Agricultural Planning Research Articles

Life cycle assessment as a prospective tool for sustainable agriculture and food planning at a local level

Owing to the far-reaching environmental consequences of agriculture and food systems, such as their contribution to climate change, there is an urgent need to reduce their impact. International and national governments set sustainability targets and implement corresponding measures. Nevertheless, critics of the globalized system claim that a territorial administrative scale is better suited to address sustainability issues. Yet, at the sub-national level, local authorities rarely apply a systemic environmental assessment to enhance their action plans. This paper employs a territorial life cycle assessment methodology to improve local environmental agri-food planning. The objective is to identify significant direct and indirect environmental hotspots, their origins, and formulate effective mitigation strategies. The methodology is applied to the administrative department of Finistere, a strategic agricultural region in North-Western France. Multiple environmental criteria including climate change, fossil resource scarcity, toxicity, and land use are modeled. The findings reveal that the primary environmental hotspots of the studied local food system arise from indirect sources, such as livestock feed or diesel consumption. Livestock reduction and organic farming conversion emerge as the most environmentally efficient strategies, resulting in a 25% decrease in the climate change indicator. However, the overall modeled impact reduction is insufficient following national objectives and remains limited for the land use indicator. These results highlight the innovative application of life cycle assessment led at a local level, offering insights for the further advancement of systematic and prospective local agri-food assessment. Additionally, they provide guidance for local authorities to enhance the sustainability of planning strategies.

Agricultural Information Utilization Pattern Among Rural Women Farmers: A Community Level Study in Ebonyi State, Nigeria

This study was to investigate the pattern of agricultural information utilization among the rural women farmers in Effium community of Ebony State. Three research questions and null hypothesis were developed to guide the study. A structured questionnaire was also developed, validated and used for eliciting information from 540 rural women farmers. Data were analyzed with mean and standard deviation to answer the research questions; and analysis of variance (ANOVA) was used to test the hypothesis at 0.05 level of significance. From the findings, rural women farmers are mostly within middle age for active use of agricultural information; but have small farm holdings, and depend to great extent on inter-personal communication for agricultural information. The findings also revealed that most of rural women farmers have low education. The respondents agreed that women farmers use information on fertilizer, herbicides, improved seeds, labour sources and market locations. But, women rarely use information on livestock management, record keeping, farm planning, sources of credit and farm mechanization. It was then-recommended that government should deploy extension agents into rural areas to educate farmers on the need and how to use new agricultural information technologies. Again farmers should be encouraged to form co-operative societies to enable them secure greater inputs to improve their income.

