Search Results (175)

Sustainable cropland management is essential in maintaining national food security. In the black soil regions of China, which are key areas for commercial grain production, sustainable land use must be achieved urgently. To address the absence of integrated, large-scale, remote sensing-based sustainability frameworks in China’s black soil zones, we developed a comprehensive evaluation system with 13 indicators from four dimensions: the soil capacity, the natural capacity, the management level, and crop productivity. With this system and the entropy weight method, we systematically analyzed the spatiotemporal patterns of cropland sustainability in the selected black soil regions from 2010 to 2020. Additionally, a diagnostic model was applied to identify the key limiting factors constraining improvements in cropland sustainability. The results revealed that cropland sustainability in Heilongjiang Province has increased by 7% over the past decade, largely in the central and northeastern regions of the study area, with notable gains in soil capacity (+15.6%), crop productivity (+22.4%), and the management level (+4.8%). While the natural geographical characteristics show no obvious improvement in the overall score, they display significant spatial heterogeneity (with better conditions in the central/eastern regions than in the west). Sustainability increased the most in sloping dry farmland and paddy fields, followed by plain dry farmland and arid windy farmland areas. The soil organic carbon content and effective irrigation amount were the main obstacles affecting improvements in cropland sustainability in black soil regions. Promoting the implementation of technical models, strengthening investment in cropland infrastructure, and enhancing farmer engagement in black soil conservation are essential in ensuring long-term cropland sustainability. These findings provide a solid foundation for sustainable agricultural development, contributing to global food security and aligning with SDG 2 (zero hunger). Full article

Wetland monitoring is a key means of protecting wetland ecosystems. In order to achieve continuous monitoring of wetlands and predict future patterns, this paper analyzes the spatiotemporal evolution characteristics of wetlands in the upper reaches of the Yellow River from 1990 to 2020, and uses the Patch Generation Land Use Simulation (PLUS) model to simulate the spatial distribution of wetlands from 2040 to 2060 under four scenarios: farmland protection (FPS), wetland protection (WPS), comprehensive protection (CPS) and natural development (NDS). The results show that the total area of wetlands in the upper reaches of the Yellow River is on the rise, increasing by 7.12% in 2020 compared with 1990. The changes in various types of wetlands are different: the areas of river and canals increased by 26.39% and 57.97%, respectively, paddy fields increased by 7.95%, lakes remained basically stable, and tidal flats decreased by 5.67%. The simulation results of the future spatial pattern of wetlands show that: under the FPS scenario, farmland and related land use will expand significantly, mainly through the development of beaches, dry land and unused land, while under the WPS scenario, wetlands will be strictly protected, the area of water resource features such as rivers, lakes and reservoirs will increase significantly, and land use changes will be more ecologically oriented. Compared with the CPS and NDS scenarios, the wetland protection and urbanization process in the upper reaches of the Yellow River can be balanced under the FPS and WPS scenarios. This study has important reference value for the protection and sustainable development of wetland ecosystems in the upper reaches of the Yellow River. Full article

This study aimed to address the challenge of monitoring and managing soil moisture in dryland agriculture with supplemental irrigation under increasingly extreme climate conditions. Using unmanned aerial vehicles (UAVs) equipped with hyperspectral sensors, we collected imagery of wheat fields on Kinmen Island at various growth stages. The Modified Perpendicular Drought Index (MPDI) was calculated to quantify soil drought conditions. Simultaneously, soil samples were collected to measure the actual soil moisture content. These datasets were used to develop a Gradient Boosting Regression (GBR) model to estimate soil moisture across the entire field. The resulting AI-based model can guide decisions on the timing and scale of supplemental irrigation, ensuring water is applied only when needed during crop growth. Furthermore, MPDI values and wheat spike samples were used to construct another GBR model for yield prediction. When applying MPDI values from multispectral imagery collected at a similar stage in the following year, the model achieved a prediction accuracy of over 90%. The proposed approach offers a reliable solution for enhancing the resilience and productivity of dryland crops under climate stress and demonstrates the potential of integrating remote sensing and machine learning in precision water management. Full article

