Cropland Area Research Articles

Comparative analysis of actual evapotranspiration values estimated by METRIC model using LOCAL data and EEFlux for an irrigated area in Northern Sinaloa, Mexico

Landsat images combined with the METRIC model have been used in applications such as EEFlux to estimate actual evapotranspiration (ETa) in irrigated areas, with uncertainty as to whether the results are sufficiently accurate at local scales. This work compares temporally and spatially ETa estimates using Landsat imagery and the METRIC model with LOCAL measured weather data and EEFlux for an irrigated area in northern Sinaloa, Mexico, from 1995 to 2018. A regression analysis and error metrics such as coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and slope were used to compare both models. The temporal analysis results confirm that ETa differences are closely related to crop growth and that the daily mean absolute error was 1.17 mm/day. Spatial analysis showed that when using only cropland pixels without non-cropland pixels in EEFlux, the R2 increases from 0.36 to 0.73, and the RMSE decreases from 2.52 to 1.98 mm/day. Therefore, EEFlux should be limited to cropland areas, and it is suggested to exclude Landsat images close to the planting and harvesting season to reduce the differences.

Complementarity of Sentinel-1 and Sentinel-2 Data for Soil Salinity Monitoring to Support Sustainable Agriculture Practices in the Central Bolivian Altiplano

Soil salinization will affect 50% of global cropland areas by 2050 and represents a major threat to agricultural production and food sovereignty. As soil salinity monitoring is costly and time consuming, many regions of the world undertake very limited soil salinity observation (in space and time), preventing the accurate assessment of soil salinity hazards. In this context, this study assesses the relative performance of Sentinel-1 radar and Sentinel-2 optical images, and the combination of the two, for monitoring changes in soil salinity at high spatial and temporal resolution, which is essential to evaluate the mitigation measures required for the sustainable adaptation of agriculture practices. For this purpose, an improved learning database made of 863 soil electrical conductivity (i.e., soil salinity) observations is considered for the training/validation step of a Random Forest (RF) model. The RF model is successively trained with (1) only Sentinel-1, (2) only Sentinel-2 and (3) both Sentinel-1 and -2 features using the Genetic Algorithm (GA) to reduce multi-collinearity in the independent variables. Using k-fold cross validation (3-fold), overall accuracy (OA) values of 0.83, 0.88 and 0.95 are obtained when considering only Sentinel-2, only Sentinel-1 and both Sentinel-1 and -2 features as independent variables. Therefore, these results highlight the clear complementarity of radar (i.e., Sentinel-1) and optical (i.e., Sentinel-2) images to improve soil salinity mapping, with OA increases of approximately 10% and 7% when compared to Sentinel-2 and Sentinel-1 alone. Finally, pre-sowing soil salinity maps over a five-year period (2019–2023) are presented to highlight the benefit of the proposed procedure to support the sustainable management of agricultural lands in the context of soil salinization on a regional scale.

Assessing changes in the ecosystem service value in response to land use and land cover dynamics in Malawi.

Land use and land cover (LULC) changes are inevitable outcomes of socioeconomic changes and greatly affect ecosystem services. Our study addresses the critical gap in the existing literature by providing the first comprehensive national analysis of LULC changes and their impacts on ecosystem service values (ESVs) in Malawi. We assessed changes in ecosystem service values (ESVs) in response to LULC changes using the benefit transfer method in ArcGIS 10.6 software. Our findings revealed a significant increase in grasslands, croplands, and urban areas and a notable decline in forests, shrubs, wetlands, and water bodies. Grassland, cropland, and built-up areas expanded by 52%, 1%, and 23.2%, respectively. In contrast, permanent wetlands, barren land, and water bodies declined by 27.6%, 34.3%, and 1%, respectively. The ESV declined from US$90.87 billion in 2001 to US$85.60 billion in 2022, marking a 5.8% reduction. Provisioning services increased by 0.5% while regulating, supporting, and cultural ecosystem service functions declined by 12.2%, 3.16%, and 3.22%, respectively. The increase in provisioning services was due to the expansion of cropland. However, the loss of regulating, supporting, and cultural services was mainly due to the loss of natural ecosystems. Thus, environmental policy should prioritise the conservation and restoration of natural ecosystems to enhance the ESV of Malawi.