INSTITUTIONS AND MANDATES FOR CLIMATE CHANGE ADAPTATION IN BULGARIAN RURAL AREAS

Climate change is undeniably the leading challenge in the 21st century (IPCC 2022). Agriculture is simultaneously a major driver of climate change and is seriously affected by it (EEA, 2019). The adaptation efforts and the level of readiness to adapt to climate change vary between regions, countries and sectors (e.g. agriculture and environment). The overall goal of the research is to assess the level of readiness for climate change adaptation of the agricultural and environmental institutions in Bulgaria. The specific objective of the paper is to analyse their institutional setups and mandates for adapting to climate change in the Bulgarian rural areas. The research findings and results will support the identification of enabling conditions and key barriers to stronger institutional adaptation capacities of the main agricultural and environmental institutions in the country. The assessment is based on documentary analysis of the national climate adaptation strategy and the legal acts, regulating the institutions’ mandates and functions. The methodological approach is motivated by the conceptual model developed by Ford and King (2015) for assessing the climate adaptation readiness by governments at various scales. The focus is on three of their adaptation readiness factors – political leadership on adaptation, institutional organisation for adaptation and adaptation decision making. The results indicate a reactive rather than proactive political leadership on climate adaptation in Bulgaria. The institutional organisation for adaptation suffers serious understaffing in the Ministry of Environment and Water and in the relevance sectoral ministries. The Strategy on Climate Change and Adaptation stated the need for increased capacities and training in sectoral institutions and stakeholders in 2019. In 2023, there is only one environmental institution with an official mandate on climate change – the Climate Policy Department in the Ministry of Environment and Water. Its mandate is on policy development at the global, European and national level addressing both mitigation and adaptation needs; and it is the smallest unit in the ministry in terms of staff numbers. The other environmental institution with a mandate on climate change is the Executive Environmental Agency which monitors the greenhouse gas emissions and the related permits and registers, with no explicit mandate on climate adaptation. As regards the agriculture and rural development institutions, the Rural Development Directorate in the Ministry of Agriculture has a mandate to propose measures addressing climate change needs during the programming of the Common Agricultural Policy Strategic Plan 2023-2027. The identified needs (i.e. introduce climate adapted breeds and plant species) relate to the farming sector and not to the rural areas. Overall, the key weakness of the Bulgarian climate adaptation approach in rural areas is its reactive and ad-hoc basis. It either is driven by EU regulations and requirements or is implemented when project opportunities arise, without systemic planning and ownership of responsibility in the agriculture or environmental institutions. Key words: climate change governance, adaptation readiness, agriculture JEL code: Q54, Q58, Q18

Empowering Crop Selection with Ensemble Learning and K-means Clustering: A Modern Agricultural Perspective

Introduction Agriculture is an intricate blend of scientific principles and practical techniques that facilitate the growth of crops and the cultivation of livestock. It involves the careful cultivation of the land to produce essential food, fibers, and various other agricultural products. Methods Effective agricultural planning fosters self-sufficiency in food production, offers a source of income for farmers, and contributes to government revenue. This research focuses on utilizing ensemble learning techniques and K-means clustering to predict optimal crop types for specific environmental conditions and categorize crops according to their environmental requirements. This approach aims to refine crop selection strategies significantly. Results The study, employing a comprehensive dataset, applies these advanced methods, yielding accurate predictions and deeper insights into the interaction between crops and their growing environments. Conclusion These findings suggest a potential revolution in agricultural decision-making, highlighting the benefits of these methodologies in enhancing crop yield, reducing environmental impact, and promoting sustainable agricultural practices. The outcomes underscore the impact of data-driven approaches in modern agriculture, offering a promising direction for future agricultural development.

Plastic-covered greenhouses mapping in Morocco with Google Earth engine: Comparing Sentinel-2 and Landsat-8 data using pixel- and object-based methods

With the ever-increasing demand for food production, monitoring plastic-covered greenhouse (PCG) spatiotemporal distribution accurately holds significant importance for monitoring agricultural expansion and environmental impacts. For this purpose, in this paper, we used a random forest (RF) algorithm to produce multi-temporal PCGs maps in three different intensive agricultural Moroccan areas (Loukkos (LS), Gharb (GB), and Souss-Mass (SM)). Two classification approaches, object-based (OB) and pixel-based (PB), were applied to Landsat-8 (L8) and Sentinel-2 (S2) imagery via the Google Earth Engine (GEE) platform. Additionally, the RF performance is compared across five feature scenarios, combining spectral bands, spectral indices, and texture information. Further, we estimated the PCGs' spatiotemporal dynamics from 2018 to 2023. Findings indicated that the Blue and SWIR bands, Normalized Difference Tillage Index (NDTI), Plastic-Mulched Landcover Index (PMLI), variance, and sum average texture were the most significant variables that contributed to accuracy in PCGs classification. The 6-year average overall accuracy achieved >93.10% in mapping PCGs. Notably, the band combination “All” (Bands + Indices + Texture) produced high overall accuracy (>94.9%) and F1-score (>91.8%) compared to other feature combinations. Nevertheless, we found considerably improved classification accuracy by combining only spectral indices with texture (OA >94.6%). It was noticed that both OB and PB approaches reveal their similar performance in most cases, with slight differences. Besides, the PCGs estimated area analysis showed that SM area had the largest PCG coverage, approximately three times larger than the other areas, and it exhibited an overall growth of +8.45% between 2018 and 2023. Thus, this research demonstrates the RF and OB-PB's capability to accurately identify PCGs in GEE. In addition, these results can serve as valuable insights for policymakers and agricultural planners to efficiently monitor and manage PCGs expansion in intensive agricultural areas.

