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Soil organic matter (SOM) plays a critical role in terrestrial ecosystem functioning and is closely related to many global issues like soil fertility, soil health and climate regulation. Therefore, obtaining accurate information on the spatial distribution of SOM and its potential controlling factors is of global interest. However, this remains a great challenge since SOM is affected by numerous natural and anthropogenic factors and usually showed strong heterogeneity. In this study, we collected a total of 16,580 surface soil (0–20 cm) samples from the farmland throughout Jiangxi Province. And the Random Forest (RF), Cubist and gradient‐boosted models were compared and used to define the factor which is most associated with SOM. Then the ordinary kriging (OK) and machine learning‐ordinary co‐kriging (ML‐COK) were used to map SOM. We found that on average, 30.86 g kg−1 SOM was present in farmland soil of Jiangxi Province. Anthropogenic activities strongly affected SOM level, with five of the top 10 most important factors are anthropogenic related. The straw return amount was proved to have the largest importance (31.46%) for modelling SOM and a significant (p < 0.001) positive relationship between SOM content and the amount of straw returned to farmland was detected. Additionally, returning straw improved crop production. Soil derived from the Quaternary Subred Sand has the highest SOM content (37.82 g kg−1). Crop rotation also improved SOM content and the rice‐bean rotation system has the highest SOM content (34.27 g kg−1). With the best performance, the RF algorithm (R2 = 0.49, RMSE = 6.77 g kg−1) was selected to identify the primary control of SOM and integrated with COK, which we termed as ML‐COK, to map the SOM in the farmland of Jiangxi Province. ML‐COK outperformed OK method for mapping the SOM in farmland of Jiangxi Province with R2 of 0.351 and Lin's concordance correlation coefficient of 0.549. Farmland distributed in the central part of the province had high SOM content. In contrast, farmland in the north, south and east parts had relatively low SOM. Our study offers new insight for mapping soil properties, identifying potential factors driving variation in SOM, and also provides valuable information for making more reasonable and environmentally friendly farmland management measures. [ABSTRACT FROM AUTHOR]

[Display omitted] • A finest map (30 m) of soil pH in cropland of Southern China was produced. • The performance of different variable selection methods was compared. • The performance of different spatial predictive algorithms on mapping soil pH was evaluated. • We analyzed the application of our produced pH map on soil heavy metal pollution. • The best applicable scenarios of RFE and FRFS were validated and analyzed. Soil pH is one of the critical indicators of soil quality. A fine resolution map of soil pH is urgently required to address practical issues of agricultural production, environmental protection, and ecosystem functioning, which often fall short of meeting the demands for local applications. To fill this gap, we used data from an extensive survey of 13,424 surface soil samples (0–0.2 m) across the cropland of Jiangxi Province in Southern China. Using digital soil mapping techniques with 46 covariates, we produced a 30 m resolution soil map of topsoil pH in cropland of Southern China. We integrate different variable selection algorithms and machine learning methods. Our results indicate the Random Forest with covariates selected by recursive feature selection had the best performance for mapping soil pH with r of 0.583 and RMSE of 0.41. The prediction interval coverage probability for our prediction was 0.92, indicating a low estimated prediction uncertainty. Climate was identified as the most critical variable for predicting soil pH with a contribution of 37.42 %, followed by soil properties (29.09 %), soil management (21.86 %), parent material (6.22 %), and biota (5.39 %) factors. The mean topsoil pH in the cropland of Jiangxi Province was 5.21, indicating a great pressure of soil acidification in this region. The high soil pH values were mainly distributed in the Northern, Western, and Eastern parts of the survey region while low soil pH values were majorly located in the central part. Compared with past surveys in 1980 s, there was no significant soil pH change in the surveyed region. Our soil pH map can provide important implications and guidance decisions on soil heavy metal pollution remediation, precision agriculture, and prevention of soil acidification. [ABSTRACT FROM AUTHOR]

[Display omitted] • We produced a high resolution (30 m) SOC map in cropland of Southern China. • Our produced SOC map has high accuracy with R2 = 0.73 and RMSE = 2.91 g kg−1. • The map accuracy was enhanced by incorporating soil management information. • Soil properties and soil management measures are most relevant to SOC variation in cropland. An accurate and fine map of soil organic carbon (SOC) plays a vital role in understanding the global carbon cycle and achieving soil carbon sequestration potential. Although global and national maps of SOC are already available with various spatial resolutions, limited sample size, and relative coarse resolution hinder its accuracy and application in local regions. Here, we collected 13,424 soil samples and information of 45 environmental covariates from cropland in Jiangxi Province, Southern China during 2012 and 2013. Then, the optimal covariates were selected by a recursive feature elimination algorithm for mapping SOC. After that, we deployed the random forest (RF) to produce a fine map (30 m) of SOC in the cropland of Jiangxi Province and 100 times bootstrapping was performed to calculate the prediction uncertainty. Finally, we determined the impacts of various covariates on SOC variability using RF and partial least squares structural equation modeling. Our results showed that compared with the predictive model without soil management information, introducing soil management information improved the predictability of SOC with an increase in R2 by 7.35 % (0.73 vs 0.68) and decrease in RMSE by 7.03 % (2.91 vs 3.13 g kg−1). Our results well estimated the uncertainty of the predicted result with a PICP of 0.91 for a 90 % prediction interval. Soil properties and soil management activities make the largest contribution for modelling SOC. Specifically, the total nitrogen content, straw return amount, total potassium content, and multi-resolution valley bottom flatness were found as the most important factors for mapping SOC in the cropland of our study area. Overall, this study deepened our knowledge of the variation of SOC and also emphasizes that incorporating soil management information could help us to achieve more accurate predictions of SOC. [ABSTRACT FROM AUTHOR]

