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Abstract Nutrient budgeting for cropland is a crucial tool for assessing nutrient mining or excess application. We estimated the nutrient budget of nitrogen (N), phosphorus (P), and potassium (K) in cropland for South Asia during the last five decades (from 1970 to 2018) using equation-based empirical methods. Nutrient budget for the last five decades shows a negative balance of N (3.94 million tons, Mt), P (23.87 Mt), and K (247.23 Mt). Inorganic fertilizer remained the major input source for N and P, and its decadal average share increased for N (from 27.9% to 72.8%) and P (from 72.1% to 94.5%) from 1970 to 2010s and the share of manure, deposition, and crop residue to N, P and K input decreased. Deposition remained a major source of K input and its share decreased from 64.0% to 35.5% during the period. The share of crop removal to the decadal output of N (58.6% to 53.4%) and P (49.0% to 23.1%) decreased, and K (72.5% to 76.0%) increased from 1970 to 2010s. The higher losses of fertilizer N, and accumulation of P and K fertilizers in soils, resulted in decreasing partial factor productivity of N (from 72.2% to 16.9%), P (from 217.0% to 42.2%), and K (from 480.3% to 113.8%) from 1970 to 2018. Nutrient budget helps in identifying the regional imbalance (mining/accumulation) of the major nutrients, it will provide valuable information on the present status of country-level nutrient use for reorientation of their nutrient/fertilizer use policies.

[Objective] The rich and coarse sediment areas of middle Yellow River basin is a typical ecologically fragile area in China. Accurately quantifying the spatiotemporal dynamic changes of cropland in this region is crucial for evaluating regional food security, ecological restoration benefits, soil erosion conditions, and the downstream tranquility of the Yellow River. [Methods] Based on five high-resolution (30 m) land use/cover dataset products (CNLUCC, GLC_FCS30, CLCD, AGLC-2000-2015, GlobeLand30), this study employed methods such as cropland dynamic degree, deviation coefficient, transfer matrix, and spatial consistency analysis to comparatively analyze the spatiotemporal characteristics of cropland in the rich and coarse sediment areas. A high-precision fused dataset with a 30 m resolution was formed and validated. Based on this fused product, an analysis of the quantity, distribution, and structural characteristics of cropland in rich and coarse sediment areas of middle Yellow River basin in 1990—2020 years was conducted. [Results] There are significant differences in cropland characteristics among existing multi-source dataset products, with the fused products demonstrating higher accuracy compared to using any of the five existing products individually. Analysis based on the fused products reveals a trend of initially increasing followed by fluctuating decline in cropland area in 1990—2020, with the implementation of the Grain for Green project serving as a turning point. In 1990—2020, there has been a net decrease of 3 170.59 km2 in cropland area, representing a reduction of 17.63%. Furthermore, the proportion of cropland with slopes greater than 15° has been decreasing annually. The main types of dynamic transfer of cropland are into grassland, followed by artificial surfaces. The implementation of ecological projects such as the Grain for Green program, along with urbanization construction, is the primary cause of the decrease in cropland. [Conclusion] Researchers are encouraged to comprehensively analyze the strengths and weaknesses of multi-source products based on their research objectives. Exploring effective methods for fusing and interpreting multi-source data, with a focus on target land classes, can lead to a more in-depth understanding of the land characteristics in a specific region and, consequently, more precise research conclusions.

Abstract Nigeria is the most populous country in Africa, and the essential foods for Nigerians are cereal crops, including maize, rice, sorghum, millet, and wheat. However, their productivity is significantly affected by population pressure, poor cropland utilization, and high fertilizer costs. Against this backdrop, this study examines the relationship between cereal production, cereal cropland area, fertilizer usage, and the rural population (farm labor). The study utilizes the novel dynamic autoregressive distributed lag simulation (DYARDLS) model and analyzes annual time series data for Nigeria from 1980 to 2021. The unit root test results suggest that the chosen variables are stationary. Furthermore, the bound test affirms that all variables are cointegrated, with a significance level of 1%. The results from the DYARDLS show that in the long run, a percentage change in rural population and cereal cropland area boosts cereal food production by 0.018% and 0.51%, respectively. Meanwhile, a 1% change in the food production index exacerbates cereal output by 0.25% in the long run and 1.06% in the short run. We also find that fertilizer consumption could improve cereal production in the short and long run, but the results are insignificant. In conclusion, we demonstrate that our study variables are the decisive determining factors of cereal productivity and cannot be disregarded in the mission to attain food security.

Background: Soil salinization is one of the key factors restricting the production of cropland. Once rice is subjected to alkali stress at the bud burst stage, the yield will suffer irreparable serious loss. Compared with salt tolerance, studies on QTL mapping and candidate gene analysis of rice alkali tolerance are limited. Results: In this study, we used the F2:3 population derived from the alkali-tolerant cultivar LD21 and the alkali-sensitive cultivar WL138 to construct an alkali-tolerant DNA mixing pool, and the BSA (Bulked Segregation Analysis) method was used for re-sequencing. The main QTL qRSLB9 controlling the relative shoot length of rice under alkali stress was mapped by QTL-seq. The candidate interval was narrowed to 346.5 kb by regional linkage mapping, which containing 6 DEGs screened through transcriptome sequencing. The qRT-PCR and candidate gene sequencing showed that LOC_Os09g24260 was most likely to control relative shoot length (RSL) in rice as a major gene who encodes the WD domain, G-beta repeat domain-containing protein. Conclusions: Based on these results, LOC_Os09g24260 was the candidate gene of qRSLB9 conferring alkalinity tolerance to rice at the bud burst stage. Our study provides valuable genetic information and materials for breeding new rice varieties with alkalinity tolerance.How to cite: Wang J, Bian J, Liu L, et al. Screening and analysis of candidate genes conferring alkalinity tolerance in rice (Oryza sativa L.) at the bud burst stage based on QTL-seq and RNA-seq. Electron J Biotechnol 2024;71. https://doi.org/10.1016/j.ejbt.2024.07.002.

Soil organic carbon (SOC) is significantly affected by land use change (LUC). Consequently, LUC is a major controlling factor of total SOC contents and SOC pool dynamics. Several methods have been developed to assess distinct SOC pools, which includes particle size separation, thermal analysis and soil reflectance mid-infrared spectroscopy. All of which are considered to have a potential as high through put methods to generate large datasets. Here, we used 23 sites covering six different types of LUC to assess differences in fast and slow cycling SOC derived from three approaches. We used i) particle size fractionation to obtain coarse (>50 µm) and fine (

Monitoring crop production has a direct effect on national and global economies and plays a significant role in food security. This study creates a possible autoregressive integrated moving average (ARIMA) model that can estimate the past (2010 to 2022) and future trends (2023 to 2035) for cultivated cropland and fertilizer consumption and their effects on rice and wheat production. The study results demonstrated past and future trends for different variables such as cultivated cropland, fertilizer consumption and rice, and wheat production over time. Based on the ARIMA model analysis, a 2.4% and 113% total reduction in cropland and fertilizer consumption over the next 13 years respectively was predicted. Over the next 13 years, the production of major crops, specifically rice and wheat, is expected to increase by 12.4% and 25.9%, respectively. However, the multiple linear regression model showed a significant change for dependent variables such as cropland and fertilizer consumption, with R2 values of 61% and 74%, respectively, for rice and wheat production. The predictive results from the ARIMA model analysis possibly showed an increasing trend for estimating crop yields, with a minor change in cultivated cropland and highly decreased fertilizer consumption. These results highlight that higher crop production can be achieved with less cropland and with minor fertilizer inputs.

