Search Results (36)

Groundwater is a critical resource for agriculture and livelihoods, particularly in semi-arid regions such as Gujarat and Rajasthan in India. However, unsustainable extraction has led to aquifer depletion and increased water insecurity. This study uses Ostrom’s design principles to evaluate how Village Groundwater Cooperatives (VGCs) are transitioning toward self-governance in managing groundwater commons. Through field research in Dharta (Rajasthan) and Meghraj (Gujarat), including 33 key informant interviews and nine focus group discussions, this study assesses institutional robustness, rule enforcement, and community participation. Findings reveal that VGCs have the potential to enhance groundwater security through collective water budgeting and recharge interventions, though institutional robustness is constrained by limited formal enforcement. In Hinta, pipelines connected four wells to distribute water equitably, while in Dharta and Meghraj, traditional water-sharing agreements (two-part and three-part systems) sustained cooperation. Groundwater monitoring by trained “Bhujal Jankaars” helped farmers plan crop cycles, supporting informed crop choices that better aligned with available water supply. Despite these successes, to strengthen VGCs for effective groundwater management, formal sanctioning mechanisms are needed to address rule violations. Additionally, women’s participation in groundwater management decisions and operationalising VGCs is low. Conflict resolution mechanisms are currently informal. This study suggests that because women primarily manage domestic water needs while men manage irrigation, integrating women into decision-making is essential to reconcile competing water demands and ensure the long-term viability of VGCs. The findings provide policy insights for scaling up community-led groundwater governance in semi-arid regions. Full article

Balancing efficiency and equity within agricultural supply chains is crucial for rural revitalization and sustainable development. This study focuses on the three-tiered chain of ‘farmers–cooperatives–retailers’, constructing a joint decision-making model linking pricing, sales effort, and order volume. It compares the performance differences between decentralized and centralized decision-making structures. Methodologically, we introduce four corrective factors—risk-bearing capacity, cooperation level, capital investment, and information access—to the traditional Shapley value. By employing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to calculate proximity, we derive an enhanced Shapley–TOPSIS allocation coefficient. Furthermore, we design a secondary distribution rule of ‘effort-based value-added distribution according to labor contribution,’ tightly binding the marginal returns of sales effort to input intensity, thereby reconciling structural fairness with incentive compatibility. Empirical findings indicate that, compared with decentralized approaches, centralized decision-making significantly enhances overall system revenue and reduces retail prices. The refined distribution scheme outperforms the baseline Shapley value in fairness and stability, effectively mitigating the misalignment where effort contributors receive disproportionately low returns. The optimal sales effort level is approximately 0.35. Under the ‘distribution according to labor’ approach, retailers (the primary effort providers) see a marked increase in their value-added share, whereas farmers and cooperatives also gain positive benefits, enhancing alliance stability. Unlike existing studies that rely mainly on revenue-sharing contracts or a single Shapley allocation, this study, on the one hand, explicitly endogenizes sales effort into demand and profit functions and systematically characterizes the joint mechanism between effort and profit allocation under both centralized and decentralized structures. On the other hand, an improved Shapley–TOPSIS modeling procedure and an ‘effort added-value allocation according to contribution’ rule are proposed. By adjusting demand parameters and the weights of the adjustment factors, the proposed framework can be readily extended to other agricultural products and green supply chain settings, providing a replicable tool and managerial implications for designing sustainable profit allocation schemes. Full article

A game-changing strategy for increasing crop productivity while preserving vital resources is precision agriculture. The development of cloud computing and the Internet of Things (IoT) has made it possible and efficient to monitor soil and environmental data in real time. In order to monitor temperature, soil moisture, humidity, and light intensity, this work proposes an inexpensive, IoT-enabled smart agriculture system that uses low-cost sensors. The real-time data is wirelessly transmitted by an ESP32 edge computing device and stored and analyzed on cloud platforms like Firebase or ThingSpeak. A rule-based algorithm generates alerts when sensor values surpass predefined thresholds, enabling prompt and informed decision-making. Field experiments reveal that the proposed system is accurate, economical, and energy-efficient, making it ideal for automation and remote monitoring in precision agriculture. A user-friendly dashboard allows farmers to easily visualize data trends and receive timely notifications. The system supports scalability and can be adapted to different crop types and soil conditions with minimal effort. Moreover, by optimizing water and resource usage, the system contributes to sustainable farming practices and environmental conservation. This deployable solution offers a practical and affordable pathway for small- and medium-sized farmers to adopt smart agriculture technologies and improve crop yield outcomes efficiently. Full article

