Sustainable Agricultural Development Research Articles

Combining magnetized water with biodegradable film mulching reshapes soil water-salt distribution and affects processing tomatoes' yield in the arid drip-irrigated field of Northwest China

In arid areas, biodegradable film has recently had the potential to replace polyethylene (PE) film to address plastic pollution. However, the positive effect of biodegradable film on soil moisture and salt control is weaker than that of PE film. Magnetized irrigation water technology is expected to compensate for this limitation. This study comprised a field experiment in 20212023 to study how two types of biodegradable film (M1, black; M2, transparent) and six magnetization intensity on irrigation water (T0, 0 Gs; T1, 1000 Gs; T2, 2000 Gs; T3, 3000 Gs; T4, 4000 Gs; T5, 5000 Gs) affect the degradation rate of biodegradable film, soil watersalt distribution, growth, and quality of processing tomatoes. The traditional PE film mulching and non-magnetized irrigation were used as the control group (MPET0). The results demonstrated that magnetized water irrigation slowed down the degradation rate of biodegradable film. In addition, the magnetized irrigation water can redistributed the soil water-salt patterns under the biodegradable film, improving the soil water content and salt leaching efficiency, with better results in M1 than in M2. Moreover, magnetized water irrigation promoted the growth of tomato leaf area under the biodegradable film, enhancing photochemical efficiency and potential activity of PSII, thereby improving fruit yield, quality, and water use efficiency of tomato. Principal component analysis showed that the comprehensive score of M1T3 treatment was the highest throughout the three years. Furthermore, M1T3 treatment has the highest processing tomato economic benefits during 2021–2023 (24634986 CNY hm2 more than MPET0). Therefore, the use of 3000 Gs magnetized irrigation water combined with black biodegradable film is conducive to improving soil water and salt conditions, reducing residual film pollution, and improving the yield and quality of processing tomatoes, thus ensuring the sustainable development of oasis agriculture.

Analyzing the Economic Viability of the Shared Farming Model

Innovative agricultural models have emerged in recent decades, with the shared farming model gaining prominence for its cooperative approach to resource sharing and mutual benefits among stakeholders. This research focuses on conducting an empirical investigation of the shared farming model involving farmers in the context of sustainable agricultural development. It investigates farmers' experiences with the shared farming model as well as the shared farming model's economic viability. The study was conducted in the Shanghai region, involving 40 participants who were sampled purposely. Findings revealed that shared farming has the potential to promote farmers' income, with most respondents reporting satisfaction with the economic returns. Resource access, risk-sharing, and market expansion were recognized as major drivers of this success. Challenges, including resource allocation disputes, are acknowledged, highlighting the need for structured agreements and conflict resolution mechanisms. This research highlights the economic potential of shared farming models, suggesting that they offer an avenue for promoting agricultural sustainability and rural development.

Partial organic substitution increases soil quality and crop yields but promotes global warming potential in a wheat-maize rotation system in China

Excessive application of synthetic fertilizer has resulted in serious soil degradation and significant greenhouse gases (GHGs) fluxes in farmlands. Partial organic substitution for synthetic fertilizer was considered as a possible strategy for sustainable agricultural development, but its potential effects on soil quality, GHGs emissions, and crop productivity remain unclear. A field experiment across 3-year was conducted to evaluate the responses of soil quality, nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) emissions, and crop yields to different ratios of organic fertilizer (OF) to synthetic fertilizer (SF). Six treatments were included: non-fertilization (CK); total SF; total OF; 15 %, 30 %, and 45 % organic substitution (LO, MO, and HO). Soil cumulative N2O emission was decreased with increasing organic substitution ratios, mainly attributing to the reducing soil NH4+ content. However, organic substitution increased soil CO2 and CH4 emissions due to the high manure-driven C input, consequently promoting global warming potential (GWP). Meanwhile, soil organic C, total N, P, available P, K, and C-acquisition enzyme activities were increased with organic substitution, resulting the higher soil quality index (SQI) under HO and OF. HO enhanced the annual yield of wheat and maize by 7.2 % and 13.0 % compared with SF and OF, respectively. The positive relationship between crop yield and SQI indicated that the yield-enhancing effect with partial organic substitution was mainly attributed to the improved synchronization in nutrient supply and soil fertility. Overall, partial organic substitution, especially 45 % organic substitution represents a viable strategy to improve soil quality and crop productivity while mitigating N2O emission in wheat-maize rotation systems. However, organic substitution promoted the GWP through stimulating soil CO2 and CH4 emissions. Further investigations of optimize fertilization managements are still needed to reduce manure-induced CO2 and CH4 emissions to achieve higher climate change mitigation.