Life cycle assessment (LCA) and ecosystem services assessment (ESA) are often used for environmental assessment. LCA has been increasingly used over the past two decades to assess agri-food systems and has established that ruminant products have higher impacts per kg of protein than products from monogastric species. Conversely, ESA is used less but is likely to rank ruminant systems higher than monogastric systems, as the former often include grasslands that can provide high levels of regulating ecosystem services (ESs). Here, we applied both methods to a selection of contrasting meat-oriented animal-production systems that included either ruminants or monogastrics (6 of each). We considered 16 environmental impact categories in the LCA and two functional units: 1 kg of human-edible protein (HEP) and 1 m2yr of land occupied. We used the life-cycle inventory step of LCA to characterise the land occupation of the systems, i.e. the land cover types used, such as croplands and grasslands. Based on these land covers and quantification of the ES they provide, we performed ESA. We estimated that ruminant systems had higher environmental impacts than monogastric systems per kg of HEP for all 16 LCA impact categories studied. For example, for ruminants and monogastrics, mean greenhouse gas (GHG) emissions were 280 vs 32 kg CO2-eq., respectively (P = 0.002), and mean fossil energy use was 351 vs 189 MJ, respectively (P = 0.009). The trend was the opposite for impacts per m2yr, with mean GHG emissions of 0.50 vs 0.57 kg CO2-eq. (P = 0.485) and mean fossil energy use of 0.71 vs 3.63 MJ (P = 0.002) for ruminants and monogastrics, respectively. We also estimated that ruminant systems had a higher capacity to supply regulating ES than monogastric systems did, with mean scores of 2.4 and 1.2, respectively (P = 0.002), due to multiple types of grasslands in ruminant systems. Applying both LCA and ESA to a range of contrasting animal-production systems was a novelty of this study, and ESA indicated that ruminant systems have higher positive environmental contributions than monogastric systems. The study also found that LCA and ESA frameworks can agree or disagree on the assessments of animal-production systems depending on functional unit used (i.e. agreement per unit of land occupied but disagreement per unit of HEP).

Soil erosion is still a vector of environmental and economic concern affecting most parts of the world, especially in Sub-Saharan African countries. Nevertheless, recent human activities in the hills, coupled with poor conservation measures and practices, could have amplified the rate at which soil is lost in the southwestern highlands of Ethiopia. This study focuses on quantifying and prioritizing micro-watersheds that require conservation actions by piloting spatial modeling of soil loss in the upper Bilate watershed. Sentinel image, soil, DEM, rainfall, and support practice data were used. A Revised Universal Soil Loss Equation (RUSLE) using GIS and satellite images was applied. The estimated average annual soil loss rate was demonstrated to be 24.1 t ha−1yr−1 and varied between 0.05 and 498.24 t ha−1yr−1. About 51.2% of the total revealed has a high soil truncation trait, of which 40% of the cropland has exceeded the soil loss tolerances of Ethiopia and tropical regions. The most affected micro-watersheds are MWS 16, 8, 6, and 3, which contributed 39.4% of the average annual loss rate, indicating the hotspots of soil loss problems in the region. This will have far-reaching off-site impacts on food security, soil productivity, human lives, infrastructures, and ecosystem service provisions.

Faced with increasing global soil degradation, spatially explicit data on cropland soil organic matter (SOM) provides crucial data for soil carbon pool accounting, cropland quality assessment and the formulation of effective management policies. As a spatial information prediction technique, digital soil mapping (DSM) has been widely used to spatially map soil information at different scales. However, the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance. To overcome this limitation, this study systematically assessed a framework of “information extraction-feature selection-model averaging” for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou, China in 2021. The results showed that using the framework of dynamic information extraction, feature selection and model averaging could efficiently improve the accuracy of the final predictions (R2: 0.48 to 0.53) without having obviously negative impacts on uncertainty. Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM, which improved the R2 of random forest from 0.44 to 0.48 and the R2 of extreme gradient boosting from 0.37 to 0.43. Forward recursive feature selection (FRFS) is recommended when there are relatively few environmental covariates (500). The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty. When the structures of initial prediction models are similar, increasing in the number of averaging models did not have significantly positive effects on the final predictions. Given the advantages of these selected strategies over information extraction, feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales, so this approach can provide more reliable references for soil conservation policy-making.

Abstract Despite fertilization efforts, phosphorus (P) availability in soils remains a major constraint to global plant productivity. Soil incorporation of biochar could promote soil P availability but its effects remain uncertain. To attain further improvements in soil P availability with biochar, we developed, characterized, and evaluated magnesium-oxide (MgO) and sepiolite (Mg4Si6O15(OH)2·6H2O)-functionalized biochars with optimized P retention/release capacity. Field-based application of these biochars for improving P availability and their mechanisms during three growth stages of maize was investigated. We further leveraged next-generation sequencing to unravel their impacts on the plant growth-stage shifts in soil functional genes regulating P availability. Results showed insignificant variation in P availability between single super phosphate fertilization (F) and its combination with raw biochar (BF). However, the occurrence of Mg-bound minerals on the optimized biochars’ surface adjusted its surface charges and properties and improved the retention and slow release of inorganic P. Compared to BF, available P (AP) was 26.5% and 19.1% higher during the 12-leaf stage and blister stage, respectively, under MgO-optimized biochar + F treatment (MgOBF), and 15.5% higher under sepiolite-biochar + F (SBF) during maize physiological maturity. Cumulatively, AP was 15.6% and 13.2% higher in MgOBF and SBF relative to BF. Hence, plant biomass, grain yield, and P uptake were highest in MgOBF and SBF, respectively at harvest. Optimized-biochar amendment stimulated microbial 16SrRNA gene diversity and suppressed the expression of P starvation response and P uptake and transport-related genes while stimulating P solubilization and mineralization genes. Thus, the optimized biochars promoted P availability via the combined processes of slow-release of retained phosphates, while inducing the microbial solubilization and mineralization of inorganic and organic P, respectively. Our study advances strategies for reducing cropland P limitation and reveals the potential of optimized biochars for improving P availability on the field scale. Graphical Abstract

Abstract To investigate the distribution characteristics of selenium (Se) in mountainous soil-crop systems and examine the threshold value of Se-rich soil, 275 soil samples and 153 associated crop samples (rice, maize, tea, nuts, konjac, mushrooms, buckwheat, and coffee) were collected in Ximeng County, a typical mountainous area in southwest China. The total Se, available Se, organic matter, pH, sampling point elevation, and crop Se content were analyzed to examine the distribution characteristics of soil Se and the ability of primary crops to enrich Se in Ximeng County. Random forest and multiple regression models were established to identify the factors influencing the available soil Se and the crop Se enrichment coefficient. Finally, the Se-rich soil threshold was examined based on the total Se, available Se, and Se content in primary crops (rice, maize, and tea). The results showed soil Se resource abundance in the study region, with high Se soil accounting for 64.72% of the entire area. The soil Se content displayed significant spatial autocorrelation. The average Se enrichment coefficient of the main cultivated crops included mushrooms > nuts > rice > coffee > tea > maize > buckwheat > konjac. The total Se content in the soil had the highest impact on the available Se content in the soil and the Se enrichment coefficient of crops. A Se-rich soil threshold of 0.3 mg·kg−1 was used for rice and maize, while that of tea was 0.4 mg·kg−1. This result provided a theoretical basis for developing and utilizing Se resources in mountainous soil in southwestern China and dividing the Se-rich soil threshold.

In Ethiopia, several factors are driving the conversion of agricultural land into Eucalyptus plantations. This study focuses on the Sinan and Mecha districts in northwest Ethiopia to identify the drivers behind this shift from agriculture to Eucalyptus forests. We used purposive and simple random sampling to select study areas and households. Data were collected through household surveys, key informant interviews, and focus group discussions. For data analysis, we employed descriptive statistics, ranking of drivers with a ranking index using the principle of weighted average, t-test, and a binary logistic regression model. The results reveal several key drivers of land use change. The most significant direct factors include using trees as an alternative income source, declining soil fertility, increasing demand for Eucalyptus wood products, and anticipated future returns from Eucalyptus plantations. Additionally, high agricultural input costs, rising wood prices, land degradation, and the shading effects of Eucalyptus on adjacent plots were identified as proximate drivers. Econometric regression analysis shows that family size and landholding size positively influence land use decisions, while distance to nearby towns negatively affects these choices. Respondents also reported challenges such as limited cropland, insufficient grazing land and fodder, land fragmentation, reduced agricultural production, and food shortages following land use conversion. Addressing these challenges is vital for the well-being and resilience of rural households. Implementing strategies like sustainable land management and effective land use policies can help enhance food security, economic stability, and overall quality of life for these communities. Allocating resources to improve soil fertility and landscape management will support sustainable agricultural practices and environmental conservation, benefiting rural households. The findings of this research will provide valuable insights for guiding future research, practices, and policies aimed at sustainable land use planning and food self-sufficiency in rural Ethiopia.

