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The soil organic carbon pool of farmland is the only carbon pool that can be appropriately adjusted by rational utilization in a short time scale. High precision mapping of soil organic carbon is helpful to further enhance the potential of regional soil carbon sequestration, analyze the geographical environment background, and promote carbon trading and carbon neutralization. This study, The study took Guangdong Province as the study area, which was divided into 13 comprehensive characteristic zones on medium and large spatial scale. The variable structure of soil organic carbon spatial differentiation in farmland was determined by Geodetector, and hierarchical multivariate composite models（MCM）was constructed. According to the data of 208 503 soil sampling points, we chart a highprecision spatial distribution map of soil organic carbon density in the study area. The results show that comprehensive feature zoning on a medium and large spatial scale, which was carried out by coupling natural geographical characteristics with socio-economic characteristics, and introducing multidistance spatial clustering, can significantly converge the degree of sample dispersion.The mean standard deviation and mean variance of soil organic carbon samples decrease by 0.55 and 3.53 respectively, and Moran′s I index increase by 0.08. Under the dual influence of natural environment and human disturbance, there are many variables of spatial variation of soil organic carbon in farmland, and the variable structure in different comprehensive characteristic zones is quite different. Average annual precipitation, altitude, terrain slope and other variables play a significant role in mountainous and hilly areas, but not in plain and hilly areas. However, variables such as land use modes and soil physical and chemical properties have extensive and significant influence on different characteristic zoning. The hierarchical multivariate composite model based on Geodetector better solves the contradiction between the synchronous expression of spatial differentiation law and spatial mutation of soil organic carbon in medium and large-scale and complex scenarios, and suppresses the increase of multivariable interpolation noise. Its comprehensive accuracy is 6.45%, 10.45% and 7.50% higher than geographically weighted regression model−Kriging（GWRK）, radial basis function neural network（RBFNN）, ordinary Kriging （OK）respectively. With the support of high-density sample set, the high precision soil organic carbon map of Guangdong Province integrates the methods of regional comprehensive feature zoning, Geodetector and hierarchical multivariate composite models. Its prediction results are accurate and the spatial details are clearly expressed, which explores an effective path for compiling the large-scale spatial soil organic carbon distribution map. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Resources & Environment / Nongye Ziyuan yu Huanjing Xuebao is the property of Journal of Agricultural Resources & Environment Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ensuring national food security and healthy land for grain production is a priority because of changes to international agricultural supply and demand and domestic non-agricultural commodities. Considerations for cultivated land quality still focus on coupling the "natural environmental background" and "input-output" and lack a multi-dimensional perspective. Paddy field grain productivity in subtropical southern China（Conghua District, Guangzhou City） was studied to identify the key limiting factors. Sampling, on-site investigations, and production measurements were completed with a Geodetector. The three primary health dimensions that influenced paddy field grain productivity were the geographical environment health background（q=2.702）, the soil health status（q=2.025）, and the farmland health artificial environment（q=1.200）. In total, 36 influencing factors were observed, and the contributions（q values）ranged from 0.004 to 0.537. The key limiting factors were the farmland hydrological conditions, the soil physical and trace element health status, the soil erosion status, the production security measures, and the human management intensity. Their contributions ranged from 0.299 to 0.537 in the following order: soil texture > average water level change > farming activity frequency > soil erosion degree > irrigation support capacity > available silicon content > irrigation method. Using a modern agricultural planting strategy, the soil fertility, pH, and microbial activity health levels were high, but the available silicon content was insufficient. These results suggest that managing the farmland water environment（i. e., irrigation guarantee and irrigation methods） is important. The farmland grain production capacity "barrel effect" continues to threaten food security, leading to instability and spatial differences. Therefore, soil trace elements and water management during crop growth are critical factors for land health. [ABSTRACT FROM AUTHOR]

