Cropland Area Research Articles

Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland.

Land use & land cover dynamics and its relationship with nitrate pollution in groundwater around inactive mine areas using geospatial techniques, SW part of Cuddapah basin, Southern India

Geospatial maps can show how the ineffective operations of inactive mines affect water and aquifer quality. As such, the purpose of this study is to assess the impact of mining and irrigation on the aquifer ecosystem through the evaluation of LULC and slope maps through the application of Landsat 8 OLI/TIRS and DEM data. A total of 50 groundwater samples were prepared from villages in the close proximity to inactive mines during pre and post monsoon periods in 2021. The results of the analysis revealed alarming statistics, that 14% of groundwater samples exceeded the WHO nitrate limit in pre & post monsoon season, indicating a high-risk in the study area. According to guidelines (USEPA, 2014), 34% in pre-monsoon and 26% post-monsoon of samples exceeded the THI levels for adults and children respectively, indicating non-carcinogenic health risks. In addition, 80% of the samples in both seasons exhibited high NPI values, indicating nitrate contamination associated with blue baby syndrome. From the Geospatial analysis the findings from the LULC classification indicate that there has been a significant increase in cropland area from 2016 to 2021 due to changes in forest land, fallow land, and water resources. These problems have been exacerbated by the expansion of cultivated land, which has increased from 71.1 square kilometers in 2016 to 118 square kilometers in 2021, accounting for 13.1% of the total area. This expansion, coupled with elevated water body resource availability, has compounded the nitrate pollution including in intensely irrigated regions. The slope map analysis revealed that the inactive mines occur at low slope, high rainfall areas and these are compounded by runoff from other sources such as domestic and agricultural wastes. For these matters, sealing and remediating these inactive mines is essential so as to prevent further nitrate leakage.

The Effects of Land Use and Landform Transformation on the Vertical Distribution of Soil Nitrogen in Small Catchments

The diversity of land use and consolidation is fundamental to ensuring sustainable development. However, the impact of diverse land uses and consolidation on the well-known shallow accumulation pattern of soil nitrogen (N) remains unclear. This existence of this knowledge gap severely constrains the sustainable production of newly created farmland. Therefore, the objective of this study was to investigate the effects of land use and gully land transformation on the vertical distribution of soil N in agricultural and nature catchments. Methodologically, soil nitrate (NO3−), ammonium (NH4+) and total nitrogen (TN) were measured to a depth of 100 cm in the hillslope forestland, grassland and gully cropland areas of the treated (gully landform reshaping) and untreated (natural gully) catchments on the Chinese Loess Plateau (CLP). The results indicated that soil N in the hillslope forestland and grassland exhibited a shallow accumulation pattern, while the vertical distribution of soil N in the gully cropland areas displayed a homogeneous, random or deep accumulation pattern. In the hillslope areas, vegetable cover was the dominant factor controlling N variation in the topsoil. In contrast, in the gully areas, the interaction of landform transformation and hydrology was the primary factor influencing the distribution of soil N. In the treated catchment, soil NO3− exhibited a significant deep accumulation pattern in the newly created farmland through gully landform reshaping. In the untreated catchment, soil NH4+ showed a significant deep accumulation pattern in the undisturbed natural gully. This study provides valuable insights into how land use and gully landform transformation affect the soil N profile. This information is crucial for the sustainable development and scientific management of valley agriculture at the catchment scale.

Climate change and food security causality in ECOWAS region: Do countries interdependence matter?

Studies abound on the causal relationship between climate change and food security; however, the ECOWAS sub-region, despite its unique climatic, agricultural, and socioeconomic characteristics, remains insufficiently explored. Utilizing data from 14 ECOWAS countries spanning 1986 to 2022, this study departs from earlier studies by incorporating the Dumitrescu–Hurlin Panel Causality test, the Pooled Mean Group (PMG), Common Correlated Effect Mean Group (CCEMG), Augmented Mean Group (AMG) and the Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL) technique to analyze the complex relationship between climate change and food security in the ECOWAS sub-region. Unlike the prior empirical studies, the findings reveal a robust mutual influence of climate change and food security in the ECOWAS sub-region. Furthermore, the findings reported a positive impact of climate change on food security, aided by increased cropland area and population growth. At the same time, drought incidence and erratic rainfall between July and September annually remained a severe concern in the region. However, while the conversion of forests and natural habitats into cropland in the sub-region can lead to heightened carbon emissions and reduced ecosystem services, policy recommendations advocate the need for ECOWAS sub-regional policymakers to carefully balance the benefits of food security through sustainable practices and environmental considerations to ensure long-term food security benefits.