Pathways to Enhancing the Quality of Agricultural Statistical Services in the Context of Rural Revitalization

In the strategic context of rural revitalization, optimizing the quality of agricultural statistical services is a crucial element for advancing agricultural modernization and sustainable rural economic development. This paper focuses on the significance of enhancing agricultural statistical service quality under the backdrop of rural revitalization. It addresses current issues such as inadequate implementation of agricultural statistical survey systems, an imperfect data quality control system, and a shortage of statistical service personnel. Proposals are made to improve the statistical survey system, enhance the data quality control framework, and strengthen personnel training. These pathways offer references for elevating the quality of agricultural statistical services and implementing the rural revitalization strategy in the new era.

Weighted Variable Optimization-Based Method for Estimating Soil Salinity Using Multi-Source Remote Sensing Data: A Case Study in the Weiku Oasis, Xinjiang, China

Soil salinization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinization. Remote sensing technology enables large-scale inversion of soil salinity, facilitating the monitoring and assessment of soil salinization levels, thus supporting the prevention and management of soil salinization. This study employs multi-source remote sensing data, selecting 8 radar polarization combinations, 10 spectral indices, and 3 topographic factors to form a feature variable dataset. By applying a normalized weighted variable optimization method, highly important feature variables are identified. AdaBoost, LightGBM, and CatBoost machine learning methods are then used to develop soil salinity inversion models and evaluate their performance. The results indicate the following: (1) There is generally a strong correlation between radar polarization combinations and vegetation indices, and a very high correlation between various vegetation indices and the salinity index S3. (2) The top five feature variables, in order of importance, are Aspect, VH2, Normalized Difference Moisture Index (NDMI), VH, and Vegetation Moisture Index (VMI). (3) The method of normalized weighted importance scoring effectively screens important variables, reducing the number of input feature variables while enhancing the model’s inversion accuracy. (4) Among the three machine learning models, CatBoost performs best overall in soil salt content (SSC) prediction. Combined with the top five feature variables, CatBoost achieves the highest prediction accuracy (R2 = 0.831, RMSE = 2.653, MAE = 1.034) in the prediction phase. This study provides insights for the further development and application of methods for collaborative inversion of soil salinity using multi-source remote sensing data.

Optimizing cropping patterns and resource allocation for sustainable agricultural development: A case study of Ilam province, Iran

Establishing connections between ecosystems and agricultural mechanization can help address erroneous decision-making in agricultural management and prevent further harm to ecosystems. It is essential for authorities and decision-makers in the agricultural sector to understand ecosystem dynamics and influencing factors to develop appropriate policies for sustainable development. This study focuses on identifying optimal conditions for determining crop patterns and input allocation in Ilam province's agricultural practices, considering technical and energy parameters. Cultivating one hectare of wheat and barley in the region requires an average energy consumption of 1.79 GJ from various sources, with an estimated energy output of 38.04 GJ ha−1 for these crops. The research involved farmers cultivating wheat, barley, fodder maize, and canola in the southern region of Ilam province, covering cities like Abdanan, Dareshahr, and Dehloran. The study suggests adjustments to cropping patterns, including reducing wheat cultivation and increasing irrigated fodder maize cultivation under optimal conditions. It also recommends exploring rainfed cultivation or enhancing the yield of rainfed wheat to offset the reduced wheat cultivation. The goal planning model indicates a shift towards sustainability, with changes in cropping patterns focusing on wheat, barley, fodder maize, and canola crops, including expanding the cultivation areas of irrigated wheat, barley, and fodder maize. Efforts aim to enhance the productivity of irrigated crops to promote sustainability and improve agricultural practices in the province for long-term sustainability goals.

