Agricultural Management Practices Research Articles

Geospatial Land Classification Via Advanced Image Processing using CNN

High resolution analysis of remote sensing images is pivotal for various classification including land use determination, environmental detection, environmental planning and geospatial object recognition. This paper introduces a robust method for categorizing satellite images into distinct groups, facilitating accurate classification for global geographical areas. It includes image compression, image preprocessing, image segmentation and feature extraction. This innovative approach enables precise identification and understanding of different areas, contributing to optimize resource allocation and improved land management practices in agriculture. CNN is the classifier that is employed in this experiment. The outcome demonstrates that our suggested strategy offers excellent accuracy, outperforming many recently published publications.

Exogenous application of different antagonists and their secretory metabolites to manage root-knot nematodes in pea

Pea (Pisum sativum L.) is a grain legume, a member of the Leguminosae family. Root-knot nematodes cause severe losses ranging from 15 to 85%. Different species of nematodes including root-knot nematodes reduce the yield of pea significantly. To control root-knot nematodes biological control is a more environment-friendly approach. The main objective of the study is to assess the effect of different antagonistic microbes and their secretory metabolites to manage root-knot nematodes in peas. Through this research, we aim to identify potential biological control agents that can be used as eco-friendly alternatives to chemical nematicides, contributing to sustainable pest management practices in agriculture. For this purpose, firstly, a brinjal seedling was transplanted for inoculum development which was inoculated with infective 2nd stage juveniles of root-knot nematodes (Meloidogyne spp.) in Department of Plant Pathology research area, University of Agriculture Faisalabad (UAF). Then two pea varieties viz. Matar sabaz and Pea-2009 were sown in pots and inoculated with nematode larvae after four weeks of sowing. Moreover, the management of nematodes was done by using the antagonists and their secretory metabolites application. The data of different plant growth and nematode-related parameters was taken and subsequently analyzed using analysis of variance and least significant difference test (LSD). Results showed that the antagonist’s treatment subsequently controlled root-knot nematodes (RKNs). Maximum plant/shoot length (47 cm), root length (25.25 cm), shoot weight (22.25 g) pod length (8.50 cm), No. of secondary shoots (8) and minimum number of galls (2.5 cluster), was observed in plants treated with Bacillus spp. (T1) while maximum No. of fruits (5.75) was observed in plants treated with Pseudomonas spp. (T2). Similarly, the number of nodules (7.50 clusters) primary shoots (5) Root weight (4.6 g) was observed in plants treated with Enterobacter spp. (T3).

‘Cropaganda’: Mythology of Corn Belt agriculture

Even as scholars have increasingly recognized the role of industrial agricultural practices in contributing to non-point source pollution, drinking water in the Corn Belt remains perilously contaminated with excess nitrates, which pose a significant risk to human health and the environment. A recent lawsuit filed by the city of Des Moines, Iowa against three upstream counties over chronic nitrate pollution sparked heated debate around the roles and responsibilities of agricultural production. Drawing on Barthes’ theory of mythology, this paper explores how three core myths influence agricultural management paradigms and practices that contribute to the water quality crisis in Iowa and shape how key stakeholders have responded (Barthes, R. 1972 [1957]. Mythologies. New York: Hill and Wang). This article relies on ethnographic data to explain agricultural nitrate pollution and the stalled progress on water quality improvement. Together these myths draw on post-enlightenment ideas of nature, security, and modernity to perpetuate productivist behavior, claim resources for conventional commodity agriculture, and impede widespread adoption of alternative agricultural practices. By identifying and interrogating each myth, I aim to reveal and complicate the inherent contradictions in its representation of reality to strip it of ideological function and, in doing so, cultivate opportunities to imagine and create an alternative system that benefits both people and the planet.

