Search Results (72)

Water and nitrogen are the key factors restricting the productivity improvement of onion in the Hexi Oasis. Unreasonable water and fertilizer management not only increases input costs, but also causes environmental pollution of farmland soil, thereby affecting the sustainable development of agriculture. To explore the effects of the water–nitrogen interaction and optimized combination schemes on onion yield, water–nitrogen use efficiency, and economic benefits under mulched drip irrigation in the Hexi Oasis, a four-year (2020–2023) water–nitrogen coupling regulation experiment was conducted at the Yimin Irrigation Experimental Station in Minle County, Hexi Corridor. The onion was used as the test crop and three irrigation levels were established, based on reference crop evapotranspiration (ET_c): low water (W1, 70% ET_c), medium water (W2, 85% ET_c), and sufficient water (W3, 100% ET_c), as well as high nitrogen N3 (330 kg·ha⁻¹), medium nitrogen N2 (264 kg·ha⁻¹), and low nitrogen N1 (198 kg·ha⁻¹). Meanwhile, no nitrogen application N0 (0 kg·ha⁻¹) was set as the control at three irrigation levels. This study analyzed the effects of different water and nitrogen supply conditions on onion quality, yield, water–nitrogen use efficiency, and economic benefits. A water–nitrogen economic benefit coupling model was established to optimize water–nitrogen combination schemes targeting different economic objectives. The results revealed that medium-to-high water–nitrogen combinations were beneficial for improving onion quality, while excessive irrigation and nitrogen application inhibited bulb quality accumulation. Both yield and economic benefits increased with the increasing amount of irrigation, whereas excessive nitrogen application showed a diminishing yield-increasing effect, simultaneously increasing farm input costs and ultimately reducing the economic benefits. In the four-year experiment, the N3W3 treatment in 2020 achieved the highest yield, economic benefits, and net profit, reaching 136.93 t·ha⁻¹, 20,376.3 USD·ha⁻¹, and 14,320.8 USD·ha⁻¹, respectively, with no significant difference from the N2W3 treatment. From 2021 to 2023, the N2W3 treatment achieved the highest yield, economic benefits, and net profit, averaging 130.87 t·ha⁻¹, 28,449.5 USD·ha⁻¹, and 21,881.5 USD·ha⁻¹, respectively. Lower irrigation and nitrogen application rates mutually restricted the water and nitrogen utilization, resulting in low water use efficiency, irrigation water use efficiency, nitrogen partial factor productivity, and nitrogen agronomic use efficiency. The relationship between the irrigation amount, nitrogen application rate, and the economic benefits of onion fits a bivariate quadratic regression model. This model predicts that onion’s economic benefits are highly correlated with the actual economic benefits, with analysis revealing a parabolic trend in economic benefits as water and nitrogen inputs increase. By optimizing the model, it was determined that when the irrigation amount reached 100%, the ET_c and nitrogen application rate was 264 kg·ha⁻¹, and the economic benefits were close to the target range of 27,000–29,000 USD·ha⁻¹; this can be used as the optimal water and nitrogen management model and technical reference for onion in the Hexi Oasis irrigation area, which can not only ensure high yield and quality but also improve the use efficiency of water and nitrogen. Full article

Against the backdrop of China’s slowing urbanization and increasing regional disparities, existing research on the spatiotemporal evolution and multidimensional drivers of urban–rural transformation (URT) requires further elaboration, particularly regarding county-level differentiation and the dynamic interactions among these drivers. This study integrates spatiotemporal hot spot analysis with a multi-factor geographical detector model to systematically examine China’s URT from 1990 to 2023. The findings reveal the following: (1) The area of urban–rural construction land increased by 149.54% overall from 1990 to 2023, but the annual average growth rate dropped sharply to 4.32% during 2000–2023, indicating overall deceleration in spatial expansion. (2) Significant structural adjustments occurred at the county level: the proportion of counties with high spatial expansion degree decreased by 20%, while counties experiencing spatial contraction increased by 6%, suggesting that growth dynamics have become increasingly concentrated in limited counties. (3) Spatially, a clear “northern contraction and southern expansion” divergence emerged, which was primarily driven by the synergistic effects of policy reorientation, market-driven factor mobility, and differential natural endowments. (4) Expanding counties benefited from urban agglomeration plans, population influx, industrial upgrading, and favorable terrain, whereas contracting counties were constrained by rigid ecological and farmland conservation policies, population outmigration, undiversified industries, and topographical limitations. These findings provide an important premise for formulating feasible policies on differentiated spatial governance and urban–rural sustainable development. Full article