Mapping rice-crayfish co-culture (RCC) fields with Sentinel-1 and -2 time series in China's primary crayfish production region Jianghan Plain

Crayfish is a high-risk invasive species with devastating impacts on freshwater ecosystems. Meanwhile, nicknamed “little lobster”, it is a popular food in many countries including China. The crayfish production in China increased from 1.13 to 2.39 million tons in 2017–2020, accounting for 97% global production. This phenomenal increase is attributed to the expansion of the rice-crayfish co-culture (RCC) farming mode whose area increased by 123% from 0.57 to 1.26 million ha in 2017–2020. However, the fast expansion of RCC is undertaken in an uncontrolled and unregulated manner, referred by some researchers as a “blind expansion”. It raises wide concerns on ecological risks (crayfish can escape in high-magnitude floods), endangerment of riverbanks (crayfish burrows), food security (reduced rice production), excessive water consumption, and greenhouse gas (methane) emission. It is thus urgent to accurately map the spatial distributions of RCC fields using satellite remote sensing data, so as to assess the ecological and environmental impacts and risks, and to better regulate the expansion. However, there are currently no practically-scalable approaches to reliably map RCC fields in large areas. In particular, there lack the knowledge on the relationship between satellite observations and on-ground biophysical processes in RCC fields. In this study, we conducted field surveys in RCC fields, and in particular, the daily water levels in RCC fields were measured for the complete year of 2020. The comparison of annual water-level time series and satellite-NDVI time series, combined with the RCC farming information collected in surveys, reveals how satellite observations vary in correspondences to on-ground biophysical processes in RCC fields; and importantly, it provides information on how RCC fields can be efficiently distinguished from other land covers using satellite data. Based on that, we propose an approach to map RCC fields from annual Sentinel-2 optical-wavelength and Sentinel-1 Synthetic Aperture Radar (SAR) time series, utilizing the annual water-occurrence frequency (AWF) and characteristic phenological features derived from the satellite data. This method was demonstrated in Jianghan Plain, the primary crayfish production region in China with an area of approximately 37,000 km2. A total of 273,365 ha (2733.65 km2) RCC field area in year 2020 was mapped, which accounted for 24.6% of the whole plain's cropland area (approximately 11,100 km2), meaning a significant proportion of the rice paddies were converted to RCC fields. The RCC mapping accuracies were validated using the samples collected in field surveys and also from Google Earth images, and was compared with the state-of-practice RCC mapping method using bi-seasonal optical-wavelength satellite images. The proposed method obtained 93.8% overall accuracy and 0.91 kappa coefficient, and outperformed the compared bi-seasonal method. The proposed method is scalable for large-area and multi-annual applications. Future research regarding improvements and applications is recommended.

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

Solar irrigation potential in Sub-Saharan Africa: a crop-specific techno-economic analysis

In this study, we introduce an integrated modeling framework that combines a hydrologic model, a biophysical crop model, and a techno-economic model to assess solar irrigation potential in Sub-Saharan Africa (SSA) based on seven commonly grown food crops-maize, wheat, sorghum, potato, cassava, tomato, and onion. The study involves determining the irrigation requirements, location-specific capital investment costs, crop-specific profitability, and the cropland area under various cost scenarios (low and high) and soil fertility (low, moderate, near-optimal, and optimal) scenarios. Our research reveals considerable potential for solar irrigation, with profitability and viable cropland areas that vary according to crop type, irrigation system cost scenarios, and soil fertility levels. Our assessment shows that approximately 9.34 million ha of SSA’s current rainfed cropland are hydrologically and economically feasible for solar irrigation. Specifically, maize and onion display the lowest and highest viability, spanning 1–4 million ha and 29–33 million ha, respectively, under optimal soil fertility conditions. In terms of profitability, maize and onion rank as the least and most economically viable crops for solar irrigation, yielding average annual returns of $50-$125/ha and $933-$1450/ha, respectively, under optimal soil fertility conditions. The lower and upper bounds of profitability and cropland range correspond to high-cost and low-cost scenarios, respectively. Furthermore, our study reveals distinct regional differences in the economic feasibility of solar irrigation. Eastern Africa is more economically favorable for maize, sorghum, tomato, and cassava. Central Africa stands out for onion cultivation, whereas West and Southern Africa are more profitable for potato and wheat, respectively. To realize the irrigation benefits highlighted, an energy input of 940-2,168 kWh/ha/yr is necessary, varying by crop and geographic sub-region of the SSA sub-continent. Our model and its results highlights the importance of selecting the right crops, applying fertilizers at the appropriate rates, and considering regional factors to maximize the benefits of solar irrigation in SSA. These insights are crucial for strategic planning and investment in the region’s agricultural sector.