More than half of the cultivated land belongs to the Irrigation Association. Therefore, there have been no farmland consolidation, irrigation, and drainage projects. The cultivation in the non-irrigation area suffers from poor geographical conditions and a lack of water sources. A practical planning strategy is required for expanding irrigation services. The mountainous area of Nantou County, Taiwan, has 7477 ha of available land and 4656 ha of agricultural land outside the irrigation area. Rain and streams are the main water source. There are 82 ponds, 80% of which belong to the loam soil, and the rainfall from October to February is limited. The water requirement of crops is 1.5–3.1 mm/day. Wild streams, groundwater, and rainwater are the only potential water sources due to elevation and terrain. The potential runoff is estimated to be 0–0.927 cms (m³/s) when using the SCS-CN method. Water supply and demand from October to April are limited, and the rainfall comprises 22% of the total water supply. Large reservoirs and water storage towers are required for flooding and in dry seasons. To address water storage challenges and stabilize the balance between water supply and demand, it is essential to construct additional ponds. Full article

As global warming worsens, there is a growing need to reduce emissions of methane, a greenhouse gas. In agriculture, a water management method called alternate wetting and drying (AWD) has proven effective in mitigating methane emissions from paddy fields. It is, therefore, advisable to disseminate it efficiently. This study was conducted in Shiga Prefecture, Japan, to determine what influences AWD adoption behavior and examine the effectiveness of human networks in promoting AWD. Spatial statistical methods, including Moran’s I and Global G* and the spatial probit model, were employed for the purpose. The analysis results indicate that the behavior of surrounding farmers, which constitutes a spatial factor, influences that of the individual farmers. Moreover, farmers who acquire and use data, those with large-scale production, and those who mainly sell paddy rice tend to implement AWD, whereas corporate-managed farms do not. Therefore, to more efficiently improve the AWD implementation rate in Shiga Prefecture, this study makes several recommendations. Farmers’ active information sharing and technology exchange should be leveraged to strengthen networks and promote best practices for AWD dissemination. Advancing agricultural digitalization and data utilization is crucial, particularly by reducing digital equipment costs and securing technical personnel through public investment. Additionally, the approach toward corporate entities in AWD dissemination should be reconsidered, with market incentives playing a role. Lastly, promoting larger farmland parcels and increasing large-scale management farmers who are motivated to adopt AWD is essential. These strategies constitute this study’s original contribution. Full article

This study examines how nitrogen and phosphorus fertilization influence soil microbial necromass carbon (MNC) content of farmland on the Loess Plateau, central Gansu. Based on an extensive (6 years) experiment, a control (CK, no fertilization) and three treatment groups employing different fertilization methods, namely, nitrogen fertilization (N, 115 kg·ha⁻¹), phosphorus fertilization (P, 115 kg·ha⁻¹), and combined fertilization of nitrogen and phosphorus (NP, 115 kg·ha⁻¹ each), were set up in this research. The results show that, in the tillage soil layer (within a depth range of 0–20 cm), the application of nitrogen and/or phosphorous fertilizers can significantly reduce the ratio between glucosamine and muramic acid (GluN/MurA) (p < 0.05), with a reduction range of 12.70–35.29%. Phosphorus fertilization can also reduce the content of fungal necromass carbon (FNC) and MNC and their contributions to SOC (p < 0.05). In addition, phosphorus fertilization and combined fertilization of nitrogen and phosphorus can both increase the content of bacterial necromass carbon (BNC) and contribute to the content of SOC (p < 0.05). Primarily because of the reduced accumulation efficiency of FNC, the combined fertilization of nitrogen and phosphorus can significantly decrease the accumulation efficiency of MNC. In the non-tillage soil layer (within depth range of 20–40 cm), both nitrogen fertilization and the combined fertilization of nitrogen and phosphorus can increase the content of FNC and MNC in soils and their impacts on SOC (p < 0.05). The addition of nitrogen and/or phosphorus fertilizers does not alter the accumulation efficiency of soil MNC. Total phosphorus (TP), total nitrogen (TN), soil pH, nitrogen-to-carbon ratio of microbial biomass (MBN/MBC), leucine aminopeptidase (LAP), and β-glucosidase activities (BG) are the primary factors that affect changes in FNC, BNC, and MNC. In summary, phosphorus fertilization alone decreases soil MNC contribution to SOC and reduces carbon pool stability in the tillage layer. On the contrary, both nitrogen fertilization and the combined fertilization of nitrogen and phosphorus can increase the content of soil MNC in the non-tillage layer and its impact on SOC, thus improving the stability of SOC. Full article

