Search Results (1,267)

Slow-release nitrogen fertilizers enhance crop production and reduce environmental pollution, but their slow nitrogen release may cause insufficient nitrogen supply in the early stages of crop growth. Mixed nitrogen fertilization (MNF), combining slow-release nitrogen fertilizer with urea, is an effective way to increase yield and income and improve nitrogen fertilizer efficiency. This study used urea alone (Urea) and slow-release nitrogen fertilizer alone (C/SRF) as controls and employed meta-analysis and a random forest model to assess MNF effects on crop yield and nitrogen partial factor productivity (PFPN), and to identify key influencing factors. Results showed that compared with urea, MNF increased crop yield by 7.42% and PFPN by 8.20%, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 20 °C, and elevations of 750–1050 m; in soils with a pH of 5.5–6.5, where 150–240 kg·ha⁻¹ nitrogen with 25–35% content and an 80–100 day release period was applied, and the blending ratio was ≥0.3; and when planting rapeseed, maize, and cotton for 1–2 years. The top three influencing factors were crop type, nitrogen rate, and soil pH. Compared with C/SRF, MNF increased crop yield by 2.44% and had a non-significant increase in PFPN, with higher improvement rates in Northwest China, regions with an average annual temperature ≤ 5 °C, average annual precipitation ≤ 400 mm, and elevations of 300–900 m; in sandy soils with pH > 7.5, where 150–270 kg·ha⁻¹ nitrogen with 25–30% content and a 40–80 day release period was applied, and the blending ratio was 0.4–0.7; and when planting potatoes and rapeseed for 3 years. The top three influencing factors were nitrogen rate, crop type, and average annual precipitation. In conclusion, MNF should comprehensively consider crops, regions, soil, and management. This study provides a scientific basis for optimizing slow-release nitrogen fertilizers and promoting the large-scale application of MNF in farmland. Full article

Biochar has garnered considerable attention as a soil amendment due to its unique physicochemical properties. Its application not only enhances soil carbon sequestration but also improves nutrient availability. Incorporating biochar into soil is regarded as a promising strategy for mitigating global climate change while delivering substantial environmental and agricultural benefits. In this study, biochar was extracted from Siraitia grosvenorii and subsequently modified through alkali treatment. A laboratory incubation experiment was conducted to assess the effects of unmodified (JMC) and modified (GXC) biochar, applied at different rates (1%, 2%, and 4%), on organic carbon mineralization and soil nutrient dynamics. Results indicated that, at equivalent application rates, JMC-treated soils exhibited lower CO₂ emissions than those treated with GXC, with emissions increasing alongside biochar dosage. After the incubation, the 1% JMC treatment exhibited a mineralization rate of 17.3 mg·kg⁻¹·d⁻¹, which was lower than that of the control (CK, 18.8 mg·kg⁻¹·d⁻¹), suggesting that JMC effectively inhibited organic carbon mineralization and reduced CO₂ emissions, thereby contributing positively to carbon sequestration in Siraitia grosvenorii farmland. In contrast, GXC application significantly enhanced soil nutrient levels, particularly increasing available phosphorus (AP) by 14.33% to 157.99%. Furthermore, partial least squares structural equation modeling (PLS-SEM) identified application rate and pH as the key direct factors influencing soil nutrient availability. Full article

This study combined field survey data with Gaofen 5 (GF-5) satellite hyperspectral images of the Yuncheng Basin (China), considering 15 environmental variables. Random forest (RF) was used to select the optimal satellite hyperspectral model, sequentially introducing natural and farmland management factors into the model to analyze the spatial distribution of farmland soil organic carbon (SOC). Furthermore, RF factorial experiments determined the contributions of farmland management, climate, vegetation, soil, and topography to the SOC. Structural equation modeling (SEM) elucidated the driving mechanisms of SOC variations. Integrating satellite hyperspectral data and environmental variables improved the prediction accuracy and SOC-mapping precision of the model. The integration of natural variables significantly improved the RF model performance (R² = 0.78). The prediction accuracy enhanced with the introduction of crop phenology (R² = 0.81) and farmland management factors (R² = 0.87). The model that incorporated all 15 variables demonstrated the highest prediction accuracy (R² = 0.89) and greatest spatial SOC variability, with minimal uncertainty. Farmland management activities exerted the strongest influence on SOC (0.38). The proposed method can support future investigations on soil carbon sequestration processes in river basins worldwide. Full article

