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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. [ABSTRACT FROM AUTHOR]

The Rothamsted Carbon (RothC) model was developed to simulate soil organic carbon dynamics under different types of land use, different climate conditions, and different agricultural technologies. The model has been effectively applied across different scales and in different environmental zones; however, it is sensitive to key input variables. This study estimates the sensitivity of soil carbon storage projections in response to variations in climate inputs using the RothC model. Two climate data sets were used for the sensitivity analysis: the Climatic Research Unit (CRU) dataset (CRU TS v4.05, 1901–2020), which has a spatial resolution of about 50 × 50 km2 and is derived from interpolated data from ground-based weather stations, and TerraClimate database, which is built based on CRU data and enhances the resolution to around 4 × 4 km2 through additional modeling and data inputs. Sequestration estimates for Rostov oblast have been derived using the CRU database. The accuracy of estimates for Rostov oblast is of particular importance, since the proportion of Chernozems in the total land structure is almost 65%. The sensitivity analysis has revealed that the replacement of the CRU data with TerraClimate meteorological data significantly underestimates the sequestration rate. On average, this underestimation was about 100 kg C ha–1 year–1 across all scenarios. For application in Rostov oblast, the TerraClimate database should be adjusted, particularly in the range of minimum precipitation values, using data from local meteorological stations or other models. [ABSTRACT FROM AUTHOR]

ABSTRACT Accurate information on cropland area is essential for agricultural development and planning. Here, we evaluated the performance of six land cover products (CLCD, MCD12Q1, CCI‐LC, GLC‐FCS, CNLUCC, and CACD) in identifying cropland extent in a large irrigation district of China from 1995 to 2020 based on visual interpretation samples. The results indicate that CACD performs best with an average kappa coefficient (KC) of 0.89, followed by CLCD (KC = 0.87), GLC‐FCS (KC = 0.75), CNLUCC (KC = 0.65), MCD12Q1 (KC = 0.49), and CCI‐LC (KC = 0.29). Additionally, the cropland area provided by statistical yearbooks is significantly lower than that identified by CACD, with an average underestimation of −34%. We also find that these land cover products exhibit poor consistency in identifying cropland. The average percentage of grids labeled as “completely consistent”—where all six products identify those grids as cropland—is only 16.0% across the entire irrigation district, highlighting the uncertainty of existing land cover products in identifying cropland areas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Using the US Geological Survey groundwater model, we compared spatial distribution, flow exchange, and storage of groundwater between cropland and forestland in the Upper Yazoo River Watershed, Mississippi, from 1900 to 2014. Under normal climate, average groundwater head declined 2.7 m in cropland but only 1 m in forestland over the 115 years. The average groundwater flow from forestland to cropland surpassed the reverse flow by a factor of 238, owing to a higher elevation in forestland and intensive groundwater pumping in cropland. Cropland was a net groundwater sink while forestland was a net groundwater source under all climates. Our findings underscore the discernible impacts of climate changes on groundwater storage and suggest that afforestation in the region would be an alternative for saving groundwater resources and supplying more waters to croplands. This study offers valuable insights into groundwater supply planning not only in Mississippi but also in comparable situations worldwide. [ABSTRACT FROM AUTHOR]

Land use and land cover change determined by numerous situation specific factors at different locations and times. In Ethiopia inappropriate land uses land cover changes become pressing challenges. Similarly, in the Gurage zone, there is a significant change from agriculture to Eucalyptus plantations. Therefore, this study investigates the direct and indirect drivers of the change, as well as factors affecting farmers' decisions regarding the conversion to provide important policy input. The data collected from 311 households through household surveys, key informant interviews (KIIs), and focused group discussions (FGDs). Descriptive statistics and a binary logit model used for analysis. The result indicated that the direct driver for this land conversion included the ability to generate high income from forest, soil infertility, and increasing demand for forest products. On the other hand, the allelopathic effect of neighboring plantations, lack of adequate agricultural technology and increased accessibility to forest products market were the top indirect drivers. The binary logit results show that farmers' decision to convert agricultural lands to forestland is significantly influenced by land size, forest income, education level, and years lived in the area. The findings suggest creating awareness about appropriate land use techniques to sustain the development. [ABSTRACT FROM AUTHOR]