This study investigates the complex interaction of biophysical and meteorological factors that drive evapotranspiration (ET) in saline environments. Leveraging a total of 182 cloud-free Landsat 5/8 time-series data from 1988 to 2019, we employed the Surface Energy Balance System (SEBS) model to quantify ET and investigate its relationships with soil salinity, vegetation cover, groundwater depth, and landscape metrics. We validated the predicted ET at two experimental sites using ET observation calculated by a water balance model. The result shows an R² of 0.78 and RMSE of 0.91 mm for the SEBS predicted ET, indicating high accuracy of the ET estimation. We detected abandoned saline farmland patches across Hetao and extracted the normalized difference vegetation index (NDVI), salinization index (SI), and the predicted ET for analysis. The results indicate that ET is negatively correlated with SI with a Pearson correlation coefficient (r) up to −0.7, while ET is positively correlated with NDVI (r = 0.4). In addition, we designed a control-variable experiment in the Yichang subdistrict to investigate the effects of groundwater depth, land aggregation index, soil salinity index, and the area of abandoned saline farmland patches on ET. The results indicate that increased NDVI could significantly enhance ET, while smaller saline farmland patches exhibited greater sensitivity to groundwater recharge, with higher averaged ET than larger patches. Moreover, we analyzed factor importance using Lasso regression and Random Forest (RF) regression. The result shows that the ranking of the importance of the features is consistent for both methods and for all the features, with NDVI being the most important (with an RF importance score of 0.4), followed by groundwater table depth (GWTD), and the influence of the surface area of abandoned saline farmland being the weakest. We found that smaller patches of abandoned saline farmland were more sensitive to changes in groundwater levels induced by nearby irrigation, affecting their averaged ET more dynamically than larger patches. Decreasing patch size over time indicates ongoing changes in land management and ecological conditions. This study, through a multifactor analysis of ET in abandoned saline farmland and its intrinsic factors, provides a reference for evaluating the dry drainage efficiency of abandoned saline farmland in a dry drainage system. Full article

Soil salinity, affecting 20–50% of irrigated farmland globally, poses a significant threat to agriculture and food security, worsened by climate change and increasing droughts. Traditional methods for managing saline soils—such as leaching, gypsum addition, and soil excavation—are costly and often unsustainable. An alternative approach using plant growth-promoting microorganisms (PGPMs) offers promise for improving crop productivity in saline conditions. This study tested twenty-three bacterial strains, one yeast, and one fungal strain, isolated from diverse sources including salicornia plants, sandy soils, tomato stems or seeds, tree leaves, stems, and flowers. They were initially submitted to in vitro selection tests to assess their ability to promote plant growth under salt stress. In vitro tests included auxin production, phosphate solubilization, and co-culture of microorganisms and tomato seedlings in salt-supplemented media. The Bacillus sp. strain 44 showed the highest auxin production, while Bacillus megaterium MJ had the strongest phosphate solubilization ability. Cryptococcus sp. STSD 4 and Gliomastix murorum (4)10-1(iso1) promoted germination and the growth of tomato seedlings in an in vitro co-culture test performed on a salt-enriched medium. This innovative test proved particularly effective in selecting relevant strains for in planta trials. The microorganisms that performed best in the various in vitro tests were then evaluated in vivo on tomato plants grown in greenhouses. The results showed significant improvements in growth, including increases in fresh and dry biomass and stem size. Among the strains tested, Gliomastix murorum (4)10-1(iso1) stood out, delivering an increase in fresh biomass of 94% in comparison to the negative control of the salt modality. These findings highlight the potential of specific PGPM strains to enhance crop resilience and productivity in saline soils, supporting sustainable agricultural practices. Full article