ABSTRACT Animal‐mediated pollination is a key ecosystem service required to some extent by almost three‐quarters of the leading human food crops in global food production. Anthropogenic pressures such as habitat loss and land‐use intensification are causing shifts in ecological community composition, potentially resulting in declines in pollination services and impacting crop production. Previous research has often overlooked interspecific differences in pollination contribution, yet such differences mean that biodiversity declines will not necessarily negatively impact pollination. Here, we use a novel species‐level ecosystem service contribution matrix along with mixed‐effects models to explore how groups of terrestrial species who contribute differently to crop pollination respond globally to land‐use type, land‐use intensity, and availability of natural habitats in the surrounding landscape. We find that the species whose contribution to crop pollination is higher generally respond less negatively (and in some cases positively) to human disturbance of land, compared to species that contribute less or not at all to pollination. This result may be due to these high‐contribution species being less sensitive to anthropogenic land conversions, which has led humans to being more reliant on them for crop pollination. However, it also suggests that there is potential for crop pollination to be resilient in the face of anthropogenic land conversions. With such a high proportion of food crops requiring animal‐mediated pollination to some extent, understanding how anthropogenic landscapes impact ecological communities and the consequences for pollination is critical for ensuring food security.

Knowledge-based evidence about potential and existing rainfed cropping is crucial for decision-making for sustainable land use and food security. Using multi-criteria spatial analysis techniques, this study assessed the current status of cropland availability and projected impacts on future crop production in Ethiopia. The study primarily defined the extent of the Rainfed Cropping Area (RCA) and assessed the performances of different cropping practices. After precisely mapping cultivated area, cropping intensification potentials were estimated. Subsequently, disregarding the existing cultivated area, completely unsuitable areas, and protected and intact forest areas, the potentially available arable land using suitability analysis techniques was determined. In addition, the performance of existing crop production systems was evaluated against the natural potential. The findings reveal that RCA covers ~60% of the country’s landmass, of which cropping is practiced in only 33%. The coverage of Potentially Available Cropland (yet uncultivated) accounts for 16% of the country’s RCA. This is dominantly located in sparsely populated western and southwestern parts of the country. This study confirms that Horizontal Cropping Intensification (HCI) in the RCA of Ethiopia reaches only 33%. On the other hand, Vertical Cropping Intensification (VCI) practices cover only 10%, while about 1/3 of the RCA is suitable for VCI strategies at various levels of suitability. The performance of existing VCI-oriented cropping (which covers only 10% of the RCA) is very poor. Challenges to the use of the available cropland and ways of addressing land shortage for needy farmers are highlighted to inform efforts to readdress landlessness and food insecurity in Ethiopia.

Satellite-based field delineation has entered a quasi-operational stage due to recent advances in machine learning for computer vision. Transfer learning allows for the resource-efficient transfer of pre-trained field delineation models across heterogeneous geographies. However, the scarcity of labeled data for complex and dynamic smallholder landscapes remains a major bottleneck. The key innovation of this study is to overcome this challenge by using pre-trained models to generate sparse (i.e., not fully annotated) field delineation pseudo-labels for fine-tuning models across geographies and sensor characteristics. We build on a FracTAL ResUNet trained for crop field delineation in India (median field size of 0.24 ha) based on multi-spectral imagery at 1.5 m spatial resolution. We use this model to generate pseudo-labels for the use in Northern Mozambique (median field size of 0.06 ha) based on sub-meter resolution true-color satellite imagery. We designed multiple pseudo-label selection strategies based on field-level probability scores and compared the quantities, area properties, seasonal distribution, and spatial agreement of the pseudo-labels against human-annotated training labels (n = 1,512). We then used the human-annotated labels and the pseudo-labels for model fine-tuning and compared predictions against human field annotations (n = 2,199). We evaluated performance with regards to object-level spatial agreement and site-level field size estimation. Our results indicate i) a good baseline performance of the pre-trained model in both field delineation (mean intersection over union (mIoU) of 0.634) and field size estimation (mean root mean squared error (mRMSE) of 0.071 ha), and ii) the added value of regional fine-tuning with performance improvements in nearly all experiments (mIoU increases of up to 0.060, mRMSE decreases of up to 0.034 ha). Moreover, we found iii) substantial performance increases when using only pseudo-labels (up to 77 % of the mIoU increases and 68 % of the mRMSE decreases obtained by human-annotated labels), and iv) additional performance increases (mIoU+0.008, mRMSE: −0.003 ha) when complementing human annotations with pseudo-labels. Pseudo-labels are architecture-agnostic, can be efficiently generated at scale, and thus facilitate domain adaptation in label-scarce settings. The workflow presented here is a stepping stone for overcoming the persisting challenges in mapping heterogeneous smallholder agriculture.

Accurate and detailed spatial–temporal soil information is crucial for soil quality assessment worldwide, particularly in the countries with large populations and extensive agricultural areas. Using remote sensing technology to generate bare soil reflectance composites has been shown as a prerequisite for effectively modeling soil properties. However, most bare soil extraction methods rely on the single-period satellite imagery, making it difficult to produce a complete bare soil map. Although some developed methods have explored the advantages of multitemporal images, single indicators (e.g., Normalized Difference Vegetation Index and Normalized Burn Ratio 2) are prone to misidentifying bare soil as other land cover types such as impervious surface. Additionally, these methodologies were designed for specific areas and coarse spatial resolution images, leaving their generalizability to other areas or larger scales underexplored. Therefore, we proposed a Two-Dimensional Bare Soil Separation (TDBSS) framework to generate the bare soil composites of Chinese cropland at 10-m spatial resolution using multi-temporal Sentinel-2 images. This method employs the Normalized Difference Red/Green Redness Index and Soil Adjusted Vegetation Index as bidimensional indicators. We identified optimal thresholds for these indicators by analyzing ecoregion-specific samples and then implemented them across nine major agricultural zones in China. Additionally, we evaluated the framework against three prevalent bare soil extraction methods (i.e., Barest Pixel Composite, Soil Composite Mapping Processor, and Geospatial Soil Sensing System) based on spatial accuracy. The results showed that TDBSS outperformed the others with the highest overall accuracy of 78.28% and the lowest omission error of 0.198. The findings indicated that the TDBSS algorithm is competent in mapping bare soil at a national scale. The produced composite map of bare soil reflectance is particularly valuable for retrieving soil attributes in Chinese cropland. The TDBSS method can be easily implemented across broad areas with computational efficiency, contributing to land management, food security, and the development of policies for precision agriculture.

During the last two decades, smallholder farmers in north-western Ethiopia have expanded eucalyptus fields into large areas of croplands until they recently started to reverse that trend. This study assessed the extent, drivers, and impacts of cropland to eucalyptus plantation changes during the 2000–2023 period and the recent land use reversal eucalyptus to cropland. It also analyzed the effect of the shift on land productivity and food security by comparing maize yields obtained from eucalyptus-cleared fields with those from permanent croplands. The assessment was conducted in the north-western highlands of Ethiopia and employed remote sensing techniques, yield difference comparisons, focus group discussions, and key informant interviews. Landsat-and Sentinel 2A-based multi-temporal image analyses were used to identify and map the coverage of eucalyptus plantation since 2000. Maize yield per plot was collected from 125 systematically selected paired 2mX2m plots, and yield differences were compared. One of the paired plots represented eucalyptus-cereal field changes, while the second represented cropland-maize plots. The multi-temporal image analysis result showed that eucalyptus plantation coverage was increased from 1000 ha in 2000 to 249,260 ha in 2023. Approximately 98% of that expansion was made onto crop fields. Latter, a large portion of that area was reconverted to cropland, mainly maize field due to substantial falls of market demand for eucalyptus logs. The oscillating land use changes imply that smallholders’ land use decisions are informed by intrinsic and extrinsic economic considerations, not by scientific-evidence-based landscape suitability and ecological analyses. Moreover, to check the effects of eucalyptus on subsequent productivity of croplands, we compared maize yield differences between cropland-maize and eucalyptus-maize field plots. The yield comparison result showed 35% average yield increment from eucalyptus-maize plots than yields from cropland-maize plots. This finding tends to defy the widely held perception that ‘growing eucalyptus tree plants on farmlands negatively affects the subsequent productivity of those plots’. However, this finding was based on a 1-year cross-sectional data. Further cross-sectional studies are important to arrive at conclusive results on the impacts of eucalyptus trees on productivity of those plots when converted to croplands.

