Authors: Runwei Li*, Florida State University, Chenyang Wei, University at Buffalo, Mahnaz Dil Afroz, Florida A&M University, Jun Lyu, Florida State University, Gang Chen, Florida State University
Topics: Agricultural Geography, Regional Geography, Environmental Perception
Keywords: CropSyst, Crop production, Decision-making, GIS, Management.
Session Type: Paper
Presentation File: No File Uploaded
Decision-making is a selection among several alternatives, based on values, possibilities, and decision-makers’ preferences. In agricultural activities, decision-making is central to cropland management and subsequent crop production. A well-supported decision-making system could help agricultural activities to be more profitable and sustainable. In this study, to support the agricultural decision-making process, we applied a local cropping system model, CropSyst, at a regional level by establishing a GIS-based framework. Based on obtained soil, weather, and cropland databases, the regional production of major crops (i.e., corn, cotton, soybean, and peanuts) was predicted for a cropland-concentrated sub-watershed (HUC 12: 0314020101) in Geneva County, AL. The crop phenological parameters used in the CropSyst model were calibrated based on four independent field studies located in nearby states. The simulated major crops’ yields were significantly correlated with the recorded values. Based on the simulation results, we identified low production fields in the study area. After adding cover crops or applying different rotation management in these fields, we observed that the predicted crop yields could be improved by 21% and 26%, respectively. Besides, we simulated the local emission of greenhouse gas (i.e., N2O) from croplands in the study area and found its amount could be efficiently decreased (i.e., more than 50%) by avoiding certain types of soil. The result of this study bridges the gap between local cropping system models and the regional prediction of crop production, which demonstrates the potential of GIS-based frameworks to provide reliable information in supporting agricultural decision-making at a regional scale.
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