(":")[0]) + fips_code: int = self.im.get_data("config.FIPS_county_code") + values: List[float] = [] + for key, value in price_data.items(): + if not isinstance(value, dict) or "fips" not in value or not isinstance(value["fips"], list): + self.om.add_warning( + "MissingPriceData", + f"Price data missing for key: {key}, FIPS: '{fips_code}' is not in expected format." + "Using fallback price.", + info_map, + ) + values.extend(self._get_fallback_price(start_year, end_year, key)) + continue + fips_idx = value["fips"].index(fips_code) + for year in range(start_year, end_year + 1): + try: + price = value[f"{year}"][fips_idx] + values.append(price) + except (KeyError, IndexError): + self.om.add_warning( + "MissingPriceData", + f"Price data missing for year '{year}' and FIPS '{fips_code}' in '{key}'." + "Using fallback price.", + info_map, + ) + values.extend(self._get_fallback_price(start_year, end_year, key)) + continue + return values + + def _get_fallback_price(self, start_year: int, end_year: int, commodity: str) -> List[float]: + """Get a fallback price for a commodity.""" + info_map = {"class": self.__class__.__name__, "function": self._get_fallback_price.__name__} + defaults: Dict[str, List[float | str]] = self.im.get_data("_default_values") + defaults_fallback: Dict[str, List[float | str]] = self.im.get_data("_default_fallback_values") + if commodity not in defaults["commodity"] and commodity not in defaults_fallback["commodity"]: + self.om.add_warning( + "MissingFallbackPrice", + f"No fallback price found for commodity: {commodity}", + info_map, + ) + return [ECONOMIC_PRICE_FALLBACK.get("cost", 1.0)] * (end_year - start_year + 1) + commodity_idx = defaults["commodity"].index(commodity) + values: List[float] = [] + use_fallback = False + for year in range(start_year, end_year + 1): + price = defaults[f"{year}"][commodity_idx] + if math.isnan(price): + use_fallback = True + break + values.append(price) + if use_fallback: + commodity_idx = defaults_fallback["commodity"].index(commodity) + values = [] + for year in range(start_year, end_year + 1): + price = defaults_fallback[f"{year}"][commodity_idx] + if math.isnan(price): + self.om.add_warning( + "MissingFallbackPrice", + f"No fallback price found for commodity: {commodity} in year: {year}", + info_map, + ) + price = ECONOMIC_PRICE_FALLBACK.get("cost", 1.0) + values.append(price) + return values + + def _infer_flow_type(self, item: EconomicItem) -> str | None: + """Infer if an item is a revenue or cost based on naming conventions.""" + + category = item.category.lower() + if "revenue" in category: + return "revenue" + if "cost" in category: + return "cost" + + haystack = " ".join([item.name, *item.biophysical_simulation, *item.input_manager]).lower() + if "_products" in haystack: + return "revenue" + if "_inputs" in haystack: + return "cost" + return None + + def _fetch_prices(self, economics_files: Any) -> Dict[str, Any]: + """Collect commodity pricing using the InputManager.""" + + prices: Dict[str, Any] = {} + info_map = {"class": self.__class__.__name__, "function": self._fetch_prices.__name__} + + if economics_files is None: + return prices + + if isinstance(economics_files, list): + for file_key in economics_files: + price_data = self._get_data_with_handling(file_key, info_map) + if price_data is None: + self.om.add_warning( + "MissingEconomicsFile", + f"Commodity pricing '{file_key}' not found in InputManager", + info_map, + ) + continue + prices[file_key] = price_data + return prices + + if not isinstance(economics_files, dict): + return prices + + selector_path = economics_files.get("input_manager_location") + if selector_path: + selection = self._get_data_with_handling(selector_path, info_map) + if selection is None: + self.om.add_warning( + "MissingSelection", + f"Selector value not found at '{selector_path}'", + info_map, + ) + for option, file_key in economics_files.items(): + if option == "input_manager_location": + continue + if not isinstance(file_key, str): + continue + price_data = self._get_data_with_handling(file_key, info_map) + if price_data is not None: + prices[file_key] = price_data + if prices: + self.om.add_warning( + "MissingSelectionFallback", + f"No selector match; using all available pricing options for '{selector_path}'.", + info_map, + ) + return prices + selection_key = str(selection).lower() + selected_file = None + for option, file_key in economics_files.items(): + if option == "input_manager_location": + continue + if option.lower() == selection_key: + selected_file = file_key + break + if selected_file is None: + self.om.add_warning( + "UnknownSelection", + f"No price file matched selection '{selection}' at '{selector_path}'", + info_map, + ) + for option, file_key in economics_files.items(): + if option == "input_manager_location": + continue + if not isinstance(file_key, str): + continue + price_data = self._get_data_with_handling(file_key, info_map) + if price_data is not None: + prices[file_key] = price_data + if prices: + self.om.add_warning( + "UnknownSelectionFallback", + f"No matching selection; using all available pricing options for '{selector_path}'.", + info_map, + ) + return prices + price_data = self._get_data_with_handling(selected_file, info_map) + if price_data is None: + self.om.add_warning( + "MissingEconomicsFile", + f"Commodity pricing '{selected_file}' not found in InputManager", + info_map, + ) + return prices + prices[selected_file] = price_data + return prices + + for label, file_key in economics_files.items(): + if not isinstance(file_key, str): + continue + price_data = self._get_data_with_handling(file_key, info_map) + if price_data is None: + self.om.add_warning( + "MissingEconomicsFile", + f"Commodity pricing '{file_key}' not found in InputManager", + info_map, + ) + continue + prices[label] = price_data + return prices + + def _aggregate(self, values: List[float], desc: str) -> float | None: + """Aggregate values according to a textual description.""" + if not values: + return None + d = desc.lower() if isinstance(desc, str) else "" + if "average" in d or "mean" in d: + return Aggregator.average(values) + if "product" in d: + return Aggregator.product(values) + if "divide" in d or "ratio" in d: + result = Aggregator.division(values) + if result is not None: + return result + if "subtract" in d or "difference" in d: + result = Aggregator.subtraction(values) + if result is not None: + return result + if "standard deviation" in d or "std" in d: + return Aggregator.standard_deviation(values) + # Default aggregation is sum + return Aggregator.sum(values) + + def preprocess(self) -> Dict[str, Dict[str, Dict[str, Dict[str, Any]]]]: + """Run preprocessing and store results in the InputManager.""" + + results: Dict[str, Dict[str, Dict[str, Dict[str, Any]]]] = {} + info_map = {"class": self.__class__.__name__, "function": self.preprocess.__name__} + + for item in self.mapping: + section_data = results.setdefault(item.section, {}) + category_data = section_data.setdefault(item.category, {}) + + values_by_scenario = self._fetch_values_by_scenario(item.biophysical_simulation) + input_values = self._fetch_input_values(item.input_manager) + + if values_by_scenario: + for scenario, biophysical_values in values_by_scenario.items(): + if not biophysical_values and input_values: + values_by_scenario[scenario] = list(input_values) + elif input_values: + values_by_scenario = {"baseline": list(input_values)} + else: + values_by_scenario = {"baseline": [ECONOMIC_QUANTITY_FALLBACK]} + + biophysical_values: List[float] = [] + for scenario_values in values_by_scenario.values(): + biophysical_values.extend(scenario_values) + + aggregated_value = self._aggregate(biophysical_values, item.preprocessing or "") + if aggregated_value is None and biophysical_values: + aggregated_value = Aggregator.sum(biophysical_values) + if not biophysical_values and not input_values: + self.om.add_warning( + "MissingBiophysicalData", + "No values found for " + f"'{item.name}' using patterns {item.biophysical_simulation} " + f"and input paths {item.input_manager}", + info_map, + ) + + price_data = self._fetch_prices(item.economics_files) + if item.economics_files and not price_data: + self.om.add_warning( + "MissingEconomicsFile", + f"No commodity pricing retrieved for '{item.name}'", + info_map, + ) + + price_values = self._extract_price_values(price_data) + price_aggregate = self._aggregate(price_values, "average") + if price_aggregate is None: + flow_type = self._infer_flow_type(item) or "cost" + if flow_type in ECONOMIC_PRICE_FALLBACK: + price_aggregate = ECONOMIC_PRICE_FALLBACK[flow_type] + aggregates_by_scenario: Dict[str, float | None] = {} + for scenario, scenario_values in values_by_scenario.items(): + scenario_aggregate = self._aggregate(scenario_values, item.preprocessing or "") + if scenario_aggregate is None and scenario_values: + scenario_aggregate = Aggregator.sum(scenario_values) + aggregates_by_scenario[scenario] = scenario_aggregate + line_item_values_by_scenario: Dict[str, float] = {} + if price_aggregate is not None: + for scenario, aggregate_value in aggregates_by_scenario.items(): + if aggregate_value is None: + continue + line_item_values_by_scenario[scenario] = aggregate_value * price_aggregate + else: + for scenario, aggregate_value in aggregates_by_scenario.items(): + if aggregate_value is None: + continue + line_item_values_by_scenario[scenario] = aggregate_value + if line_item_values_by_scenario: + self.om.add_warning( + "MissingPriceForLineItem", + f"No price found for '{item.name}'. Using aggregated values as totals.", + info_map, + ) + if not line_item_values_by_scenario and aggregated_value is not None: + fallback_flow_type = self._infer_flow_type(item) or "cost" + fallback_price = ECONOMIC_PRICE_FALLBACK.get(fallback_flow_type, 1.0) + line_item_values_by_scenario["baseline"] = aggregated_value * fallback_price + + flow_type = self._infer_flow_type(item) or "cost" + category_data[item.name] = { + "biophysical_values": biophysical_values, + "biophysical_aggregate": aggregated_value, + "biophysical_values_by_scenario": values_by_scenario, + "biophysical_aggregate_by_scenario": aggregates_by_scenario, + "price_data": price_data, + "price_values": price_values, + "price_aggregate": price_aggregate, + "line_item_values_by_scenario": line_item_values_by_scenario, + "flow_type": flow_type, + } + + # Store aggregated results back into the InputManager + self.im.add_runtime_variable_to_pool( + variable_name="economic_preprocessed", + data=results, + properties_blob_key="economic_preprocessing_properties", + eager_termination=False, + ) + self.om.add_log( + "Economic preprocessing", + "Economic preprocessing completed", + info_map, + ) + return results + + +__all__ = ["EconomicPreprocessor"] diff --git a/RUFAS/util.py b/RUFAS/util.py index f849e3ad8e..56f5d92609 100644 --- a/RUFAS/util.py +++ b/RUFAS/util.py @@ -58,7 +58,11 @@ def convert_dict_of_lists_to_list_of_dicts(dict_of_lists: dict[str, list[Any]]) A list of dictionaries with string keys and integer values. """ - return [dict(zip(dict_of_lists.keys(), values)) for values in zip(*dict_of_lists.values())] + try: + return [dict(zip(dict_of_lists.keys(), values)) for values in zip(*dict_of_lists.values())] + except AttributeError: + print(dict_of_lists) + raise AttributeError @staticmethod def flatten_keys_to_nested_structure(input_dict: Dict[str, Any]) -> Dict[str, Any]: diff --git a/changelog.md b/changelog.md index 1971677f92..856a61f8b9 100644 --- a/changelog.md +++ b/changelog.md @@ -29,6 +29,7 @@ v1.0.0 - [2728](https://github.com/RuminantFarmSystems/RuFaS/pull/2728) - [minor change] [InputManager] [NoInputChange] [NoOutputChange] Enforces the required file blobs. - [2743](https://github.com/RuminantFarmSystems/RuFaS/pull/2743) - [minor change] [NoInputChange] [NoOutputChange] Fix broken IM unit test. - [2757](https://github.com/RuminantFarmSystems/RuFaS/pull/2757) - [minor change] [NoInputChange] [NoOutputChange] Updates version number in `pyproject.toml`. +- [2496](https://github.com/RuminantFarmSystems/MASM/pull/2496) - [minor change] [EEE] Adds economic map file to generate partial budget analysis. - [2737](https://github.com/RuminantFarmSystems/RuFaS/pull/2737) - [minor change] [NoInputChange] [NoOutputChange] Update CONTRIBUTING.md file to provide forking guidelines for contributors. - [2690](https://github.com/RuminantFarmSystems/RuFaS/pull/2690) - [minor change] [TaskManager] [NoInputChange] [NoOutputChange] Remove hard-coded name for output folder, use user-defined name instead. - [2720](https://github.com/RuminantFarmSystems/MASM/pull/2720) - [minor change] [Emissions] [NoInputChange] [NoOutputChange] Adds FGF emissions reset when there's a harvest-kill operation and there's none of that feed left in storage. diff --git a/docs/economic_docs/DCFROR Flow Chart (1).pdf b/docs/economic_docs/DCFROR Flow Chart (1).pdf new file mode 100644 index 0000000000..a818722a83 Binary files /dev/null and b/docs/economic_docs/DCFROR Flow Chart (1).pdf differ diff --git a/docs/economic_docs/Documentation of Economic Data and Analytical Methods (2).pdf b/docs/economic_docs/Documentation of Economic Data and Analytical Methods (2).pdf new file mode 100644 index 0000000000..bc5fd97f79 Binary files /dev/null and b/docs/economic_docs/Documentation of Economic Data and Analytical Methods (2).pdf differ diff --git a/docs/economic_docs/RuFaS Econ Mapping.xlsx b/docs/economic_docs/RuFaS Econ Mapping.xlsx new file mode 100644 index 0000000000..7f49555ac5 Binary files /dev/null and b/docs/economic_docs/RuFaS Econ Mapping.xlsx differ diff --git a/docs/economic_docs/RuFaS_Economics_Module_Overview_Slides.pdf b/docs/economic_docs/RuFaS_Economics_Module_Overview_Slides.pdf new file mode 100644 index 0000000000..768a85b008 Binary files /dev/null and b/docs/economic_docs/RuFaS_Economics_Module_Overview_Slides.pdf differ diff --git a/docs/economic_docs/RuFaS_FAB_Pre-Meeting_Materials_4_18.pdf b/docs/economic_docs/RuFaS_FAB_Pre-Meeting_Materials_4_18.pdf new file mode 100644 index 0000000000..057df7ffea Binary files /dev/null and b/docs/economic_docs/RuFaS_FAB_Pre-Meeting_Materials_4_18.pdf differ diff --git a/docs/economic_docs/Variables Update for Joe and Pooya.xlsx b/docs/economic_docs/Variables Update for Joe and Pooya.xlsx new file mode 100644 index 0000000000..fead9457f8 Binary files /dev/null and b/docs/economic_docs/Variables Update for Joe and Pooya.xlsx differ diff --git a/docs/economic_docs/diagram.md b/docs/economic_docs/diagram.md new file mode 100644 index 0000000000..65c532d181 --- /dev/null +++ b/docs/economic_docs/diagram.md @@ -0,0 +1,56 @@ +```mermaid +--- +config: + layout: elk + theme: neo +--- +flowchart TB + subgraph Orchestration["Orchestration"] + TM["TaskManager"] + IM["InputManager"] + OM["OutputManager"] + SE["SimulationEngine"] + EEE["EEEManager"] + EF["EconomicFramework"] + end + subgraph Economics_Module["Economics_Module"] + EP["EconomicPreprocessor"] + DCFROR["DCFRORCalculator"] + PB["PartialBudget"] + MAP["ECONOMIC_MAP"] + AGG["Aggregator"] + EQ["Economics Equations"] + MET["EconomicMetrics"] + DCC["DigesterCostCalculator"] + end + TM -- "load metadata and inputs (task_manager.py:L72-L150)" --> IM + TM -- "startup and logging (task_manager.py:L109-L138)" --> OM + TM -- "launch simulations (task_manager.py:L150-L210)" --> SE + IM -- "simulation config and runtime data (simulation_engine.py:L49-L58)" --> SE + SE -- "biophysical outputs (simulation_engine.py:L59-L79)" --> OM + SE -- "end-of-sim call (simulation_engine.py:L71-L78)" --> EEE + EEE -- "load runtime econ metadata (EEE_manager.py:L29-L36)" --> IM + EEE -- "invoke economics (EEE_manager.py:L37-L39)" --> EF + IM -- "commodity prices and mapped econ inputs (preprocessing.py:L217-L238)" --> EP + IM -- "capital and cashflow inputs (dcfror.py:L23-L63)" --> DCFROR + IM -- "partial budget inputs (partial_budget.py:L68-L94)" --> PB + EF -- "orchestrate preprocessing (framework.py:L124-L136)" --> EP + EF -- "orchestrate DCFROR (framework.py:L131-L136)" --> DCFROR + EF -- "orchestrate partial budget (framework.py:L127-L136)" --> PB + EF -. "capital cost presence check (framework.py:L21-L123)" .-> IM + EF -. "logs and warnings (framework.py)" .-> OM + OM -- "biophysical outputs consumed (preprocessing.py:L196-L215)" --> EP + EP -- "store economic_preprocessed (preprocessing.py:L347-L353)" --> IM + EP -- "mapping lookup (mapping.py)" --> MAP + EP -- "aggregate helpers (preprocessing.py:L180-L215)" --> AGG + DCFROR -- "equations (equations.py)" --> EQ + DCFROR -- "metrics (metrics.py)" --> MET + DCFROR -- "export DCFROR results (dcfror.py:L359-L392)" --> OM + PB -- "export PBA metrics (partial_budget.py:L99-L144)" --> OM + DCC -. "CAPEX/OPEX curves used by DCFROR (digester_costs.py)" .-> DCFROR + OM -- "reports (report_generator.py)" --> RG["ReportGenerator"] + OM -- "graphs (graph_generator.py)" --> GG["GraphGenerator"] + + DCC:::aux + classDef aux fill:#eef,stroke:#666,stroke-dasharray: 5 5 +``` diff --git a/docs/economic_docs/economics_map_summary.md b/docs/economic_docs/economics_map_summary.md new file mode 100644 index 0000000000..7cb0d73782 --- /dev/null +++ b/docs/economic_docs/economics_map_summary.md @@ -0,0 +1,62 @@ +# Economics map narrative + +This document summarizes how each cost or revenue item in `input/data/EEE/economics_map.json` is intended to connect biophysical outputs with commodity price inputs and any preprocessing needed before aggregation. It is meant to stay in sync with the map so later code changes keep the economic intent intact. + +## Animal module + +- **Purchased heifers** – Uses the counted number of purchased heifers reported by `AnimalModuleReporter.report_life_cycle_manager_data.bought_heifer_num` and multiplies by the bred heifer price from `commodity_prices.cow_dairy_heifer_bred_t3.dollar_per_animal.csv`. +- **Semen purchased** – Sums daily AI events across heifers and cows (`AnimalModuleReporter._record_heiferIIs_conception_rate.heiferII_total_num_ai_performed` and `AnimalModuleReporter._record_cows_conception_rate.cow_total_num_ai_performed`). The semen price file is chosen from the user’s management input (`animal.animal_config.management_decisions.semen_type`) and can be conventional, sexed, or beef semen price per straw. Preprocessing aggregates daily counts and selects the matching semen price series. +- **Bedding requirements** – Daily head counts per pen from `AnimalModuleReporter.report_daily_pen_total.number_of_animals_in_pen_.*` are matched to the bedding type declared for each pen (`animal.pen_information..*.manure_streams.0.bedding_name`). Price files differ by bedding material (sand, straw, sawdust, manure solids, compost bedded pack) and are annual dollars per head; preprocessing maps head counts to the correct bedding price by pen. +- **Animal – Labor hours** – Uses per-day labor hours from `Economic_inputs.Animal.labor_hours_per_day` scaled by hourly wage in `farm_services.labor_hours.dollar_per_hour.csv`. +- **Animal investment (fuel, utilities, water)** – Daily consumption from the economic inputs (`gasoline_liters_per_day`, `diesel_liters_per_day`, `propane_liters_per_day`, `natural_gas_megajoule_per_day`, `electricity_kwh_per_day`, `water_cubic_meter_per_day`) is paired with the corresponding commodity price series for gasoline, diesel, wholesale propane, industrial natural gas, industrial electricity, and municipal water. + +### Animal revenue + +- **FPCM (Milk Production)** – Uses fat- and protein-corrected milk computed during preprocessing (`Economic_preprocessing.Animal.FPCM`) and multiplies by the retail milk price series in `commodity_prices.milk_retail.dollar_per_liter.csv`. Preprocessing follows the FPCM calculation logic in `economic_preprocessing.json` to transform raw milk outputs. +- **Sold calves** – Revenues come from sold calf weights (`AnimalModuleReporter.report_sold_animal_information_sort_by_sell_day.sold_calves_sold_weight`) valued with `commodity_prices.calves_all.dollar_per_kilogram.csv`. +- **Sold heifer IIs** – Sold weights from `report_sold_animal_information_sort_by_sell_day.heiferII_sold_weight` use `commodity_prices.cow_dairy_heifer_open.dollar_per_animal.csv`. +- **Sold heifer IIIs** – Counts from life-cycle manager reports (`sold_heiferII_num` and `sold_heiferIII_oversupply_num`) are summed before applying the bred heifer price (`cow_dairy_heifer_bred_t3`). +- **Sold cows** – Sold cow counts (`report_sold_animal_information_sort_by_sell_day.sold_cows_sold_count`) are multiplied by `commodity_prices.cows_milk.dollar_per_animal.csv` after summing the counts. +- **Carbon credits from enteric emissions reduction** – Daily enteric methane emissions per pen (`report_enteric_methane_emission.enteric_methane_emission.