评估在遗留土壤库存部分可观测条件下的磷肥投入最优农场管理

摘要

Decades of intensive fertilizer application have led to the accumulation of phosphorus in cropland soils across the US. An empirical question is: Why does fertilizer use remain high even in the presence of large soil phosphorus stocks? A previously proposed hypothesis is that farmers lack sufficient information about soil phosphorus, and that enhanced testing could reduce fertilizer use. We evaluate this argument using a dynamic farm‐level optimization model with imperfectly observed soil phosphorus and costly testing. We parameterize the model using data from corn field trials spanning over a decade in eastern North Carolina. To account for uncertain soil phosphorus beliefs and the value of information from testing, we structure our model as a “partial‐observability Markov decision process,” accounting for and investigating the roles of fertilizer price volatility, farmer risk aversion, and intertemporal preferences. Simulation results show that information from enhanced soil testing is most valuable at low levels of soil phosphorus, enabling profit‐maximizing farmers to dynamically tailor their subsequent fertilizer inputs with more accurate soil phosphorus measurements. However, when legacy soil phosphorus is high, enhanced testing does not reduce optimal fertilizer inputs, contrary to the hypothesis we set out to investigate. Sensitivity analysis identifies important biophysical and economic factors that shape farmer fertilizer and soil testing decisions in the model. To our knowledge, this work reflects the first dynamic analysis of the economics of soil nutrient testing. We discuss implications of these dynamics for policies seeking to reduce environmental externalities from phosphorus fertilizer use.