Eco-enzymatic stoichiometry provides a basis for understanding soil ecosystem functions, with implications for land management and ecological protection. Long-term climatic factors and human interferences have caused significant land-use transformations in the Qinghai–Tibet Plateau region, affecting various ecological functions, such as soil nutrient cycling and chemical element balance. It is currently unclear how large-scale land-use conversion affects soil ecological stoichiometry. In this study, 763 soil samples were collected across three land-use types: farmland, grassland, and forest land. In addition, changes in soil physicochemical properties and enzyme activity and stoichiometry were determined. The soil available phosphorus (SAP) and total phosphorus (TP) concentrations were the highest in farmland soil. Bulk density, pH, SAP, TP, and NO₃⁻-N were lower in forest soil, whereas NH₄⁺-N, available nitrogen, soil organic carbon (SOC), available potassium, and the soil nutrient ratio increased. Land-use conversion promoted soil β-1,4-glucosidase, N-acetyl-β-glucosaminidase, and alkaline phosphatase activities, mostly in forest soil. The eco-enzymatic C:N ratio was higher in farmland soils but grassland soils had a higher enzymatic C:P and N:P. Soil microorganisms were limited by P nutrients in all land-use patterns. C limitation was the highest in farmland soil. The redundancy analysis indicated that the ecological stoichiometry in farmland was influenced by TN, whereas grass and forest soils were influenced by SOC. Overall, the conversion of cropland or grassland to complex land-use types can effectively enhance soil nutrients, enzyme activities, and ecosystem functions, providing valuable insights for ecological restoration and sustainable land management in alpine regions. Full article

Sustainable farmland management is vital for global food security and for mitigating environmental degradation and climate change. While individual practices such as crop rotation and no-tillage are well-documented, this review synthesizes current evidence to illuminate the critical synergistic effects of integrating four key strategies: crop rotation, conservation tillage, organic amendments, and soil microbiome management. Crop rotation enhances nutrient cycling and disrupts pest cycles, while conservation tillage preserves soil structure, reduces erosion, and promotes carbon sequestration. Organic amendments replenish soil organic matter and stimulate biological activity, and a healthy soil microbiome boosts plant resilience to stress and enhances nutrient acquisition through key functional groups like arbuscular mycorrhizal fungi (AMFs). Critically, the integration of these practices yields amplified benefits that far exceed their individual contributions. Integrated management systems not only significantly increase crop yields (by up to 15–30%) and soil organic carbon but also deliver profound global ecosystem services, with a potential to sequester 2.17 billion tons of CO₂ and reduce soil erosion by 2.41 billion tons annually. Despite challenges such as initial yield variability, leveraging these synergies through precision agriculture represents the future direction for the field. This review concludes that a holistic, systems-level approach is essential for building regenerative and climate-resilient agroecosystems. Full article

The salinization of agricultural soils is a serious threat to farming and ecological balance in arid and semi-arid regions. Accurate estimation of soil water-soluble ions (calcium, carbonate, magnesium, and sulfate) is necessary for correct monitoring of soil salinization and sustainable land management. Hyperspectral ground-based data are valuable in soil salinization monitoring, but the acquisition cost is high, and the coverage is small. Therefore, this study proposes a two-stage deep learning framework with multispectral remote-sensing images. First, the wavelet transform is used to enhance the Transformer and extract fine-grained spectral features to reconstruct the ground-based hyperspectral data. A comparison of ground-based hyperspectral data shows that the reconstructed spectra match the measured data in the 450–998 nm range, with R² up to 0.98 and MSE = 0.31. This high similarity compensates for the low spectral resolution and weak feature expression of multispectral remote-sensing data. Subsequently, this enhanced spectral information was integrated and fed into a novel multiscale self-attentive Transformer model (MSATransformer) to invert four water-soluble ions. Compared with BPANN, MLP, and the standard Transformer model, our model remains robust across different spectra, achieving an R² of up to 0.95 and reducing the average relative error by more than 30%. Among them, for the strongly responsive ions magnesium and sulfate, R² reaches 0.92 and 0.95 (with RMSE of 0.13 and 0.29 g/kg, respectively). For the weakly responsive ions calcium and carbonate, R² stays above 0.80 (RMSE is below 0.40 g/kg). The MSATransformer framework provides a low-cost and high-accuracy solution to monitor soil salinization at large scales and supports precision farmland management. Full article

The ameliorative mechanism of biochar in reducing soil greenhouse gas emissions in arid saline farmland remains unclear. A two-year field study in sorghum farmland in China’s Hetao Irrigation District was conducted to assess the influence of corn straw-derived biochar on GHG emissions and explore the role of soil physicochemical properties in regulating GHG fluxes. Four different biochar application rates were tested: 0 (CK), 15 (C15), 30 (C30), and 45 t hm⁻² (C45). Compared to CK, C15 reduced CH₄ emissions by 15.2% and seasonal CH₄ flux by 77.0%. The N₂O flux followed CK > C45 > C30 > C15 from 2021 to 2022. C15 and C30 significantly decreased GWP, mitigating GHG emission intensity. Biochar application enhanced sorghum grain yield. Soil temperature was the primary determinant of CH₄ flux (total effect = 0.92). In the second year, biochar’s influence on CH₄ emissions increased by 0.76. Multivariate SEM identified soil moisture (total effect = −0.72) and soil temperature (total effect = −0.70) as primary negative regulators of N₂O fluxes. C40 lead to salt accumulation, which increases CH₄ emissions but inhibits N₂O emissions. Averaged over two years, GWP under C15 and C30 decreased by 76.5–106.7% and 5.3–56.1%, respectively, compared to CK. Overall, the application of biochar at a rate of 15 t hm⁻² significantly reduced CH₄ and N₂O emissions and increased sorghum yield. Full article