Cropland is a critical component of food security. Under the multiple contexts of climate change, urbanization, and industrialization, China’s cropland faces unprecedented challenges. Understanding the spatiotemporal dynamics of cropland non-agriculturalization (CLNA) and quantifying the contributions of its driving factors are vital for effective cropland management and the optimal allocation of land resources. This study investigated the spatiotemporal dynamics and driving mechanisms of CLNA in Shaanxi Province (SP), a major grain-producing region in China, from 2001 to 2020, using geospatial statistical analysis and machine learning techniques. The results showed that, between 2001 and 2020, approximately 17,200.8 km2 of cropland (8.4% of the total area) was converted to non-cropland, with a pronounced spatial clustering pattern. XGBoost-SHAP attribution analysis revealed that among the 15 selected driving factors, precipitation, road network density, rural population, population density, grain yield, registered population, and slope length exerted the most significant influence on CLNA in SP. Notably, the interaction effects between these factors contributed more substantially than the individual factors. These findings highlight the pronounced regional disparities in CLNA across SP, driven by a complex interplay of multiple factors, underscoring the urgent need to implement water-saving agricultural practices and optimize rural land-use planning to maintain the dynamic balance of cropland and ensure food security in the region.

Cropland is a fundamental basis for agricultural development and a prerequisite for ensuring food security. The segmentation and extraction of croplands using remote sensing images are important measures and prerequisites for detecting and protecting farmland. This study addresses the challenges of diverse image sources, multi-scale representations of cropland, and the confusion of features between croplands and other land types in large-area remote sensing image information extraction. To this end, a multi-source self-annotated dataset was developed using satellite images from GaoFen-2, GaoFen-7, and WorldView, which was integrated with public datasets GID and LoveDA to create the CRMS dataset. A novel semantic segmentation network, the Global–Local Context Aggregation Network (GLCANet), was proposed. This method integrates the Bilateral Feature Encoder (BFE) of CNNs and Transformers with a global–local information mining module (GLM) to enhance global context extraction and improve cropland separability. It also employs a multi-scale progressive upsampling structure (MPUS) to refine the accuracy of diverse arable land representations from multi-source imagery. To tackle the issue of inconsistent features within the cropland class, a loss function based on hard sample mining and multi-scale features was constructed. The experimental results demonstrate that GLCANet improves OA and mIoU by 3.2% and 2.6%, respectively, compared to the existing advanced networks on the CRMS dataset. Additionally, the proposed method also demonstrated high precision and practicality in segmenting large-area croplands in Chongzhou City, Sichuan Province, China.

Agricultural drought is a major natural disaster affecting biomass accumulation and causing food loss, exacerbated by the increasing frequency of flash droughts and compounded drought-heatwave events. Traditional optical remote sensing indices cannot directly represent the water content of vegetation, resulting in a limited understanding of crop response to drought. To address this gap, we investigated the responsiveness of microwave Vegetation Optical Depth (VOD) with four bands (L-, C-, X-, KU-) and four emerging VOD-derived products to drought conditions in crops in the Midwest US. These products include the normalized Difference Between Night and Day VOD (nVOD), Slope of the Regression of Day and Night VOD (σ), Standardized VOD Index (SVODI), and VOD to estimate Gross Primary Productivity (VOD2GPP). They employ different theoretical modeling approaches to crop growth and water use strategies. We comprehensively analyzed the trend, seasonality, and residual of VODs, using Leaf Area Index (LAI) for comparison, and further assessed the lagged and cumulative effects, quantified drought sensitivity, and captured responsiveness to cumulative drought using thresholds. The results showed a time lag in the response of VOD series to drought as indicated by the Standardized Precipitation Evapotranspiration Index (SPEI). VODs achieved faster responses and higher correlations compared to LAI. Among them, VOD_L exhibited the most statistically significant pixels (39.84%) and positive Rmax-lag with 96.81% of all pixels. For cumulative effects, VOD_L, VOD_C, VOD_X, VOD_KU, and SVODI were highly correlated in the early stages of droughts. We also found that crops in Iowa exhibited medium to high drought sensitivity (average values of 0.55 to 0.74), with the highest drought sensitivity calculated using the isohydricity indicator, σ. Based on threshold comparison, σ showed a timely response in the first month of drought (average of −0.62), whereas VOD_L and VOD_C performed best in the second month (both averaging −1.85), and VOD2GPP (−2.94) was the most responsive in the third month. Due to water use strategy, maize responded more quickly to the onset of drought compared to soybeans. Overall, the results demonstrated that VOD is promising for crop phenology and drought research. This study provides the first comprehensive investigation of the diverse capabilities of multiple VOD-based indices in drought monitoring across various timescales and croplands.

Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman–Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5–6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them.

The change in soil microbial biomass and stoichiometry in different land uses of Northeast India were assessed by using the fumigation extraction method. The pattern of microbial biomass C, N, and P followed the sequence of forest (650.36, 62.86 and 254.54μgg−1) > grassland (62.86, 39.30 and 31.76 μgg−1> cropland (31.84,18.62 and 15.54 μgg−1), and exhibits seasonality with the lowest concentrations during the winter season across all land uses. The maximum concentrations were recorded during the rainy season in the forest and the summer season in both grassland and cropland. Microbial ratios (C: N, C: P and N: P) and proportions of microbial biomass to total soil nutrients vary with a variance in land use types. Soil microbial biomass exhibited a positive significant relation with soil nutrients but negatively related with soil temperature and moisture in all the study sites. Two-way ANOVA of soil microbial biomass exhibits significant differences due to seasons and land use types (p< 0.01). The microbial stoichiometry of the present study indicates N limitations in the study sites, while the proportion of microbial nutrients to total soil nutrients reveals nutrient mineralization in the forest and immobilization and competition in grassland and cropland. Furthermore, this study indicates that landuse and soil management practices interfere with microbial stoichiometry and nutrient release and immobilization patterns. Improving the retention of organic matter in ecosystems may enhance microbial activity and soil health.

This study addresses the critical global issue of food security, particularly under the influence of climate change on agricultural land suitability. The primary objective of our research is to predict the risks associated with land suitability degradation and changes in irrigation patterns, directly impacting food security. This research aligns with the United Nations’ sustainable development goals to reduce hunger and malnutrition. Central Eurasia, a region facing unique economic and social challenges, serves as the focal point of our investigation, providing a pertinent example for analyzing the effects of climate change on food security. In our approach, we employ interpretable machine learning techniques to analyze the impact of climate change on agricultural land suitability under different carbon emission scenarios. The developed model demonstrates strong performance, evidenced by an accuracy of 86% and a mean average precision of 72% in a multi-class land suitability classification task. Focusing on the most vulnerable regions in Eastern Europe and Northern Asia, our research provides crucial insights for policymakers. These insights are instrumental for strategic planning, including the allocation of critical resources like water and fertilizers, to prevent humanitarian crises. The results demonstrate that machine learning can be a powerful tool in predicting and managing the impacts of climate change on food security.

The conversion of agricultural lands, termed “nonagriculturalization,” poses profound threats to food security and ecological stability. Remote sensing image change detection offers an invaluable tool for monitoring this phenomenon. However, most change detection techniques prioritize image comparison over exploiting accumulated vector datasets. Additionally, many current methods are not readily applicable in practical scenarios due to inadequate model generalization capabilities and a scarcity of samples, resulting in a continued reliance on manual intervention for nonagriculturalization detection. In response, this article introduces a novel change detection approach for nonagriculturalization based on the vector data and contrastive learning. Initially, the boundary-constrained simple noniterative clustering algorithm is applied to segment two-phase images under vector data guidance. Samples are then generated using an adaptive cropping method. For early phase image samples, a collaborative validation-based sample annotation framework is employed to optimize and annotate the samples, with the purified high-quality samples serving as the training set for subsequent classification. For later-phase image samples, only those within the cropland vector polygons are retained for prediction. Building on this, a semi-supervised cross-domain contrastive learning framework is proposed for remote sensing scene classification. Ultimately, by integrating nonagriculturalization rules and postprocessing techniques, areas undergoing nonagriculturalization are further detected. Validating our methodology on Wuxi and Yangzhou datasets yielded precision rates of 91.57% and 89.21%, with recall rates of 93.68% and 90.51%, respectively. These outcomes affirm the effectiveness of our method in nonagriculturalization detection, offering robust technical support for research in this domain.

The redistribution of cropland to areas of higher elevation in China has long affected agricultural development and could seriously threaten national food security. However, there is currently little research reported on this phenomenon, which may limit the improvement of cropland protection policies. To fill this gap, we analyzed the spatiotemporal characteristics and driving mechanisms of increased cropland elevation in China during the period 1980–2020. The average cropland elevation in China increased by 17.38 m from 1980 to 2020. The gravity center of the cropland area and average cropland elevation in China moved to the northwest by 81.00 km and 51.47 km, respectively. The amount of newly added cropland in eastern China was less than that in occupied regions; however, the average elevation of newly added cropland was greater than that of occupied cropland, though the opposite phenomenon was observed in western China. Slope, temperature, land-use intensity, population, economic density, and distance to main roads were the main factors affecting the redistribution of cropland to areas of higher elevation. The effects of these major driving factors exhibited significant spatial and temporal variations in China. This study has important implications for improving existing cropland protection policies and developing more effective cropland management systems in China.