This study presents an intelligent method for evaluating the risk of Asian rust (Phakopsora pachyrhizi) based on its development stage in soybean crops (Glycine max (L.) Merrill). It has been designed using smart computer systems supported by image processing, environmental sensor data, and an embedded model for evaluating favorable conditions for disease progression within crop areas. The approach also includes the use of machine learning techniques and a Markov chain algorithm for data fusion, aimed at supporting decision-making in agricultural management. Rules derived from time-series data are employed to enable scenario prediction for risk evaluation related to disease development. Measured data are stored in a customized system designed to support virtual monitoring, facilitating the evaluation of disease severity stages by farmers and enabling timely management actions. Full article

This study employs the social–ecological system (SES) framework to investigate the operational mechanisms of farmer apartment housing in Village L, demonstrating how such mechanisms ensure housing security for villagers in land-constrained contexts. Through a case analysis of Village L, we reveal that the effective implementation of farmer apartments relies on four interconnected elements: socio-political and economic conditions, homestead resource allocation within the resource system, institutional governance rules, and collaborative interactions among the government, village collectives, villagers, and enterprises. By integrating fragmented resources, optimizing participatory governance, and fostering multi-stakeholder cooperation, Village L has established a closed-loop operational model of “resource intensification–democratic decision-making–synergistic co-construction”. This model preserves villagers’ homestead entitlements and addresses housing demands through centralized construction, striking a balance between equity and efficiency in land-scarce areas. The findings underscore that farmer apartment housing represents a viable pathway for achieving “housing-for-all” in resource-limited areas, contingent upon institutionalizing village collectives’ self-governance capabilities and incentivizing broader societal participation (e.g., NGOs and enterprises) to form a diversified investment framework. Policy refinements should prioritize scaling context-specific governance innovations while safeguarding farmers’ land rights during urbanization transitions, offering replicable insights for regions facing similar land use challenges. Full article

The aim of this study is to construct and validate an expert system to predict the adaptation of irrigation technologies, water-saving strategies, and monitoring tools by small-scale farmers in Egypt. The research investigates the impact of economic, educational, environmental, and social factors on adaptation rates. To build the expert system, extensive knowledge was collected from experts, key concepts were identified, and production rules were created to generate tailored scenarios. These scenarios utilize the empirical cumulative distribution function (ECDF), selecting the scenario with the highest ECDF as the optimal irrigation technology. This approach ensures well-informed, data-driven decisions that are tailored to specific conditions. The expert system was evaluated under the conditions of ten small farms in Egypt. The results indicate that water cost and availability are significant drivers of technology adaptation. Specifically, subsurface drip irrigation (SDI) demonstrated an adaptation percentage of 75% at high water costs, with probabilities of 0.67 and 0.33, while soil mulching (SM) showed a 75% adaptation rate with a probability of 0.33 in high-cost scenarios. Conversely, when water availability was high, the adaptation percentage for all techniques was zero, but it reached 100% adaptation with a probability of 0.76 for SM and SDI and a probability of 1 for variable number of drippers (VND) and the use of sensors as monitoring tools during water shortages. Educational attainment and professional networks enhance the adaptation of advanced technologies and monitoring tools, emphasizing the role of knowledge and community engagement. Environmental conditions, including soil texture and salinity levels, directly affect the choice of irrigation methods and water-saving practices, highlighting the need for localized solutions. The source of irrigation water, whether groundwater or surface water, influences the preference for water-saving technologies. The study underscores the importance of tailored approaches to address the challenges and opportunities faced by small farmers in Egypt, promoting sustainable agriculture and efficient water management. The evaluation findings reveal that SDI is the most favored irrigation technology, with a probability of 0.55, followed by variable number of drippers (VND) at 0.38 and ultralow drip irrigation (ULDI) at 0.07 across various scenarios for small farmers. Regulated deficit irrigation (RDI) and SM are equally preferred water-saving strategies, each with a probability of 0.50. Sensors emerged as the preferred monitoring tool, boasting a high probability of 0.94. The analysis reveals the critical roles of economic pressures, educational levels, environmental conditions, and social networks in shaping the adaptation of sustainable agricultural practices. Full article