Spatial Mapping of Soil CO2 Flux in the Yellow River Delta Farmland of China Using Multi-Source OpticalRemote Sensing Data

The spatial prediction of soil CO2 flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO2 flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO2 flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO2 flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO2 flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha−1 d−1, R2 = 0.73), showing a stronger fitting ability for the spatial variability of soil CO2 flux. The spatial mapping results of soil CO2 flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO2 flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO2 flux, thereby supporting regional agricultural sustainable development and climate change response strategies.

Modeling Tomato Yield and Quality Responses to Water and Nitrogen Deficits with a Modified Crop Water Production Function

Increasingly severe crises, such as climate change, water scarcity and environmental pollution, pose significant challenges to global food security and sustainable agricultural development. For efficient and sustainable tomato cultivation management under resource constraints, quantitatively describing the relationship between yield-quality harvest and water-nitrogen application is practically beneficial. Two successive greenhouse experiments with three irrigation levels (1/3 FI, 2/3 FI, and full irrigation (FI)) and four nitrogen fertilizer treatments (0 FN, 1/3 FN, 2/3 FN, and full nitrogen (FN)) were conducted on tomatoes during the whole phenological stage. The tomato evapotranspiration and nitrogen application amount, yield, comprehensive quality, solid–acid ratio, and lycopene content were measured. Based on crop water production functions, three equation forms of water-nitrogen production functions containing 20 models were established and evaluated to predict tomato harvest parameters. The results show that water increased tomato yield while decreasing fruit quality, and the effect of nitrogen was primarily contrary. Water most significantly impacted tomato formation, and the interaction of water and nitrogen changed among different harvest parameters. Tomato yield and quality formation was more sensitive to water and nitrogen at the flowering and fruit maturation stages. Model Singh-2 outweighed other models for yield estimates, with an R2 of 0.71 and an RMSE of 0.11. Singh-Log, Singh-sigmoid and Rao-Root models were effective models for comprehensive quality, solid–acid ratio, and lycopene content prediction, with an R2 of 0.41, 0.62, and 0.42, and an RMSE of 0.33, 0.50, and 0.16, respectively. Finally, models in the form of f(ETi)·f(N) were ideal for tomato harvest prevision and are recommended for water and nitrogen management in tomato cultivation.

Combination of recycled cellulose with nano-thick flower layered double hydroxides: Biomass water retention slow-release fertilizer for sustainable agriculture

The development of crop nutrient, water utilization and increasing the renewability of waste resources sustainable agriculture is essential for improving sustainable agriculture. In this study, a type of nanothick floral layered double hydroxides (nLDHs) for fast and effective pesticide adsorption was created using cyclodextrin (CD) induction. After that, it was composited with recycled cellulose (PCe) derived from waste biomass (pomelo peel). A novel slow-release hydrogel fertilizer (N@nLDHs/PCe-PAA) was synthesized through the graft copolymerization of acrylic acid (AA), which was then employed as a nutrient (urea) carrier in the nLDHs/PCe composite. The swelling, water retention and slow-release capabilities of slow-release fertilizer was evaluated. Additionally, crop cultivation tests were conducted on the material to ascertain its potential use in sustainable agriculture. The results shown that N@nLDHs/PCe-PAA had a swelling rate of 197.02 g/g at 600 min, the water retention rate reached 21.02 % in soil at 60 d, and a nutrient release of 68.78 % in soil at 60 d, which were in accordance with European Committee for Standardization (CEN) standards. Besides, the experiment of crop cultivation showed that N@nLDHs/ CPE-PAA had obvious promotion effect on plant growth, which could improve the root system, rhizomes, chlorophyll content, and relative water content in the leaves of the plants. Therefore, this novel slow-release hydrogel fertilizer (N@nLDHs/PCe-PAA) not only promotes resource reproducibility, but also has excellent soil water retention, slow-release nutrient properties and a function of promoting plant growth. It has great value in promoting the sustainable development of agriculture.