Stones in farmland soil affect the efficiency of agricultural mechanization and the efficient growth of crops. In order to solve the problems of traditional stone pickers, such as large soil disturbance, high soil content and low picking rate, this paper introduces the Archimedean curve with constant radial expansion characteristics into the design of the core working parts of the drum picker and designs a new type of drum stone picker. The key components such as spiral blades, rollers, and scrapers were theoretically analyzed, the structural parameters of the main components were determined, and the reliability of the spiral blades was checked using ANSYS Workbench software. Through the preliminary stone-picking performance test, the forward speed of the stone picker, the rotation speed of the drum, and the starting sliding angle of the spiral blade were determined as the test influencing factors. The picking rate and soil content of the stone picker were determined as the test indicators. The response surface test was carried out in the Design-Expert13.0 software. The results show that, when the forward speed of the stone picker is 0.726 m/s, the drum speed is 30 rpm, and the initial sliding angle of the spiral blade is 26.214°, the picking rate is 91.458% and the soil content is 3.513%. Field tests were carried out with the same parameters, and the picking rate was 91.42% and the soil content was 3.567%, with errors of 0.038% and 0.054% compared with the predicted values, indicating that the stone picker meets the field operation requirements. These research results can provide new ideas and technical paths for improving the performance of pickers and are of great value in promoting the development of advanced harvesting equipment and the efficient use of agricultural resources. Full article

Prometryn and acetochlor are herbicides used to control weeds in farmlands and other areas. They enter the soil through direct application, residual accumulation in crops, and atmospheric deposition. The pollution of their residues in the environment has attracted people’s attention. Bioremediation is one of the main methods to solve such problems. In this study, the effects of prometryn and acetochlor-degrading strain NM-1 on soil enzymes, soil microbial communities, and physiological indexes of soybean seedlings during soil remediation were studied, and the relationship between them was discussed. The results showed that 81.54% of prometryn (50 mg·L⁻¹) and 89.47% of acetochlor (50 mg·L⁻¹) were degraded within 15 days after NM-1 inoculation in soil. NM-1 positively affected soil enzyme activities and soil microbial communities, and the abundance of beneficial bacteria in soil increased. More importantly, the inoculation of strain NM-1 under prometryn and acetochlor stress significantly increased plant height, root length, root volume, water content, chlorophyll concentration, and root activity of soybean. The results of these studies showed that the NM-1 strain showed significant potential in bioremediation in order to provide technical support for solving the problem of prometryn and acetochlor pollution. Full article

Microplastics (MPs) present in farmland soils, where urban compost has been distributed since 2005, were extracted using a device based on elutriation, a method developed for marine sediments but not yet used in soil. Since (i) fine earth (diameter < 2 mm) is the standard fraction used for soil analysis and (ii) the size of MPs contained in urban compost may exceed that value, MP were recovered from both the entire soil and fine earth. The recovered MPs pieces were weighed, counted, and characterized using FTIR photoacoustic spectroscopy (FTIR-PAS). Both the mass and number of recovered MPs pieces (>34 µm) were comparable to those reported in the literature for soils. Polystyrene, polyethylene, and polypropylene are the primary polymers. Nevertheless, some issues were highlighted: (i) the importance of sampling the soil by volume, and (ii) the need of analyzing the entire soil sample rather than just the fraction below 2 mm, commonly used in soil analysis; (iii) the necessity of breaking up (i.e., by ultrasonication and/or dispersion) soil aggregates that may withstand the elutriation process. Full article

This study examines Poland’s agricultural land market between 2009 and 2023 through fixed effects and spatial econometric models, highlighting economic and spatial determinants of land prices. Key results show that GDP per capita strongly increases land values (β = +0.699, p < 0.001), while agricultural gross value added (–2.698, p = 0.009), soil quality (–6.241, p < 0.001), and land turnover (–0.395, p < 0.001) are associated with lower prices. Spatial dependence is confirmed (λ = 0.74), revealing strong regional spillovers. The volume of state-owned WRSP land sales declined from 37.4 thousand hectares in 2015 to 3.1 thousand hectares in 2023, while non-market transfers, such as donations, exceeded 49,000 annually. Although these trends support farmland protection and family farms, they also reduce market mobility and hinder generational renewal. The findings call for more flexible, sustainability-oriented land governance that combines ecological performance, regional equity, and improved access for young farmers. Full article

