The Spatial Distribution Characteristics of the Cultivated Land Quality in the Diluvial Fan Terrain of the Arid Region: A Case Study of Jimsar County, Xinjiang, China

Abstract

Environmental constraints are not only important aspects that affect the cultivated land quality but also necessary factors that shall be considered when evaluating the cultivated land quality scientifically. Moreover, identifying the quality condition of cultivated land accurately is the premise for guaranteeing food security. Based on the case study of diluvial fan terrain in Jimsar County, Xinjiang in the arid region of Northwest China, this study utilizes a geographic information system spatial analysis and a multifactor comprehensive evaluation method and constructs a comprehensive evaluation index system for cultivated land quality on account of three dimensions, namely soil properties, farming conditions, and natural environmental conditions. To reduce the Modifiable Areal Unit Problem (MAUP) effect and improve the accuracy of the quality evaluation results of cultivated land, this study compares the spatial interpolation methods of Inverse Distance Weighted Matrix (IDW), Ordinary Kriging (OK), and Spline Functions (Spline) based on different cultivated land evaluation units. Through the assessment on the comparison results, we finally adopted large-scale cultivated land as the quality evaluation unit of cultivated land and Ordinary Kriging (OK) as the spatial interpolation method. The results indicated that the average grade of the quality index of cultivated land in the diluvial fan terrain of Jimsar County is 6.66 at the middle or lower level; the quality of cultivated land and natural environment conditions reduce with the rise of elevation of the diluvial fan terrain, indicating a vertical zonality differentiation rule; the farming conditions keep sliding from the middle part of diluvial fan terrain to the edge of the diluvial fan terrain and the piedmont slope. The major factors affecting the quality of the cultivated land include the soil capacity, soil pH, soil organic matter, the quantity of straw returning to the field, source of irrigation water, water delivery method, part of the diluvial fan, groundwater level depth, and geomorphic type. Therefore, the measures to improve the quality of the cultivated land are put forward, mainly including improving the soil, carrying out land consolidation projects, and developing highly efficient water-saving irrigation agriculture. This study provides favorable references and directions for the sustainable utilization and quality improvement of cultivated land resources in arid regions.

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction

“…This result indicates that model performance can be further improved by building samples with large, cultivated land patches as units. Sheng et al evaluated the quality of cultivated land on the alluvial fan in Jimusar County, Xinjiang, China, based on different cultivated land evaluation units and found that when large areas of cultivated land were used as the evaluation units for CLQ, the evaluation result was accurate [31]. The reason may be that a large area of cultivated land is more representative of the characteristics of CLQ.…”

“…In this study, the proposed ASP sample construction method further improved the accuracy of predicting CLQ using machine learning. A rapid and efficient CLQ evaluation method can provide a timely and accurate basis for orderly demarcation of permanent basic farmland, occupation or protection compensation of cultivated land, and calculation of compensation for expropriation of land [31]. Furthermore, land use planning and land reclamation planning can manage cultivated land differently, according to the spatial distribution of CLQ grade [58].…”

“…Thus, the quality of the sample data profoundly influences the learning ability of these models. The construction of samples has predominantly been based on spatial points [15,28,31], while cultivated land area has been neglected, despite previous studies demonstrating that CLQ is correlated with cultivated land area [32,33]. Therefore, studies aimed at optimizing the construction of samples for the evaluation of CLQ using machine learning models are required.…”

Spatiotemporal Evolution of Non-Grain Production of Cultivated Land and Its Underlying Factors in China

“…Some fruits can destroy the original soil structure of cultivated land and reduce the quality of cultivated land, but it is relatively easy to restore cultivated land fertility. However, if the cultivated land is used for the production of fast-growing trees, such as poplar, or other types of economic trees with developed root systems, soil degradation, e.g., soil compaction and acidification, often arises because the trees absorb water and fertilizers during long-term planting [27][28][29][30][31][32]. Digging ponds and breeding fish in many areas completely removes the cultivated layer of arable land.…”

Impacts of Farming Layer Constructions on Cultivated Land Quality under the Cultivated Land Balance Policy

“…The CLQ evaluation at the field scale has higher accuracy and greater diversity of indicators, and thus, can better detect the changes in CLQ factors [35], which is conducive to the fine and differentiated management of cultivated land and meets the work needs of CLBP. The recent indicator systems of CLQ evaluation at the field scale are generally based on the physical, chemical, and biological aspects of the land and are used to explore the input-output efficiency of the field [36], the soil nutrient dynamics [37], and cultivated land effects on crops [38]. However, this assessment system, which focuses on determining soil fertility, obviously lacks in-depth research on the constituent elements, operation processes, and functions of the cultivated land system, and cannot fully consider the natural, economic, and ecological conditions of the cultivated land [39].…”