The southwestern region of China is one of the major maize (Zea mays L.)-producing areas and a concentrated zone of farmland contaminated by heavy metals (HMs). Selection of maize varieties with low accumulation of HMs under complex HM pollution conditions is one the most feasible and effective ways for safe utilization of HM-polluted farmland. In this study, we conducted field experiments to investigate the differences in biological traits among 28 local maize varieties under combined soil pollution with Cd, Pb, As, Cr, and Hg. We analyzed the absorption, accumulation, and transport characteristics of Cd, Pb, As, and Cr in various parts of the maize plant (Hg was not detected in any part of maize plants) and explored the relationships of HM contents in different parts of maize with soil HM contents through cluster analysis, correlation analysis, and principal component analysis. The results indicated that among different biological traits of maize, root length, root dry weight, and plant height were the most significantly influenced by soil HM content, while stem dry weight was the least affected. The accumulation capacity of various maize parts for HMs followed the order of grains < stems < cobs < leaves < roots, while the transport capacity followed the order of root–grain < root–stem < cob–grain < stem–cob < stem–leaf. In addition, the accumulation capacity of maize grains for HMs followed the order of As < Cr < Pb < Cd. Different HMs exhibited synergistic effects in various maize parts, except for the stem, particularly in the grains. A synchronous transport mechanism was observed for As and other HMs in different parts. The accumulation of HMs in maize was primarily derived from human activities such as the extraction, storage, and smelting of non-ferrous metals, while the HMs in soil parent material and weathering products played a secondary role. The yield of the tested maize varieties ranged from 7377.6 to 11,037.0 kg·hm⁻², with M5 (Haoyu 1511) achieving the highest yield. M2, M4, M5, M9, M10, M21, and M25–28 were identified as suitable varieties with low Cd, Pb, As, and Cr accumulation for popularization in HM-contaminated soils in southwestern China due to their low accumulation of HMs. Full article

Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of the above-based model in the plains of the arid zone remains underexplored. Fortunately, soil salinity and soil moisture may provide an optimized solution for GWL prediction based on the application of apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted in previous studies in oases in arid regions. The study proposed two strategies to predict GWL, included an ECa-based GWL model and a remote sensing-based GWL model (RS_GWL), and then compared and explored their performances and cooperation possibilities. To this end, this study first constructed the ECa prediction model and the RS_GWL with ensemble machine learning algorithms using environmental variables and field observations (474 ECa reads and 436 groundwater level observations from a mountain–oasis–desert system, respectively). Subsequently, a strategy to improve the prediction accuracy of GWL was proposed by comparing the correlation between GWL observations and the two models. The results showed that the RS_GWL prediction model explains 30% and 90% of the spatial variability in the two value domain intervals, GWL < 10 m and GWL > 10 m, respectively. The R² of the modeling and the validation of the ECa was 79% and 73%, respectively. Careful analysis of the scatter plots between predicted ECa and GWL revealed that when ECa varies between 0–600 mS/m, 600–800 mS/m, 800–1100 mS/m, and >1100 mS/m, the fluctuation ranges of the corresponding GWL correspond to 0–31 m, 0–15 m, 0–10 m, and 0–5 m. Finally, combining the spatial variability of ECa and RS_GWL spatial distribution map, the following optimization strategies were finally established: GWL < 5 m (in natural land with ECa > 1100 mS/m), GWL < 5 m (occupied by farmland from RS_GWL) and GWL > 10 m (from RS_GWL), and 3 < GWL < 10 m (speculated). In conclusion, this study has demonstrated that the integration of EMI technology has significantly improved the precision of forecasting shallow GWL in oasis plain regions, outperforming the outcomes achieved by the use of remote sensing data alone. Full article