Building upon a theoretical framework, this study utilized 126 field survey questionnaires from farmers in Shouyang County, Shanxi Province, China, coupled with corresponding farmland soil heavy metal monitoring data, to investigate the extent of heavy metal pollution and its mechanistic relationship with farmers’ behavior. The single-factor pollution index (P_i), Nemerow composite pollution index (P_N), and geographical detector were employed to assess pollution levels and elucidate the underlying mechanisms linking farmer practices to soil heavy metal accumulation. Analysis revealed that the mean concentrations of Cu, Ni, Cr, Pb, Cd, and Zn (25.54, 31.47, 98.50, 16.63, 0.16 and 76.92 mg/kg, respectively) in the farmland soil exceeded the background values for soil elements in Shanxi Province, whereas As (1.92 mg/kg) levels were lower. Assessment using P_i indicated that Cr, Pb, Cd, Ni, Cu, and Zn (1.78, 1.13, 1.55, 1.05, 1.07 and 1.21, respectively) were predominantly in a state of mild pollution. Similarly, the P_N (1.50) suggested an overall mild level of composite heavy metal pollution in the soil. Geographical detector(Geo-Detector) analysis demonstrated that the explanatory power (q-value) of interactions among factors-including agricultural film and fertilizer application intensity, farmland fragmentation degree, per capita annual household income, farmland area, and years engaged in farming-on soil heavy metal accumulation was significantly enhanced compared to that of individual behavioral factors. While individual farmers’ behaviors are associated with heavy metal accumulation, the interaction effects among multiple behaviors constitute the dominant factor influencing localized accumulation in farmland soil. Consequently, local authorities should enhance farmers’ requisite knowledge, skills, and practices for mitigating soil heavy metal accumulation through strategies such as promoting large-scale farming, implementing agricultural input reduction initiatives, and intensifying technical and environmental protection training. The Geo-Detector exhibits significant advantages in identifying nonlinear influencing factors and analyzing factor interactions, yielding more comprehensive insights compared to conventional linear models. Full article

Based on cross-sectional survey data from 574 grain farmers in Hebei Province, China, this study systematically analyzed, using an ordered Logit model and Bootstrap mediation effect tests, the mechanism by which information acquisition influences farmers’ adoption of green production technologies. The results showed that the diversity of information acquisition channels, content quality, and source credibility were all significantly and positively correlated with the degree of technology adoption, with content quality exhibiting the strongest correlation. Perceived usefulness played a partial mediating role between information acquisition and adoption behavior. Digital skills significantly and positively moderated the path through which information acquisition affects technology adoption—farmers with higher digital skills were more adept at converting information into technical knowledge and practices. Further heterogeneity analysis revealed that farmers with high digital skills in plain areas benefited more noticeably from information acquisition. Therefore, it is recommended that county-level agricultural technology extension centers take the lead in developing visualized technical materials to improve the quality of information content; conduct special digital skills training for elderly farmers to enhance their ability to acquire and identify information; and in regional practices, implement the supporting service of “targeted information & high-standard farmland” in plain areas while establishing a “technology demonstration household” dissemination network in mountainous areas. These measures will collectively form a differentiated and implementable technology promotion system, providing a feasible, practical path for advancing agricultural green transformation. Full article