Ground Water Resource and Their Characteristics of the Eastcentral Rajmahal Highlands, Jharkhand, India

This study examines the ground water resources and their characteristics of the Eastcentral Rajmahal Highlands in Jharkhand, India, The study aims to provide a comprehensive analysis of the ground water resources in the region and their geographical distribution. The study categorizes the area into four water table classes on the basis of 67 water samples sites, and explores the role of ground water in maintaining agriculture, the economy, and the environment in the region. The results of the study will provide valuable information for water resource management, agricultural planning, and sustainable development in the Eastcentral Rajmahal Highlands in Jharkhand.

Farmer Preferences Regarding Andean Fruit Crops Across Six Municipalities in the Department of Nariño, Colombia

Understanding the dynamics of crop choices in different regions is critical to designing effective strategies for sustainable agriculture and rural development. The experiment was carried out in six municipalities in the Andean region of the department of Nariño, Colombia. The correspondence analysis was used to explore the association between the preferences of farmers and cultivated fruit species. On farms of 90 fruit growers, seven Andean fruit crops were planted, including Solanum betaceum, S. quitoense, Physalis peruviana, Passiflora pinatistipula, P. ligularis, P. edulis, R. fruticosus. At the end of the experiment, a structured survey was carried out to ask the producers which crop they preferred to implement on their farm, and which one produced the worst experience. In addition, a general diagnosis of the main phytosanitary problems they had throughout the cultivation period was carried out. Arboleda and Sandoná farmers demonstrate a distinctive inclination towards S. betaceum, while Ipiales exhibits a preference for P. peruviana. Conversely, La Florida, Providencia, and El Peñol distinctly favor S. quitoense, with widespread acceptance for R. glaucus and P. ligularis. Farmers do not have a preference for planting P. pinatistipula in Ipiales. S. betaceum was intensified Anthracnosis and Potyvirus-related issues, affecting crop periods and fruit quality. S. quitoense faces altitude-dependent pathogen susceptibility, while R. fruticosus contends with downy mildew. Passion fruit confronts Fusarium sp., gray mold, and dieback. Tailored interventions are crucial for sustainable agriculture amidst diverse challenges. This research contributes valuable insights for informed agricultural planning and regional development policies.

CROP PREDICTION

Cereal crops such as rice, wheat, and different pulses account for the majority of India's food output. Predicting crop yields far ahead of harvest would assist policymakers and farmers in making informed decisions about agronomy, crop selection, and agricultural planning. Such forecasts will also assist related sectors in planning their logistical operations. The goal of the research is to create a machine learning model that can generate such predictions. The model is trained using a dataset that incorporates soil data from the previous decade, with features such as Ph value, temperature, and crop name, as well as labels such as crop yield. The model learns the link between the yield and variables such as soil type, location, and forecast using appropriate machine learning techniques.

