Identifying the Relationship between Soil Properties and Rice Growth for Improving Consolidated Land in the Yangtze River Delta, China

Using different multivariate approaches to assess water quality of qanats in arid zones of Southern Central Mexico

Qanats in the aquifer of the Tehuacán Valley (Mexico) represent an ancient way of using groundwater that is still practiced today. They are used mainly for agricultural irrigation. However, anthropogenic activities have jeopardized the use of these aquifers. We analyzed 24 qanats in the Tehuacán Valley to assess water quality. Based on 24 physicochemical variables, a water quality index (WQI) was constructed on a zero-to-100 scale, divided into five water quality classes. A decision-tree analysis was applied to identify the parameters with the highest influence on the WQI, considering the water quality classes as categorical responses and the values of physicochemical variables as drivers of these categories. We produced interpolation maps to identify trends. The relationship between the WQI and the normalized difference indices of vegetation and salinity (NDVI and NDSI, respectively) was analyzed using a ternary diagram. WQI scores showed that 12.5% of the qanats have very good quality; 25%, good quality; and the remaining (62.5%) range from moderate to unacceptable quality. The CHAID classification-tree method correctly explained 83.3% of the categories, with sulfates, alkalinity, conductivity, and nitrates as the main parameters that explain water quality. WQI was inversely related to NDVI and NDSI, showing seasonal differences. Interpolation maps suggest a better water quality in the northern zone of the aquifer.

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

The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influenced by training samples. However, in the existing research, samples have mainly been constructed by random point (RPO). Little attention has been devoted to the optimization of sample construction, which may affect the accuracy of evaluation results. In this study, we present two optimization methods for sample construction of random patch (RPA) and area sequence patch (ASP). Differing from RPO samples, it aims to include cultivated land area and its size into sample construction. Based on landsat-8 Operational Land Manager images and agricultural land grading data, the proposed sample construction methods were applied to the machine learning model to predict the CLQ in Dongtai City, Jiangsu Province, China. Four machine learning models (the backpropagation neural network, decision tree, random forest (RF), and support vector machine) were compared based on RPO samples to determine the accurate evaluation model. The best machine learning model was selected to compare RPA and ASP samples with RPO samples. Results determined that the RF model generated the highest accuracy. Meanwhile, a high correlation was noted between the cultivated land area and CLQ. Thus, incorporating cultivated land area in the sample construction attributes can improve the prediction accuracy of the model. Among the three sample construction methods, the ASP yielded the highest prediction accuracy, indicating that the use of a large, cultivated land patch as the sample unit can further elevate the model performance. This study provides a new sample construction method for predicting CLQ using a machine learning model, as well as providing a reference for related research.

Assessing the Effectiveness for Achieving Policy Objectives of Land Consolidation in China: Evidence from Project Practices in Jiangsu Province from 2001 to 2017

Land consolidation (LC) is an important measure taken to increase the quantity and productivity of farmland while reducing land fragmentation and ensuring food security. However, long-term land consolidation project (LCP) practices are rarely analyzed to assess the effectiveness for achieving current policy objectives of LC in China. Taking the practices of LCPs in Jiangsu Province from 2001 to 2017 as a case study, we used the spatial self-related analysis, the consistency analysis, and the redundant analysis (RDA), and found that the construction scale and the investment amount of LC in Jiangsu Province displayed varying trends, and that the newly increased farmland rate is clearly divided into three stages and gradually decreases. The newly increased farmland area, the investment funds, and reserved land resources for farmlands are not spatially synchronized in Jiangsu Province. Only the positive relationship between the LC rate and the Normalized Difference Vegetation Index (NDVI) growth rate continue to rise. The earlier stage of land consolidation projects (LCPs)’s practices is mainly affected by natural and social factors, and the late stage is mainly affected by economic and strategic factors. Finally, a new implementation scheme framework of LC planning has been proposed. This framework provides reference for top-level design, planning, and management of LC policies at the national level in China and other developing countries.

Influence of Gully Land Consolidation on Phreatic Water Transformation in the Loess Hilly and Gully Region

Gully Land Consolidation (GLC) is a proven method to create farmlands and increase crop yields in the Loess Hilly and Gully Region, China. However, GLC influences phreatic water transformation and might cause the farmlands water disasters, such as salinization and swamping. For exploring the influence of GLC on phreatic water transformation and mitigating disasters, a series of indoor experiments were conducted in the artificial rainfall hall. Then, we simulated the phreatic water transformation patterns under more conditions with HYDRUS-3D. Finally, an engineering demonstration in the field was performed to validate our research. The indoor experiments indicated that GLC could increase phreatic water outflow rate 4.39 times and phreatic water coefficient (PWC) 2.86 times with a considerable delay. After calibration and validation with experimental data, the HYDRUS-3D was used to simulate phreatic water transformation under more soil thickness and rainfall intensities. Accordingly, we summarized the relationship among PWC, rainfall intensities, and soil thickness, and therefore suggested a blind ditch system to alleviate farmlands disasters. Field application showed that a blind ditch system could avoid disasters with 3.2 times the phreatic water transformation rate compared to loess. Our research provides implications for sustainable land uses and management in the region with thick soil covers.

Probing Influence Factors of Implementation Patterns for Sustainable Land Consolidation: Insights from Seventeen Years of Practice in Jiangsu Province, China

Land consolidation is a key measure in the implementation of agricultural sustainability and has a strategic importance in farmland fragmentation reduction and rural revitalization. In order to understand spatiotemporal patterns of land consolidation implementation and their influences at a large scale, a comprehensive evaluation of 1046 land consolidation projects was conducted in Jiangsu Province, China. The results of this study showed that the construction scale (CS) and investment amount (IA) rose in waves in Jiangsu province during the period 2001–2017, while the newly increased farmland rate (NIFR) continued to decline. Spatial patterns of land consolidation aggregated, whereas the scale and the kernel density of the newly increased farmland area (NIFA) was differentiated in different time periods. In addition, the regional differentiation was significant. The gravity center of CS, IA, and NIFA moved with an overall trend from South Jiangsu to North Jiangsu, and finally stopped at the Li-Xia River plain area in North Jiangsu. The key factors that promoted land consolidation included natural farmland quality and the proportion of the primary industry production in GDP. The potential of NIFA, the farmland production amount, and the income of the financial transferring payment were also important factors. Spatial patterns were initially influenced by natural conditions and were later influenced more significantly by economic and policy conditions. In the future, differentiated land consolidation policy oriented by public involvement should be formulated to improve new frameworks of system implementation, as well as to provide evidence for spatial configuration, district cooperation, policy adjustment, and the systematic improvement of sustainable land consolidation.