Precision agriculture which facilitates and enables crop management through site-specific recommendations, is essential to optimize agricultural inputs in space and time. In this study, we used Landsat and MODIS-NDVI product data with climatic, topographic data and laboratory-analyzed soil samples to map the spatial distribution of seven soil properties; soil texture (T), electrical conductivity (EC), potential hydrogen (pH), nitrogen (N), phosphorus (P), potassium (K), and organic matter (OM) in the Punjab, Pakistan from 2000 to 2020. We examined and compared three statistical prediction models: the support vector machine (SVM), the random forest regression (RFR), and the multiple linear regression (MLR). The predictions were validated against a separate set of soil samples while considering the modeling region and an extrapolation area. Model performance statistics showed that the RFR often provided the highest accuracy, with the machine learning algorithms performing slightly better than the MLR. It was discovered that one obstacle to accurately forecasting soil parameters at unsampled areas with MLR was its inability to handle non-linear connections between independent and dependent variables. The results indicate that the cultivated area decreased from 43.16 % in 2000 to 38.24% in 2020. The soil has a high level of EC due to salinity. In general, the soils contained

● Soil N mineralization (Nmin) rates varied spatially among cropland fields.● Soil Nmin rates increased with a decreasing elevation.● Soil Nmin was mainly affected by SOC, TN, and available C and N.● Nmin in cropland soil should be considered when evaluating regional water pollution. Soil nitrogen mineralization (Nmin) is a key process that converts organic N into mineral N that controls soil N availability to plants. However, regional assessments of soil Nmin in cropland and its affecting factors are lacking, especially in relation to variation in elevation. In this study, a 4-week incubation experiment was implemented to measure net soil Nmin rate, gross nitrification (Nit) rate and corresponding soil abiotic properties in five field soils (A–C, maize; D, flue-cured tobacco; and E, vegetables; with elevation decreasing from A to E) from different altitudes in a typical intensive agricultural area in Dali City, Yunnan Province, China. The results showed that soil Nmin rate ranged from 0.10 to 0.17 mg·kg−1·d−1 N, with the highest value observed in field E, followed by fields D, C, B, and A, which indicated that soil Nmin and Nit rates varied between fields, decreasing with elevation. The soil Nit rate ranged from 434.2 to 827.1 µg·kg−1·h−1 N, with the highest value determined in field D, followed by those in fields E, C, B, and A. The rates of soil Nmin and Nit were positively correlated with several key soil parameters, including total soil N, dissolved organic carbon and dissolved inorganic N across all fields, which indicated that soil variables regulated soil Nmin and Nit in cropland fields. In addition, a strong positive relationship was observed between soil Nmin and Nit. These findings provide a greater understanding of the response of soil Nmin among cropland fields related to spatial variation. It is suggested that the soil Nmin from cropland should be considered in the evaluation of the N transformations at the regional scale.

In the face of global population growth and climate change, the protection and rational utilization of cropland are crucial for food security and ecological balance. However, the complex topography and unique ecological environment of the Qinghai-Tibet Plateau results in a lack of high-precision cropland monitoring data. Therefore, this paper constructs a high-quality cropland dataset for the YarlungZangbo-Lhasa-Nyangqv River region of the Qinghai-Tibet Plateau and proposes an MSC-ResUNet model for cropland extraction based on Landsat data. The dataset is annotated at the pixel level, comprising 61 Landsat 8 images in 2023. The MSC-ResUNet model innovatively combines multiscale features through residual connections and multiscale skip connections, effectively capturing features ranging from low-level spatial details to high-level semantic information and further enhances performance by incorporating depthwise separable convolutions as part of the feature fusion process. Experimental results indicate that MSC-ResUNet achieves superior accuracy compared to other models, with F1 scores of 0.826 and 0.856, and MCC values of 0.816 and 0.847, in regional robustness and temporal transferability tests, respectively. Performance analysis across different months and band combinations demonstrates that the model maintains high recognition accuracy during both growing and non-growing seasons, despite the study area’s complex landforms and diverse crops.

Since China’s reform and opening up in 1978, the reclamation and abandonment of cropland in Xinjiang have become significant features of the land use change in the arid land of Northwest China. However, the spatiotemporal changes and driving mechanisms of cropland reclamation and abandonment over long time periods are still unclear, but this is crucial in understanding cropland changes in inland arid land, providing important insights for land management and agricultural development. Based on 40 years of remote sensing data on resources and the environment, this study examines the spatiotemporal characteristics of cropland reclamation and abandonment in Xinjiang over four periods since 1980. Additionally, it uses an optimal parameter geographical detector model to quantify the driving factors for each period. The results indicate that cropland reclamation experiences a “slow decrease–rapid increase” trend, forming a “V-shaped” pattern, while abandonment shows a “rapid decrease–slow decrease–slow increase” trend, forming a “U-shaped” pattern. These trends can be divided into three periods: 1980–1990 (unstable growth), 1990–2010 (stable growth), and 2010–2020 (growth with constraints). The movement pattern of cropland reclamation’s center of gravity is “slightly southeast–slightly northeast–southwest”, whereas the abandonment’s center of gravity shifts “northeast–southwest–northeast”. Further analysis reveals that the impact of agricultural technological investment and infrastructure on cropland reclamation has increased, while the influence of natural environmental factors has decreased. Although climate and water resources remain key factors in cropland abandonment, the influence of economic and social factors has gradually diminished, and the impact of agricultural mechanization has steadily risen.

Accurate cropland distribution data are essential for efficiently planning production layouts, optimizing farmland use, and improving crop planting efficiency and yield. Although reliable cropland data are crucial for supporting modern regional agricultural monitoring and management, cropland data extracted directly from existing global land use/cover products present uncertainties in local regions. This study evaluated the area consistency, spatial pattern overlap, and positional accuracy of cropland distribution data from six high-resolution land use/cover products from approximately 2020 in the alpine agricultural regions of the Hehuang Valley and middle basin of the Yarlung Zangbo River (YZR) and its tributaries (Lhasa and Nianchu Rivers) area on the Qinghai-Tibet Plateau. The results indicated that (1) in terms of area consistency analysis, European Space Agency (ESA) WorldCover cropland distribution data exhibited the best performance among the 10 m resolution products, while GlobeLand30 cropland distribution data performed the best among the 30 m resolution products, despite a significant overestimation of the cropland area. (2) In terms of spatial pattern overlap analysis, AI Earth 10-Meter Land Cover Classification Dataset (AIEC) cropland distribution data performed the best among the 10 m resolution products, followed closely by ESA WorldCover, while the China Land Cover Dataset (CLCD) performed the best for the Hehuang Valley and GlobeLand30 performed the best for the YZR area among the 30 m resolution products. (3) In terms of positional accuracy analysis, the ESA WorldCover cropland distribution data performed the best among the 10 m resolution products, while GlobeLand30 data performed the best among the 30 m resolution products. Considering the area consistency, spatial pattern overlap, and positional accuracy, GlobeLand30 and ESA WorldCover cropland distribution data performed best at 30 m and 10 m resolutions, respectively. These findings provide a valuable reference for selecting cropland products and can promote refined cropland mapping of the Hehuang Valley and YZR area.