DEVELOPMENT OF WEB GIS APPLICATIONS FOR MONITORING AGRICULTURAL ACTIVITIES IN SUNGAI KAKAP DISTRICT, KUBU RAYA REGENCY

<p>Good agricultural development planning requires an accurate and real-time database. This is very important to see the development and level of welfare of farmers as the spearhead of food security. Sungai Kakap Sub-district, Kubu Raya Regency is one of the sub-districts with the number of Gapoktan (23), Poktan (284), the number of members (6305) with Field Agricultural Extension Workers (PPL) since 2019 totalling 9 civil servants and 14 people helping themselves. The number of PPLs is not proportional to the number of farmer group members, making supervision of agricultural activities difficult. The presentation of information in the form of Web GIS is expected to be a solution so that assistance, guidance, and counselling carried out by the government through the agriculture office can be right on target. The purpose of this research is to build a Web GIS-based Agricultural Information System in Sungai Kakap District so that it can be used as a model for agricultural monitoring activities in other areas. The engineering method in this research uses Remote Sensing and Waterfall methods. Remote Sensing is a method of building spatial data using aerial photo interpretation techniques, while Waterfall is used to build a web GIS as a place to display spatial data. The result of this research is Software Engineering in the form of a Web GIS-based Agricultural Information System Application. This Web GIS application can then be used as a tool to monitor the agricultural activities of each farmer group member in each farmer group.</p>

Synergistic impacts of Landscape, Soil, and environmental factors on the spatial distribution of soil aggregates stability in the Danjiangkou reservoir area

Soil aggregate stability (SAS) plays a crucial role in determining the soil's resistance to erosion. Conducting a spatial variability analysis of SAS in the reservoir area, which is highly susceptible to soil degradation and erosion, can yield valuable insights for agricultural practices, landscape planning, and environmental management in this region. Nevertheless, existing research remains constrained in its capacity to accurately map and forecast soil aggregates at a spatial level. This study aims to investigate the spatial distribution of SAS in the Danjiangkou reservoir area, as well as to explore the intrinsic linkage mechanisms between changes in soil properties, environmental factors and landscape patterns and SAS. Results revealed significant spatial variations in SAS within the vicinity of the Danjiangkou reservoir area. Notably, soil properties (including clay, available potassium, soil organic matter, and available phosphorus), environmental factors (such as digital elevation model, slope, land surface temperature difference between seasons (ΔLST)), and landscape pattern (largest patch index (LPI), shannon’s diversity index (SHDI), edge density (ED)) significantly affected the spatial variability of SAS, explaining 18.91%, 8.22%, and 8.17% of the total variability of soil SAS, respectively. Structural equation modeling results indicated that altering soil properties serves as the primary approach to enhance SAS. Furthermore, interventions targeting landscape factors such as LPI, SHDI, and ED, alongside environmental factors like ΔLST and slope, exert direct influences on SAS, while also indirectly impacting it through their effects on soil properties. These findings contribute to the design of land planning and soil and water conservation measures in the reservoir watershed, promoting its protection of soil structure and its ecological service function.

Capability of satellite data to estimate observed precipitation in southeastern South America

AbstractPrecipitation is a fundamental component of the water cycle. Satellite‐derived precipitation estimates with high spatial resolution and daily to subdaily temporal resolution become very important in regions with a limited ground‐based measurement network, such as southeastern South America (SESA). This study evaluates the performance of four state‐of‐the‐art satellite products, including IMERG V.06 Final Run, PERSIANN, PERSIANN CCS‐CDR and PDIR‐NOW in representing observed precipitation over SESA during the 2001–2020 period. The ability of each product to represent observed annual and seasonal precipitation patterns was assessed. Statistical and categorical evaluation metrics were used to evaluate the performance of satellite precipitation estimates at monthly and daily timescales. Our results report that IMERG and CCS‐CDR achieve the best performance in estimating observed precipitation patterns at annual and seasonal timescales. While all satellite products effectively identify autumn and spring precipitation patterns, they struggle to represent winter and summer patterns. Notably, all satellite precipitation products have a better agreement with observed precipitation in wetter regions compared to drier regions, as indicated by the spatial distribution of continuous validation metrics. IMERG stands out as the most accurate product, reaching the highest correlation coefficients (0.75 < CC < 0.95) and Kling–Gupta efficiencies (0.65 < KGE < 0.85, rate as good to very good performance). Regarding categorical statistical metrics, IMERG correctly estimates the fraction of observed rainy days (POD > 0.7, CSI > 0.6) and shows the lowest fraction of estimated precipitation events that did not occur. PERSIANN, CCS‐CDR and PDIR‐NOW exhibit lower performances, mainly in drier areas. Moreover, PERSIANN and PDIR‐NOW tend to overestimate observed precipitation in almost the entire SESA region. We expect this validation study will provide greater reliability to satellite precipitation estimates, in order to provide an alternative that complement the scarce observed information available for decision‐making in water management and agricultural planning.