There is still controversy over the influence of no-tillage (NT) on CO₂ emissions in farmland soil. Few studies focus on the impact of monitoring hours on the response of soil CO₂ emissions to NT. Therefore, an in situ experiment was conducted in maize cropland in the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The soil CO₂ emissions, soil water content (SWC), and soil temperature (ST) were automatically monitored using the morning sampling (MonS) and continuous sampling (multi-hour sampling in one day, DayS) methods during the whole maize growth stages. The results showed that the MonS method decreased the sum of soil CO₂ emissions by 146.39 g CO₂ m⁻² in the wet year 2018 and increased that by 93.69 g CO₂ m⁻² in the dry year 2019 when compared to the DayS method. The influence intensity of NT on soil CO₂ effluxes was decreased with the MonS method. In contrast, the MonS method had no significant effect on the differences in SWC between NT and conventional tillage. However, the MonS method increased the variance in ST between NT and conventional tillage by 0.45 °C, which was higher than that with the DayS method (0.20 °C) across years. Compared to the DayS method, the MonS method increased the regression coefficient of soil CO₂ emissions with SWC but decreased that with ST. This study is beneficial for reducing the artificial impact of monitoring hours on the data accuracy of soil CO₂ effluxes and deepening the understanding of the influence of NT on soil CO₂ emissions. Full article

This study focused on three drought-tolerant grasses, namely Miscanthus sinensis ‘Gracillimus’ (Mis), Pennisetum alopecuroides ‘Ziguang’ (Pen), and Elytrigia repens (L.) Nevski ‘Jingcao No. 2′ (Ely), selected from nine species. Despite limited knowledge regarding their tolerance to submergence and responses to this stress, these three grasses were chosen for investigation. The three grass species were exposed to varying durations of submergence (0, 1, 3, 5, 7, 9, and 11 days) in a greenhouse setting. Subsequently, their growth characteristics, physiological traits, and nitrogen accumulation were evaluated. The study found that all three grass species exhibited flood tolerance, with Mis showing the strongest resistance. Under an 11-day flooding treatment, there was no significant trend in the above-ground biomass of Mis. Flooding significantly reduced the root-to-stem ratio, with Pen and Ely exhibiting more pronounced declines than Mis. The chlorophyll content in Mis decreased by 38%, compared to 41% in Pen and 60% in Ely. The root activity of the most affected species dropped by 88.6%, and nitrogen accumulation was inhibited with longer flooding durations. Pen’s nitrogen levels decreased significantly across treatments, while no significant changes were observed in Mis. Ely’s nitrogen assimilation initially increased until T4, after which it began to decline, reflecting similar trends in above-ground biomass. These findings suggest that flood tolerance is linked to nutrient uptake and photosynthetic capacity, highlighting Mis as the most suitable grass species for flood-prone areas and recommending its use in ecological ditch construction in China. This study provides material selection for the construction of ecological ditches. Full article