Validating the potential of multi-frequency synthetic aperture radar (SAR) data for multi-layer and profile soil moisture (SM) estimation modeling, we conducted an airborne multi-frequency SAR joint observation experiment (AMFSEX) over the Liao River Basin in China. The experiment simultaneously acquired airborne high spatial resolution quad-polarization (quad-pol) SAR data at five frequencies, including the Ka-, X-, C-, S-, and L-band. A preliminary “vegetation–soil” parameter estimation model based on the multi-frequency SAR data was established. Theoretical penetration depths of the multi-frequency SAR data were analyzed using the Dobson empirical model and the Hallikainen modified model. On this basis, a water cloud model (WCM) constrained by multi-polarization weighted and penetration depth weighted parameters was used to analyze the estimation accuracy of the multi-layer and profile SM (0–50 cm depth) under different vegetation types (grassland, farmland, and woodland). Overall, the estimation error (root mean square error, RMSE) of the surface SM (0–5 cm depth) ranged from 0.058 cm³/cm³ to 0.079 cm³/cm³, and increased with radar frequency. For multi-layer and profile SM (3 cm, 5 cm, 10 cm, 20 cm, 30 cm, 40 cm, 50 cm depth), the RMSE ranged from 0.040 cm³/cm³ to 0.069 cm³/cm³. Finally, a multi-input multi-output regression model (Gaussian process regression) was used to simultaneously estimate the multi-layer and profile SM. For surface SM, the overall RMSE was approximately 0.040 cm³/cm³. For multi-layer and profile SM, the overall RMSE ranged from 0.031 cm³/cm³ to 0.064 cm³/cm³. The estimation accuracy achieved by coupling the multi-source data (multi-frequency SAR data, multispectral data, and soil parameters) was superior to that obtained using the SAR data alone. The optimal SM penetration depth varied across different vegetation cover types, generally falling within the range of 10–30 cm, which holds true for both the scattering model and the regression model. This study provides methodological guidance for the development of multi-layer and profile SM estimation models based on the multi-frequency SAR data. 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

Exploring the mechanisms by which green agricultural production reduces emissions and enhances carbon sequestration in soil can provide a scientific basis for greenhouse gas reduction and sustainable development in farmland. This study uses a combination of meta-analysis and field experiments to evaluate the impact of different agricultural management practices and climatic conditions on soil organic carbon (SOC) and the emissions of CO₂ and CH₄, as well as the role of microorganisms. The results indicate the following: (1) Meta-analysis reveals that the long-term application of organic fertilizers in green agriculture increases SOC at a rate four times higher than that of chemical fertilizers. No-till and straw return practices significantly reduce CO₂ emissions from alkaline soils by 30.7% (p < 0.05). Warm and humid climates in low-altitude regions are more conducive to soil carbon sequestration. (2) Structural equation modeling of plant–microbe–soil carbon interactions shows that plant species diversity (PSD) indirectly affects microbial biomass by influencing organic matter indicators, mineral properties, and physicochemical characteristics, thereby regulating soil carbon sequestration and greenhouse gas emissions. (3) Field experiments conducted in the typical green farming research area of Chenzhuang reveal that soils managed under natural farming absorb CH₄ at a rate three times higher than those under conventional farming, and the stoichiometric ratios of soil enzymes in the former are close to 1. The peak SOC (19.90 g/kg) in the surface soil of Chenzhuang is found near fields cultivated with natural farming measures. This study provides theoretical support and practical guidance for the sustainable development of green agriculture. Full article

In recent years, heavy metal pollution in farmland soil has become a crisis due to human activities or natural impacts, with particular emphasis on cases from China, where this issue is prominent, greatly affecting crop production and food safety. In the context of a low heavy metal (HM) content in farmland soil, which is difficult to monitor in real time, effective and rapid monitoring of soil plays a decisive role in subsequent targeted protection measures. To this end, this paper provides a narrative review of the application of spectral sensing technology on the basis of the quantitative inversion of heavy metal content in farmland soil using different platforms (ground, airborne, and spaceborne). The sensing process evaluates the mechanism by which soil produces different weak spectral features from the perspective of the heterogeneity of farmland soil. Different methods used for the quantitative inversion of heavy metals (by studying the correlation between soil heavy metals and organic matter, clay minerals, metal oxides, crop vegetation index, etc.) and their feasibility were clarified. At the same time, relevant research on key technologies used in various processes—such as follow-up pretreatment, spectral feature extraction, and the establishment of inversion models for spectral data of different farmland soil types—was summarized, with a primary focus on cases in China. Finally, the challenges, applications, and research directions related to heavy metal spectral inversion in farmland soil were discussed. Full article