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Land abandonment in European mountains threatens habitats shaped for centuries by low-intensity agriculture and grazing. Hence, it is important to identify spatiotemporal patterns in rural abandonment, and relate them to biophysical and socioeconomic drivers. We pursued these goals in the theoretical context of transitions from traditional to productivist and then to post-productivist agriculture. We conducted a case study in a representative of southern Europe sub-mountainous marginal area that was once traditionally exploited (Pindus range, Epirus, Greece). Land cover was mapped from the outset of abandonment (years 1945, 1970, 1996 and 2015), and we subsequently calculated landscape metrics. An Intensity Analysis facilitated the comparison of rates of land cover change between time periods. By employing random forest modelling, we related socioeconomic, physiographic, geological and climatic predictors to land type occurrence and succession intensity. We found that farmland decreased from 30% to 3% during the 70 years of the study period, and that forest increased from 22% to 63%. The landscape's heterogeneity, ecotone diversity, and spatial aggregation decreased. Abandonment and succession accelerated and relocated to lower elevation, especially during the latest time period, which was related to a second depopulation wave and livestock decrease. The remaining lowland farmlands were of productivist agriculture, and no widespread post-productivist regime was found. Thus, our study supports the view that policies, which have been mainly based on the linear transition of agricultural regimes in northern Europe, must take into account southern European mountains, where widespread abandonment can coexist with limited intensification and extensification. [ABSTRACT FROM AUTHOR]

Context: Most data collection and analyses in soundscape ecology have focused on summer or breeding seasons in urban or protected landscapes, missing important acoustic dynamics in winter, non-breeding periods and in agricultural landscapes, a land-use that constitutes 39% of ice-free surface globally. Objectives: To address these gaps, we examined the variation of winter soundscapes across a rural agricultural landscape of Nebraska, USA. We compared high and low traffic sites, testing if traffic levels affected soundscape structure. Methods: We recorded sound over two winters at 19 sites located adjacent to major and minor roadways. We calculated eight unique soundscape indexes to quantify the soundscape over time as a function of traffic and land cover. We applied filters at 80, 1000, and 2000 Hz. Results: We found clear statistical differences between high and low traffic sites in 7 of 8 soundscape indexes. Soundscape varied throughout the day, but not throughout the season. There was a clear negative correlation between technophony (human-derived sounds) and biophony (ecologically derived sounds) across sites. We found that not all indices may be suitable for all ecosystems. Conclusions: We quantified the effects of noise pollution on the soundscape of understudied habitats during winter months. By using soundscape indexes as surrogates for biodiversity, acoustic sampling could be an effective method for monitoring biodiversity when traditional methods may be ineffective or too costly. However, caution needs to be taken when choosing indices. [ABSTRACT FROM AUTHOR]

In recent years, Kabul city's rapid urbanization has adversely affected the urban land cover, such as surface water bodies and croplands. Surface water resources are threatened due to overpopulation in the city either qualitatively or quantitatively, also croplands are being lost with the development of urbanization activities through the city. To monitor and assess surface changes accurately, we classified the city area using satellite images of both Landsat-8 and Sentinel-2 and compared both of their findings. The Support Vector Machine classifier was applied to multi-senor data to classify four different land categories using the same training sites and samples with the same period. All the procedures were conducted in Google Earth Engine (GEE) cloud platform. The surface reflectance bands of both satellites were used for classification. Confusion matrixes were created using the same reference points for Sentinel-2 and Landsat-8 classification to compare the results and determine the best approach for classification of land cover. Results show that overall accuracy was 94.26% for Sentinel-2 while it was 85.04% for Landsat-8, similarly, the Kappa coefficient was calculated 91.7% and 78.3% for Sentinel-2 and Landsat-8, respectively. [ABSTRACT FROM AUTHOR]

Local habitat and riparian modifications imposed by surrounding land use drastically impact the water quality of streams. However, whether these effects could still be discernible when the watercourse also receives urbanization effluents has not been fully explored. We evaluated the water quality of a Neotropical prairie stream exposed to urbanization and explored the role of downstream patches of different surrounding land uses (cropland and livestock) in further regulating water quality. Forty-two variables of water quality, habitat structure and riparian condition were measured at four reaches of the Langueyú stream. Significant differences in water quality were observed. Water conductivity, dissolved oxygen, salinity, dissolved solids, chloride, inorganic nitrogen and bacteriological loads displayed a continuum of recovery from the urban reach. Indeed, almost 24 percent of the total variation in water quality was explained by the longitudinal arrangement of sites. Alternatively, pH, phosphorous, suspended solids and chemical oxygen demand showed a disruption in this continuum of recovery and were highly related with local aspects of habitat structure and riparian conditions imposed by cropland and livestock. Key aspects of effluent treatment, riparian integrity and in-stream habitat must be addressed within a comprehensive social context in order to design sustainable management of fluvial urbanised ecosystems. [ABSTRACT FROM AUTHOR]

In Mongolia, greenhouse gas (GHG) and emission factors (EFs) from land-use categories including cropland, wetland, and peatland, have not been estimated. The main purpose of this study was to estimate GHG EF based on field measurement data for the wetland, peatland, and cropland using the IPPC guideline’s Volume-2 method. The determination of EFs for the land-use categories is crucial as it contributes to the national GHG inventory. Study sites were selected at the wetland, peatland, and cropland in (1) Khurkh river valley, and (2) near Ulaanbaatar city. Fieldwork was conducted during the spring and summer seasons of 2020-2021 and CO2 concentrations at study sites were measured using EGM-4 equipment with replicates (n=87). At the study sites, soil samples were collected from four different depths including 0-5 cm, 5-10 cm, 10-15 cm, and 15-30 cm. As the results, estimated EFs (± standard error) of the CO2 and CH4 were 357.2 g [CO2] m-2 yr-1 (±125.7) and 18.8 g [CH4] m-2 yr-1 (±6.6) in the peatland, 193.4 g [CO2] m-2 yr-1 (±53.8) and 10.2 g [CH4] m-2 yr-1 (±2.8) in the wetland, and 118.0 g [CO2] m-2 yr-1 (±29.4) and 6.2 g [CH4] m-2 yr-1 (±1.5) in the cropland. As these EFs measurements were determined by direct-field measurement method with seasonal and daily replicates within a year, they can be used as a “default value” for land-use categories in Mongolia. [ABSTRACT FROM AUTHOR]

Accurate regional identification of cropland quantities and spatial distributions is important for cropland monitoring, food security, and sustainable regional development. Various countries and organizations have produced series of land-cover products. However, variability among remote sensors, land-cover classification schemes, and classification methods has resulted in discrepancies. In this study, we develop a novel method to improve cropland data accuracy for the Belt and Road (B&R) region, by fusing and correcting four cropland products: CCI-LC, GFSAD30, MCD12Q1, and FROM-GLC. Spatial analysis techniques are implemented, including climate stratification, consistency assessment, and statistical filtering, to develop training samples for model correction. The Google Earth Engine (GEE) platform and random forest (RF) algorithm are executed with these training samples to correct fused multi-data product and generate a corrected 2015 cropland product. The corrected product indicates that cropland accounts for 14.94% of the B&R region, which is closer to the results found via FAO statistics than the results from any of the four individual land-cover products. On the national scale, the root mean square error between the corrected cropland product quantities and FAO statistics is 11.39% and the correlation coefficient value is 0.77. This indicates that the method exhibits better fitting characteristics. The accuracies of the areas of inconsistency among the four cropland products and our corrected product are assessed using 3112 visually interpreted samples and Google Earth. The overall accuracy of the corrected cropland product is 77.54% in inconsistent areas. The highest accuracy produced by the corrected cropland product indicates the effectiveness of our method, which can rapidly improve cropland data accuracy in heterogeneous regions. Combining the training samples produced by fusing existing cropland products and updating techniques with multi-source remote sensing data from the GEE platform, we foresee potential applications to update global cropland product.

