Search Results (878)

Virginia’s coastal location and abundant marine resources make its seafood industry a vital contributor to the state’s economy, supporting both local communities and tourism. This study applied input–output models and updates the economic contributions of the Virginia seafood industry using 2023 data, building on models developed for 2019 that capture both direct effects and broader economic ripple effects. In 2023, the industry generated USD 1.27 billion in total economic output and supported over 6500 jobs—including watermen, aquaculture farmers, processors, and distributors—resulting in USD 238.3 million in labor income. Contributions to state GDP totaled USD 976.7 million, and tax revenues exceeded USD 390.4 million. The study also evaluates the economic role of Virginia seafood products sold in retail stores and restaurants, based on secondary data sources. In 2023, these sectors generated USD 458 million in economic output, supported more than 3600 jobs, produced USD 136.7 million in labor income, and USD 280.8 million in value-added. Combined tax contributions surpassed USD 74 million. Importantly, the analysis results for the Virginia seafood products from retail and restaurant should not be summed to the seafood industry totals to avoid double-counting, as seafood products move as output from one sector as an input to another. These results provide evidence-based insights to guide decision-making, inform stakeholders, and support continued investment in Virginia’s seafood supply chain and related economic activities. Full article

The aim of this research is to identify policy and practice interventions that support a just transition towards resilient practices for resource-dependent communities. We focus on Thai Nguyen and Phu Tho, two provinces in the Northern Midlands and Mountains of Vietnam. The region is reliant on agriculture but is assessed as highly vulnerable to climate change. We surveyed 105 farming households. A Likert-type questionnaire asked respondents to self-assess their experiences of weather extremes and of changes they had made to their farming practices. Our results show that for both Thai Nguyen and Phu Tho, farmers see the effects of climate change on their crops. Respondents in Thai Nguyen were more likely to report technically driven adaptation and engagement with extension services. Respondents in Pho Tho were more likely to continue traditional practices. For both, use of traditional knowledge and practices was related to taking measures to adapt to climate change. Our main conclusion is that at least three actions could support a just transition to resilient livelihoods. First is incorporating natural science and traditional knowledge into decision-making for just transitions. Second is considering long-term implications of interventions that appear to support livelihoods in the short term. Third is tailoring messaging and engagement strategies to the requirements of the most vulnerable people. The main message of this study is that a just transition for resource-dependent communities will inevitably be context-specific. Even in centralized and authoritarian contexts, flexibility to adapt top-down policies to locals’ own experiences of changing climates is needed. Full article

The transition to sustainable agriculture is a critical challenge for the U.S. food system. A sustainable food system must support the production of healthy and nutritious food while ensuring economic sustainability for farmers and ranchers. It should also reduce negative environmental impacts on soil, water, biodiversity, and climate, and promote equitable and inclusive access to land, farming resources, and food. This narrative review synthesizes U.S. social science literature to identify the key factors that support or impede the adoption of sustainable agricultural practices in the U.S. Our analysis reveals seven overarching factors that influence producer decision-making: awareness and knowledge, social factors, psychological factors, technologies and tools, economic factors, implementation capacity, and policies and regulations. The review highlights the critical role of social science in navigating complexity and uncertainty. Key priorities emerging from the literature include developing measurable, outcome-based programs; ensuring credible communication through trusted intermediaries; and designing tailored interventions. The findings demonstrate that initiatives will succeed when they emphasize measurable benefits, address uncertainties, and develop programs that capitalize on identified opportunities while overcoming existing barriers. Full article

This work presents a parallel agent-based framework designed to analyze the dynamics of vegetable trade within a metropolitan area. The system integrates agent-based and discrete event techniques to capture the complex interactions among farmers, vendors, and consumers in urban agricultural supply chains. Decision-making processes are modeled in detail: farmers select crops based on market trends and environmental risks, while vendors and consumers adapt their purchasing behavior according to seasonality, prices, and availability. To efficiently handle the computational demands of large-scale scenarios, we adopt an optimistic approximate parallel execution strategy. Furthermore, we introduce a credit-based load balancing mechanism that mitigates the effects of heterogeneous communication patterns and improves scalability. This framework enables detailed analysis of food distribution systems in urban contexts, offering insights relevant to smart cities and digital agriculture initiatives. 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

