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The straw return practice is essential to soil organic matter (SOM) accumulation in the black soil area with high carbon sequestration potential. However, due to lacking accurate spatial distribution of straw return, few studies have carried out rigorous research on the impact of long-term straw return on SOM spatiotemporal variation on a regional scale. This study was carried out across an approximately 3000 km 2 area in Lishu County, northeastern China, a typical agricultural plain. Based on a total of 619 soil samples and 16 environmental covariates, the study mapped the spatial distributions of SOM in 2006 and 2018 by random forest (RF) and evaluated the effects of the interaction of soil properties, land use, and straw return on SOM spatial–temporal variation. The results show that in the context of long-term straw return, the mean SOM content increased from 18.93 to 20.84 g kg -1 during 2006–2018. And 74.49 % of the region had a significant increase (maximum of 24.41 g kg -1) of SOM. The severest SOM loss occurred in the northwest due to the light texture and the transition from paddy fields to dryland. Nevertheless, for areas from paddy fields to dryland, the SOM loss decreased with the increased cumulative crop residue coverage (CRC). The SOM even increased by 1.79 g kg -1 when the cumulative crop residue coverage reached 0.60–1.00. In addition, soil with higher initial SOM and sand content had a lower response to straw return. The study revealed that straw return is beneficial to carbon sink in farmland and is a better way to prevent a carbon source caused by the conservation of paddy field to dryland. [ABSTRACT FROM AUTHOR]

Systematically recognizing spatial patterns and driving factors of cultivated land fragmentation is of great significance for the exploration of locally appropriate path to relieve cultivated land fragmentation. This study aims to estimate cultivated land density, mean patch size and area-weighted mean shape index to respectively indicate the characteristics of cultivated land fragmentation from three dimensions, namely, natural resource endowment, spatial partition, and convenience of utilization. The regional leading factors of cultivated land fragmentation are also analyzed. The results demonstrate that the distribution of cultivated land density is higher in Northern regions compared with those of southern regions. The significant positive correlation between cultivated land density and mean patch size is found to be universal across nearly all cities, exceeding differences in terrain, elevation, climate, soil, and social economic condition. For cities in the southern part of China, cultivated land of regular shape is partitioned to smaller blocks compared with irregular ones; alternatively, intensive and meticulous farming under small-scale agricultural operation leads to clusters with low mean patch size - low area-weighted mean shape index. Random forest model explains the impact of driving factors on cultivated land fragmentation, with an explanatory power ranging from 66% to 95%. The terrain factor emerges as the primary driver negatively affecting cultivated land density. Gross domestic product emerges as the dominant factor with a significant (p < 0.01) negative correlation to mean patch size for nearly all agricultural climatic zones. Terrain, gross domestic product and population is the most important factor affecting area-weighted mean shape index. Rural development degree influences correlation between dominant factors and cultivated land fragmentation. This study is greatly instructive for recognizing the spatial patterns of cultivated land fragmentation at the national scale and for exploring the barriers that impede regionally scaled cultivated land use. • First reveal 1-km grid scale cultivated land fragmentation in mainland China. • Detect the polarizing differences of cultivated land fragmentation between peri-urban and out-of-urban areas. • Analyze China national spatial clustering characteristics of cultivated land fragmentation. • Recognize regional dominant factor to cultivated land fragmentation. • Generate China national 1-km grid-based cultivated land fragmentation maps. [ABSTRACT FROM AUTHOR]