We have analyzed the loss of agricultural land in a case study of the community Eggelsberg in the Innviertel in Upper Austria. The trigger for the project was the attribution of Austria as Europe’s leader in soil sealing. We compared the results of the Austrian agricultural soil survey of the 1970s with the actual land use and quantified the land use change. Within half a century, 15% of the agricultural area was converted to settlement area or to other forms of infrastructure. According to the original soil assessment, 96 ha of high-quality cropland, 1,220 ha of medium-quality cropland, 121 ha of low-quality cropland, and 409 ha of grassland were converted. The forest cover remained stable. The highest share of soil sealing was owed to the expansion of the settlement area and the construction of a successful enterprise. Minor contributions were expansions of farm buildings. The loss of agricultural land was reasonable and reflected the needs of the local population. A special protection status because of the future relevance of the agricultural land was not communicated to land managers and is not reflected in the market prices of agricultural products. The relevance of the agricultural land beyond the regional level is not fully recognized.

We report soil carbon stock (SCS) and nutrient characteristics of a pure stand of Senna siamea grove in comparison with adjacent cropland using t-test. This study was conducted in 2018 at the Kwame Nkrumah University of Science and Technology, Kumasi. Soil sampling up to 50 cm depth was carried out from five subplots in each ecosystem. The SCS of the grove at 0–15 cm depth was over 100% greater (30.78 Mg/ha) than that of the cropland (15.16 Mg/ha). Soil pH and total N content of the grove were 5.75 ± 1.22 and 0.10 ± 0.03% in the topsoil (0–15 cm) and 5.52 ± 0.80, 0.06 ± 0.01% and 5.03 ± 1.22, 0.04 ± 0.01% in the 15–30 and 30–50 cm depths, respectively. Although these values were greater in the grove than the cropland, the available phosphorus content was 3–4 fold greater in the latter soil. The two ecosystems affected soil organic carbon and total nitrogen contents significantly (p < 0.05) only in the topsoil, but had a significant influence on soil available phosphorus in both the topsoil and the subsoil. Sand content of the grove seemed to explain greater variability in its SCS (R2 = 0.81) than clay content. The greater SCS of the Senna grove demonstrates its role in soil carbon storage in tropical climate in the era of climate change.

In the study, a method of automated detection of croplands and natural grasslands using multi-year time series of the vegetation indices was developed and implemented. The metho­dology is based on a new recognition feature enabling identification of all lands cultivated over a long period. The new recognition feature was calculated from time series of the MODIS vegetation indices. It takes into account both intraseasonal and interannual characteristics of the vegetation period for arable lands with spring and winter crops, as well as for natural grasslands. The methodology determines: the procedure of obtaining time series of the NDVI and EVI vegetation indices for the period from March to November in the given years; gene­ralization of the time series by land cover categories and agro-climatic zones; dates for calculating the recognition feature in each zone; calculation of the recognition feature; adaptation of the decision rules to the regional environmental conditions (threshold values for the agro-climatic zones); procedure of classification into croplands and grasslands. Croplands within the territory of the European part of Russia were mapped for the period from 2000 to 2016. The assessment of recognition accuracy and comparison of the results with the existing products confirm that the developed recognition feature can be successfully used for cropland detection and can improve the accuracy of data interpretation when applied along with other features.

The objective of this study was to find out how land use affects the soil microbial attributes in different soil types and to which depth. The study was performed in Slovakia (Europe) in three areas differing in soil type (Chernozem, Stagnosol, Cambisol). Within each area, three localities with different land use (forest, grassland, cropland), representing a gradient with different intensity of management, were chosen. The soil samples were taken along a single soil profile up to a depth of 1 m with 10 cm increments at each locality. In the soil samples, the basic soil chemical properties and microbial attributes were determined. The effect of the land use on the microbial biomass and basal respiration was mainly observed in the Chernozem in the top 30 cm, while in the Stagnosol, no difference in the trend in the microbial biomass between the different ecosystems along the soil profile was found. The N-mineralisation reflected the different management practices especially in the Cambisol in the top 20 cm. The most distinct differences in the catalase activity between the soils differing in land use were found in the Cambisol along the whole profile. The richness and diversity of the functional groups did not differ significantly between the soils with the different land use and also no uniform responses of the functional groups composition to the land use were observed. The microbial biomass and activity were mainly affected by the amount of the soil organic matter; the intensity of the impact differed according to the soil type.

Water resources become scarce due to the melting of glaciers caused by climate change, and water pollution caused by human activities and overuse of freshwater resources are aggravating this phenomenon. Cropland is an essential part of the terrestrial ecosystem, and it is of great importance to make rational use of the limited water resources to have higher water use efficiency (WUE). The quantitative estimation of the ecosystem WUE and tracking its dynamics of croplands is crucial for addressing the impact of future climate change on crop production. Scaling up field observations of flux towers to a large scale remains a challenge and there are few reports on estimating WUE directly from remote sensing data. This study examined the main environmental factors that contribute to changes in WUE of typical crops, and found that the EVI showed strong correlation with the ecosystem WUE of typical croplands, which can be developed to track ecosystem WUE. This study proposed an alternative method exclusively based on MODIS EVI data analysis with an R2 of nearly 0.90 and a small RMSE, which was much better than the calculations of the MODIS GPP and ET products, and provided an accessible method for modelling the ecosystem WUE to effectively manage water use in croplands.

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.

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

Land use change affects soil aggregate composition and stability, which impacts soil structure and health. To reveal how land use change impacted soil aggregates of alpine grassland in a high-altitude watershed, soil samples from 161 sites including alpine grassland, cropland and abandoned land were selected to measure and analyze the distribution of aggregate fractions (macro-aggregates, micro-aggregates, silt+clay), soil aggregate stability (mean weight diameter, geometric mean diameter, fractal dimension, etc.) and related soil properties (soil organic carbon content, soil particle composition, etc.) in the Huangshui River watershed of the Qinghai–Tibet Plateau. The results showed: (1) As alpine grasslands were converted to croplands and croplands to abandoned lands, the proportion of macro-aggregates and the aggregate stability index showed a trend of first decreasing and then increasing (p < 0.05), indicating that tillage and abandonment have significant influences on soil aggregate structure. (2) Compared with temperate grassland, alpine grassland had richer soil organic carbon, and a higher ratio of macro-aggregates and aggregate stability. (3) Soil organic carbon and sand content had distinct influences on the fractions and stability of aggregates during land use change. These results suggested that cultivation can substantially reduce the soil aggregate stability in alpine grassland, whereas abandonment can effectively improve soil aggregate structure.

Accurate extraction of cropland distribution information using remote sensing technology is a key step in the monitoring, protection, and sustainable development of black soil. To obtain precise spatial distribution of cropland, an information extraction method is developed based on a fractal algorithm integrating temporal and spatial features. The method extracts multi-seasonal fractal features from the Landsat 8 OLI remote sensing data. Its efficiency is demonstrated using black soil in Lishu County, Northeast China. First, each pixel’s upper and lower fractal signals are calculated using a blanket covering method based on the Landsat 8 OLI remote sensing data in the spring, summer, and autumn seasons. The fractal characteristics of the cropland and other land-cover types are analyzed and compared. Second, the ninth lower fractal scale is selected as the feature scale to extract the spatial distribution of cropland in Lishu County. The cropland vector data, the European Space Agency (ESA) WorldCover data, and the statistical yearbook from the same period are used to assess accuracy. Finally, a comparative analysis of this study and existing products at different scales is carried out, and the point matching degree and area matching degree are evaluated. The results show that the point matching degree and the area matching degree of cropland extraction using the multi-seasonal fractal features are 90.66% and 96.21%, and 95.33% and 83.52%, respectively, which are highly consistent with the statistical data provided by the local government. The extracted accuracy of cropland is much better than that of existing products at different scales due to the contribution of the multi-seasonal fractal features. This method can be used to accurately extract cropland information to monitor changes in black soil, and it can be used to support the conservation and development of black soil in China.

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.