Efficient cadmium-resistant plant growth-promoting bacteria loaded on pig bone biochar has higher efficiency in reducing cadmium phytoavailability and improving maize performance than on rice husk biochar

Green agriculture faced challenges due to the shortage of efficient cadmium (Cd)-resistant plant growth-promoting bacteria (CdR-PGPB) and their low survival rate and activity during application. In this study, a diverse range of efficient CdR-PGPB were isolated from the rhizosphere soil of Desmodium elegans, especially those with high phosphate-solubilizing capabilities (272.87–450.45 mg L−1). Two highly efficient CdR-PGPB namely, XH1 and XH3 were loaded on to rice husk biochar (RHB) and pig bone biochar (PBB), labelled as RHBM and PBBM respectively. This study aimed to explore their effectiveness and mechanisms in promoting maize growth in a Cd-contaminated planting system. Results showed that PBBM performed best among all treatments. It significantly decreased soil phytoavailable Cd by 53.19 % and Cd content in maize shoot by 85.89 %. It also increased soil available phosphorus by 145.72 %, soil alkaline phosphatase activity by 76.34 %, maize shoot/root biomass by 47.06 %/67.98 %, Chlorophyll (a/b) content by 66.80 %/134.13 % and peroxidase activity by 171.96 %. These results were achieved through the synergistic action of efficient CdR-PGPB and PBB. Therefore, PBBM proved to be a promising and innovative application technique for sustainable agricultural development in Cd-contaminated farmland ecosystems.

Is auxin the key to improve crop nitrogen use efficiency for greener agriculture?

Strengthening future food security through the application of unsustainable levels of inorganic nitrogen (N) fertilizers to crop fields may exacerbate environmental damage. Coordination of N-use efficiency (NUE) and plant growth is, therefore, crucial for sustainable agriculture. Auxin plays pivotal roles in developmental and signaling responses that affect NUE. Hence, a better understanding of these processes provides great potential to improve crop NUE. This review summarizes the effects of auxin on N-related and root developmental processes that either directly or indirectly affect NUE in the model plant Arabidopsis and major crop species to highlight the potential of fostering sustainable agricultural development in the future through modulating auxin-related processes.

The Impact of Land Transfer on Sustainable Agricultural Development from the Perspective of Green Total Factor Productivity

China’s agricultural sector is transitioning from extensive management to intensive management, and land transfer brings about changes in land use and management methods, which may encourage the agricultural sector to enter a sustainable development track, but this mechanism has not been effectively proven. Using the SBM-GML index to construct a green total factor productivity index to measure the level of sustainable agricultural development in each province (or autonomous region or municipality directly under the central government) and provincial panel data from 2010 to 2022, we applied a panel interactive fixed-effects model to empirically test the impact of land transfer on sustainable agricultural development, with a focus on analyzing the heterogeneity and related mechanisms of this impact. The results indicate that (1) land transfer significantly promotes sustainable agricultural development, and this conclusion still held true after robustness tests such as controlling for regional omitted variables, replacing dependent variables, changing the sample size, IV estimation, and GMM estimation. (2) The mechanism testing found that land transfer mainly promotes sustainable agricultural development by increasing the desirable output, and has no significant effect on reducing non-point source pollution. At the same time, land transfer mainly improves the desirable output through factor allocation effects rather than scale operation effects, thereby promoting sustainable agricultural development. (3) The heterogeneity analysis found that the higher the quantile of agricultural development level is, the weaker the role of land transfer in promoting sustainable agricultural development, indicating that land transfer has a greater impact on areas with poor agricultural development foundations, and areas with poor agricultural development foundations are more likely to obtain sustainable development space through land transfer. The impact of different land transfer methods and land transfer objects on sustainable agricultural development was heterogeneous. Compared with non-market transfer methods such as exchange and transfer, market-oriented transfer methods such as leasing and equity had a more significant impact on sustainable agricultural development. Compared to transferring land to ordinary farmers, transferring land to new business entities such as family farms, professional cooperatives, and enterprises can significantly promote sustainable agricultural development.