Understanding the status of soil properties and revealing their spatiotemporal variation patterns in study areas are important for achieving precision agriculture and promoting the steady improvement of farmland quality. By combining field survey sampling with ArcGIS spatial interpolation, the change rate, correlation, and transfer matrix were applied to analyze the spatiotemporal variations in soil properties in Feixi County farmlands in 2010 and 2022. The average soil pH increased from 5.80 to 5.96, maintaining weak acidity. The analyzed levels remained moderate, and the average available phosphorus (AP) decreased by 5.31 mg·kg−1. The average organic matter (OM), total nitrogen (TN), and available potassium (AK) increased by 4.89 g·kg−1, 0.23 g·kg−1, and 16.41 mg·kg−1, respectively. Soil nutrient contents were higher in the coastal areas of Chaohu Lake. The coefficients of variation and the rate of change of OM, TN, and AK were similar, whereas the coefficient of variation of pH was relatively small. The coefficients of variation and the rate of change for AP were relatively high. Moderate-level soil properties significantly changed in the area, with direct conversion between low and high levels. The spatial and temporal characteristics of OM and TN were similar. In the absence of targeted agricultural technical guidance, the soil property grade is usually medium, the soil property grade will fluctuate up and down without direction, and there is a direct transition between low and high content. Taking Feixi County as a case study in East China, the research results clearly show the changes and trends of major nutrient elements, providing a research idea for cities in East Asia, which mainly engage in rice cultivation and in the development of agriculture and urbanization, and providing data support and references for the future soil nutrient zoning management of farmers' precise fertilization, production, and planting for sustainable development. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) storage is a critical ecosystem service for reducing CO2 emissions. Terrestrial ecosystems, including the agriculture lands, hold the second largest carbon reserves after oceans, containing 2,344 Gt of carbon. Agroforestry plays an important role in sequestering (SOC) which is essential for mitigating CO2. Due to increased anthropogenic activities, global CO2 emissions continue to rise. In agroecological zones, SOC serves as a key reservoir for atmospheric CO2. This research evaluates sustainability comparison between two land types for carbon sequestration potential and soil fertility parameters in District Multan, Pakistan; mango-based agroforestry (MBA) and cropland (CL) respectively. Soil samples were collected randomly at a depth of 20 cm from the locations; under tree shade in mango-based agroforestry (MBAUS), outside the tree shade (MBA), and cropland (CL). These samples were tested for analysis for (SOC), organic matter (OM %), nitrogen percentage (N %), saturation percentage (SP %), bulk density (BD), and carbon-to-nitrogen (C: N) ratio between mean values of agroforestry and CL. Results indicated that the amount of SOC was higher in agroforestry (0.64) compared to CL (0.43). Similarly, OM% was 1.15 in agroforestry and 0.75% in CL. N% was 0.055 in agroforestry and 0.037 in CL, C: N ratio (12:1) in agroforestry versus (11:1) in CL. SP was greater in CL (36.1) than in agroforestry (34.1), similarly, BD was higher in CL (1.361 g/cm3) and (1.077 g/cm3) in agroforestry. The study employed a completely randomized design (CRD) with three treatments and four replications. ANOVA was used for data analysis. According to the results, the carbon sequestration potential and fertility index of agroforestry was quite double that of cropland. [ABSTRACT FROM AUTHOR]

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.

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.

