A machine learning approach to predicting plant available phosphorus that accounts for soil heterogeneity and regional variability

Abstract

Purpose Mehlich-3 extractable P, Al, Ca, and Fe combined with pH can be used to help explain soil chemical processes which regulate P retention, such as the role of Al, Ca, Fe, and pH levels in P fixation and buffering capacity. However, Mehlich-3 is not always the standard test used in agriculture. The objective of this study is to assess the most reliable conversion of Mehlich-3 Al, Ca, Fe, and P and pH into a commonly used soil P test, Morgan’s P, and specifically to predict values into decision support for fertiliser recommendations. Methods A geochemical database of 5631 mineral soil samples which covered the northern area of Ireland was used to model soil test P and P indices using Mehlich-3 data. Results A random forest machine learning algorithm produced an R2 of 0.96 and accurately predicted soil P index from external validation in 90% of samples (with an error range of ± 1 mg L−1). The model accuracy was reduced when predicted Morgan’s P concentration was outside of the sampled area. Conclusions It is recommended that random forest is used to produce Mehlich-3 conversions, especially when data covers large spatial scales with large heterogeneity in soil types and regional variations. To implement conversion models into P testing regimes, it is recommended that representative soil types/geochemical attributes are present in the dataset. Furthermore, completion of a national scale geochemical survey is needed. This will enable accurate predictions of Morgan’s P concentration for a wider range of soils and geographical scale.

Prediction and Interpretation of Total N and Its Key Drivers in Cultivated Tropical Peat using Machine Learning and Game Theory

“…SHAP is widely known for its strong alignment with human intuition (Lundberg and Lee 2017). SHAP has been employed in explaining ML models in several soil studies, e.g., soil organic C mapping (Padarian et al 2020b), liquefaction potentials (Jas andDodagoudar 2023;Sui et al 2023), soil moisture , available P (Hall et al 2023) and permeability coefficients (Tran 2022).…”