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Land use is a major cause of biodiversity decline worldwide. Agricultural and forestry diversification measures, such as the inclusion of natural elements or diversified crop types, may reduce impacts on biodiversity. However, the extent to which such measures may compensate for the negative impacts of land use remains unknown. To fill that gap, we synthesised data from 99 studies that recorded mammal populations or assemblages in natural reference sites and in cropland and forest plantations, with or without diversification measures. We quantified the responses to diversification measures based on individual species abundance, species richness and assemblage intactness as quantified by the mean species abundance indicator. In cropland with natural elements, mammal species abundance and richness were, on average, similar to natural conditions, while in cropland without natural elements they were reduced by 28% and 34%, respectively. We found that mammal species richness was comparable between diversified forest plantations and natural reference sites, and 32% lower in plantations without natural elements. In both cropland and plantations, assemblage intactness was reduced compared with natural reference conditions, but the reduction was smaller if diversification measures were in place. In addition, we found that responses to land use were modified by species traits and environmental context. While habitat specialist populations were reduced in cropland without diversification and in forest plantations, habitat generalists benefited. Furthermore, assemblages were impacted more by land use in tropical regions and landscapes containing a larger share of (semi)natural habitat compared with temperate regions and more converted landscapes. Given that mammal assemblage intactness is reduced also when diversification measures are in place, special attention should be directed to species that suffer from land use impacts. That said, our results suggest potential for reconciling land use and mammal conservation, provided that the diversification measures do not compromise yield. [ABSTRACT FROM AUTHOR]

Context: Conversion of native vegetation to cropland is one of the most widespread anthropogenic landscape alterations, particularly in the Great Plains region of the United States. Mule deer occur throughout the Great Plains; however, it is the south-eastern edge of their geographical distribution, and few populations coincide with dense cropland. The rapidly depleting Ogallala Aquifer supplies irrigation to row-crops throughout the region, which will likely shift towards dryland agricultural practices in the near future. Aims: We sought to understand how cropland use influences morphology, body condition indices, reproductive output, and survival of free-ranging mule deer. Methods: We accumulated a multi-year, longitudinal dataset of movement and morphology for 146 mule deer in the Texas Panhandle. We linked seasonal cropland use with observed morphology, body condition metrics, and reproductive output via linear mixed-effect modelling and assessed the influence of cropland on annual survival by using Cox proportional hazard models. Key results: Mule deer that did not use cropland at any time during the year exhibited morphological and nutritional indices similar to those that did; except body-fat percentage being greater for mature (≥4-year-old) males that used cropland. Further, cropland use did not predict survival probability. Analyses of cropland use during seasons defined by life-stage showed context-dependent nutritional benefits. Use of cropland during winter following reproduction demonstrated an increase in young (≤3-year-old) male antler size and body mass and summer crop use increased body condition for all males. Female mule deer that utilised cropland before pregnancy had increased probability of successful reproduction, demonstrating a potential capital investment strategy in reproduction. Conclusions: Cropland does not limit morphology or survival of mule deer; however, additive use of row-crops can provide a nutritional buffer and enhanced reproductive output for individuals that choose to utilise it. Implications: Our study demonstrates important population-level interactions with the environment for a species near the extent of their geographical distribution. Conversion of row-crop farming from aquifer depletion or climate shifts may not diminish mule deer populations, but these changes may alter specific habitat-nutritional health relationships that can influence population performance and future conservation efforts. The rapidly depleting Ogallala Aquifer supplies irrigation to row-crops throughout the United States Great Plains, which will likely shift towards dryland agricultural practices in the next 20 years. These areas are the fringe of the mule deer distribution and cropland serves as a nutritional buffer against naturally occurring periods of nutrient limitation. Row-crop farming conversion from aquifer depletion may not diminish mule deer populations, but these changes may alter specific habitat–nutritional relationships that influence population performance and conservation efforts. [ABSTRACT FROM AUTHOR]

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 < 0.05) among crops in the order: early successional (0.288 ± 0.012SE) >> 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. [ABSTRACT FROM AUTHOR]

The problem of choosing the type of land use is now more relevant than ever. Against the backdrop of the growth of urbanized territories, the challenge is to preserve cropland, maintain the quality of soil resources, and find a balance between competing land uses. Forecasting and modeling changes in the area of cropland is a sought-after area of research against the backdrop of a growing shortage of fertile land and a threat to food security. In this study, on the example of one of the agriculturally most developed administrative regions of Russia (Belgorod Oblast), an approach to statistical modeling of agricultural land areas over the past 30 years is shown. Two approaches were used: statistical modeling of the dynamics of the total area of the study region's cropland depending on the balance of other types of land and spatial interaction modeling of cropland in a key area. For the study region, administrative districts with positive and negative cropland dynamics were identified; the main types of land were revealed, due to which cropland is withdrawn, and a regression balance model was developed. It was revealed that the implementation of the planned regional programs to expand the development and conservation of meadow lands will reduce cropland by 3.07% or 83.2 thousand ha. On the example of one of the administrative districts with high rates of urbanization, the probability of cropland transformation into other types of land was estimated and a predictive spatial model of land use was developed. According to the forecast, about 6.2 thousand ha of cropland will turn into residential development land, and 2/3 of their area will be concentrated within 6 km from the borders of the regional capital city (Belgorod). The presented approach to forecasting the area of cropland and the threats of its reduction due to the need to replace other types of land is relevant for all agricultural regions and countries with developing urbanization processes. [ABSTRACT FROM AUTHOR]

Accurate quantification of ecosystem water use efficiency (eWUE) over agroecosystems is crucial for managing water resources and assuring food security. Currently, the uncoupled Moderate Resolution Imaging Spectroradiometer (MODIS) product is the most widely applied dataset for simulating local, regional, and global eWUE across different plant functional types. However, it has been rarely investigated as to whether the coupled product can outperform the uncoupled product in eWUE estimations for specific C4 and C3 crop species. Here, the eWUE as well as gross primary production (GPP) and evapotranspiration (ET) from the uncoupled MODIS product and the coupled Penman–Monteith–Leuning version 2 (PMLv2) product were evaluated against the in-situ observations on eight-day and annual scales (containing 1902 eight-day and 61 annual samples) for C4 maize and C3 soybean at the five cropland sites from the FLUXNET2015 and AmeriFlux datasets. Our results show the following: (1) For GPP estimates, the PMLv2 product showed paramount improvements for C4 maize and slight improvements for C3 soybean, relative to the MODIS product. (2) For ET estimates, both products performed similarly for both crop species. (3) For eWUE estimates, the coupled PMLv2 product achieved higher-accuracy eWUE estimates than the uncoupled MODIS product at both eight-day and annual scales. Taking the result at an eight-day scale for example, compared to the MODIS product, the PMLv2 product could reduce the root mean square error (RMSE) from 2.14 g C Kg−1 H2O to 1.36 g C Kg−1 H2O and increase the coefficient of determination (R2) from 0.06 to 0.52 for C4 maize, as well as reduce the RMSE from 1.33 g C Kg−1 H2O to 0.89 g C Kg−1 H2O and increase the R2 from 0.05 to 0.49 for C3 soybean. (4) Despite the outperformance of the PMLv2 product in eWUE estimations, both two products failed to differentiate C4 and C3 crop species in their model calibration and validation processes, leading to a certain degree of uncertainties in eWUE estimates. Our study not only provides an important reference for applying remote sensing products to derive reliable eWUE estimates over cropland but also indicates the future modification of the current remote sensing models for C4 and C3 crop species. [ABSTRACT FROM AUTHOR]

