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Plausible maize planting distribution under future global change scenarios

播种 分布(数学) 灌溉 粮食安全 社会经济地位 气候变化 地理 环境科学 作物产量 作物 农业 农林复合经营 农业经济学 农学 人口 数学 生态学 林业 经济 生物 人口学 数学分析 考古 社会学
作者
Peng Su,Anyu Zhang,Jing’ai Wang,Wei Xu
出处
期刊:Field Crops Research [Elsevier BV]
卷期号:302: 109079-109079 被引量:7
标识
DOI:10.1016/j.fcr.2023.109079
摘要

Population expansion has become a trend. To meet the growing demand for crops, the area required for planting crops will also increase in the future. Current research generally assumes that land suitable for crops will generally shrink under global change, which, together with the increased crop demand, poses challenges to food security. The objective of this study is to access the global maize planting distribution by improving the crop distribution model with a special focus on both environmental and socioeconomic factors under the global change scenarios of SSP245 and SSP385. We constructed a crop distribution model that considers both environmental and socioeconomic factors. The environmental factors include topography, soil, and climate variables, while socioeconomic factors include supply-demand balancing, planting inertia, trade, irrigation ratio, and yield progress variables. Through those two aspects, we conducted a comprehensive prediction for the maize planting distribution. Compared with the statistics, the modelling results of most countries differ slightly from the statistical data (within 20%), which shows that the model has good performance. Based on the projections of the model, the predicted planting area under the SSP245 and SSP385 scenarios in the near-term (2016–2035) increases by 39.86% and 48.70%, and in the mid-term (2046–2065) increases by 56.17% and 75.73%, respectively. In addition, the maize planting zone has decreased in size in the United States, Mexico, northern Brazil, South Africa, and southern Europe. Our study provided a comprehensive crop distribution model and projected the global maize planting distribution, emphasizing the importance of socioeconomic factors in crop distribution modelling. Our research could benefit agricultural risk and regional field management.

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