Development of a leaf area index-based relative threshold method for identifying agricultural drought areas

Considering the global aggravated agricultural drought condition, the availability of reliable historical agricultural drought information with high spatiotemporal resolution is crucial for accurate drought monitoring, effective water resources management, and sustainable agricultural development. However, the coarse spatiotemporal resolution of available historical agricultural drought datasets imposes great limitations on drought prevention and mitigation. Recognizing the research gap, this study developed a novel approach of leaf area index (LAI) relative thresholds to generate a 500 m-resolution agricultural drought areas dataset in the North China Plain (NCP) spanning the timeframe from 2006 to 2019. A range of key LAI relative thresholds were established to capture various levels of agricultural drought severity. Subsequently, a 500 m-resolution agricultural drought area dataset was generated, encompassing critical parameters of drought-covered area, drought-damaged area, and crop failure area for both summer-harvest and autumn-harvest seasons in each year. The relative thresholds for drought-covered area, drought-damaged area, and crop failure area yielded percentages of 56 %, 51 %, 34 % for summer-harvest crops and 45 %, 41 %, 28 % for autumn-harvest crops, respectively. To validate the credibility of the generated approach, historical agricultural drought areas data from the Bulletin of Flood and Drought Disasters in China were juxtaposed across various harvest seasons and multiple years. The spatial verification in the Hebei Province (one main province of the NCP) revealed a remarkable consistency between the newly generated dataset and the authentic dataset, demonstrating correlation efficiency estimates ranging from 0.70 to 0.83. The developed approach gives insights into the spatial distribution and coherence of agricultural drought impacted areas and sheds light on revealing the inter-annual dynamics of crop growing seasons. It can support for impact-based agricultural drought monitoring and prediction, and subsequently assist in optimizing agricultural water management and ensuring global food security.

The Crop Phosphorus Uptake, Use Efficiency, and Budget under Long-Term Manure and Fertilizer Application in a Rice–Wheat Planting System

Little is known about the effect of the long-term application of organic and inorganic fertilizers on P-use efficiency, P budget, and the residual effect of P fertilizer. To clarify the effect of different fertilization on soil P balance in a rice (Oryza sativa L.)–wheat (Triticum aestivum L.) rotation system is helpful to promote the sustainable development of agriculture. Thus, a thirty-five-year fertilizer experiment was conducted with eight treatments, including an unfertilized control (CK); chemical nitrogen (N), phosphorus (P), and potassium (K) fertilizers; and organic manure (M) either alone or in combination treatments (N, NP, NPK, M, MN, MNP, and MNPK). The results indicated that crop yields and P uptake were higher in the combined application of manure and chemical fertilizer treatments than in the manure or chemical fertilizer alone treatments. Soil P budget indicated a 23.4–55.4 kg P ha−1 yr−1 surplus in the organic combined with or without mineral fertilizer treatments, but the soil P budget indicated a 20.0 and 21.9 kg P ha−1 yr−1 deficit in the control and N treatments. The proportion of residual fertilizer P converted to soil available P in NP, NPK, M, MN, MNP, and MNPK treatments was 4.5%, 4.8%, 19.1%, 19.0%, 11.5%, and 13.3%, respectively, over a 35-year period. Furthermore, according to the higher P content and crop uptake in organic manure treatment compared with chemical P fertilizer alone, an organic addition could effectively reduce the use of chemical fertilizer and become an effective way of sustainable development in practice. Therefore, the combined application of organic and inorganic fertilizer will be a practical method to increase crop yields and soil P status in a rice–wheat planting system.

Analysis of the effect of digital financial inclusion on agricultural carbon emissions in China

<p style="text-align:justify"><font _mstmutation="1">In the context of China's "dual carbon" goal, digital financial inclusion plays an important role in agricultural carbon reduction. Based on the provincial panel data of 31 provinces in China from 2012 to 2022, this paper analyzes the impact of digital financial inclusion on agricultural carbon emissions and its transmission mechanism. The results show that digital financial inclusion has a significant inhibitory effect on agricultural carbon emissions. From the perspective of mechanism, inclusive digital finance can reduce agricultural carbon emissions through the upgrading of agricultural industrial structure. In addition, in the mechanism of digital inclusive finance affecting agricultural carbon emissions, there is a threshold effect, and only when rural human capital is higher than a certain threshold, digital inclusive finance can show an inhibitory effect on agricultural carbon emissions. Therefore, it is necessary to strengthen the multi-dimensional development of digital technology facilities, the development of digital inclusive finance and the upgrading of agricultural industrial structure, promote the green and low-carbon transformation of agriculture, comprehensively reduce agricultural carbon emissions, and realize the low-carbon and sustainable development of China's agriculture.</font></p>