Understanding the relationship between climate change and cropland spatiotemporal patterns is critical for developing government policy and assisting agriculture in adapting to future climate change. The land use dataset was used to extract the area of cropland expansion and contraction as well as to calculate the cropland landscape indices. The impacts of climatic factors on the spatiotemporal patterns of cropland were quantified for different time periods by Random Forest (RF) classification and regression models, and the accuracy of the models was used as the intensity of the influence of climatic factors on cropland change. The results revealed that the total cropland area has consistently increased by 17.74% in the last four decades. The Sanjiang Plain (SJP) and Songnen Plain (SNP), with high aggregation and a simple shape of cropland landscapes, were the main regions where the cropland area has increased. Cropland landscape aggregation was low in mountainous areas. Before 2000, the total cropland area expanded more (46,748 km2) in response to the pressure to ensure food security, whereas there was less cropland loss. The accuracy of the RF model during this period revealed that the changes in cropland spatiotemporal patterns were highly influenced by climatic factors. After 2000, the climate conditions gradually became warmer and wetter. The total area of cropland increased slightly (10,587 km2) under the influence of the contradictory relationships among economic development, food production and ecological conservation, and the conversion between cropland and natural landscape types was drastic. The impact of climatic factors on changes in cropland spatiotemporal patterns has declined. Our results suggest that the response of spatiotemporal pattern changes in cropland to climatic factors differs under different policy contexts in different periods. The findings are intended to aid in the balance of agricultural production and ecological conservation in Northeast China in the face of climate change. [ABSTRACT FROM AUTHOR]

Metal beneficiation activities may cause soil pollution in the surrounding cropland, making it crucial to conduct heavy metal pollution assessment and source analysis of the cultivated land in mining areas for the protection of the ecological environment and human health. In this study, a total of 205 surface soil samples (0–20 cm) were collected on site from the Tianheba cropland near the lead–zinc concentrator in Xieping Village, Huixian County, Gansu Province, China; their pH values and their SOM, Zn, As, Cd, Cr, Hg, and Pb contents were determined. Based on the data, we used the Kriging spatial interpolation, the Nemero index, the index of geoaccumulation, and the PMF model to analyze the characteristics of the spatial distribution of soil heavy metals in the region, the degree of contamination, the sources, and the contribution rate. The results indicated that the heavy metals with contamination levels in the Tianheba cropland were Cd, Zn, Pb, Hg, As, and Cr in descending order, with the average concentrations of Cd (0.39 mg/kg), Zn (122 mg/kg), Pb (30.4 mg/kg), and Hg (0.07 mg/kg) being significantly higher than the background values of the Gansu soils. The soil in the region as a whole was heavily polluted; it was heavily polluted with Cd, moderately polluted with Zn, and mildly polluted with Pb. Hg had a larger value in the index of geoaccumulation. By analyzing the spatial distribution and sources of the soil metals, it was found that the cropland contaminated with heavy metals in Tianheba was distributed around the mineral processing plant and the infiltration area of the canal. The PMF model revealed three pollution sources: the industrial source related to mining activities, the fertilizer application source, and the natural source. This study provides a scientific basis for the precise management of heavy metal pollution in the area. [ABSTRACT FROM AUTHOR]

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.

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.

In croplands, soil erosion resistance varies with both natural processes and human disturbances. To clarify the temporal variation in soil erosion resistance, nine cropland plots with three treatments (continuous fallow, fallow after tillage and tillage with corn) were established in the dry–hot valley region of China. A total of 144 field runoff simulation experiments were conducted from May to October to measure the soil detachment rate (Dc), rill erodibility (Kr) and critical shear stress (τc). The results revealed that the natural dry—wet alternation had little influence on the continuous-fallowed soil erosion resistance. On the other hand, the tillage disturbance that occurred in May sharply increased the Dc and Kr to 2.24 and 3 times that of the continuous-fallow treatment, respectively. Then, the erosion resistance could be enhanced with surface consolidation for the fallow-after-tillage treatment. However, after three months of fallow, the Kr was still 89.5% of the fresh tilled soil. In contrast, crop growth could significantly improve aggregate stability and reduce the Kr to 38.2% in August and even further to 23.7% in October compared to the fresh tilled soil. It could be concluded that crop growth is more efficient in enhancing erosion resistance than the mechanical effect. The above results would benefit from the accurate modeling of cropland soil erosion dynamics and guide agricultural management in dry–hot climate regions.

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.

