A GIS-based framework for local agricultural decision-making and regional crop yield simulation

Abstract

CONTEXTIn agricultural activities, the decision-making process is central to agricultural system management and subsequent crop yield. As a powerful tool in field-specific decision-making processes, crop simulation models have the potential to simulate crop yields on a large scale. However, their performance is often biased by the spatial heterogeneity of environment and management factors when applied over a large scale. OBJECTIVEThe major objectives of this study include: (1) Predicting and evaluating the annual yields of dominant crops with real rotation scenarios; (2) Locating fields with low crop yield and determining possible reasons; and (3) Evaluating the improvement for crop yield with different management strategies. METHODSThis study proposed a crop yield simulation framework at the regional level by coupling a cropping system model (CropSyst) with a geographic information system (QGIS) to provide more reliable information for the decision-making process. In the study of a cropland concentrated USGS sub-watershed (Hydrologic Unit Code: 031402030101) in Geneva County, Alabama, we estimated the annual yields of four regionally dominant crops (i.e., corn, cotton, soybean, and peanuts) from 2016 to 2018. Low yield fields were identified in the simulation results visualization. Moreover, four management strategies were tested at a field scale to improve annual yields. RESULTS AND CONCLUSIONSOverall, the simulated crop yields were significantly correlated with the recorded values (Pearson's r = 0.99). However, the performance of the regional model varied for different crops. The model achieved the best performance for soybean with a high index of agreement (0.93) and modeling efficiency (0.86). For cotton, the model achieved positive model efficiency (0.23) and a good index of agreement (0.59). For peanut and maize, the model fitted records well but not sensitive enough. According to the visualization of simulation results, we located fields with low yields. The low organic matter content and high sand percentage of the soil were the potential causes of the nitrogen deficiency, which leads to the low yield subsequently. In field scale tests, four proposed management strategies could increase the cotton yields as high as 74.4%. But some strategies would also increase greenhouse gas emissions at the same time. SIGNIFICANCEThis study bridges the gap between local cropping system models and the regional estimation of crop yields. The GIS-based crop simulation framework developed here demonstrates the potential of cropping system models to provide reliable information at a regional scale and hence significantly broadens their application in the agricultural decision-making process.