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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. [ABSTRACT FROM AUTHOR]

[Objective] The objective of the paper is to provide the scientific basis for rational fertilization in Jianhe County of Guizhou Province and similar areas. [Method] The effects of soil nutrient indexes(total N .organic matter, available N .available K, available P, total P and total K)on soil fertility are explored by using variation coefficient method, correlation coefficient method and weighted mean method, and then the soil fertility in paddy field and dry' land is evaluated by using improved Nemerow comprehensive evaluation method combined with the weight coefficient mean value of the three methods. [Result] The soil available k in paddy field and total P in dry land were at a lower level, while other nutrient indexes were at a medium-high, high and higher level. The soil available N content in six dry' land sampling sites with different plants was high. Soil total P and available k fluctuate greatly among sampling sites, while organic matter content fluctuates less. The soil available P in fertility factor weighted coefficient of paddy field and dry land was the highest, respectively 0. 2710 and 0. 2358 comparing the mean value of the three weighted method weighted coefficient. The available P is the main factor influencing soil fertility in paddy field and dry' land and soil fertility indexes is 1.52 and 1.63 separately. The soil comprehensive fertility was at a fertile level. [Conclusion] The soil fertility of paddy field and dry land in Jianhe County is at a fertile level, and available P is the main factor influencing soil fertility in paddy field and dry land. The soil N content in paddy field and dry land is high, which can meet the demand of crop production. Therefore, N fertilization should be reduced in production. The soil K content in paddy field is low, and the corresponding fertilization measures should be taken to improve K supply level in paddy field. [ABSTRACT FROM AUTHOR]