Assessing the relationship between the spatial distribution of land consolidation projects and farmland resources in China, 2006–2012

Unraveling patterns, causes, and nature-based remediation strategy for non-grain production on farmland in hilly regions

The surge in non-grain production on farmland (NGPF) poses significant threats to food security and land sustainability, particularly in hilly regions. However, there remains a lack of clarity on how to effectively balance grain and non-grain production in relation to land remediation. Using Wannian County as a case study, we investigate the evolution of this by leveraging high-precision land surveys and satellite imagery. Through the application of bootstrapped partial linear regression models, we identify key influencers behind each type of NGPF. In proposing land remediation solutions, we integrate the results of NGPF and land quality evaluations to identify mismatches between non-grain production and land attributes (i.e., topography, geology, soil, and land use). Our findings reveal a substantial growth in NGPF, expanding from 3838.72 ha to 5659.64 ha (2010-2020), and predominantly occurring on farmland with favorable natural conditions and connected locations such as proximity to roads, town centers, and industrial plants. Surprisingly, the basic farmland protection policy shows limited effectiveness in curbing NGPF, except for garden operations. We identify 1674 NGPF patches suitable for conversion to grain production and provide land remediation suggestions tailored to low-quality farmland with specific natural barriers, thus complementing the demand for regional non-grain production. This study thereby innovatively proposes nature-based land remediation strategies to address the non-grain production dilemma by tailoring NGPF and land quality, offering valuable insights for sustainable farmland management in China and beyond.

The role of harmonized Landsat Sentinel-2 (HLS) products to reveal multiple trajectories and determinants of cropland abandonment in subtropical mountainous areas

Cropland abandonment is a widespread land-change process globally, which can stem from the accelerated outmigration of the population from rural to urban areas, socio-economic and political changes, catastrophes, and other trigger events. Clouds limit the utility of optical satellite data to monitor cropland abandonment in highly fragmented mountain agricultural landscapes of tropical and subtropical regions, including the south of China. Taking Nanjing County of China as an example, we developed a novel approach by utilizing multisource satellite (Landsat and Sentinel-2) imagery to map multiple trajectories of cropland abandonment (transitioning from cropland to grassland, shrubs and forest) in subtropical mountainous landscapes. Then, we employed a redundancy analysis (RDA) to identify the spatial association of cropland abandonment considering agricultural productivity, physiography, locational characteristics and economic factors. Results indicate the great suitability of harmonized Landsat 8 and Sentinel-2 images to distinguish multiple trajectories of cropland abandonment in subtropical mountainous areas. Our framework of mapping cropland abandonment resulted in good producer's (78.2%) and user's (81.3%) accuracies. The statistical analysis showed 31.85% of croplands cultivated in 2000 were abandoned by 2018, and more than a quarter of townships experienced cropland abandonment with high abandoned rates (>38%). Cropland abandonment mainly occurred in relatively unfavorable areas for agricultural production, for instance with a slope above 6°. Slope and the proximity to the nearest settlement explained 65.4% and 8.1% of the variation of cropland abandonment at the township level, respectively. The developed approaches on both mapping cropland abandonment and modeling determinants can be highly relevant to monitor multiple trajectories of cropland abandonment and ascribe their determinants not only in mountainous China but also elsewhere and thus promote the formulation of land-use policies that aim to steer cropland abandonment.

Farmland quality assessment using deep fully convolutional neural networks

Farmland is the cornerstone of agriculture and is important for food security and social production. Farmland assessment is essential but traditional methods are usually expensive and slow. Deep learning methods have been developed and widely applied recently in image recognition, semantic understanding, and many other application domains. In this research, we used fully convolutional networks (FCN) as the deep learning model to evaluate farmland grades. Normalized difference vegetation index (NDVI) derived from Landsat images was used as the input data, and the China National Cultivated Land Grade Database within Jiangsu Province was used to train the model on cloud computing. We also applied an image segmentation method to improve the original results from the FCN and compared the results with classical machine learning (ML) methods. Our research found that the FCN can predict farmland grades with an overall F1 score (the harmonic mean of precision and recall) of 0.719 and F1 score of 0.909, 0.590, 0.740, 0.642, and 0.023 for non-farmland, level I, II, III, and IV farmland, respectively. Combining the FCN and image segmentation method can further improve prediction accuracy with results of fewer noise pixels and more realistic edges. Compared with conventional ML, at least in farmland evaluation, FCN provides better results with higher precision, recall, and F1 score. Our research indicates that by using remote sensing NDVI data, the deep learning method can provide acceptable farmland assessment without fieldwork and can be used as a novel supplement to traditional methods. The method used in this research will save a lot of time and cost compared with traditional means.