Locust swarms are destructive agricultural and biological disasters in China. The green prevention and control (GPC, such as ecological regulation and physical control) of locusts is a comprehensive and complex process, especially in information technology. In this study, a web-based decision support system (DSS) integrated with geographic information system (GIS) is developed to prevent and control locusts efficiently, accurately, and rapidly. The locust prevention and control DSS (LPCDSS) is developed to assist farmers and local government agencies in Chinese provinces with high incidence of locust by providing spatial decision-making information. LPCDSS offers online access to county, city, provincial, and national level data queries and is capable of storing, spatial analyzing, and displaying geographically referenced information of locust data. The system can also provide the real-time tracking of global positioning system (GPS) location, as well as goods scheduling of locust plagues prevention. Six types of web service, real-time data synchronization model, and locust population estimation model are developed and implemented to improve the decision-making usability and feasibility of LPCDSS by adopting a three-layer system architecture. The system is developed by using several programming languages, libraries, and software components. As a result, this system has been running successfully for several years and has improved efficiency of the locust prevention and control in China with high efficiency and great accuracy. The approaches and methodologies presented in this paper can serve as a reference for those who are interested in developing integrated pest control system applications. [ABSTRACT FROM AUTHOR]

Lodging is one of the major problems in maize production which causes severe yield loss every year all over the world. In the present study, the lodging suitability of different maize varieties in target growing environments was investigated based on geographical information science. A total of 401 maize planting counties in northeast China and northern China were selected as study areas. The mean and standard deviation of environment accumulated temperature in vegetative stage of maize were calculated from raw temperature data obtained from 167 meteorology stations in these areas. The variety lodging resistance was determined based on the data of national regional variety trials for maize, and the environment lodging stress was measured using field survey data on lodging. Probability analysis based on the calculated values of environment accumulated temperature in vegetative stage of maize was utilized to determine whether a maize variety can be physically mature in a planting county, and lodging suitability of the variety was evaluated with geographical information science combining variety lodging resistance and local environment lodging stress together. A new maize variety NH1101 was taken as an example to illustrate the modeling and calculating procedures. The result shows that, from southwest to northeast of the study areas, the overall suitability trend changes from nonsuitable to suitable, then to not very suitable. And it is demonstrated that the lodging suitability is not only related to the variety resistance but also to the local environment stress. [ABSTRACT FROM AUTHOR]

Agricultural greenhouse (AG), one of the fastest-growing technology-based approaches worldwide in terms of controlling the environmental conditions of crops, plays an essential role in food production, resource conservation and the rural economy, but has also caused environmental and socio-economic problems due to policy promotion and market demand. Therefore, long-term monitoring of AG is of utmost importance for the sustainable management of protected agriculture, and previous efforts have verified the effectiveness of remote sensing-based techniques for mono-temporal AG mapping in a relatively small area. However, currently, a continuous annual AG remote sensing-based dataset at large-scale is generally unavailable. In this study, an annual AG mapping method oriented to the provincial area and long-term period was developed to produce the first Landsat-derived annual AG dataset in Shandong province, China from 1989 to 2018 on the Google Earth Engine (GEE) platform. The mapping window for each year was selected based on the vegetation growth and the phenological information, which was critical in distinguishing AG from other misclassified categories. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement. Finally, the average User's Accuracy, Producer's Accuracy and F1-score of AG based on visually-interpreted samples over 30 years reached 96.56%, 86.64% and 0.911, respectively. Furthermore, we also found that the ranked features via calculating the importance of each tested feature resulted in the highest accuracy and the strongest stability in the initial classification stage, and the proposed temporal consistency correction algorithm improved the final products by approximately five percent on average. In general, the resultant AG sequence dataset from our study has revealed the expansion of this typical object of "Human–Nature" interaction in agriculture and has a potential application in use of greenhouse-related technology and the scientific planning of protected agriculture. [ABSTRACT FROM AUTHOR]

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China's commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited. [ABSTRACT FROM AUTHOR]