Farming System Choice Is Key to Preserving Surface Water Quality in Agricultural Watersheds

Despite numerous published literature on the impacts of agriculture on water quality, knowledge gaps persist regarding which farming systems are of most concern for their impact on water quality, which would allow water resource planners to better target water management efforts. Seeking to understand how these relationships vary across different farming systems, we used data on water quality status in watersheds of an agricultural region in southern Portugal and overlaid it with a map of farming systems for the same region provided by a previous study. By intersecting both data layers, we characterized the areal shares of the farming systems in each watershed and inspected how these shares relate to water quality status using logistic regression. The results show that the impact of agriculture on water quality is primarily related to specific farming systems. We believe this type of information can be of significant interest to agricultural planners and policymakers interested in meeting water quality standards, and we conclude this study by suggesting innovative policy options based on payments to farmers operating selected farming systems as a cost-effective way to reconcile agricultural and environmental policy objectives.

Hyperspectral Imaging of Soil and Crop: A Review

The remote sensing is one of the precision technologies, can be used to monitor and assess the target area or object such as soil, crop, and water. Hyperspectral imaging (HSI), also known as imaging spectrometry or hyperspectral remote sensing, is a combined technique of spectroscopy and imaging system for sensing spectral information of an area or object. It involves capturing images of an object using multiple distinct optical bands that cover a wide range of the electromagnetic spectrum (350-2500 nm). The hyperspectral bands are continuous, narrow, and contagious and contain hundreds and thousands of numbers. Hyperspectral remote sensing is particularly valuable for gathering precise and up-to-date information necessary for agricultural planning and precision farming. HSI technology is the employment of hyperspectral sensors aids in analyzing soil physical (bulk density, texture, water content), chemical (pH, EC, SOC, and macro and micro nutrients), biological (SOM) properties and helps to categorize different crop varieties, identify pests and diseases, and assess crop yield and water stress in plants. The spectral reflectance of soil is affected by its properties such as mineral composition (Fe oxides), organic matter, soil moisture, and texture. For example, the spectral reflectance will be more if soil has less organic matter. The chemical bonds of soil molecules interact with the electromagnetic spectrum, and produce distinct pattern of reflectance. But the data collected from hyperspectral imaging are required big storage due to its large amount of data and finding the most appropriate hyperspectral image classification algorithm is a challenging task. So, these problems should be solved in future and national soil spectral library is needed for calibration of models which helps for efficient use of hyperspectral imaging technology.

Optimization models for the installation planning of offshore wind farms

Challenges posed by global warming motivate the increasing interest in green energy sources, such as wind energy. To make wind energy attractive from an economic viewpoint, the decision-making problems faced in designing, installing, and maintaining wind farms have to be optimized. In this paper, we focus on the problem of optimally planning the installation process of offshore wind farms, as faced by Vattenfall, a leading European energy company. We first formulate the deterministic version of this Installation Planning of Offshore Wind Farms (IPOWF) problem as a Mixed Integer Linear Programming (MILP) model. From this model, we then derive other MILPs that can provide lower and upper bounds to the optimal installation cost. To cope with weather uncertainty, we also provide a light-robust MILP. These models are tested on real-life instances corresponding to two wind farms that Vattenfall has recently built in Denmark and Germany. Computational results show that tight primal and dual solutions can be computed for the deterministic case with the proposed models and that the light-robust model yields solutions that are robust to weather uncertainty.