Rational optimization of irrigation regimes for drip-irrigated cotton fields without mulch can alleviate the problem of residual film contamination in arid zones

The serious problem of residual film pollution in the arid region of northwestern China has a severe negative impact on both the farmland environment and the benefits of cotton planting. Drip irrigation without mulch (DIWM) has the potential to serve as an effective alternative to mulch drip irrigation and address the issue of residual film pollution. However, there are few studies on how to formulate a rational irrigation regime under DIWM conditions and achieve water savings and productivity gains. Consequently, from 2020 to 2021, we established three DIWM treatments: W4, W6, and W8. None of the treatments were irrigated at the seedling stage, and the irrigation regime was the same for all the treatments at the squaring stage (2 irrigations at an irrigation quota of 45 mm). From the initial flowering stage, the frequency of irrigation was once every 6 d, 8 d, and 12 d for W4, W6, and W8, respectively, and the corresponding total numbers of watering times were 8, 6, and 4, respectively. The irrigation amount was 69 mm at each time point during the flowering and boll-forming stages in all the treatments, and the final irrigation amount was 52.2 mm in W8. The total irrigation amounts were 366 mm, 504 mm and 625.2 mm for W4, W6 and W8, respectively. This study explored the spatiotemporal characteristics of soil moisture via sensors combined with the spatial grid method. Additionally, the growth indices, biomass accumulation, yield components, and water use efficiency (WUE) of cotton were assessed across various treatments. The results revealed that, in 2020 and 2021, the W8 treatment resulted in the highest soil water content (SWC) in the 70–110 cm soil layer and soil water consumption (WC) in the 10–110 cm layer, with the WC in the 10 cm layer being 20 % and 44 % greater than that in W6, respectively. The excessive total irrigation amount (IA) in the W8 treatment led to high vegetative growth of cotton, diminishing the positive impact on yield. Across both years, the WUE observed in the W6 treatment significantly exceeded that of W8, while the seed cotton yield demonstrated only marginal decreases of 5 % and 0.9 % compared with that of W8. The irrigation amount and average WC in the 10–40 cm soil layer were 19 % and 29 % lower in W6 than in W8, respectively, but the economic benefits were only 2 % lower than those in W8. There was a significant positive correlation between the SWC and WC in all the different soil layers, and the effect of the SWC on the WC gradually decreased with increasing soil depth. Overall, the irrigation regime employed in the W6 treatment within the arid zone, devoid of mulch cover, proves to be an effective water-saving strategy, ensuring a consistent cotton yield and enhancing WUE. This study serves as a reference for developing an efficient irrigation system tailored to the DIWM cotton industry in arid zones, contributing to the promotion of green and sustainable agricultural development in this region.

Environmental variables improve the accuracy of remote sensing estimation of soil organic carbon content

Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in the monitoring of global carbon. Environmental variables play a significant role in improving the accuracy of the SOC content estimation model. This study focuses on modeling methodologies and environmental variables, which significantly influence the SOC content estimation model. The modeling methods used in this research comprise multiple linear regression (MLR), partial least squares regression (PLSR), random forest, and support vector machines (SVM). The analyzed environmental variables include terrain, climate, soil, and vegetation cover factors. The original spectral reflectance (OSR) of Landsat 5 TM images and the spectral reflectivity after the derivative processing were combined with the above environmental variables to estimate SOC content. The results showed that: (1) The SOC content can be efficiently estimated using the OSR of Landsat 5 TM, however, the derived processing method cannot significantly improve the estimation accuracy. (2) Environmental variables can effectively improve the accuracy of SOC content estimation, with climate and soil factors producing the most significant improvements. (3) Machine learning modeling methods provide better estimation accuracy than MLR and PLSR, especially the SVM model which has the highest accuracy. According to our observations, the best estimation model in the study area was the “OSR + SVM” model (R2 = 0.9590, RMSE = 13.9887, MAE = 10.8075), which considered four environmental factors. This study highlights the significance of environmental variables in monitoring SOC content, offering insights for more precise future SOC assessments. It also provides crucial data support for soil health monitoring and sustainable agricultural development in the study area.