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article

Accurate measurement and understanding of the spatiotemporal distribution of soil water content (SWC) are crucial in various environmental and agricultural sectors. The present study implements a novel precision agriculture (PA) approach under sugarbeet field conditions of two moisture-irrigation treatments with two subfactors, clay loam (CL) and clay (C) soils, for geostatistics modeling (seven models’ evaluation) of time domain reflectometry (TDR) multisensor network measurements. Two different sensor calibration methods (M1 and M2) were trialed, as well as the results of laboratory soil analysis for geospatial two-dimensional (2D) imaging for accurate GIS maps of root zone moisture profiles, granular, and hydraulic profiles in multiple soil layers (0–75 cm depth). Modeling results revealed that the best-fitted semi-variogram models for the granular attributes were circular, exponential, pentaspherical, and spherical, while for hydraulic attributes were found to be exponential, circular, and spherical models. The results showed that kriging modeling, spatial and temporal imaging for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps, the exponential model was identified as the most appropriate with TDR sensors using calibration M1, and the exponential and spherical models were the most appropriate when using calibration M2. The resulting PA profile maps depict spatiotemporal soil water variability with very high resolutions at the centimeter scale. The best validation measures of PA profile SWC ${\theta v}_{TDR}$ maps obtained were Nash-Sutcliffe model efficiency NSE = 0.6657, MPE = 0.00013, RMSE = 0.0385, MSPE = −0.0022, RMSSE = 1.6907, ASE = 0.0418, and MSDR = 0.9695. The sensor results using calibration M2 were found to be more valuable in environmental irrigation decision-making for a more accurate and timely decision on actual crop irrigation, with the lowest statistical and geostatistical errors. The best validation measures for accurate profile SWC ${\theta v}_{TDR}$ (m³·m⁻³) maps obtained for clay loam over clay soils. Visualizing the SWC results and their temporal changes via root zone profile geostatistical maps assists farmers and scientists in making informed and timely environmental irrigation decisions, optimizing energy, saving water, increasing water-use efficiency and crop production, reducing costs, and managing water–soil resources sustainably. Full article

Climate change presents major challenges to agriculture, especially in economically underdeveloped regions. In these areas, farmers often lack access to resources and timely information, which limits their ability to respond effectively to drought and threatens agricultural sustainability. This study uses survey data from farmers in underdeveloped regions of China to examine the association between their ability to acquire information and their drought response behaviors. The results indicate that better information acquisition ability is significantly correlated with more effective and scientifically informed decision-making in drought adaptation strategies. To explore the underlying mechanism, we introduce value perception—that is, farmers’ beliefs about the usefulness and benefits of drought adaptation strategies—as a mediating variable. A mechanism model is constructed to test how information acquisition ability relates to behavior indirectly through this perception. We apply a threshold regression model to identify potential nonlinear associations, finding that the relationship between information acquisition ability and drought response behaviors becomes stronger once a certain threshold is surpassed. Additionally, we employ the Item Response Theory (IRT) model to measure the intensity and quality of farmers’ adaptation behaviors more accurately. These findings provide theoretical insights and empirical evidence for enhancing agricultural resilience, while acknowledging that causality cannot be definitively established due to the cross-sectional nature of the data. The study also offers useful guidance for policymakers seeking to strengthen farmers’ access to information, improve value recognition of adaptive actions, and promote sustainable agricultural development in underdeveloped areas. Full article