Revealing the spatio-temporal heterogeneity of cropland ecosystem services and identifying its main driving factors is a prerequisite for managing cropland and ensuring sustainable agriculture. The cropland ecosystem services are impacted by both cultivation practices and the natural environment, which lead to high heterogeneity. Therefore, making clear the cropland ecosystem service value (CESV) dynamic and its driving factors are still a great challenge in agricultural production areas. Jiangxi Province is located in eastern China and it is one of the main grain production regions in China but face great pressure of cropland degradation due to rapid industrialization and urbanization in the past two decades. However, the knowledge on the spatio-temporal variation of CESV and related dominators is largely retain unclear in Jiangxi Province. To fill this gap, we integrated different biophysical models and value estimation methods to map the CESV in Jiangxi Province. The CESV from 2001 to 2020 was mapped with a spatial resolution of 1 km, and the temporal trend was explored using Theil-Sen and Mann-Kendall test method. Subsequently, the influencing pathways of both natural and anthropogenic driving factors on CESV were contrasted utilizing partial least squares structural equation modeling. The spatial correlation and heterogeneity between CESV and its driving factors was also revealed by the bivariate Moran's I method. Finally, the geographically weighted regression was adopted to identify spatially-varying primary driving factors which dominate the variation of CESV across the study area. Our results revealed that: (1) the CESV in Jiangxi province increased from 257.84 billion yuan to 312.37 billion yuan from 2001 to 2020; (2) the CESV in most of the study area (97.47%) was featured by an increasing trend; (3) vegetation and terrain factors have the highest contribution to the variation of CESV, followed by soil properties; (4) the CESV was positively correlated with saturated soil hydraulic conductivity, clay, elevation, slope, leaf area index, vegetation cover fraction in the study area, while significantly negatively correlated with population density and nighttime light index; (5) the proportion of areas where CESV was dominated by vegetation cover fraction was the largest, followed by saturated soil hydraulic conductivity, elevation, and slope. Based on our findings, we put forward several suggestions to underpin sustainable cropland ecosystem management and development. Our research offers an insightful understanding of spatio-temporal heterogeneity of CESV and can provide important implications and useful guidance for enhancing the ecosystem functions of cropland. [Display omitted] • The total CESV of Jiangxi Province increased from 257.84 billion yuan in 2001 to 312.37 billion yuan in 2020. • The CESV across Jiangxi Province was mapped from 2001 to 2020 with a spatial resolution of 1 km. • The temporal trend of CESV was explored using Theil-Sen and Mann-Kendall test. • The CESV in Jiangxi Province was promoted directly by terrain and vegetation. • Spatial varying primary drivers of spatio-temporal variation of CESV were mapped. [ABSTRACT FROM AUTHOR]

• The farmland soil in Jiangxi Province is facing a serious threat of acidification. • parental materials and soil group greatly affect soil C, N and P stoichiometry. • The MAT and MAP is significantly related to soil C, N and P stoichiometry. • The cropping system has essential effect on soil C, N and P stoichiometry. • Straw return could clearly increase the soil N:P ratio. Soil carbon (C), nitrogen (N), and phosphorus (P) contents and its stoichiometry are important indicators of the elemental balance in ecological interactions and processes. Farmland usually is featured by extensive anthropogenic actions which profoundly alters the soil C, N, P contents and its stoichiometry. However, it remains largely unknown how the soil C, N and P stoichiometry in farmlands is comprehensively affected by natural factors including parental material, soil types, annual mean temperature, annual precipitation, and soil management measures such as crop rotation and straw return at large spatial scale. The main aim of this study is to analyze the summary statistics, spatial variability, as well as main controls of soil C, N and P stoichiometry in farmland of the whole Jiangxi Province, which is one of the most important food production base. Our results found that the average concentration of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) is 17.91, 1.58 and 0.52 g kg−1 in farmland soil of Jiangxi Province, respectively. The averaged value of the soil C:N, the soil C:P ratio, and the soil N:P ratio is 11.73, 38.31 and 3.38, correspondingly. Great differences were detected for C, N and P stoichiometry in farmland soil derived from different parental materials or different soil groups. The mean annual temperature (MAT) is significantly and negatively related to soil C:N ratio, soil C:P ratio as well as soil N:P ratio while the annual precipitation (MAP) is significantly and positively related to soil C:P ratio, soil N:P ratio but significantly and negatively related to soil C:N ratio. The structural equation model analysis showed that soil properties and soil management have greater influences on soil C, N and P stoichiometry than climate and lithology. Our results could provide important implications for soil management and agricultural production. [ABSTRACT FROM AUTHOR]