*`) are summed and converted to tonnes before applying the carbon credit price in `commodity_prices.carbon_credits_enteric.dollar_per_tonne.csv`. + +## Manure module + +### Costs + +- **Purchased manure** – Off-farm manure mass (`ManureManager._record_manure_request_results.off_farm_manure.total_manure_mass`) is priced using `commodity_prices.bedding_manure_solids.dollar_per_kilogram.csv`. +- **General manure operational labor** – Daily labor hours from `Economic_inputs.Manure.general.labor_hours` are valued with `farm_services.labor_hours.dollar_per_hour.csv`. +- **Slurry storage operational costs** – Daily labor, diesel, gasoline, propane, natural gas, electricity, and water consumption from `Economic_inputs.Manure.slurry_storage` are paired with the corresponding wage or commodity price files (labor hours; diesel/gasoline per liter; propane per liter; industrial natural gas per megajoule; residential, commercial, or industrial electricity per kWh; municipal water per cubic meter). +- **Digester operational costs** – Similar daily inputs from `Economic_inputs.Manure.digester` map to wage or commodity price series for labor, diesel, gasoline, propane, industrial natural gas, multiple electricity tariffs, and municipal water. Additional digester operating costs reference helper methods in `digester_costs.py`, applying natural gas, industrial electricity, and digester carbon credit price files as needed. +- **Manure disposal quantity and transport** – Manually provided disposal mass and transport distance (`Economic_inputs.Manure.manure_disposal_*`) use user-specified per-kilogram and per-kilometer prices; these entries are fully manual with no linked commodity series. + +### Revenue + +- **Sold manure** – Manual manure sales values (`Economic_inputs.Manure.manure_sales`) are multiplied by `commodity_prices.bedding_manure_solids.dollar_per_kilogram.csv`; future logic could scale sales to total manure production. +- **Digester co-products** – Placeholders for fertilizer nutrients, bedding solids, RNG, electricity, and carbon credits reference future outputs from the manure module. Current mappings list the relevant commodity price files (net fertilizer N/P/K per kilogram; manure solids per kilogram; industrial natural gas per megajoule for RNG; industrial electricity per kWh; carbon credit price per tonne), with preprocessing deferred until those outputs are available. + +## Feed storage module + +### Costs + +- **Purchased feed costs** – Feed purchase costs recorded per ration interval (`FeedManager.purchase_feed.ration_interval_*_cost`) should be either prorated across days until the next ration reformulation or left as interval totals with zeroes on non-purchase days. Commodity price series for each feed ingredient are available for historical comparisons or scaling, though the current logic uses the recorded costs directly. +- **Feed storage labor** – Daily labor hours from `Economic_inputs.Feed_storage.labor_hours_per_day` use the labor wage series in `farm_services.labor_hours.dollar_per_hour.csv`. + +### Revenue + +- **Feed sales** – Harvest yields per field (`CropManagement._record_yield.harvest_yield.field='field_.*'`) are matched to the corresponding feed commodity price series (same set used for purchased feed) to value feed sold from each harvest event. + +## Soil and crop module + +### Costs + +- **Nitrogen, phosphorus, potassium fertilizer use** – Fertilizer masses from the schedules are summed across applications (see fertilizer schedule inputs) and priced using the net N/P/K commodity price series per kilogram. +- **Other fertilizer types** – Placeholder entries for ammonium nitrate, super phosphate, and urea using the listed net fertilizer price files when those outputs are available. +- **Seed costs** – Each field’s `crop_specification` selects the matching seed price file (e.g., corn_seed, soybean_seed), and field area is converted from hectares to square meters (divide by 10,000). Because seed purchases are scenario-level events, total seed cost per field is evenly allocated across all simulation days. +- **Land purchase and rental** – Manual entries for land purchased or rented over the whole simulation (`Economic_inputs.Soil_and_crop.land_ha_purchased` and `land_ha_rented`) are valued with per-hectare purchase (`capital_costs.land_ha`) or rental (`farm_services.land_ha_rent`) price files. Land purchases are single-scenario values rather than per-day series. +- **Crop labor hours** – Daily labor from `Economic_inputs.Soil_and_crop.labor_hours_per_day` multiplied by the labor wage series. +- **Crop water consumption** – Daily irrigation uptake (`FieldDataReporter.send_crop_daily_variables.water_uptake.*`) uses `commodity_prices.water_irrigation.dollar_per_cubic_meter.csv`. +- **Tractor hours for crops** – Pending tractor and implement outputs will map to hourly tractor service price files for small, medium, and large tractors. + +### Revenue + +- **Carbon credits from soil conservation practice** – Scenario-level carbon credits (`Economic_inputs.Soil_and_crop.carbon_credits_whole_simulation_tonnes`) are priced with `commodity_prices.carbon_credits_soil.dollar_per_tonne.csv`; future work may scale credits to crop outputs or conservation scenarios. diff --git a/input/data/EEE/econ/_default_values.csv b/input/data/EEE/econ/_default_values.csv index 002cc7c00e..a0b7cb55ae 100644 --- a/input/data/EEE/econ/_default_values.csv +++ b/input/data/EEE/econ/_default_values.csv @@ -1,108 +1,107 @@ -,1866,1867,1868,1869,1870,1871,1872,1873,1874,1875,1876,1877,1878,1879,1880,1881,1882,1883,1884,1885,1886,1887,1888,1889,1890,1891,1892,1893,1894,1895,1896,1897,1898,1899,1900,1901,1902,1903,1904,1905,1906,1907,1908,1909,1910,1911,1912,1913,1914,1915,1916,1917,1918,1919,1920,1921,1922,1923,1924,1925,1926,1927,1928,1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025 -commodity_prices.digester_bedding_coproduct.dollars_per_kg_bedding.csv,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0, -commodity_prices.alfalfa_silage.dollar_per_kilogram.csv,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0630931948830723,0.0835670783798233,0.0910881462987152,0.0633717488715091,, -commodity_prices.elec_commercial.dollar_per_kwh.csv,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0792,0.0789,0.0803,0.0817,0.0867,0.0946,0.0965,0.1026,0.1016,0.1019,0.1024,0.1009,0.1026,0.1074,0.1064,0.1043,0.1066,0.1067,0.1068,0.1059,0.1122,0.1241,0.1259,, 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+commodity_prices_soybean_meal_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.227398,0.329003,0.337773,0.303152,0.330106,0.445305,0.442683,0.387732,0.32026,0.319079,0.316148,0.338912,0.303891,0.319633,0.382786,0.4433519999999999,0.438291,0.335356, +commodity_prices_calf_heifer_1_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.93862,1.58092,0.937086,0.854145,0.80478,1.38506,2.89926,11.0253,12.9687 +commodity_prices_bedding_sawdust_dollar_per_head,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.22,,,, +commodity_prices_net_fertilizer_nitrogen_solutions_30pct_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.073634374,0.072532063,0.068067704,0.058422483,0.059083869,0.060957798,0.061288491,0.06564262,0.1361354078826259,0.1537723838026422,0.1240099869376147,0.1322773194001223,0.1259390311788665,0.1259390311788665,0.1444027403451336,0.1628664495114006,0.1614885607676494,0.1532212283051417,0.1573548945363955,0.1543235393001427,0.1289703864151193,0.1218053649476126,0.1504654508176392,0.1499142953201387,0.1471585178326361,0.1526700728076412,0.1521189173101407,0.1499142953201387,0.1537723838026422,0.1862905581551723,0.2006206010901856,0.1763697592001631,0.1477096733301366,0.1410958073601305,0.1444027403451336,0.2083367780551927,0.1399934963651295,0.1774720701951641,0.1962113571101815,0.2369968639252192,0.2557361508402366,0.3053401456152824,0.4420267089954089,0.3527395184003263,0.3119540115852885,0.3869111592453579,0.4442313309854109,0.4519475079504181,0.3957296472053661,0.377243892,0.3061448326416332,0.293016309,0.3158121000677921,0.3222826656084481,0.2952650231209731,0.4300997040294979,0.7076616125707547,,, +commodity_prices_natgas_commercial_dollar_per_megajoule,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.000687708,0.000678414,0.000687708,0.000715588,0.000762055,0.000817815,0.000873575,0.000994388,0.001254602,0.001524109,0.001895843,0.002072417,0.002537084,0.003150445,0.003717339,0.004479394,0.005194982,0.005157808,0.005111342,0.004721021,0.004432927,0.00430282,0.004405047,0.004488687,0.004470101,0.004535154,0.004851128,0.005055582,0.004693141,0.005018408,0.005390142,0.005092755,0.004953355,0.006124317,0.007834293,0.00616149,0.007806413,0.008763628,0.010538657,0.011152018,0.010538657,0.011365765,0.009349109,0.008800801,0.008280374,0.007527612,0.007509026,0.00827108,0.007351039,0.006765558,0.007323159,0.007239519,0.007072238,0.006951425,0.008168853,0.010520071,0.010148337,, +commodity_prices_corn_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00658535,0.00709439,0.0074181,0.00748482,0.0074181,0.00799138,0.00786783,0.00860667,0.00909841,0.0100003,0.0107614,0.012118,0.0148313,0.0195015,0.0201588,0.0208483,0.0227436,0.024115,0.0249675,0.0251108,0.0243053,0.0239865,0.0231315,0.0224816,0.0226324,0.0241768,0.0272013,0.0285827,, +commodity_prices_propane_residential_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7581741475824283,0.5681902356679989,0.5426976206012031,0.6326043942399898,0.6560716012268155,0.5761595177272739,0.5047889132220815,0.6555432568730996,0.7302160135890167,0.6759286312446999,0.6805954969210732, +commodity_prices_distiller_grains_modified_wet_50pct_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0616265,0.0751592,0.076337,0.0779986,0.108108,0.135124,0.126786,0.0863367,0.0822324 +commodity_prices_net_fertilizer_super_phosphate_20pct_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.04161224,0.042163396,0.042328742,0.044423133,0.044312902,0.044919173,0.045525444,0.046407293,0.047564719,0.04866703,0.050871652,0.053351852,0.055721821,0.060461758,0.1076957842115996,0.1218053649476126,0.104884891,0.1119396815423535,0.1165693877213578,0.1292459641638695,0.1441271625963833,0.1510166063151397,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +commodity_prices_net_fertilizer_nitrogen_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2606414347679911,0.2591974073645398,0.2522859174258834,0.2411635994863231,0.2391904628052713,0.2374818807630196,0.2331828678825157,0.2319923720079146,0.2127239758152967,0.1943374284186798,0.1945358443977799,0.1969058130370321,0.200025353,0.2220715730529054,0.4776313541339418,0.5312587840407414,0.4041182338773239,0.4192198945088378,0.4174121044770361,0.4440770074461108,0.5226607582797335,0.5817997431615382,0.5810171023550875,0.5411134443360506,0.575108715,0.5532057959512118,0.4872765753402008,0.4593550378368249,0.5038884020348662,0.529616341,0.522010395,0.5358223515600457,0.527543996,0.5410693518962506,0.5761669339770831,0.678902319,0.6956574458351936,0.6663580195880664,0.5834862789838898,0.53390433,0.5563914747267648,0.753495704,0.5768724130138837,0.7087418773458557,0.7536720735020972,0.8779907075183122,1.0044919173046292,1.1161449979882825,1.5086338508683454,1.3369158440670867,1.1619239736106748,1.471673363205961,1.722967200736344,1.719792545070741,1.695023617013068,1.470251382,1.193130397879154,1.1419721446011564,1.2308294339081884,1.256017240143962,1.1507575632313145,1.6762402376582508,2.757993132602502,,, +commodity_prices_net_fertilizer_potassium_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.094523168,0.096639605,0.097830101,0.098844227,0.098469441,0.098381256,0.099483567,0.095901057,0.089650953,0.085429102,0.096364027,0.1064611958971984,0.1079382926304998,0.1149159212288563,0.1582698126622464,0.1803160325622668,0.1746281078280615,0.1750800553360119,0.1805034254314169,0.2135727552814475,0.2539944994681349,0.2824671924690113,0.2751147781323545,0.2503127807448316,0.2562873063377371,0.219547281,0.2002568384618352,0.2296444495885624,0.2884417180619169,0.2902715543136185,0.2801743855994091,0.2792484443636083,0.2709811119011007,0.2645546388002447,0.2663954981618964,0.2847599993386134,0.281089304,0.2792484443636083,0.299464828,0.3086470786002855,0.3031355236252804,0.3123177742136389,0.3012946642636287,0.3031355236252804,0.3325341578619576,0.4501066485887663,0.501551503,0.5144154720371259,1.0306607803259535,1.567122472,0.9388052051125184,1.1041518543626714,1.221724345,1.0931287444126612,1.1041518543626714,1.1745454345034363,1.0619774356939324,0.9876706515209136,0.9918043177521676,1.027023154,0.9803733527340068,0.9913964626840172,1.2473640988332038,,, +commodity_prices_net_fertilizer_super_phosphate_44to46pct_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.087909302,0.088405342,0.088129764,0.089066728,0.088736035,0.088515573,0.089838346,0.090665079,0.084602369,0.08124032,0.083389827,0.084437022,0.086531413,0.1000898383460925,0.1862905581551723,0.2166041105177003,0.167551271,0.1628664495114006,0.1681024267376555,0.2083367780551927,0.2706173492727503,0.2629011723077432,0.2441618853927258,0.2323120421964649,0.2419572634027238,0.2193598880052029,0.2050298450701896,0.2204621990002039,0.2441618853927258,0.2386503304177207,0.223769132,0.2358945529302182,0.2204621990002039,0.2083367780551927,0.2353433974327177,0.2579407728302386,0.2843962367102631,0.2832939257152621,0.278884682,0.281089304,0.2568384618352376,0.2601453948202407,0.2436107298952253,0.2678615717852478,0.2932147246702712,0.3295909875053049,0.3571487623803304,0.4607659959104263,0.8818487960008158,0.7043767258056516,0.558871674,0.6977628598356455,0.8035847153557434,0.7727200074957148,0.6845351278956333,0.6477399868824992,0.5230134977981339,0.4975501138136103,0.5779637008989347,0.523244983,0.4727370933161373,0.820527235,1.0562454185199273,,, +commodity_prices_hay_all_dollar_per_kilogram,0.015961463,0.015763047,0.015322123,0.014065488,0.015928394,0.018265293,0.016920474,0.015873278,0.015267007,0.014054465,0.010802648,0.009612152,0.008068916,0.010615255,0.013029316,0.013481263,0.011012087,0.009667267,0.009909776,0.011100272,0.009612152,0.011122318,0.010185354,0.008531887,0.008939742,0.00953499,0.009678291,0.010449908,0.009898753,0.010615255,0.008377564,0.007947662,0.007187068,0.00903895,0.010780602,0.010890833,0.009975915,0.010119215,0.009722383,0.00935862,0.011464034,0.012786808,0.010008984,0.011243572,0.012566345,0.014660736,0.011684497,0.012456114,0.011464034,0.01102311,0.012456114,0.018739287,0.021825758,0.023148531,0.0180779,0.01201519,0.012786808,0.014219812,0.014219812,0.014219812,0.013889119,0.011353803,0.012566345,0.012566345,0.012125421,0.009479875,0.006613866,0.008487795,0.012897039,0.008377564,0.011574265,0.009920799,0.007716177,0.011353803,0.010780602,0.013558425,0.016093741,0.021935989,0.023589455,0.022376913,0.02502246,0.025242922,0.026786157,0.023258762,0.023258762,0.028329393,0.029652166,0.024140611,0.024140611,0.024801997,0.024471304,0.021274602,0.020723447,0.024581535,0.023920149,0.022817838,0.02403038,0.02711685,0.026345233,0.025573615,0.027557775,0.027006619,0.026014539,0.027227082,0.028770317,0.030974939,0.034502334,0.045856137,0.05610763,0.057430403,0.066359122,0.0591941,0.054895088,0.065477273,0.078264081,0.07418553,0.076390152,0.083555173,0.081571014,0.074516223,0.065918198,0.071650215,0.093916897,0.094137359,0.088846266,0.078484543,0.081901707,0.093365741,0.095570363,0.090609964,0.1056013933210976,0.1102310995001019,0.09325551,0.084767716,0.09325551,0.1063730110175984,0.1018535359380942,0.09424759,0.1014126115400938,0.1082469397091001,0.1212542094501121,0.1399934963651295,0.167551271,0.1190495874601101,0.1256634534301162,0.1962113571101815,0.2105414000451947,0.1940067351201794,0.1895974911401754,0.1598350942751478,0.1421981183551315,0.1565281612901448,0.1829836251701692,0.1796766921851662,0.171960515,0.2127460220351968,0.2634523278052437,0.2292806869602121,, +commodity_prices_calf_bull_1_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.37761,2.01718,1.74913,2.1327,2.74353,3.41298,6.47891,12.4277,14.3706 +commodity_prices_cows_steers_and_heifers_ge_500_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.8562937617947408,1.9885712270057676,2.0789608282333023,2.0348683398296266,2.103211696855324,2.0833700770736696,1.8827492548369464,2.1539180585195505,2.5794105716150195,2.7116880368260463,2.7778267694315595,3.373075363,3.262844141871991,2.667595548422371,2.6896417926242084,2.5794105716150195,2.6014568158168574,2.447133106403993,2.733734281027884,3.1967054092664773,3.902185223725286,4.166740154147339, +commodity_prices_sundan_silage_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.085378616, +commodity_prices_steer_holstein_500_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.0384,1.94514,1.81525,1.93028,2.15871,2.65001,3.46916,4.62007,5.17425 +commodity_prices_rice_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00576002,0.0052411,0.00502118,0.00470982,0.00667431,0.00681022,0.00908111,0.0100695,0.011142,0.0161804,0.0162225,0.0166549,0.0168723,0.0208532,0.0244412,0.0251479,0.0244708,0.0260721,0.0259189,0.0243226,0.0219306,0.0256718,0.0292103,0.0300653,, +commodity_prices_diesel_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2929669441355097,0.3262526384196164,0.3164782678758707,0.2757957526397405,0.2961370102578056,0.3938807156952615,0.3701052197780425,0.3484431012756874,0.3986358148787053,0.47815164,0.6345415688128895,0.7145857384008601,0.76213673,1.0046467885909318,0.6517127603086588,0.7904031531591029,1.0144211591346777,1.0482351977725002,1.0360832776370326,1.0104585764818077,0.715114083,0.6086526954808065,0.7000562686736707,0.8395391780546888,0.8073101724780142,0.6739032231647298,0.8683339453322096,1.3179549903445067,1.1132215532795655,, +commodity_prices_semen_sexed_dollar_per_straw,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,35,,, +commodity_prices_digester_fertilizer_coproduct_dollars_per_kg_phosphorus,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0, +commodity_prices_bedding_compost_bedded_pack_dollar_per_head,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.22,,,, +commodity_prices_milk_all_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2962542565266743,0.3125993189557322,0.3089670828603859,0.3082860385925085,0.3055618615209989,0.2896708286038593,0.2839954597048808,0.2846765039727582,0.2783200908059024,0.3078320090805903,0.3119182746878547,0.2785471055618615,0.2985244040862656,0.2914869466515324,0.2953461975028377,0.2901248581157775,0.3348467650397276,0.3032917139614075,0.3509648127128264,0.3264472190692395,0.2814982973893303,0.3414301929625425,0.2765039727582293,0.2849035187287174,0.3661748013620886,0.3448354143019296,0.294211124,0.4360953461975029,0.4188422247446084,0.2935300794551646,0.3711691259931896,0.459477866,0.4213393870601589,0.4565266742338252,0.5464245175936436,0.3906923950056754,0.3709421112372305,0.401589103,0.3695800227014756,0.4233825198637911,0.4122587968217934,0.4208853575482407,0.5763904653802499,0.4626560726447219,, +commodity_prices_milk_class_4_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.320469,0.323004,0.369559,0.306205,0.36521,0.555467,0.433995,0.47098,0.470602 +farm_services_manure_hauling_dollars_per_tonne_km,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.140842863,0.145814982, +farm_services_labor_hours_crop_hours_per_ha_per_year,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6.08,6.23,6.72,6.42,6.35,6.18,6.15,5.98,6.18,6.47,6.94,6.65,6.42,6.72,6.75,6.7,6.38,6.52,6.3,6.3,6.45,6.42,6.89,6.47,6.5, +commodity_prices_manure_sales_dollars_per_kg_dry_matter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0, +commodity_prices_net_fertilizer_potassium_chloride_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.056713901,0.057981558,0.05869806,0.059304332,0.059083869,0.059028754,0.05969014,0.057540634,0.053792777,0.051257461,0.057816212,0.063878922,0.064760771,0.068949553,0.094964092,0.1081918241593501,0.1047746600748469,0.1050502378235971,0.1083020552588501,0.1281436531688685,0.1523944950588909,0.1694803154814068,0.1650710715014027,0.1501898730688889,0.1537723838026422,0.1317261639026218,0.1201518984551111,0.1377888743751274,0.1730628262151601,0.1741651372101611,0.1681024267376555,0.167551271,0.1625908717626504,0.1587327832801468,0.1598350942751478,0.170858204,0.168653582,0.167551271,0.1796766921851662,0.1851882471601713,0.1818813141751682,0.1873928691501733,0.1807790031801672,0.1818813141751682,0.1995182900951845,0.2700661937752498,0.3009309016352784,0.3086470786002855,0.618396468,0.9402712787358698,0.5632809184455211,0.6624889079956129,0.7330368116756781,0.6558750420256068,0.6624889079956129,0.704729465,0.6371908706603394,0.5926023909125482,0.5950825906513005,0.616213892,0.5882262162623941,0.5948400822324003,0.7484140500559424,,, +commodity_prices_alfalfa_hay_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.009986938,0.010262515,0.014109581,0.022817838,0.022928069,0.025132691,0.017416514,0.011904959,0.014219812,0.014881198,0.015211892,0.014330043,0.01499143,0.013117501,0.015101661,0.015101661,0.013337963,0.01102311,0.007705154,0.009281459,0.015873278,0.009402713,0.013668656,0.011353803,0.008664164,0.012125421,0.011794728,0.014881198,0.017416514,0.023589455,0.024912228,0.024140611,0.027998699,0.02810893,0.02910101,0.02502246,0.025463384,0.02810893,0.028660086,0.02502246,0.025353153,0.024250842,0.024140611,0.020502985,0.020502985,0.023920149,0.023148531,0.023148531,0.023589455,0.025904308,0.026455464,0.026455464,0.027227082,0.026014539,0.025353153,0.026345233,0.027227082,0.029872628,,,,,,,,,,,,,,,,,,0.1033967713310956,0.095460132,0.0822324,0.08620072,0.098436372,0.1015228426395939,0.096121519,0.1122152592911038,0.1179472764651091,0.097113599,0.088405342,0.097995447,0.114640343,0.1102310995001019,0.1000898383460925,0.1086878641071005,0.114640343,0.1245611424351152,0.1510166063151397,0.1818813141751682,0.1245611424351152,0.1355842523851254,0.2160529550201998,0.2325876199452151,0.2193598880052029,0.2160529550201998,0.1741651372101611,0.1499142953201387,0.169755893,0.1984159791001835,0.1973136681051825,0.1884951801451743,0.2380991749202202,0.3009309016352784,0.2480199738752294,, +commodity_prices_barley_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00238456,0.00229314,0.00232279,0.00227337,0.00255507,0.0035682,0.00348171,0.00312835,0.00416866,0.005036,0.0054462,0.0054289,0.00575261,0.00570319,0.0057452,0.00570813,0.00500141,0.00498411,0.00517438,0.00574272,0.00596512,, +commodity_prices_net_fertilizer_urea_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1289703864151193,0.1245611424351152,0.1196007429576106,0.116844965,0.115742654,0.1140891879826055,0.1107822549976024,0.1090736729553509,0.099097758,0.09176739,0.091546928,0.089397422,0.090444617,0.1027905002838451,0.2287295314627116,0.2463665073827279,0.1840859361651703,0.1862905581551723,0.1862905581551723,0.2031008008289379,0.2466420851314781,0.2686883050314985,0.2549094175939858,0.2323120421964649,0.2491222848702304,0.2306585757039634,0.1835347806676698,0.1763697592001631,0.2044786895726891,0.2116437110401958,0.2110925555426953,0.2287295314627116,0.2188087325077024,0.2215645099952049,0.2287295314627116,0.2932147246702712,0.3064424566102834,0.2832939257152621,0.2149506440251988,0.1940067351201794,0.2204621990002039,0.3086470786002855,0.2105414000451947,0.2877031696952661,0.3042378346202814,0.3659672503403385,0.3990365801903691,0.4993468807354619,0.6084756692405628,0.5357231435704956,0.4938353257604568,0.5798155833705364,0.7098882807806567,0.6525681090406037,0.6294195781455822,0.5819761129207385,0.4722741226982369,0.4520356928300181,0.4871994135705507,0.4971753280753099,0.4555079724642714,0.6635140572209638,1.091706763,,, +commodity_prices_cotton_seed_hulls_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.190248,0.231492,,,0.418878,0.409663,0.431279,0.449836,0.440924 +commodity_prices_carbon_credits_soil_dollar_per_tonne_CO2e,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12.01,15.91,, +commodity_prices_limestone_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.284832451, +commodity_prices_corn_silage_dollar_per_kilogram,0.007966402,0.009469954,0.007481385,0.00879093,0.006317344,0.005626195,0.004644036,0.005856578,0.007772395,0.005080551,0.004377277,0.004328775,0.003795257,0.004413653,0.004728914,0.007614764,0.005832327,0.005068426,0.004231772,0.003904386,0.004328775,0.005262433,0.004013514,0.003334491,0.006014209,0.004825918,0.00476529,0.004377277,0.005468565,0.003055606,0.00259484,0.003152609,0.003455745,0.003613375,0.004243897,0.007275253,0.004862294,0.005080551,0.005286684,0.004922921,0.00474104,0.006123338,0.007881524,0.006984242,0.005759575,0.007869398,0.00619609,0.007990652,0.008160408,0.007893649,0.013701726,0.016854335,0.01758186,0.017460606,0.006547727,0.005626195,0.00834229,0.00921532,0.012367929,0.007930025,0.008669676,0.009748838,0.009676086,0.009263822,0.006668982,0.003564874,0.003540623,0.005989958,0.009724588,0.007663266,0.012489184,0.005941456,0.005686822,0.006571978,0.007287378,0.00892431,0.010840126,0.013095455,0.012489184,0.014914268,0.018551894,0.026190909,0.015520539,0.015035522,0.01843064,0.020128199,0.01843064,0.017945623,0.017339352,0.016369318,0.015641793,0.013459217,0.013580471,0.012731692,0.012125421,0.013337963,0.013580471,0.013459217,0.014186743,0.014065488,0.015035522,0.012489184,0.013095455,0.014065488,0.01612681,0.013095455,0.019036911,0.030919823,0.036618771,0.030798569,0.026069655,0.02449335,0.027282197,0.030556061,0.037710059,0.030313552,0.030919823,0.038922601,0.031889857,0.027039689,0.018188131,0.023523317,0.030798569,0.028615993,0.02764596,0.028737248,0.025099621,0.030313552,0.027403451,0.039286364,0.032859891,0.029464773,0.023523317,0.022068266,0.022432029,0.023887079,0.028130977,0.029343519,0.024978367,0.024250842,0.03686128,0.050926768,0.049229209,0.043045244,0.06280968,0.075420118,0.08354415,0.054079377,0.044864057,0.04377277,0.040741414,0.040741414,0.04377277,0.043166499,0.054928157,0.072752526,0.079300253,0.055170665,, +farm_services_land_ha_rent_dollar_per_hectare,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,223.6291386,, +commodity_prices_natgas_industrial_dollar_per_megajoule,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.003336312,0.002918111,0.002899525,0.00413554,0.004869715,0.003735926,0.005473782,0.006068557,0.007955106,0.007313865,0.007137292,0.008968081,0.004953355,0.005102048,0.004767488,0.003605819,0.004312114,0.005222862,0.003652286,0.003261965,0.003791686,0.003893913,0.003624406,0.003085392,0.005055582,0.007118705,0.004265647,, +commodity_prices_water_municipal_dollar_per_cubic_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.7465589458693244,0.7661290796486323,0.7866729029343876,0.8082536292551419,0.8309399535030793,0.8548066519917054,0.8799352605182631,0.9064148417125784,0.9343428546880864,0.9638261420204828,0.994982051,1.0279397120824216,1.0628414891308635,1.0998446426157915,1.139123222493723,1.180870235326678,, +commodity_prices_barley_silage_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.052408494,0.069415279,0.075662627,0.052639869,, +capital_costs_land_ha_dollar_per_hectare,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,14944.61693,, +commodity_prices_calf_bull_2_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.91442,1.61818,1.30113,1.65187,2.34682,2.98971,5.50607,10.9598,12.4517 +commodity_prices_cotton_seed_whole_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.281633,0.299474,0.346428,0.343124,0.366089,0.548459,0.423343,0.435692,0.40974 +commodity_prices_water_irrigation_dollar_per_cubic_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.02671304,,,,,0.034349969,,,,,0.043956935,, +commodity_prices_bedding_sand_dollar_per_head,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.22,,,, +farm_services_grain_transportation_dollars_per_tonne_km,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.140842863,0.145814982, +commodity_prices_net_fertilizer_diammonium_phosphate_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1201518984551111,0.1190495874601101,0.108136709,0.1025700380848448,0.1044439667763466,0.104940007,0.1080815930598499,0.1256634534301162,0.2254225984777085,0.2640034833027442,0.2017229120851866,0.2025496453314373,0.2053054228189399,0.254082684,0.3177411443090439,0.3003797461377779,0.2832939257152621,0.2703417715240001,0.2860497032027646,0.2582163505789889,0.2364457084277187,0.2485711293727299,0.2739242822577534,0.2612477058152417,0.2463665073827279,0.2551849953427361,0.2358945529302182,0.2155017995226993,0.2496734403677309,0.2899077916852682,0.3240794325302998,0.2998285906402774,0.2910101026802692,0.2910101026802692,0.2645546388002447,0.2689638827802488,0.2502245958652315,0.2755777487502549,0.3042378346202814,0.334000231,0.3714788053153436,0.4872214597904507,0.9369643457508668,0.7032744148106506,0.559973985,0.7749246294857168,0.7440599216256883,0.7054790368006526,0.6735120179456231,0.6348539713509372,0.5126187051152742,0.4876623841884511,0.5664665972210741,0.5128391673142745,0.4633343805287786,0.804213033,1.0352353709552076,,, +commodity_prices_carbon_credits_enteric_dollar_per_tonne_CO2e,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,80, +farm_services_labor_hours_dollar_per_hour,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,5.36,5.52,5.79,6.06,6.25,6.39,6.54,6.78,7.35,7.47,7.77,8.1,8.45,8.81,9.08,9.23,9.5,9.87,10.23,10.6,10.83,10.95,11.07,11.51,11.87,12.07,12.54,12.98,13.32,14.17,14.91,15.49,16.38,17.56,18.53,19.1, +commodity_prices_cow_dairy_heifer_bred_t3_dollar_per_animal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1253.7,951.459,953.015,984.977,991.693,1265.96,1452.13,1936.87, +commodity_prices_winter_wheat_grain_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.047766898,0.039315831,0.034539141,0.034906579,0.041153019,0.045194834,0.047032022,0.041153019,0.081938601,0.077529349,0.077161911,0.054380776,0.03472286,0.038213518,0.03472286,0.048501773,0.054380776,0.044459958,0.04262277,0.03784608,0.038213518,0.025500175,0.014036119,0.014366813,0.028549907,0.03101174,0.030387096,0.037478643,0.035898661,0.021090922,0.025500175,0.025353199,0.035163785,0.040785582,0.051073837,0.052543587,0.055115651,0.06944572,0.082306039,0.073487535,0.068343407,0.073854972,0.078631662,0.076794474,0.074589848,0.077529349,0.072017784,0.072017784,0.070548033,0.063566717,0.063934155,0.063934155,0.065771344,0.073487535,0.067241094,0.049604086,0.048869211,0.059892341,0.049971524,0.044092521,0.044459958,0.047766898,0.049604086,0.062831842,0.1366868144990906,0.1433006926200143,0.1238264959306277,0.100310485,0.08377579,0.1105987396887803,0.1392588782127831,0.1407286289063218,0.134114751,0.125663684,0.125663684,0.1219893075637045,0.1094964266686263,0.085612978,0.091491981,0.134114751,0.1388914405393985,0.09626867,0.1072918006283184,0.1190498061766273,0.1113336150355496,0.1238264959306277,0.1620400139626316,0.1591005125755544,0.1186823685032426,0.092594294,0.084143227,0.092226856,0.099943047,0.1252962466241663,0.1201521191967812,0.1219893075637045,0.1219893075637045,0.1532215098013999,0.2252392937847918,0.2414065514137165,0.1730631441641711,0.1973140306075582,0.2502250555749481,0.2774154434054124,0.2531645569620253,0.217523103,0.176370083,0.1311752493983208,0.1587330749021697,0.1866583380794033,0.1664492660432474,0.1829839613455568,0.2568389336958719,0.3152615237640315,0.2458158034943323,, +commodity_prices_wheat_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00188047,0.00157653,0.00151723,0.00156665,0.00164325,0.001878,0.00194966,0.00199167,0.00209051,0.00241422,0.00395862,0.003457,0.00273545,0.00330874,0.00378812,0.00396851,0.0039092,0.00398334,0.00377082,0.00348665,0.00366457,0.00366457,0.00349901,0.00373376,0.00446766,0.00487538,, +farm_services_labor_hours_animal_total_hours_per_head_per_year,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,36.02,37.42,42.62,41.71,40.89,40.88,41.4,42.5,41.06,40.05,43.05,39.45,40.29,40.04,40.85,42.95,42.88,44.93,40.78,39.08,40.89,38.69,38.66,38.75,39.8, +commodity_prices_steer_holstein_700_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.01929,1.88435,1.81735,1.83436,2.05326,2.53131,3.25987,4.0121,4.59025 +commodity_prices_net_fertilizer_ammonium_nitrate_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.089562768,0.090499733,0.089728115,0.088625804,0.087303031,0.086531413,0.083500058,0.08124032,0.073413912,0.06713074,0.0666347,0.070051864,0.07170533,0.081956822,0.1703070487276575,0.1884951801451743,0.1499142953201387,0.1573548945363955,0.1521189173101407,0.1584572055313965,0.184912669,0.209714667,0.211919289,0.2036519563264384,0.2171552660152008,0.2061321560651907,0.1846370916626708,0.1714093597226585,0.1851882471601713,0.2033763785776881,0.198967135,0.2028252230801876,0.1962113571101815,0.2050298450701896,0.2171552660152008,0.2458153518852274,0.2568384618352376,0.2502245958652315,0.2127460220351968,0.1995182900951845,0.2138483330301978,0.2866008587002651,0.2149506440251988,0.2678615717852478,0.2899077916852682,0.3218748105402977,0.4034458241703732,0.4210828000903895,0.561076296,0.4828122158104466,0.4387197760104058,0.5280069666054884,0.6327265111305853,0.5996571812805548,0.617294157,0.5341578619575941,0.433483799,0.4148988354084338,0.4471745013420637,0.4563236826005721,0.4180845141839867,0.6090047785181634,1.002011717565877,,, +commodity_prices_cotton_seed_meal_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.270386,0.319347,0.283386,0.337925,0.422776,0.454729,0.447417,0.384192,0.324267 +commodity_prices_soybean_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00487291,0.00505577,0.00475677,0.00473948,0.00558211,0.00628883,0.00677562,0.00734149,0.00806057,0.0079815,0.00982737,0.0109591,0.013655,0.0146286,0.0137267,0.0136699,0.0143766,0.0145248,0.0146311,0.0145273,0.0143494,0.0154169,0.0150561,0.0150709,0.0156195,0.0175667,0.0184859,, +commodity_prices_semen_beef_dollar_per_straw,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,,, +commodity_prices_calves_all_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.248716908587453,2.6235030600186957,2.9762429672480994,2.932150478844424,2.6235030600186957,2.4250868622021557,2.3148556411929664,2.5794105716150195,3.130566676660964,3.791954002716097,3.990370200532637,5.754069736679659,5.4454223178539305,3.4833065838903683,3.703769025908746,3.747861514312422,3.505352828092206,3.549445316495882,3.769907759,4.299017619358367,5.754069736679659,7.142983121, +commodity_prices_digester_carbon_credits_dollar_per_tonne_CO2e,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,60, +commodity_prices_cows_all_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.9457838762588404,1.108926083,1.139790825235013,1.0273549798056405,1.0560150972680291,1.1155399566129915,0.9876717402423324,1.208134182,1.5696925871708496,1.8011781512901464,1.814405897811249,2.358948129596642,2.270763153,1.6380359441965466,1.5233954743469902,1.399936506816699,1.366867140513942,1.4153688777579851,1.547646342969012,1.8452706396938217,2.248716908587453,2.843965502037073, +commodity_prices_cows_milk_dollar_per_animal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1330,1420,1430,1380,1830,1990,1760,1620,1360,1200,1300,1360,1590,1760,2240, +commodity_prices_cow_dairy_bred_t3_dollar_per_animal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1224.02,1013.55,986.045,1026.73,1030.74,1302.51,1498.13,2189.16, +commodity_prices_distiller_grains_dried_10pct_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.152799,0.177417,0.171344,0.187648,0.230727,0.28637,0.251039,0.181721,0.170946 +commodity_prices_soybean_grain_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.065771344,0.081203726,0.0789991,0.08046885,0.1164777424629347,0.117212618,0.1297054987047822,0.098105859,0.079366537,0.073854972,0.08377579,0.091859418,0.085980416,0.073487535,0.066138781,0.068343407,0.068343407,0.049236648,0.017673752,0.019437453,0.033694035,0.035384248,0.02623505,0.045929709,0.030754533,0.024397862,0.029468501,0.03277544,0.056952839,0.058790028,0.066506219,0.075324723,0.076427036,0.094431482,0.1223567452370891,0.083408352,0.079366537,0.090757105,0.100310485,0.099943047,0.099943047,0.090389668,0.081571163,0.080101413,0.076059598,0.073487535,0.072017784,0.078264224,0.08377579,0.085980416,0.092226856,0.09626867,0.093329169,0.1010453601807793,0.091491981,0.089287355,0.086347853,0.1047197369146258,0.1113336150355496,0.160570263,0.2087045984824824,0.243978615,0.1807793353052488,0.2502250555749481,0.2160533519501754,0.2447134904741783,0.2311182965589462,0.2792526317723356,0.2230346677444838,0.2098069115026364,0.2877036982601826,0.2145836012566368,0.1855560250592493,0.1756352078778637,0.2160533519501754,0.2726387536514119,0.2090720361558671,0.2109092245227903,0.2050302217486359,0.2042953464018665,0.2351601109661774,0.2013558450147894,0.2469181165144862,0.2700666899377193,0.2377321746798699,0.1811467729786335,0.1701236427770939,0.1668167037166321,0.1609377009424776,0.2031930333817126,0.2696992522643346,0.2109092245227903,0.2079697231357131,0.2362624239863313,0.3711120501184987,0.3663353603644982,0.3523727287758814,0.415204571,0.4592970917308152,0.5291102496738992,0.4776689754000478,0.3711120501184987,0.3288567176792636,0.3479634766952656,0.3428193492678805,0.3115871470301851,0.3148940860906469,0.3968326872554243,0.4886921056015874,0.521761496,0.4556227149969687,, +commodity_prices_milk_class_3_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.370866,0.331763,0.380002,0.412107,0.38776,0.498411,0.38636,0.428907,0.461748 +commodity_prices_cows_ge_500_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.7570856628864708,1.891567752517681,1.9775481049048484,1.922432494400254,1.981957353745216,1.9643203583837456,1.7703134094075732,2.032663715409443,2.4912255948076685,2.667595548422371,2.755780525229722,3.351029118679342,3.240797897670153,2.6235030600186957,2.645549304220533,2.535318083211344,2.557364327413182,2.403040618000317,2.667595548422371,3.130566676660964,3.814000246917935,4.122647665743664, +commodity_prices_elec_industrial_dollar_per_kwh,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0505,0.0488,0.0511,0.0525,0.0573,0.0616,0.0639,0.0696,0.0683,0.0677,0.0682,0.0667,0.0689,0.071,0.0691,0.0676,0.0688,0.0692,0.0681,0.0667,0.0718,0.0832,0.0804,, +commodity_prices_whey_dry_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.765519,0.754386,0.853671,0.798221,1.26639,1.33543,0.797707,1.08313,1.5913 +commodity_prices_gasoline_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.4023342253547171,0.3856913782126638,0.3661426371251726,0.4234679995033563,0.5006062751458891,0.6112944172493864,0.691602759,0.7510414988072626,0.8715040114545055,0.6355982575203214,0.7489281213923986,0.9446797044441684,0.9721536108373994,0.9444155322673105,0.907959772,0.6657138856821321,0.5943873979304751,0.667827263,0.743116334,0.7108873279248482,0.5965007753453391,0.8189337482597657,1.072274865866577,0.9602658628787898,, +commodity_prices_net_fertilizer_anhydrous_ammonia_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1548746947976432,0.1537723838026422,0.1482608288276371,0.140544652,0.1366865633801264,0.1333796303951233,0.1295215419126198,0.1223565204451131,0.097554523,0.081791476,0.083665405,0.087413262,0.088625804,0.099263105,0.227076065,0.2667592607902467,0.2055810005676902,0.1992427123464343,0.1851882471601713,0.2055810005676902,0.2502245958652315,0.2722708157652518,0.2689638827802488,0.2568384618352376,0.2937658801677718,0.269239461,0.2199110435027034,0.2022740675826871,0.2199110435027034,0.222666821,0.2149506440251988,0.2193598880052029,0.2188087325077024,0.2281783759652111,0.2783335262377575,0.3637626283503365,0.334000231,0.334000231,0.278884682,0.2325876199452151,0.2502245958652315,0.4398220870054068,0.2755777487502549,0.4111620011353804,0.4177758671053865,0.4585613739204242,0.5743040283955313,0.5765086503855333,0.8322448012257699,0.7495714766006935,0.5500531865055088,0.8256309352557638,0.8653141310758005,0.9336574127658636,0.9380666567458678,0.7742191504489162,0.6282841978207312,0.6013547402128563,0.6481368188406996,0.6614086432205118,0.6059734232819106,0.8826865523570165,1.4523167821337435,,, +commodity_prices_almond_hulls_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2924805855476006,0.1375132966263772,0.1099555217513517, +commodity_prices_cow_dairy_fresh_dollar_per_animal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1329.95,1093.28,1061.69,1099.57,1096.22,1451.72,1641.07,2191.82, +commodity_prices_peanut_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.018009,0.0184835,0.0183574,0.0186515,0.0180115,0.017967,0.0182166,0.0181795,0.0185971,0.0166623,0.0164399,0.0168946,0.0177743,0.024241,0.0223136,0.022064,0.0267244,0.0279649,0.0276486,0.0288619,0.0314515,0.0302457,0.0300826,0.0292251,0.0284517,0.0284665,0.029623,0.0332949,0.0348319,, +commodity_prices_cow_dairy_heifer_open_dollar_per_animal,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,711.893,591.297,517.573,469.054,503.217,1050,1108.75,1076.14, +commodity_prices_calf_starter_18cp_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4.712522046, +commodity_prices_hay_excluding_alfalfa_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.010020007,0.009920799,0.011739612,0.01399935,0.019257373,0.016733081,0.016887404,0.021384833,0.019400674,0.028197115,0.020932886,0.019764436,0.03423778,0.029850582,0.018739287,0.023148531,0.027006619,0.025970447,0.025761008,0.02466972,0.026223979,0.025772031,0.019340046,0.02186985,0.022575329,0.020976978,0.022398959,0.022806814,0.024366585,0.021076186,0.02346269,0.025072064,0.026912923,0.030680989,0.03633217,0.04931372,0.050063291,0.049115304,0.053599872,0.045034916,0.046710428,0.054173074,0.064347404,0.054564394,0.052266076,0.054178585,0.054933668,0.053125878,0.052084195,0.059701163,0.07220137,0.071760446,0.066689815,0.068563744,0.074295761,0.074846917,0.072091139,0.084326791,0.083444942,0.079145929,0.073634374,0.07815385,0.080799396,0.081350551,0.07815385,0.0822324,0.089728115,0.112435721,0.1212542094501121,0.1300726974101203,0.1072548598135992,0.1069241665150989,0.1455050513401346,0.1587327832801468,0.1532212283051417,0.1433004293501325,0.1333796303951233,0.1300726974101203,0.1322773194001223,0.1532212283051417,0.1488119843251376,0.1455050513401346,0.168653582,0.1951090461151804,0.1895974911401754,, +commodity_prices_milk_class_2_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.346273,0.336076,0.380374,0.32448,0.378165,0.576996,0.456205,0.484487,0.489898 +farm_services_labor_hours_animal_nonmanure_hours_per_head_per_year,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,34.644036,35.990556,40.991916,40.116678,39.328002,39.318384,39.81852,40.8765,39.491508,38.52009,41.40549,37.94301,38.750922,38.510472,39.28953,41.30931,41.241984,43.213674,39.222204,37.587144,39.328002,37.212042,37.183188,37.26975,38.27964, +farm_services_labor_hours_animal_manure_hours_per_head_per_year,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.375964,1.429444,1.628084,1.593322,1.561998,1.561616,1.58148,1.6235,1.568492,1.52991,1.64451,1.50699,1.539078,1.529528,1.56047,1.64069,1.638016,1.716326,1.557796,1.492856,1.561998,1.477958,1.476812,1.48025,1.52036, +commodity_prices_milk_retail_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.6561165104968814,0.6929685291685709,0.69054607,0.7142555231797876,0.7509754557630481,0.7345756470237041,0.7619386011026547,0.728388735,0.7294005140790562,0.8337062563896644,0.841871818,0.8140016537178271,0.9254611785777498,1.0026205879944312,0.8213112978514876,0.860805038,0.943512063,0.9226213276765264,0.9145429425082092,0.975764844,0.9031386296332498,0.846473698,0.8520873564554433,0.7649977149106701,0.8019395521224914,0.8766342351290877,0.936358281,1.0807283755260328,1.063932308521402,1.0524408188280792, +commodity_prices_natgas_residential_dollar_per_megajoule,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.000966508,0.000966508,0.000975802,0.001012975,0.001068735,0.001124495,0.001198842,0.001328949,0.001589163,0.001840083,0.002183937,0.002379097,0.002769418,0.003419952,0.003986847,0.004804661,0.005631769,0.005687529,0.005687529,0.005418022,0.005148515,0.005083462,0.005241449,0.005390142,0.005408729,0.005473782,0.005724703,0.005957036,0.005631769,0.005891983,0.006449584,0.006338064,0.00621725,0.007211639,0.008949495,0.007332452,0.008949495,0.00999035,0.011802553,0.012759768,0.0121557,0.012908461,0.011282125,0.010585124,0.010250564,0.009897416,0.009590736,0.010194803,0.009646496,0.009339815,0.010139043,0.009758016,0.009767309,0.01001823,0.011319299,0.013707689,0.01415377,, +commodity_prices_semen_conventional_dollar_per_straw,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,15,,, +commodity_prices_calf_ heifer_2_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.08006,1.1142,0.636856,0.643218,0.513034,0.876338,1.8,7.86384,9.31564 +commodity_prices_soybean_hulls_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.12099,0.135434,0.139022,0.148417,0.180407,0.227495,0.193953,0.136762,0.140027 +commodity_prices_net_fertilizer_sulfate_of_ammonium_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.063768691,0.064319847,0.062721496,0.057320172,0.057981558,0.058642945,0.058146905,0.060406643,0.059138985,0.057926443,0.057595749,0.056713901,0.057926443,0.063217536,0.1361354078826259,0.1504654508176392,0.1063730110175984,0.112435721,0.1218053649476126,0.1328284748976228,0.1543235393001427,0.1722360929689093,0.1782988034414149,0.1609374052701488,0.1681024267376555,0.1689291599839062,0.1653466492501529,0.1570793167876453,0.1576304722851458,0.169755893,0.168653582,0.167551271,0.1681024267376555,0.1747162927076616,0.1851882471601713,0.2006206010901856,0.2028252230801876,0.2039275340751886,0.2061321560651907,0.1884951801451743,0.1840859361651703,0.2116437110401958,0.2061321560651907,0.2149506440251988,0.225973754,0.2689638827802488,0.2932147246702712,0.3174655665602937,0.4310035990453987,0.4166735561103855,0.3593533843703324,0.4662775508854313,0.5544624304855129,0.5754063393905323,0.5875317603355436,0.479902115,0.3894464745338603,0.3727574860695448,0.4017592883480217,0.4099825283707293,0.3756234946565475,0.5471430854787062,0.9002353433974327,,, +commodity_prices_milk_nonfat_dry_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.68213,1.7515,2.26208,2.2965,2.79826,3.71494,2.61382,2.7382,3.0426 +commodity_prices_corn_grain_dollar_per_kilogram,0.025865123,0.030746821,0.024290382,0.028542183,0.020511004,0.018266997,0.015078147,0.019014999,0.025235227,0.016495414,0.014212039,0.014054565,0.01232235,0.014330144,0.015353726,0.024723436,0.018936262,0.016456045,0.013739617,0.012676666,0.014054565,0.017085941,0.013030983,0.010826345,0.01952679,0.015668674,0.015471832,0.014212039,0.017755206,0.009920869,0.008424865,0.010235817,0.011220031,0.011731822,0.013778985,0.023621117,0.01578678,0.016495414,0.017164679,0.015983623,0.015393095,0.019881107,0.025589544,0.022676273,0.018700051,0.025550175,0.020117318,0.02594386,0.02649502,0.025628912,0.044486438,0.054722255,0.057084367,0.056690681,0.021259006,0.018266997,0.027085548,0.029920082,0.040155899,0.025747018,0.028148498,0.031652297,0.031416086,0.030077556,0.021652691,0.011574347,0.01149561,0.019448053,0.03157356,0.02488091,0.040549585,0.019290579,0.01846384,0.021337743,0.023660486,0.028975237,0.035195465,0.042518011,0.040549585,0.04842329,0.060233849,0.085036022,0.050391717,0.048816976,0.059840164,0.065351758,0.059840164,0.058265423,0.056296996,0.053147514,0.050785402,0.043699067,0.044092752,0.041336955,0.039368529,0.043305382,0.044092752,0.043699067,0.046061179,0.045667493,0.048816976,0.040549585,0.042518011,0.045667493,0.052360143,0.042518011,0.06180859,0.1003897484351009,0.118892957,0.099996063,0.084642337,0.079524428,0.08857919,0.099208693,0.1224361245620251,0.098421322,0.1003897484351009,0.126372977,0.1035392307389472,0.087791819,0.059052793,0.076374946,0.099996063,0.092909728,0.089760246,0.093303413,0.081492855,0.098421322,0.088972875,0.1275540333057754,0.1066887130427935,0.095665525,0.076374946,0.071650722,0.072831778,0.077556002,0.091334987,0.09527184,0.081099169,0.078737058,0.1196803275461596,0.165347821,0.159836227,0.1397582772331798,0.2039289791740482,0.2448722491240502,0.271249163,0.1755836384394315,0.1456635565528916,0.1421203889610645,0.1322782567615448,0.1322782567615448,0.1421203889610645,0.1401519625211606,0.178339435,0.2362111727884729,0.2574701783394355,0.1791268060312586,, +commodity_prices_net_fertilizer_phosphorus_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.2017118889752366,0.2036409332164884,0.2037401412060385,0.2100233138775443,0.2093729503904937,0.2106516311446948,0.2214873482255549,0.2307688068034634,0.2203078754609038,0.2156120306221994,0.2222369197021556,0.2275059662582604,0.2352882818829676,0.265965597,0.4808390791293948,0.5547600544541632,0.4450911335615117,0.453986783,0.4675782778595325,0.5538561594382624,0.6709215871073706,0.6368160849220391,0.5792203354332359,0.5519712076368106,0.5797604678207864,0.5244024096518352,0.4848184218213485,0.5151429972938265,0.5690349818394265,0.549127245,0.5164216780480277,0.5394820240634491,0.5013641098563137,0.4657263953879308,0.532879181,0.6017185028412066,0.6682539944994682,0.6406741734045427,0.6261898069302293,0.6286369373391315,0.572937163,0.5814029112033379,0.5426566797290521,0.5971659584318525,0.6564923361828072,0.7292558849628247,0.8006084756692405,1.0415516129565636,1.9982693717378488,1.5470714352640311,1.2296389380335877,1.6175972927241964,1.7016374829830745,1.6254016545688037,1.492672387660731,1.4097675777267042,1.138323495207703,1.0828992983790515,1.2579132150553638,1.1388195351554533,1.028897082733952,1.785843019891202,2.298858556964676,,, +commodity_prices_cotton_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00435646,0.00441577,0.00453438,0.00743787,0.00934552,0.0118586,0.00918984,0.0113446,0.0131929,0.0152439,0.0149103,0.0160075,0.0180807,0.0200106,0.0238728,0.0243646,0.0248934,0.0249131,0.0174036,0.017075,0.0168896,0.0165709,0.0208211,0.0203639,0.0211596,0.0236702,0.0253901,, +commodity_prices_elec_residential_dollar_per_kwh,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0858,0.0844,0.0872,0.0895,0.0945,0.104,0.1065,0.1126,0.1151,0.1154,0.1172,0.1188,0.1213,0.1252,0.1265,0.1255,0.1289,0.1287,0.1301,0.1315,0.1366,0.1504,0.16,, +commodity_prices_bedding_manure_solids_dollar_per_head,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,53.22,,,, +commodity_prices_oat_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00212263,0.00274534,0.00246611,0.00308634,0.0030987,0.00295785,0.00313576,0.00389438,0.00382272,0.00402287,0.00510272,0.00482843,0.00483338,0.0046011,0.00451708,0.00449979,0.00468511,0.00505577,0.00553021,, +commodity_prices_propane_wholesale_dollar_per_liter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.1543679548582584,0.2489009116581823,0.2563154321460555,0.2045749337588266,0.1675461839008191,0.3338222279753051,0.3347875131095443,0.2483421875041276,, +commodity_prices_sorghum_seed_dollar_per_square_meter,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.00133931,0.00148263,0.00162348,0.00150734,0.00166055,0.00156418,0.00156912,0.00163831,0.00115151,0.00120587,0.00131213,0.00132943,0.00138873,0.00163831,0.00184588,0.00187306,0.0026119,0.00337793,0.003457,0.00350889,0.00351136,0.0036794,0.00364727,0.00357561,0.00334827,0.0033804,0.0034916,0.00370905,0.00407476,, +farm_services_tractor_medium_dollar_per_hour,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,76.94048454,80.23602955,81.27501555,80.26872001,82.27781041,87.82752818,88.16618902,88.68278401,92.2386242,98.08278799,109.1271157,114.8597394,119.431736,133.8876349,120.6901214,128.8179162,141.4993197,145.176592,145.9008894,146.6784441,134.6764261,131.1503026,136.4890271,145.2124361,147.3699011,143.4645789,157.6563492,194.9876452,193.1281324,, +commodity_prices_steer_holstein_300_dollar_per_kilogram,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2.37997,2.22456,2.13873,2.13709,2.43396,2.86248,4.16285,5.73829,6.06911 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cost", + "Cost": 15000.0 + } + ] + } +} diff --git a/input/data/EEE/economic_preprocessing.json b/input/data/EEE/economic_preprocessing.json new file mode 100644 index 0000000000..a06bc07357 --- /dev/null +++ b/input/data/EEE/economic_preprocessing.json @@ -0,0 +1,111 @@ +{ + "Animal": { + "FPCM": [ + { + "name": "Total milk, kg", + "filters": [ + "AnimalModuleReporter.report_herd_statistics_data.daily_milk_production" + ], + "vertical_aggregation": "sum", + "slice_start": -365 + }, + { + "name": "Milk per cow/d, kg", + "filters": [ + "AnimalModuleReporter.report_herd_statistics_data.daily_milk_production", + "AnimalModuleReporter.report_daily_animal_population.num_lactating_cows" + ], + "horizontal_aggregation": "division", + "horizontal_order": [ + "AnimalModuleReporter.report_herd_statistics_data.daily_milk_production", + "AnimalModuleReporter.report_daily_animal_population.num_lactating_cows" + ], + "horizontal_first": true, + "vertical_aggregation": "average", + "slice_start": -365 + }, + { + "name": "% milk fat", + "filters": [ + "AnimalModuleReporter.report_life_cycle_manager_data.herd_milk_fat_percent" + ], + "constants": { + "By 100": 100 + }, + "vertical_aggregation": "average", + "horizontal_aggregation": "division", + "slice_start": -365 + }, + { + "name": "% milk protein", + "filters": [ + "AnimalModuleReporter.report_life_cycle_manager_data.herd_milk_protein_percent" + ], + "constants": { + "By 100": 100 + }, + "vertical_aggregation": "average", + "horizontal_aggregation": "division", + "slice_start": -365 + }, + { + "name": "Milk fat, kg", + "filters": [ + "AnimalModuleReporter.report_life_cycle_manager_data.herd_milk_fat_kg" + ], + "vertical_aggregation": "sum", + "slice_start": -365 + }, + { + "name": "Milk protein, kg", + "filters": [ + "AnimalModuleReporter.report_life_cycle_manager_data.herd_milk_protein_kg" + ], + "vertical_aggregation": "sum", + "slice_start": -365 + }, + { + "name": "weighted fat value", + "cross_references": [ + "Milk fat, kg_ver_agg" + ], + "constants": { + "fat weight": 12.2 + }, + "horizontal_aggregation": "product" + }, + { + "name": "weighted protein value", + "cross_references": [ + "Milk protein, kg_ver_agg" + ], + "constants": { + "protein weight": 7.7 + }, + "horizontal_aggregation": "product" + }, + { + "name": "weighted milk value", + "cross_references": [ + "Total milk, kg_ver_agg" + ], + "constants": { + "milk weight": 0.25 + }, + "horizontal_aggregation": "product" + }, + { + "name": "Herd FPCM, kg", + "cross_references": [ + "weighted fat value_hor_agg", + "weighted protein value_hor_agg", + "weighted milk value_hor_agg" + ], + "horizontal_aggregation": "sum" + } + ] + }, + "Crop and Soil": {}, + "Feed": {}, + "Manure": {} +} \ No newline at end of file diff --git a/input/data/EEE/economics_map.json b/input/data/EEE/economics_map.json new file mode 100644 index 0000000000..f720410a1e --- /dev/null +++ b/input/data/EEE/economics_map.json @@ -0,0 +1,591 @@ +{ + "Animal": { + "Costs": { + "Purchased heifers": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_life_cycle_manager_data.bought_heifer_num" + ], + "economics_files": [ + "commodity_prices.cow_dairy_heifer_bred_t3.dollar_per_animal.csv" + ] + }, + "Semen purchased": { + "biophysical_simulation": [ + "AnimalModuleReporter._record_heiferIIs_conception_rate.heiferII_total_num_ai_performed", + "AnimalModuleReporter._record_cows_conception_rate.cow_total_num_ai_performed" + ], + "economics_files": { + "input_manager_location": "animal.animal_config.management_decisions.semen_type", + "conventional": "commodity_prices.semen_conventional.dollar_per_straw.csv", + "sexed": "commodity_prices.semen_sexed.dollar_per_straw.csv", + "beef": "commodity_prices.semen_beef.dollar_per_straw.csv" + }, + "preprocessing": "daily sum of both biophysical outputs, select econ file from user input", + "processing": "" + }, + "Bedding requirements": { + "biophysical_simulation": ["AnimalModuleReporter.report_daily_pen_total.number_of_animals_in_pen_.*"], + "economics_files": { + "input_manager_location": "animal.pen_information..*.manure_streams.0.bedding_name", + "sand": "commodity_prices.bedding_sand.dollar_per_head.csv", + "staw": "commodity_prices.bedding_straw.dollar_per_head.csv", + "sawdust": "commodity_prices.bedding_sawdust.dollar_per_head.csv", + "manure_solids": "commodity_prices.bedding_manure_solids.dollar_per_head.csv", + "CBPB": "commodity_prices.bedding_compost_bedded_pack.dollar_per_head.csv" + }, + "preprocessing": "dollar per head is reported on an annual basis, Map bedding price to number of animals for each bedding type, depending on their pen." + }, + "Animal - Labor hours": { + "input_manager": [ + "economic_inputs.Animal.labor_hours_per_day" + ], + "economics_files": [ + "farm_services.labor_hours.dollar_per_hour.csv" + ], + "notes": "", + "future_expansion": "more subcategories, e.g. vet, milker" + }, + "Animal investment - Gasoline consumption": { + "input_manager": [ + "economic_inputs.Animal.gasoline_liters_per_day" + ], + "economics_files": [ + "commodity_prices.gasoline.dollar_per_liter.csv" + ], + "notes": "" + }, + "Animal investment - Diesel consumption": { + "input_manager": [ + "economic_inputs.Animal.diesel_liters_per_day" + ], + "economics_files": [ + "commodity_prices.diesel.dollar_per_liter.csv" + ], + "notes": "" + }, + "Animal investment - Propane consumption": { + "input_manager": [ + "economic_inputs.Animal.propane_liters_per_day" + ], + "economics_files": [ + "commodity_prices.propane_wholesale.dollar_per_liter.csv" + ], + "notes": "" + }, + "Animal investment - Natural gas consumption": { + "input_manager": [ + "economic_inputs.Animal.natural_gas_megajoule_per_day" + ], + "economics_files": [ + "commodity_prices.natgas_industrial.dollar_per_megajoule.csv" + ], + "notes": "" + }, + "Animal investment - Electricity consumption": { + "input_manager": [ + "economic_inputs.Animal.electricity_kwh_per_day" + ], + "economics_files": [ + "commodity_prices.elec_industrial.dollar_per_kwh.csv" + ], + "notes": "" + }, + "Animal investment - Water consumption": { + "input_manager": [ + "economic_inputs.Animal.water_cubic_meter_per_day" + ], + "economics_files": [ + "commodity_prices.water_municipal.dollar_per_cubic_meter.csv" + ], + "notes": "" + } + }, + "Revenue": { + "FPCM (Milk Production)": { + "biophysical_simulation": [ + "Economic_preprocessing.Animal.FPCM" + ], + "economics_files": [ + "commodity_prices.milk_retail.dollar_per_liter.csv" + ], + "preprocessing": "FPCM calculation from raw values, see economic_preprocessing.json as example logic" + }, + "Sold calves": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_sold_animal_information_sort_by_sell_day.sold_calves_sold_weight" + ], + "economics_files": [ + "commodity_prices.calves_all.dollar_per_kilogram.csv" + ] + }, + "Sold heifer IIs": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_sold_animal_information_sort_by_sell_day.heiferII_sold_weight" + ], + "economics_files": [ + "commodity_prices.cow_dairy_heifer_open.dollar_per_animal.csv" + ] + }, + "Sold heifer IIIs": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_life_cycle_manager_data.sold_heiferII_num", + "AnimalModuleReporter.report_life_cycle_manager_data.sold_heiferIII_oversupply_num" + ], + "economics_files": [ + "commodity_prices.cow_dairy_heifer_bred_t3.dollar_per_animal.csv" + ], + "preprocessing": "sum from both biophysical simulation locations" + }, + "Sold cows": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_sold_animal_information_sort_by_sell_day.sold_cows_sold_count" + ], + "economics_files": [ + "commodity_prices.cows_milk.dollar_per_animal.csv" + ], + "preprocessing": "sum number sold" + }, + "Carbon credits from enteric emissions reduction": { + "biophysical_simulation": [ + "AnimalModuleReporter.report_enteric_methane_emission.enteric_methane_emission.*" + ], + "economics_files": [ + "commodity_prices.carbon_credits_enteric.dollar_per_tonne.csv" + ], + "notes": "Sum across all pens (e.g. each of the pens captured by .*), then convert enteric_methane_emission to tonnes" + } + } + }, + "Manure": { + "Costs": { + "Purchased manure": { + "biophysical_simulation": [ + "ManureManager._record_manure_request_results.off_farm_manure.total_manure_mass" + ], + "economics_files": [ + "commodity_prices.bedding_manure_solids.dollar_per_head.csv" + ], + "notes": "" + }, + "General manure operational costs - Labor hours": { + "input_manager": [ + "economic_inputs.Manure.general.labor_hours" + ], + "economics_files": [ + "farm_services.labor_hours.dollar_per_hour.csv" + ], + "notes": "" + }, + "Slurry storage operational costs - Labor hours": { + "input_manager": [ + "economic_inputs.Manure.slurry_storage.labor_hours" + ], + "economics_files": [ + "farm_services.labor_hours.dollar_per_hour.csv" + ], + "notes": "" + }, + "Slurry storage operational costs - Diesel consumption": { + "input_manager": [ + "economic_inputs.Manure.slurry_storage.diesel_liters_per_day" + ], + "economics_files": [ + "commodity_prices.diesel.dollar_per_liter.csv" + ], + "notes": "" + }, + "Slurry storage operational costs - Gasoline consumption": { + "input_manager": [ + "economic_inputs.Manure.slurry_storage.gasoline_liters_per_day" + ], + "economics_files": [ + "commodity_prices.gasoline.dollar_per_liter.csv" + ], + "notes": "" + }, + "Slurry storage operational costs - Propane consumption": { + "input_manager": [ + "economic_inputs.Manure.slurry_storage.propane_liters_per_day" + ], + "economics_files": [ + "commodity_prices.propane_wholesale.dollar_per_liter.csv" + ], + "notes": "" + }, + "Slurry storage operational costs - Natural gas consumption": { + "input_manager": [ + "economic_inputs.Manure.slurry_storage.megajoules_per_day" + ], + "economics_files": [ + "commodity_prices.natgas_industrial.dollar_per_megajoule.csv" + ], + "notes": "" + }, + "Slurry storage operational costs - Electricity consumption": { + "input_manager": [ + "economic_inputs.Manure.slurry_storage.kwh_per_day" + ], + "economics_files": [ + "commodity_prices.elec_residential.dollar_per_kwh.csv", + "commodity_prices.elec_commercial.dollar_per_kwh.csv", + "commodity_prices.elec_industrial.dollar_per_kwh.csv" + ], + "notes": "" + }, + "Slurry storage operational costs - Water consumption": { + "input_manager": [ + "economic_inputs.Manure.slurry_storage.cubic_meters_water_per_day" + ], + "economics_files": [ + "commodity_prices.water_municipal.dollar_per_cubic_meter.csv" + ], + "notes": "" + }, + "Digester operational costs - Labor hours": { + "input_manager": [ + "economic_inputs.Manure.digester.labor_hours_per_day" + ], + "economics_files": [ + "farm_services.labor_hours.dollar_per_hour.csv" + ], + "notes": "" + }, + "Digester operational costs - Diesel consumption": { + "input_manager": [ + "economic_inputs.Manure.digester.diesel_liters_per_day" + ], + "economics_files": [ + "commodity_prices.diesel.dollar_per_liter.csv" + ], + "notes": "" + }, + "Digester operational costs - Gasoline consumption": { + "input_manager": [ + "economic_inputs.Manure.digester.gasoline_liters_per_day" + ], + "economics_files": [ + "commodity_prices.gasoline.dollar_per_liter.csv" + ], + "notes": "" + }, + "Digester operational costs - Propane consumption": { + "input_manager": [ + "economic_inputs.Manure.digester.propane_liters_per_day" + ], + "economics_files": [ + "commodity_prices.propane_wholesale.dollar_per_liter.csv" + ], + "notes": "" + }, + "Digester operational costs - Natural gas consumption": { + "input_manager": [ + "economic_inputs.Manure.digester.megajoules_per_day" + ], + "economics_files": [ + "commodity_prices.natgas_industrial.dollar_per_megajoule.csv" + ], + "notes": "" + }, + "Digester operational costs - Electricity consumption": { + "input_manager": [ + "economic_inputs.Manure.digester.kwh_per_day" + ], + "economics_files": [ + "commodity_prices.elec_residential.dollar_per_kwh.csv", + "commodity_prices.elec_commercial.dollar_per_kwh.csv", + "commodity_prices.elec_industrial.dollar_per_kwh.csv" + ], + "notes": "" + }, + "Digester operational costs - Water consumption": { + "input_manager": [ + "economic_inputs.Manure.digester.cubic_meters_water_per_day" + ], + "economics_files": [ + "commodity_prices.water_municipal.dollar_per_cubic_meter.csv" + ], + "notes": "" + }, + "Digester operational costs": { + "biophysical_simulation": [ + "SEE NOTES" + ], + "economics_files": [ + "commodity_prices.natgas_industrial.dollar_per_megajoule.csv", + "commodity_prices.elec_industrial.dollar_per_kwh.csv", + "commodity_prices.digester_carbon_credits_dollar_per_tonne.csv" + ], + "notes": "Get outputs from methods in digester_costs.py" + }, + "Manure disposal quantity": { + "input_manager": [ + "economic_inputs.Manure.manure_disposal_kg" + ], + "economics_files": [ + "economic_inputs.Manure.manure_disposal_price_per_kg" + ], + "notes": "FULLY MANUAL" + }, + "Manure disposal transport distance": { + "input_manager": [ + "economic_inputs.Manure.manure_disposal_transport_km" + ], + "economics_files": [ + "economic_inputs.Manure.manure_disposal_price_per_km" + ], + "notes": "FULLY MANUAL" + } + }, + "Revenue": { + "Sold manure": { + "input_manager": [ + "economic_inputs.Manure.manure_sales" + ], + "economics_files": [ + "commodity_prices.bedding_manure_solids.dollar_per_head.csv" + ], + "future_expansion": "perhaps scale as a percentage of total manure produced? Or preprocessing for excess e.g. not applied to fields" + }, + "Digester fertilizer production": { + "biophysical_simulation": [ + "see future_expansion" + ], + "economics_files": [ + "commodity_prices.net_fertilizer_nitrogen.dollar_per_kilogram.csv", + "commodity_prices.net_fertilizer_phosphorus.dollar_per_kilogram.csv", + "commodity_prices.net_fertilizer_potassium.dollar_per_kilogram.csv" + ], + "future_expansion": "Placeholder for the future, scale to manure module outputs" + }, + "Digester bedding co-product": { + "biophysical_simulation": [ + "see future_expansion" + ], + "economics_files": [ + "commodity_prices.bedding_manure_solids.dollar_per_head.csv" + ], + "future_expansion": "Placeholder for the future, scale to manure module outputs" + }, + "Renewable natural gas (RNG) production": { + "input_manager": [ + "economic_inputs.Manure.digester.digester_rng_produced" + ], + "economics_files": [ + "commodity_prices.natgas_industrial.dollar_per_megajoule.csv" + ], + "future_expansion": "Placeholder for the future, scale to manure module outputs" + }, + "Electricity production from anaerobic digester": { + "input_manager": [ + "economic_inputs.Manure.digester.kwh_per_day_produced" + ], + "economics_files": [ + "commodity_prices.elec_industrial.dollar_per_kwh.csv" + ], + "future_expansion": "Placeholder for the future, scale to manure module outputs" + }, + "Carbon credits from digester products and activities": { + "biophysical_simulation": [ + "see future_expansion" + ], + "economics_files": [ + "commodity_prices.carbon_credits_enteric.dollar_per_tonne.csv" + ], + "future_expansion": "Placeholder for the future, scale to manure module outputs" + } + } + }, + "Feed_storage": { + "Costs": { + "Purchased feed costs": { + "biophysical_simulation": [ + "FeedManager.purchase_feed.ration_interval_*_cost" + ], + "economics_files": [ + "commodity_prices.alfalfa_hay", + "commodity_prices.alfalfa_silage", + "commodity_prices.almond_hulls", + "commodity_prices.barley_silage", + "commodity_prices.cotton_seed_hulls", + "commodity_prices.cotton_seed_meal", + "commodity_prices.cotton_seed_whole", + "commodity_prices.corn_grain", + "commodity_prices.corn_silage", + "commodity_prices.distiller_grains_dried_10pct", + "commodity_prices.distiller_grains_modified_wet_50pct", + "commodity_prices.distiller_grains_wet_65pct", + "commodity_prices.hay_excluding_alfalfa", + "commodity_prices.hay_all", + "commodity_prices.rye_grain", + "commodity_prices.soybean_grain", + "commodity_prices.soybean_hulls", + "commodity_prices.soybean_meal", + "commodity_prices.sundan_silage", + "commodity_prices.winter_wheat_grain", + "commodity_prices.calcium_phosphate_di", + "commodity_prices.calf_starter_18cp", + "commodity_prices.limestone" + ], + "processing": "no commodity price needed, just loop over each feed .