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A new sugarcane yield model using the SiPAR model

2021; Wiley; Volume: 114; Issue: 1 Linguagem: Inglês

10.1002/agj2.20949

1435-0645

Shun Hu, Liangsheng Shi, Yuanyuan Zha, Kai Huang,

Plant Water Relations and Carbon Dynamics

Abstract Physical process–based crop yield models are subject to extensive input requirements, and traditional statistical models often lack robustness in a changing environment. The purpose of this study was to develop a new and simple semi‐physical sugarcane ( Saccharum officinarum L.) yield model, called the SiPAR model (intercepted photosynthetically active radiation partitioned to stem), with less data requirement than the process‐based models and strong robustness. The SiPAR model was developed using the normalized difference vegetation index, the leaf area index, and solar radiation data. A 3‐yr field experiment was used to evaluate model performance. The SiPAR model was also compared with three traditional statistical models. The results showed that (a) the SiPAR model obtained the highest accuracy among the compared methods and reproduced the spatial pattern of yield well (RMSE = 5.88–8.65 t ha –1 ; R 2 = .44–.87; normalized RMSE (Ry) = 7.22–11.77%) and (b) the SiPAR model had better spatial and temporal stability than the other three statistical models through cross‐validation because the SiPAR model considered the stem biomass accumulation features of sugarcane by introducing a weighting factor to reflect the stem potential growth rate and using intercepted photosynthetically active radiation to represent the energy available for photosynthesis. The SiPAR model has the potential to be applied at a regional scale because it requires less data and fewer parameters and is robust and highly accurate.