This study aims to clarify the grain production efficiency in western Guangdong and its key influencing factors and to optimize the efficiency of resource allocation, promoting the transformation and enhancement of grain production. Based on the grain production data of three cities in western Guangdong, from 1999 to 2018, a cross efficiency DEA model was employed to calculate the regional grain production efficiency and reveal almost two decades of temporal and spatial evolution characteristics of the grain production efficiency. The key influencing factors of the grain production efficiency were compared and analyzed by using the Tobit model and geographical detector model. Results showed that the average grain production efficiency in western Guangdong was 0.77; Maoming City exhibited the highest efficiency（0.88）, followed by Yangjiang City（0.78）and Zhanjiang City（0.65）. The most important input factor（contribution）of the Tobit and geographical detector models was the total power of agricultural machinery（- 1.082 and 0.942, respectively）. However, the Tobit model demonstrated that the seven input factors affecting the grain production efficiency were positively correlated only with the amount of chemical fertilizer applied（0.015）; that the effective irrigated area, grain sown area, and pesticide use amount were not correlated with the grain production efficiency; and that the remaining factors were negatively correlated. All factors measured by the geographical detector had a significant influence on grain production efficiency. The contribution of input factors in Yangjiang was similar to that in western Guangdong Province, but the contribution of the amount of chemical fertilizer was 0.008 higher than that of the effective irrigation area. The contribution of chemical fertilizer and pesticide input in Zhanjiang and Maoming was second only to the total power of agricultural machinery and the contribution of effective irrigation area was the lowest. However, the contribution of the area in which grain was sown in Maoming was higher than that of the employed population and agricultural plastic film, whereas Zhanjiang showed the opposite trend. In general, the grain production efficiency of western Guangdong during the past 20 years has not been high, and has remained at a medium level. The data demonstrate that grain production efficiency decreases first and then increases significantly. There are no insignificant factors in the measurement results of the geographical detector and the influence of detection factors is more stable and less biased than in the Tobit model. The total power of agricultural machinery is the leading factor affecting the grain production efficiency of the three cities in western Guangdong, but the contribution of the factors in different cities is quite different，Zhanjiang and Maoming are more dependent on the chemical fertilizer and pesticide pollution [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Resources & Environment / Nongye Ziyuan yu Huanjing Xuebao is the property of Journal of Agricultural Resources & Environment Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

:[Objective] The objective and accurate evaluation of farmland productivity enhancement potential on medium and large spatial scales is crucial to the prediction of regional food security capacity. However, the existing methods are too big and comprehensive, with significant multicollinearity of evaluation indicators, strong mutual interference noise and obvious dispersion of index system. [Method] In Guangdong province, in 2011 -2015 ,before and after the construction of high standard farmland farmland which can realize the change of the capacity potential value of the dependent variable, by the method of literature of 17 factors as independent variables, the influence factors of using random forest model to measure the importance of construction of farmland productivity promotion potential evaluation index system, taking Guangdong province as an example verifies the measure in the study area. [Result] (i) According to the measurement results of the random forest model, the importance (/value) of different factors to farmland productivity improvement potential was between 0.21 and 12. 71 , in which the importance of matching coefficient between water source and farmland and soil properties was greater than that of supporting infrastructure and traffic conditions. (ii) According to the measurement results of the random forest model, the significance of five indicators, including terrain slope, matching coefficient of water source and farmland, organic matter content, effective soil layer thickness and profileconfiguration, was greater than 5.00, which was the core index for the evaluation of farmland productivity improvement potential. (iii) According to the calculation, the farmland productivity improvement potential of guangdong province was between 1.04 x 104 -6. 06 x 104 hm2 , and the farmland productivity had great potential for improvement. [Conclusion] Large spatial scales of farmland productivity potential and topography and hydrology, soil conditions, farmland infrastructure, traffic conditions and so on, hut there are large difference between the importance of different factors, through the random forest model to build the evaluation index system of dimension reduction of convergence effect is remarkable, the calculated result with strong scientific and rationality [ABSTRACT FROM AUTHOR]