Spatial and Temporal Dynamics in Vegetation Greenness and Its Response to Climate Change in the Tarim River Basin, China

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p < 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB.

Remote sensing monitoring of irrigated area in the non-growth season and of water consumption analysis in a large-scale irrigation district

Globally, water scarcity threatens food security, especially in arid and semi-arid food-producing regions, such as the Hetao Irrigation District (HID), where the water consumption during the non-growth season (spring irrigation, autumn irrigation) accounts for more than 30 % of the total irrigation volume. Meanwhile, mapping the characteristics of spatial and temporal evolution of the area of cropland irrigated in the spring and autumn and the pattern of water consumption is crucial for strengthening the management of agricultural water use. Evaporation and infiltration of irrigation water are difficult to capture in time due to insufficient timeliness or accuracy of single remote sensing data. This study integrates seven remote sensing datasets (Landsat5, Landsat7, Landsat8, HJ-1A/B, GF-1, Sentinel-2 and MODIS) for the first time to build a high spatial-temporal resolution dataset, and combines the water index NDWI and MNDWI to extract the maximum water range formed after irrigation by manually adjusting the threshold method. In addition to the global land use/cover dataset (GLC FCS30–1985_2020), the cropland layer was superimposed to accurately identify the cropland spring irrigation and autumn irrigation range from 2000 to 2021 in the HID. In the past 20 years, spring-irrigated (SI) areas increased by 1012.60 km2 (+49.96 %), and the autumn-irrigated (AI) areas decreased by 512.63 km2 (-11.02 %). Combined with the quantity of water diversion and displacement, the characteristics of irrigation water consumption in the non-growth season were further analysed. The results showed that the water consumption of spring irrigation increased by 0.67×108 m3 (+22.26 %), and that of autumn irrigation increased by 4.34×108 m3 (+38.82 %). The decrease of autumn- irrigated area and the increase of water consumption were mainly related to the stability of autumn irrigation water, imperfect irrigation and drainage conditions and insufficient field water management. In 2020­2021, the area of non-growth “autumn irrigation - spring irrigation” repeated irrigation is about 1271.52 km2, which can be used as a key control area. This study provides a theoretical basis and direction for rational water use in non-growth seasons of irrigation districts.

Thirty years’ spatio-temporal evolution of desertification degrees and driving factors in Turpan–Hami Basin, Xinjiang, China

Turpan-Hami (Tuha) Basin is in an extremely arid area, surrounded by mountains and desert with sparse vegetation. It has experienced serious desertification over the past 30 years, resulting in ecological environment and socio-economic issues. To reveal the temporal and spatial evolutions of desertification degrees in Tuha Basin, we first constructed an evaluation system for the desertification degree classification using vegetation and soil-related indices; then long-time series Landsat 5 TM/ Landsat8 OLI images and a C5.0 decision tree (DT) model were used to monitor large-scale desertification from 1990 to 2020. We applied a geographic detector to discover the driving forces between desertification changes and natural and human factors. The results demonstrated that the severe desertification (SD) accounted for the largest desertification degree, and the transitions of various desertification degrees mainly occurred between extremely severe desertification (ESD) and SD, which were considered the most active degrees. Only 9.4 % of Tuha Basin showed an expansion trend in desertification degree, with the most significant expansion occurred in 1990–1995. The proportion of 13.6 % desertification showed a reversion trend, with the most significant reversion occurred in 1995–2010. The main driving factors affecting the evolution of desertification degrees were precipitation and temperature, with the average q value higher than 0.95. Human activity factors such as year-end cropland area and population also impacted the patterns of desertification degree, with the q values of 0.9 and 0.7, respectively. The interactive detection enhanced the driving forces and the interaction between precipitation and other factors played a leading role in desertification degree changes. The results provide scientific data and a basis for desertification control and ecological protection in arid areas.