Integrating key ecosystem services to study the spatio-temporal dynamics and determinants of ecosystem health in Wuhan’s central urban area

The central areas of many Chinese cities have experienced large-scale urbanization in recent decades, a trend that is affecting the quality of life of residents and posing challenges to the sustainable development of urban ecosystems. Hence, it is vital to conduct assessments of urban ecosystems’ health conditions to ensure their sustainability. In this study, the spatial and temporal dynamics of ecosystem health in the central city of Wuhan were analyzed using the Vigor-Organization-Resilience-Service (VORS) model. The sensitivity of ecosystem health at different scales was also investigated using hierarchical health assessment delineation and random forest regression methods. The results showed that the composite index of ecosystem health has been declining since 2000, highlighting the negative impacts of shrinking ecological space, declining service capacity and urban expansion. Health zoning divides areas into five categories. Meanwhile, the health zones are located in areas of dense shrubs, cropland and water bodies on the edge of the central city, while the weak zones are mainly in the core of the city, which is 99.33 % impervious surface. The main factors influencing ecosystem health, including impervious surface area, water bodies and topography, vary from region to region. The study proposes targeted ecological management strategies for different health zones in Wuhan, emphasizing ecosystem protection and providing guidance for sustainable urban development.

Assessing land-use changes and carbon storage: a case study of the Jialing River Basin, China

Land-use change is the main driver of carbon storage change in terrestrial ecosystems. Currently, domestic and international studies mainly focus on the impact of carbon storage changes on climate, while studies on the impact of land-use changes on carbon storage in complex terrestrial ecosystems are few. The Jialing River Basin (JRB), with a total area of ~ 160,000 km2, diverse topography, and elevation differences exceeding 5 km, is an ideal case for understanding the complex interactions between land-use change and carbon storage dynamics. Taking the JRB as our study area, we analyzed land-use changes from 2000 to 2020. Subsequently, we simulated land-use patterns for business-as-usual (BAU), cropland protection (CP), and ecological priority (EP) scenarios in 2035 using the PLUS model. Additionally, we assessed carbon storage using the InVEST model. This approach helps us to accurately understand the carbon change processes in regional complex terrestrial ecosystems and to formulate scientifically informed land-use policies. The results revealed the following: (1) Cropland was the most dominant land-use type (LUT) in the region, and it was the only LUT experiencing net reduction, with 92.22% of newly designated construction land originating from cropland. (2) In the JRB, total carbon storage steadily decreased after 2005, with significant spatial heterogeneity. This pattern was marked by higher carbon storage levels in the north and lower levels in the south, with a distinct demarcation line. The conversion of cropland to construction land is the main factor driving the reduction in carbon storage. (3) Compared with the BAU and EP scenarios, the CP scenario demonstrated a smaller reduction in cropland area, a smaller addition to construction land area, and a lower depletion in the JRB total carbon storage from 2020 to 2035. This study demonstrates the effectiveness of the PLUS and InVEST models in analyzing complex ecosystems and offers data support for quantitatively assessing regional ecosystem services. Strict adherence to the cropland replenishment task mandated by the Chinese government is crucial to increase cropland areas in the JRB and consequently enhance the carbon sequestration capacity of its ecosystem. Such efforts are vital for ensuring the food and ecological security of the JRB, particularly in the pursuit of the “dual-carbon” objective.

Spatially-explicit land use change emissions and carbon payback times of biofuels under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA)