*, report separately, but expanded evenly across the subsequent ration interval. To explain this a little more...this isn't reported daily, but on each ration formulation (usually the interval). So this price should be either prorated over the rest of the days, or the 'gaps' for each day should be filled in with 0s. So if the ration interval cost was reported for day 0, and the next on day 30, either the price of day 0 should be evenly divided across days 0-29, or the whole price for 0 should be assigned to the 0th day in the list, and for days 1-29 a price of 0 is assigned.'", + "future_expansion": "cases where historical references are desired: Instead of cost, select from economics_input options, all end in '.dollar_per_kilogram.csv', compare price in input to this price, scale accordingly (e.g. price was 1, in file is 2, double the final cost)" + }, + "Feed storage - Labor hours": { + "input_manager": [ + "economic_inputs.Feed_storage.labor_hours_per_day" + ], + "economics_files": [ + "farm_services.labor_hours.dollar_per_hour.csv" + ] + } + }, + "Revenue": { + "Feed_sales": { + "biophysical_simulation": [ + "CropManagement._record_yield.harvest_yield.field='field_.*'" + ], + "economics_files": [ + "commodity_prices.alfalfa_hay", + "commodity_prices.alfalfa_silage", + "commodity_prices.almond_hulls", + "commodity_prices.barley_silage", + "commodity_prices.cotton_seed_hulls", + "commodity_prices.cotton_seed_meal", + "commodity_prices.cotton_seed_whole", + "commodity_prices.corn_grain", + "commodity_prices.corn_silage", + "commodity_prices.distiller_grains_dried_10pct", + "commodity_prices.distiller_grains_modified_wet_50pct", + "commodity_prices.distiller_grains_wet_65pct", + "commodity_prices.hay_excluding_alfalfa", + "commodity_prices.hay_all", + "commodity_prices.rye_grain", + "commodity_prices.soybean_grain", + "commodity_prices.soybean_hulls", + "commodity_prices.soybean_meal", + "commodity_prices.sundan_silage", + "commodity_prices.winter_wheat_grain", + "commodity_prices.calcium_phosphate_di", + "commodity_prices.calf_starter_18cp", + "commodity_prices.limestone" + ], + "processing?": "Needs to loop over each field, each harvest, get total yield and map against the commodity prices here" + } + } + }, + "Soil_and_crop": { + "Costs": { + "Nitrogen fertilizer used": { + "biophysical_simulation": [ + "fertilizer_schedule SEE NOTES" + ], + "economics_files": [ + "commodity_prices.net_fertilizer_nitrogen.dollar_per_kilogram.csv" + ], + "notes": "likely as 'simple' as taking the sum of nitrogen_masses for each fertilizer_schedule file used in the simulation, but this requires some consulting with the soil and crop SMEs" + }, + "Phosphorus fertilizer used": { + "biophysical_simulation": [ + "fertilizer_schedule SEE NOTES" + ], + "economics_files": [ + "commodity_prices.net_fertilizer_phosphorus.dollar_per_kilogram.csv" + ], + "notes": "likely as 'simple' as taking the sum of phosphorus_masses for each fertilizer_schedule file used in the simulation, but this requires some consulting with the soil and crop SMEs" + }, + "Potassium fertilizer used": { + "biophysical_simulation": [ + "fertilizer_schedule SEE NOTES" + ], + "economics_files": [ + "commodity_prices.net_fertilizer_potassium.dollar_per_kilogram.csv" + ], + "notes": "likely as 'simple' as taking the sum of potassium_masses for each fertilizer_schedule file used in the simulation, but this requires some consulting with the soil and crop SMEs" + }, + "Other fertilizer types used": { + "biophysical_simulation": [ + "fertilizer_schedule SEE NOTES" + ], + "economics_files": [ + "commodity_prices.net_fertilizer_ammonium_nitrate.dollar_per_kilogram.csv", + "commodity_prices.net_fertilizer_super_phosphate_44to46pct.dollar_per_kilogram.csv", + "commodity_prices.net_fertilizer_urea.dollar_per_kilogram.csv" + ], + "notes": "Placeholder for the future" + }, + "Seeds costs": { + "biophysical_simulation": [ + "both below are from multiple input files, see preprocessing", + "field.crop_specification", + "field.field_size" + ], + "economics_files": [ + "commodity_prices..*.dollar_per_square_meter.csv", + "commodity_prices.barley_seed.dollar_per_square_meter.csv", + "commodity_prices.corn_seed.dollar_per_square_meter.csv", + "commodity_prices.cotton_seed.dollar_per_square_meter.csv", + "commodity_prices.oat_seed.dollar_per_square_meter.csv", + "commodity_prices.peanut_seed.dollar_per_square_meter.csv", + "commodity_prices.rice_seed.dollar_per_square_meter.csv", + "commodity_prices.sorghum_seed.dollar_per_square_meter.csv", + "commodity_prices.soybean_seed.dollar_per_square_meter.csv", + "commodity_prices.wheat_seed.dollar_per_square_meter.csv" + ], + "preprocessing": "Map 'crop_specification' in each field input file, e.g. CornSilage to corn_seed to get appropriate economics_file, ALSO convert the field_size from Ha to square meters (divide by 10000). However - note this one isn't reported daily! So easiest way to handle this could be to divide the price for each field evenly across each day of the simulation. e.g. if the cost for corn_seed for a field_size of 100 was 365, and the simulation was two years, the preprocessing would take all that and compute a price of 0.5 for each day in the simulation (e.g. a list consisting of 0.5 repeated for each day of the sim)." + }, + "Land purchase costs": { + "input_manager": [ + "economic_inputs.Soil_and_crop.land_ha_purchased" + ], + "economics_files": [ + "capital_costs.land_ha.dollar_per_hectare.csv" + ], + "notes": "This 'manual' entry in Economic_inputs fields is meant to be for an entire simulation, so is different from the 'per_day' values, and instead should result only in a single value. The idea for this (and others that are similar, see variables with 'whole_simulation' in them) is that it's a per scenario type value. Adding fields, giving a single cost to rent or buy manually." + }, + "Land rental costs": { + "input_manager": [ + "economic_inputs.Soil_and_crop.land_ha_rented" + ], + "economics_files": [ + "farm_services.land_ha_rent.dollar_per_hectare.csv" + ] + }, + "Crop labor hours": { + "input_manager": [ + "economic_inputs.Soil_and_crop.labor_hours_per_day" + ], + "economics_files": [ + "farm_services.labor_hours.dollar_per_hour.csv" + ] + }, + "Crop water consumption": { + "biophysical_simulation": [ + "FieldDataReporter.send_crop_daily_variables.water_uptake.*" + ], + "economics_files": [ + "commodity_prices.water_irrigation.dollar_per_cubic_meter.csv" + ] + }, + "Tractor hours for crops": { + "biophysical_simulation": [ + "Waiting on tractor_implement and other parts of EEE outputs" + ], + "economics_files": [ + "farm_services.tractor_small.dollar_per_hour.csv", + "farm_services.tractor_medium.dollar_per_hour.csv", + "farm_services.tractor_large.dollar_per_hour.csv" + ] + } + }, + "Revenue": { + "Carbon credits from soil conservation practice": { + "input_manager": [ + "economic_inputs.Soil_and_crop.carbon_credits_whole_simulation_tonnes" + ], + "economics_files": [ + "commodity_prices.carbon_credits_soil.dollar_per_tonne.csv" + ], + "future_expansion": "scale this to crop outputs/scenarios" + } + } + } +} \ No newline at end of file diff --git a/input/data/tasks/available_simulation_tasks.json b/input/data/tasks/available_simulation_tasks.json index f7669d582c..06bcae7452 100644 --- a/input/data/tasks/available_simulation_tasks.json +++ b/input/data/tasks/available_simulation_tasks.json @@ -10,16 +10,6 @@ "cross_validation_file_paths": [ "input/metadata/cross_validation/example_cross_validation.json" ] - }, - { - "task_type": "SIMULATION_SINGLE_RUN", - "metadata_file_path": "input/metadata/example_open_lot_metadata.json", - "output_prefix": "example", - "log_verbosity": "errors", - "random_seed": 42, - "cross_validation_file_paths": [ - "input/metadata/cross_validation/example_cross_validation.json" - ] } ] } \ No newline at end of file diff --git a/input/data/tasks/example_freestall_task.json b/input/data/tasks/example_freestall_task.json index e36254fe3a..42d7f795ba 100644 --- a/input/data/tasks/example_freestall_task.json +++ b/input/data/tasks/example_freestall_task.json @@ -13,4 +13,4 @@ ] } ] -} +} \ No newline at end of file diff --git a/input/metadata/EEE/econ_metadata.json b/input/metadata/EEE/econ_metadata.json index a216fa1df7..182f6652c2 100644 --- a/input/metadata/EEE/econ_metadata.json +++ b/input/metadata/EEE/econ_metadata.json @@ -7,728 +7,735 @@ "type": "csv", "properties": "_default_values_csv_properties" }, - "capital_costs.land_ha.dollar_per_hectare": { + "_default_fallback_values": { + "title": "Defaults Fallabacks", + "description": "Fallback series providing default economic values used when default data is unavailable.", + "path": "input/data/EEE/econ/_default_values_fallback.csv", + "type": "csv", + "properties": "_default_values_csv_properties" + }, + "capital_costs_land_ha_dollar_per_hectare": { "title": "Capital costs", "description": "County-level 2023 cropland value expressed in dollars per hectare.", "path": "input/data/EEE/econ/capital_costs.land_ha.dollar_per_hectare.csv", "type": "csv", "properties": "capital_costs_land_ha_dollar_per_hectare_csv_properties" }, - "commodity_prices.alfalfa_hay.dollar_per_kilogram": { + "commodity_prices_alfalfa_hay_dollar_per_kilogram": { "title": "Crops", "description": "Alfalfa hay price received by farmers", "path": "input/data/EEE/econ/commodity_prices.alfalfa_hay.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_alfalfa_hay_dollar_per_kilogram_csv_properties" }, - "commodity_prices.alfalfa_silage.dollar_per_kilogram": { + "commodity_prices_alfalfa_silage_dollar_per_kilogram": { "title": "Feed prices", "description": "Alfalfa Silage", "path": "input/data/EEE/econ/commodity_prices.alfalfa_silage.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_alfalfa_silage_dollar_per_kilogram_csv_properties" }, - "commodity_prices.almond_hulls.dollar_per_kilogram": { + "commodity_prices_almond_hulls_dollar_per_kilogram": { "title": "Feed prices", "description": "Almond Hulls", "path": "input/data/EEE/econ/commodity_prices.almond_hulls.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_almond_hulls_dollar_per_kilogram_csv_properties" }, - "commodity_prices.barley_seed.dollar_per_square_meter": { + "commodity_prices_barley_seed_dollar_per_square_meter": { "title": "Seeds", "description": "Barley Seed", "path": "input/data/EEE/econ/commodity_prices.barley_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_barley_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.barley_silage.dollar_per_kilogram": { + "commodity_prices_barley_silage_dollar_per_kilogram": { "title": "Feed prices", "description": "Barley Silage", "path": "input/data/EEE/econ/commodity_prices.barley_silage.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_barley_silage_dollar_per_kilogram_csv_properties" }, - "commodity_prices.bedding_compost_bedded_pack.dollar_per_head": { + "commodity_prices_bedding_compost_bedded_pack_dollar_per_head": { "title": "Bedding", "description": "Estimated annual bedding cost for compost bedded pack barns in dollars per head.", "path": "input/data/EEE/econ/commodity_prices.bedding_compost_bedded_pack.dollar_per_head.csv", "type": "csv", "properties": "commodity_prices_bedding_compost_bedded_pack_dollar_per_head_csv_properties" }, - "commodity_prices.bedding_manure_solids.dollar_per_head": { + "commodity_prices_bedding_manure_solids_dollar_per_head": { "title": "Bedding", "description": "Estimated annual bedding cost using manure solids in dollars per head.", "path": "input/data/EEE/econ/commodity_prices.bedding_manure_solids.dollar_per_head.csv", "type": "csv", "properties": "commodity_prices_bedding_manure_solids_dollar_per_head_csv_properties" }, - "commodity_prices.bedding_sand.dollar_per_head": { + "commodity_prices_bedding_sand_dollar_per_head": { "title": "Bedding", "description": "Estimated annual bedding cost using sand in dollars per head.", "path": "input/data/EEE/econ/commodity_prices.bedding_sand.dollar_per_head.csv", "type": "csv", "properties": "commodity_prices_bedding_sand_dollar_per_head_csv_properties" }, - "commodity_prices.bedding_sawdust.dollar_per_head": { + "commodity_prices_bedding_sawdust_dollar_per_head": { "title": "Bedding", "description": "Estimated annual bedding cost using sawdust in dollars per head.", "path": "input/data/EEE/econ/commodity_prices.bedding_sawdust.dollar_per_head.csv", "type": "csv", "properties": "commodity_prices_bedding_sawdust_dollar_per_head_csv_properties" }, - "commodity_prices.bedding_straw.dollar_per_head": { + "commodity_prices_bedding_straw_dollar_per_head": { "title": "Bedding", "description": "Estimated annual bedding cost using straw in dollars per head.", "path": "input/data/EEE/econ/commodity_prices.bedding_straw.dollar_per_head.csv", "type": "csv", "properties": "commodity_prices_bedding_straw_dollar_per_head_csv_properties" }, - "commodity_prices.calcium_phosphate_di.dollar_per_kilogram": { + "commodity_prices_calcium_phosphate_di_dollar_per_kilogram": { "title": "Feed prices", "description": "Calcium Phosphate (di)", "path": "input/data/EEE/econ/commodity_prices.calcium_phosphate_di.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_calcium_phosphate_di_dollar_per_kilogram_csv_properties" }, - "commodity_prices.calf_ heifer_2.dollar_per_kilogram": { + "commodity_prices_calf_ heifer_2_dollar_per_kilogram": { "title": "Cattle", "description": "No.2 heifer calf prices (0-14 days, 85-120 lbs) expressed in dollars per kilogram.", "path": "input/data/EEE/econ/commodity_prices.calf_ heifer_2.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_calf__heifer_2_dollar_per_kilogram_csv_properties" }, - "commodity_prices.calf_bull_1.dollar_per_kilogram": { + "commodity_prices_calf_bull_1_dollar_per_kilogram": { "title": "Cattle", "description": "No.1 Bull Calves (0-14 days) with weight 90-130 lbs", "path": "input/data/EEE/econ/commodity_prices.calf_bull_1.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_calf_bull_1_dollar_per_kilogram_csv_properties" }, - "commodity_prices.calf_bull_2.dollar_per_kilogram": { + "commodity_prices_calf_bull_2_dollar_per_kilogram": { "title": "Cattle", "description": "No.2 Bull Calves (0-14 days) with weight 85-120 lbs", "path": "input/data/EEE/econ/commodity_prices.calf_bull_2.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_calf_bull_2_dollar_per_kilogram_csv_properties" }, - "commodity_prices.calf_heifer_1.dollar_per_kilogram": { + "commodity_prices_calf_heifer_1_dollar_per_kilogram": { "title": "Cattle", "description": "No.1 Heifer Calves (0-14 days) with weight 80-130 lbs", "path": "input/data/EEE/econ/commodity_prices.calf_heifer_1.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_calf_heifer_1_dollar_per_kilogram_csv_properties" }, - "commodity_prices.calf_heifer_2.dollar_per_kilogram": { + "commodity_prices_calf_heifer_2_dollar_per_kilogram": { "title": "Cattle", "description": "No.2 Heifer Calves (0-14 days) with weight 85-120 lbs", "path": "input/data/EEE/econ/commodity_prices.calf_heifer_2.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_calf_heifer_2_dollar_per_kilogram_csv_properties" }, - "commodity_prices.calf_starter_18cp.dollar_per_kilogram": { + "commodity_prices_calf_starter_18cp_dollar_per_kilogram": { "title": "Feed prices", "description": "Calf Feed 18% Crude Protein", "path": "input/data/EEE/econ/commodity_prices.calf_starter_18cp.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_calf_starter_18cp_dollar_per_kilogram_csv_properties" }, - "commodity_prices.calves_all.dollar_per_kilogram": { + "commodity_prices_calves_all_dollar_per_kilogram": { "title": "Cattle", "description": "Price received for calves", "path": "input/data/EEE/econ/commodity_prices.calves_all.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_calves_all_dollar_per_kilogram_csv_properties" }, - "commodity_prices.carbon_credits_enteric.dollar_per_tonne_CO2e": { + "commodity_prices_carbon_credits_enteric_dollar_per_tonne_CO2e": { "title": "Carbon credits", - "description": "Carbon credit price applied to enteric methane reductions in dollars per tonne CO\u2082e.", + "description": "Carbon credit price applied to enteric methane reductions in dollars per tonne CO2e.", "path": "input/data/EEE/econ/commodity_prices.carbon_credits_enteric.dollar_per_tonne_CO2e.csv", "type": "csv", "properties": "commodity_prices_carbon_credits_enteric_dollar_per_tonne_CO2e_csv_properties" }, - "commodity_prices.carbon_credits_soil.dollar_per_tonne_CO2e": { + "commodity_prices_carbon_credits_soil_dollar_per_tonne_CO2e": { "title": "Carbon credits", - "description": "Carbon credit price for soil carbon sequestration in dollars per tonne CO\u2082e.", + "description": "Carbon credit price for soil carbon sequestration in dollars per tonne CO2e.", "path": "input/data/EEE/econ/commodity_prices.carbon_credits_soil.dollar_per_tonne_CO2e.csv", "type": "csv", "properties": "commodity_prices_carbon_credits_soil_dollar_per_tonne_CO2e_csv_properties" }, - "commodity_prices.corn_grain.dollar_per_kilogram": { + "commodity_prices_corn_grain_dollar_per_kilogram": { "title": "Crops", "description": "Historical U.S. corn grain prices received by farmers in dollars per kilogram.", "path": "input/data/EEE/econ/commodity_prices.corn_grain.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_corn_grain_dollar_per_kilogram_csv_properties" }, - "commodity_prices.corn_seed.dollar_per_square_meter": { + "commodity_prices_corn_seed_dollar_per_square_meter": { "title": "", "description": "Corn Seed", "path": "input/data/EEE/econ/commodity_prices.corn_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_corn_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.corn_silage.dollar_per_kilogram": { + "commodity_prices_corn_silage_dollar_per_kilogram": { "title": "Crops", "description": "Corn silage price received by farmers assuming the silage is already harvested and stored", "path": "input/data/EEE/econ/commodity_prices.corn_silage.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_corn_silage_dollar_per_kilogram_csv_properties" }, - "commodity_prices.cotton_seed.dollar_per_square_meter": { + "commodity_prices_cotton_seed_dollar_per_square_meter": { "title": "", "description": "Cotton Seed", "path": "input/data/EEE/econ/commodity_prices.cotton_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_cotton_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.cotton_seed_hulls.dollar_per_kilogram": { + "commodity_prices_cotton_seed_hulls_dollar_per_kilogram": { "title": "Feeds", "description": "Cotton Seed Hulls", "path": "input/data/EEE/econ/commodity_prices.cotton_seed_hulls.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_cotton_seed_hulls_dollar_per_kilogram_csv_properties" }, - "commodity_prices.cotton_seed_meal.dollar_per_kilogram": { + "commodity_prices_cotton_seed_meal_dollar_per_kilogram": { "title": "Feeds", "description": "Cotton Seed Meal", "path": "input/data/EEE/econ/commodity_prices.cotton_seed_meal.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_cotton_seed_meal_dollar_per_kilogram_csv_properties" }, - "commodity_prices.cotton_seed_whole.dollar_per_kilogram": { + "commodity_prices_cotton_seed_whole_dollar_per_kilogram": { "title": "Feeds", "description": "Whole Cotton Seeds", "path": "input/data/EEE/econ/commodity_prices.cotton_seed_whole.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_cotton_seed_whole_dollar_per_kilogram_csv_properties" }, - "commodity_prices.cow_dairy_bred_t3.dollar_per_animal": { + "commodity_prices_cow_dairy_bred_t3_dollar_per_animal": { "title": "Cattle", "description": "T3 Bred Dairy Cow (mature and pregnant) with weight 1,000-1,600 lbs", "path": "input/data/EEE/econ/commodity_prices.cow_dairy_bred_t3.dollar_per_animal.csv", "type": "csv", "properties": "commodity_prices_cow_dairy_bred_t3_dollar_per_animal_csv_properties" }, - "commodity_prices.cow_dairy_fresh.dollar_per_animal": { + "commodity_prices_cow_dairy_fresh_dollar_per_animal": { "title": "Cattle", "description": "Fresh Dairy Cow Replacements (recently gave birth and lactating) with weight 1,000-1,600 lbs", "path": "input/data/EEE/econ/commodity_prices.cow_dairy_fresh.dollar_per_animal.csv", "type": "csv", "properties": "commodity_prices_cow_dairy_fresh_dollar_per_animal_csv_properties" }, - "commodity_prices.cow_dairy_heifer_bred_t3.dollar_per_animal": { + "commodity_prices_cow_dairy_heifer_bred_t3_dollar_per_animal": { "title": "Cattle", "description": "T3 Bred Dairy Heifer (pregnant and never calved before) with weight 800-1,400 lbs", "path": "input/data/EEE/econ/commodity_prices.cow_dairy_heifer_bred_t3.dollar_per_animal.csv", "type": "csv", "properties": "commodity_prices_cow_dairy_heifer_bred_t3_dollar_per_animal_csv_properties" }, - "commodity_prices.cow_dairy_heifer_open.dollar_per_animal": { + "commodity_prices_cow_dairy_heifer_open_dollar_per_animal": { "title": "Cattle", "description": "Open Dairy Heifer (not yet been bred or is not currently pregnant) with weight 600-900 lbs", "path": "input/data/EEE/econ/commodity_prices.cow_dairy_heifer_open.dollar_per_animal.csv", "type": "csv", "properties": "commodity_prices_cow_dairy_heifer_open_dollar_per_animal_csv_properties" }, - "commodity_prices.cows_all.dollar_per_kilogram": { + "commodity_prices_cows_all_dollar_per_kilogram": { "title": "Cattle", "description": "Price received for cows", "path": "input/data/EEE/econ/commodity_prices.cows_all.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_cows_all_dollar_per_kilogram_csv_properties" }, - "commodity_prices.cows_ge_500.dollar_per_kilogram": { + "commodity_prices_cows_ge_500_dollar_per_kilogram": { "title": "Cattle", "description": "Price received for cattle greater than or equal to 500 lbs", "path": "input/data/EEE/econ/commodity_prices.cows_ge_500.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_cows_ge_500_dollar_per_kilogram_csv_properties" }, - "commodity_prices.cows_milk.dollar_per_animal": { + "commodity_prices_cows_milk_dollar_per_animal": { "title": "Cattle", "description": "Price received for Milk Cows", "path": "input/data/EEE/econ/commodity_prices.cows_milk.dollar_per_animal.csv", "type": "csv", "properties": "commodity_prices_cows_milk_dollar_per_animal_csv_properties" }, - "commodity_prices.cows_steers_and_heifers_ge_500.dollar_per_kilogram": { + "commodity_prices_cows_steers_and_heifers_ge_500_dollar_per_kilogram": { "title": "Cattle", "description": "Price received for Steers and Heifers greater than or equal to 500 lbs", "path": "input/data/EEE/econ/commodity_prices.cows_steers_and_heifers_ge_500.