Driving Forces behind the Reduction in Cropland Area on Hainan Island, China: Implications for Sustainable Agricultural Development

Driving Forces behind the Reduction in Cropland Area on Hainan Island, China: Implications for Sustainable Agricultural Development

Identifying factors influencing reservoir eutrophication using interpretable machine learning combined with shoreline morphology and landscape hydrological features: A case study of Danjiangkou Reservoir, China

Reservoir nearshore areas are influenced by both terrestrial and aquatic ecosystems, making them sensitive regions to water quality changes. The analysis of basin landscape hydrological features provides limited insight into the spatial heterogeneity of eutrophication in these areas. The complex characteristics of shoreline morphology and their impact on eutrophication are often overlooked. To comprehensively analyze the complex relationships between shoreline morphology and landscape hydrological features, with eutrophication, this study uses Danjiangkou Reservoir as a case study. Utilizing Landsat 8 OLI remote sensing data from 2013 to 2022, combined with a semi-analytical approach, the spatial distribution of the Trophic State Index (TSI) during flood discharge periods (FDPs) and water storage periods (WSPs) was obtained. Using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), explained the relationships between landscape composition, landscape configuration, hydrological topography, shoreline morphology, and TSI, identified key factors at different spatial scales and validated their reliability. The results showed that: (1) There is significant spatial heterogeneity in the TSI distribution of Danjiangkou Reservoir. The eutrophication levels are significant in the shoreline and bay areas, with a tendency to extend inward only during the WSPs. (2) The importance of landscape composition, landscape configuration, hydrological topography, and shoreline morphology to TSI variations during the FDPs are 25.12 %, 29.6 %, 23.09 %, and 22.19 % respectively. Besides shoreline distance, the Landscape Shape Index (LSI) and Hypsometric Integral (HI) are the two most significant environmental variables overall during the FDPs. Forest and grassland areas become the most influential factors during the WSPs. The influence of landscape patterns and hydrological topography on TSI varies at different spatial scales. At the 200 m riparian buffer zone, the increase in cropland and impervious areas significantly elevates eutrophication levels. (3) Morphology complexity, shows a noticeable threshold effect on TSI, with complex shoreline morphology increasing the risk of eutrophication.

Driving Analysis and Multi Scenario Simulation of Ecosystem Carbon Storage Changes Based on the InVEST-PLUS Coupling Model: A Case Study of Jianli City in the Jianghan Plain of China

The carbon storage capacity of terrestrial ecosystems serves as a crucial metric for assessing ecosystem health and their resilience to climate change. By evaluating the effects of land use alterations on this storage, carbon management strategies can be improved, thereby promoting carbon reduction and sequestration. While county-level cities are pivotal to ecological conservation and high-quality development, they often face developmental challenges. Striking a balance between economic growth and meeting peak carbon emissions and carbon neutrality objectives is particularly challenging. Consequently, there is an urgent need to bolster research into carbon storage management. The study focuses on Jianli City, employing the InVEST model and land use data to examine the response patterns of land use changes and terrestrial system carbon storage from 2000 to 2020. Using the PLUS model, the study simulated the land use and carbon storage in Jianli City for the year 2035 under three scenarios: Natural Development scenario, Urban Expansion scenario, and Ecology and food security scenario. Our findings indicate the following: (1) Between 2000 and 2020, significant shifts in land use were observed in Jianli City. These changes predominantly manifested as the interchange between Cropland and Water areas and the enlargement of impervious surfaces, leading to a decrease of 691,790.27 Mg in carbon storage. (2) Under the proposed scenarios—Natural Development scenario, Urban Expansion scenario, and Ecology and food security scenario—the estimated carbon storage capacities in Jianli City were 39.95 Tg, 39.90 Tg, and 40.14 Tg, respectively. When compared with the 2020 data, all these estimates showed an increase. In essence, our study offers insights into optimizing land use structures from a carbon storage standpoint to ensure stability in Jianli’s carbon storage levels while mitigating the risks associated with carbon fixation. This has profound implications for the harmonious evolution of regional eco-economies.