The rapid population growth has intensified the need for increased food production, making cropland resources crucial for ensuring food security. Comprehending the evolutionary trends of cropland resources can aid in making well-informed decisions concerning their judicious utilization and contribute to the enhancement of ecological stability. In this investigation, the focus was directed towards the economic zone of the northern slope of Tianshan Mountain, situated in the arid expanse of northwest China. By analyzing land use data from 2000 to 2020, a range of methodologies, including Lorenz curves, local spatial autocorrelation, and Geodetector were employed to investigate the spatial and temporal distribution patterns, evolution, driving forces, heterogeneity, and driving mechanisms of cropland in this region. The findings revealed that the cropland in research area underwent three distinct stages: steady expansion, rapid expansion, and relative stability. The overall annual increase rate was 11.993%, primarily driven by the conversion of grassland and unused land. The spatial distribution of cropland resources was uneven, with concentrations in the central oasis plains in the form of strips and patches. The center of gravity of cropland exhibited a circular movement pattern of ‘northwest-southeast-northwest’. The migration speed followed a sequence of ‘slow-fast-slow’, and the spatial distribution appeared scattered. The main driving forces influencing cropland change included mean annual temperature, elevation, total grain output and agricultural output value. Among these, socioeconomic factors (0.403, 0.430) exerted a greater influence on the spatial distribution of cropland compared to natural factors (0.167). Most interactions between the factors demonstrated non-linear enhancement and two-factor enhancement. The discoveries of this study can assist decision-makers in formulating conservation strategies for cropland based on the current status of cropland resources and drivers in different regions. Furthermore, these findings can stimulate the coordinated development of cropland, water resources, population, and industries, facilitating informed decision-making across various domains.

Many antibiotics and their related antibiotic resistance genes (ARGs) are found in agricultural soil, raising concerns for public and environmental health. As a result, it is crucial to examine the abundance of sulfonamides, fluoroquinolones, tetracyclines, macrolides, and β -lactam antibiotics and their corresponding ARGs in agricultural soils. We investigted the abundance of aad A, sul 1, aac (6’)-Ib, tet X, tet A, erm B, mph B, qnr A, int I1, int I2, and ISCR 1 genes and explore their relationships with antibiotics in rice, peach orchard, maize, peanut, and garlic croplands along the Yellow River in Zhongmu and Yuanyang, Henan Province. There was no significant difference across the five croplands (P > 0.05), however, the paddy field had the highest average concentration (>10 μ g kg ^−1 ) of oxytetracycline, tetracycline, ciprofloxacin and norfloxacin antibiotics. The predominant ARGs were aad A, sul 1, with the the highest number of ARGs and MGEs detected in paddy rice, followed by maize, peanut, garlic, and peach soils. Integrase gene int I1 was significantly greater than int I2 and ISCR 1, which was positively linked with sul 1, aad A, aac (6′)-Ib, tet A, tet X, and erm B genes (P < 0.05). According to redundancy analysis, antibiotics may accelerated the prevalence of ARGs. Redundancy analysis demonstrated that antibiotics contributed 78.7% of the total ARG variations. Notably, the concentration of oxytetracycline in all the croplands ranged from 10.33 to 14.42 μ g kg ^−1 , which had impacted the frequency of erm B, bla _CTX-M , int I2, and mph B genes as demonstrated by RDA analysis. Results revealed that effective techniques are required to lower the prevalence of ARGs in the agroecosystems.

Port cities in Southeast Asia (SEA) have been undergoing intensive land use and land cover change (LUCC) because of rapid socio-economic development in recent decades. However, various development statuses among these cities could result in divergent LUCC transitions and therefore affect the local ecosystem at different levels. This study investigated the historic land-use transition in the SEA port cities by classifying Landsat images from 1990 to 2020 with the random forest algorithm, and further integrated these patterns with land-use modeling to inform the future land uses under the current developing mode. DIVISON was used to assess land-use fragmentation and investigated its relationship with development levels represented by the average nighttime light index (ANLI). The results showed an intensive LUCC in SEA port cities in 1990–2020, with artificial surface increasing by 9.2% (175.9 km ^2 ) of the total area, mainly converted from cultivated land which decreased by 136.9 km ^2 . An inverted V-shaped relationship between fragmentation and development level was found at the landscape level and for cultivated land, in which both turning points (TPs) occurred in the mid-developed stage (ANLI = 41.1 and 20.0, respectively). Artificial surfaces tended to be more aggregated in later developed stages, showing a TP of ANLI around 53.2. Under the current developing mode, the aggregation trend will be generally continued for the next three decades, mainly resulting from the increased connection in artificial surfaces. The findings also highlighted a tradeoff between city development and agricultural production in SEA port cities, leading to increased food insecurity in more developed stages. Recent developments also negatively affected aqua ecosystems, such as aquaculture, mangroves, and natural waterbodies in several cities. This study underscored the importance of the sustainable LUCC strategy in SEA port cities and provides insights into rationalizing land-use policies for other port cities worldwide.

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.

Land use and cover (LUC) of southern European mountains is dramatically changing, mainly due to observed socioeconomic demands and climatic changes. It is therefore important to understand LUC changes to accurately predict future landscapes and their threats. Simulation models of LUC change are ideal for this task because they allow the in silico experimentation under different socioeconomic and climatic scenarios. In the present study, we employed the trans-CLUE-S model, to predict for 2055 the LUC of a typical southern European sub-mountainous area, which has experienced widespread abandonment until recently. Four demand scenarios were tested, and under each demand scenario, we compared three climatic scenarios, ranging from less to more warm and dry conditions. We found that farmland declined from 3.2% of the landscape in 2015 to 0.4% in 2055 under the business-as-usual demand scenario, whereas forest further increased from 62.6% to 79%. For any demand scenario, differences in LUC between maps predicted under different climatic scenarios constituted less than 10% of the landscape. In the less than 10% that differed, mainly farmland and forest shifted to higher elevation under a warmer and drier climate, whereas grassland and scrubland to lower. Such insights by modelling analyses like the present study’s can improve the planning and implementation of management and restoration policies which will attempt to conserve ecosystem services and mitigate the negative effects of socioeconomic and climatic changes in the mountainous regions of southern Europe.

Changes in climate and land use are major drivers of biodiversity loss. These drivers likely interact and their mutual effects alter biodiversity. These interaction mechanisms are rarely considered in biodiversity assessments, as only the combined individual effects are reported. In this study, we explored interaction effects from mechanisms that potentially affect biodiversity under climate change. These mechanisms entail that climate-change effects on, for example, species abundance and species' range shifts depend on land-use change. Similarly, land-use change impacts are contingent on climate change. We explored interaction effects from four mechanisms and projected their consequences on biodiversity. These interactions arise if species adapted to modified landscapes (e.g. cropland) differ in their sensitivity to climate change from species adapted to natural landscapes. We verified these interaction effects by performing a systematic literature review and meta-analysis of 42 bioclimatic studies (with different increases in global mean temperature) on species distributions in landscapes with varying cropland levels. We used the Fraction of Remaining Species as the effect-size metric in this meta-analysis. The influence of global mean temperature increase on FRS did not significantly change with different cropland levels. This finding excluded interaction effects between climate and landscapes that are modified by other land uses than cropping. Although we only assessed coarse climate and land-use patterns, global mean temperature increase was a good, significant model predictor for biodiversity decline. This emphasizes the need to analyse interactions between land-use and climate-change effects on biodiversity simultaneously in other modified landscapes. Such analyses should also integrate other conditions, such as spatial location, adaptive capacity and time lags. Understanding all these interaction mechanisms and other conditions will help to better project future biodiversity trends and to develop coping strategies for biodiversity conservation. [ABSTRACT FROM AUTHOR]

Although agricultural land abandonment (LA) is accompanied by land degradation, it could be considered a kind of self-rehabilitation. Studies have shown that long-term LA has profound ecological and environmental benefits, whereas few studies have compared LA with human intervention (HI), which involves planting and fertilization in agroecosystem restoration. Here, we established four different scenarios based on local livestock husbandry, including LA without HI, LA with slight human intervention (HIS), medium human intervention (HIM), and intensive human intervention (HII). LA experiments were conducted for 3 years and repeatedly sampled three times. The soil bacterial and fungal communities were determined to present the ecological impacts. In this study, LA and HIS could save soil inorganic carbon and total calcium (Ca) contents and benefit soil mycorrhizal fungi and plant growth-promoting rhizobacteria. LA and HIM benefited some microbial communities associated with complicated organic compounds. Human interference methods did not significantly increase soil nutrients after 3 years of farmland abandonment. However, indigenous vegetation increased the risk of plant diseases based on soil microbial communities. Forage grass may control the risk, and HIS was a cost-effective scenario in our study. Moreover, we should maintain a cautious attitude toward HII to prevent excessive intervention. [ABSTRACT FROM AUTHOR]