Developing low-carbon utilization of cropland is critical to coordinate agricultural production and environmental protection. Based on a theoretical analysis of greenhouse gas (GHG) emissions and crop production, this study examined the GHG emissions from cropland utilization in China and the decoupling process from crop yields with consideration of different sources and then explored the driving factors in different regions. The results showed that the GHG emissions from cropland utilization in China rose first and then fell between 2003 and 2020, and the decoupling process has undergone three stages, namely "expansive coupling", "weak decoupling", and "strong decoupling". And the eastern and southern provinces are relatively ahead of the western and northwestern provinces. Additionally, crop yields have been basically decoupled from GHG emissions caused by agricultural inputs, but they were still not decoupled from GHG emissions from cropland in Northeastern and Northern China. Among the influencing factors, utilization efficiency has promoted the decoupling progress, the input structure has played a promoting role in the early stage and hindered it later, and the input intensity and the scale have worked as hindering factors. Policy implications have been proposed to support the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in regions with abundant ground labels for model training, yet these labels remain difficult to obtain for many regions and years. NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR instrument, originally designed for forest monitoring, has shown promise for distinguishing tall and short crops. In the current study, we leverage GEDI to develop wall-to-wall maps of short vs. tall crops on a global scale at 10 m resolution for 2019–2021. Specifically, we show that (i) GEDI returns can reliably be classified into tall and short crops after removing shots with extreme view angles or topographic slope, (ii) the frequency of tall crops over time can be used to identify months when tall crops are at their peak height, and (iii) GEDI shots in these months can then be used to train random forest models that use Sentinel-2 time series to accurately predict short vs. tall crops. Independent reference data from around the world are then used to evaluate these GEDI-S2 maps. We find that GEDI-S2 performed nearly as well as models trained on thousands of local reference training points, with accuracies of at least 87% and often above 90% throughout the Americas, Europe, and East Asia. A systematic underestimation of tall crop area was observed in regions where crops frequently exhibit low biomass, namely Africa and South Asia, and further work is needed in these systems. Although the GEDI-S2 approach only differentiates tall from short crops, in many landscapes this distinction is sufficient to map individual crop types (e.g., maize vs. soy, sugarcane vs. rice). The combination of GEDI and Sentinel-2 thus presents a very promising path towards global crop mapping with minimal reliance on ground data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Plastic mulch has been widely used to increase the soil temperature with the constant moisture for the high crop yields in the arid regions in northwestern China. Particularly, there has also the application in maize, wheat, and cotton cultivation in Hexi Corridor since the last century. Nevertheless, the mulch residues have significantly damaged the soil structure, leading to environmental degradation. Therefore, it is very necessary for the spatial and temporal distribution of plastic mulch, in order to quantify the impact on the local environment. However, the wide region and the complex interlocking surface types have posed a great challenge to the spatial and temporal distribution of plastic mulched cropland (PMC) from the field work. Fortunately, large-scale ground objects can be extracted with the development of remote sensing. However it is still lacking in the acquisition and processing of remote sensing data. This study aims to extract the spatiotemporal PMC variations in Hexi Corridor from 2013 to 2022. The multi-source remote sensing data was first collected using Google Earth Engine, including Sentinel-2, Landsat 8, Landsat 7, and MODIS. A time window of PMC extraction was also proposed using the variance of the Normalized Difference Vegetation Index (NDVI) within one year. Then, the Timely Plastic-mulched cropland Extraction Method (TPEM) was applied to the complex and variable ground objects for the extraction of the PMC area. About 500 sample points were randomly selected and then labelled as the PMC and non-PMC for the accuracy assessment. The Overall Accuracy (OA), User Accuracy (UA), Quantity Disagreement (QD), and Allocation Disagreement (AD) were also used to evaluate the performance of TPEM. Finally, the spatiotemporal pattern of PMC was analyzed to explore the potential controls on the interannual variation in the PMC area using Water Yearbook Bulletin data. The results showed that: 1) The best time window was the first half of April for the PMC extraction, which also coincided with the sowing of major local crops. The half month before and after this period was also beneficial to extracting the PMC information. 2) The TPEM reached 80% overall accuracy with the QD less than 4%. The error was attributed to the disagreement between the sample points and the real PMC area. 3) The average PMC area was about 383 000 hm², of which about 55.6% was in the Heihe Basin. The PMC was located near the inland river systems. Approximately 41.9% of the cropland used plastic mulch, while the PMC area decreased by 32.4% in the last decade. Meanwhile, there was a 46% increase in the water-saving irrigation area, whereas, there was a 14.9% decrease in the irrigation water from 2013 to 2019, indicating the significant correlation with the decrease in the PMC area. In conclusion, the application of plastic mulch was ever decreasing with the development of water-saving irrigation. This finding can be an essential reference for the survey of agricultural resources in the Northwest Arid Region of China. [ABSTRACT FROM AUTHOR]

Nutrient management is arguably a key factor for achieving several of the UN Sustainability Development Goals. It plays a key role in the production of sufficient amounts of nutritious food, while poor nutrient management leads to nutrient losses (notably of nitrogen (N) and phosphorus (P)) and contributes to air and water pollution and biodiversity loss. Nutrient management in China is complicated because of the diversity in farming systems and environments, the countless number of farm households and small farms, and the fragmented agricultural supply and advisory system. Current nutrient management in China's agriculture has become a global concern because of the implications of the large N and P inputs and losses. In this perspective, we identify and discuss several barriers for implementing environmentally-sound nutrient management, and highlight that farmers currently have limited farm-specific techniques, tools and incentives for precision and environmentally sound nutrient management. Most crop farmers manage primarily by choosing fertilizer bag size and nutrient formulation, while many livestock farmers have a headache of manure management, because of a lack of appropriate manure recycling opportunities. We argue that nutrient management in China is at the crossroads; decisions have to be made about the development and testing of farm-specific techniques and tools to provide farmers a customary dashboard with indicators and a steering wheel for precision and environmentally-sound nutrient management. Likewise, integrated crop-livestock systems and sustainability-driven farm business models have to be developed, within the context of Agriculture Green Development, which aims at greening agriculture and enhancing ecosystem services. There is also a need for in-depth and long-term studies of whole farming systems to better understand the impact of farmers' decisions on nutrient cycling, use efficiency and losses, to test and improve nutrient management tools, and to explore the effects of policy measures. Such approaches could be applied and tested first in Science and Technology Backyards and then up-scaled to other villages and counties. [ABSTRACT FROM AUTHOR]

Agricultural intensification is a key driver of water pollution in many parts of the world. A frequent policy response is to implement mitigation measures in the context of agri-environmental schemes (AESs). We develop a hydro-economic modeling procedure to design cost-effective AESs to mitigate water pollution from nitrogen and phosphorus from cropland. Our procedure considers spatial heterogeneity of mitigation impacts and of costs of cropland management measures. It further takes into account in the optimization that AES participation is voluntary and farmers who may participate in an AES will select between different cropland management measures. We demonstrate how the procedure works by applying it to the Baishahe watershed in Shanxi Province, China. As a result, we obtain a cost-effective AES with a set of cropland management measures and the associated payment levels that are able to achieve weighted minimized nitrogen and phosphorus loads in the watershed for different budgets. We find, for example, that a cost-effective AES for a budget of 100,000 RMB consists of the two measures of implementing a filter strip of 5 m and reducing chemical fertilizers by 25% for which the farmers receive payments of 134 RMB and 88 RMB, respectively, which induces the reduction of nitrogen by 6.9% and phosphorus by 7.8%. [ABSTRACT FROM AUTHOR]

Mapping 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. [ABSTRACT FROM AUTHOR]

Winter climate conditions have a great effect on spring soil moisture (SM) in cold regions. The frequent occurrence of spring drought events in the Songnen Plain poses a significant threat to food security. In this study, we selected the random forest (RF) algorithm from three currently popular machine learning algorithms to predict the spatial pattern of average SM for the period of April 1–15 and April 16–30 with 1 km resolution in the dry cropland of southern Songnen Plain (SSNP) using 0–10 cm SM data for each April from 81 agro‐meteorological stations and winter climate data (e.g., temperature, snowfall), soil properties and topographic relief. Compared to the existing ERA5_Land and SMAP SM products, we obtain higher precision SM data (April 1–15: r = 0.74, RMSE = 0.050 m3 m−3; April 16–30: r = 0.73, RMSE = 0.051 m3 m−3). Drought grades were classified based on predicted SM data, and the results indicate agricultural drought was mainly influenced by the change of winter snowfall/snow depth, with western SSNP being more susceptible to drought because of soil properties. Compared with current SM data products, the RF model proposed in this study can implement more accurate prediction of spring soil drought based on winter climate, provide important information for agricultural management departments to prepare for spring cropping and irrigation, and avoid further soil salinization caused by drought. [ABSTRACT FROM AUTHOR]