Safety assessment of agricultural products and the pesticide regulation trend in China

The sustainable development of Chinese agriculture needs to coordinate the quality and quantity of agricultural products. To verify whether government regulation can promote agricultural development by controlling pesticides, the indices of government regulation, quantity and quality of agricultural products, and pesticide use per area of cropland were calculated, and a comprehensive evaluation index and the coupling degree and coordination degree of agricultural products were analyzed by coupling function. The results showed that the coupling and coordination degree of the quality and quantity of agricultural products and the comprehensive index are improving. The regulation of pesticide use per area of cropland is a key mechanism in promoting the coordinated development of agricultural product safety. The regulation trend of agricultural development in China is to reduce pesticide usage appropriately and expand the production-possibility frontier of agricultural products.Graphical abstract

Analysis of the Spatiotemporal Changes in Cropland Occupation and Supplementation Area in the Pearl River Delta and Their Impacts on Carbon Storage

In recent years, the “dual carbon” issue has become a major focus of the international community. Changes in land use driven by anthropogenic activities have a profound impact on ecosystem structure and carbon cycling. This study quantitatively assesses the spatiotemporal changes in cropland occupation and supplementation in the Pearl River Delta from 2000 to 2020 using the InVEST model, analyzing the spatial clustering of carbon storage changes caused by variations in cropland area. The PLUS model was employed to simulate land-use patterns and the spatial distribution of carbon storage in four future development scenarios. The results indicate the following: (1) From 2000 to 2020, the net change rate of cropland area in the Pearl River Delta was −0.81%, with a decrease of 16.49 km2 in cropland area, primarily converted to built-up land and forest land. (2) Carbon storage in the Pearl River Delta exhibited a pattern of lower values in the center and higher values in the periphery. The terrestrial ecosystem carbon storage in the Pearl River Delta was 534.62 × 106 t in 2000, 518.60 × 106 t in 2010, and 512.57 × 106 t in 2020, showing an overall decreasing trend. The conversion of cropland and forest land was the main reason for the decline in total regional carbon storage. (3) The area of carbon sequestration lost due to cropland occupation was significantly greater than the area of carbon loss compensated by new cropland, indicating an imbalance in the quality of cropland occupation and supplementation as a crucial factor contributing to regional carbon loss. (4) Under the ecological priority scenario, the expansion of built-up land and the reduction in ecological land such as cropland and forest land were effectively controlled, resulting in the minimal loss of carbon storage. The soil organic carbon pool of cropland is the most active carbon pool in terrestrial ecosystems and has a significant impact on carbon storage. Clarifying the relationship between “cropland protection measures–land use changes–ecosystem carbon storage” will improve cropland protection policies, provide references for regional carbon sequestration enhancement, and support sustainable socio-economic development.

Ecosystem carbon storage assessment and multi-scenario prediction in the Weihe River Basin based on PLUS-InVEST model.

Land use changes are the main cause for the changes of carbon storage, which is of great importance for maintaining regional carbon balance to make multi-scenario projections of future land use change and explore its impacts on carbon storage. In recent years, under the combination of natural factors and policies, with the land use changing significantly, carbon storage of the Weihe River Basin has also changed. Based on the PLUS-InVEST model, we assessed and predicted the spatial and temporal variations of ecosystem carbon storage in the Weihe River Basin and explored the impacts of land-use change. The results showed that land use distribution pattern of the Weihe River Basin did not change much from 2000 to 2020, which was characterized by the decreases of cropland area and the increases of the area of the remaining land use types. The main ways of land use type conversion were cropland to built-up land and inter-conversion of cropland, forest, grassland. Carbon storage in the Weihe River Basin showed an upward trend from 2000 to 2020, with a total increment of 15.31×106 t. The areas with high carbon storage presented the characteristics of "northeast patch-western scatter-central and southern belt", while low carbon storage distributed in the Guanzhong Plain urban agglomeration located in the lower basin. Compared to 2020, carbon storage in the Weihe River Basin in 2030 would increase under the four scenarios. Carbon storage would increase the least under the economic development scenario, and the most under the ecological protection scenario. The variation of carbon storage in spatial distribution would be embodied in the staggered zone of cropland, forest, and grassland in the upper basin. The results could provide data support for land use management decisions and carbon storage enhancement in the Weihe River Basin.