Climate change poses significant threats to coffee supply chains, highlighting the crucial role of coffee genetic resources in enhancing resilience and improving the livelihoods of coffee farmers. Increasing climate change effects are intensifying pressure to develop new high-performance resilient varieties. Current cultivated coffee species include Coffea arabica and C. canephora, while uncultivated genetic resources include C. stenophylla, C. racemosa, and many others among the 130 known coffee species. To protect and recognize the property rights of countries and people hosting and conserving genetic resources, the international community has developed regulations embodied in the Plant Treaty and the Nagoya Protocol, among others. The majority of coffee genetic resources originate in Africa and are maintained in large field collections, particularly in Côte d’Ivoire, Ethiopia, and Madagascar. The 2023 International Coffee Convention (ICC) highlighted the need for community awareness in applying these international regulations. To foster a common understanding and establish precise rules for exchanging coffee genetic resources, the Crop Trust and the International Coffee Organization organized an invitation-only satellite workshop in Mannheim, Germany, on 16 October 2024, in connection with ICC 2024. International experts on the Nagoya Protocol and Plant Treaty and genebank experts were invited to participate. This presentation summarizes key outcomes from the workshop, covering topics such as (i) key requirements of the Convention on Biological Diversity (CBD), its Nagoya Protocol, and the Plant Treaty specifically applicable to the coffee sector; (ii) assessment of the coffee sector’s readiness to implement these international regulations for the transparent use and exchange of coffee genetic resources; (iii) suggestions for mechanisms enabling transparent use and exchange of coffee genetic resources in compliance with international regulations; (iv) evaluation of strategies for generating benefits for communities hosting coffee genetic resources; (v) a practical, user-friendly checklist to ensure the correct handling of coffee genetic resources in line with international regulations; and (vi) a practical decision-making tree with examples to differentiate genetic resources falling under Nagoya/CBD and the Plant Treaty from others. The workshop’s discussions and outcomes expanded on these topics, yielding several concrete initiatives and recommendations. Most importantly, the workshop identified critical gaps in existing coffee genetic resource collections and proposed a global safety duplication strategy. Participants conceptualized a global platform to facilitate the exchange and use of coffee genetic resources, including a centralized database and a system for tracking benefit-sharing obligations. A comprehensive list categorizing coffee varieties based on their status under the Nagoya Protocol may be initiated to clarify access and benefit-sharing requirements. The workshop concluded with a clear roadmap for advancing coffee genetic resource conservation and exchange. Full article

Focusing on sustainability, the new Common Agricultural Policy (2023–2027) sets ambitious goals for water management, as reducing irrigation water use is a vital issue. Cooperation among farmers, relevant authorities, and researchers plays a significant role in achieving these objectives. Therefore, this study applies a multicriteria mathematical programming model to optimize land use, considering water use, profit, labor, and cost. The model was applied to three farmer groups located in Greece and proved to be valuable in the implementation of irrigation water use. Using the same methodology, two additional cases of farmer groups that utilize drylands are presented in complementary ways to investigate how the new CAP affects non-irrigated land uses. Regarding the irrigated case, reducing water usage involves decreasing the land dedicated to crops characterized by high water demand, such as rice, corn, vetch, and clover. This adjustment stems from the necessity to replace irrigated land with non-irrigated land because climate change demands low water consumption for crops and underscores the importance of the new policy framework to promote sustainable agriculture. As for the non-irrigated case, achieving optimal farm planning entails reducing the cultivated areas of vetch, grassland, and sunflower. This result is driven by the need to increase crops receiving primary subsidies, highlighting the necessity for non-irrigated farms to enhance their profitability through the benefits provided by the Common Agricultural Policy. Lastly, it is important to note that this study significantly contributes to guiding decision-makers in achieving alternative agricultural land uses and farm plans while also aiding in the comprehension of the new cross-compliance rules. Full article

The farmers’ deep participation in the classification of domestic waste plays a crucial role in reducing the amount of waste out of the village from the source, lowering the cost of waste treatment, and realizing the sustainable development of rural waste resocialization, reduction, and harmlessness. This paper aims to identify the key factors and logical structure that influence the farmers’ behavior in classifying domestic waste and provide recommendations for improving it. Based on the Participants’ Intellectual Decision (PID) Model, we constructed a theoretical analysis framework for farmers’ decision-making on domestic waste classification, and the PID model was further extended by combining with the practice of rural domestic waste management in China and proposing the research hypothesis that factors, such as community attributes, rules of operation, the status of the participants, and the situation of external actions, have a significant impact on the farmers’ behavior in classifying domestic waste. Empirical analyses were carried out with the help of the ordered logistic model and the DEMATEL-ISM using 939 research data of farmers in Jiangsu and Gansu provinces of China. The results show the following: (1) classification of domestic waste by farmers in the sample area was predominantly unclassified (34.40%) and two-classified (40.58%); (2) 17 factors, including regional disparity, Party affiliation, organizational support perception, environmental emotions, conscious governance attitudes, trust in village cadres, social reference norms, and expected outcomes, have a significant impact on the farmers’ behavior in classifying domestic waste; (3) trust in village cadres, organizational support perception, and environmental emotion are superficial direct factors; incentive measures, fee level, waste transport situation, difficulty perception, self-consciousness perception, social reference norms, and expected outcomes are middle indirect factors; whether or not it is a demonstration village, Party membership and regional disparity are deep root factors affecting farmers to classify their domestic waste. Full article

