This paper presents a methodology for practitioners in agricultural and applied economics, focusing on Positive Mathematical Programming (PMP). PMP is a flexible approach that uses minimal data to construct models that are consistent with microeconomic theory and prior estimates of demand and supply elasticities. Unlike traditional linear programming models, PMP allows for more flexible responses to policy changes and can self-calibrate without relying on "flexibility" constraints. The paper discusses the calibration of PMP models, emphasizing the use of dual values from the base year to determine parameters that ensure accurate calibration. It also addresses challenges in calibrating models, such as the need for sufficient empirical constraints and the impact of resource and policy constraints on model outcomes. The paper highlights the importance of using nonlinear cost functions and the role of elasticity priors in improving model accuracy. It also discusses the application of PMP in various agricultural policy models, including sectoral, regional, and farm-level analyses. The paper concludes that PMP provides a robust framework for calibrating agricultural models, ensuring consistency with empirical data and economic theory. The methodology is illustrated with examples, including the calibration of a two-crop model and the derivation of quadratic cost functions. The paper also discusses the use of PMP in policy analysis, emphasizing its ability to respond to changes in prices, technology, and constraints. Overall, the paper demonstrates that PMP is a valuable tool for agricultural economists in developing and calibrating models that accurately reflect real-world agricultural production and resource use.This paper presents a methodology for practitioners in agricultural and applied economics, focusing on Positive Mathematical Programming (PMP). PMP is a flexible approach that uses minimal data to construct models that are consistent with microeconomic theory and prior estimates of demand and supply elasticities. Unlike traditional linear programming models, PMP allows for more flexible responses to policy changes and can self-calibrate without relying on "flexibility" constraints. The paper discusses the calibration of PMP models, emphasizing the use of dual values from the base year to determine parameters that ensure accurate calibration. It also addresses challenges in calibrating models, such as the need for sufficient empirical constraints and the impact of resource and policy constraints on model outcomes. The paper highlights the importance of using nonlinear cost functions and the role of elasticity priors in improving model accuracy. It also discusses the application of PMP in various agricultural policy models, including sectoral, regional, and farm-level analyses. The paper concludes that PMP provides a robust framework for calibrating agricultural models, ensuring consistency with empirical data and economic theory. The methodology is illustrated with examples, including the calibration of a two-crop model and the derivation of quadratic cost functions. The paper also discusses the use of PMP in policy analysis, emphasizing its ability to respond to changes in prices, technology, and constraints. Overall, the paper demonstrates that PMP is a valuable tool for agricultural economists in developing and calibrating models that accurately reflect real-world agricultural production and resource use.