Comparative Analysis of Artificial Neural Network (ANN) and Wavelet Integrated Artificial Neural Network (W-ANN) Approaches for Rainfall Modeling of Southern Rajasthan, India

This paper addresses the challenge of predicting erratic rainfall in Rajasthan state of India, particularly in southern regions. Reliable rainfall predictions are crucial for water resource management and agriculture planning. The research involved selecting 58 stations across seven districts of southern Rajasthan and identifying the best fit computational neural (ANN) and wavelet integrated computational neural (W-ANN) architectures based on performance metrics. Different combinations of input characters, hidden layer neurons, learning algorithms, and training cycles were tested to determine optimal models. Hybrid models, combining wavelet analysis with ANN, were explored to tackle non-stationary hydrologic signals effectively. Results showed that ANN Model C with ten input layer neurons performed best for 74% of stations, followed by Model B (21% of stations) and Model A (5% of stations). Models with increased input and hidden layer neurons performed better. Among the selected stations, 81% of stations demonstrated improved performance using W-ANN models due to effective signal decomposition and information extraction. The hybrid W-ANN models outperformed simple ANN models for rainfall prediction. Both ANN and W-ANN models accurately forecasted weekly rainfall, as observed in the comparison of actual and forecasted values.

Первый опыт разведения русской комолой породы крупного рогатого скота в условиях Республики Калмыкия

Purpose. Analysis of the economic and biological features of the Russian hornless breed of cattle bred in the Republic of Kalmykia. Materials and Methods. The work was carried out in the conditions of the farm of IE KFF (Individual entrepreneur peasant (farm) farm) Iskam I.A. Maloderbetovsky district of the Republic of Kalmykia. The object of the study is bathing cattle of the Russian polled breed. The material was the results of the bounty sheets of cattle of the Russian hornless breed for 2023, which were provided by the individual entrepreneur by the head of the farm Iskam I.A. Maloderbetovsky district of the Republic of Kalmykia. Results. Studies have shown that animals with a strong constitution have a pro-dignity of life longer and retain reproducible ability and high productivity until old age. Cattle on the farm are characterized by well-defined meat forms of build and a strong constitution. Animals have a rather wide body with developed muscles, massive hams, a powerful anterior part with a dilapidated chest; are well adapted to the sharply continental and arid climate of the zone of location of the farm. Studies conducted by employees of VRIMMP have established that high growth in large animals is combined with a wide body, which determines high indicators of their living weight. One of the main among the economic and useful signs of a meat cow is its milk productivity. Calves are weaned from mother cows on the farm at the age of 7-7,5 months, as well as depending on their condition, although overexposure of them on suction after 8 months of age negatively affects the preparation of the mother stock for wintering and further reproduction. Conclusion. The farm of the individual entrepreneur of the head of the farm Iskam I.A. on the basis of a high-class livestock is engaged in targeted breeding work to consolidate and improve the economic and useful prizes of the Russian hornless breed. Due to the improvement of the existing highly productive lines of combed animals, which produce high growth energy, give a good slaughter yield, are adapted to pasture content and more efficiently use coarse and juicy feeds, the farm plans to reduce the productivity of the herd.

Irrigation planning for development of an effective cropping pattern using genetic algorithm

The majority of surface irrigation schemes are diverse in character, consisting of a diversity of crops and soils as well as a huge network of canals with varying qualities (design capacities, efficiencies, command area, length, duration of operation, etc.). The programmes in semiarid and arid locations are similarly related to limited water supplies and operate on a rotating water distribution system. As a result, managing irrigation in such settings is tough. It demands decisions on how much water and space should be allotted to different crops grown on different soils and in different areas or regions of the scheme (the allocation plan), based on water availability, benefit maximisation, varied needs, and the physical boundaries of the scheme. The current study focuses on the use of genetic algorithms (GA) in irrigation planning. In India, the GA technique is being used to create an effective farming plan for an irrigation project. Constraints include land and water limitations, as well as crop and storage limits. The model is run for various choices of population, generations, cross-over, and mutation probabilities to determine GA parameters. The results of GA are compared to those of linear programming. This case study is about a problem with linear constraints that was addressed using a genetic algorithm. The model's future use will be to address issues with non-linear constraints. Traditional nonlinear programming approaches become difficult and time-intensive in such instances. Future research is being conducted to improve the efficiency and usability of these artificial intelligence systems.