In recent years, the sustainable utilization of China's arable land has been confronted with several challenges. The China government has been very strict in arable land protection, and a package of policies and measures have been promulgated. All these endeavors are of great significance for proposing an innovative policy system for sustainable land use in China. However, above stated policies are all designed from the perspective of space control with the purpose of reducing arable land loss or increasing arable land area, few policies have been designed from the perspective of utilization control, namely guide the actual arable land farming in sustainable ways and constraint unreasonable land use behavior such as overuse, rough use, land abandonment. In this paper, we analyze spatial distribution of average land-use intensity (ALUI) at the county-level in Mainland China, which can be used as a significant index for evaluating the rationality of arable land use and providing effective decision-making supporting information for design of regional arable land protection policy. Based on the experimental results, there is still considerable room for yield improvement as the ALUI of ∼73.1 % counties are lower than 0.7 while the 53.60 % counties are lower than 0.6. Furthermore, the ALUI dataset shows significant global spatial autocorrelation characteristic. Boundaries of regions that aggregated by counties with high ALUI are more consistent with that of provincial administrative districts, comparing with that of sub-standard farming system regions. On the other hand, counties with low ALUI are mostly cluster in mountains, hills, or plateaus, where grain yield is mainly limited by regional hydrothermal conditions. In addition, counties with different ALUI status have been divided into six classes, using k-means clustering algorithm. This will facilitate the understanding of appropriate arable land protection and utilization paths for different regions and the rethinking of current support policies on farmland protection. • Present arable land-use intensity of mainland China at county scale. • Analyze spatial aggregation characteristics of arable land-use intensity. • Key points for regional arable land protection were found among different counties. • Present a new, output-oriented measure for arable land-use intensity. [ABSTRACT FROM AUTHOR]

Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase. [ABSTRACT FROM AUTHOR]

The extraction and evaluation of crop production units are important foundations for agricultural production and management in modern smallholder regions, which are very significant to the regulation and sustainable development of agriculture. Crop areas have been recognized efficiently and accurately via remote sensing (RS) and machine learning (ML), especially deep learning (DL), which are too rough for modern smallholder production. In this paper, a delimitation-grading method for actual crop production units (ACPUs) based on RS images was explored using a combination of a mask region-based convolutional neural network (Mask R-CNN), spatial analysis, comprehensive index evaluation, and cluster analysis. Da'an City, Jilin province, China, was chosen as the study region to satisfy the agro-production demands in modern smallholder areas. Firstly, the ACPUs were interpreted from perspectives such as production mode, spatial form, and actual productivity. Secondly, cultivated land plots (C-plots) were extracted by Mask R-CNN with high-resolution RS images, which were used to delineate contiguous cultivated land plots (CC-plots) on the basis of auxiliary data correction. Then, the refined delimitation-grading results of the ACPUs were obtained through comprehensive evaluation of spatial characteristics and real productivity clustering. For the conclusion, the effectiveness of the Mask R-CNN model in C-plot recognition (loss = 0.16, mean average precision (mAP) = 82.29%) and a reasonable distance threshold (20 m) for CC-plot delimiting were verified. The spatial features were evaluated with the scale-shape dimensions of nine specific indicators. Real productivities were clustered by the incorporation of two-step cluster and K-Means cluster. Furthermore, most of the ACPUs in the study area were of a reasonable scale and an appropriate shape, holding real productivities at a medium level or above. The proposed method in this paper can be adjusted according to the changes of the study area with flexibility to assist agro-supervision in many modern smallholder regions. [ABSTRACT FROM AUTHOR]

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon's entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies. [ABSTRACT FROM AUTHOR]

Increasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson’s r < 0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1–5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield ( r > 0.40, p < 0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts. [ABSTRACT FROM AUTHOR]