Rapid evaluations of farmland soil quality can provide data support for farmland protection and utilization. This study focuses on the soda–saline soil region of Da’an City, Jilin Province, covering an area of 4879 km²; it proposes a framework for evaluating farmland soil quality based on multi-source remote sensing data (Sentinel-2 MSI, GF-5 AHSI hyperspectral and field hyperspectral data). Soil organic matter content, salt content, and pH were selected as indicators of cultivated land soil quality in soda–saline soil areas. A threshold of 20% crop residue cover was set to mask high-cover areas, extracting bare soil information. The spectral indices SI1 and SI2 were utilized to predict the comprehensive grade of soil organic matter + salinity based on the cloud model ($M{E}_c$ = 0.74 and $M{E}_v$ = 0.68). The pH grade was predicted using the red-edge ratio vegetation index (RVIre) ($M{E}_c$ = 0.95 and $M{E}_v$ = 0.98). The short-board method was used to construct a soil quality evaluation system. The results indicate that 13.73% of the cultivated land in Da’an City is of high quality (grade 1), 80.63% is of medium quality (grades 2–3), and 5.65% is of poor quality (grade 4). This study provides a rapid assessment tool for the sustainable management of cultivated land in saline–alkali areas at the county level. Full article

Whether voluntary carbon markets can effectively contribute to climate mitigation remains a debated issue. Taking Chinese Certified Emission Reduction (CCER) projects as a quasi-natural experiment, this study employed a difference-in-difference approach calibrated with a county-level panel dataset spanning 2008–2021 to examine the carbon abatement effect of CCER projects. The results show that CCER projects reduced county-level emissions by 2.8%, though this reduction falls short of the levels self-declared by project developers, implying the possibility of overstating emission reductions. The carbon abatement effect is more pronounced in biogas projects and projects verified by large agencies, underscoring the mitigation potential of biogas deployment as well as the importance of professional expertise in enhancing project quality. In addition, CCER projects generate a range of socio-economic benefits, including raising income, creating employment opportunities, and preserving farmland. Overall, this study identified the effectiveness of voluntary carbon markets, providing valuable insights for fostering their further sustainable development. Full article

Given that the increasing non-agricultural conversion of cultivated land (NACCL) endangers food security, studying the spatial and temporal variation characteristics and driving mechanisms of NACCL in Sichuan Province can offer a scientific foundation for developing local farmland preservation measures and controlling further conversion. Guided by the theoretical framework of land use transition, this study utilizes land use datasets spanning multiple periods between 2000 and 2023. Comprehensively considering population scale factors, natural geographical factors, and socioeconomic factors, the county-level annual NACCL rate is calculated. Following this, the dynamic evolution and underlying driving forces of NACCL across 183 counties in Sichuan Province are examined through temporal and spatial dimensions, utilizing analytical tools including Nonparametric Kernel Density Estimation (KDE) and the Geographical Detector model with Optimal Parameters (OPGD). The study finds that: (1) Overall, NACCL in Sichuan Province exhibits phased temporal fluctuations characterized by “expansion—contraction—re-expansion—strict control,” with cultivated land mainly being converted into urban land, and the differences among counties gradually narrowing. (2) In Sichuan Province, the spatial configuration of NACCL is characterized by the expansion of high-value agglomerations alongside the dispersed and stable distribution of low-value areas. (3) Analysis through the OPGD model indicates that urban construction land dominates the NACCL process in Sichuan Province, and the driving dimension evolves from single to synergistic. The findings of this study offer a systematic examination of the spatiotemporal evolution and underlying drivers of NACCL in Sichuan Province. This analysis provides a scientific basis for formulating region-specific farmland protection policies and supports the optimization of territorial spatial planning systems. The results hold significant practical relevance for promoting the sustainable use of cultivated land resources. Full article

