Near-Optimal Nitrogen Recommendations for Precision Agriculture via Sequential Screening and Hierarchical Refinement

2026-06-30

AI Summary

This study addresses the challenge of deriving near-optimal nitrogen fertilizer recommendations across multiple locations, where spatial heterogeneity complicates decision-making. The authors propose a novel approach that integrates sequential screening with hierarchical refinement: first, inferior options are safely eliminated at an aggregated level through sequential hypothesis testing; then, the remaining candidates are fine-tuned locally using a hierarchical model. This framework balances decision simplicity with explicit modeling of spatial heterogeneity while avoiding overreliance on a single point estimate of optimality. Empirical evaluation using multi-year, multi-state corn trials demonstrates that the recommended strategies significantly reduce nitrogen application rates without compromising agronomic performance, further revealing substantial within-state spatial variability in optimal nitrogen management.

Abstract

Nitrogen fertilizer management plays a central role in balancing agricultural productivity and environmental sustainability, yet identifying optimal application strategies remains difficult because treatment responses vary substantially across locations and many fertilizer choices are statistically indistinguishable near the optimum. This paper develops a hierarchical refinement procedure, built on sequential screening, for fertilizer recommendation in multi-site experiments that explicitly accounts for spatial heterogeneity while prioritizing parsimonious, decision-oriented selection. Rather than targeting a single estimated best treatment, the proposed method first conducts sequential screening at a higher aggregation level to eliminate clearly inferior fertilizer choices and then refines recommendations locally among the surviving candidates. We study the asymptotic properties of the proposed estimators and show that it provides screening-safety guaranteed recommendations. The efficacy of the new approach is investigated through a multi-state, multi-year corn nitrogen trial. The results show that no single fertilizer regime is uniformly optimal within a state; instead, each state is associated with multiple recommended choices, and the most common recommendation typically covers only about one-third to one-half of decision units, underscoring substantial within-state heterogeneity. Representative site-level comparisons further demonstrate that the proposed method often yields lower total nitrogen recommendations than state-level or hindsight benchmarks while maintaining competitive agronomic performance.

Problem

• Nitrogen recommendation
• Precision agriculture
• Spatial heterogeneity
• Fertilizer management
• Multi-site experiments

Innovation

• sequential screening
• hierarchical refinement
• precision agriculture
• nitrogen recommendation
• spatial heterogeneity