The influence of farm resources and management decisions on the severity of Striga infestation and maize productivity in western Kenya

AbstractThe control of Striga is of critical importance for smallholder farmers cultivating maize and other cereals in the western region of Kenya. Therefore, advanced knowledge and analysis is needed to understand farmers' resource endowment and decision making with regards to crop management, and the possible effect on Striga infestation and crop productivity. The overall objective of the study was to understand the role of resource endowment and allocation decisions on Striga infestation and maize productivity and offer effective and locally adapted options in western Kenya. The study used semi‐structured questionnaires, focus group discussions and resource flow mapping to identify farm resources and key management decisions that are critical in management of Striga on smallholder farms. Soil was sampled from fields of selected farms and taken to the laboratory for analysis including Striga seed counts. In the fields, Striga shoots counts, and maize yield were recorded. Socio‐economic variables such as land size, household income, labour availability, were used to construct a farm typology and assign farmers to different resource groups (RGs). Our results showed that household diversity leads to different land‐use patterns, resource‐use intensity and agricultural management practices which affect soil fertility and Striga infestation. Poor‐resourced farmers tend to be associated with poor crop and soil management which results in maize fields with low soil fertility and high level of Striga infestation. Soil organic carbon showed a strong negative association with Striga infestation. Improving soil fertility is, therefore, the central component of any integrated and sustainable Striga management approaches, and technological solutions to address Striga and soil fertility issues, and they must be tailored to the farm and field diversity. The inclusion of legumes is highly recommended either as rotation or intercropping depending on land availability. Where possible, farmers may use mulch from previous crop residues to suppress weed emergence and prevent flowering and seed setting. Hand weeding though tiresome, remains a low‐cost effective method to control Striga especially in areas where fields are small as in western Kenya.

Biosynthesized Metallic Nanoarchitecture for Photocatalytic Degradation of Emerging Organochlorine and Organophosphate Pollutants: A Review

AbstractThe use of efficacious and cost‐effective pesticides (OP and OC) has undoubtedly proven to be a blessing and a baron because these pesticides are safeguarding the world from food insecurity. Unfortunately, their presence in aquatic bodies brings about an upsurge in water pollution. Amazingly, the photocatalytic degradation approach utilizing biogenic nanoparticles (BNPs) is a trendy state‐of‐the‐art approach and has been established to be a sustainable methodology for the complete mineralization of contaminants into harmless molecules. Thus, this work holistically explores the use of BNPs for photocatalytic degradation of OP and OC. Based on the review, it was found that the least amount of time needed for degradation was less than 5 minutes, while the maximum degradation efficiency was >80 %. The dominant radicals participating in the degradation are ⋅OH and O2⋅ and this radical dominance was enhanced by the oxygenated functional groups present in the biogenic entities employed for the biosynthesis of BNPs. The photocatalytic degradation data fits the pseudo‐first‐order and Langmuir isotherm models (R2 > 0.9), which indicates that the main adsorption mechanisms involved during electron‐hole pair formation and photocatalytic degradation are physisorption and monolayer at the surface of the BNPs. BNPs can sustain a >80 % degradation efficiency for approximately 5 cycles and are reusable for up to 8 cycles. It was also revealed that plants constitute 80 % of the engaged biogenic entities for BNP synthesis. Ultimately, this work offers novel avenues and future research hotspots that might accelerate the use of BNPs for sustainable agricultural and wastewater management practices.

Evaluating the machine learning based Efficacy of Decision Tree and Support Vector Machines in Smart Irrigation Systems for Precise Irrigation Status Classification for Optimizing Water Management in Agriculture

The research paper deals with the improvement of water management practices in agriculture through the use of machine learning in smart irrigation systems. The efficiency of the Decision Tree and the Support Vector Machine models with real-time sensor data was analyzed. The Internet of Things system was developed to analyze the data collected at the field with the help of various sensors, including temperature, humidity, and soil moisture sensors. The layout and choice of sensors were based on the official parameters required for most average plants and corps. After data collection and development of classifiers, the Decision Tree and Support Vector Machine models were used and analyzed regarding their efficiency for making irrigation-related decisions. As the result, it was found that both models showed a good performance, but the SVM model was slightly better due to a smaller number of false positives and false negatives, which allow it to divide the data into the corresponding categories more accurately. At the same time, both models use history data, and the more recent data are better for decision as it was also noted during the development when the use of models based on history data for irrigation decision result in regular irrigation, which in long term would cause the loss of productivity of plans, due to the issue that ancient and no crops are watered too. As the final results, it can be stated that the models show good performance, and the algorithms that were tested can be stated viable for the use at practice. At the same time, the SVM model shows slightly better performance than DT.