The estimation method is the primary source of uncertainty in cropland nitrate leaching estimates in China

Cropland nitrate leaching is the major nitrogen (N) loss pathway, and it contributes significantly to water pollution. However, cropland nitrate leaching estimates show great uncertainty due to variations in input datasets and estimation methods. Here, we presented a re-evaluation of Chinese cropland nitrate leaching, and identified and quantified the sources of uncertainty by integrating three cropland area datasets, three N input datasets, and three estimation methods. The results revealed that nitrate leaching from Chinese cropland averaged 6.7±0.6 Tg N yr−1 in 2010, ranging from 2.9 to 15.8 Tg N yr−1 across 27 different estimates. The primary contributor to the uncertainty was the estimation method, accounting for 45.1%, followed by the interaction of N input dataset and estimation method at 24.4%. The results of this study emphasize the need for adopting a robust estimation method and improving the compatibility between the estimation method and N input dataset to effectively reduce uncertainty. This analysis provides valuable insights for accurately estimating cropland nitrate leaching and contributes to ongoing efforts that address water pollution concerns.

Simulation and evaluation of ecosystem service value along the Yellow River in Henan Province, China

The unprecedented growth in population and swift industrial advancements exert considerable strains on the ecosystem, particularly within medium-sized and large urban landscapes. The critical investigation into the intricate links between current and prospective land utilization, as well as the ecosystem service value (ESV), holds considerable empirical relevance for the calibration of land usage frameworks, thereby contributing to the sustainable evolution of extensive urban zones. Utilizing GlobeLand 30 data, the present research probes into the pattern of land transformation and the spatial-temporal dispersal of ESV in Henan’s Yellow River vicinity over a span from 2000 to 2020. For the enhancement of land usage alignment, a Markov-PLUS fusion model was devised to gauge three disparate ESV transition scenarios slated for 2030, namely, natural development scenario (NDS), cropland protection scenario (CPS), and ecological protection scenario (EPS). The principal determinants of land transformation within the 2000–2020 period were recognized as elevation, populace concentration, and atmospheric temperature. Amid the rapid accretion of construction land engulfing substantial cropland and grassland areas, there was an ESV diminution to the tune of 1.432 billion RMB between 2000 and 2020. The ESV’s high-value regions were discerned within relatively undisturbed ecosystem zones, with the lower-value sections identified in cropland and constructed areas, where human interventions exerted pronounced effects on the ecosystem. In accordance with the 2030 land usage simulations and analyses, in contrast to alternative scenarios, the EPS exhibited the least fluctuation in land type alterations in 2030, demonstrated the most pronounced escalation in cold spot concentration, and reached a peak agglomeration level. This underscores that the EPS not only offers a refinement in land utilization configuration but also mediates the equilibrium between economic and ecological considerations. The insights derived from this investigation afford innovative evaluative methods for spatial planning, ecological recompense, and sustainable land exploitation within large- and medium-scale urban domains.