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_cows_steers_and_heifers_ge_500_dollar_per_kilogram_csv_properties" }, - "commodity_prices.diesel.dollar_per_liter": { + "commodity_prices_diesel_dollar_per_liter": { "title": "Diesel fuel", "description": "Retail prices", "path": "input/data/EEE/econ/commodity_prices.diesel.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_diesel_dollar_per_liter_csv_properties" }, - "commodity_prices.digester_bedding_coproduct.dollars_per_kg_bedding": { + "commodity_prices_digester_bedding_coproduct_dollars_per_kg_bedding": { "title": "Digester coproducts", "description": "Assumed market value of anaerobic digester bedding coproduct in dollars per kilogram.", "path": "input/data/EEE/econ/commodity_prices.digester_bedding_coproduct.dollars_per_kg_bedding.csv", "type": "csv", "properties": "commodity_prices_digester_bedding_coproduct_dollars_per_kg_bedding_csv_properties" }, - "commodity_prices.digester_carbon_credits.dollar_per_tonne_CO2e": { + "commodity_prices_digester_carbon_credits_dollar_per_tonne_CO2e": { "title": "Carbon credits", - "description": "Carbon credit price assumed for anaerobic digester projects in dollars per tonne CO\u2082e.", + "description": "Carbon credit price assumed for anaerobic digester projects in dollars per tonne CO2e.", "path": "input/data/EEE/econ/commodity_prices.digester_carbon_credits.dollar_per_tonne_CO2e.csv", "type": "csv", "properties": "commodity_prices_digester_carbon_credits_dollar_per_tonne_CO2e_csv_properties" }, - "commodity_prices.distiller_grains_dried_10pct.dollar_per_kilogram": { + "commodity_prices_distiller_grains_dried_10pct_dollar_per_kilogram": { "title": "Feeds", "description": "Dried Distiller Grains with 10% moisture", "path": "input/data/EEE/econ/commodity_prices.distiller_grains_dried_10pct.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_distiller_grains_dried_10pct_dollar_per_kilogram_csv_properties" }, - "commodity_prices.distiller_grains_modified_wet_50pct.dollar_per_kilogram": { + "commodity_prices_distiller_grains_modified_wet_50pct_dollar_per_kilogram": { "title": "Feeds", "description": "Modified Wet Distiller Grains with 50-55% moisture", "path": "input/data/EEE/econ/commodity_prices.distiller_grains_modified_wet_50pct.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_distiller_grains_modified_wet_50pct_dollar_per_kilogram_csv_properties" }, - "commodity_prices.distiller_grains_wet_65pct.dollar_per_kilogram": { + "commodity_prices_distiller_grains_wet_65pct_dollar_per_kilogram": { "title": "Feeds", "description": "Wet Distiller Grains with 65-70% moisture", "path": "input/data/EEE/econ/commodity_prices.distiller_grains_wet_65pct.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_distiller_grains_wet_65pct_dollar_per_kilogram_csv_properties" }, - "commodity_prices.elec_commercial.dollar_per_kwh": { + "commodity_prices_elec_commercial_dollar_per_kwh": { "title": "Electricity prices", "description": "Commercial rates", "path": "input/data/EEE/econ/commodity_prices.elec_commercial.dollar_per_kwh.csv", "type": "csv", "properties": "commodity_prices_elec_commercial_dollar_per_kwh_csv_properties" }, - "commodity_prices.elec_industrial.dollar_per_kwh": { + "commodity_prices_elec_industrial_dollar_per_kwh": { "title": "Electricity prices", "description": "Industrial rates", "path": "input/data/EEE/econ/commodity_prices.elec_industrial.dollar_per_kwh.csv", "type": "csv", "properties": "commodity_prices_elec_industrial_dollar_per_kwh_csv_properties" }, - "commodity_prices.elec_residential.dollar_per_kwh": { + "commodity_prices_elec_residential_dollar_per_kwh": { "title": "Electricity prices", "description": "Residential rates", "path": "input/data/EEE/econ/commodity_prices.elec_residential.dollar_per_kwh.csv", "type": "csv", "properties": "commodity_prices_elec_residential_dollar_per_kwh_csv_properties" }, - "commodity_prices.gasoline.dollar_per_liter": { + "commodity_prices_gasoline_dollar_per_liter": { "title": "Gasoline fuel", "description": "Retail prices", "path": "input/data/EEE/econ/commodity_prices.gasoline.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_gasoline_dollar_per_liter_csv_properties" }, - "commodity_prices.hay_all.dollar_per_kilogram": { + "commodity_prices_hay_all_dollar_per_kilogram": { "title": "Crops", "description": "Hay price received by farmers", "path": "input/data/EEE/econ/commodity_prices.hay_all.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_hay_all_dollar_per_kilogram_csv_properties" }, - "commodity_prices.hay_excluding_alfalfa.dollar_per_kilogram": { + "commodity_prices_hay_excluding_alfalfa_dollar_per_kilogram": { "title": "Crops", "description": "Hay excluding alfalfa price received by farmers", "path": "input/data/EEE/econ/commodity_prices.hay_excluding_alfalfa.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_hay_excluding_alfalfa_dollar_per_kilogram_csv_properties" }, - "commodity_prices.limestone.dollar_per_kilogram": { + "commodity_prices_limestone_dollar_per_kilogram": { "title": "Feed prices", "description": "Limestone", "path": "input/data/EEE/econ/commodity_prices.limestone.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_limestone_dollar_per_kilogram_csv_properties" }, - "commodity_prices.milk_all.dollar_per_liter": { + "commodity_prices_milk_all_dollar_per_liter": { "title": "Commodity prices", "description": "Milk", "path": "input/data/EEE/econ/commodity_prices.milk_all.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_milk_all_dollar_per_liter_csv_properties" }, - "commodity_prices.milk_class_2.dollar_per_liter": { + "commodity_prices_milk_class_2_dollar_per_liter": { "title": "Commodity prices", "description": "Class 2 Milk (Used for soft dairy products like yogurt, ice cream)", "path": "input/data/EEE/econ/commodity_prices.milk_class_2.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_milk_class_2_dollar_per_liter_csv_properties" }, - "commodity_prices.milk_class_3.dollar_per_liter": { + "commodity_prices_milk_class_3_dollar_per_liter": { "title": "Commodity prices", "description": "Class 3 Milk (Used for cheese production)", "path": "input/data/EEE/econ/commodity_prices.milk_class_3.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_milk_class_3_dollar_per_liter_csv_properties" }, - "commodity_prices.milk_class_4.dollar_per_liter": { + "commodity_prices_milk_class_4_dollar_per_liter": { "title": "Commodity prices", "description": "Class 4 Milk (Used for butter and nonfat dry milk)", "path": "input/data/EEE/econ/commodity_prices.milk_class_4.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_milk_class_4_dollar_per_liter_csv_properties" }, - "commodity_prices.milk_nonfat_dry.dollar_per_kilogram": { + "commodity_prices_milk_nonfat_dry_dollar_per_kilogram": { "title": "Commodity prices", "description": "Nonfat Dry Milk", "path": "input/data/EEE/econ/commodity_prices.milk_nonfat_dry.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_milk_nonfat_dry_dollar_per_kilogram_csv_properties" }, - "commodity_prices.milk_retail.dollar_per_liter": { + "commodity_prices_milk_retail_dollar_per_liter": { "title": "Feed prices", "description": "Whole Milk Retail Prices", "path": "input/data/EEE/econ/commodity_prices.milk_retail.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_milk_retail_dollar_per_liter_csv_properties" }, - "commodity_prices.natgas_commercial.dollar_per_megajoule": { + "commodity_prices_natgas_commercial_dollar_per_megajoule": { "title": "Natural Gas", "description": "Commercial prices", "path": "input/data/EEE/econ/commodity_prices.natgas_commercial.dollar_per_megajoule.csv", "type": "csv", "properties": "commodity_prices_natgas_commercial_dollar_per_megajoule_csv_properties" }, - "commodity_prices.natgas_industrial.dollar_per_megajoule": { + "commodity_prices_natgas_industrial_dollar_per_megajoule": { "title": "Natural Gas", "description": "U.S. dollars per megajoule of natural gas", "path": "input/data/EEE/econ/commodity_prices.natgas_industrial.dollar_per_megajoule.csv", "type": "csv", "properties": "commodity_prices_natgas_industrial_dollar_per_megajoule_csv_properties" }, - "commodity_prices.natgas_residential.dollar_per_megajoule": { + "commodity_prices_natgas_residential_dollar_per_megajoule": { "title": "Natural Gas", "description": "Residential prices", "path": "input/data/EEE/econ/commodity_prices.natgas_residential.dollar_per_megajoule.csv", "type": "csv", "properties": "commodity_prices_natgas_residential_dollar_per_megajoule_csv_properties" }, - "commodity_prices.net_fertilizer_ammonium_nitrate.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_ammonium_nitrate_dollar_per_kilogram": { "title": "Fertilizer", "description": "Ammonium Nitrate prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_ammonium_nitrate.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_ammonium_nitrate_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_anhydrous-ammonia.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_anhydrous_ammonia_dollar_per_kilogram": { "title": "Fertilizer", "description": "Anhydrous Ammonia prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_anhydrous-ammonia.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_anhydrous-ammonia_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_anhydrous_ammonia.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_anhydrous_ammonia_dollar_per_kilogram": { "title": "Fertilizer", "description": "Anhydrous ammonia fertilizer prices expressed in dollars per kilogram.", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_anhydrous_ammonia.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_anhydrous_ammonia_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_diammonium_phosphate.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_diammonium_phosphate_dollar_per_kilogram": { "title": "Fertilizer", "description": "Diammonium Phosphate 18-46-0 prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_diammonium_phosphate.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_diammonium_phosphate_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_nitrogen.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_nitrogen_dollar_per_kilogram": { "title": "Fertilizer", "description": "Nitrogen prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_nitrogen.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_nitrogen_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_nitrogen_solutions_30pct.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_nitrogen_solutions_30pct_dollar_per_kilogram": { "title": "Fertilizer", "description": "Nitrogen Solutions (30% nitrogen) prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_nitrogen_solutions_30pct.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_nitrogen_solutions_30pct_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_phosphorus.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_phosphorus_dollar_per_kilogram": { "title": "Fertilizer", "description": "Phosphorus prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_phosphorus.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_phosphorus_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_potassium.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_potassium_dollar_per_kilogram": { "title": "Fertilizer", "description": "Potassium prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_potassium.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_potassium_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_potassium_chloride.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_potassium_chloride_dollar_per_kilogram": { "title": "Fertilizer", "description": "Potassium Chloride (60pct potassium) prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_potassium_chloride.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_potassium_chloride_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_sulfate_of_ammonium.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_sulfate_of_ammonium_dollar_per_kilogram": { "title": "Fertilizer", "description": "Sulfate of Ammonium prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_sulfate_of_ammonium.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_sulfate_of_ammonium_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_super_phosphate_20pct.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_super_phosphate_20pct_dollar_per_kilogram": { "title": "Fertilizer", "description": "Super Phosphate (20% phosphate) prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_super_phosphate_20pct.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_super_phosphate_20pct_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_super_phosphate_44to46pct.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_super_phosphate_44to46pct_dollar_per_kilogram": { "title": "Fertilizer", "description": "Super Phosphate (44-46% phosphate) prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_super_phosphate_44to46pct.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_super_phosphate_44to46pct_dollar_per_kilogram_csv_properties" }, - "commodity_prices.net_fertilizer_urea.dollar_per_kilogram": { + "commodity_prices_net_fertilizer_urea_dollar_per_kilogram": { "title": "Fertilizer", "description": "Urea (44-46% nitrogen) prices", "path": "input/data/EEE/econ/commodity_prices.net_fertilizer_urea.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_net_fertilizer_urea_dollar_per_kilogram_csv_properties" }, - "commodity_prices.oat_seed.dollar_per_square_meter": { + "commodity_prices_oat_seed_dollar_per_square_meter": { "title": "", "description": "Oat Seed", "path": "input/data/EEE/econ/commodity_prices.oat_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_oat_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.peanut_seed.dollar_per_square_meter": { + "commodity_prices_peanut_seed_dollar_per_square_meter": { "title": "", "description": "Peanut Seed", "path": "input/data/EEE/econ/commodity_prices.peanut_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_peanut_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.propane_residential.dollar_per_liter": { + "commodity_prices_propane_residential_dollar_per_liter": { "title": "Propane", "description": "Residential prices", "path": "input/data/EEE/econ/commodity_prices.propane_residential.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_propane_residential_dollar_per_liter_csv_properties" }, - "commodity_prices.propane_wholesale.dollar_per_liter": { + "commodity_prices_propane_wholesale_dollar_per_liter": { "title": "Propane", "description": "Wholesale prices", "path": "input/data/EEE/econ/commodity_prices.propane_wholesale.dollar_per_liter.csv", "type": "csv", "properties": "commodity_prices_propane_wholesale_dollar_per_liter_csv_properties" }, - "commodity_prices.rice_seed.dollar_per_square_meter": { + "commodity_prices_rice_seed_dollar_per_square_meter": { "title": "", "description": "Rice Seed", "path": "input/data/EEE/econ/commodity_prices.rice_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_rice_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.rye_grain.dollar_per_kilogram": { + "commodity_prices_rye_grain_dollar_per_kilogram": { "title": "Crops", "description": "Rye price received by farmers", "path": "input/data/EEE/econ/commodity_prices.rye_grain.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_rye_grain_dollar_per_kilogram_csv_properties" }, - "commodity_prices.semen_beef.dollar_per_straw": { + "commodity_prices_semen_beef_dollar_per_straw": { "title": "Genetics", "description": "Price per straw for beef semen.", "path": "input/data/EEE/econ/commodity_prices.semen_beef.dollar_per_straw.csv", "type": "csv", "properties": "commodity_prices_semen_beef_dollar_per_straw_csv_properties" }, - "commodity_prices.semen_conventional.dollar_per_straw": { + "commodity_prices_semen_conventional_dollar_per_straw": { "title": "Genetics", "description": "Price per straw for conventional dairy semen.", "path": "input/data/EEE/econ/commodity_prices.semen_conventional.dollar_per_straw.csv", "type": "csv", "properties": "commodity_prices_semen_conventional_dollar_per_straw_csv_properties" }, - "commodity_prices.semen_sexed.dollar_per_straw": { + "commodity_prices_semen_sexed_dollar_per_straw": { "title": "Genetics", "description": "Price per straw for sexed dairy semen.", "path": "input/data/EEE/econ/commodity_prices.semen_sexed.dollar_per_straw.csv", "type": "csv", "properties": "commodity_prices_semen_sexed_dollar_per_straw_csv_properties" }, - "commodity_prices.sorghum_seed.dollar_per_square_meter": { + "commodity_prices_sorghum_seed_dollar_per_square_meter": { "title": "", "description": "Sorghum Seed", "path": "input/data/EEE/econ/commodity_prices.sorghum_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_sorghum_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.soybean_grain.dollar_per_kilogram": { + "commodity_prices_soybean_grain_dollar_per_kilogram": { "title": "Crops", "description": "Soybean price received by farmers", "path": "input/data/EEE/econ/commodity_prices.soybean_grain.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_soybean_grain_dollar_per_kilogram_csv_properties" }, - "commodity_prices.soybean_hulls.dollar_per_kilogram": { + "commodity_prices_soybean_hulls_dollar_per_kilogram": { "title": "Feeds", "description": "Soybean Hulls", "path": "input/data/EEE/econ/commodity_prices.soybean_hulls.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_soybean_hulls_dollar_per_kilogram_csv_properties" }, - "commodity_prices.soybean_meal.dollar_per_kilogram": { + "commodity_prices_soybean_meal_dollar_per_kilogram": { "title": "Crops", "description": "Soybean meal commodity price", "path": "input/data/EEE/econ/commodity_prices.soybean_meal.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_soybean_meal_dollar_per_kilogram_csv_properties" }, - "commodity_prices.soybean_seed.dollar_per_square_meter": { + "commodity_prices_soybean_seed_dollar_per_square_meter": { "title": "", "description": "Soybean Seed", "path": "input/data/EEE/econ/commodity_prices.soybean_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_soybean_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.steer_holstein_300.dollar_per_kilogram": { + "commodity_prices_steer_holstein_300_dollar_per_kilogram": { "title": "Cattle", "description": "Feeder Holstein Steers (300-500 lbs)", "path": "input/data/EEE/econ/commodity_prices.steer_holstein_300.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_steer_holstein_300_dollar_per_kilogram_csv_properties" }, - "commodity_prices.steer_holstein_500.dollar_per_kilogram": { + "commodity_prices_steer_holstein_500_dollar_per_kilogram": { "title": "Cattle", "description": "Feeder Holstein Steers (500-700 lbs)", "path": "input/data/EEE/econ/commodity_prices.steer_holstein_500.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_steer_holstein_500_dollar_per_kilogram_csv_properties" }, - "commodity_prices.steer_holstein_700.dollar_per_kilogram": { + "commodity_prices_steer_holstein_700_dollar_per_kilogram": { "title": "Cattle", "description": "Feeder Holstein Steers (700-1,000 lbs)", "path": "input/data/EEE/econ/commodity_prices.steer_holstein_700.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_steer_holstein_700_dollar_per_kilogram_csv_properties" }, - "commodity_prices.sundan_silage.dollar_per_kilogram": { + "commodity_prices_sundan_silage_dollar_per_kilogram": { "title": "Feed prices", "description": "Sudangrass Silage", "path": "input/data/EEE/econ/commodity_prices.sundan_silage.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_sundan_silage_dollar_per_kilogram_csv_properties" }, - "commodity_prices.water_irrigation.dollar_per_cubic_meter": { + "commodity_prices_water_irrigation_dollar_per_cubic_meter": { "title": "Water", "description": "Irrigation prices", "path": "input/data/EEE/econ/commodity_prices.water_irrigation.dollar_per_cubic_meter.csv", "type": "csv", "properties": "commodity_prices_water_irrigation_dollar_per_cubic_meter_csv_properties" }, - "commodity_prices.water_municipal.dollar_per_cubic_meter": { + "commodity_prices_water_municipal_dollar_per_cubic_meter": { "title": "Water", "description": "Municipal prices", "path": "input/data/EEE/econ/commodity_prices.water_municipal.dollar_per_cubic_meter.csv", "type": "csv", "properties": "commodity_prices_water_municipal_dollar_per_cubic_meter_csv_properties" }, - "commodity_prices.wheat_seed.dollar_per_square_meter": { + "commodity_prices_wheat_seed_dollar_per_square_meter": { "title": "", "description": "Wheat Seed", "path": "input/data/EEE/econ/commodity_prices.wheat_seed.dollar_per_square_meter.csv", "type": "csv", "properties": "commodity_prices_wheat_seed_dollar_per_square_meter_csv_properties" }, - "commodity_prices.whey_dry.dollar_per_kilogram": { + "commodity_prices_whey_dry_dollar_per_kilogram": { "title": "Commodity prices", "description": "Dry Whey (A byproduct of cheese production)", "path": "input/data/EEE/econ/commodity_prices.whey_dry.