The aim of this study is to change land use by applying a decision support model that will contribute to the assimilation of the new cross-compliance rules, to optimal water management, and to the enhancement of the effectiveness and profitability of the farms. The research objective will be achieved by establishing 50-acre pilot fields for five farmer groups through the optimal allocation of limited economic and land resources. The result extracted will lead to the gradual incorporation of the new directives to reduce production costs and recognize the new cross-compliance rules. Full article

As one of the major new agricultural business entities, agricultural leading enterprises (ALEs) are responsible for ensuring national food security, leading agricultural and rural modernization, and increasing farmers’ employment prospects and incomes. From the perspective of headquarters and branches, this study used a point pattern analysis, the local Moran’s index, the rank-size rule, and the geographical detector to depict the spatial pattern of ALEs in Heilongjiang Province, detect influencing factors, and reveal the spatial layout mechanism. The main conclusions are as follows. (1) ALE headquarters and branches in Heilongjiang Province had different location requirements, and their layout orientation, clustering areas, and influencing factors were different. (2) The headquarters displayed a pronounced urban and agglomeration orientation, while branches exhibited a significant farm dependence and raw material orientation. (3) Both the headquarters and the branches showed a significant trend towards spatial clustering. The headquarters were mainly in the Harbin municipal district and surrounding counties, including Wuchang, Zhaodong, and Beilin, which showed a high–high cluster pattern. The branches were mainly in the Sanjiang Plain. Tongjiang, Fujin, Hulin, Mishan, Raohe, Baoqing, and Suibin showed a high–high cluster pattern, while the Harbin municipal district and Bei’an showed a high–low outlier pattern. (4) The ALEs’ regional connection network in Heilongjiang Province was radially distributed with the Harbin municipal district as the centre. The agricultural reclamation system deeply influenced it. (5) The number of supporting enterprises, number of permanent residents, gross domestic product, railway mileage, number of people with Bachelor’s degrees or above, and distance from the provincial capital were the main influencing factors of the headquarters spatial pattern in Heilongjiang Province. The number of state farms in Heilongjiang Province, the per capita grain yield, highway mileage, and distance from the provincial capital were the main influencing factors of the branch spatial patterns in Heilongjiang Province. The interaction results indicated that the explanatory power of two-factor interaction was stronger than that of a single factor regardless of headquarters or branches, and most interaction types were bilinear enhancements. This study aims to provide a decision-making reference for the long-term development of ALEs in Heilongjiang Province at the present stage and accelerate the development of agricultural industrialization in major grain-producing areas. Full article

Agent-based models (ABMs) are particularly suited for simulating the behaviour of agricultural agents in response to land use (LU) policy. However, there is no evidence of their widespread use by policymakers. Here, we carry out a review of LU ABMs to understand how farmers’ decision-making has been modelled. We found that LU ABMs mainly rely on pre-defined behavioural rules at the individual farmers’ level. They prioritise explanatory over predictive purposes, thus limiting the use of ABM for policy assessment. We explore the use of machine learning (ML) as a data-driven alternative for modelling decisions. Integration of ML with ABMs has never been properly applied to LU modelling, despite the increased availability of remote sensing products and agricultural micro-data. Therefore, we also propose a framework to develop data-driven ABMs for agricultural LU. This framework avoids pre-defined theoretical or heuristic rules and instead resorts to ML algorithms to learn agents’ behavioural rules from data. ML models are not directly interpretable, but their analysis can provide novel insights regarding the response of farmers to policy changes. The integration of ML models can also improve the validation of individual behaviours, which increases the ability of ABMs to predict policy outcomes at the micro-level. Full article