Groundwater geochemical modeling under severe climate conditions for sustainable agricultural development, the case of Chougafiya Basin, central Tunisia

ABSTRACT Monitoring groundwater quality (GWQ) is vital for water sustainability, mainly in arid to semi-arid regions with intense agriculture and inadequate irrigation management systems. It was, closely, linked to groundwater hydrochemistry (GWH). The present work aims to identify factors controlling this in the Chougafiya shallow aquifer (Central Tunisia), which is known for groundwater hypersalinization and intensive agricultural activities under severe climate conditions. Based on 30 inventoried wells data, the proposed approach combines (i) exploratory factor analysis and (ii) confirmatory factor analysis as a structural equation model (SEM) to detect the most relevant factors involved in GWH and their spatial variability. It has been demonstrated that GWH was governed by Na–Cl and Ca-SO4 type, resulting from evaporated minerals. The results showed a total explained variance of 86% and allowed the identification of two main factors that control groundwater salinization: (1) the natural factor, mainly, attributed to the water–rock interactions and (2) the anthropogenic factor, mainly related to anarchic farming practices. As a confirmatory analysis, the development of SEM led to the identification of the most important factors that control water mineralization. These results could be helpful for the strategic plans for water resources management in the studied area.

A study on the classification of complexly shaped cultivated land considering multi-scale features and edge priors.

Obtaining accurate cultivated land distribution data is crucial for sustainable agricultural development. The current cultivated land extraction studies mainly analyze crops on a regular shape and a small block scale. Aiming at the problem of fragmentation of plots in complexly shaped cultivated land leads to variable scales and blurred edges and the difficulty of extracting the context information by kernel convolution operation of the CNN-based model. We propose a complexly shaped farmland extraction network considering multi-scale features and edge priors (MFEPNet). Specifically, we design a context cross-attention fusion module to couple the local-global features extracted by the two-terminal path CNN-transformer network, which obtains more accurate cultivated land plot representations. This paper constructs the relation maps through a multi-scale feature reconstruction module to realize multi-scale information compensates by combining the gated weight parameter based on information entropy. Additionally, we design a texture-enhanced edge module, which uses the attention mechanism to fuse the edge information of texture feature extraction and the reconstructed feature map to enhance the edge features. In general, the network effectively reduces the influence of variable scale, blurred edges, and limited global field of view. The novel model proposed in this paper is compared with classical deep learning models such as UNet, DeeplabV3 +, DANet, PSPNet, RefineNet, SegNet, ACFNet, and OCRNet on the regular and irregular farmland datasets divided by IFLYTEK and Netherlands datasets. The experimental results show that MFEPNet achieves 92.40 and 91.65 MIoU on regular and irregular farmland datasets, which is better than the benchmark experimental model.

Meta-Analysis of the Effects of Straw Incorporation on Maize Yield and Water Use Efficiency in China under Different Production Conditions

Maize plays a crucial role in China’s grain production, with a cultivation area reaching 44.22 million hectares and an annual yield of 289 million tons in 2023. However, the challenge remains on how to further increase maize yield and water use efficiency (WUE) without adding to the environmental burden. To systematically evaluate the impact of straw incorporation under varying production conditions on maize yield and WUE, this study collected experimental data from multiple locations across China. A meta-analysis was conducted to compare the effects of straw incorporation versus no incorporation, and the main influencing factors were identified using correlation analysis and a random forest model. The results indicate that straw incorporation significantly enhances both maize yield and WUE, with the most pronounced improvements observed under conditions of an average growing season temperature of 19–23 °C, soil pH of 6.5–7.5, low initial soil organic matter content, and deep plowing for straw incorporation. Additionally, moderate nitrogen application rates and straw incorporation amounts (9000–15,000 kg·ha−2) also significantly boost maize yield and WUE. Field management practices and meteorological conditions are identified as the primary factors affecting maize yield and WUE under straw incorporation conditions. Therefore, straw incorporation stands out as an effective agricultural practice for achieving high maize yields and efficient resource utilization. The findings of this study provide valuable insights for global food security and the sustainable development of agriculture.