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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Monitoring agricultural land over vast geographical areas presents challenges due to the absence of accurate, comprehensive and precise data, which has become a complex process that is difficult to do in terms of both timespans and consistency. Hence, this study presents an improved approach for the identification of agricultural land by utilizing the capabilities of Sentinel-1 and Sentinel-2 satellites with a variety of vegetation and non-vegetation indices and machine learning algorithms. The Multispectral Correlation Mapper (MCM) and Random Forest (RF) algorithms are adopted to train different agricultural lands, crop types and sowing and cultivation seasons. The 45-bands mega-file data cube (MFDC) fusion for each season incorporates essential indices and features derived from the Sentinel-1 and Sentinel-2 datasets for both seasons, i.e. Rabi (winter-spring season) and Kharif (summer-autumn season). The proposed method demonstrated resilience when applied to satellite datasets while effectively reducing the impact of non-agricultural elements such as shrubs, grass, bare soil and orchards. The results demonstrate a notable ability to differentiate between the Rabi and Kharif seasons, resulting in a high level of precision in measuring the extent of cultivated land during the Rabi and Kharif seasons with an area of 626,947 acres and 590,858 acres, respectively. The total land area, ascertained from the observation of the comprehensive cropping pattern and agricultural modifications during the entire year (June 2021–May 2022) is 635,655 acres. The validation exercise shows the higher accuracy of this method for cropland, with an overall accuracy of 98.8%, kappa of 0.97, user accuracy of 98.69% and producer accuracy of 99.13%. Additionally, it was spatially compared with the ESRI, ESA and MODIS cropland layers and government statistical data. Furthermore, the research investigates the temporal dynamics of agricultural growth phases using spectral bands and indices. This approach improves the accuracy of precise cropland identification and provides useful insights into crop phenology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sediment connectivity indexes serve as a diagnostic tool for investigating the overall hydro-geomorphological functioning. The primary factors influencing sediment connectivity are rainfall and changes in land cover. For the period 1995–2016, we investigated changes in sediment connectivity in two ca.1000 km2 catchments located in the Ethiopian Highlands and evaluated the potential sediment sources of the catchment. These catchments are characterized by severe land degradation and high sediment yields. The sediment connectivity of the catchments was computed using SedIn-Connect, incorporating variables such as a digital elevation model, rainfall erosivity, soil erodibility, surface roughness, cover management, and surface runoff to formulate the weight factor. Rainfall variability was calculated using TAMSAT data, and land cover maps were derived using Landsat data in Google Earth Engine. The findings show substantial spatial variability in sediment connectivity across the two catchments. The modified index of connectivity value has a positive correlation with sediment yield from two stations whereby the sediment connectivity is higher in the Gumara Catchment than in the Rib Catchment. In both catchments, bare land, cropland, shrubland, and grassland exhibited higher connectivity, whereas forest and rural settlements displayed lower connectivity. Croplands registered the most pronounced increase in connectivity, mirroring similar trends observed in shrublands and grasslands. Conversely, forested areas demonstrated relatively stable connectivity patterns. Notably, croplands nestled in steep slopes and proximate to rivers emerged as potential sediment sources capable of influencing sediment connectivity within the two catchments. [ABSTRACT FROM AUTHOR]

Agricultural expansion is one of the main causes of deforestation of tropical montane forests in the central Andes of Peru. The hydrological impacts of converting forests to cropland are well known; however, an issue that is not entirely clear is related to the hydrological effects that can result from the conversion of montane forests to agroforestry systems and the recovery of the hydrological functions of montane forests after a disturbance. In this study, we compare the hydrological processes of different land covers previously modified by agricultural expansion, in order to determine the impact of the conversion of tropical montane forest to agricultural land on the ecosystem service of water provision and regulation. To achieve this, we establish study plots in four land cover types located in the central Andes of Peru (mature montane forest (BMC), natural regenerating secondary forest (BMR), coffee agroforestry systems (AF), and cropland (C)), for the purpose of measuring the vegetation structure and soil properties in them, and subsequently carry out a soil water balance in each plot to calculate the actual evapotranspiration, surface runoff, and groundwater recharge. The results revealed the following percentages (based on precipitation) for the hydrological components in the four land cover types: annual actual evapotranspiration—BMR (41.2%), AF (40.4%), BMC (40.0%), and C (38.0%); annual surface runoff—C (16.1%), BMC (8.3%), BMR (5.2%), and AF (4.6%); and annual groundwater recharge—AF (43.0%), BMR (41.6%), C (34.0%), and BMC (31.7%). Furthermore, the study also examined the relationship between vegetation structure and the hydrological components across the four land cover types. The findings indicated that the reduction of thicker and taller trees could increase surface runoff generation, whereas the presence of thinner and shorter trees could facilitate groundwater recharge. These results shed light on the complex interactions between land cover types, vegetation structure, and hydrological processes, emphasizing the importance of considering these factors in water resource management and land use planning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Converting natural vegetation for agriculture has resulted in the loss of approximately 5% of the current global terrestrial soil organic carbon (SOC) stock to the atmosphere. Increasing the agricultural area under grassland may reverse some of these losses, but the effectiveness of such a strategy is limited by how quickly SOC recovers after conversion from cropland. Using soil data and extensive land‐use histories gathered during the national German agricultural soil inventory, this study aims to answer three questions regarding agricultural land‐use change (LUC): (i) how do SOC stocks change with depth following LUC; (ii) how long does it take to reach SOC equilibrium after LUC; and (iii) what is the legacy effect of historic LUC on present day SOC dynamics? By using a novel approach that substitutes space for time and accounts for differences in site properties using propensity score balancing, we determined that sites that were converted from cropland to grassland reached a SOC equilibrium level 47.3% (95% confidence interval (CI): 43.4% to 49.5%) above permanent cropland levels 83 years (95% CI: 79 to 90 years) after conversion. Meanwhile, sites converted from grassland to cropland reached a SOC equilibrium level −33.6% (95% CI: −34.1% to −33.5%) below permanent grassland levels after 180 years (95% CI: 151 to 223 years). We estimate that, over the past century, today's German agricultural soils (16.6 million ha) have gained about 40 million Mg C. Furthermore, croplands with historic LUC from grassland are losing SOC by −0.26 Mg ha−1 year−1 (10% of agricultural land) while grasslands historically converted from cropland are gaining SOC by 0.27 Mg ha−1 year−1 (18% of agricultural land). This study shows that even long‐standing temperate agricultural sites likely have ongoing SOC change as a result of historical LUC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Heavy metal composite pollution, such as Cd–Pb, occurs commonly in agriculture soils around the world, potentially harming the ecological environment and human health via food chain/network migration. There are numerous strategies for treating and rehabilitating polluted farmland today, including the use of attractive plants for phytoremediation. However, it is unclear how to use attractive plants to properly treat and fully utilize Cd–Pb composite damaged farms. The combined Cd–Pb pollution of agriculture in Xieping village, Huixian county, Gansu province is increasingly prevalent. Therefore, based on the results of literature research and the actual situation of the study area, nine kinds of ornamental plants with Cd–Pb composite pollution remediation potential and comprehensive utilization value were first selected in this paper, and the greenhouse test was carried out in the Cd–Pb composite pollution farmland in the study area. Second, the bioconcentration factor (BCF), transfer coefficient (TF), and economic feasibility principle were utilized to select the best treatment plants, and a novel model of effective remediation and comprehensive exploitation of Cd–Pb composite damaged farmland was developed. The results showed that the nine tested plants grew well under the Cd–Pb combined pollution stress, and all of them showed certain tolerance. However, the biomass, Cd–Pb accumulation, enrichment and transport capacity of the different plants were significantly different. Among them, Paeonia suffruticosa Andr., Rumex acetosa L., Calendula officinalis L., Rose chinensis Jacq., Tagetes erecta L. and Impatiens balsamina L. show excellent restorative abilities. Combining the ornamental value, safety and economy of flowering plants, P. suffruticosa, R. chinensis, C. officinalis, T. erecta and I. balsamina were finally selected as the plants for the effective remediation and comprehensive utilisation of Cd–Pb composite pollution in farmland. In conclusion, this study proposes a new model for the effective remediation and comprehensive utilisation of heavy metal composite pollution in farmland by flowering plants with economic feasibility. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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.