Utilizing geodetectors to identify conditioning factors for gully erosion risk in the black soil region of northeast China

In the black soil region of Northeast China, the issue of gully erosion persists as a significant threat, resulting in extensive damage to farmland, severe degradation of the black soil, and decreased productivity. It is therefore of utmost importance to accurately identify areas that are susceptible to gully erosion to effectively prevent and control its negative impact. This study tried to utilize geographical detectors (geodetectors) as a means to identify the factors that contribute to the distribution of gullies and assess the risk of gully erosion (GER) in five catchments within the region, with areas ranging from approximately 80 km2–200 km2. By employing the geodetectors method, fourteen geo-environmental factors were analyzed, including topographic attributes (such as aspect, catchment area, convergence index, elevation, plan curvature, profile curvature, slope length, slope, stream power index, and topographic wetness index), channel network distance, vegetation index (NDVI and EVI), as well as land use/land cover (LULC). The modeling of GER was conducted using the random forest algorithm (RFA). Out of the fourteen examined geo-environmental factors, only a subset, comprising less than or equal to 50%, demonstrated a significant (p < 0.05) influence on the spatial distribution of gullies. These selected factors were sufficient in assessing GER, with LULC (mean q-value = 0.270) and elevation (mean q-value = 0.113) identified as the two most important factors. Furthermore, the RFA exhibited satisfactory performance across all catchments, achieving AUC values ranging from 0.712 to 0.933 (mean = 0.863) in predicting GER. Overall, the catchment areas were classified into high, moderate, low, and very low-risk levels, representing 9.67%–15.95%, 19.28%–26.08%, 24.59%–30.55%, and 30.54%–39.08% of the total area, respectively. Importantly, a significant positive linear relationship (r2 = 0.722, p < 0.05) was observed between the proportion of cropland area and the occurrence of high-level GER. Although the primary risk levels were categorized as low and very low, the proportion of high-risk levels exceeded the existing gully coverage (0.34%–3.69%). These findings highlight the substantial potential for gully erosion and underscore the necessity for intensified efforts in the prevention and control of gully erosion within the black soil region of Northeast China.

Opportunity Analysis of Phosphorus Recovery from Municipal Wastewater for Cropland Based on the Simulated Vehicle Transport Distance in the Yangtze River Delta, China.

With the rapid depletion of phosphate rocks and increasing agricultural demand, establishing a phosphorus (P) flow "loop" rather than a one-way trajectory between cropland and urban areas was imperative. Recovering P from municipal wastewater stood as a viable strategy to mitigate reliance on traditional P-containing chemical fertilizer. This study analyzed the intricate relationships between the potentials of P recovery from municipal wastewater and the P demand of croplands in the populated Yangtze River Delta (YRD), China. An indicator of the P vehicle transport distance was constructed and calculated to estimate the potential to recover and reuse P in agriculture, applying the simulated annealing (SA) algorithm and road networks obtained from OpenStreetMap (OSM). The results indicated that, on a regional scale, recovered P from municipal wastewater could fulfill 14.0% of the cropland P demands in the YRD, with a median P vehicle transport distance of 3.1 km/Mg of P. Notably, the P vehicle transport distance varied largely depending upon the cropland distributions, road density, and P recovery potential from municipal wastewater. The novel methodology developed here determined the optimal transportation routes for P recovery from wastewater treatment plants (WWTPs) to cropland, which played a crucial role in refining the wastewater management strategies aligned with the United Nations Sustainable Development Goals.

Characteristics of Changes in Typical Mountain Wetlands in the Middle and High Latitudes of China over the Past 30 Years

