Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks
Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks
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DOI：
10.1177/0309133320956631
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
B. Liang;Hongyan Liu;T. Quine;Xiaoqiu Chen;P. Hallett;E. Cressey;Xinrong Zhu;Jing Cao;Shunhua Yang
中科院分区：
文献类型：
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作者：
B. Liang;Hongyan Liu;T. Quine;Xiaoqiu Chen;P. Hallett;E. Cressey;Xinrong Zhu;Jing Cao;Shunhua Yang
中国喀斯特地貌面积为3.63× 106km2，其中西南地区占40%以上，分布在贵州高原。岩溶包括该地区约1.30×106 km2的裸露碳酸盐岩基岩，土壤退化，作物产量低。本文旨在更好地了解作物产量的环境控制，以便更可持续地利用自然资源进行粮食生产和发展。利用4种人工神经网络对贵州省7种作物产量的空间格局进行了分析和模拟，探讨了作物产量与气象、土壤、灌溉和施肥等因子的关系。空间分类结果表明，贵州省粮食单产和粮食总产的高水平区主要集中在中北部地区。此外，三个人工神经网络用于模拟作物产量的空间格局都表现出良好的相关系数之间的模拟和真实的产量。然而，反向传播网络具有基于准确性和运行时间的最佳性能。在所调查的13个影响因子中，温度（16.4%）、辐射（15.3%）、土壤水分（13.5%）、氮肥（13.5%）和磷肥（12.4%）对作物产量空间分布的贡献最大。这些结果表明，神经网络在确定作物产量的环境控制和作物产量的空间格局建模方面具有潜在的应用，这可以使当地利益相关者实现可持续发展和作物生产目标。
The area of karst terrain in China covers 3.63×106 km2, with more than 40% in the southwestern region over the Guizhou Plateau. Karst comprises exposed carbonate bedrock over approximately 1.30×106 km2 of this area, which suffers from soil degradation and poor crop yield. This paper aims to gain a better understanding of the environmental controls on crop yield in order to enable more sustainable use of natural resources for food production and development. More precisely, four kinds of artificial neural network were used to analyse and simulate the spatial patterns of crop yield for seven crop species grown in Guizhou Province, exploring the relationships with meteorological, soil, irrigation and fertilization factors. The results of spatial classification showed that most regions of high-level crop yield per area and total crop yield are located in the central-north area of Guizhou. Moreover, the three artificial neural networks used to simulate the spatial patterns of crop yield all demonstrated a good correlation coefficient between simulated and true yield. However, the Back Propagation network had the best performance based on both accuracy and runtime. Among the 13 influencing factors investigated, temperature (16.4%), radiation (15.3%), soil moisture (13.5%), fertilization of N (13.5%) and P (12.4%) had the largest contribution to crop yield spatial distribution. These results suggest that neural networks have potential application in identifying environmental controls on crop yield and in modelling spatial patterns of crop yield, which could enable local stakeholders to realize sustainable development and crop production goals.