Water resources have become a critical factor limiting agricultural development and ecological health in arid regions. The ecological efficiency of agricultural water use (EEAWU) serves as an indicator of the sustainable utilization of agricultural water resources, taking into account both economic output and environmental impact. This paper, grounded in the social–ecological system (SES) framework, integrates multidimensional variables related to social behavior, economic decision-making, and ecological constraints to construct an analytical system that examines the impact mechanism of farmers’ part-time employment on the EEAWU. Utilizing survey data from 448 farmers in the western Tarim River Basin, and employing the super-efficiency SBM model alongside Tobit regression for empirical analysis, the study reveals the following findings: (1) the degree of farmers’ part-time employment is significantly negatively correlated with EEAWU (β = −0.041, p < 0.05); (2) as the extent of part-time employment increases, farmers adversely affect EEAWU by altering agricultural labor allocation, adjusting crop structures, and inadequately adopting water-saving measures; (3) farm size plays a negative moderating role in the relationship between farmers’ part-time engagement and the EEAWU, where scale expansion can alleviate the EEAWU losses associated with part-time employment through cost-sharing and factor substitution mechanisms. Based on these findings, it is recommended to enhance the land transfer mechanism, promote agricultural social services, implement tiered water pricing and water-saving subsidy policies, optimize crop structures, and strengthen environmental regulations to improve EEAWU in arid regions. Full article

Climate change is affecting farmers’ livelihoods and their ability to adapt. Therefore, solutions for adaptation and resilience are required. The objective of the work was to assess how nature-based solutions contribute to improving the adaptive capacity of farmers, taking coffee production in Mexico as a case study. It followed the theoretical approach of the Sustainable Livelihoods Framework, which involves identifying the capacities, resources, and activities that a population possesses, considering the following six dimensions: natural, social, human, economic, physical, and political. A rapid systematic review was carried out to identify measurement indicators for each dimension. A semi-structured survey was constructed to collect information on the indicators in the field. The surveys were administered to a sample of 60 randomly selected farmers who utilized various management types incorporating nature-based solutions, including diversified polyculture, simple polyculture, and simplified shade. In addition, farmers who do not use nature-based solutions and who grow coffee in full sun were considered. An index of adaptive capacity was then calculated for each coffee agroecosystem assessed, and finally, actions were proposed to strengthen the livelihood dimensions and increase the adaptive capacity of farmers. It was found that farmers using the management types diverse polyculture and simple polyculture had an average value of the adaptive capacity index classified as high (15.06 and 11.61, respectively). Farmers using the simplified shade management type had an average index value classified as medium (8.59). Whereas, farmers producing coffee in full sun were classified with low adaptive capacity in the average index value (−0.49). The results obtained in this research can contribute to informed government decision making (local, state, or federal) in generating policies to improve or design nature-based solutions in the agricultural sector, thereby increasing the adaptive capacity of producers in the face of climate variability. Full article

Addressing the interconnected challenges of food security, climate change, and population growth requires innovative and adaptive approaches to sustainable agriculture. Agricultural best management practices (BMPs) provide a promising framework for enhancing resilience, improving resource efficiency, and promoting biodiversity. However, the effectiveness of BMPs’ implementation largely depends on their alignment with local environmental, social, and economic conditions. This study presents a novel methodology for selecting and implementing BMPs based on stakeholder preferences, ensuring solutions are contextually relevant and widely accepted. Developed within the European Commission-funded WATDEV project, this methodology integrates a bottom-up and top-down decision-making framework, incorporating the perspectives of farmers, policymakers, and experts. The approach has been tested in four East African countries: Kenya, Ethiopia, Sudan, and Egypt, demonstrating its adaptability across diverse agroecological settings. Through a structured assessment involving stakeholder engagement, data-driven BMP selection, and participatory decision support tools, the study identifies and prioritizes BMPs that optimize water use, soil conservation, and climate resilience. Findings highlight that community-driven BMP selection enhances adoption rates and ensures solutions are technically feasible, economically viable, and environmentally sustainable. The methodology provides a scalable blueprint for integrating stakeholder preferences into agricultural planning, offering valuable insights for policymakers, researchers, and practitioners working toward sustainable food systems in East Africa and beyond. Full article