Quantitative Assessment of Climate Change Impact and Anthropogenic Influence on Crop Production and Food Security in Shandong, Eastern China

Food security plays an important role in maintaining national stability and sustainable development of human society, and its research has become a hot issue at present. Shandong is the main grain producing area in China, and its grain production plays an important role in national food security. Accordingly, this paper is based on the county climate change, grain yield, sown area, fertilizer use, total power of rural machinery, and total population data in Shandong Province from 1995 to 2020. The evolution process of the food security pattern was studied by the methods of spatial analysis and comprehensive evaluation, the influencing factors of food security were quantitatively analyzed, and the adaptive countermeasures to alleviate the food security risks in this region were discussed. The results show that: Grain production increased by 30.62% from 1995 to 2020. The total population and per capita food availability also increased. Since 2000, more than a quarter of counties have experienced a high risk of food insecurity. The spatial agglomeration of grain production was enhanced, and the local agglomeration characteristics were significantly different. The average temperature in the growing season, the sown area, and the total power of agricultural machinery had a significant positive impact on grain production, while the annual average temperature had a significant negative impact on grain production. Improving the food supply system, strengthening the protection of cultivated land, improving the efficiency of fertilizer utilization, and increasing investment in agricultural science and technology can effectively alleviate food security risks.

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.

Quantifying the impacts of climate variability and human interventions on crop production and food security in the Yangtze River Basin, China, 1990-2015

Food security has become a global policy concern due to its important role in sustaining development and human well-being. Using spatial autocorrelation analysis of statistical data at the county-level, this study quantifies the change in spatial and temporal patterns of crop production in the Yangtze River Basin of China since 1990 and draws out policy implications for food security in the country. Four panel models were constructed to examine in what ways and to what extent four major factors (climate variation, sown area, fertilizer use intensity, and population size) influence the capacity for crop production. The results show that total crop production increased by 15.2% in 1990-2015, while there exists significant spatial heterogeneity in crop output across the upper, middle and lower sections of the Basin. The spatial agglomerations of crop production (hotspots) in the Basin have varied significantly over time, with the hotspots in the lower section having disappeared since 2000. Over a quarter of the total number of counties (649) in the region have experienced a high risk of food shortages, with 19.4-27.4% of counties having experienced severe or moderate shortages of per capita food availability since 1990. This percentage increased from 9.3% to 16.2% in the lower section, while it declined from 53.9% in 1990 to 41.9% in 2015 in the upper section and remained unchanged in the middle section. The variables of sown area, fertilizer use intensity, total precipitation in the growing seasons and time (Year) have significant positive effects on the growth of crop production, but mean temperature in the growing seasons of crops and total population have significant and negative relationships with crop outputs. Establishing a reliable food supply system, safeguarding high-quality cultivated land and increasing fertilizer use efficiency are suggested as imperative countermeasures to mitigate food security risks in the Yangtze River Basin.

Satellite data indicates multidimensional variation of agricultural production in land consolidation area

Land consolidation (LC) is an innovative way to improve agricultural production. Spatiotemporal pattern of agricultural production in land consolidation area (LCA) is difficult to quantify with limited field observations and survey data. Satellite data has advantages on recording vegetation status changes frequently, which is very supportive of estimating variation of agricultural production. In this paper, we used Net Primary Productivity (NPP), Normal Difference Vegetation Index (NDVI), and Multiple Band Drought Index (MBDI) from satellite data, to examine five attributes (irrigation capacity, multiple cropping index, crop phenology, farmland productivity, and production stability) of agricultural production after land consolidation (LC) at a site in China. Results show that there were no significant spatial differences in irrigation capacity for farmland in few years after LC due to consistent climatic conditions and uniform irrigation and drainage system. Multiple cropping index shows a pattern of "first reducing, then growing, last reducing", which may result from the disturbed "water-soil" environment and weak farmers' intention. Interannual variation of spatial distribution of phenology for the second-season crop is larger than that for the first-season crop since LC implementation adjusts short-term land use and management. With the improvement of production conditions and balanced distribution of production elements, farmland productivity has been improved and its differences among various farmland patches imply a reducing trend. Production in LCA is slightly less stable than that in the control area (TCA) where LC is not carried out because of limited and short-term effect from LC. We concluded that satellite data presents variation of agricultural production in LCA from different dimensions of time, space and attributes. Multidimensional variation of agricultural production is decided by several factors, including climate condition, LC activity, and farmers' intention.