To effectively plan and manage the use of agricultural land, it is crucial to identify and evaluate the multiple human and environmental factors that influence it. In this study, we propose a model framework to identify the factors potentially explaining the use of agricultural land for wheat, maize, and olive grove plantations at the regional level. By developing a machine-learning model coupled with a model-agnostic approach, we provide global and local interpretations of the most influential factors. We collected nearly 140 variables related to biophysical, bioclimatic, and agricultural socioeconomic conditions. Overall, the results indicated that biophysical and bioclimatic conditions were more influential than socioeconomic conditions. At the global interpretation level, the proposed model identified a strong contribution of conditions related to drainage density, slope, and soil type. In contrast, the local interpretation level indicated that socioeconomic conditions such as the degree of mechanisation could be influential in specific parcels of wheat. As demonstrated, the proposed analytical approach has the potential to serve as a decision-making tool instrument to better plan and control the use of agricultural land.

The expansion and intensification of agricultural lands (i.e., croplands and pasture areas) have become increasing threats to species diversity around the world. However, genus diversity should be considered for biodiversity conservation due to the important role that phylogenetic diversity and evolutionary history play shaping biodiversity patterns, particularly in the Neotropics, the most species-rich realm. Here, we explored agricultural land effects on distribution patterns of terrestrial vertebrate diversity at the genus level using cases from the Neotropics. We compiled distribution data on genera of amphibians, birds, mammals, and reptiles and explored the effects of agricultural lands on genus diversity distribution patterns for terrestrial vertebrates in the region using distribution modelling. Geography Weighting Regression (GWR) was used to explore the spatially variable relationship between agricultural lands and genus diversity. We found that there are significant, non-stationary relationships between agricultural lands and genus diversity in the Neotropics. The non-stationary relationship between croplands and genus diversity was stronger than that between pasture areas and genus diversity. Furthermore, these effects may depend on changes in genus groups (i.e., amphibians, birds, mammals, and reptiles) and biomes. The relationship was the strongest for the croplands and reptile diversity pair at the genus levels. The relationship between both croplands and pasture areas and reptile diversity was the largest in temperate broadleaf and mixed forests. Given the influence of crop and pasturelands on vertebrate distribution patterns, we argue that agriculture development should be considered in the spatial prioritization of conservation for vertebrates in the Neotropics. Agricultural lands should be used as environmental variables for distribution modelling, and understanding the impacts of agricultural lands on biodiversity is critical for effective conservation management in the Neotropics.

The global agricultural sector needs to implement good soil management practices, in particular to prevent erosion and to improve water-retention capacity. The introduction of tillage techniques along particular theoretical lines, called keylines, can make a significant contribution to improving the management of the soil and agricultural crops. The keyline system has been around for less than 100 years. With this preliminary work, we performed a comparative analysis of two small river basins (less than 100 ha) before and after keyline application, based on GIS computational models (TWI and SIMWE). The calculation models were elaborated starting from a DTM with 2 m resolution, obtained from a LIDAR survey. The comparative analysis, in qualitative terms, showed a positive effect of the keylines, both in terms of erodibility and infiltration of runoff water. The use of GIS models to verify the effectiveness in the planning phase can constitute a decision support system that guides agronomists, technicians, and farmers.

China’s East Asia monsoon zone is undergoing rapid land-use conversion and urbanization. Safeguarding remaining biodiversity requires reducing, mitigating, and/or eliminating the negative impacts of human-induced landscape modification. In this study, we sampled ground-dwelling ants at 40 plots over 12 continuous months in a suburban area in southwestern China to examine whether and how vegetation composition and habitat fragmentation affected species richness and assemblage composition for the general ant community and, specifically, for principal functional groups (including Opportunists and Generalized Myrmicinae). Warmer seasons were associated with a higher capture rate for all functional groups. Patterns of ant species richness among Opportunists were more sensitive to vegetation and fragmentation than for Generalized Myrmicinae, and these effects generally varied with season. Patterns of ant assemblage composition for Opportunists were exclusively sensitive to vegetation, whereas Generalized Myrmicinae were sensitive to both vegetation and fragmentation with variation among seasons. Overall, our findings highlight the important role of seasonality, vegetation composition, and habitat fragmentation in mediating the impacts of human-induced landscape modification on urbanized ant communities, which make an essential functional contribution to biodiversity in the East Asia monsoon zone.

In this paper, we strive to showthat the protection of the ecological environment of the Yellow River can impact regional sustainable development and human society. Based on GIS and historical documents, we selected 1640AD in the late Ming Dynasty and 1726, 1746, and 1856AD in the early and middle Qing Dynasty as time sections to reconstruct the distributions of cropland and vegetation in the Hehuang valley. Our results showed that the cropland in the Ming Dynasty was mainly distributed in the valley of Sainei;during the early and mid-Qing Dynasty, the cropland reclamation broke the boundary of the Great Wall. Furthermore, replacing vegetation with cropland resulted in the rapid decline of water conservation capacity in the medium and high mountain areas. The decline of water conservation capacity significantly contributed to the frequent occurrence of natural disasters, such as drought, flood, water erosion, and sand pressure, which led to decreased cropland output. By the mid-Qing Dynasty, the cropland area had saturated while the population was still growing, and the grain yield could not meet the demands of the expanding population. Due to both natural and social factors, two social upheavals occurred in the late Qing Dynasty, which significantly affected the development of the regional social economy. Therefore, the destruction of the ecological environment and the reduction of water conservation capacity became an important driving force for the destruction of sustainable regional development.

Soil salinization is a widespread environmental hazard and a major abiotic constraint affecting global food production and threatening food security. Salt-affected cropland is widely distributed in China, and the problem of salinization in the Hetao Irrigation District (HID) in the Inner Mongolia Autonomous Region is particularly prominent. The salt-affected soil in Inner Mongolia is 1.75 million hectares, accounting for 14.8% of the total land. Therefore, mapping saline cropland in the irrigation district of Inner Mongolia could evaluate the impacts of cropland soil salinization on the environment and food security. This study hypothesized that a reasonably accurate regional map of salt-affected cropland would result from a ground sampling approach based on PlanetScope images and the methodology developed by Sentinel multi-sensor images employing the machine learning algorithm in the cloud computing platform. Thus, a model was developed to create the salt-affected cropland map of HID in 2021 based on the modified cropland base map, valid saline and non-saline samples through consistency testing, and various spectral parameters, such as reflectance bands, published salinity indices, vegetation indices, and texture information. Additionally, multi-sensor data of Sentinel from dry and wet seasons were used to determine the best solution for mapping saline cropland. The results imply that combining the Sentinel-1 and Sentinel-2 data could map the soil salinity in HID during the dry season with reasonable accuracy and close to real time. Then, the indicators derived from the confusion matrix were used to validate the established model. As a result, the combined dataset, which included reflectance bands, spectral indices, vertical transmit–vertical receive (VV) and vertical transmit–horizontal receive (VH) polarization, and texture information, outperformed the highest overall accuracy at 0.8938, while the F1 scores for saline cropland and non-saline cropland are 0.8687 and 0.9109, respectively. According to the analyses conducted for this study, salt-affected cropland can be detected more accurately during the dry season by using just Sentinel images from March to April. The findings of this study provide a clear explanation of the efficiency and standardization of salt-affected cropland mapping in arid and semi-arid regions, with significant potential for applicability outside the current study area.