Sustainable management strategies for balancing crop yield, water use efficiency and greenhouse gas emissions

CONTEXTTo ensure food security in the face of climate change, it is crucial to use effective agricultural management practices that produce the triangular win-win for water, crops, and environments. OBJECTIVEThis study aimed to explore the sustainable management measures for balancing crop yield, water use efficiency (WUE) and greenhouse gas (GHG) emissions in China during 1981–2020. METHODSThe study selected the Beijing-Tianjin-Hebei (BTH) region of China as the research area, where there were sharp contradictions among water shortage, food security and polluted environment. Four irrigation treatments and four fertilization treatments were set referring to the local cultivated experiences. The daily dynamic changes in crop yield, WUE and GHG emissions were simulated by optimized Agricultural Production Systems sIMulator (APSIM) model. RESULTS AND CONCLUSIONSThe irrigations had positive impacts on crop yield and WUE in BTH region. The irrigation amount's rise caused a gradual augmentation in the total yield of winter wheat and summer maize from 1981 to 2020.The highest total yield was achieved under W4 irrigation treatment (320 mm water for winter wheat and 60 mm water for summer maize), with an annual mean of 24,579.2 kg·ha−1. The annual average WUE under the W3 irrigation treatment (240 mm water for winter wheat) was the highest, with an average of 13.9 kg·ha−1·mm−1. However, extensive fertilization exacerbated soil GHG emissions. The soil GHG emissions were found to be significant increase as the input amount of fertilization. The FI fertilization measure (no fertilization) was assessed to have the least impact on the environment, with an average GHG emissions of 0.4 Mg·CO2-eq·ha−1. The optimal goals of high yield, high WUE and low GHG emissions were achieved by choosing effective combination practices of water and nitrogen management. Overall, the W3 irrigation and F3 fertilization (180 kg/ha fertilizer for winter wheat and 120 kg/ha fertilizer for summer maize.) treatments had the maximum water-fertilizer production indexes in the BTH region. SIGNIFICANCEThis study highlights the importance of agricultural management measures for guaranteeing food security and environmental sustainability at the same time under climate change.

Agricultural management practices in China enhance nitrogen sustainability and benefit human health.

The potential of enhanced agricultural management practices to drive sustainability is rarely quantified at grassroots level. Here we analyse nitrogen use and loss in Chinese cropland, drawing from data collected in 2,238,550 sites in two national agricultural pollution source censuses from 2007 to 2017. We find an upswing of 10% in crop yields and an 8% reduction in nitrogen pollution during this period, owing to the promotion and adoption of various management practices (including the combination of organic and chemical fertilizers, straw recycling and deep placement of fertilizer). These practices have collectively contributed to an 18% increase in nitrogen use efficiency in the country. By fully embracing them, we project that annual cropland pollution could be further reduced by up to 1.4 Mt of nitrogen without compromising crop yields. Environmental and human health benefits are projected to consistently outweigh implementation costs in the future, with total benefits reaching US$15 billion.

Effects of agricultural land management practices on crop production and household income in Ojoje, southern Ethiopia

Farmers in Ethiopia and other sub-Saharan African countries often implement various agricultural land management (ALM) practices to mitigate the negative impact of land degradation, increase crop yield, improve income, and safeguard the security of food. Despite the widespread use of these measures, their effect on crop productivity and related income has received relatively little attention in research, particularly in the context of mixed farming systems. The objective of this study was to assess the effects of ALM practices on crop production and household income in southern Ethiopia. Data was gathered from 423 sampled respondents, which included adopters and non-adopters of ALM practices. This study adopted multistage sampling to gather the data through a survey questionnaire. This study’s findings reveal that ALM strategies have a considerable impact on crop productivity and the incomes of farm households (p<0.05). Moreover, the adoption of integrated physical and biological intervention practices for longer durations resulted in a steady increase in both crop productivity and household incomes. Furthermore, analysis of the effect of long-term treatment indicates that farms show a significant and increasing marginal benefit to production after six years of intervention. These findings imply that integrating ALM practices and maintaining them for an extended period (i.e., 10 years) will help to increase crop productivity and household incomes. Hence, adopting a wider range of physical and biological ALM measures and ensuring their continual adoption are key strategies for improving crop production and increasing household income. This strategy will have significant policy implications and provide a solid foundation for sustainable agricultural development.

