Rice Yield Simulation and Planting Suitability Environment Pattern Recognition at a Fine Scale

• Published in: ISPRS international journal of geo-information 2021-09, Vol.10 (9), p.612
• Main Authors: Li, Daichao, Liang, Jianqin, Wang, Xingfeng, Wu, Sheng, Xie, Xiaowei, Lu, Jiaqi
• Format: Article
• Language: English
• Subjects: Agricultural production; Altitude; cluster analysis; Clustering; Correlation; Crop production; Crop yield; Data; Datasets; Distribution patterns; Environmental factors; GIS; Human factors; Irrigation; Mechanical properties; multidimensional environment; Natural resources; Pattern recognition; Planting; Potassium; Rice; rice yield simulation; Simulation; Soil; Soil conditions; Soil mechanics; Soils; Spatial distribution; spatial pattern recognition; Variance analysis
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi10090612

Analyzing rice yields and multidimensional environmental factors at a fine scale facilitates the discovery of the planting environment patterns that guide the spatial layout of rice production. This study uses Pucheng County, Fujian Province, a demonstration county of China Good Grains and Oils, as the research area. Using actual rice yield sample data and environment data, a yield simulation model based on random forest regression is constructed to realize a fine-scale simulation of rice yield and its spatial distribution pattern in Pucheng County. On this basis, we construct a method system to identify spatial combination patterns between rice yields and fine-scale multidimensional environmental planting suitability using rice yield data and environmental planting suitability evaluation data. We categorize the areas into four combination model areas to analyze the spatial correlation model of planting suitability, multidimensional environment, and yield: higher-yield and higher-suitability cluster–comprehensive environmental-advantage areas, high-yield and high-suitability cluster–soil condition-limited areas, moderate-yield and moderate-suitability cluster–irrigation and drainage condition-limited areas, and low-yield and low-suitability cluster–site condition-limited areas. The following results are found. (1) The rice yield simulation model, which is based on random forest regression, considers the various complex relationships between yield and natural as well as human factors to realize the refined simulation of rice yields at a county scale. (2) The county rice yield has a strong positive spatial correlation, and the spatial clustering characteristics are obvious; these relationships can provide a basis for effectively implementing intensive rice planting in Pucheng County. (3) We construct a spatial combination pattern recognition method based on rice yield and environmental planting suitability. We can use this method to effectively identify the spatial relationship between yield and planting suitability as well as the shortcomings and advantages of different regions in terms of the climate, soil, irrigation, site, mechanical farming, and similar factors. On this basis, we can provide regional rice planting guidance for Pucheng County. In addition, this method system also provides a new perspective and method for research into spatial combination models and related spatial issues.