The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) requires airlines to offset their greenhouse gas (GHG) emissions above 2019 levels by either buying carbon offsets or using Sustainable Aviation Fuels (SAFs). These are drop-in jet fuels made from biomass or other renewable resources that reduce GHG emissions by at least 10 % compared to kerosene and meet certain sustainability criteria. This study assesses the direct land use change (DLUC) emissions of SAF, i.e., GHG emissions from on-site land conversion from previous uses (excluding primary forests, peatlands, wetlands, and protected and biodiversity-rich areas) into alternative feedstocks, considering spatial variability in global yields and land carbon stocks. The results provide DLUC values and carbon payback times at 0.5-degree resolution for six SAF pathways, with and without irrigation and a medium-input intensity, according to CORSIA sustainability criteria. When excluding CORSIA non-compliant areas, soybean SAF shows the highest mean DLUC factor (31.9 ± 20.7 gCO2/MJ), followed by reed canary grass and maize. Jatropha SAF shows the lowest mean DLUC factor (3.6 ± 31.4 gCO2/MJ), followed by miscanthus and switchgrass. The latter feedstocks show potential for reducing GHG emissions over large areas but with relatively greater variability. Country-average DLUC values are higher than accepted ILUC ones for all pathways except for maize. To ensure the GHG benefits of CORSIA, feedstocks must be produced in areas where not only carbon stocks are relatively low but also where attainable yields are sufficiently high. The results help identify locations where the combination of these two factors may be favourable for low-DLUC SAF production. Irrigated miscanthus offers the highest SAF production potential (2.75 EJ globally) if grown on CORSIA-compliant cropland and grassland areas, accounting for ∼1/5 of the total kerosene used in 2019. Quantifying other environmental impacts of SAFs is desirable to understand sustainability trade-offs and financial constraints that may further limit production potentials.

Evaluation of food-water-energy sustainable development in China’s western frontiers during 2010–2021

ABSTRACT Food, water, and energy (FWE) are foundational resources that sustain human well-being and societal advancement. The examination of climate change effects on FWE resources is imperative, particularly in regions abundant in natural resources but ecologically fragile. In this context, we investigated the spatiotemporal changes of FWE resources in China’s western frontiers, where both resource wealth and climate vulnerability are critical. This study proposes an innovative approach to comprehensively analyze the dynamics of food production, surface water, and solar energy from 2010 to 2021 by integrating easily accessible and widely applicable variables, and utilizing remote sensing and statistical data within a sustainable development framework. The results show that: (1) both crop yield and cropland area display an upward trend; (2) the expansion of overall surface water area in Xinjiang is mainly driven by changes in precipitation and temperature, while in Xizang, such a relationship is not evident; (3) Xinjiang and Xizang have advantages in solar photovoltaic (PV) development due to their abundant solar resources. Our results suggest that such spatiotemporal dynamics in food production, surface water and solar energy resources can intuitively reflect the sustainable development status of FWE resources in China’s western frontiers.

Spatiotemporal Distribution and Driving Mechanisms of Cropland Long-Term Stability in China from 1990 to 2018

Long-term stability is crucial in cropland for maintaining stable agricultural production and ensuring national food security. However, relatively few studies have been conducted on the long-term stability of cropland at the national level. This study assessed the long-term stability of cropland in China from 1990 to 2018 using a fine-resolution land use dataset. The experimental results indicated that the average area of unstable cropland in China from 1990 to 2018 amounted to 2.08 × 106 km2, 47.31% of the total. The Qinghai–Tibet Plateau exhibited the highest average proportion of unstable cropland at 65.9%, followed by the northern arid and semiarid region, Southern China, and the Yunnan–Guizhou Plateau. The quantity of unstable cropland in China initially declined before increasing, reaching a final growth rate of 5.09%. Furthermore, this study explored the relevant driving factors of cropland’s long-term stability from both natural factors and human activities based on artificial neural networks. The relative importance of distance to vegetation reached a value of 0.30, indicating that it had the most significant influence on the long-term stability of cropland, followed by relief amplitude and soil type. This phenomenon may be attributed to the inadequate execution of the Grain for Green Policy and the requisition–compensation balance of cropland policy, along with the depletion of young and middle-aged laborers due to urban migration from rural areas. Local governments should focus on addressing the unsustainable exploitation of sloped land in rural mountainous or hilly regions while preventing urban developers from appropriating fertile cropland to compensate for less fertile areas.

