The unnecessary application of nitrogen could negatively impact both the yield and quality of crops, and could also lead to nitrate leaching and soil pollution. The objective of this study was to investigate the effects of the optimized nitrogen application on 15N accumulation, utilization and distribution of processing tomato in China. Through field experiments with 15N and consisted two treatments: optimized nitrogen application (NY) and no nitrogen application (N0). The results showed that the absorption efficiency of applied fertilizer nitrogen was 46%. In processing tomato plants, 128.9 kg ha−1 of nitrogen came from applied fertilizer, while the remainder of the nitrogen (177.6 kg ha−1) came from the soil. The processing tomato fully absorbed the soil nitrogen, which reduced the nitrogen loss rate and resulted in higher production of dry matter. In the harvest stage. The residual percentage in soil of applied fertilizer nitrogen was 34%, which was mainly distributed in the 0-40 cm soil layer. This reduced the leaching of nitrogen and was conducive to the reuse of crops in the next year. This study provides a reference for the development of precise nitrogen management techniques using drip irrigation methods in arid areas..
Land use and land cover (LULC) are diverse and complex in urban areas. Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical buildings). Social media data, “marks” left by people using mobile phones, have great potential to overcome this semantic gap. Multisource remote sensing data are also expected to be useful in distinguishing different LULC types. This study examined the capability of combined multisource remote sensing images and social media data in urban LULC classification. Multisource remote sensing images included a Chinese ZiYuan-3 (ZY-3) high-resolution image, a Landsat 8 Operational Land Imager (OLI) multispectral image, and a Sentinel-1A synthetic aperture radar (SAR) image. Social media data consisted of the hourly spatial distribution of WeChat users, which is a ubiquitous messaging and payment platform in China. LULC was classified into 10 types, namely, vegetation, bare land, road, water, urban village, greenhouses, residential, commercial, industrial, and educational buildings. A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification. The overall accuracy and kappa value attained by the combination of multisource remote sensing images and WeChat data were 87.55% and 0.84, respectively. They further improved to 91.55% and 0.89, respectively, by integrating the textural and spatial features extracted from the ZY-3 image. The ZY-3 high-resolution image was essential for urban LULC classification because it is necessary for the accurate delineation of land parcels. The addition of Landsat 8 OLI, Sentinel-1A SAR, or WeChat data also made an irreplaceable contribution to the classification of different LULC types. The Landsat 8 OLI image helped distinguish between the urban village, residential buildings, commercial buildings, and roads, while the Sentinel-1A SAR data reduced the confusion between commercial buildings, greenhouses, and water. Rendering the spatial and temporal dynamics of population density, the WeChat data improved the classification accuracies of an urban village, greenhouses, and commercial buildings.
Village-level agricultural specialization in China is becoming increasingly important for rural development. However, existing knowledge of specialized agricultural villages (SAVs) based on singular assessment criteria and data describing static time points becomes insufficient in addressing multifaceted developmental questions today. We examined the long-term development patterns of SAVs in Anhui, China, with attributes from multiple angles, and explored how local factors affected SAV development across space and time using random forest regression. We found that as time elapsed, economic rationality drove specialized farmers closer to sale dependency and made SAVs more susceptible to market and economic factors, which builds upon previous findings analyzing SAVs at specific time points and consolidates the importance of market factors in the long-term development of SAVs. However, this susceptibility manifests differently in these two geographically contrasting regions north and south of Huai River. The northern SAVs received increased influences from market and economic factors, while the southern SAVs were continuously controlled by market and location factors. The dynamic spatial and temporal patterns of the two regions point to different dependencies, which emphasized local sales in the north and distant sales in the south. We propose that policies and strategies regarding SAV development accommodate these dynamics and address appropriate influencing factors accordingly.
China’s urban villages have distinct characteristics compared with the ones in western countries. Identifying urban villages provides a basis for policymakers to evaluate and improve the effectiveness of urban planning in China and other developing countries. However, perhaps due to limitations of data acquisition among others, few urban studies have successfully identified urban villages at the building level. To fill the research gap, this paper has fused multiple sources of data and utilized a three-stage model to identify urban villages in Haizhu District (Guangzhou, China). The first stage discriminates residential buildings, offices, shops, and restaurants based on various peak times of bike trajectories in different types of buildings. However, the first stage could not distinguish the regular residential buildings (in cities) and residential buildings within urban villages due to the similarity of human activities between them. It then utilized a second stage to identify residential buildings within urban villages based on the area, height, and density of buildings. In the third stage, we used correction rules to identify buildings with mixed-use and single-use buildings within urban villages. The results showed that urban villages were mainly concentrated in the western and central regions of the Haizhu District. Most of them were adjacent to shopping buildings or high-rise residential buildings. Building height and density played critical roles in the characterization of residential buildings in urban villages. Our accuracy rate was around 85% when verified against ground-truth data.
Introduction The development of specialized villages (SVs) is of great importance for rural revitalization. Methods This study integrated SVs, terrain, resource, traffic, market, and economy data to characterize the development of SVs from 2017 to 2021 and explore its influence factors by the Random Forest Regression model in Henan, China. Results The sustainably developed SVs were mainly distributed in the plain and the transition zone of mountain-hilly, mountain-plain, and hilly-plain, showing a spatially aggregated polycentric characteristic; the market is the key factor for the development of SVs in the transition zone of mountain-hill or mountain-plain, and the traffic factor mainly influences SVs in the plain and the transition zone of hill-plain; compared to the factors influencing the formation of SVs, the influence of terrain and traffic factors on the development of SVs was decreasing, and the influence of market and economic factors was showing an increasing trend. Discussion The results of this study can provide practical strategies for the development of SVs in the under-developed areas of interior.
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