Nitrous oxide (laughing gas, N2O) is a relevant greenhouse gas. Agriculture contributes significantly to its emissions. As nitrogen fertilization has been identified as one of the main sources of N2O, controlled application and reduction of the amount of fertilizer adapted to crop demand is essential to reduce N2O emissions. This requires detailed studies of the local distribution of the N2O emission fluxes on different croplands. Consequently, frequent spatially resolved field measurements of N2O concentrations are needed. A precision in the ppb range close to the ambient N2O level of 333 ppb is necessary. Tunable laser absorption spectroscopy using quantum-cascade lasers (QCL) as a light source is an established technique for the measurement of N2O traces. We present the development and validation of a compact portable setup for on-site measurement of N2O emissions from the soil. The setup differs from previous solutions by using an interband cascade laser (ICL), which has significantly lower power consumption compared to a QCL. The portable measurement setup allows N2O emission fluxes to be determined with a precision of 3.5% with a measuring duration of 10 min. The developed system enables the detection of increased N2O emissions because of the fertilization of fields. High N2O emission fluxes are indicators of the overfertilization of the field. Directly after fertilization, N2O fluxes between 2.9 and 5.3 µL m−2 min−1 depending on the gas acquisition site are measured during the field tests. Over time, the fluxes decrease. The obtained results compare well with data from more precise but also more complex and maintenance-intensive instruments for atmospheric research. With this system, the soil moisture as well as the air humidity and air temperature are recorded. Strong influences on N2O fluxes by soil moisture were observed. The presented measurement system is a contribution to the establishment of mobile N2O screening systems that are robust in the field and suitable for comprehensive and routine detection of N2O emissions from soil. [ABSTRACT FROM AUTHOR]

Enhanced rock weathering (ERW) on croplands has emerged as an economically and ecologically promising negative emissions technology. However, estimated total carbon sequestration potential from ERW on croplands and its potential sensitivity to climate conditions requires further understanding. Here we combine 1‐D reactive transport modeling with climate model experiments to simulate ERW on ∼1,000 agricultural sites globally. Applying a fixed rate of 10 tons of basalt dust per hectare on these sites sequesters 64 gigatons of CO2 over a 75‐year period; when extrapolated to all agricultural land, ERW sequesters 217 gigatons of CO2 over the same time interval. However, we find that a significant fraction of applied basalt does not weather even on a multidecadal timescale, indicating the need to optimize application strategies for cost effectiveness. We find that ERW becomes modestly more effective with global warming and predict that the payback period for a given ERW deployment is significantly shorter in hot and humid environments currently coinciding with relatively low per‐capita incomes. These results provide strong impetus for investment in agricultural reform in developing economies and highlight an additional potential co‐benefit of ERW. Plain Language Summary: Enhanced rock weathering (ERW) on croplands is a promising negative emissions concept that accelerates natural weathering by amending soils with crushed rock. Our simulations of ERW with a fixed rate of 10 tons of basalt dust per hectare on all global croplands suggest ERW can sequester >200 gigatons of CO2 over a 75‐year period. This suggests that cost and logistical concerns, rather than weathering potential, are likely to be the key limiting factors for large‐scale deployment of enhanced weathering. Notably, ERW is resilient to global climate change and becomes more effective with global warming. ERW is moreover far more efficient in hot and humid environments currently coinciding with relatively low per‐capita gross domestic product economies. Our study provides strong support for the assertion that ERW represents a resilient carbon capture strategy that is non‐competitive for arable land and can foster CO2 removal at the gigaton scale. Key Points: Enhanced rock weathering (ERW) with fixed annual application rates of 10 tons of basalt dust per hectare on 1,000 global cropland sites sequesters 64 gigatons of CO2 over 2006–2080Extrapolated to all croplands, ERW with a fixed application rate of 10 tons of basalt dust per hectare sequesters 215 gigatons of CO2 over 2006–2080ERW is resilient to global climate change but is much more efficient over hot and humid environments [ABSTRACT FROM AUTHOR]

The geographic distribution of natural ecosystems is affected by both climate and cropland. Discussions of future land use/land cover usually focus on how cropland expands and displaces natural vegetation especially as climate change impacts become stronger. Less commonly considered is the direct influence of climate change on natural ecosystems simultaneously with cropland incursion. We combine a natural vegetation model responsive to climate with a cropland allocation algorithm to assess the relative importance of climate change compared to cropland incursion. Globally, the model indicates that climate change drives larger gains and losses than cropland incursion. For example, in the Amazonian rainforests, more than one sixth of the forest area could be lost due to climate change with cropland playing virtually no role. Our findings suggest that policies to protect specific ecosystems may be undercut by climate change and that localized analyses that fully account for the impacts of a changing climate on natural vegetation and agriculture are necessary to formulate policies that preserve natural ecosystems over the long term. [ABSTRACT FROM AUTHOR]

Cropland is one of the most important resources in a complex ecosystem with the highest degree of human dependence, in order to ensure the regional food and ecological security for the better settlement environment. The coordinated development can be expected to promote in the multi-functions of cropland for the scientific protection and rational utilization of cropland. Therefore, it is very necessary to scientifically evaluate the spatiotemporal changes of multi-functions of cropland, and to clarify the change of trade-off and synergy among various functions of cropland, due to the natural supply and the demand of human society. Taking Fujian Province in southeastern China as an example, this study aims to construct a performance metrics, according to four dimensions of cropland production, ecological, social, and landscape functions. The multi-functional level of cropland was evaluated on the county scale. The comprehensive analysis was introduced to evaluate the multi-functions of cropland, and then to explore the spatial-temporal variation from 2005 to 2020. Pearson product-moment correlation coefficient was selected to analyze the trade-off and synergy relationships among the functions of each dimension. The results showed that: 1) The multi-functional level of cropland decreased slightly in the most areas. Specifically, the high-value areas were concentrated in the eastern and western regions, with the superior productive and ecological function of cropland. The higher ability was found to guarantee the living and employment of farmers. Among them, the production function showed an upward trend, while the ecological function decreased slightly. Both social and landscape functions were improved significantly. The spatial change of social function was weakened from the mountain to the coastal area, whereas, there was the strong spatial change of landscape function. 2) Synergy relationships were dominated among cropland multi-functions. Only the production-ecological and production-landscape function showed the synergy relationship in more than 50% of the areas. The proportion of grid quantity with the trade-off relationship was ranked in the descending order of the ecological-social, production-social, social-landscape, and ecological-landscape function. A spatial heterogeneity was observed in the trade-off and synergy relationship of each function combination. 3) The present condition of cropland was used to determine the characteristics of agricultural production in the different regions. The single function of cultivated land was combined with the trade-off between different functions. The cropland functional areas were divided into four types, including the superiority protection area (38.52%), strict control area (36.71%), remediation area (6.98%), and transition or exit area (17.79%). Some differentiated management and control suggestions were proposed as well. The superiority protection areas were distributed mainly in the eastern and western regions, while the strict control areas were distributed mainly in the coastal areas of northern and southern regions, and the remediation areas were in the Zhangzhou region of Southern regions, as well as the transition or exit areas were scattered throughout the cities. The findings can provide an important decision-making support to protect the cropland resources. The coordinated development can be expected to promote the multi-functions of cropland for the high efficiency of cropland land use. [ABSTRACT FROM AUTHOR]

