Linking measurable and conceptual phosphorus pools (in APSIM) enables quantitative model initialisation

Linking measurable and conceptual phosphorus pools (in APSIM) enables quantitative model initialisation

Phosphorus (P) is an essential plant macro-nutrient, yet it is deficient in 65 % of agricultural soils worldwide. Agricultural systems models enable the integration of plant-soil-climate-management interactions to investigate crop responses to P fertilisation and improve P use efficiency. However, current models cannot align their modellable P pools with values obtained from soil tests. This limits their applicability since soil testing is the most widely used tool to assess soil P status, which is then used to predict fertiliser P requirements based on assumed crop P demand for optimal growth in the field. Our study introduces a modelling framework akin to inversely modelling in the Agricultural Production Systems sIMulator (APSIM) to quantitatively derive the most likely P modelling parameters for different soils and empirically link them to common soil P test values. The methodology was first tested using data from an 8-year alfalfa (syn. lucerne) experiment (1997–2004) on two soil types in the mid-west of the United States to establish the adequacy of the P modelling framework in APSIM. We then extended this approach to eight Australian soil types using a simulation study based on known wheat yield response curves to soil P tests to derive empirical relationships between the labile P values in APSIM and common soil test P values (Bray-2 P and Colwell P) for the soils studied. Cross-validation yielded an average R2 of 0.98 and an average Lin’s Concordance Correlation Coefficient (CCC) of 0.92. Our work thus enables the initialisation of the labile P pool in APSIM using Bray-2 P and Colwell P data, enhancing the usability and accuracy of agricultural systems models in predicting crop P requirements and optimising P fertiliser use across diverse soil types in different agro-climatic regions.