Cropland suitability is assessed as a part of the land assessment process, in which arrival capacity is compared to prerequisite requirements. The goal of this research is to determine the various zones of land suitability for crops, as well as the impact of various elements on cropland. Several studies have been made to look into the suitability of cropland in various parts of India and West Bengal, but the Jungle Mahal region in the extreme western part of West Bengal, India, has not been considered, despite the fact that the livelihood of the socially backward population is largely dependent on crop cultivation. The Jungle Mahal region is in the far western section of West Bengal, India, and the people who reside there are mostly from a backward group that rely on agricultural cultivation for a living. As a result, it is critical to understand how the areal layout influences crop adaptability and which factors contribute to restrict the agricultural activity in this area. To achieve the goal of the study, the multi-dimensional parametric dataset has been used, likely topographical data, soil map, soil moisture, land use/land cover, soil texture, drainage density, slope, soil depth, and soil PH along with satellite data obtained from various government and non-government organisations. Researchers have used the Multiple Influencing Factors (MIF) technique to evaluate the weightage of various influencing variables or thematic layers, and then weighted overlay analysis to use geospatial integrated techniques for spatial mapping of viable cropland areas. From the north-western to the south-eastern parts of the territory, the results demonstrate a wide range of agricultural suitability among the community development blocks. The five types of cropland suitable zones have been determined from the results of the study: non-suitable, low suitable, moderately suitable, highly suitable, and very highly suitable. The box plot depicts the heterogeneity and inconsistency of different types of croplands across the study area. The findings of the study can lessen intra- and inter-regional variations in cropland availability, allowing stakeholders to prepare better for a strong crop yield in the future, ensuring the state's food security. [ABSTRACT FROM AUTHOR]

Conversion of grassland into cropland always induces a great degradation of soil organic carbon (SOC). However, during this process information on microbial residue C (MR‐C) and its contribution to SOC with increasing conversion age remain scarce. The relative contribution of soil properties to the accumulation of amino sugars is still poorly understood. Here, we studied variations in soil amino sugars and MR‐C, and their contribution to SOC with increasing conversion age (5 (M05), 16 (M16) and 34 (M34) years, respectively) from grassland to cropland. In contrast to the contents of glucosamine, fungal glucosamine, galactosamine, muramic acid, total amino sugars and MR‐C in grassland, those in M05 decreased in the range of 14.8–37.5%, and those in M34 decreased in the range of 31.8–65.1%, respectively. The contents of amino sugars and MR‐C did not differ between M16 and M34. The similar contribution of MR‐C to SOC (44.6–46.3%) in grassland, M16 and M34 is likely to result from a balance between microbial and non‐degraded plant residues. Decreasing ratios of fungal to bacterial residue C with conversion age indicated a decreased relative contribution of fungal residues to SOC, which probably resulted from the decreased percentage of macroaggregates. MR‐C and SOC decreased and reached a steady status in a few years (≧16 a) after conversion of grassland to cropland. The redundancy analysis model revealed that total nitrogen (TN), pH, SOC and available nitrogen (AN) played a bigger role than particle size distribution, in terms of explaining the contribution of amino sugars to soil carbon. Highlights: Conversion of grassland into cropland decreased amino sugars and microbial residues.The contribution of MR‐C to SOC peaked in M05.The relative contribution of fungal residues to SOC decreased with increasing conversion age.TN and SOC contributed more to the accumulation of amino sugars than particle size distribution. [ABSTRACT FROM AUTHOR]

Miscanthus spp. are increasingly cultivated in cropland worldwide due to their bioenergy potential and multiple ecological services. Effects of long‐term cropland‐to‐Miscanthus conversion without N fertilizer on soil microbiome and N cycling largely remain unknown. We aimed to explore the effects of Miscanthus conversion on soil microbiome and N cycling over a 15‐year period. We analyzed diversity, composition, and abundance of bacterial and archaeal communities using 16S rRNA amplicon sequencing, and abundances of N‐cycling‐related genes using quantitative polymerase chain reaction of 0–10 cm soils collected from bare land, cropland, 10‐year Miscanthus × giganteus, and 15‐year Miscanthussacchriflorus land in Beijing. Conversion decreased soil sand and micro‐aggregate proportion, nitrate N (NiN), available phosphorus levels, conductivity, temperature, and pH, while increasing proportion of soil clay and macro‐aggregate (MAA), soil organic C (SOC), available N (AN), exchangeable Mg2+ (EMg2+), and available potassium (AK) contents as well as microbial C/N. Consequently, diversity, composition, and abundance of soil bacterial community exhibited larger changes than those values of archaeal community after conversion. Soil AP, EMg2+, AK, and SOC were key factors in shifting microbiome from the cropland to Miscanthus pattern. Moreover, abundances of bacterial and archaeal communities and the N fixer gene nifH increased, whereas that of the bacterial ammonia monooxygenase gene decreased. The copies of other N‐cycling‐related genes in the two Miscanthus lands seemed similar to those values of cropland. The nifH copies negatively correlated with soil NiN and positively correlated with AN, EMg2+, ECa2+, SOC, AK, and MAA. We conclude that changes in soil microbiome pattern induced by the variation of soil properties enhance microbial N fixation potential, maintaining stable N levels and robust N cycling with lower N leakage risk after conversion. These results should inspire farmers and governments to large‐scale use Miscanthus on marginal cropland in Northern China. [ABSTRACT FROM AUTHOR]

To investigate the temporal dynamics of CO2 efflux from the soil surface in a temperate cropland and to quantify the effects of soil temperature, soil water content, N fertilization and plant growth on soil carbon dioxide efflux (Rs) field and lab experiments were performed. The field experiment was conducted in a cropland site with a conventional farming system in Central Hungary. The temporal changes of Rs were estimated using a closed chamber IRGA system about bi-weekly/monthly between November 2017–November 2019 in 10 positions. The measured average soil CO2 efflux values ranged from 0.06 ± 0.007 to 7.04 ± 0.44 µmol CO2 m–2 s–1 Soil respiration model including soil temperature (Ts), soil water content (SWC) and the incorporation of VIgreen (plant growth and functioning) gave a higher goodness-of-fit value (r2 = 0.54) than the simple temperature response. According to our field results, different variables including Ts, SWC and VIgreen play a principal role in the carbon cycle of the investigated cropland. We further investigated the effects of the main drivers in a laboratory experiment with the same soil. Closed chamber technique was used for measuring the emission of carbon dioxide by a Picarro G1101-i gas analyzer. We also introduced a fertilization experiment: three different N treatments were applied (N0, N75 and N150) with different levels of soil water content on the soil planted with maize and bare soil. According to our laboratory results, the cumulative CO2 efflux from soil was found to have a positive correlation with plant growth and with N fertilizer rate: as higher plant biomass and more N added, more CO2 was emitted, whereas, the cumulative emissions values from planted soil were around two times higher than in bare soil in all treatments. Significant positive correlations were found between CO2 efflux and SWC indicating that the soil water content was the main factor limiting the rate of the CO2 emission from soil in both planted and bare soil, in which the cumulative CO2 efflux was increased with the increase in soil water content, and it was almost three times higher in planted soils at higher soil moisture level than in the bare soil. We can conclude that the effects of plant presence and soil moisture on soil respiration had similar magnitude; however, the effect of N addition was small. [ABSTRACT FROM AUTHOR]

Soils are indispensable for the provision of several functions. Agricultural intensification and its focus on increasing primary productivity (PP) poses a threat to soil quality, due to increases in nutrient loads, greenhouse gas emissions and declining biodiversity. The EU Horizon 2020 Landmark project has developed multi‐criteria decision models to assess five soil functions: PP, nutrient cycling (NC), soil biodiversity and habitat provision (B‐HP), climate mitigation and water regulation, simultaneously in agricultural fields. Using these algorithms, we evaluated the supply of PP, NC and B‐HP of 31 grasslands and 21 croplands as low, medium or high. The multi‐criteria decision models showed that 38% of the farms had a medium to high supply of all three soil functions, whereas only one cropland had a high supply for all three. Forty‐eight per cent of the farms were characterized by a high supply of PP and NC. We observed a clear trade‐off between these two functions and B‐HP. Multivariate statistical analyses indicated that higher organic inputs combined with a lower mineral fertilization concur with higher biodiversity scores while maintaining a medium delivery of PP and NC. Additionally, we compared the outputs of the model predictions to independent variables that served as proxies for the soil functions and found: (a) croplands (but not grasslands) with high PP had a higher standardized yield than those with medium PP; (b) grasslands (but not croplands) with high NC had a significantly lower fungal to bacterial biomass ratio, suggesting faster decomposition channels; and (c) a positive though non‐significant trend between B‐HP score and rank according to soil invertebrate biodiversity. These comparisons suggest a successful upscaling of the models from field to farm level. Our study highlights the need for systematic collection of management‐related data for the assessment of soil functions. Multifunctionality can be achieved in agricultural soils; however, without specifically managing for it, biodiversity might come at a loss. Highlights: We study how well soils can provide primary productivity, nutrient cycling and biodiversity.We study trade‐offs and synergies among soil functions, as well as the drivers of these relationships.Soil biodiversity is largely sacrificed for primary productivity and nutrient cyclingChanges in pesticide and fertilizer management can increase soil multifunctionality. [ABSTRACT FROM AUTHOR]