Background: Integrating trees into agricultural landscapes can provide climate mitigation and improves soil fertility, biodiversity habitat, water quality, water flow, and human health, but these benefits must be achieved without reducing agriculture yields. Prior estimates of carbon dioxide (CO2) removal potential from increasing tree cover in agriculture assumed a moderate level of woody biomass can be integrated without reducing agricultural production. Instead, we used a Delphi expert elicitation to estimate maximum tree covers for 53 regional cropping and grazing system categories while safeguarding agricultural yields. Comparing these values to baselines and applying spatially explicit tree carbon accumulation rates, we develop global maps of the additional CO2 removal potential of Tree Cover in Agriculture. We present here the first global spatially explicit datasets calibrated to regional grazing and croplands, estimating opportunities to increase tree cover without reducing yields, therefore avoiding a major cost barrier to restoration: the opportunity cost of CO2 removal at the expense of agriculture yields. Results: The global estimated maximum technical CO2 removal potential is split between croplands (1.86 PgCO2 yr− 1) and grazing lands (1.45 PgCO2 yr− 1), with large variances. Tropical/subtropical biomes account for 54% of cropland (2.82 MgCO2 ha− 1 yr− 1, SD = 0.45) and 73% of grazing land potential (1.54 MgCO2 ha− 1 yr− 1, SD = 0.47). Potentials seem to be driven by two characteristics: the opportunity for increase in tree cover and bioclimatic factors affecting CO2 removal rates. Conclusions: We find that increasing tree cover in 2.6 billion hectares of agricultural landscapes may remove up to 3.3 billion tons of CO2 per year – more than the global annual emissions from cars. These Natural Climate Solutions could achieve the Bonn Challenge and add 793 million trees to agricultural landscapes. This is significant for global climate mitigation efforts because it represents a large, relatively inexpensive, additional CO2 removal opportunity that works within agricultural landscapes and has low economic and social barriers to rapid global scaling. There is an urgent need for policy and incentive systems to encourage the adoption of these practices. [ABSTRACT FROM AUTHOR]