Land use/cover drive functional patterns of bacterial communities in sediments of a subtropical river, China

The bacterial community in sediment serves as an important indicator for assessing the environmental health of river ecosystems. However, the response of bacterial community structure and function in river basin sediment to different land use/cover changes has not been widely studied. To characterize changes in the structure, composition, and function of bacterial communities under different types of land use/cover, we studied the bacterial communities and physicochemical properties of the surface sediments of rivers. Surface sediment in cropland and built-up areas was moderately polluted with cadmium and had high nitrogen and phosphorus levels, which disrupted the stability of bacterial communities. Significant differences in the α-diversity of bacterial communities were observed among different types of land use/cover. Bacterial α-diversity and energy sources were greater in woodlands than in cropland and built-up areas. The functional patterns of bacterial communities were shown that phosphorus levels and abundances of pathogenic bacteria and parasites were higher in cropland than in the other land use/cover types; Urban activities have resulted in the loss of the denitrification function and the accumulation of nitrogen in built-up areas, and bacteria in forested and agricultural areas play an important role in nitrogen degradation. Differences in heavy metal and nutrient inputs driven by land use/cover result in variation in the composition, structure, and function of bacterial communities.

Shallow groundwater table fluctuations promote the accumulation and loss of phosphorus from surface soil to deeper soil in croplands around plateau lakes in Southwest China

The continuous excessive application of phosphorus (P) fertilizers in intensive agricultural production leads to a large accumulation of P in surface soils, increasing the risk of soil P loss by runoff and leaching. However, there are few studies on the accumulation and loss of P from surface soil to deep soil profiles driven by shallow groundwater table (SGT) fluctuations. This study used the intensive cropland around 7 plateau lakes in Yunnan Province as an example and conducted in situ monitoring of P storage in the soil profile and SGT during the rainy season (RS) and dry season (DS) as well as simulation experiments on soil P loss. The aim was to study the spatiotemporal variation in P accumulation in the soil profile of cropland driven by SGT fluctuations in the RS and DS and estimate the P loss in the soil profile driven by SGT fluctuations. The results showed that fluctuations in the SGT promoted P accumulation from the surface soil to deeper soil. The proportions of P stored in various forms in the 30–60 cm and 60–100 cm soil layers in the RS were greater than those in the DS, while the average proportion in the 0–30 cm soil layer in the DS was as high as 48%. Compared with those in the DS, the maximum decreases in the proportion of P stored as TP and Olsen-P in the 0–100 cm soil layer in the RS were 16% and 58%, respectively, due to the rise in the SGT (SGT <30 cm), while the soil TP storage decreased by only 1% when the SGT was maintained at 60–100 cm. The critical thresholds for soil Olsen-P and TP gradually decreased with increasing soil depth, and the risk of P loss in deeper soil increased. The loss of soil P was increased by fluctuations in the SGT. Based on the cropland area around the 7 plateau lakes, P storage, and SGT fluctuations, the average loss intensity and loss amount of TP in the 0–100 cm soil layer around the 7 plateau lakes were estimated to be 25 kg/ha and 56 t, respectively. Therefore, reducing exogenous P inputs, improving soil endogenous P utilization efficiency and maintaining deep soil P retention are the basic strategies for preventing and controlling P accumulation and loss in deep soil caused by SGT fluctuations.

Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment

Flooding is a major natural hazard worldwide, causing catastrophic damage to communities and infrastructure. Due to climate change exacerbating extreme weather events robust flood hazard modeling is crucial to support disaster resilience and adaptation. This study uses multi-sourced geospatial datasets to develop an advanced machine learning framework for flood hazard assessment in the Arambag region of West Bengal, India. The flood inventory was constructed through Sentinel-1 SAR analysis and global flood databases. Fifteen flood conditioning factors related to topography, land cover, soil, rainfall, proximity, and demographics were incorporated. Rigorous training and testing of diverse machine learning models, including RF, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS algorithms, were undertaken for categorical flood hazard mapping. Model optimization was achieved through statistical feature selection techniques. Accuracy metrics and advanced model interpretability methods like SHAP and Boruta were implemented to evaluate predictive performance. According to the area under the receiver operating characteristic curve (AUC), the prediction accuracy of the models performed was around > 80%. RF achieves an AUC of 0.847 at resampling factor 5, indicating strong discriminative performance. AdaBoost also consistently exhibits good discriminative ability, with AUC values of 0.839 at resampling factor 10. Boruta and SHAP analysis indicated precipitation and elevation as factors most significantly contributing to flood hazard assessment in the study area. Most of the machine learning models pointed out southern portions of the study area as highly susceptible areas. On average, from 17.2 to 18.6% of the study area is highly susceptible to flood hazards. In the feature selection analysis, various nature-inspired algorithms identified the selected input parameters for flood hazard assessment, i.e., elevation, precipitation, distance to rivers, TWI, geomorphology, lithology, TRI, slope, soil type, curvature, NDVI, distance to roads, and gMIS. As per the Boruta and SHAP analyses, it was found that elevation, precipitation, and distance to rivers play the most crucial roles in the decision-making process for flood hazard assessment. The results indicated that the majority of the building footprints (15.27%) are at high and very high risk, followed by those at very low risk (43.80%), low risk (24.30%), and moderate risk (16.63%). Similarly, the cropland area affected by flooding in this region is categorized into five risk classes: very high (16.85%), high (17.28%), moderate (16.07%), low (16.51%), and very low (33.29%). However, this interdisciplinary study contributes significantly towards hydraulic and hydrological modeling for flood hazard management.