Abstract Changes in land surface albedo can alter ecosystem energy balance and potentially influence climate. We examined the albedo of six bioenergy cropping systems in southwest Michigan USA: monocultures of energy sorghum (Sorghum bicolor), switchgrass (Panicum virgatum L.), and giant miscanthus (Miscanthus × giganteus), and polycultures of native grasses, early successional vegetation, and restored prairie. Direct field measurements of surface albedo (αs) from May 2018 through December 2020 at half‐hourly intervals in each system quantified the magnitudes and seasonal differences in albedo (∆α) and albedo‐induced radiative forcing (RF∆α). We used a nearby forest as a historical native cover type to estimate reference albedo and RF∆α change upon original land use conversion, and a continuous no‐till maize (Zea mays L.) system as a contemporary reference to estimate change upon conversion from annual row crops. Annually, αs differed significantly (p > miscanthus (0.271 ± 0.009) ≈ energy sorghum (0.270 ± 0.010) ≥ switchgrass (0.265 ± 0.009) ≈ restored prairie (0.264 ± 0.012) > native grasses (0.259 ± 0.010) > maize (0.247 ± 0.010). Reference forest had the lowest annual αs (0.134 ± 0.003). Albedo differences among crops during the growing season were also statistically significant, with growing season αs in perennial crops and energy sorghum on average ~20% higher (0.206 ± 0.003) than in no‐till maize (0.184 ± 0.002). Average non‐growing season (NGS) αs (0.370 ± 0.020) was much higher than growing season αs (0.203 ± 0.003) but these NGS differences were not significant. Overall, the original conversion of reference forest and maize landscapes to perennials provided a cooling effect on the local climate (RFαMAIZE: −3.83 ± 1.00 W m−2; RFαFOREST: −16.75 ± 3.01 W m−2). Significant differences among cropping systems suggest an additional management intervention for maximizing the positive climate benefit of bioenergy crops, with cellulosic crops on average ~9.1% more reflective than no‐till maize, which itself was about twice as reflective as the reference forest.

Land-use and plant invasion influence biodiversity. Understanding the effects of land-use types and invasive plants on the ecosystem is crucial for better management and the development of strategic plans for increasing biodiversity in Jeju Island, Korea, a designated Biosphere Reserve by the United Nations Education, Scientific, and Cultural Organization. The effect of the most dominant invasive exotic species, Hypochaeris radicata, on the four land-use types of Jeju Island was investigated. Plant composition, soil characteristics, and plant diversity among four land-use types (cropland, green space, neglected land, and residential) were compared. Among the land-use types, croplands had the most diverse plant composition and the highest richness in exotic and native plant species. Croplands, such as tangerine orchards, which are widely distributed throughout Jeju Island, showed the highest plant diversity because of medium intensity disturbance caused by weed removal. The relative cover of H. radicata did not differ between land-use types. However, H. radicata invasion was negatively related with plant species richness, making this invasive species a threat to the biodiversity of native herbs present in land-use areas. H. radicata adapts to areas with a broad range of soil properties and a variety of land-use types. Therefore, it is crucial to monitor land-use types and patterns of plant invasion to guide the implementation of consistent management and conservation strategies for maintaining ecosystem integrity of the transformed habitat in Jeju Island.

Abstract Miscanthus spp. show excellent application prospects due to its bioenergy potential and multiple ecological services. Annual N export with biomass harvest from Miscanthus, even without fertilizer supplement, do not reduce soil N levels. The question arises regarding how Miscanthus can maintain stable soil N levels. Metagenomic strategies were used to reveal soil N‐cycling‐related microbiome and their functional contributions to processes of soil N‐cycling based on the comparison among the bare land, cropland, 10‐year Miscanthus × giganteus, and 15‐year Miscanthus sacchariflorus fields. The results showed that, after long‐term cropland‐to‐Miscanthus conversion (LCMC), 16 of 21 bacterial phyla and all the archaeal phyla exhibited significant changes. Soil microbial denitrification and nitrification functions were significantly weakened, and N fixation (NF) was significantly enhanced. The biosynthesis of amino acids, especially alanine, aspartate, and glutamate metabolism, in soil N‐cycling‐related microbiome was dramatically promoted. The genus Anaeromyxobacter contributed largely to the NF process after LCMC. Variations in the soil available potassium, available N, organic C, and total N contents drove a functional shift of soil microbiome from cropland to Miscanthus pattern. We conclude that Miscanthus can recruit Anaeromyxobacter communities to enhance NF benefiting its biomass sustainability and soil N balance.

Reducing nonpoint-source pollution and improving soil quality by applying best management practices (BMPs) on sloping farmland are essential for ensuring water environmental safety and sustainable agricultural development. However, little is known about the accumulative effect of the long-term application of BMPs on reducing pollution and improving soil quality in major land use types of sloping farmland in watersheds. We conducted a nine-year field experiment for citrus orchard and cropland of sloping farmland in the Three Gorges Reservoir area of China. The treatment settings for the citrus experimental plots were as follows: (1) citrus intercropped with white clover (WC), (2) citrus orchard soil mulched with straw (SM), (3) citrus intercropped with Hemerocallis flava contour hedgerows (HF) and (4) conventional citrus planting pattern (C-CK). The treatment settings for the crop experimental plots were as follows: (1) wheat-peanut rotation intercropped with Toona sinensis contour hedgerows (TS), (2) wheat-peanut rotation intercropped with alfalfa contour hedgerows (AF), (3) ryegrass-sesame rotation (RS) and (4) conventional wheat-peanut rotation (W-CK). We found that compared with C-CK and W-CK, (1) runoff, sediment, total nitrogen and total phosphorus losses were reduced by 18.0 %, 66.1 %, 35.6 % and 40.2 %, respectively, in WC; by 22.4 %, 61.2 %, 43.9 % and 52.4 %, respectively, in SM; by 25.4%, 69.5 %, 52.0 % and 58.1 %, respectively, in HF; by 45.1 %, 73.7 %, 64.6 % and 66.0 %, respectively, in TS; by 41.8 %, 67.4 %, 59.3 % and 60.8 %, respectively, in AF; and by 24.1 %, 42.9 %, 39.3 % and 40.3 %, respectively, in RS; (2) the soil quality index values of the BMP treatments increased; and (3) the application of these BMPs did not significantly affect yield or product value. We suggest the application of these BMPs on sloping farmland to (1) efficiently reduce total runoff, total sediment, and soil total nitrogen and total phosphorus losses; (2) improve soil quality; and (3) have no significant effect on output, of which the BMP HF applied to citrus orchard and the BMP TS applied to cropland are the most effective.

Analysing China's cropland changes highlights the intertwined dynamics of urbanization, industrialization, and ecological policies, offering insights into cropland efficiency and advancing agricultural modernization. This study analysed China's county-level cropland area and landscape fragmentation from 1990 to 2021 using land use data. The results revealed an uneven cropland distribution in China, which was mainly concentrated in regions such as Northeast China, the North China Plain, the Sichuan Basin, and the Middle-Lower Yangtze Plains, with most cropland occurring north of the Qinling Mountains-Huaihe River boundary. In the past three decades, China's cropland area has decreased by 8.68 million ha. Approximately 72.14% of the counties experienced a decline, and 24 provinces reported a net loss. Notably, there was large-scale land use type replacement in China, which was primarily manifested as the spatial displacement of cropland with forest, grassland, and impervious surfaces. As a result, the net reduction in cropland accounted for only 22.24% of the total cropland decrease. From 1990 to 2021, 52.04% of China's county-level cropland exhibited no significant changes in total area (TA) or patch density (PD), maintaining a stable cropland landscape. A total of 20.70% of the counties had significant TA reductions without PD changes, primarily in the Huang-Huai-Hai Plain. Counties without TA changes but significant PD declines represented 11.72%, mainly distributed in transitional zones of the second and third steps of China's terrain, the Loess Plateau, the Yunnan-Guizhou Plateau, and some areas of the Southeast Hills. Counties with significant TA reductions and PD increases accounted for 7.79%, primarily found in the border areas between Henan and Anhui, as well as Jiangsu. Thus, whether cropland loss in China over the past 30 years has led to increased fragmentation has shown clear spatial heterogeneity. This study provides scientific support for guiding cropland utilization to enhance sustainability and resilience in China.

National soil organic carbon (SOC) maps are essential to improve greenhouse gas accounting and support climate-smart agriculture. Large-scale SOC models based on wall-to-wall soil information from remote sensing remain a challenge due to the high diversity of natural soil conditions and the difficulty of accounting for the spatial location of the soil samples. In this study, we tested if the implementation of local ensemble models (LEM) can be used to improve the SOC predictions from Landsat-based soil reflectance composites (SRC) for Germany. For this, we divided the research area into 30 times 30 km tiles and calculated local generalized linear models (GLM) based on random, nearby observations. Based on the GLMs, local SOC maps were predicted and aggregated using a moving window approach. The local variable importance was analyzed to identify spatial dependencies in the correlation between the SRC and SOC. For the final SOC map, a Random Forest (RF) model was trained using the aggregated local SOC predictions, the SRC, and a full set of training samples from the agricultural soil inventory. The results show that the LEM was able to improve the accuracy (R2 = 0.68; RMSE = 5.6 g kg−1), compared to the maps based on a single, global model (R2 = 0.52; RMSE = 6.8 g kg−1). The local variable importance of the spectral bands showed clear spatial patterns throughout the research area. Differences can be explained by the local soil conditions, influencing the correlation between SOC and the spectral properties. Compared to the widely adopted integration of distance covariates such as geographical coordinates, the LEM was able the reduce the spatial autocorrelation to a greater extent and to improve the prediction accuracy, especially for underrepresented SOC values. The LEM presents a new method to integrate spatial information and increase the interpretability of DSM models.