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_whey_dry_dollar_per_kilogram_csv_properties" }, - "commodity_prices.winter_wheat_grain.dollar_per_kilogram": { + "commodity_prices_winter_wheat_grain_dollar_per_kilogram": { "title": "Crops", "description": "Winter wheat price received by farmers", "path": "input/data/EEE/econ/commodity_prices.winter_wheat_grain.dollar_per_kilogram.csv", "type": "csv", "properties": "commodity_prices_winter_wheat_grain_dollar_per_kilogram_csv_properties" }, - "farm_services.grain_transportation.dollars_per_tonne_km": { + "farm_services_grain_transportation_dollars_per_tonne_km": { "title": "Logistics", "description": "Cost to transport grain in dollars per tonne-kilometer.", "path": "input/data/EEE/econ/farm_services.grain_transportation.dollars_per_tonne_km.csv", "type": "csv", "properties": "farm_services_grain_transportation_dollars_per_tonne_km_csv_properties" }, - "farm_services.labor_hours.dollar_per_hour": { + "farm_services_labor_hours_dollar_per_hour": { "title": "Labor", "description": "Hired wages", "path": "input/data/EEE/econ/farm_services.labor_hours.dollar_per_hour.csv", "type": "csv", "properties": "farm_services_labor_hours_dollar_per_hour_csv_properties" }, - "farm_services.labor_hours_animal_manure.hours_per_head_per_year": { + "farm_services_labor_hours_animal_manure_hours_per_head_per_year": { "title": "Labor", "description": "Hours of labor per head per year allocated to manure-related animal tasks.", "path": "input/data/EEE/econ/farm_services.labor_hours_animal_manure.hours_per_head_per_year.csv", "type": "csv", "properties": "farm_services_labor_hours_animal_manure_hours_per_head_per_year_csv_properties" }, - "farm_services.labor_hours_animal_nonmanure.hours_per_head_per_year": { + "farm_services_labor_hours_animal_nonmanure_hours_per_head_per_year": { "title": "Labor", "description": "Hours of labor per head per year for non-manure animal tasks.", "path": "input/data/EEE/econ/farm_services.labor_hours_animal_nonmanure.hours_per_head_per_year.csv", "type": "csv", "properties": "farm_services_labor_hours_animal_nonmanure_hours_per_head_per_year_csv_properties" }, - "farm_services.labor_hours_animal_total.hours_per_head_per_year": { + "farm_services_labor_hours_animal_total_hours_per_head_per_year": { "title": "Labor", "description": "Total labor hours per head per year for animal operations.", "path": "input/data/EEE/econ/farm_services.labor_hours_animal_total.hours_per_head_per_year.csv", "type": "csv", "properties": "farm_services_labor_hours_animal_total_hours_per_head_per_year_csv_properties" }, - "farm_services.labor_hours_crop.hours_per_ha_per_year": { + "farm_services_labor_hours_crop_hours_per_ha_per_year": { "title": "Labor", "description": "Crop labor requirement in hours per hectare per year.", "path": "input/data/EEE/econ/farm_services.labor_hours_crop.hours_per_ha_per_year.csv", "type": "csv", "properties": "farm_services_labor_hours_crop_hours_per_ha_per_year_csv_properties" }, - "farm_services.land_ha_rent.dollar_per_hectare": { + "farm_services_land_ha_rent_dollar_per_hectare": { "title": "Land rent", "description": "County-level 2023 land rent in dollars per hectare.", "path": "input/data/EEE/econ/farm_services.land_ha_rent.dollar_per_hectare.csv", "type": "csv", "properties": "farm_services_land_ha_rent_dollar_per_hectare_csv_properties" }, - "farm_services.tractor_large.dollar_per_hour": { + "farm_services_tractor_large_dollar_per_hour": { "title": "Machinery", "description": "Custom rate for large tractor services in dollars per hour.", "path": "input/data/EEE/econ/farm_services.tractor_large.dollar_per_hour.csv", "type": "csv", "properties": "farm_services_tractor_large_dollar_per_hour_csv_properties" }, - "farm_services.tractor_medium.dollar_per_hour": { + "farm_services_tractor_medium_dollar_per_hour": { "title": "Machinery", "description": "Custom rate for medium tractor services in dollars per hour.", "path": "input/data/EEE/econ/farm_services.tractor_medium.dollar_per_hour.csv", "type": "csv", "properties": "farm_services_tractor_medium_dollar_per_hour_csv_properties" }, - "farm_services.tractor_small.dollar_per_hour": { + "farm_services_tractor_small_dollar_per_hour": { "title": "Machinery", "description": "Custom rate for small tractor services in dollars per hour.", "path": "input/data/EEE/econ/farm_services.tractor_small.dollar_per_hour.csv", @@ -736,4 +743,4 @@ "properties": "farm_services_tractor_small_dollar_per_hour_csv_properties" } } -} +} \ No newline at end of file diff --git a/input/metadata/example_freestall_dairy_metadata.json b/input/metadata/example_freestall_dairy_metadata.json index 3498b9c9de..c15f6be9a7 100644 --- a/input/metadata/example_freestall_dairy_metadata.json +++ b/input/metadata/example_freestall_dairy_metadata.json @@ -224,6 +224,23 @@ "type": "json", "properties": "EEE_constants_properties" }, + "economic_inputs": { + "title": "Manual economics levers", + "description": "Resource use and scenario-wide economic overrides consumed by the Economic module.", + "path": "input/data/EEE/economic_inputs.json", + "type": "json", + "properties": "economic_inputs_properties" + }, + "properties": { + "title": "Metadata Properties", + "description": "The properties of input data.", + "paths": [ + "input/metadata/properties/default.json", + "input/metadata/properties/commodity_properties.json" + ], + "type": "json", + "properties": "NA" + }, "feed_storage_configurations": { "title": "Feed Management", "description": "Configurations for feed storage units.", diff --git a/input/metadata/properties/commodity_properties.json b/input/metadata/properties/commodity_properties.json index dbbd11b4f1..5517253d59 100644 --- a/input/metadata/properties/commodity_properties.json +++ b/input/metadata/properties/commodity_properties.json @@ -16159,7 +16159,7 @@ }, "_default_values_csv_properties": { "data_collection_app_compatible": false, - "Unnamed: 0": { + "commodity": { "type": "array", "properties": { "type": "string" diff --git a/input/metadata/properties/default.json b/input/metadata/properties/default.json index 01e7294c9c..2d28e952f9 100644 --- a/input/metadata/properties/default.json +++ b/input/metadata/properties/default.json @@ -3084,6 +3084,422 @@ } } }, + "economic_inputs_properties": { + "data_collection_app_compatible": false, + "Animal": { + "type": "object", + "description": "Daily resource use for animal facilities (liters/day, kWh/day, m3/day, labor hours).", + "diesel_liters_per_day": { + "type": "number", + "description": "Diesel burned daily by animal operations (L/day).", + "minimum": 0, + "default": 0 + }, + "gasoline_liters_per_day": { + "type": "number", + "description": "Gasoline burned daily by animal operations (L/day).", + "minimum": 0, + "default": 0 + }, + "propane_liters_per_day": { + "type": "number", + "description": "Propane burned daily by animal operations (L/day).", + "minimum": 0, + "default": 0 + }, + "natural_gas_megajoule_per_day": { + "type": "number", + "description": "Natural gas energy used daily by animal operations (MJ/day).", + "minimum": 0, + "default": 0 + }, + "electricity_kwh_per_day": { + "type": "number", + "description": "Grid electricity used daily by animal operations (kWh/day).", + "minimum": 0, + "default": 0 + }, + "water_cubic_meter_per_day": { + "type": "number", + "description": "Potable/process water used daily by animal operations (m3/day).", + "minimum": 0, + "default": 0 + }, + "labor_hours_per_day": { + "type": "number", + "description": "Labor dedicated to animal operations (hours/day).", + "minimum": 0, + "default": 0 + } + }, + "Manure": { + "type": "object", + "description": "Manual manure-management levers (labor, utilities, hauled quantities, and sales).", + "general": { + "type": "object", + "description": "Whole-farm manure support labor.", + "labor_hours": { + "type": "number", + "description": "Total manure-handling labor not tied to a specific asset (hours/day).", + "minimum": 0, + "default": 0 + } + }, + "slurry_storage": { + "type": "object", + "description": "Daily resource use for slurry storages.", + "labor_hours": { + "type": "number", + "description": "Labor for slurry storage (hours/day).", + "minimum": 0, + "default": 0 + }, + "diesel_liters_per_day": { + "type": "number", + "description": "Diesel for slurry storage equipment (L/day).", + "minimum": 0, + "default": 0 + }, + "gasoline_liters_per_day": { + "type": "number", + "description": "Gasoline for slurry storage equipment (L/day).", + "minimum": 0, + "default": 0 + }, + "megajoules_per_day": { + "type": "number", + "description": "Natural-gas energy for slurry storage (MJ/day).", + "minimum": 0, + "default": 0 + }, + "kwh_per_day": { + "type": "number", + "description": "Electricity for slurry storage (kWh/day).", + "minimum": 0, + "default": 0 + }, + "propane_liters_per_day": { + "type": "number", + "description": "Propane burned daily by animal operations (L/day).", + "minimum": 0, + "default": 0 + }, + "cubic_meters_water_per_day": { + "type": "number", + "description": "Water used for slurry storage operations (m3/day).", + "minimum": 0, + "default": 0 + } + }, + "digester": { + "type": "object", + "description": "Daily resource use and coproduct outputs for the anaerobic digester.", + "labor_hours_per_day": { + "type": "number", + "description": "Labor for digester operations (hours/day).", + "minimum": 0, + "default": 0 + }, + "diesel_liters_per_day": { + "type": "number", + "description": "Diesel used by the digester complex (L/day).", + "minimum": 0, + "default": 0 + }, + "gasoline_liters_per_day": { + "type": "number", + "description": "Gasoline used by the digester complex (L/day).", + "minimum": 0, + "default": 0 + }, + "propane_liters_per_day": { + "type": "number", + "description": "Propane used by the digester complex (L/day).", + "minimum": 0, + "default": 0 + }, + "megajoules_per_day": { + "type": "number", + "description": "Natural-gas energy consumed by the digester (MJ/day).", + "minimum": 0, + "default": 0 + }, + "kwh_per_day": { + "type": "number", + "description": "Electricity drawn by the digester (kWh/day).", + "minimum": 0, + "default": 0 + }, + "cubic_meters_water_per_day": { + "type": "number", + "description": "Water used by the digester (m3/day).", + "minimum": 0, + "default": 0 + }, + "kwh_per_day_produced": { + "type": "number", + "description": "Electricity exported from the digester (kWh/day).", + "minimum": 0, + "default": 0 + }, + "digester_rng_produced": { + "type": "number", + "description": "Renewable natural gas produced (MJ/day).", + "minimum": 0, + "default": 0 + } + }, + "manure_disposal_kg": { + "type": "number", + "description": "Mass of manure hauled off-farm for disposal (kg/day or scenario total).", + "minimum": 0, + "default": 0 + }, + "manure_disposal_price_per_kg": { + "type": "number", + "description": "User-defined hauling/tipping price per kilogram (USD/kg).", + "minimum": 0, + "default": 0 + }, + "manure_disposal_transport_km": { + "type": "number", + "description": "Distance traveled to dispose manure (km per haul or scenario total).", + "minimum": 0, + "default": 0 + }, + "manure_disposal_price_per_km": { + "type": "number", + "description": "Price per kilometer for manure hauling (USD/km).", + "minimum": 0, + "default": 0 + }, + "manure_sales": { + "type": "number", + "description": "Quantity of manure sold for bedding/fertilizer markets (kg/day or scenario total).", + "minimum": 0, + "default": 0 + } + }, + "Soil_and_crop": { + "type": "object", + "description": "Scenario-wide land and crop economics levers.", + "land_ha_purchased": { + "type": "number", + "description": "Hectares of land purchased for the scenario (ha).", + "minimum": 0, + "default": 0 + }, + "land_ha_rented": { + "type": "number", + "description": "Hectares of land rented for the scenario (ha).", + "minimum": 0, + "default": 0 + }, + "labor_hours_per_day": { + "type": "number", + "description": "Daily labor devoted to soil and crop tasks (hours/day).", + "minimum": 0, + "default": 0 + }, + "carbon_credits_whole_simulation_tonnes": { + "type": "number", + "description": "Tonnes of carbon credits earned from soil practices over the scenario (t).", + "minimum": 0, + "default": 0 + } + }, + "Feed_storage": { + "type": "object", + "description": "Daily labor requirements for feed-storage operations.", + "labor_hours_per_day": { + "type": "number", + "description": "Labor dedicated to feed-storage management (hours/day).", + "minimum": 0, + "default": 0 + } + }, + "cashflow_inputs": { + "type": "object", + "description": "Inputs for discounted cash-flow rate-of-return analysis.", + "loan_term": { + "type": "number", + "description": "Loan term in years.", + "minimum": 0, + "default": 10 + }, + "project_term": { + "type": "number", + "description": "Project evaluation horizon in years.", + "minimum": 0, + "default": 20 + }, + "operating_units": { + "type": "array", + "description": "Operating units by category and year.", + "properties": { + "type": "array", + "properties": { + "type": "number" + } + } + }, + "operating_unit_costs": { + "type": "array", + "description": "Operating cost per unit by category and year.", + "properties": { + "type": "array", + "properties": { + "type": "number" + } + } + }, + "units_produced": { + "type": "array", + "description": "Revenue units produced by category and year.", + "properties": { + "type": "array", + "properties": { + "type": "number" + } + } + }, + "unit_price": { + "type": "array", + "description": "Revenue price per unit by category and year.", + "properties": { + "type": "array", + "properties": { + "type": "number" + } + } + }, + "loan_interest_rate": { + "type": "number", + "description": "Loan interest rate (fraction).", + "minimum": 0, + "default": 0.05 + }, + "loan_fraction": { + "type": "number", + "description": "Fraction of capital financed by debt.", + "minimum": 0, + "maximum": 1, + "default": 0.5 + }, + "equity": { + "type": "number", + "description": "Fraction of capital financed by equity.", + "minimum": 0, + "maximum": 1, + "default": 0.5 + }, + "const_int": { + "type": "number", + "description": "Construction-period interest rate (fraction).", + "minimum": 0, + "default": 0.04 + }, + "const_term": { + "type": "number", + "description": "Construction term in years.", + "minimum": 0, + "default": 1 + }, + "const_rate_i": { + "type": "array", + "description": "Fractional completion per construction year.", + "properties": { + "type": "number" + } + }, + "tax_rate": { + "type": "number", + "description": "Tax rate applied to taxable income (fraction).", + "minimum": 0, + "maximum": 1, + "default": 0.21 + }, + "target_internal_rate_of_return": { + "type": "number", + "description": "Target internal rate of return (fraction).", + "minimum": 0, + "default": 0.08 + }, + "depreciation_i": { + "type": "array", + "description": "Depreciation schedule fractions by year.", + "properties": { + "type": "number" + } + }, + "enable_dcfror": { + "type": "bool", + "description": "Whether to execute DCFROR calculations.", + "default": true + }, + "tax_credit_used": { + "type": "number", + "description": "Tax credit amount used in the project model.", + "default": 0.0 + }, + "tax_credit_revenue": { + "type": "number", + "description": "Tax credit revenue recognized in the project model.", + "default": 0.0 + }, + "enable_goal_seek": { + "type": "bool", + "description": "Whether to run goal-seek after DCFROR to solve an input that yields the target NPV.", + "default": false + }, + "goal_seek_target_npv": { + "type": "number", + "description": "Target net present value used by goal-seek.", + "default": 0.0 + }, + "goal_seek_bounds": { + "type": "array", + "description": "Lower and upper multiplier bounds used by goal-seek binary search.", + "properties": { + "type": "number" + } + }, + "goal_seek_unit_price_multiplier": { + "type": "number", + "description": "Multiplier applied to unit_price when solving for MPSP via goal-seek.", + "minimum": 0, + "default": 1.0 + }, + "goal_seek_fixed_variables": { + "type": "array", + "description": "Names of exactly two cashflow variables to keep fixed during goal-seek; the remaining one is solved automatically.", + "properties": { + "type": "string", + "pattern": "^(target_internal_rate_of_return|loan_term|goal_seek_unit_price_multiplier)$" + } + } + }, + "capital_costs": { + "type": "object", + "description": "Capital cost definitions for economic analysis.", + "capital_cost_breakdown": { + "type": "array", + "description": "Capital cost breakdown table with line item names and costs.", + "properties": { + "type": "object", + "Item": { + "type": "string", + "description": "Capital cost line-item name." + }, + "Cost": { + "type": "number", + "description": "Capital cost amount for the line item.", + "minimum": 0 + } + } + } + } + }, "emissions_properties": { "data_collection_app_compatible": false, "alfalfa_tillage_emissions": { @@ -6249,6 +6665,146 @@ } } }, + "economics_map_properties": { + "data_collection_app_compatible": false, + "Animal": { + "type": "object", + "description": "Mappings for animal-related economic metrics.", + "properties": { + "type": "object", + "description": "Economics metric definitions grouped within a category.", + "properties": { + "type": "object", + "description": "Mapping details for an individual economics metric.", + "biophysical_simulation": { + "type": "array", + "description": "InputManager addresses for the biophysical values feeding the metric.", + "properties": { + "type": "string" + }, + "minimum_length": 1 + }, + "economics_files": { + "type": "array", + "description": "Economics data sources referenced by the metric.", + "properties": { + "type": "string" + }, + "minimum_length": 1 + }, + "properties": { + "type": "string", + "description": "Supplemental notes or preprocessing guidance for the metric.", + "nullable": true + } + } + } + }, + "Soil_and_crop": { + "type": "object", + "description": "Mappings for crop and soil economic metrics.", + "properties": { + "type": "object", + "description": "Economics metric definitions grouped within a category.", + "properties": { + "type": "object", + "description": "Mapping details for an individual economics metric.", + "biophysical_simulation": { + "type": "array", + "description": "InputManager addresses for the biophysical values feeding the metric.", + "properties": { + "type": "string" + }, + "minimum_length": 1 + }, + "economics_files": { + "type": "array", + "description": "Economics data sources referenced by the metric.", + "properties": { + "type": "string" + }, + "minimum_length": 1 + }, + "properties": { + "type": "string", + "description": "Supplemental notes or preprocessing guidance for the metric.", + "nullable": true + } + } + } + }, + "Feed_storage": { + "type": "object", + "description": "Mappings for feed-related economic metrics.", + "properties": { + "type": "object", + "description": "Economics metric definitions grouped within a category.", + "properties": { + "type": "object", + "description": "Mapping details for an individual economics metric.", + "biophysical_simulation": { + "type": "array", + "description": "InputManager addresses for the biophysical values feeding the metric.", + "properties": { + "type": "string" + }, + "minimum_length": 1 + }, + "economics_files": { + "type": "array", + "description": "Economics data sources referenced by the metric.", + "properties": { + "type": "string" + }, + "minimum_length": 1 + }, + "properties": { + "type": "string", + "description": "Supplemental notes or preprocessing guidance for the metric.", + "nullable": true + } + } + }, + "Revenue": { + "type": "string", + "description": "Reference directing revenue metrics to crop and soil mappings.", + "nullable": false + } + }, + "Manure": { + "type": "object", + "description": "Mappings for manure-related economic metrics.", + "properties": { + "type": "object", + "description": "Economics metric definitions grouped within a category.", + "properties": { + "type": "object", + "description": "Mapping details for an individual economics metric.", + "biophysical_simulation": { + "type": "array", + "description": "InputManager addresses for the biophysical values feeding the metric.", + "properties": { + "type": "string" + }, + "minimum_length": 1 + }, + "economics_files": { + "type": "array", + "description": "Economics data sources referenced by the metric.", + "properties": { + "type": "string" + }, + "minimum_length": 1 + }, + "properties": { + "type": "string", + "description": "Supplemental notes or preprocessing guidance for the metric.", + "nullable": true + } + } + } + } + }, "EEE_constants_properties": { "data_collection_app_compatible": false, "constants": { @@ -6837,5 +7393,10 @@ "type": "number" } } + }, + "economic_preprocessing_properties": { + "data_collection_app_compatible": false, + "type": "object", + "description": "Aggregated biophysical outputs and pricing data produced by the economics preprocessor." } -} \ No newline at end of file +} diff --git a/tests/test_EEE/test_economics.py b/tests/test_EEE/test_economics.py new file mode 100644 index 0000000000..a32908ea10 --- /dev/null +++ b/tests/test_EEE/test_economics.py @@ -0,0 +1,345 @@ +from pytest_mock import MockerFixture +import pandas as pd +import math +import numpy as np +import pytest + +from RUFAS.EEE.economics.framework import EconomicFramework +from RUFAS.EEE.economics.dcfror import DCFRORCalculator +from RUFAS.EEE.economics.metrics import ( + calculate_roi, + calculate_payback_period, + calculate_net_annual_cash_flow, + calculate_mpsp, +) +from RUFAS.EEE.economics.digester_costs import ( + calculate_digester_capital_cost, + capital_recovery_factor, + scale_installed_cost, + calculate_digester_capex, + calculate_digester_operational_cost, + estimate_digester_costs, + estimate_digester_trucking_cost, + get_digester_cost_profile, +) +from RUFAS.EEE.economics.equations import ( + construct_timeline, + discount_factor, + annual_capital_spent, + equity_contribution, + loan_principal, + construction_interest, + npv_capital_plus_interest, + annual_loan_payment, + interest_payment, + principal_after_payment, + depreciation_schedule, + net_revenue, + loss_carry_forward, + taxable_income, + income_tax, + annual_cash_income, + present_value, + net_present_value, +) + + +def test_run_economic_analysis_uses_dcfror_when_capital_present( + mocker: MockerFixture, +) -> None: + im_cls = mocker.patch("RUFAS.EEE.economics.framework.InputManager") + im_cls.return_value.get_data.return_value = pd.DataFrame({"Cost": [100]}) + mocker.patch( + "RUFAS.EEE.economics.framework.PartialBudget.has_partial_budget_activity", + return_value=False, + ) + mock_calc_cls = mocker.patch("RUFAS.EEE.economics.framework.DCFRORCalculator") + sentinel = {"section": {}} + mock_preproc_cls = mocker.patch("RUFAS.EEE.economics.framework.EconomicPreprocessor") + mock_preproc_cls.return_value.preprocess.return_value = sentinel + mock_om_cls = mocker.patch("RUFAS.EEE.economics.framework.OutputManager") + + result = EconomicFramework().run_economic_analysis() + + mock_calc_cls.assert_called_once_with() + mock_calc_cls.return_value.calculate.assert_called_once_with(sentinel) + mock_om_cls.return_value.add_variable.assert_not_called() + assert result is None + + +def