Potential for and impacts of mainstreaming diversification crops through institutional catering

IntroductionIn France, land use is dominated by a narrow range of crops. As crop diversification is increasingly promoted to transform the food system, outlets for diversification crops need to be identified. We assess the scope for introducing diversification crops from farmers’ fields to canteen plates, and the agroecological, environmental, work-related and nutritional impacts of their introduction.MethodsWe focus on three diversification crops: chickpea, squash, and millet, to partly replace soft wheat, potato, and durum wheat, respectively. For each crop, we define three scenarios of low (1), medium (2) and high (3) integration into institutional catering on a national scale. We assess the scenarios against the amount of diversification cropland area needed and their expected impacts.ResultsDiversification crops can be developed through institutional catering. Taking the case of chickpea, in Scenario 1, chickpea flour is introduced in a cake served once every 20 days, replacing 30% of the soft wheat flour used. This amounts to 874 ha cropped. In Scenario 2, chickpea flour partly replaces wheat flour in all preparations, and 4,048 ha are required. In Scenario 3, raw chickpeas are also introduced into new recipes and 20,958 ha are needed. All three diversification crops support the establishment of agroecological cropping systems, with enhanced nutrient cycling in particular, and reduce their environmental footprint, notably by avoiding pesticide use. Diversification crops do not generate work constraints for farmers and cooks. However, they have a lower productivity per unit area than the reference crops, although they do improve several nutrition parameters such as fiber intake.DiscussionWe show for the first time that institutional catering provides a tangible outlet for developing diversification crops and improving the sustainability of agriculture. Our scenarios can support concerted actions between farmers, supply-chain actors, cooks and policy makers, as well as communication to eaters surrounding their impacts on agriculture.

Analysis of Long-Term Vegetation Trends and Their Climatic Driving Factors in Equatorial Africa

Understanding vegetation seasonality and its driving mechanisms improves decision-making in the management of ecological systems in a warming global climate. Using multiple statistical methods (i.e., trend analysis, abrupt changes, and partial correlation analysis), this study analyzed the spatiotemporal variations in the Normalized Difference Vegetation Index (NDVI) in the Equatorial Africa (EQA) region and their responses to climate factors from 1982 to 2021. The NDVI values declined at a rate of 0.00023 year−1, while the precipitation (P) and mean temperature (TMEAN) values increased at rates of 0.22 mm year−1 and 0.22 °C year−1, respectively. The mean minimum temperature (TMIN) had a higher rate of 0.2 °C year−1 than the mean maximum temperature (TMAX) at 0.02 °C year−1. An abrupt change analysis showed that the TMAX, P, and NDVI breakpoints occurred in 2000, 2002, and 2009, respectively; TMEAN and TMIN breakpoints occurred in 2001. The NDVI trends declined in forest and cropland areas but increased in shrubland and grassland areas. The summer NDVI trends declined for all vegetation types and were reversed in the winter season. The NDVI positively correlated with the P (r = 0.50) and TMEAN (r = 0.60). All seasonal analyses varied across four seasons. A temporal analysis was conducted using partial correlation analysis (PCR), and the results revealed that TMIN had a greater impact on the NDVI (PCR = −0.45), followed by the TMAX (PCR = 0.31) and then the P (PCR = −0.19). The annual trend showed that areas with significant greening were consistent with stronger wetter and weaker warming trends. Both precipitation and temperature showed a positive relationship with vegetation in semi-arid and arid regions but a negative relationship with humid regions. Our findings improve our insight into scientific knowledge on ecological conservation.

Predicting the Impacts of Land Use/Cover and Climate Changes on Water and Sediment Flows in the Megech Watershed, Upper Blue Nile Basin

This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) was used to assess LULC and climate changes’ impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios.