Biodiversity in agroforestry systems contributes to diversified socio-economic and cultural dimensions and to managing biodiversity. Agroforestry is one of Ethiopia's highest conservation priorities to promote forest-based ecosystem services and environmental incentive programs that minimize deforestation. This study was carried out across intensively cultivated areas of Midakegn district to assess the woody species diversity potential of agroforestry systems managed by farmers. Out of 114 plots laid down on transect lines, 33 (30 × 30 m), 44 (50 × 50 m), and 9 (10 × 10 m) plots belonged to grazing land, cropland, and woodlots, respectively, and 28 representative homegardens were systematically selected. For each sample of data encountered, the diameter and height of woody species were measured. Species richness, Shannon diversity, and evenness indices were used for diversity analysis and the mean values/land unit were calculated using a one-way ANOVA, and Tukey's pairwise comparison was done through SPSS statistics at (P

ABSTRACTMapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

Food security, a crucial issue for the development of humankind, is often severely constrained by water scarcity. As a globally recognized most advanced agricultural water-saving technology, drip irrigation under plastic mulch (DIPM) has played a significant role in grain production. However, a comprehensive review of the dual impacts of this practice in farmland remains lacking. This study has conducted an exhaustive review of DIPM research from 1999 to 2023 and employed CiteSpace software to perform a co-occurrence and clustering analysis of keywords in order to reveal research hotspots and trends. The results show that the attention to DIPM technology has increased annually and reached a peak in 2022. China leads in the number of publications in this field, reflecting its emphasis on agricultural water-saving technologies. This study critically discusses the dual impacts of DIPM on farmland. On the positive side, DIPM can improve soil temperature and moisture, enhance nutrient availability, promote water and nutrient absorption by roots, and increase the crop growth rate and yield while reducing evaporation and nitrogen loss, suppressing weed growth, decreasing herbicide usage, and lowering total greenhouse gas emissions. On the negative side, it will cause pollution from plastic mulch residues, damage the soil structure, have impacts on crop growth, and lead to increased clogging of drip irrigation systems, which will increase agricultural costs and energy consumption, hinder crop growth, hamper soil salinization management, and further reduce the groundwater level. The future development of DIPM technology requires optimization and advancement. Such strategies as mechanized residual-mulch recovery, biodegradable mulch substitution, aerated drip irrigation technology, and alternate irrigation are proposed to address existing issues in farmland triggered by DIPM. This review advocates for the active exploration of farming management practices superior to DIPM for future agricultural development. These practices could lead to higher yields, water–nitrogen efficiency, and lower environmental impact in agricultural development.

Food security is a major challenge for China at present and will be in the future. Revealing the spatiotemporal changes in cropland and identifying their driving forces would be helpful for decision-making to maintain grain supply and sustainable development. Hainan Island is endowed with rich agricultural resources due to its unique climatic conditions and is facing tremendous pressure in cropland protection due to the huge variation in natural conditions and human activities over the past few decades. The purpose of this study is to assess the spatiotemporal changes in and driving forces of cropland on Hainan Island in the past and predict future cropland changes under different scenarios. Key findings are as follows: (1) From 2000 to 2020, the cropland area on Hainan Island decreased by 956.22 km2, causing the center of cropland to shift southwestward by 8.20 km. This reduction mainly transformed into construction land and woodland, particularly evident in coastal areas. (2) Among anthropogenic factors, the increase in the human footprint is the primary reason for the decrease in cropland. Land use changes driven by population growth, especially in economically active and densely populated coastal areas, are key factors in this decrease. Natural factors such as topography and climate change also significantly impact cropland changes. (3) Future scenarios show significant differences in cropland area changes. In the natural development scenario, the cropland area is expected to continue decreasing to 597 km2, while in the ecological protection scenario, cropland conversion is restricted to 269.11 km2; however, in the cropland protection scenario, the trend of cropland reduction is reversed, increasing by 448.75 km2. Our findings provide a deep understanding of the driving forces behind cropland changes and, through future scenario analysis, demonstrate the potential changes in cropland area under different policy choices. These insights are crucial for formulating sound land management and agricultural policies to protect cropland resources, maintain food security, and promote ecological balance.

Soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and total potassium (TK) are important indicators for evaluating soil fertility. Exploring the content and spatial distribution of these indicators is of great significance for optimizing cropland management measures and developing sustainable agriculture. Yunnan Province is one of the most important agricultural regions in southwestern China, characterized by large variations in the topography and an uneven distribution of soil fertility. In this study, the data of 8571 topsoil (0–20 cm) samples selected from Yunnan Province and a portion of related spatial data were used to carry out electronic mapping of the spatial distribution of soil sand content, clay content, silt content, SOC, TN, TP, TK content, and C:N ratio at 1 km resolution using the Random Forest (RF) model. The results indicated that the average measured contents of SOC, TN, TP, TK, and C:N ratio in the topsoil of Yunnan Province were 18.78 ± 0.09 g/kg, 1.78 ± 0.01 g/kg, 0.98 ± 0.01 g/kg, 13.89 ± 0.08 g/kg, and 10.56 ± 0.02, respectively. The spatial analysis showed that higher SOC was mainly distributed in northern and eastern Yunnan, and the spatial distribution pattern of TN and TP was similar to that of SOC. While higher TK was mainly distributed in southwestern Yunnan Province. There was a significant positive correlation between SOC and TN and TP contents with correlation coefficients of 0.889 and 0.463, however, there was a significant negative correlation between SOC and TK content with correlation coefficients of −0.060. It was also indicated that elevation, temperature, precipitation, clay content, sand content, and silt content were the most important factors affecting SOC, TN, TP, and TK content. The present study provided an understanding of soil nutrients characteristics and their affecting factors, which is helpful to optimize agricultural management practices and develop sustainable agriculture according to soil fertility.

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.

This study investigates the 40-year spatiotemporal evolution of cropland in Northeast China’s black soil region at the county scale. Utilizing land use/cover maps from 1980 to 2020 with a 30 m × 30 m resolution, we employed various analytical methods, including mathematical statistics, GIS spatial analysis, land use transition matrix, landscape pattern analysis, and hotspot analysis. The findings of this study are as follows: (1) Cropland area expanded by 51,976.76 km2 from 1980 to 2020, mainly concentrated in the Sanjiang Plain, Songnen Plain, and Liaohe Plain. Notably, areas near prefecture-level city locations experienced a decrease in cropland, while regions farther from cities witnessed an increase. (2) Cropland primarily transitioned from woodland, grassland, and unused land to cropland, covering substantial areas. Conversely, cropland was converted mainly into woodland, built-up land, and grassland. (3) Over the same period, cropland in the region exhibited increased elevation and slope, with average altitude rising by 2.06 m and average slope increasing by 0.0369 degrees. (4) The study revealed an increase in cropland proportion, predominance, and aggregation, alongside more irregular shapes and reduced subdivision. These findings highlight significant changes in the cropland landscape in Northeast China’s black soil region and offer insights for policy recommendations and land management strategies. The research findings of this paper can offer valuable insights for the protection and utilization of cropland in the region. They can provide scientific references for the formulation of policies related to China’s food security.

Rapid urbanization currently poses a threat to cropland areas. Therefore, exploring the pattern of change in cropland areas and its driving mechanism is of great significance in safeguarding regional economic development and food security. In this study, data regarding cropland in the Changsha-Zhuzhou-Xiangtan city group (CZTCG) from 2000 to 2020 were collected, and dynamic changes in cropland in the past 20 years were analyzed. Based on this, the mechanisms that drive cropland change were explored in the context of multiple dimensions. Furthermore, an Inertial Development Scenario and a Cropland Priority Scenario were designed to simulate land use/land cover (LULC) changes in the CZTCG in 2025 and 2035, and, in particular, to analyze the characteristics of future spatiotemporal changes of cropland. The results demonstrated that the cropland area in the CZTCG in the past 20 years has shown a two-stage trend of increasing (2000–2015, increase of 8.25%) and decreasing (2015–2020, decrease of 2.91%), and the main reasons for cropland loss in the whole process of change were forest and building, with serious loss of cropland area in Changsha County and Wangcheng District. Between 2000 and 2020, socio-economic factors (GDP, total agricultural output), topography (elevation, slope), and climate change (average annual temperature, annual precipitation) were highly correlated with changes in cropland area, exhibiting significant temporal and regional variability. The simulation results indicate that until 2035, the Cropland Priority Scenario shows a significant increase in the area of cropland in the study area compared to the Inertial Development Scenario. Consequently, the implementation of a strategy that prioritizes cropland can effectively protect it, while helping to promote ecological development and regional socio-economic development.

