Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks

• Published in: Progress in physical geography 2021-02, Vol.45 (1), p.33-52
• Main Authors: Liang, Boyi, Liu, Hongyan, Quine, Timothy A, Chen, Xiaoqiu, Hallett, Paul D, Cressey, Elizabeth L, Zhu, Xinrong, Cao, Jing, Yang, Shunhua, Wu, Lu, Hartley, Iain P
• Format: Article
• Language: English
• Subjects: Agricultural production; Artificial neural networks; Back propagation networks; Bedrock; Correlation coefficient; Correlation coefficients; Crop production; Crop yield; Crops; Environmental degradation; Fertilization; Food production; Karst; Natural resources; Neural networks; Radiation; Simulation; Soil degradation; Soil fertility; Soil investigations; Soil moisture; Soil temperature; Spatial analysis; Spatial distribution; Sustainable development; Sustainable use
• ISSN: 0309-1333; 1477-0296
• DOI: 10.1177/0309133320956631

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.