Cropland elevation uplift (CLEU) has recently become a new challenge for agricultural modernization, food security, and sustainable cropland use in China. Uncovering the rules of CLEU is of great theoretical and practical significance for China's sustainable agricultural development and rural revitalization strategy. However, existing studies lack in-depth disclosure of multi-scale CLEU evolution rules, making it difficult to support the formulation of specific cropland protection policies. We analyzed the spatio-temporal evolution and multi-scale CLEU in China from 1980 to 2020 using the Lorenz curve, gravity center model, hotspot analysis, and cropland elevation spectrum. The results indicated that the center of gravity of cropland moved to the northeast from 1980 to 2000 and then shifted to the northwest. The spatial distribution of cropland became increasingly imbalanced from 1980 to 2000. The change hotspots clustered in the northwest and the northeast, whereas cold-spots were mainly in southeastern China. The average elevation of cropland increased by 17.38 m, and the elevation uplift rule in different regions differed evidently across scales. From 1980 to 2000, all provinces except Xinjiang, Inner Mongolia, Gansu, and Yunnan exhibited CLEU, with Qinghai, Tibet, Beijing, and Guangdong showing the most noticeable uplifting. The CLEU can alleviate the shortage of cropland to some extent. However, without a planning constraint, the CLEU will lead to the increase of ecological risk and food security risk. [ABSTRACT FROM AUTHOR]

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.

【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.

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.

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.

Long‐term extending cultivation activities resulted in the world's worst soil erosion on the Chinese Loess Plateau (LP). By converting cropland into vegetated land, the Grain for Green Project (GfGP)—the world's largest investment revegetation project—effectively alleviates the soil erosion on the LP. However, during the GfGP implementation, the positive effect of cropland to the revegetation and soil erosion control has been underestimated to date, hindering a comprehensive evaluation to the effect of cropland on ecological restoration. Here, we evaluated the effect of the GfGP on soil erosion control across the LP, analyzed the dominant driver of the LP vegetation greening, and further identified the contributions of croplands to this world's largest revegetation project. We found that the vegetation of the LP was significantly improved and its leaf area increased by 1.23 × 105 km2 after the implementation of the GfGP, which contributed 42% to the decrease of the LP soil loss. Among them, our results show that cropland contributed 39.3% to the increased leaf areas of the LP, higher than grassland (36.3%) and forestland (14.3%). With the reduction of agricultural area, the contribution of cropland to the increased leaf areas in the LP was still the largest, which was mainly due to the increase in cropland utilization intensity. This study highlights the significance of the GfGP in soil erosion control and revises our understanding of the role of cropland in ecological restoration and society development. Plain Language Summary: Long‐term extending cultivation activities resulted in the world's worst soil erosion on the Chinese Loess Plateau (LP). For controlling this soil erosion, the Grain for Green Project (GfGP)—the world's largest investment revegetation project—was carried out. This was followed by the conversion of large amounts of cropland to vegetated land. However, this manuscript offers evidence indicating that cropland, rather than grassland and forestland, contributed the most to the vegetation improvement of the LP during the implementation of GfGP. We find that vegetation improvement after the GfGP could explain 42% of the reduction in the LP soil loss. The contribution of cropland to the LP greening was 39.3%, higher than that of grassland (36.3%) and forestland (14.3%). Although the area of cropland was greatly reduced after the implementation of GfGP, the contribution of cropland to the LP vegetation greening was still dominant, which was mainly due to the intensive cropland utilization. The positive effect of cropland on ecological restoration indicates its significant potential for future human land‐use management and revegetation management. Key Points: Vegetation greening reduces the sediment yield of the Loess Plateau by 42%Cropland contributes 39.3% to the Loess Plateau vegetation greening, higher than grassland (36.3%) and forestland (14.3%)Cropland dominates the Loess Plateau greening, mainly due to the improvement of cropping intensity [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation. [ABSTRACT FROM AUTHOR]

To examine agricultural impacts on stream food webs, we used gut content analysis (GCA) and stable isotope analysis (SIA) to investigate the influence of riparian forest buffers on the diet and trophic position of five stream fish species in a row‐crop agricultural watershed. Six study streams were divided into two categories (forested and agricultural) based on percentages of riparian forest and agriculture in the watershed. Results suggest alterations in riparian vegetation (forested vs. cropland) had strong impacts on instream characteristics and basal resources that affected prey abundances and fish trophic structure. Although differences varied among species, GCA revealed fish in forested streams had a more diverse diet that included greater numbers of terrestrial invertebrates and aquatic invertebrate predators, whereas fish in agricultural streams consumed more aquatic herbivores and decapods. Diet shifts of the omnivorous creek chub followed differences in basal resource biomass, with greater proportions of algae consumed in agricultural sites contrasted by greater detritus consumed in forested sites. SIA Bayesian mixing‐model results supported diet differences shown by GCA, including higher contributions of terrestrial invertebrates to fish diets in forested streams. However, the isotopic overlap of aquatic invertebrates in agricultural streams limited diet contribution comparisons. SIA indicated fish trophic position was higher in forested than agricultural streams for all species, but GCA was required to identify that greater consumption of predatory prey likely caused this widespread change in fish trophic structure. Results highlight the benefits of integrating SIA and GCA and the importance of riparian forests to stream fishes in agricultural watersheds. [ABSTRACT FROM AUTHOR]

Timely and accurate acquisition of cropland spatial distribution data is of great significance for agricultural production management, planting structure adjustment and food security. In this study, three northeastern provinces (Heilongjiang, Liaoning and Jilin) were selected as the research area. Based on massive Sentinel-2 data, a 12-month Normalized Difference Vegetation Index (NDVI) dataset of cropland and non-cropland samples in a specific year was established in each province. The Google Earth Engine remote sensing computing platform was used to conduct supervised classification according to the difference characteristics of NDVI between cropland and non-cropland samples, and the spatial distribution data set of cropland resources with 10 m spatial resolution in the three northeastern provinces during 2020-2022 was obtained. The data set is an update of the latest available cropland resource data set, which can provide data support and scientific services for the scientific protection of phaeozem in Northeast China, the implementation of the strategy of "storing grain in land, storing grain in technology" and the guarantee of food security. [ABSTRACT FROM AUTHOR]

Abstract Enhanced rock weathering (ERW) on croplands has emerged as an economically and ecologically promising negative emissions technology. However, estimated total carbon sequestration potential from ERW on croplands and its potential sensitivity to climate conditions requires further understanding. Here we combine 1‐D reactive transport modeling with climate model experiments to simulate ERW on ∼1,000 agricultural sites globally. Applying a fixed rate of 10 tons of basalt dust per hectare on these sites sequesters 64 gigatons of CO2 over a 75‐year period; when extrapolated to all agricultural land, ERW sequesters 217 gigatons of CO2 over the same time interval. However, we find that a significant fraction of applied basalt does not weather even on a multidecadal timescale, indicating the need to optimize application strategies for cost effectiveness. We find that ERW becomes modestly more effective with global warming and predict that the payback period for a given ERW deployment is significantly shorter in hot and humid environments currently coinciding with relatively low per‐capita incomes. These results provide strong impetus for investment in agricultural reform in developing economies and highlight an additional potential co‐benefit of ERW.

