Improving sugarcane growth simulations by integrating multi-source observations into a crop model

Abstract Accurate crop growth simulations and yield estimation play a crucial role in agricultural development and food security. Incorporating multi-source observations into crop growth models could reduce the prediction uncertainty propagated from input data and model parameters. However, the value of different data sources varies. Incorporating redundant data into models not only increases computational cost, but also introduces additional prediction uncertainties. The objective of this study is to investigate the value of three common agronomy variables (plant height, PH; leaf area index, LAI; and soil moisture, SM) for sugarcane growth simulations and explore which variable(s) have the largest information content and hence should be included in data assimilation system. The measurements of PH, LAI and SM data are collected through a two-year sugarcane experiment (in 2016–2017) at Chongzuo station (Guangxi, China). Results show that the value of SM is the lowest among all three variables for sugarcane yield estimation if the spatial heterogeneity of water and nutrient both exist. When sugarcane plots have relatively homogeneous cultivation density, it is preferable to incorporate PH data into the model. In contrast, assimilation of LAI might be more suitable when the cultivation density and tiller number contain strong spatial variability. Moreover, compared with traditional LAI & SM fusion strategy, the fusion of LAI and PH data is recommended to obtain more robust sugarcane simulation results. Furthermore, observations during the elongation period provide the most valuable information for sugarcane growth simulation and yield estimation, while those in the emergence and tillering period are less informative.