Assessing green manure impact on wheat productivity through Bayesian analysis of yield monitor data

TL;DR

This paper introduces a Bayesian method to analyze large-scale wheat yield data, showing how green manure improves productivity in real-world farming conditions.

Contribution

A novel Bayesian approach for analyzing yield monitor data is introduced to assess green manure effects on wheat productivity at scale.

Findings

Green manure application significantly enhances wheat yields across diverse agricultural landscapes.

Bayesian analysis of yield monitor data provides more precise and contextually relevant insights than traditional small-scale trials.

The Python script included enables replication and extension of the analysis for broader agronomic use.

Abstract

Agronomy research traditionally relies on small, controlled trial plots, which may not accurately represent the complexities and variabilities found in larger, real-world settings. To address this gap, we introduce a Bayesian methodology for the analysis of yield monitor data, systematically collected across extensive agricultural landscapes during the 2020/21 and 2021/22 growing seasons. Utilizing advanced yield monitoring equipment, our method provides a detailed examination of the effects of green manure on wheat yields in a real-world context. The results from this comprehensive analysis reveal significant insights into the impact of green manure application on wheat production, demonstrating enhanced yield outcomes across varied landscapes. This evidence suggests that the Bayesian approach to analyzing yield monitor data can offer more precise and contextually relevant information than traditional experimental designs. This research underscores the value of integrating large-scale data analysis techniques in agronomy, moving beyond small-scale trials to offer a broader, more accurate perspective on agricultural practices. The adoption of such methodologies promises to refine farming strategies and policies, ultimately leading to more effective and sustainable agricultural outcomes. The inclusion of a Python script in the appendix illustrates our analytical process, providing a tangible resource for replicating and extending this research within the agronomic community.

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