Maintaining udder health is the primary indication for antimicrobial use (AMU) in dairy production, and modulating this application is a key factor in decreasing AMU. Defining the optimal AMU and the associated practical rules is challenging since AMU interacts with many parameters. To define the trade-offs between decreased AMU, labor and economic performance, the bioeconomic stochastic simulation model DairyHealthSim (DHS)© was applied to dairy cow mastitis management and coupled to a mean variance optimization model and marginal abatement cost curve (MACC) analysis. The scenarios included three antimicrobial (AM) treatment strategies at dry-off, five types of general barn hygiene practices, five milking practices focused on parlor hygiene levels and three milk withdrawal strategies. The first part of economic results showed similar economic performances for the blanked dry-off strategy and selective strategy but demonstrated the trade-off between AMU reduction and farmers’ workload. The second part of the results demonstrated the optimal value of the animal level of exposure to AM (ALEA). The MACC analysis showed that reducing ALEA below 1.5 was associated with a EUR 10,000 loss per unit of ALEA on average for the farmer. The results call for more integrative farm decision processes and bioeconomic reasoning to prompt efficient public interventions. Full article

The urban expansion process involves multiple stakeholders whose interactions and decision-making behaviors have a complex impact on urban land conversion. In this study, we established an urban expansion simulation model that couples two sub-models: the residential location selection model and the land acquisition bargaining model. Those sub-models include four types of agents: resident agent (RA), real estate developer agent (DA), government agent (GA), and farmer agent (FA). The residential location selection model is composed of three agents, RA, DA, and GA, and is first used to select residential locations, while an artificial neural network (ANN) is used to define the behavior rules of RA and RA selects pixels as candidate locations according to the joint decision probability. Then the land acquisition bargaining model is used, which is composed of GA and FA. If the land acquisition is successful, a pixel is converted into urban land, which is occupied by the corresponding RA; otherwise, the RA selects the next pixel and enters the bargaining process again, and so on, until the RA successfully selects a residential location. Each iteration represents the selection process of an agent. We used this model to simulate urban expansion within the Wuhan Urban Development Zone (WHUDZ) of central China from 2009 to 2019. The overall accuracy and Kappa coefficient of the simulation results were 92.78% and 55.24%, respectively, which were higher than the results using logistic regression cellular automata. Moreover, we obtained the relative contributions of various influencing factors in the ANN on the residential location selection, revealing the influence of the land acquisition process on land expansion. In addition, the coupled model predicted that the WHUDZ’s urban land area will reach 1415.82 km² in 2029, mainly through extensional expansion, and the southeast and northwest will be expansion hot spots. Full article

The soil–environmental relationship identified and standardised over the years has expedited the growth of digital soil-mapping techniques; hence, various machine learning algorithms are involved in predicting soil attributes. Therefore, comparing the different machine learning algorithms is essential to provide insights into the performance of the different algorithms in predicting soil information for Indian landscapes. In this study, we compared a suite of six machine learning algorithms to predict quantitative (Cubist, decision tree, k-NN, multiple linear regression, random forest, support vector regression) and qualitative (C5.0, k-NN, multinomial logistic regression, naïve Bayes, random forest, support vector machine) soil information separately at a regional level. The soil information, including the quantitative (pH, OC, and CEC) and qualitative (order, suborder, and great group) attributes, were extracted from the legacy soil maps using stratified random sampling procedures. A total of 4479 soil observations sampled were non-spatially partitioned and intersected with 39 environmental covariate parameters. The predicted maps depicted the complex soil–environmental relationships for the study area at a 30 m spatial resolution. The comparison was facilitated based on the evaluation metrics derived from the test datasets and visual interpretations of the predicted maps. Permutation feature importance analysis was utilised as the model-agnostic interpretation tool to determine the contribution of the covariate parameters to the model’s calibration. The R² values for the pH, OC, and CEC ranged from 0.19 to 0.38; 0.04 to 0.13; and 0.14 to 0.40, whereas the RMSE values ranged from 0.75 to 0.86; 0.25 to 0.26; and 8.84 to 10.49, respectively. Irrespective of the algorithms, the overall accuracy percentages for the soil order, suborder, and great group class ranged from 31 to 67; 26 to 65; and 27 to 65, respectively. The tree-based ensemble random forest and rule-based tree models’ (Cubist and C5.0) algorithms efficiently predicted the soil properties spatially. However, the efficiency of the other models can be substantially increased by advocating additional parameterisation measures. The range and scale of the quantitative soil attributes, in addition to the sampling frequency and design, greatly influenced the model’s output. The comprehensive comparison of the algorithms can be utilised to support model selection and mapping at a varied scale. The derived digital soil maps will help farmers and policy makers to adopt precision information for making decisions at the farm level leading to productivity enhancements through the optimal use of nutrients and the sustainability of the agricultural ecosystem, ensuring food security. Full article