Application of Rice Straw Inhibits Clubroot Disease by Regulating the Microbial Community in Soil.

Straw return is an effective agricultural management practice for alleviating soil sickness, but only a few studies have focused on the incorporation of straw with deep plowing and rotary tillage practices in vegetable production. To determine the effects of rice straw return on Chinese cabbage clubroot, a field experiment for three consecutive years in the same area was performed. Soil microbial high-throughput sequencing, quantitative real-time polymerase chain reaction (PCR) and other methods were used to detect Chinese cabbage plant growth, clubroot occurrence, soil chemical properties and soil microbial diversity and abundance. The results showed that straw addition could significantly reduce the clubroot disease incidence. Through Illumina Miseq sequencing, the diversity of the fungi decreased obviously. The relative abundance of the phyla Proteobacteria and Firmicutes was strikingly reduced, while that of Chloroflexi was significantly increased. Redundancy analysis suggests that soil properties may also affect the soil microbial composition; changes in the microbial structure of bacteria and fungi were associated with the available phosphorus. In conclusion, the continuous addition of rice straw can promote the growth and control the occurrence of clubroot, which is closely related to the microbial composition, and the inhibition effect is proportional to the age of addition.

Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

This research presents a comprehensive assessment of a sophisticated approach for the accurate detection and classification of various tomato leaf diseases using an improved ResNet-50 based deep learning classifier. The alarming increase in plant diseases has prompted the need for advanced technologies that can promptly identify and categorize these ailments to ensure agricultural sustainability. The proposed method harnesses the potential of deep convolutional neural networks (CNNs) and builds upon the ResNet-50 architecture, renowned for its depth and performance. However, the approach's innovation lies in the incorporation of enhancements such as advanced data augmentation techniques and transfer learning from a vast plant disease dataset. These modifications empower the model to learn intricate disease-specific features and patterns, leading to heightened accuracy and robustness. The evaluation of the approach is conducted on an extensive dataset encompassing high-resolution images of tomato leaves affected by a range of diseases. The dataset is meticulously preprocessed to ensure consistency and quality, followed by a rigorous training regimen that fine-tunes the improved ResNet-50 model.The results underscore the efficacy of the proposed method in accurate disease detection and classification. The improved ResNet-50 based classifier demonstrates exceptional performance, achieving an impressive accuracy exceeding 95%. Notably, the model showcases resilience against variations in lighting conditions, angles, and disease severity, highlighting its applicability in real-world agricultural scenarios. The implications of this research are significant, offering an efficient and reliable tool for early detection and classification of tomato leaf diseases. The integration of advanced deep learning techniques and the enhancements introduced in this work signify a substantial advancement in precision agriculture and sustainable crop management practices. As future work, this approach can be extended to address diseases in other plant species, contributing to a versatile framework that can safeguard global food production and alleviate the challenges posed by plant diseases.

The role of theoretical models in IoT-based irrigation systems: A Comparative Study of African and U.S. Agricultural Strategies for Water Scarcity Management

This review paper presents a comparative study of the theoretical models underpinning the Internet of Things (IoT)-based irrigation systems, focusing on their application in managing water scarcity within agricultural sectors in Africa and the United States. By examining the adaptation and implementation of these models, the paper sheds light on the diverse strategies employed to optimize water use and enhance crop yields in response to the pressing challenge of water scarcity. In Africa, the emphasis is on low-cost, innovative solutions tailored to local conditions and resource constraints, whereas in the U.S., the focus shifts to leveraging advanced technology, scalability, and economic viability. The study underscores the significance of IoT-based irrigation systems in promoting sustainable agriculture and water management practices across different environmental and socio-economic contexts. Through a detailed discussion, the paper reflects on the theoretical and practical implications of these findings for policymakers, farmers, and technology developers, acknowledging the limitations of the current analysis while suggesting directions for future research. The comparative insights highlight the potential of IoT technologies to revolutionize water scarcity management strategies globally, advocating for increased collaboration and innovation in the development of sustainable irrigation solutions.