Abstract Nitrous oxide (N2O) is the third important long‐lived greenhouse gas next to carbon dioxide and methane and croplands are considered biogeochemical hotspots of soil N2O emissions. To reduce soil N2O and other greenhouse emissions, climate‐smart agricultural practices including biochar application have been applied. Many studies have been conducted with biochar application but results from these studies are not conclusive. To address this issue, meta‐analysis, a quantitative review that synthesizes results from multiple independent studies, has been widely used. The results from different meta‐analyses also differ but are seldomly evaluated. In this study, we evaluated meta‐analyses on the effects of biochar application on soil N2O emissions. A grand mean response ratio (RR) was further proposed to estimate an overall effect and the impacts of experiment setting, properties of biochar and soil, and agricultural practices. We found 18 meta‐analysis papers were published between 2014 and 2022. Sample size (publications or experiments) varied from less than 30 to more than 1000, with a mean sample size of 275. RR was calculated in all studies except one. While four meta‐analyses did not find a significant effect of biochar application on soil N2O emissions, all others reported reductions of soil N2O emissions, but the magnitude ranged from −10.5% to −54.8%. Synthesizing all results from these meta‐analyses, we found that biochar application overall significantly reduced the soil N2O emissions by 38.8%. The impacts increased with experimental duration till one and half years and reduced after that. Biochar application rate and C:N ratio had large influence on the effects of biochar application on soil N2O emissions. This study demonstrated that while meta‐analysis provides a more comprehensive and better estimation, the inconsistence among these studies may need to be further evaluated. A grand mean RR based on meta‐analyses could be more accurate and representative than single meta‐analysis.

Abstract Since the beginning of the Holocene era, human activities have seriously impacted animal habitats and vegetative environments. Species that are dependent on natural habitats or with narrow niches might be more severely affected by habitat changes. Malcus inconspicuus is distributed in subtropical China and highly dependent on the mountain environment. Our study investigated the role of the mountainous landscape in the historical evolution of M. inconspicuus and the impact of Holocene human activities on it. A phylogeographical approach was implemented with integrative datasets including double‐digest restriction site‐associated DNA (ddRAD), mitochondrial data, and distribution data. Three obvious clades and an east–west phylogeographical pattern were found in subtropical China. Mountainous landscape has “multifaceted” effects on the evolutionary history of M. inconspicuus, it has contributed to population differentiation, provided glacial refuges, and provided population expansion corridors during the postglacial period. The effective population size (Ne) of M. inconspicuus showed a sharp decline during the Holocene era, which revealed a significantly negative correlation with the development of cropland in a hilly area at the same time and space. It supported that the species which are highly dependent on natural habitats might undergo greater impact when the habitat was damaged by agricultural activities and we should pay more attention to them, especially in the land development of their distribution areas.

【Objective】Quickly gathering information of accurate cropland distribution and spatial and temporal change is an important technical means for scientific protection and effective management of cropland resources, which is an important guiding significance for ensuring regional food security and formulating cropland protection policies. The study of spatial and temporal change of cropland in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA) can provide data support for local cropland conservation and food security assurance.【Method】Based on the multi-temporal Landsat remote sensing image, the support vector machine method was used to obtain the land cover types and cropland distribution of the GBA from 2010 to 2020. Then, the characteristics of spatial and temporal changes of cropland in the GBA were analyzed from three aspects: quantitative changes, spatial transfer and landscape patterns.【Result】In terms of quantitative changes, from 2010to 2020, the area of cropland in the GBA showed a trend of decreasing first and then increasing, from 16 155.56 km2 in 2010 to 15 740.54 km2 in 2015, and then increasing to 16 473.93 km2 in 2020. At the municipal scale, the net increase of cropland in Jiangmen is the largest, with the net increase area of 176.99 km2, and the net decrease of cropland in Dongguan is the largest, with the net decrease area of 74.68 km2. In terms of spatial transfer, the area of 60.25% of the increased cropland were coming from forest, and the area of 35.85% of the increased cropland were coming from water body. Meanwhile, the area of 49.53%of the decrease cropland were converted to forest, and the area of the 43.51% of the decrease cropland were converted to impervious surface. In terms of landscape patterns, the patch density aggregation index of cropland in the GBA showed a trend of increasing and then decreasing.【Conclusion】In total, the change of cropland resources in the GBA maintained a dynamic balance from 2010 to 2020. The increased areas of cropland were mainly coming from forest and water body, and the decrease areas of cropland were converted to forest and impervious surface. Besides, landscape pattern of cropland generally tended to be less fragmented and more aggregated.

Mapping land use and delimiting study areas in the peripheral zones of cities has been one of the central themes of the literature on urban and territorial studiesin Latin American countries with difficult access to data where transformation processes are accelerated. The objective of this study was to propose an applicable methodology for the detection of home garden areas in rural areas of Cuenca. It was based onthe use of Sentinel-2 satellite images and unsupervised classification processes to identify possible cultivation areas. Based on this, spatial criteria were defined to classify and identify home gardens, based on the characteristics of the gardens themselves. A total of 699 gardens were identified in the field. Therefore, this study is a first approximation to the identification of home gardens in rural areas and constitutes very useful information for local governments, who are responsible for promoting these activities and in turn improving the production, mobilization,and marketing conditions of the vulnerable group that engages in subsistence agriculture. Thus, this research can be replicated in different localities and geographical contexts.

Recent declines of grassland bird populations in North America are linked to habitat loss and fragmentation associated with agricultural practices. One tool used to conserve soil, water and wildlife habitat on agricultural fields is the U.S. Department of Agriculture’s Conservation Reserve Program (CRP), the largest agricultural conservation program in the United States. Managers and conservationists recognize CRP as an important component of conserving grassland birds in the central portion of the United States. However, recent widespread expiration of CRP contracts could negatively influence grassland bird populations. Few studies have evaluated how former CRP-enrolled fields may function as grassland bird habitat. In this paper, we analyzed data from a long-term (1990–2017) study aimed at comparing grassland bird abundance (24 species) between idled CRP grasslands and fields where the CRP contracts expired. Some of these fields where contracts expired were maintained as pasture or hayland, and others were converted back to cropland. Estimated abundances of most species were considerably higher in idled CRP than in fields with expired CRP contracts. Post-CRP land use also appeared to affect most bird abundances, with lower abundance in grazed grasslands and haylands relative to idled CRP, but higher abundance than cropland. The responses of obligate and facultative grassland specialists to post-CRP management varied among species, with some being negative and some being positive depending on post-CRP land use, which is unsurprising given the variable habitat requirements of grassland birds. Our results have implications for wildlife managers who must design conservation strategies around the land use decisions of private landowners. Our results support the idea of maintaining a mosaic of undisturbed CRP grasslands and post-CRP grasslands that are hayed or grazed, which should guarantee some undisturbed nesting cover in the landscape for some bird species and some disturbed grasslands that may have long-term benefits for other species.

In mining areas with high groundwater tables, mining subsidence can lead to the inundation of cropland by water, causing damage to cropland and posing a threat to national food security. The implementation of concurrent mining and reclamation techniques can effectively enhance the reclamation rate of cropland. This technique requires engineers to initiate reclamation measures before cropland waterlogging occurs. Therefore, when mining a panel underground, an accurate calculation of the time when cropland becomes waterlogged, known as the theoretical reclamation timing, is crucial. To address this issue, this study proposes a computational method for the theoretical reclamation timing of cropland under the conditions of single-panel mining based on intelligent optimization algorithms. In addition, this paper also proposes an optimization method for the theoretical reclamation timing of cropland within a district based on an intelligent optimization algorithm. Utilizing this method makes optimizing the layout of multiple panels possible, thereby delaying the theoretical reclamation timing for cropland within a district. This approach aims to shorten the duration of reclamation projects and minimize their interference with agricultural activities. Through experimental validation, this paper demonstrates the reliability of these two methods. This study is beneficial for the rational planning of reclamation projects.