Aim: Land‐use change leads to local climatic changes, which can induce shifts in community composition. Indeed, human‐altered land uses favour species able to tolerate greater temperature and precipitation extremes. However, environmental changes do not impact species uniformly across their distributions, and most research exploring the impacts of climatic changes driven by land use has not considered potential within‐range variation. We explored whether a population's climatic position (the difference between species' thermal and precipitation tolerance limits and the environmental conditions a population experiences) influences their relative abundance across land‐use types. Location: Global. Methods: Using a global dataset of terrestrial vertebrate species and estimating their realized climatic tolerance limits, we analysed how the abundance of species within human‐altered habitats relative to that in natural habitats varied across different climatic positions (controlling for proximity to geographic range edge). Results: A population's thermal position strongly influenced abundance within human‐altered land uses (e.g. agriculture). Where temperature extremes were closer to species' thermal limits, population abundances were lower in human‐altered land uses (relative to natural habitat) compared to areas further from these limits. These effects were generally stronger at tropical compared to temperate latitudes. In contrast, the influences of precipitation position were more complex and often differed between land uses and geographic zones. Mapping the outcome of models revealed strong spatial variation in the potential severity of decline for vertebrate populations following conversion from natural habitat to cropland or pasture, due to their climatic position. Main conclusions: We highlight within‐range variation in species' responses to land use, driven (at least partly), by differences in climatic position. Accounting for spatial variation in responses to environmental changes is critical when predicting population vulnerability, producing successful conservation plans, and exploring how biodiversity may be impacted by future land‐use and climate change interactions. [ABSTRACT FROM AUTHOR]

Biodiversity conservation is the cornerstone for sustainable development. Bold conservation targets provide the last opportunities to halt the human-driven mass extinction. Recently, bold conservation targets have been proposed to protect 30% or 50% of Earth. However, little is known about its potential impacts on cropland. We identify potential cropland losses when 30% and 50% of global terrestrial area is given back to nature by 2030/2050, at three spatial scales (global, biome and country) and using two approaches (“nature-only landscapes” and “shared landscapes”). We find that different targets, applied scales and approaches will lead to different cropland losses: (1) At the global scale, it is possible to protect 50% of the Earth while having minimum cropland losses. (2) At biome scale, 0.64% and 8.54% cropland will be lost globally in 2030 and 2050 under the nature-only approach while by contrast, the shared approach substantially reduces the number of countries confronted by cropland losses, demanding only 0% and 2.59% of global cropland losses in 2030 and 2050. (3) At the national scale, the nature-only approach causes losses of 3.58% and 10.73% of global cropland in 2030 and 2050, while the shared approach requires 0.77% and 7.55% cropland in 2030 and 2050. Our results indicate that bold conservation targets could be considered, especially when adopting the shared approach, and we suggest adopting ambitious targets (protecting at least 30% by 2030) at the UN Biodiversity Conference (COP 15) to ensure a sustainable future for Earth. [ABSTRACT FROM AUTHOR]

Soil erosion is one of the major natural risk factors for developing high-value crops and an accurate estimation of spatial distribution and rates of soil degradation can be crucial to prevent crop degradation. In this paper, we use comparisons between high-resolution DEMs and soil erosion models to uncover the short-term landscape evolution of hazelnut crop yields, which are affected by incipient processes of rill development. Maps of rill initiation and evolution were extracted from the analysis of UAV-based multitemporal DEMs and the application of soil erosion models. A comparison between such a short-term analysis and historical orthophotos was carried out. Such a comparison shows how the USPED model predicts, very reliably, where linear erosion occurred. In fact, a reliable overlay between the linear erosive forms predicted by the USPED model and those captured by the UAV images can be observed. Furthermore, land use changes from 1974 to 2020 are characterized by a transition from abandoned areas (1974) to areas with high-value cultivation (2020), which has a strong impact on the spatial distribution of erosion processes and landslide occurrence. Such data represent a key tool for both the investigation of the spatial distribution of hot-spots of soil degradation and the identification of effective mitigation practices of soil conservation. [ABSTRACT FROM AUTHOR]

Soil microbial indicators help monitor soil quality. Limited studies have determined how land use in drylands affects soil microbial indices. Top soil (0–10 cm) from four land use systems in African drylands: (1) shrubland (natural), (2) grassland (natural), (3) pasture (agricultural) and (4) cropland (agricultural) occurring on two soil types: (1) Vertisol and (2) Acrisol, was used in laboratory incubations (6 days) to assess the effects of land use changes on organic carbon (C org) mineralization, microbial biomass C (C mic), mineralization quotient (q M), metabolic quotient (q CO 2), C mic :C org ratio and sensitivity indices of these microbial indicators. Experimental plots were organized into a completely randomized design (n = 3) for every combination of land use and soil type. Cumulative CO 2 produced from native C org mineralization was the highest in Acrisol (108 ± 2.7 μg CO 2 –C g−1 soil) and the lowest in Vertisol (53 ± 2.5 μg CO 2 –C g−1 soil) croplands. Vertisol shrubland (1.34 ± 0.09 mg C g−1 soil) and Acrisol cropland (0.28 ± 0.07 mg C g−1 soil) had the highest and the lowest C mic , respectively. Acrisol cropland (1.29 μg CO 2 –C g−1 h−1) had the highest q M, approximately five times higher than the lowest q M (0.26 μg CO 2 –C g−1 h−1) in a Vertisol cropland. Highest q CO 2 was observed in an Acrisol pasture (12.04 μg CO 2 –C g−1 C mic h−1), which was approximately 30 times higher compared to the lowest q CO 2 observed in a Vertisol shrubland (0.41 μg CO 2 –C g−1 C mic h−1). The C mic :C org ratio was the highest in a Vertisol shrubland (0.097), approximately five times higher than the lowest observed in an Acrisol pastureland (0.019). Our study demonstrated that the measured soil quality indicators' magnitude, direction, and sensitivity varied depending on land use and soil type. Higher N availability in Vertisols increased the biological stability of soil organic carbon (SOC) resulting to decreased SOC mineralization than Acrisols. In conclusion, the measured microbial soil quality indicators showed that Acrisols are prone to accelerated SOC mineralization after disturbance than Vertisols in the studied semi-arid dryland ecosystems. Thus, there is a need to manage natural ecosystem conversions to support sustainable crop and pasture production in African drylands. [Display omitted] [ABSTRACT FROM AUTHOR]

The "conflict-coordination" theoretical model of regional land use transitions put forward by Long (2022) offers a fresh perspective for understanding the process of regional land use and the decision-making dynamics involved. However, being a novel theory, empirical evidence supporting this theoretical model is currently lacking. Consequently, this paper aims to validate the theoretical model using the lower Yellow River basin in China as a case study, focusing on the interconversion between cropland and rural settlements. The findings reveal that the rural land use transitions primarily manifest as an initial conversion of cropland to rural settlements, followed gradually by the reverse conversion of rural settlements back to cropland. The conversion of cropland to rural settlements prior to 2015 are regarded as the land use conflict phase, resulting in conflicts between construction departments and cropland protection departments, and deviation from national cropland protection goals. The conversion of rural settlements to cropland between 2015 and 2020 is regarded as the land use conflict-coordination phase, which mitigated pressure on cropland protection and resolved conflicts between the above-mentioned departments. The land use conflict phase was driven by socio-economic factors such as urbanization and rural economic development, whereas the land use conflict-coordination phase was primarily driven by cropland protection policies. This paper effectively elucidates the "conflict-coordination" theoretical model of regional land use transitions and provides a framework for evaluating rural land management policies in China. [ABSTRACT FROM AUTHOR]