Viruses of Nitrogen-Fixing Mesorhizobium Bacteria in Globally Distributed Chickpea Root Nodules

Legume nodules are specialized environments on plant roots that are induced and dominated by nitrogen-fixing bacteria. Bacteriophages (phages) in these nodules could potentially provide top-down controls on the population size and, therefore, the function of nitrogen-fixing symbionts. Here we sought to characterize the diversity and biogeographical patterns of phages that infect nitrogen-fixing Mesorhizobium symbionts isolated from root nodules, leveraging 266 genomes of Mesorhizobium isolated from nodules and 648 nodule metagenomes collected from three species of chickpea plants ( Cicer spp.) under different agricultural management practices, spanning eight countries on five continents. We identified 106 phage populations (viral operational taxonomic units [vOTUs]) in Mesorhizobium draft genomes, 37% of which were confirmed as likely prophages. These vOTUs were detected in 64% of the Mesorhizobium-dominated nodule metagenomes and 58% of the Mesorhizobium isolates. Per metagenome, 1 to 16 putative Mesorhizobium vOTUs were detected, with more than half of the nodules containing only one such vOTU. The majority of vOTUs were detected exclusively in Ethiopia, followed by India and Morocco, with the lowest richness of putative Mesorhizobium phages in countries that applied industrial Mesorhizobium inoculants to crops. Two vOTUs were identified in five or more countries and in nodules dominated by different strains of Mesorhizobium, suggesting infection of diverse Mesorhizobium hosts and long-term interactions. Beta-diversity of these Mesorhizobium phage assemblages was significantly correlated with the dominant Mesorhizobium strain, but not with measured environmental parameters. Our findings indicate that nitrogen-fixing nodules in chickpea plants can contain distinct viral assemblages, with potential impacts on the nodule microbiome that bear further exploration.

CARBON EMISSIONS IN AGRICULTURAL GROWTH: A COMPREHENSIVE ANALYSIS OF FACTORS INFLUENCING ORGANIC CROP PRODUCTION IN KAZAKHSTAN

The article examines the problem of carbon emissions into the atmosphere caused by agricultural activities. She analyzes various aspects of the problem, including the impact of fertilizer use, tillage, and animal husbandry on carbon emissions. The article examines modern methods and technologies that can reduce agriculture’s carbon footprint and improve its environmental sustainability. The authors also discuss the role of renewable energy, effective waste management and sustainable practices in agriculture to reduce its contribution to climate change. This study is anticipated to make significant contributions to both recent literature and policymaking in Kazakhstan across several dimensions. Economic regression analysis was conducted using the panel data models such as Random Effects and Fixed Effects, and two econometric models were assessed. The findings of this study shed light on the intricate relationship between carbon emissions and key determinants within the agricultural sector of Kazakhstan. By employing a comprehensive regression model, incorporating various variables such as oilseed imports and exports, investments in the green economy, fertilizer usage, arable land, and economic indicators, this research delves into the nuanced dynamics influencing carbon emissions

Regenerative Agriculture

There is an emerging trend towards adoption of regenerative agriculture which is perceived to be more sustainable than conventional agricultural production. This paper introduces the concept, definition and principles of regenerative agriculture and its relevance to the oil palm industry. While there is apparent overlapping of best management practices (BMPs) for sustainable agriculture and regenerative agriculture, the regenerative agriculture philosophy has a stronger focus and emphasis on soil health, the soil-water nexus, biodiversity and integration with livestock. Sustainable agriculture and regenerative agriculture should be regarded as complementary systems and they should not be mutually exclusive. Building on the progress made in sustainable production of palm oil through the various certification systems, growers and supply chain actors should consider opportunities for improvements by adopting regenerative agriculture practices. Keywords: Regenerative agriculture, sustainable agriculture, BMPs, soil health, regenagri.