Mapping and monitoring cropland extents are among the most appreciated element for resource management, monitoring, and assessment. India, being the most densely inhabited and agronomical country, mapping high-resolution cropland extent provides appreciated statistics and data for various resource management practices from an individual farm unit to larger administrative units. However, currently available cropland extent maps at global, continental, and regional extents are derived from coarser resolution satellite imageries. For smaller administrative unit with areas less than 10,000 Ha, these coarse resolution maps, often misclassify, resulting in uncertainties in accurate cropland accuracy. To overcome these limitations, in this study an effort is made to develop 10-m fine-resolution cropland extent maps using multi-temporal Sentinel-2 datasets for a watershed in Tadepalligudem, India. The selected study area is an agriculture dominant watershed covering an area of 5375 Ha. The satellite datasets used for the present study are selected with minimum cloud cover and are time-composited over three crop seasons (Kharif, Rabi, and Zaid). With the successful advent of machine learning methods in remote sensing applications, cropland extent maps are derived using Random Forest (RF) and Support Vector Machine (SVM) supervised machine learning algorithms in R-Studio, an R-programming interface. RF and SVM are CART-based machine learning algorithms, where the performance of RF depends on the number of trees (“ntrees”) and the number of sampled features (“mtry”), while for SVM it depends on Kernel functions used. To rejoice the need to develop a fine resolution, a comparative analysis is performed between 10-m resolution Sentinel-2 derived cropland extent maps and commonly used 30-m resolution Landsat-8 OLI-derived products. Training and testing samples collected from the field observations and high-resolution Google Earth Pro-is divided in a ratio of 75:25. In the present study, the accuracy of the classified cropland extent maps are validated with ground truth for both satellite sources and machine learning methods. The accuracy of the maps is assessed using Overall Accuracy (OA), and kappa coefficient derived from the confusion matrix. With an OA and kappa co-efficient of 92.8% and 0.86 (RF) respectively and 94.3% and 0.89 (SVM) respectively, classified cropland extent maps from Sentinel-2 datasets have been shown to outperform Landsat-8 derived maps. From the comparative analysis between the two machine learning methods, classified cropland extent maps obtained using the SVM algorithm, have been shown to outperform maps obtained using RF with greater OA and kappa coefficient values. The methodology adopted in this study finds its potential in delineating cropland from non-cropland regions predominantly in small agriculture watersheds with an accuracy of 0.01 ha per pixel.

Planning and management of water resources is crucial to enhance agricultural productivity and ensure food security in drylands. For this, adaptable and reliable runoff prediction models are urgently needed to support water harvesting and irrigation development. In this study we explored the potential of the runoff Curve Number (CN) method to estimate daily surface runoff from representative land use types and management conditions in Tigray (north Ethiopia). For this, we use the National Engineering Handbook (NEH-Tables) to derive CN values for runoff plots treated with and without soil and water conservation (SWC) structures. Moreover, the rainfall-runoff data collected from 21 large (600–1000 m2) runoff plots during three years (2010 to 2012) were used to calculate CN-values. Results show that CN values derived from the NEH-Tables are larger by 21% compared to those values calculated from plot data. The calculated CN values vary widely (34 to 91) in response to land use type, slope gradient, and applied SWC structures. Our results show that land use and SWC structures strongly influence runoff production. Considering plots with otherwise similar characteristics, CN values are consistently larger for rangeland sites as compared to those values for cropland sites. Likewise, SWC structures greatly reduce runoff production and their presence also leads to a clearly lower performance of the CN model. The results of this study are relevant contributions towards addressing several goals of agenda 2063 of the African union either directly or indirectly and also international sustainable development goals (SDG). Based on our analysis and field observations, we propose calibrated CN values that may be used for runoff yield assessments in similar environments.

Cropland monitoring is important for ensuring food security in the context of global climate change and population growth. Freely available satellite data allow for the monitoring of large areas, while cloud-processing platforms enable a wide user community to apply remote sensing techniques. Remote sensing-based estimates of cropped area and crop types can thus assist sustainable land management in developing countries such as Ethiopia. In this study, we developed a method for cropland and crop type classification based on Sentinel-1 and Sentinel-2 time-series data using Google Earth Engine. Field data on 18 different crop types from three study areas in Ethiopia were available as reference for the years 2021 and 2022. First, a land use/land cover classification was performed to identify cropland areas. We then evaluated different input parameters derived from Sentinel-2 and Sentinel-1, and combinations thereof, for crop type classification. We assessed the accuracy and robustness of 33 supervised random forest models for classifying crop types for three study areas and two years. Our results showed that classification accuracies were highest when Sentinel-2 spectral bands were included. The addition of Sentinel-1 parameters only slightly improved the accuracy compared to Sentinel-2 parameters alone. The variant including S2 bands, EVI2, and NDRe2 from Sentinel-2 and VV, VH, and Diff from Sentinel-1 was finally applied for crop type classification. Investigation results of class-specific accuracies reinforced the importance of sufficient reference sample availability. The developed methods and classification results can assist regional experts in Ethiopia to support agricultural monitoring and land management.

Crop harvested carbon (HC) is one of the most important components of the carbon cycle in cropland ecosystems, with a significant impact on the carbon budget of croplands. China is one of the most important crop producers, however, it is still unknown on the spatial and temporal variations of HC. This study collected statistical data on crop production at the province and county levels in China for all ten crop types from 1981 to 2020 and analyzed the magnitude and long-term trend of harvested crop carbon. Our results found a substantial increase of HC in cropland from 0.185 Gt C yr ^−1 in 1981 to 0.423 Gt C yr ^−1 in 2020 at a rate of 0.006 Gt C yr ^−1 . The results also highlighted that the average annual carbon sink removal from crop harvesting in China from 1981 to 2020 was 0.32 Gt C yr ^−1 , which was comparable to the net carbon sink of the entire terrestrial ecosystems in China. This study further generated a gridded dataset of HC from 2001 to 2019 in China by using jointly the statistical crop production and distribution maps of cropland. In addition, a model-data comparison was carried out using the dataset and results from seven state-of-the-art terrestrial ecosystem models, revealing substantial disparities in HC simulations in China compared to the dataset generated in the study. This study emphasized the increased importance of HC for estimating cropland carbon budget, and the produced dataset is expected to contribute to carbon budget estimation for cropland ecosystems and the entire China.