There is an urgent need for a comprehensive assessment of cropland fragmentation that can provide valuable insights for guiding policies related to cropland protection, restoration, and other relevant measures. Despite the growing number of studies focusing on cropland fragmentation, a nationwide, long-term, and fine-scale understanding of the spatiotemporal changes in cropland fragmentation across China remains lacking. This study aimed to construct a novel comprehensive index that quantitatively assesses the level of farmland fragmentation, to fully elucidate the spatial and temporal dynamics of farmland fragmentation in China at the county level over the last two decades, and to identify the relationships between farmland fragmentation and key factors from anthropogenic and natural perspectives. Utilizing the 30 m annual and continuous time series Landsat-derived annual China land cover dataset (CLCD) from 2000 to 2021, we integrated three landscape pattern metrics- patch density (PD), mean patch area (MPA), and edge density (ED)- to devise a new comprehensive cropland fragmentation index (CFI). We also developed a Beta regression method to identify the relationships between CFI and key factors from human and natural perspectives. The results show that counties with high cropland fragmentation (CFI > 0.8) are predominantly located in the central part of China and the coastal areas, and the counties with low cropland fragmentation (CFI < 0.2) are mainly in the North China Plain, the Northeast Plain, and some areas in the northwest. Among counties experiencing an increase or decrease in fragmentation, the primary types of cropland conversions are between cropland and grassland, as well as between cropland and forests. A third major type of conversion involves the transformation of cropland into impervious surfaces. CFI had a significantly negative linear relationship with total area of cropland in each county while showing a positive relationship with the total cropland patches in each county. The variables CFI and slope, and CFI and nighttime light (NTL) data, exhibited a quadratic parabolic relationship. The findings presented in this study provide crucial insights into the underlying causes and patterns of farmland fragmentation in China. These insights will serve as a valuable resource for policymakers and land managers, enabling them to devise effective strategies for sustainable land use planning and promoting rural development in the country. [Display omitted] • Limited knowledge on fine-scale cropland fragmentation in China • A new synthetic cropland fragmentation index (CFI) was developed. • Counties with high and low CFI were identified during the lastest two decades. • Primary cropland conversions: cropland to grassland and forests, and cropland to impervious surfaces. • The relationships between CFI and influencing factors and their thresholds were detected. [ABSTRACT FROM AUTHOR]

Soil microbial biomass and activity strongly depend on land use, vegetation cover, climate, and soil physicochemical properties. In most cases, this dependence was assessed by one-to-one correlations while by employing network analysis, information about network robustness and the balance between stochasticity and determinism controlling connectivity, was revealed. In this study, we further elaborated on the hypothesis of Smith et al. (2021) that cropland soils depended more on climate variables and therefore are more vulnerable to climate change. We used the same dataset with that of Smith et al. (2021) that contains seasonal microbial, climate and soil variables collected from 881 soil points representing the main land uses in Europe: forests, grassland, cropland. We examined complete (both direct and indirect relationships) and incomplete networks (only direct relationships) and recorded higher robustness in the former. Partial Least Square results showed that on average more than 45% of microbial attributes' variability was predicted by climate and habitat drivers denoting medium to strong effect of habitat filtering. Network architecture slightly affected by season or land use type; it followed the core/periphery structure with positive and negative interactions and no hub nodes. Microbial attributes (biomass, activity and their ratio) mostly belong to core block together with Soil Organic Carbon (SOC), while climate and soil variables to periphery block with the exception of cropland networks, denoting the higher dependence between microbial and climate variables in these latter. All complete networks appeared robust except for cropland and forest in summer, a finding that disagrees with our initial hypothesis about cropland. Networks' connectivity was controlled stronger by stochasticity in forest than in croplands. The lack of human interventions in forest soils increase habitat homogeneity enhancing the influence of stochastic agents such as microbial unlimited dispersal and/or stochastic extinction. The increased stochasticity implies the necessity for proactive management actions. • Complete networks exhibited higher robustness than incomplete ones. • Cropland in summer and forest in autumn and summer exhibited the lowest robustness. • Connectivity of complete networks was controlled mostly by stochasticity than determinism. • Stochasticity was higher in forest (natural soil) than cropland (man-affected soils). • Stochasticity implies the necessity for more proactive management practices and less remedial actions. [ABSTRACT FROM AUTHOR]

Reducing nonpoint-source pollution and improving soil quality by applying best management practices (BMPs) on sloping farmland are essential for ensuring water environmental safety and sustainable agricultural development. However, little is known about the accumulative effect of the long-term application of BMPs on reducing pollution and improving soil quality in major land use types of sloping farmland in watersheds. We conducted a nine-year field experiment for citrus orchard and cropland of sloping farmland in the Three Gorges Reservoir area of China. The treatment settings for the citrus experimental plots were as follows: (1) citrus intercropped with white clover (WC), (2) citrus orchard soil mulched with straw (SM), (3) citrus intercropped with Hemerocallis flava contour hedgerows (HF) and (4) conventional citrus planting pattern (C-CK). The treatment settings for the crop experimental plots were as follows: (1) wheat-peanut rotation intercropped with Toona sinensis contour hedgerows (TS), (2) wheat-peanut rotation intercropped with alfalfa contour hedgerows (AF), (3) ryegrass-sesame rotation (RS) and (4) conventional wheat-peanut rotation (W-CK). We found that compared with C-CK and W-CK, (1) runoff, sediment, total nitrogen and total phosphorus losses were reduced by 18.0 %, 66.1 %, 35.6 % and 40.2 %, respectively, in WC; by 22.4 %, 61.2 %, 43.9 % and 52.4 %, respectively, in SM; by 25.4%, 69.5 %, 52.0 % and 58.1 %, respectively, in HF; by 45.1 %, 73.7 %, 64.6 % and 66.0 %, respectively, in TS; by 41.8 %, 67.4 %, 59.3 % and 60.8 %, respectively, in AF; and by 24.1 %, 42.9 %, 39.3 % and 40.3 %, respectively, in RS; (2) the soil quality index values of the BMP treatments increased; and (3) the application of these BMPs did not significantly affect yield or product value. We suggest the application of these BMPs on sloping farmland to (1) efficiently reduce total runoff, total sediment, and soil total nitrogen and total phosphorus losses; (2) improve soil quality; and (3) have no significant effect on output, of which the BMP HF applied to citrus orchard and the BMP TS applied to cropland are the most effective. • Clarified accumulative effects of long-term application of best management practices for two major land use types. • Best management practices reduce the runoff, sediment, and soil nutrient losses of the land use types. • Best management practices improve the soil quality without reducing the output of the land use types. • The optimal best management practices for the two major land use types in the watershed are selected. • This study is essential for identifying the optimal best management practices for watershed sloping farmland use. [ABSTRACT FROM AUTHOR]

Accurately reconstructing cropland cover in topographically complex areas has long been challenging for historical anthropogenic land cover change (ALCC) datasets. Sichuan Province, one of the most topographically complex areas in China, was chosen as a case study. This study integrated historical gazetteers and modern cropland statistics and survey data to examine and correct the prefecture-level cropland area of Sichuan Province. Subsequently, a novel cropland reconstruction model was proposed based on the order of cropland reclamation. Using this model, cropland changes in Sichuan Province from 1671 to 2019 were reconstructed with a spatial resolution of 5′ × 5′. The main findings were as follows: (1) The cropland reconstruction model designed in this study can well reproduce with the cropland cover in Sichuan Province. Compared with the prevailing ALCC datasets, our reconstructions were more consistent with descriptions of cropland changes in Sichuan Province in the literature. (2) The cropland cover in Sichuan Province expanded from northeastern and southern Sichuan to the Chengdu Plain and the hills north of the Yangtze River and then to the mountainous areas in the center and periphery of the basin from 1671 to 1985, after which it shrank in mountainous and urban areas. (3) The changes in cropland cover in Sichuan Province over the past 350 years were shaped by multiple factors such as war, famine, migration, agricultural policies and urbanization. The reconstructions in this study captured the spatial differences in cropland distribution in Sichuan Province, but their accuracy was limited by the cropland estimation method and the satellite-based cropland data used in the reconstruction model. • A new cropland reconstruction model based on the order of cropland reclamation was designed. • The 5′ × 5′ cropland cover change in Sichuan province over the past 350 years was reconstructed. • The cropland cover in Sichuan Province changed dramatically over the past 350 years. • Obvious differences in cropland cover from other datasets were observed in our reconstructions. • Our reconstructions were consistent with literature-based descriptions of agricultural reclamation in Sichuan Province. [ABSTRACT FROM AUTHOR]