Expected effects of climate change on the soil organic matter content related to contrasting agricultural management practices based on a crop model ensemble for locations in Czechia

The organic matter stored in soils is a major carbon pool with fundamental importance for the global atmospheric carbon balance. Its decomposition contributes not only to the emission of greenhouse gases into the atmosphere but also to the release of minerals that serve as nutrients for plants growing in these soils. SOC is an indicator of soil fertility, reflecting the influences of agricultural practices on this property. Using crop models, the amount of soil organic carbon (SOC) can be simulated under the assumption of different climate scenarios and different agricultural practices. The conventional agricultural practice in Czechia includes short crop rotations of mainly cereals and oil-seed rape, mineral fertilisation and removal of crop residues for technical and energy use. However, the conventional approach is often associated with soil degradation and constant depletion of soil carbon stocks. Based on the standard crop rotation method, we compared the conventional practice (CR1) to an alternative practice (CR2), in which more effort is made towards stabilising soil carbon stocks by including cover crops in the rotation, organic fertilizers and leaving crop residues in the field. We used an ensemble of crop models (APSIM, DAISY, DSSAT, HERMES, and MONICA) to assess the carbon loss from two typical agricultural soils (Chernozem and Cambisol) at three locations in Czechia under current and future climate conditions (RCP 8.5, as represented by five global climate models). The ensemble simulations revealed that using CR2 could lead to an average increase in the SOC content by 15.427 kg/ha for Chernozem and 12.624 kg/ha for Cambisol until 2080. With the use of CR1 the SOC values on average decreased by 34.462 kg/ha for Chernozem and 24.096 kg/ha for Cambisol until 2080. The 1990 value was taken as the SOC reference level. Furthermore, both the increase (CR2) and decrease (CR1) amounts SOC stabilised after 2050. As such, even at the cost of high levels of nitrogen fertilisation and the associated risk of nitrogen leaching (CR2), the additional carbon that can be stored in soils is limited. The differences due to the different climate models are negligible in the case of CR1, while in the case of CR2, the different climate scenarios (baseline vs. future) yielded different SOC equilibrium levels, with a lower level (by 4.400 kg/ha on average) under the RCP 8.5 scenario for both soils. The results showed that carbon can be sequestered by increasing organic inputs. The crop models predicted that CR2 could lead to a higher SOC content, which occurs at the cost of high manure application levels and increased risk of nitrogen leaching.

Geospatial Technology for Sustainable Agricultural Water Management in India—A Systematic Review

Effective management of water resources is crucial for sustainable development in any region. When considering computer-aided analysis for resource management, geospatial technology, i.e., the use of remote sensing (RS) combined with Geographic Information Systems (GIS) proves to be highly valuable. Geospatial technology is more cost-effective and requires less labor compared to ground-based surveys, making it highly suitable for a wide range of agricultural applications. Effectively utilizing the timely, accurate, and objective data provided by RS technologies presents a crucial challenge in the field of water resource management. Satellite-based RS measurements offer consistent information on agricultural and hydrological conditions across extensive land areas. In this study, we carried out a detailed analysis focused on addressing agricultural water management issues in India through the application of RS and GIS technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we systematically reviewed published research articles, providing a comprehensive and detailed analysis. This study aims to explore the use of RS and GIS technologies in crucial agricultural water management practices with the goal of enhancing their effectiveness and efficiency. This study primarily examines the current use of geospatial technology in Indian agricultural water management and sustainability. We revealed that considerable research has primarily used multispectral Landsat series data. Cutting-edge technologies like Sentinel, Unmanned Aerial Vehicles (UAVs), and hyperspectral technology have not been fully investigated for the assessment and monitoring of water resources. Integrating RS and GIS allows for consistent agricultural monitoring, offering valuable recommendations for effective management.

The stress history of soil bacteria under organic farming enhances the growth of wheat seedlings.