Analyzing the relative importance of habitat quantity and quality for boosting pollinator populations in agricultural landscapes.

To increase pollinator populations, international policy targets minimum levels of seminatural habitat cover, but it is unknown whether improving the quality of existing habitats could bring similar benefits without the need of reducing cropland area. Using data we collected in 26 Italian agricultural landscapes during the entire flying season, we explored the relative importance of habitat quantity (seminatural habitat cover) and quality (flower availability) on pollinator densities in seminatural habitats. We obtained transect-based counts and estimated the effect of habitat quantity (proportion of seminatural habitat) and quality (flower cover and richness) on wild bee and hoverfly densities. We used the relationships revealed in the data to simulate pollinator population sizes in landscapes with varying habitat quantity and quality. Wild bee densities were only related to flower availability, whereas hoverfly densities were additionally related to seminatural habitat cover. We found that in complex agricultural landscapes (above 15% seminatural habitat cover), improving habitat quality increased pollinator populations more effectively than increasing habitat quantity. However, increasing habitat quantity was by far the most effective approach for boosting pollinator populations in simple landscapes.

Contributing to Carbon Neutrality Targets: A Scenario Simulation and Pattern Optimization of Land Use in Shandong Province Based on the PLUS Model

Land use profoundly impacts the sustainable development of the ecological environment. Optimizing land use patterns is a vital approach to mitigate climate change and achieve carbon neutrality. Using Shandong Province as a case study, this research evaluates the impacts of land use and land cover change (LUCC) on regional carbon storage and emissions. Employing a coupled PLUS–InVEST–GM(1,1) model, simulations were conducted for scenarios including the natural scenario (NS), cropland protection scenario (CPS), high-speed development scenario (HDS), and low-carbon scenario (LCS), to assess LUCC and changes in carbon storage and emissions from 2030 to 2060 under these scenarios. The findings indicate that due to the expansion of construction land and significant declines in arable and grassland areas, carbon emissions increased by 40,436.44 × 104 t over a 20-year period, while carbon storage decreased by 4881.13 × 104 t. Notably, forests contributed the most to carbon sequestration, while construction land emerged as the primary source of carbon emissions. Simulating four scenarios demonstrates that measures such as protecting cropland, expanding forest, grassland, and aquatic areas, controlling construction land expansion, and promoting intensive development positively affect emission reductions and carbon sequestration in Shandong. These findings underscore the importance of rational planning of land use patterns, which can enhance contributions to carbon neutrality by harmonizing the relationships among cropland protection, ecological conservation, and economic development.

Increased straw return promoted soil organic carbon accumulation in China's croplands over the past 40 years

Quantifying changes in soil organic carbon (SOC) stocks within croplands across a broad spatiotemporal scale in response to anthropogenic and environmental factors offers valuable insights for sustainable agriculture aimed to improve soil health. Using a validated and widely used soil carbon model RothC, we simulated the SOC dynamics across intensive croplands in China that support ∼22 % of the global population using only 7 % of the global cropland area. The modelling results demonstrate that the optimized RothC effectively captures SOC dynamics measured across 29 long-term field trials during 40 years. Between 1980 and 2020, the average SOC at the top 30 cm in croplands increased from 40 Mg C ha−1 to 49 Mg C ha−1, resulting in a national carbon sequestration of 1100 Tg C, with an average carbon sequestration rate of 27 Tg C yr−1. The annual increase rate of SOC (relative to the SOC stock of the previous year), starting at <0.2 % yr−1 in the 1980s, reached around 0.4 % yr−1 in the 1990s and further rose to about 0.8 % yr−1 in the 2000s and 2010s. Notably, the eastern and southern regions, comprising about 40 % of the croplands, contributed about two-thirds of the national SOC gain. In northeast China, SOC slightly decreased from 58 Mg C ha−1 in 1980 to 57 Mg C ha−1 in 2020, resulting in a total decline of 28 Tg C. Increased organic C inputs, particularly from the straw return, was the crucial factor in SOC increase. Future strategies should focus on region-specific optimization of straw management. Specifically, in northeast China, increasing the proportion of straw returned to fields can prevent further SOC decline. In regions with SOC increase, such as the eastern and southern regions, diversified straw utilization (e.g., bioenergy production), could further mitigate greenhouse gas emissions.