A quantitative study on the spatial structure and spatiotemporal variation characteristics of net carbon sinks in regional farmland ecosystems is of significant importance for uncovering the multifunctional roles of farmland ecosystems and formulating region-specific agricultural policies and management strategies. Based on the measurement of net carbon sinks in county-level farmland ecosystems across Hunan Province from 2005 to 2020, this research employs methodologies, including the standard deviational ellipse (SDE), spatial autocorrelation, and exploratory spatiotemporal data analysis (ESTDA) to investigate the spatiotemporal evolution characteristics of net carbon sinks in Hunan’s county-level farmland ecosystems. The results show that the net carbon sinks of county-level farmland ecosystems in Hunan Province exhibits a “northeast–southwest” spatial distribution pattern, with a trend toward spatial agglomeration during contraction, and the center of gravity of net carbon sinks has generally shifted northwestward over time. A significant positive spatial correlation exists globally in the net carbon sinks of Hunan’s county-level farmland ecosystems, and the degree of spatial agglomeration has gradually intensified amid fluctuations. The dynamic evolution of local spatial patterns of net carbon sinks in county-level farmland ecosystems in Hunan Province varied significantly, showing strong stability in both local spatial structure and spatial dependence direction. In contrast, eastern and central Hunan exhibited more dynamic local spatial structures compared to southern and northern regions. The local spatial association patterns of the net carbon sinks in county-level farmland ecosystems remained relatively stable, with weak spatial synergy and a pronounced path-dependent locking effect in spatial agglomeration. Full article

The Luohe River boasts a profound historical heritage. Due to long-term impacts of human activities along its banks, significant variations in soil environmental conditions may exist across different land use types within the region. This study focused on four land use types (farmland, bamboo forest, grassland, and abandoned land) in Luoning County of the Luohe River Basin and employed high-throughput sequencing technology to analyze the characteristics of soil microbial communities and differences in soil nutrients. The results showed the following: There were significant differences in soil nutrients and microbial diversity among different land use types. Specifically, the organic matter content in farmland was significantly higher than that in bamboo forests (p < 0.05), and the available phosphorus content in farmland was significantly higher than that in abandoned land (p < 0.05); the abandoned land had a significant advantage in alkali-hydrolyzable nitrogen and available potassium contents (p < 0.05) but the lowest soil water content (p < 0.05). Microbial diversity indices indicated that Pielou’s evenness index (Pieloue) in farmland was significantly higher than that in grassland. The bacterial community was dominated by Acidobacteria, Proteobacteria, and Actinobacteria. At the genus level, available potassium was the key factor affecting the top 20 dominant bacterial genera. Redundancy Analysis (RDA) showed that pH was the core environmental variable driving the variation of bacterial community structure. Metabolic pathway analysis revealed that biosynthetic metabolism was the main pathway, and grassland exhibited outstanding performance in the secondary metabolite synthesis pathway. The results of this study fill the gap in soil microbial ecology research in this region and provide a theoretical basis for the sustainable utilization of land resources and agricultural ecological management in the Luohe River Basin. Full article

In southern China, the long-term irrational utilization of land resources has caused severe damage to the ecology and environment of the entire region. Serious issues such as soil degradation and water erosion have led to the decline of soil quality and productivity. In this study, the spatial distribution characteristics of soil carbon, nitrogen, and phosphorus in Zhuxi watershed, Changting County, southern China, were analyzed by coupling geostatistics with GIS. The analysis generated several important results: (1) The concentrations of soil organic matter (OM), alkali-hydrolyzable nitrogen (AN), and available phosphorus (AP) are at moderate levels, and AP exhibits local enrichment in the downstream farmland, while the concentrations of total nitrogen (TN) and total phosphorus (TP) remain at low levels. (2) The optimal theoretical model for AN is an exponential model, while other nutrients follow spherical models. Except for AP, which has a nugget effect exceeding 75%, the nugget effects of other nutrients range between 25% and 75%, indicating that their spatial distribution is moderately correlated. According to Kriging interpolation results, the distribution of OM, TN, and AN shows a clear trend of decreasing from northeast to southwest, followed by a gradual increase, which is generally consistent with the direction of rivers. The trends of TP and AP are more irregular, generally decreasing from downstream to upstream. (3) OM, TN, and AN exhibit a negative correlation with the degree of soil erosion, indicating that soil erosion is associated with the loss of carbon and nitrogen nutrients. However, the impact on phosphorus is relatively insignificant. Full article