The effects of stress factors associated with climate change and agricultural management practices on microorganisms are often studied separately, and it remains to be determined how these factors impact the soil microbiome and, subsequently, plant growth characteristics. The aim of this study was to understand how the historical climate and agriculture to which soil microbes have been exposed can influence the growth characteristics of wheat seedlings and their associated bacterial communities. We collected soil from organic and conventional fields with different histories of climate conditions to extract microbes to inoculate wheat seeds under agar-based cultivation conditions. Within a growth period of 8 days, we monitored germination rates and time as well as seedling above-ground biomass and their associated bacterial communities. The results showed a positive interaction between conventional farming practices and an ambient climate for faster and higher germination rates. We demonstrate that soil microbial extracts from organic farming with experience of the future climate significantly enhanced above-ground biomass along with the diversity of bacterial communities associated with seedlings than other treatments. Such findings support the idea that organic agricultural practices not only mitigate the adverse effects of climate change but also promote the diversity of seedling-associated bacteria.

Seasonal nitrate variations, risks, and sources in groundwater under different land use types in a thousand-year-cultivated region, northwestern China

The global public health concern of nitrate (NO3−) contamination in groundwater is particularly pronounced in irrigated agricultural regions. This paper aims to analyze the spatial distribution of groundwater NO3−, assess potential health risks for local residents, and quantitatively identify nitrate sources during different seasons and land use types in the Jinghuiqu Irrigation District, a region in northwestern China with a longstanding agricultural history. The investigation utilizes hydrochemical parameters, dual isotopic data, and the Bayesian stable isotope mixing model (MixSIAR). The findings underscore significant seasonal variations in the average concentrations of NO3−, with values of 87.72 mg/L and 101.87 mg/L during the wet and dry seasons, respectively. Furthermore, distinct fluctuations in nitrate concentration were observed across different land use types, whereby vegetable lands manifested the maximum concentration. Prolonged exposure to elevated nitrate concentrations may pose potential health risks to residents, especially in the dry season when the non-carcinogenic groundwater nitrate risk surges past its wet season counterpart. The MixSIAR analysis revealed that chemical fertilizers accounted for the majority of nitrate pollution in vegetable lands, both during the dry season (49.6%) and wet season (41.2%). In contrast, manure and sewage contributed significantly to NO3−concentrations in residential land during the wet (74.9%) and dry seasons (67.6%). For croplands, soil nitrogen emerged as a dominant source during the wet season (42.2%), while chemical fertilizers prevailed in the dry season (38.7%). In addition to source variations, the nitrate concentration of groundwater is further affected by hydrogeological conditions, with more permeable aquifers tending to display higher nitrate concentrations. Thus, targeted measures were proposed to modify or impede the nitrogen migration pathway, taking into consideration hydrogeological conditions and incorporating domestic sewage, organic fertilizer, and agricultural management practices.

Soil nitrogen availability and microbial carbon use efficiency are dependent more on chemical fertilization than winter drought in a maize-soybean rotation system.

Soil nitrogen (N) availability is one of the limiting factors of crop productivity, and it is strongly influenced by global change and agricultural management practices. However, very few studies have assessed how the winter drought affected soil N availability during the subsequent growing season under chemical fertilization. We conducted a field investigation involving snow removal to simulate winter drought conditions in a Mollisol cropland in Northeast China as part of a 6-year fertilization experiment, and we examined soil physicochemical properties, microbial characteristics, and N availability. Our results demonstrated that chemical fertilization significantly increased soil ammonium and total N availability by 42.9 and 90.3%, respectively; a combined winter drought and fertilization treatment exhibited the highest soil N availability at the end of the growing season. As the growing season continued, the variation in soil N availability was explained more by fertilization than by winter drought. The Mantel test further indicated that soil Olsen-P content and microbial carbon use efficiency (CUE) were significantly related to soil ammonium availability. A microbial community structure explained the largest fraction of the variation in soil nitrate availability. Microbial CUE showed the strongest correlation with soil N availability, followed by soil available C:P and bacteria:fungi ratios under winter drought and chemical fertilization conditions. Overall, we clarified that, despite the weak effect of the winter drought on soil N availability, it cannot be ignored. Our study also identified the important role of soil microorganisms in soil N transformations, even in seasonally snow-covered northern croplands.