A crop yield prediction is critical to increase agricultural sustainability because it allows for the more effective use of natural resources, including water, fertilizers, and soil. Accurate yield estimates enable farmers and governments to more accurately manage resources, decreasing waste and minimizing adverse environmental effects such as the degradation of soil and water quality issues. In addition, predictive models serve to alleviate the consequences of climate change by promoting adaptable farming techniques and improving the availability of food by means of early decision-making. Thus, including a crop yield prediction into farming practices is critical for combining productivity and sustainability. In contrast to conventional machine learning models, which frequently require long feature engineering, deep learning may obtain complicated yield-related characteristics directly from initial or merely preprocessed data from different sources. This research paper aims to demonstrate the strength of deep learning models and their ensembles in agricultural yield prediction in Saudi Arabia, where agriculture faces issues such as scarce water resources and harsh climate conditions. We first define and evaluate a Multilayer Perceptron (MLP), a Gated Recurrent Unit (GRU), and a Convolutional Neural Network (CNN) as baseline deep models for the crop yield prediction. Then, we investigate combining these three models based on stacking, blending, and boosting ensemble methods. Finally, we study the uncertainty quantification for the proposed models, which involves a discussion of many enhancements’ techniques. As a result, this research shows that, by applying the right architectures with strong parametrization and optimization techniques, we obtain models that can explain 96% of the variance in the crop yield with a very low uncertainty rate (reaching an MPIW of 0.60), which proves the reliability and trustworthiness of the prediction. Full article

The green transition of smallholder farmers is a critical component in expanding the scale of green agricultural production in China. This research investigates how Science and Technology Backyards facilitate the ecological transformation process for small-scale agricultural producers by developing a three-party evolutionary game framework that incorporates Science and Technology Backyards (STBs), smallholder farmers, and research institutions. The main findings are as follows: (1) Under specific parameter conditions, the system converges to two stable equilibrium points: (0,0,0), where none of the three parties engage in cooperation, and (1,1,1), where full participation and collaboration among all parties are achieved. (2) Science and Technology Backyards exhibit a strong tendency to avoid bearing research costs and demonstrate high sensitivity to economic returns, indicating a clear preference for profit maximization. (3) Research institutes can effectively reduce the cost of technology trials through cooperation with Science and Technology Backyards; however, excessively high trial costs significantly weaken the willingness to collaborate. This study provides a scientific basis for decision-making by stakeholders involved in Science and Technology Backyard initiatives and offers theoretical support for advancing the green transformation of smallholder farmers through the Science and Technology Backyard. Full article

Climate change has increasingly disrupted traditional farming systems, particularly in highland areas where environmental changes are more pronounced. This study explores how rice farmers in the Lake Toba highlands, Indonesia—both irrigated and non-irrigated—have gradually shifted away from traditional knowledge (TK) in response to climate challenges and what new adaptation strategies have emerged to sustain rice production. This study employed a descriptive qualitative approach with a broad and holistic perspective. Data were collected from 130 purposively selected rice farmers in two sub-districts: Harian (irrigated) and Pangururan (non-irrigated). Data were gathered through in-depth interviews guided by semi-structured statements, focusing on farmers’ lived experiences and adaptation strategies across the rice farming cycle—from planting to harvesting. The findings revealed that while the two groups differ in water access and environmental conditions, they show similar trends in shifting away from traditional indicators. Farmers increasingly adopted new adaptation strategies such as joining farmer groups, using water pumps in non-irrigated areas, switching to more climate-resilient crop varieties, and adjusting planting calendars based on personal observation rather than inherited natural signs. This shift from traditional to practical, experience-based strategies reflects farmers’ responses to the fading reliability of traditional knowledge under changing climatic conditions. Despite the loss of symbolic TK practices, farmers continue to demonstrate resilience through peer collaboration and contextual decision-making. This study highlights the need to strengthen farmer-led adaptation while preserving valuable elements of TK. Future research should expand across the Lake Toba highlands and incorporate quantitative methods to capture broader patterns of local adaptation. Full article