The accurate classification of staple crops is of great significance for scientifically promoting food production. Crop classification methods based on deep learning models or medium/low-resolution images have been applied in plain areas. However, existing methods perform poorly in complex mountainous scenes with rugged terrain, diverse planting structures, and fragmented farmland. This study introduces the Complex Scene Crop Classification U-Net+ (CSCCU+), designed to improve staple crop classification accuracy in intricate landscapes by integrating supplementary spectral information through an additional branch input. CSCCU+ employs a multi-branch architecture comprising three distinct pathways: the primary branch, auxiliary branch, and supplementary branch. The model utilizes a multi-level feature fusion architecture, including layered integration via the Shallow Feature Fusion (SFF) and Deep Feature Fusion (DFF) modules, alongside a balance parameter for adaptive feature importance calibration. This design optimizes feature learning and enhances model performance. Experimental validation using GaoFen-2 (GF-2) imagery in Xifeng County, Guizhou Province, China, involved a dataset of 2000 image patches (256 × 256 pixels) spanning seven categories. The method achieved corn and rice classification accuracies of 89.16% and 88.32%, respectively, with a mean intersection over union (mIoU) of 87.04%, outperforming comparative models (U-Net, DeeplabV3+, and CSCCU). This research paves the way for staple crop classification in complex land surfaces using high-resolution imagery, enabling accurate crop mapping and providing robust data support for smart agricultural applications. Full article

Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil moisture retrieval by integrating Sentinel-1 and Sentinel-2 remote sensing data. Dual-polarization parameters (VV + VH and VV × VH) were constructed and tested. Pearson correlation analysis showed that these polarization combinations carried the most useful information for soil moisture estimation. We then applied Shapley Additive exPlanations (SHAP) for feature selection, and a Stacking model was used to perform soil moisture inversion based on the selected features. SHAP values derived from the coupled support vector regression (SVR) and random forest regression (RFR) models were used to select an additional six key features for model construction. Building on this framework, a comparative analysis was conducted to evaluate the predictive performance of multivariate linear regression (MLR), RFR, SVR, and a Stacking model that integrates these three models. The results demonstrate that the Stacking model outperformed other approaches in soil moisture retrieval, achieving a higher R² of 0.70 compared to 0.52, 0.61, and 0.62 for MLR, RFR, and SVR, respectively. This process concluded with the use of the Stacking model to generate a county-level farmland soil moisture distribution map, which provides an objective and practical approach to guide agricultural management and the optimized allocation of water resources in arid regions. Full article

Soil organic carbon (OC) storage in crop rotation systems benefits soil productivity and global climate change. However, the regulatory mechanisms and pathways by which soil OC storage is affected under medium-term crop rotation at the aggregate level are not fully understood. Herein, fifteen soil samples from five cropping systems (abandoned farmland, continuous cropping of tobacco, tobacco–pea rotation, continuous cropping of dasheen, and dasheen–ryegrass rotation for over 10 years) were collected from soil at 0 to 20 cm depths in Miyi County, Sichuan Province, China. The soil aggregates and aggregate-associated OC, enzyme activities, and microbial biomass were evaluated. The effects of medium-term crop rotation on soil aggregate-associated OC content and biochemical properties varied between crop types. Specifically, tobacco–pea rotation significantly decreased the proportion of macro-aggregates (0.25–2 mm); the contents of OC, Ca-OC, aliphatic C, alcohols, and phenols; enzyme activities; and fungal biomass in the aggregate fractions, compared with those associated with the continuous cropping of tobacco. In contrast, dasheen–ryegrass rotation significantly increased the recalcitrant OC content, β-glucosidase and polyphenol oxidase activities, microbial biomass in mega-aggregates (>2 mm) and macro-aggregates, and the recalcitrant OC content and enzyme activity in microaggregates (0.053–0.25 mm) and slit clay (<0.053 mm), relative to those in the continuous cropping of dasheen. Moreover, for the continuous-cropping soils, the OC contents were positively correlated with POD activity but negatively correlated with other enzymes. For the rotational soils, the OC content was positively related to the Fe/Al-OC, aromatic-C, aliphatic-C, and microbial biomass contents but negatively related to the carbohydrate content. The increased OC content was driven by the microbial biomass in the aggregate fractions, and medium-term crop rotation changed the negative effect of microorganisms on the OC content into a positive effect at the aggregate level. Overall, medium-term crop rotation enhances OC storage by improving soil structural stability and microbial community dynamics. Full article