Analysis of the driving mechanisms of wetland change can help identify spatial differences in the mechanisms affecting various elements, enabling a more scientific approach to the conservation and utilization of wetlands. This study investigated the impacts of natural and anthropogenic factors on the spatiotemporal evolution of the Altay and Greater and Lesser Khingan Mountains areas using Landsat satellite image data from 1980 to 2018 and fieldwork data from 2019 to 2020. A transfer matrix, correlation analysis, and dynamic characteristics were applied to calculate and analyze the transformation types and areas of wetland resources across all consecutive periods. Finally, the dominant factors influencing the spatiotemporal evolution of the wetland were explored and revealed using the drought index (Standardized Precipitation Index, SPEI) and statistical almanacs. The results showed: (1) From 1980 to 2018, the wetlands area in the Altay Mountains exhibited a decreasing trend, whereas the wetlands area in the Greater and Lesser Khingan Mountains showed an increasing trend. The primary type of wetland transfer in the Altay Mountains was grassland, whereas in the Greater and Lesser Khingan Mountains regions, the primary types of wetland transfer were grassland and forestland. The wetlands area transferred out of the Altay Mountain region was larger than the area of wetland types transferred into during 2010–2018, whereas the wetland areas of the Greater and Lesser Khingan Mountain areas showed the opposite trend. (2) From 1980 to 2018, the wetland ecosystem types in the Altay Mountains exhibited the highest dynamic and conversion degrees of the channels. Similarly, the mountain areas of the Greater Khingan Mountains showed the highest dynamic and conversion degrees of marshes and channels among the wetland types. In addition, the mountainous areas of the Lesser Khingan Mountains showed the highest dynamic and conversion degrees for reservoirs and rivers. (3) Natural driving factor analysis revealed that the SPEI values in the Altay Mountains and the Greater and Lesser Khingan Mountains areas exhibited an increasing trend, indicating that the climate has been warm and humid over the past 30 years and that the expansion of cropland and human-made wetland areas has been significantly influenced by human activities. Therefore, the wetland areas of the Greater and Lesser Khingan Mountains in the northeast are strongly influenced by human activities, whereas the wetland in the Altay Mountains in the northwest is strongly influenced by the climate.

Per Capita Cropland Estimations for Traditional Agricultural Areas of China over Past Millennium

Studying changes in land use per capita is critical for understanding the interactions between humans and ecosystems, and for modeling the impacts of land use changes on climate systems. However, many uncertainties in historical estimates significantly hinder climate modeling. This study estimated the per capita cropland area in traditional agricultural regions of China over the past millennium using historical-document-based and modern statistical cropland and population data. The findings showed that changes in the per capita cropland area in the provinces of the middle and lower reaches of the Yellow River could be characterized into three stages: slow decrease, rapid increase, and fluctuating decrease, whereas, in the provinces of the middle and lower reaches of the Yangtze River, there was a continuous decrease. Spatially, the per capita cropland area was higher in the middle and lower reaches of the Yellow River and lower in the middle and lower reaches of the Yangtze River during the study period. The per capita cropland areas showed clear differences in the HYDE dataset and our study; the corresponding values of our study were 2.1–8.0, 1.7–8.2, and 1.6–8.8 times higher than those from the HYDE dataset for the early Song, Yuan, and Ming dynasties, respectively.

Assessing the risk of subsoil compaction in croplands using the soil compaction stratification ratio as an indicator

AbstractAccurate prediction of subsoil compaction in cropland areas is important for food security and sustainable agricultural production development. This study evaluated the validity and advantages of the soil compaction stratification ratio (SR) as an indicator for assessing the risk of subsoil compaction and presented a theoretical framework for predicting and evaluating subsoil compaction risk. Geographic information system and remote sensing technologies were utilized in combination with a state‐of‐the‐art ensemble machine learning model to quantitatively predict the spatial distribution and uncertainty of subsoil compaction risk in croplands. The results showed that the subsoil bulk density (BD) was significantly greater than the topsoil BD in Shaanxi Province, China. Compared with that of the subsoil BD, the stratification ratio of the bulk density (SRBD) exhibited greater sensitivity to subsoil compaction, and it accurately reflected spatial variations in subsoil compactness. The SRBD exhibited obvious spatial heterogeneity, and the mean annual precipitation, elevation, and mean land surface temperature substantially influenced its spatial distribution. The critical thresholds of the SRBD for the low‐risk, medium‐risk, and high‐risk groups were 1.01, 1.21, and 1.32, respectively. Low subsoil compaction dominated in the mountainous and hilly areas, whereas the Guanzhong Plain and Hanzhong Basin exhibited medium levels of subsoil compaction. Some croplands in the central Guanzhong Plain experienced severe subsoil compaction issues. In conclusion, the soil compaction SR was found to be an effective indicator of subsoil compaction by effectively eliminating soil background differences. This approach has potential applications in cropland management, digital soil mapping, and early warning of soil compaction risk.