Evaluation of arable land ecosystem services capacity and arable land-use intensity (ALUI) is important for recognising key regional factors that impact arable land attributes changes, which is crucial for planning sustainable patterns of arable land use. The chronic lack of coordination between these two types of evaluation studies has made it hard to provide enough information for developing arable land-use management and control policies. Here, we generated a 1-km-grid map of arable land potential yield and county-level arable land productive-capacity. The impact of land-use and land-cover change on county-level total arable land productive-capacity during 1990–2010 had been estimated. Then we determined the aggregation–distribution characteristics of four indexes (i.e. average arable land potential yield, average ALUI, total arable land area and arable land productive-capacity reserves) at the county-level by the k-means algorithm to assess the regional coordination between arable land productive-capacity protection and arable land use. The results show that during 1990–2010, land-use change led to arable land productive-capacity decreases in 2007 of China's 2733 counties (nearly 73.5% of the total counties' count). Most of these counties are in central and southern China, and their corresponding arable land productive-capacity decrement rates are generally < 6.15%. Counties with decrement rates > 6.15% are mainly in the Yangtze and Pearl River delta regions. The geographical detector shows that county-level arable land-area change is a primary factor that drives county-level arable land productive-capacity increase. Its determinant power can be quantified as 74.154%. In contrast, its determinant power to county-level arable land productive-capacity decrease is only 38.542%, which demonstrates that occupy high-capacity arable land and supplement low-capacity arable land have a greater role in causing reduction of county arable land productive-capacity. Total arable land productive-capacity and use intensity show only slight determinant power to county-level arable land productive-capacity decrease. It indicates that insufficient attention has been paid to the protection of arable land productive-capacity and the farmers' willingness in the implementation of China requisition-compensation balance policy. The aggregation–distribution characteristics show that arable land productive-capacity protection is in coordination with arable land use for most of the provinces in China. Arable land with high or medium potential yield tends to be used at high or medium intensity. A lack of coordination is most evident in the insufficient ALUI, particularly in seven of the thirteen major grain-producing provinces. Other evidence of weak coordination is in the low potential yield versus high farming conditions and willingness, where unsuited planting modes should be prevented. Lastly, challenges for exploring sustainable arable land use path have been discussed. This study is greatly instructive for recognising interrelations between natural conditions and arable land-use patterns and for exploring shortcomings that impede regionally sustainable arable land use. • Impact of LUCC on county-level arable land productivity during 1990–2010 is estimated. • Difference in determinant power to arable land productivity decrease and increase is detected. • Regional coordination between arable land productivity and use intensity is assessed. • Correlation between arable land potential yield and its distance to urban is assessed. • Generate county-level normalised total arable land productive capacity dataset. [ABSTRACT FROM AUTHOR]

Abstract: Lodging in maize, which is the result of the genotype-by-environment interactions, causes severe yield losses annually. There are many studies on lodging resistance of maize varieties now, but information about maize planting environment lodging stress is very rare. The environment lodging stress, which is a result of various factors, is characterized by uncertainty. Fuzzy sets theory and fuzzy logic are advantageous for dealing with the questions with uncertainties. The objective of this study is to assess the environment lodging stress for maize planting. Based on the daily weather data in the recent 59 years (1951–2009) and soil data, five counties (or cities) in ShanDong Province and HeNan Province in Huang-Huai-Hai-Plain, China, are selected as study sites; the maximum wind speed, rainfall, and potassium content in soil are chosen as stress indicators, and the overall stress level of each study site is obtained by a fuzzy synthetic evaluation approach. The consistency is shown between our calculation results and the results obtained with actual survey data on lodging in maize, indicating that the employed model is a promising approach for assessing environment lodging stress, and providing a scientific basis for maize variety extension and recommendation and comprehensive management to reduce maize planting risk and loss. [Copyright &y& Elsevier]

Abstract: Firstly, this paper analyzes the basic principles and processes of the spatial pattern changes of land use in towns and villages, and the result shows that the land resource demands of urban development and population growth lead to the spatial pattern changes. Secondly, in order to grip land use changes better, the paper proposes a method for the simulation of spatial patterns. The simulating method can be divided into two parts: one is a quantitative forecast by using the Markov model, and the other is simulating the spatial pattern changes by using the CA model. The above two models construct the simulative model of the spatial pattern of land use in towns and villages. Finally, selecting Fangshan which is a district of Beijing as the experimental area, both the quantity and spatial pattern changing characteristics are investigated through building a changing dataset of land use by using spatial analysis methods based on the land use data in 2001, 2006 and 2008; CA–Markov is used to simulate the spatial pattern of land use in Fangshan for 2015. [Copyright &y& Elsevier]