test_run_economic_analysis_uses_pba_when_no_capital(mocker: MockerFixture) -> None: + im_cls = mocker.patch("RUFAS.EEE.economics.framework.InputManager") + im_cls.return_value.get_data.return_value = pd.DataFrame({"Cost": [0]}) + sentinel = {"section": {"category": {}}} + mock_preproc_cls = mocker.patch("RUFAS.EEE.economics.framework.EconomicPreprocessor") + mock_preproc_cls.return_value.preprocess.return_value = sentinel + mock_pba = mocker.patch("RUFAS.EEE.economics.framework.PartialBudget.calculate_partial_budget") + mocker.patch( + "RUFAS.EEE.economics.framework.PartialBudget.has_partial_budget_activity", + return_value=True, + ) + mock_om_cls = mocker.patch("RUFAS.EEE.economics.framework.OutputManager") + + result = EconomicFramework().run_economic_analysis() + + mock_pba.assert_called_once_with(sentinel) + mock_om_cls.return_value.add_variable.assert_not_called() + assert result is None + + +def test_calculate_roi() -> None: + assert calculate_roi(150.0, 100.0) == 50.0 + + +def test_calculate_payback_period() -> None: + cash_flows = [-50, 20, 20, 20] + assert calculate_payback_period(cash_flows) == 2.5 + + +def test_calculate_net_annual_cash_flow() -> None: + result = calculate_net_annual_cash_flow([100, 120], [80, 90]) + assert result.tolist() == [20, 30] + + +def test_calculate_mpsp() -> None: + assert math.isnan(calculate_mpsp(100.0, 50.0)) + + +def test_calculate_digester_capital_cost() -> None: + result = calculate_digester_capital_cost( + animal_units=10.0, + digester_volume=20.0, + farm_type_flag=1.0, + below_ground_flag=0.0, + concrete_flag=1.0, + steel_flag=0.0, + alpha=1.0, + r1=0.5, + r2=0.3, + psi1=0.1, + psi2=0.2, + psi3=0.05, + psi4=0.07, + epsilon=0.0, + ) + expected = math.exp( + 1.0 + 0.5 * math.log(10.0) + 0.3 * math.log(20.0) + 0.1 * 1.0 + 0.2 * 0.0 + 0.05 * 1.0 + 0.07 * 0.0 + ) + assert result == expected + + legacy = calculate_digester_capital_cost( + alpha=1.0, + r1=0.5, + r2=0.3, + area=10.0, + volume=20.0, + psi1=0.1, + psi2=0.2, + psi3=0.05, + psi4=0.07, + f_j=1.0, + g_j=0.0, + c_j=1.0, + s_j=0.0, + epsilon=0.0, + ) + assert legacy == expected + + +def test_capital_recovery_factor() -> None: + result = capital_recovery_factor(0.05, 10) + expected = 0.05 * (1 + 0.05) ** 10 / ((1 + 0.05) ** 10 - 1) + assert pytest.approx(result, rel=1e-6) == expected + + +def test_scale_installed_cost() -> None: + assert scale_installed_cost(1000.0, 150.0, 100.0, 0.6) == pytest.approx(1000.0 * (150.0 / 100.0) ** 0.6) + + +def test_calculate_digester_capex() -> None: + crf = capital_recovery_factor(0.05, 20) + afc = 5000.0 + assert calculate_digester_capex(afc, crf) == pytest.approx(afc / crf) + + +def test_calculate_digester_operational_cost() -> None: + result = calculate_digester_operational_cost( + True, + animal_units=10.0, + farm_type_flag=1.0, + below_ground_flag=0.0, + concrete_flag=1.0, + steel_flag=0.0, + beta=1.0, + omega1=0.3, + phi1=0.1, + phi2=0.2, + phi3=0.05, + phi4=0.07, + epsilon=0.0, + ) + expected = math.exp(1.0 + 0.3 * math.log(10.0) + 0.1 * 1.0 + 0.2 * 0.0 + 0.05 * 1.0 + 0.07 * 0.0) + assert pytest.approx(result) == expected + + assert ( + calculate_digester_operational_cost( + False, + animal_units=10.0, + farm_type_flag=1.0, + below_ground_flag=1.0, + concrete_flag=1.0, + steel_flag=0.0, + ) + == 0.0 + ) + + +def test_estimate_digester_costs_linear_equations() -> None: + estimates = estimate_digester_costs("Covered Lagoon - RNG", 1000) + + assert pytest.approx(estimates["capital_expenditure"]) == 875 * 1000 + 625_000 + + operating = estimates["operating_costs"] + assert pytest.approx(operating["labor"]) == 94.5 * 1000 + 67_500 + assert pytest.approx(operating["energy"]) == 34.80 * 1000 + 21.0 + assert pytest.approx(operating["repairs"]) == 66.69 * 1000 + 40.25 + + assert estimates["useful_life_years"] == 20 + assert pytest.approx(estimates["salvage_value_fraction"]) == 0.15 + assert pytest.approx(estimates["biogas_yield_ft3_per_cow_day"]) == 72.0 + assert pytest.approx(estimates["methane_content_fraction"]) == 0.6 + + +def test_get_digester_cost_profile_normalizes_names() -> None: + profile = get_digester_cost_profile("plug flow chp") + + assert pytest.approx(profile.capital_cost(5000)) == 2_625 * 5000 + 2_000_000 + operating = profile.annual_operating_costs(5000) + assert pytest.approx(operating["labor"]) == 75 * 5000 + 45_000 + assert pytest.approx(operating["energy"]) == 12.5 * 5000 + 7_500 + assert pytest.approx(operating["repairs"]) == 37.5 * 5000 + 22_500 + + +def test_dcfror_goal_seek_reads_scalar_npv_from_output_pool(mocker: MockerFixture) -> None: + calc = DCFRORCalculator.__new__(DCFRORCalculator) + calc.inputs = {"unit_cost": 10.0} + + om = mocker.Mock() + om.filter_variables_pool.return_value = { + "DCFRORCalculator.calculate.econ_dcfror_npv": { + "values": [1.0], + "info_maps": [{}], + } + } + calc.om = om + + def calculate(*args, **kwargs) -> None: + return None + + calc.calculate = calculate + + multiplier = calc.goal_seek("unit_cost", target_npv=1.0, max_iter=1) + + om.filter_variables_pool.assert_called_once() + assert om.filter_variables_pool.call_args.args[0]["slice_start"] == -1 + assert multiplier == pytest.approx(50.005) + assert calc.inputs["unit_cost"] == pytest.approx(500.05) + + +def test_dcfror_goal_seek_returns_nan_for_invalid_bounds(mocker: MockerFixture) -> None: + calc = DCFRORCalculator.__new__(DCFRORCalculator) + calc.inputs = {"unit_cost": 10.0} + calc.om = mocker.Mock() + + multiplier = calc.goal_seek("unit_cost", bounds=(2.0, 1.0)) + + assert np.isnan(multiplier) + calc.om.add_error.assert_called() + + +def test_dcfror_prepare_costs_applies_goal_seek_unit_price_multiplier(mocker: MockerFixture) -> None: + calc = DCFRORCalculator.__new__(DCFRORCalculator) + calc.om = mocker.Mock() + calc.inputs = {} + + prepared = calc._prepare_costs( + { + "cost_capital_multiple": [{"Cost": 100.0}], + "interest_rate_construction": 0.05, + "construction_term": 1, + "construction_finish_pcts": [1.0], + "cost_operational_units": [[1.0, 1.0]], + "cost_operational_unit_cost": [[1.0, 1.0]], + "units_produced": [[2.0, 2.0]], + "unit_cost": [[10.0, 10.0]], + "goal_seek_unit_price_multiplier": 3.0, + "project_term": 2, + } + ) + + assert prepared["revenue"].tolist() == [60.0, 60.0] + + +def test_estimate_digester_trucking_cost() -> None: + assert pytest.approx(estimate_digester_trucking_cost(1000)) == 137.5 * 1000 + assert pytest.approx(estimate_digester_trucking_cost(5000)) == 137.5 * 5000 + + +def test_equation_helpers() -> None: + years = construct_timeline(2, 5) + assert years.tolist() == [-1, 0, 1, 2, 3, 4, 5] + + df = discount_factor(0.1, 2) + assert pytest.approx(df) == 1 / (1.1**2) + assert pytest.approx(discount_factor(0.1, -1)) == 1 / 1.1 + + construction_rates = [0.5, 0.5] + cap = annual_capital_spent(100.0, construction_rates) + assert cap.tolist() == [50.0, 50.0] + + equity = equity_contribution(100.0, construction_rates, 0.4) + assert equity.tolist() == [20.0, 20.0] + + lp = loan_principal(100.0, construction_rates, 0.6) + assert lp.tolist() == [30.0, 30.0] + + ci = construction_interest(lp, 0.05) + assert ci.tolist() == [1.5, 3.0] + + npv_ci = npv_capital_plus_interest(cap, ci, 0.1, np.array([-1, 0])) + expected_npv = (cap + ci) * np.array([discount_factor(0.1, -1), discount_factor(0.1, 0)]) + assert np.allclose(npv_ci, expected_npv) + + payment = annual_loan_payment(1000.0, 0.05, 5, 0.8) + expected_payment = 1000.0 * 0.05 * 0.8 / (1 - (1 + 0.05) ** -5) + assert pytest.approx(payment) == expected_payment + + int_pay = interest_payment(800.0, 0.05) + assert pytest.approx(int_pay) == 40.0 + + remaining = principal_after_payment(800.0, payment, int_pay) + assert pytest.approx(remaining) == 800.0 - payment + int_pay + + dep = depreciation_schedule(1000.0, np.array([0.1, 0.2])) + assert dep.tolist() == [100.0, 200.0] + + nr = net_revenue(500.0, 200.0, 30.0, 20.0) + assert nr == 250.0 + + loss = loss_carry_forward(-50.0) + assert loss == -50.0 + + taxable = taxable_income(nr, loss) + assert taxable == 200.0 + + tax = income_tax(taxable, 0.3) + assert tax == 60.0 + + aci = annual_cash_income(500.0, 200.0, 50.0, tax) + assert aci == 190.0 + + pv = present_value(aci, discount_factor(0.1, 1)) + assert pytest.approx(pv) == aci / 1.1 + + npv = net_present_value(np.array([pv]), np.array([10.0])) + assert pytest.approx(npv) == pv - 10.0 diff --git a/tests/test_EEE/test_economics_missing_inputs.py b/tests/test_EEE/test_economics_missing_inputs.py new file mode 100644 index 0000000000..88e6c4fb0e --- /dev/null +++ b/tests/test_EEE/test_economics_missing_inputs.py @@ -0,0 +1,64 @@ +import pytest + +from RUFAS.EEE.economics import framework, partial_budget + + +class DummyOutputManager: + def __init__(self): + self.warnings = [] + + def add_warning(self, code, message, info): + self.warnings.append((code, message, info)) + + def add_log(self, *args, **kwargs): + return None + + +class MissingInputManager: + def __init__(self): + self.requested = [] + + def get_data(self, key): + self.requested.append(key) + return None + + +def test_partial_budget_ignores_missing_inputs(monkeypatch: pytest.MonkeyPatch) -> None: + dummy_im = MissingInputManager() + dummy_om = DummyOutputManager() + + monkeypatch.setattr(partial_budget, "InputManager", lambda: dummy_im) + monkeypatch.setattr(partial_budget, "OutputManager", lambda: dummy_om) + + pb = partial_budget.PartialBudget() + + assert pb.has_partial_budget_activity() is False + assert dummy_im.requested == [] + assert dummy_om.warnings == [] + + +def test_capital_cost_present_handles_missing_table(monkeypatch: pytest.MonkeyPatch) -> None: + dummy_im = MissingInputManager() + dummy_om = DummyOutputManager() + + class DummyPartialBudget: + def __init__(self): + self.called = False + + def has_partial_budget_activity(self): + return False + + class DummyPreprocessor: + def preprocess(self): + return {} + + monkeypatch.setattr(framework, "InputManager", lambda: dummy_im) + monkeypatch.setattr(framework, "OutputManager", lambda: dummy_om) + monkeypatch.setattr(framework, "PartialBudget", lambda: DummyPartialBudget()) + monkeypatch.setattr(framework, "EconomicPreprocessor", lambda: DummyPreprocessor()) + + ef = framework.EconomicFramework() + + assert ef._capital_cost_present() is False + assert dummy_im.requested == ["economic_inputs.capital_costs.capital_cost_breakdown"] + assert dummy_om.warnings == [] diff --git a/tests/test_EEE/test_economics_preprocessing.py b/tests/test_EEE/test_economics_preprocessing.py new file mode 100644 index 0000000000..c2cae98246 --- /dev/null +++ b/tests/test_EEE/test_economics_preprocessing.py @@ -0,0 +1,281 @@ +import pytest + +from RUFAS.EEE.economics import preprocessing + + +class DummyOutputManager: + def __init__(self, pool): + self._pool = pool + self.warnings = [] + self.logs = [] + + def _get_flat_variables_pool(self): + return self._pool + + def add_warning(self, code, message, info): + self.warnings.append((code, message, info)) + + def add_log(self, title, message, info): + self.logs.append((title, message, info)) + + +class DummyInputManager: + def __init__(self, data): + self._data = data + self.added_runtime = [] + + def get_data(self, key): + return self._data.get(key) + + def add_runtime_variable_to_pool(self, variable_name, data, properties_blob_key, eager_termination=False): + self.added_runtime.append( + { + "variable_name": variable_name, + "data": data, + "properties_blob_key": properties_blob_key, + "eager_termination": eager_termination, + } + ) + return True + + +def test_preprocess_collects_values_and_prices(monkeypatch: pytest.MonkeyPatch) -> None: + dummy_im = DummyInputManager({"price.csv": {"cost": 10}}) + dummy_om = DummyOutputManager({"Economic_inputs.Animal.labor": {"values": [1, 2, 3]}}) + + monkeypatch.setattr(preprocessing, "InputManager", lambda: dummy_im) + monkeypatch.setattr(preprocessing, "OutputManager", lambda: dummy_om) + monkeypatch.setattr( + preprocessing, + "ECONOMIC_MAP", + { + "Section": { + "Category": { + "Item": { + "biophysical_simulation": ["Economic_inputs.Animal.labor"], + "economics_files": ["price.csv"], + } + } + } + }, + ) + + preprocessor = preprocessing.EconomicPreprocessor() + results = preprocessor.preprocess() + + assert results == { + "Section": { + "Category": { + "Item": { + "biophysical_values": [1.0, 2.0, 3.0], + "biophysical_aggregate": 6.0, + "biophysical_values_by_scenario": {"baseline": [1.0, 2.0, 3.0]}, + "biophysical_aggregate_by_scenario": {"baseline": 6.0}, + "price_data": {"price.csv": {"cost": 10}}, + "price_values": [10.0], + "price_aggregate": 10.0, + "line_item_values_by_scenario": {"baseline": 60.0}, + "flow_type": "cost", + } + } + } + } + assert dummy_im.added_runtime[-1]["variable_name"] == "economic_preprocessed" + assert dummy_im.added_runtime[-1]["properties_blob_key"] == "economic_preprocessing_properties" + assert dummy_om.warnings == [] + assert dummy_om.logs[-1][0] == "Economic preprocessing" + + +def test_preprocess_selects_price_by_selector(monkeypatch: pytest.MonkeyPatch) -> None: + dummy_im = DummyInputManager( + { + "selector": "Premium", + "price_premium": {"cost": 25}, + "price_standard": {"cost": 5}, + } + ) + dummy_om = DummyOutputManager({"Economic_inputs.Feed.energy": [2, 3]}) + + monkeypatch.setattr(preprocessing, "InputManager", lambda: dummy_im) + monkeypatch.setattr(preprocessing, "OutputManager", lambda: dummy_om) + monkeypatch.setattr( + preprocessing, + "ECONOMIC_MAP", + { + "Section": { + "Category": { + "Energy": { + "biophysical_simulation": ["Economic_inputs.Feed.energy"], + "economics_files": { + "input_manager_location": "selector", + "premium": "price_premium", + "standard": "price_standard", + }, + "preprocessing": "average", + } + } + } + }, + ) + + preprocessor = preprocessing.EconomicPreprocessor() + results = preprocessor.preprocess() + + energy_data = results["Section"]["Category"]["Energy"] + assert energy_data["biophysical_values"] == [2.0, 3.0] + assert energy_data["biophysical_aggregate"] == pytest.approx(2.5) + assert energy_data["biophysical_values_by_scenario"] == {"baseline": [2.0, 3.0]} + assert energy_data["biophysical_aggregate_by_scenario"] == {"baseline": pytest.approx(2.5)} + assert energy_data["price_data"] == {"price_premium": {"cost": 25}} + assert energy_data["price_values"] == [25.0] + assert energy_data["price_aggregate"] == 25.0 + assert energy_data["line_item_values_by_scenario"] == {"baseline": pytest.approx(62.5)} + assert energy_data["flow_type"] == "cost" + assert dummy_om.warnings == [] + + +def test_preprocess_reads_input_manager_values(monkeypatch: pytest.MonkeyPatch) -> None: + dummy_im = DummyInputManager({"economic_inputs.Animal.labor_hours_per_day": 4}) + dummy_om = DummyOutputManager({}) + + monkeypatch.setattr(preprocessing, "InputManager", lambda: dummy_im) + monkeypatch.setattr(preprocessing, "OutputManager", lambda: dummy_om) + monkeypatch.setattr( + preprocessing, + "ECONOMIC_MAP", + { + "Section": { + "Category": { + "Item": { + "input_manager": ["economic_inputs.Animal.labor_hours_per_day"], + } + } + } + }, + ) + + preprocessor = preprocessing.EconomicPreprocessor() + results = preprocessor.preprocess() + + item = results["Section"]["Category"]["Item"] + assert item["biophysical_values"] == [4.0] + assert item["biophysical_aggregate"] == 4.0 + assert item["biophysical_values_by_scenario"] == {"baseline": [4.0]} + assert item["biophysical_aggregate_by_scenario"] == {"baseline": 4.0} + assert item["price_values"] == [] + assert item["price_aggregate"] is None + assert item["flow_type"] == "cost" + warning_codes = [code for code, _, _ in dummy_om.warnings] + assert "MissingPriceForLineItem" in warning_codes + + +def test_preprocess_handles_invalid_economics_path(monkeypatch: pytest.MonkeyPatch) -> None: + class RaisingInputManager(DummyInputManager): + def get_data(self, key): # type: ignore[override] + if key == "commodity_prices..broken_path.csv": + raise ValueError("invalid literal for int() with base 10: ''") + return super().get_data(key) + + dummy_im = RaisingInputManager({}) + dummy_om = DummyOutputManager({"Economic_inputs.Misc.value": [1]}) + + monkeypatch.setattr(preprocessing, "InputManager", lambda: dummy_im) + monkeypatch.setattr(preprocessing, "OutputManager", lambda: dummy_om) + monkeypatch.setattr( + preprocessing, + "ECONOMIC_MAP", + { + "Section": { + "Category": { + "Item": { + "biophysical_simulation": ["Economic_inputs.Misc.value"], + "economics_files": ["commodity_prices..broken_path.csv"], + } + } + } + }, + ) + + preprocessor = preprocessing.EconomicPreprocessor() + preprocessor.preprocess() + + warning_codes = [code for code, _, _ in dummy_om.warnings] + assert "InvalidEconomicsFilePath" in warning_codes + + +def test_preprocess_skips_pool_check_on_wildcard_path(monkeypatch: pytest.MonkeyPatch) -> None: + class CheckingInputManager(DummyInputManager): + def check_property_exists_in_pool(self, key): # type: ignore[override] + if "*" in str(key): + raise ValueError("invalid literal for int() with base 10: '*'") + return key in self._data + + dummy_im = CheckingInputManager({}) + dummy_om = DummyOutputManager({"Economic_inputs.Misc.value": [1]}) + + monkeypatch.setattr(preprocessing, "InputManager", lambda: dummy_im) + monkeypatch.setattr(preprocessing, "OutputManager", lambda: dummy_om) + monkeypatch.setattr( + preprocessing, + "ECONOMIC_MAP", + { + "Section": { + "Category": { + "Seeds": { + "biophysical_simulation": ["Economic_inputs.Misc.value"], + "economics_files": ["commodity_prices.*.dollar_per_square_meter.csv"], + } + } + } + }, + ) + + preprocessor = preprocessing.EconomicPreprocessor() + preprocessor.preprocess() + + warning_codes = [code for code, _, _ in dummy_om.warnings] + assert "MissingEconomicsFile" in warning_codes + + +def test_preprocess_skips_input_manager_on_wildcard_path(monkeypatch: pytest.MonkeyPatch) -> None: + called_keys = [] + + class TrackingInputManager(DummyInputManager): + def get_data(self, key): # type: ignore[override] + called_keys.append(key) + if "*" in str(key): + raise AssertionError("Wildcard path should be skipped") + return super().get_data(key) + + def check_property_exists_in_pool(self, key): # type: ignore[override] + called_keys.append(f"check:{key}") + if "*" in str(key): + raise AssertionError("Wildcard path should not reach pool check") + return key in self._data + + dummy_im = TrackingInputManager({}) + dummy_om = DummyOutputManager({"Economic_inputs.Misc.value": [1]}) + + monkeypatch.setattr(preprocessing, "InputManager", lambda: dummy_im) + monkeypatch.setattr(preprocessing, "OutputManager", lambda: dummy_om) + monkeypatch.setattr( + preprocessing, + "ECONOMIC_MAP", + { + "Section": { + "Category": { + "Seeds": { + "biophysical_simulation": ["Economic_inputs.Misc.value"], + "economics_files": ["commodity_prices.*.dollar_per_square_meter.csv"], + } + } + } + }, + ) + + preprocessor = preprocessing.EconomicPreprocessor() + preprocessor.preprocess() + + assert not called_keys + warning_codes = [code for code, _, _ in dummy_om.warnings] + assert "MissingEconomicsFile" in warning_codes diff --git a/tests/test_EEE/test_partial_budget_outputs.py b/tests/test_EEE/test_partial_budget_outputs.py new file mode 100644 index 0000000000..7a252d5083 --- /dev/null +++ b/tests/test_EEE/test_partial_budget_outputs.py @@ -0,0 +1,105 @@ +import pytest + +from RUFAS.EEE.economics import partial_budget + + +class DummyOutputManager: + def __init__(self) -> None: + self.variables = [] + self.logs = [] + + def add_variable(self, name, value, info): + self.variables.append((name, value, info)) + + def add_warning(self, *args, **kwargs): + return None + + def add_log(self, *args, **kwargs): + self.logs.append(args) + + +def test_partial_budget_exports_all_series(monkeypatch: pytest.MonkeyPatch) -> None: + preprocessed = { + "Section": { + "Revenue": { + "Milk": { + "flow_type": "revenue", + "line_item_values_by_scenario": {"baseline": 100.0, "alternative": 110.0}, + }, + "Cull cows": { + "flow_type": "revenue", + "line_item_values_by_scenario": {"baseline": 50.0, "alternative": 47.0}, + }, + }, + "Costs": { + "Feed": { + "flow_type": "cost", + "line_item_values_by_scenario": {"baseline": 25.0, "alternative": 27.0}, + }, + "Labor": { + "flow_type": "cost", + "line_item_values_by_scenario": {"baseline": 10.0, "alternative": 8.0}, + }, + }, + } + } + + dummy_im = object() + dummy_om = DummyOutputManager() + + monkeypatch.setattr(partial_budget, "InputManager", lambda: dummy_im) + monkeypatch.setattr(partial_budget, "OutputManager", lambda: dummy_om) + + pb = partial_budget.PartialBudget() + pb.calculate_partial_budget(preprocessed) + + exported = {name: value for name, value, _ in dummy_om.variables} + + assert exported["econ_pba_additional_revenue"] == [10.0] + assert exported["econ_pba_reduced_costs"] == [2.0] + assert exported["econ_pba_additional_costs"] == [2.0] + assert exported["econ_pba_reduced_revenue"] == [3.0] + assert exported["econ_pba_net_change"] == pytest.approx([7.0]) + assert exported["econ_pba_cumulative_net_change"] == pytest.approx([7.0]) + assert "econ_pba_summary" in exported + + +def test_partial_budget_exports_net_annual_cash_flow_for_single_scenario( + monkeypatch: pytest.MonkeyPatch, +) -> None: + preprocessed = { + "Section": { + "Revenue": { + "Milk": { + "flow_type": "revenue", + "line_item_values_by_scenario": {"baseline": 120.0}, + } + }, + "Costs": { + "Feed": { + "flow_type": "cost", + "line_item_values_by_scenario": {"baseline": 80.0}, + } + }, + } + } + + dummy_im = object() + dummy_om = DummyOutputManager() + + monkeypatch.setattr(partial_budget, "InputManager", lambda: dummy_im) + monkeypatch.setattr(partial_budget, "OutputManager", lambda: dummy_om) + + pb = partial_budget.PartialBudget() + pb.calculate_partial_budget(preprocessed) + + exported = {name: value for name, value, _ in dummy_om.variables} + + assert exported["econ_pba_net_annual_cash_flow"] == [40.0] + assert exported["econ_pba_revenue_total"] == [120.0] + assert exported["econ_pba_cost_total"] == [80.0] + assert exported["econ_pba_additional_revenue"] == [0.0] + assert exported["econ_pba_reduced_costs"] == [0.0] + assert exported["econ_pba_additional_costs"] == [0.0] + assert exported["econ_pba_reduced_revenue"] == [0.0] + assert "econ_pba_summary" in exported