Decoding the inconsistency of six cropland maps in China

Accurate cropland information is critical for agricultural planning and production, especially in food-stressed countries like China. Although widely used medium-to-high-resolution satellite-based cropland maps have been developed from various remotely sensed data sources over the past few decades, considerable discrepancies exist among these products both in total area and in spatial distribution of croplands, impeding further applications of these datasets. The factors influencing their inconsistency are also unknown. In this study, we evaluated the consistency and accuracy of six cropland maps widely used in China in circa 2020, including three state-of-the-art 10-m products (i.e., Google Dynamic World, ESRI Land Cover, and ESA WorldCover) and three 30-m ones (i.e., GLC_FCS30, GlobeLand 30, and CLCD). We also investigated the effects of landscape fragmentation, climate, and agricultural management. Validation using a ground-truth sample revealed that the 10-m-resolution WorldCover provided the highest accuracy (92.3%). These maps collectively overestimated Chinese cropland area by up to 56%. Up to 37% of the land showed spatial inconsistency among the maps, concentrated mainly in mountainous regions and attributed to the varying accuracy of cropland maps, cropland fragmentation and management practices such as irrigation. Our work shed light on the promotion of future cropland mapping efforts, especially in highly inconsistent regions.

Random Forest Analysis of Exogenous Variables Impacting Rice Production in the Philippines

This research examines the relationship of rice production as the endogenous variable in a production function theory that considers key exogenous factors such as fertilizer consumption, irrigation water use, agricultural machinery, poverty rate, and agricultural land area. The study reveals the interdependencies shaping rice production in the Philippines. Applying the Cobb- Douglas function enhanced through the random forest regression algorithm establishes fertilizer consumption's focal role, focusing its essential impact on rice yields. Proper allocation of irrigation, access to agriculture machinery, poverty alleviation, and effective land use appear as significant contributors to overall production, defining 98% of the variability in rice production in random forests in both the in-sample and out-of-sample results. The findings emphasize the necessity for holistic strategies in agricultural planning, aiming for targeted interventions in fertilizer management, irrigation infrastructure, mechanized farming, poverty alleviation, and land-use.

Flood irrigation increases the release of phosphorus from aquifer sediments into groundwater

Nonpoint source pollution caused by agricultural activities has long attracted widespread attention from people in society and academia. Many studies have found that human activities not only convey exogenous pollutants into aquifers but also affect the mobilization and transport of geogenic pollutants in aquifers. Geogenic groundwater with high phosphorus concentrations has been found, but it is unclear whether the changes in hydrogeochemical conditions caused by flood irrigation in paddy fields affect the fate of phosphorus. We investigated the temporal and spatial distribution characteristics of phosphorus in groundwater under the influence of flood irrigation through laboratory experiments, proved its impact on phosphorus in groundwater, and explored the mechanisms influencing P concentrations. The results show that flood irrigation can increase the release of phosphorus in the aquifer media and greatly increase the phosphorus concentration in the groundwater of the study area, which has a negative impact on groundwater quality. The main mechanism of increase in phosphorus concentration in groundwater involves an increase in the reducibility of the aquifer via flood irrigation; as a result, iron oxides are reductively dissolved and iron-bound phosphorus is released into the groundwater. Changes in pH also result in the dissolution of calcium phosphate minerals and the release calcium-bound phosphorus. This study not only advances the theory of multielement-coupled hydrogeochemistry but also provides a reference for agricultural planning and groundwater pollution prevention and control in rice-growing areas.