Quantifying soil conservation service is critical for sustainable land management. However, the traditional algorithm includes a gap between barren land and real soil erosion (BSERef), and its paradox is that a mass of barren land rarely exists in ecologically adaptable areas with fertile soils, which might overestimate soil conservation service. This study suggests a practical algorithm to effectively evaluate soil conservation service of different ecosystems. We propose a new algorithm based on the difference between cropland without control practices and real soil erosion (CSERef), which is compared with the BSERef algorithm. Our results show that: (1) a nearly five times smaller mean soil conservation amount of cropland occurred in the CSERef algorithm (3.76×106 t·yr–1) than in the BSERef algorithm (20.04×106 t·yr–1); (2) land use has higher explanatory power for the spatial differentiation of soil conservation rate in the CSERef algorithm (15.93% – 46.34%) than in the BSERef algorithm (5.95% – 44.49%). Our results demonstrate that the BSERef algorithm overestimates the soil conservation service of cropland in ecologically adaptable areas, whereas the CSERef algorithm can effectively assess the influence of land use change induced by anthropogenic activities on soil conservation service. Furthermore, we develop an assessment framework in terms of land use classification system following the Chinese Academy of Sciences. The framework considers that for water bodies, there is no soil conservation service; for non-ecologically adaptable areas (unused land) with vast barren lands and vegetation cover below 5%, the BSERef algorithm is recommended; the CSERef algorithm is suggested to use in ecologically adaptable areas (including woodland, grassland, cropland and construction land). This assessment framework can provide scientific assistance for decision-makers to formulate strategies for sustainable land management.

Abstract Long‐term extending cultivation activities resulted in the world's worst soil erosion on the Chinese Loess Plateau (LP). By converting cropland into vegetated land, the Grain for Green Project (GfGP)—the world's largest investment revegetation project—effectively alleviates the soil erosion on the LP. However, during the GfGP implementation, the positive effect of cropland to the revegetation and soil erosion control has been underestimated to date, hindering a comprehensive evaluation to the effect of cropland on ecological restoration. Here, we evaluated the effect of the GfGP on soil erosion control across the LP, analyzed the dominant driver of the LP vegetation greening, and further identified the contributions of croplands to this world's largest revegetation project. We found that the vegetation of the LP was significantly improved and its leaf area increased by 1.23 × 105 km2 after the implementation of the GfGP, which contributed 42% to the decrease of the LP soil loss. Among them, our results show that cropland contributed 39.3% to the increased leaf areas of the LP, higher than grassland (36.3%) and forestland (14.3%). With the reduction of agricultural area, the contribution of cropland to the increased leaf areas in the LP was still the largest, which was mainly due to the increase in cropland utilization intensity. This study highlights the significance of the GfGP in soil erosion control and revises our understanding of the role of cropland in ecological restoration and society development.

IntroductionSoil extracellular enzymes are central in terrestrial ecosystem responses to climate change, and their research can be crucial for assessing microbial nutrient demand. However, the effects of climate-induced precipitation patterns on soil microbial nutrient demand in different soil profiles of agroecosystems are rarely studied.MethodsHere, we present how the precipitation gradient affects soil enzymes related to carbon (C), nitrogen (N) and phosphorus (P) cycling and identified microbial nutrient limitation determinants at five depth intervals (0–10, 10–20, 20–30, 30–40, and 40–50 cm) in seven agroecosystems.Results and DiscussionWe found that N- and P-acquiring enzymes have a tendency to decrease or increase, respectively, but C-acquiring enzymes did not change along the precipitation gradient throughout soil profiles. Soil pH and moisture were the most important factors affecting the enzyme activity in 0–50 cm. Our results also revealed a crucial soil boundary (at 20 cm) that differentiated responses of microbial nutrient limitation to precipitation changes. In the topsoil (0–20 cm), the stoichiometry of soil nutrients did not vary with precipitation. Microbial P limitation was exacerbated with increased precipitation, which was controlled by soil pH and moisture in the topsoil. In contrast, in the subsoil (20–50 cm), soil nutrient stoichiometry decreased with increasing precipitation, and microbial C and P limitation displayed a positive correlation with precipitation. Furthermore, microbial P limitation tended to be stronger in the subsoil than in the topsoil along the precipitation gradient. Microbial C and P limitation was regulated by the soil nutrients and their stoichiometry in the subsoil. Our study is an essential step in soil enzyme activity and stoichiometry response to precipitation in agroecosystems and provides novel insights into understanding microbial nutrient limitation mechanisms in soil profiles along the precipitation gradient.

Mapping agricultural land cover data is important as an effort to support national food security, especially in Boyolali, Central Java Province, Indonesia which is one of the national rice granaries. However, mapping in the mountain slope area using optical data only is challenging due to cloud cover. The development of remote sensing technology has encouraged the possibility to integrate data with different sensors. This data integration is needed to optimize the ability to detect and map cropland that has a variety of characteristics. Therefore, this study aims to identify the cropland through the integration of time-series optical and Synthetic Aperture Radar (SAR) data. Detection of cropland was carried out using 2021 data. Polarisation of VV, VH, and ratio of VV/VH data was derived from the Sentinel-1, whereas image indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil Adjusted Vegetation Index (SAVI) data were obtained from Sentinel-2. Data of Sentinel-1 and Sentinel-2 was combined and several features were selected based on their importance score. Random Forest (RF) classification was then performed. The result show that the mapping using integrated data could improve the accuracy. This indicates the possibility of data to be implemented in further studies such as the cropland type mapping and the estimation of food productivity. [ABSTRACT FROM AUTHOR]

A remote sensing (RS) platform consisting of a remote-controlled aerial vehicle (RAV) can be used to monitor crop, environmental conditions, and productivity in agricultural areas. However, the current methods for the calibration of RAV-acquired images are cumbersome. Thus, a calibration method must be incorporated into RAV RS systems for practical and advanced applications. Here, we aimed to develop a standalone RAV RS-based calibration system without the need for calibration tarpaulins (tarps) by quantifying the sensor responses of a multispectral camera, which varies with light intensities. To develop the standalone RAV-based RS calibration system, we used a quadcopter with four propellers, with a rotor-to-rotor length of 46 cm and height of 25 cm. The quadcopter equipped with a multispectral camera with green, red, and near-infrared filters was used to acquire spectral images for formulating the RAV RS-based standardization system. To perform the calibration study process, libraries of sensor responses were constructed using pseudo-invariant tarps according to the light intensities to determine the relationship equations between the two factors. The calibrated images were then validated using the reflectance measured in crop fields. Finally, we evaluated the outcomes of the formulated RAV RS-based calibration system. The results of this study suggest that the standalone RAV RS system would be helpful in the processing of RAV RS-acquired images. [ABSTRACT FROM AUTHOR]