Atmospheric deposition is an important source of heavy metal in soil and the use of dust collection cylinders is a traditional monitoring method. This method has limitations in agricultural areas because polluted soil particles may become resuspended and eventually deposited into these cylinders, leading to overestimates in the amount of atmospheric deposition in soil. To address this concern, we propose that frequent snowfall can help suppress local soil dust resuspension and that fresh snow can serve as an efficient surrogate surface when collecting atmospheric deposition samples. To investigate the rationality of this method, 52 snow samples were collected from sites surrounding smelting plants in Anyang, an industrial region of North China. The results revealed that the concentration of cadmium in the melted snow ranged between 0.03 and 41.09 μg/L, with mean values three times higher around the industrial sites (5.31 μg/L) than background farmlands (1.54 μg/L). In addition, the cadmium concentration in the snow from sites surrounding the factories was higher in the north than in the south because of prevailing winds blowing from the southwest. Moreover, snow samples from sites with high concentrations of cadmium and sulfate can be categorized into different groups via the clustering method, conforming to the spatial distribution of particulate matter emissions and sulfur dioxide satellite column concentrations. Finally, a positive correlation was found between the cadmium content in the snow and the production capacity (R² = 0.90, p < 0.05) and total permitted emissions (R² = 0.69, p > 0.05) of the nearby factories. These findings demonstrate that snow is a reliable medium for documenting atmospheric dry deposition associated with specific industrial emissions. Full article

The direct input of straw to the field can increase the source and supply of soil carbon and nitrogen, change the soil microbial biomass and enzyme activity, and affect the soil organic carbon sequestration, which in turn affects soil fertility and quality. In this study, a three-year field orientation experiment was conducted to investigate the effects of straw input on soil microbial biomass, enzyme activity, and soil organic carbon at different straw incorporation amounts (0, 3000, 7000, and 14,000 kg/hm²). The results showed that soil microbial biomass, enzyme activity, and organic carbon content increased with the increase in straw amount, and the increase in 4-fold straw input (T4) treatment was significantly larger than that of other treatments; the microbial biomass, enzyme activity, and SOC (soil organic carbon) in different soil layers were 0–10 cm > 10–20 cm > 20–30 cm; and straw incorporation amounts had a significant effect on soil microbial biomass, enzyme activity, and SOC. The amount of straw input to the field had a highly significant positive effect on soil microbial carbon and nitrogen and SOC (p < 0.001). In conclusion, four times the amount of straw input to the field had the most obvious effect on enhancing soil organic carbon content, microbial biomass, and enzyme activity. This has important implications for the development of sustainable agriculture. Full article

Xinjiang is a representative dry area in China characterized by oasis agriculture. In recent decades, the amount of farmland has increased considerably. For the regional objectives of “carbon peaking and carbon neutrality”, it is essential to investigate the carbon effects induced by large-scale changes in farmland. This research integrates the PLUS and InVEST models to calculate the carbon effects resulting from the spatiotemporal changes in farmland distribution in Xinjiang. It quantitatively assesses the changes in land-use patterns and carbon storage under four scenarios for 2035—natural development (ND), economic development (ED), ecological protection (EP), and farmland protection (FP)—and explores the spatial agglomeration degree of the carbon effect of cultivated land area change. The analysis reveals the following: (1) From 1990 to 2020, the farmland area in Xinjiang showed a trend of first decreasing and then increasing, resulting in a total increase of 33,328.53 km² over the 30-year period. The newly added farmland primarily came from grassland and unused land. (2) The terrestrial ecosystem carbon storage in Xinjiang showed a trend of decreasing first and then increasing, with an increase of 57.49 Tg in 30 years. The centroid of carbon storage was located in the northwestern part of the Bayingolin Mongol Autonomous Prefecture, showing an overall southwestward shift. Changes in farmland area contributed to a regional carbon storage increase of 45.03 Tg. The contribution of farmland to carbon storage increased by 3.42%. (3) In 2035, the carbon storage value of different scenarios will increase compared with 2020, and the carbon sink of cultivated land will be the maximum under the cultivated land protection scenario. (4) There is a strong spatial positive correlation between the changes in carbon storage caused by the change in cultivated land area in Xinjiang, and there are more hot spots than cold spots. The carbon storage changes under farmland transformation have the characteristics of “high-high” clustering and “low-low” clustering. Future territorial spatial planning in Xinjiang should comprehensively coordinate ecological protection and farmland conservation measures, improve regional carbon sink capacity, and achieve green and sustainable development. Full article