Abstract
With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity.
Highlights
Arable land is the basis of food production, the most valuable input in agricultural production, and an important factor in sustainable agricultural development and national food security [1,2].In recent years, China has experienced rapid economic and social development
The study study area area is is divided divided into into five five partitions based on on spatial spatial variations variations in in the the amount amount of of Figure partitions based soil organic matter; this property is one of low variation based on the spatial autocorrelation analysis soil organic matter; this property is one of low variation based on the spatial autocorrelation analysis mentioned in in the the preceding preceding section
The results indicate that the mean spatial variations in soil properties based on the optimal sample points from the conventional Simulated Annealing (SA) differ slightly from those of the raw sample points; the values for the soil organic matter and pH are smaller, and the optimal sample points from the conventional SA are selected according to their statistical characteristics
Summary
Arable land is the basis of food production, the most valuable input in agricultural production, and an important factor in sustainable agricultural development and national food security [1,2].In recent years, China has experienced rapid economic and social development. The reduction and degradation of arable land due to industrialization and urbanization has gradually emerged as one of the most prominent challenges in China [3,4,5]. In this context, the long-term dynamic monitoring of arable land quality becomes important for protecting arable land resources. With regards to the monitoring and evaluation of the arable land quality, several relatively complete networks of sample points have been established, and numerous soil data have been accumulated by the departments of land and resources and the departments of agriculture [6]. One efficient practice is to select representative sample points with the goal of achieving a certain level of accuracy [7,8,9,10]
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