This study aims to systematically examine the concept and characteristics of medium–low yield farmland (MLYF), to identify the key factors influencing the coordination between soil carbon sequestration (SCS) and production capacity (PC) in MLYF, and develop an evaluation indicator system (EIS) to provide innovative approaches for transforming MLYF to enhance food security and emission reduction capabilities. Focusing on the synergistic improvement of SCS and PC in MLYF, this research employs theoretical analysis, systematic inference, and inductive deduction to analyze the relationships between these factors and construct the EIS. The findings reveal that (1) MLYF is characterized by inherent limitations and suboptimal management practices, resulting in low grain PC but significant potential for yield improvement. (2) A positive correlation exists between the soil organic carbon (SOC) content and crop yield, with MLYF demonstrating substantially greater potential for synergistic improvement than high-yield fields. (3) On the basis of soil science principles, the key factors affecting the synergistic enhancement of carbon sequestration and productivity in MLYF include climatic conditions, soil properties, and biological factors. (4) A comprehensive “Demand-Function-Dimension-Element-Indicator” framework was established, incorporating an EIS designed for national, provincial, and city/county levels to address the management requirements of MLYF across various scales, thereby facilitating comprehensive quality improvement. This research contributes to the theoretical understanding of MLYF transformation, offering valuable insights for ensuring national food security and achieving carbon emission reduction goals. Full article

The accelerating conversion of agricultural land to non-agricultural uses poses critical threats to food security and sustainable land management, particularly in ecologically fragile karst mountainous regions. This study investigated the spatiotemporal patterns and driving mechanisms of cropland non-agricultural conversion (CNAC) in the Qian-Gui karst region (Guangxi and Guizhou, China) from 2000 to 2020, employing land use datasets and socioeconomic indicators through geographically weighted regression (GWR) modeling. The results showed that (1) from 2000 to 2020, the CNAC rate in the Qian-Guizhou karst mountainous region reached 2.03%. The area of CNAC increased by 14.60 × 10⁴ hm², increasing 1.74 times in 2010–2020 compared to 2000–2010, showing a trend of rapid growth. Specifically, the growth rate of the CNAC area was the highest in apparent mountainous (110.36%) and quasi-mountainous counties (100.5%), followed by semi-mountainous counties (95.28%), while entirely mountainous (40.89%) and pure hilly counties (37.68%) experienced the lowest growth, revealing distinct regional disparities. (2) Spatially, CNAC exhibited a pattern of “high in the north and south, low in the central region”, and the overall level of CNAC displayed significant regional imbalances, with extreme grades distributed in provincial capitals, high and medium grades concentrated in prefecture-level city districts, and light and low grades mainly located in counties and districts (accounting for more than 55.56% of the total number of research units in the two time periods). (3) There was significant spatial heterogeneity in the driving effect of factors influencing CNAC. Agricultural output and population density showed the strongest positive correlations; effectively irrigated areas exhibited a growing influence over time (except for pure hilly counties); rocky desertification areas exerted a strengthened influence on CNAC in pure hilly counties, while their impact was relatively lower in other regions compared to other indicators. Therefore, when formulating policies to protect farmland, it is essential to take into account the specific conditions of different types of counties in mountainous areas and adopt management measures tailored to these regional characteristics. Full article