Plant available soil moisture and its spatial variability is among the most important limiting factors for crop and livestock productivity in semi-arid Ethiopia. The objectives of this study were therefore: 1) to measure the effects of land use and slope gradient on spatial variability of topsoil moisture content. 2) to analyze the variability of topsoil moisture content and crop responses under different soil and water conservation (SWC) structures and 3) to analyze whether topsoil moisture content, grain and biomass yields vary in relation to SWC structures. In total, 21 large runoff plots (600–1000 m2) were installed and treated with stone bunds, trenches, stone bunds with trenches and a control plot at each site. Plot sites were located on gentle (5%), medium (12%) and steep (16%) slopes of cropland and rangeland. Topsoil (0–15 cm) moisture content for plots were monitored daily using soil moisture probes (Theta-Probes) and weekly using manual sampling over two growing seasons. Crop performance was monitored during two growing seasons and grain and biomass yield were measured at harvest. Results show that average gravimetric moisture content of the topsoil during growing seasons is remarkably higher for cropland (33–25 g/100 g) compared to rangelands (24–20 g/100 g). This is due to tillage-induced runoff retention and infiltration. Soil moisture content decreases with increasing slope gradients, owing to increased drainage. Considering the effects of SWC structures, average topsoil moisture contents at accumulation and erosion zones are generally higher as compared to middle zone between two successive SWC structures. Average seasonal volumetric soil moisture content of 38.2 (cm3/100 cm3) in 2011 and 39.4 (cm3/100 cm3) in 2012 for accumulation zone of plots with stone bunds with trenchers are significantly higher than at other sampling locations. Crop growth performance, grain and biomass yield varied among plots and depended on slope gradients and SWC structures. Grain yields of wheat in 2011 and barley in 2012 were significantly higher for plots with SWC structures as compared to control plots and are highly correlated to topsoil moisture content (r = 0.64; p < 0.04 in 2011 and r = 0.59, p < 0.01 in 2012). Even when taking into account the cropland area occupied by SWC structures, grain yield increased by (4.2%−33%) for plots with SWC structures as compared to control plots. Overall, our results show that implemented measures may not only reduce runoff and soil loss, but also increase crop productivity by enhancing soil moisture and thus can significantly contribute to sustainability of agro-ecosystems in semi-arid environments. • Spatial distribution of topsoil moisture is related to land use and slope gradient. • Topsoil moisture content varies within plot due to water conservation structures. • Soil moisture content is significantly higher for treated compared to control plots. • Grain yield for both wheat and barley significantly correlated to topsoil moisture. • Soil and water conservation structures increased grain yield by (4.2 - 33%). [ABSTRACT FROM AUTHOR]

Ammonia (NH 3) emissions can have significant impacts on the environment, ecology, and human health, with a key source being fertilizer application in croplands. NH 3 emissions from croplands are influenced not only by natural factors but also by human management practices and social factors. However, our current understanding of the mechanisms and driving factors behind these emissions is limited. In this study, we conducted a quantitative analysis of the factors influencing NH 3 emissions using data from in-situ experiments and historical emissions data from 1980 to 2020. Our analysis revealed that fertilization management is the most significant contributor, accounting for 47% of NH 3 emission flux at the point level. Climate conditions and soil properties contribute 21% and 32%, respectively, to cropland NH 3 flux. At the county level, the contributions of management practices and socioeconomic factors such as farm size to NH 3 emission factors are nearly equal, with proportions of 51% and 49%, respectively. Advancements in agricultural mechanization and practices focused on reducing fertilizer use can achieve a 29% reduction in NH 3 emissions by 2030, all while maintaining rapid socioeconomic development. This highlights a strategy that can improve environmental quality, ensure Chinese grain production security, and deliver significant socioeconomic and ecological benefits. [Display omitted] • Simultaneously using in-suit experiments and county-level data to analyze the driving forces of NH 3 emissions. • Natural and socioeconomic factors contribute equally to the NH 3 emissions from cropland. • The practice of deep fertilizer application exhibits a substantial emission reduction effect. • Rural residents' education levels, farm size, and mechanization exhibit negative correlations with NH 3 emissions. • The average annual growth rate of NH3 emissions was 3.3%, with the greatest influence on per capita GDP. [ABSTRACT FROM AUTHOR]

• Topsoil SOC saturation (or steady-state) is reached after 12–13 years of straw return. • Soil N 2 O and NO show high interannual variability, largely driven by changes in rainfall. • Increases in SOC stocks are associated with increases in N 2 O emissions and crop yields. • Long-term straw return shows no stimulating effect on yield-scaled N 2 O and NO fluxes. Straw return to agricultural soils is considered as a key strategy to improve soil organic C (SOC) sequestration and crop production. However, changes in SOC can affect soil N-turnover and associated N 2 O and NO fluxes, as soil biogeochemical C- and N-cycles are strongly coupled. Understanding how and to what extent N-trace gas emissions per unit crop yield respond to changes in SOC following straw return, is critical for balancing food security and climate change mitigation. Here we report on an experiment in a Chinese wheat-maize system designed to understand the effects of long-term (2005–2020) different straw management options (straw return, burning or removal) on SOC stocks and yields, completed by N 2 O and NO flux measurements over the period 2016–2020. Our results showed that the increase in SOC stocks following straw return tended to level off over time, indicating that SOC saturation is reached after 12–13 years under straw return. Soil N-trace gas emissions showed high inter- and intra-annual variability, which were largely driven by changes in rainfall, soil hydrothermal conditions or soil C- and N-availability. Compared to straw burning or removal, long-term straw return increased annual N 2 O emissions by 9 %-49 %, to 2.72–2.98 kg N ha−1 yr−1, while NO emissions were decreased by 12 %-28 %, to 0.83–0.90 kg N ha−1 yr−1. The annual N 2 O emissions roughly offset the total C sink (SOC sequestration and CH 4 uptake) by 40 %-46 %, but will override the climate benefits of total C sink as the SOC pool becomes saturated. However, because yields were higher in straw return treatments, yield-scaled annual N 2 O emissions remained unchanged. Our unique dataset allows for the first time a better understanding of the dynamics between SOC sequestration, yields and N-trace gas emissions, with results calling for action to address the risk of increasing N 2 O emissions following long-term SOC increases induced by straw return. [Display omitted] [ABSTRACT FROM AUTHOR]

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

Abstract By using the latest China population grid and land-use data, we assess the changing exposure of China’s population and land uses to the hazards of storm surges, droughts, earthquakes, floods, and landslides from 1995 to 2015. We found that the single-hazard areas and the multi-hazard areas covered 43% and 26% of China’s territory, respectively. Population grew faster in the hazard-prone areas than in the non-hazard areas. Built-up area expanded more rapidly in the areas prone to earthquakes and landslides. Cropland changed rapidly in many hazard-prone areas. The hazard-prone areas affected by floods featured the highest cropland loss rates, while the areas prone to earthquakes and landslides featured the highest cropland growth rates. We detected areas with significant exposure changes by using hot spot analysis. It was found that population and built-up land in the Pearl River Basin were increasingly exposed to storm surges, floods, and landslides. The Haihe River Basin and Huaihe River Basin also showed a consistent increase of population and built-up land exposure to droughts and earthquakes. These findings can provide a foundation for the design and implementation of protection and adaptation strategies to improve the resilience of Chinese society to natural hazards.