Cropland on steep slopes is crucial for smallholders to maintain food security and their livelihoods, but the social‐ecological predicament on global steeply sloped cropland is largely overlooked and remains unclear. Here we find that global steeply sloped cropland supports ∼168 million people's livelihoods. Large disparities in population pressure on steeply sloped cropland exist across the development spectrum, among which lower‐middle‐income countries have the highest population density (∼222 per km2) and low‐income countries have the largest population growth rate (∼62.1% from 2000 to 2020). Moreover, inhabitants of steeply sloped cropland, especially those belonging to low‐income countries, are marginalized from public services and face more environmental pressure represented by severe soil erosion and low cropland productivity. Our findings imply that residents of steeply sloped cropland deserve more attention to eradicate global poverty and inequality. Plain Language Summary: Steeply sloped areas support many people's livelihoods. Meanwhile, these areas are socially and ecologically fragile. Many of the dwellers in steeply sloped areas are smallholder farmers who are vulnerable to poverty and hunger, especially in developing and transition countries. Understanding the social‐ecological predicament on the global steeply sloped cropland is crucial for alleviating the contradiction between environmental degradation and economic development in steeply sloped areas and achieving the UN Sustainable Development Goals with "no one left behind." Our findings show that steeply sloped cropland supports ∼168 million people's livelihoods, and these people are marginalized from public services and face more environmental pressure. We identify these lagging regions to efficiently allocate resources, propose targeted decision‐making, and call for more attention to the plight of steeply sloped cropland residents to reduce global inequality. Key Points: Around 168 million people worldwide depend on the approximately 1 million km2 of cropland on steep slopes for their livelihoodsPopulation living around steeply sloped cropland has grown from 2000 to 2020, with low‐income countries having the highest growth ratePeople that rely on steeply sloped cropland are more likely to experience ecological poverty and exclusion from modern society [ABSTRACT FROM AUTHOR]

The study explores the usefulness of infra-red CO2 gas analyzer in capturing diurnal and seasonal variations in atmospheric CO2 over three ecosystems, viz., mixed forest plantation (HFS), deciduous Sal forest (BFS), and sugarcane–wheat cropland (SFS). Half-hourly datasets of CO2 and meteorological parameters collected in the respective study sites were analyzed to depict the diurnal, seasonal, and annual variation in atmospheric CO2. The influence of meteorological variables on atmospheric CO2 concentration was depicted through biplot technique based on principal component analysis. Minimum and maximum CO2 values observed in late-afternoon and pre-dawn period, respectively. Among all the sites under study, SFS exhibited the highest range [438.55 ppm (pre-dawn maximum) – 345.99 ppm (late-afternoon minimum)] of CO2 concentration. Minimum CO2 concentration was observed in monsoon and post-monsoon seasons for HFS/BFS and SFS, respectively. Also, maximum "morning down-drop" was observed in monsoon and post-monsoon season in HFS/BFS and SFS, respectively which can be ascribed to rapid consumption of CO2 concentration due to higher biomass. Highest CO2 concentration was observed in SFS owing to the more anthropogenic emission in terms of biomass burning data obtained through long-term Fire Energetics and Emission Research version 1.0. The peak in normalized difference vegetation Index derived from MODIS satellite across three sites coincides with months of the lowest atmospheric CO2 due to strong photosynthetic activity. [ABSTRACT FROM AUTHOR]

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using artificial neural network (ANN) models relating locations of recent past conversions (2007–2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil, and accessibility. Cross-validation of the models indicated good performance with area under the curve (AUC) values of 0.88–0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83–0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments. [ABSTRACT FROM AUTHOR]

Non‐grain cropland is a typical phenomenon with some common characteristics globally. As the most populous country, China's non‐grain cropland threatens global food supply stability and affects food production sustainability under the current complex situation of international food security. This paper used Sichuan Province, China, to explore the formation mechanism of non‐grain cropland and its potential impact on sustainable productivity, given the regional environment complexity and socioeconomic representativeness. High‐precision land survey data were used to detect multiscale non‐grain influencing factors and discuss non‐grain impacts on soil state to fill the knowledge gap on microlevel development of non‐grain issues. Results show that rotation type is the region's most abundant and widely distributed cropland type, accounting for about 59.38% of the total cropland area; the second is abandonment type. Urbanization indicators are the common factors affecting the non‐grain spatial distribution on the global scale; environmental variables' impact on non‐grain shows significant differences in geographical locations on the local scale. Overall, cash type and intercrop type aim at improving production conditions. Rotation type and fallow type mostly occur in areas with low production capacity and are prominently driven by policies. Abandonment type is subject to terrain constraints, production conditions, and social economy. Additionally, non‐grain trends have not caused significant damage to soil sustainable productivity except for the positive promotion of field biodiversity. Non‐grain cropland can be divided into three clusters according to the corresponding average soil fertility state, rotation, intercrop, cash, abandonment, and fallow. Policymakers must implement specific policies for different cropland types and formulate more refined farming management plans. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on the soil properties and crop-vegetation dynamics. Mostly coarse-scale meteorological forcing is used to estimate only the water balance at the catchment outlet. The mesoscale Hydrologic Model (mHM) is one of the rare models that incorporate remote sensing data, i.e., leaf area index (LAI) and aspect, to improve the actual evapotranspiration (AET) simulations and water balance together. The user can select either LAI or aspect to scale PET. However, herein we introduce a new weight parameter, "alphax", that allows the user to incorporate both LAI and aspect together for potential evapotranspiration (PET) scaling. With the mHM code enhancement, the modeler also has the option of using raw PET with no scaling. In this study, streamflow and AET are simulated using the mHM in The Main Basin (Germany) for the period of 2002–2014. The additional value of PET scaling with LAI and aspect for model performance is investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) AET and LAI products. From 69 mHM parameters, 26 parameters are selected for calibration using the Optimization Software Toolkit (OSTRICH). For calibration and evaluation, the KGE metric is used for water balance, and the SPAEF metric is used for evaluating spatial patterns of AET. Our results show that the AET performance of the mHM is highest when using both LAI and aspect indicating that LAI and aspect contain valuable spatial heterogeneity information from topography and canopy (e.g., forests, grasslands, and croplands) that should be preserved during modeling. This is key for agronomic studies like crop yield estimations and irrigation water use. The additional "alphax" parameter makes the model physically more flexible and robust as the model can decide the weights according to the study domain. [ABSTRACT FROM AUTHOR]

Land use change is a major predictor for variations in soil bacteria, which plays a key role in maintaining soil multifunctionality and function of terrestrial ecosystems. The effects of land use change on the soil bacterial community in an alpine region at the Qinghai-Tibetan Plateau (QTP) were still unclear. In this study, we investigated soil bacterial profiles under grazed grassland, enclosed grassland, continuous cropland, rotation cropland and abandoned cropland in the Tianzhu alpine agro-pastoral ecotone. Our results showed that Proteobacteria, Acidobacteria and Actinobacteria were the most three abundant phyla regardless of land use change, accounting for over 60% of the total. Cultivation declined soil bacterial alpha diversity without recovering even after abandonment. Over 73% variations in bacterial diversity can be explained by soil physical and chemical properties. In addition, soil moisture could be the main driver for the difference in bacterial structure between croplands and others. Soil bulk density, pH, organic carbon and total nitrogen contents seem to be the key factors determining the difference in bacterial structure between abandoned croplands and others. Our results have implications for comprehensive understanding about the responses of the soil bacterial community to land use change in alpine regions. [ABSTRACT FROM AUTHOR]