Farmer-veterinarian interaction as multi-level situated learning: Negotiating health, risk, and responsibility in intensive pig farming - a scoping review.

Multimodal large language models for food safety detection within deep learning frameworks: a review.

ABSTRACT Agricultural insurance stands out as an important risk management tool capable of minimising losses arising from the occurrence of accidents, contributing to the resilience of rural farmers. This research aimed to analyse the panorama of developing patents within the scope of the agricultural insurance market. The search was conducted in the Questel Orbit patent records database based on previously established parameters. This resulted in a final portfolio composed of 104 patent families. The results were grouped into records into three blocks subdivided into categories: (i) commercialisation: product creation; contracting and underwriting; and pricing; (ii) management: risk management; and (iii) claims indemnities: identification of losses; inspections; and claims regulation. It was also identified that the first patent deposit registration under the scope of the investigation was made in 1994 and gradually intensified, especially over the 2017 to 2023. The China has the largest number of patents registered, corresponding to 72.81% of the investigated portfolio. Considering the greater dependence on the adoption of agricultural insurance in contemporary agricultural production due to environmental pressures, our study addressed in a unique and in-depth way the technological innovations found in this area of knowledge to mitigate the economic risks to which farmers are subjects.

This study assessed the microbiological quality of irrigation water in Jega and Alero Local Government Areas, focusing on well water fed and stagnant water-fed ponds. A total of 80 water samples were analyzed for bacterial contamination, with Staphylococcus aureus and Staphylococcus epidermidis identified as the most frequently isolated organisms. Stagnant water fed ponds exhibited significantly higher microbial loads, reaching a maximum of 1.8 × 10⁶ CFU/mL, and accounted for a greater proportion of bacterial isolates (63.4%) compared to well water-fed ponds (36.6%). S. aureus alone constituted 56.1% of all identified isolates, underscoring considerable public health and agricultural risks associated with the use of microbiologically contaminated irrigation water. Prolonged exposure to such water sources may facilitate the spread of waterborne pathogens, threatening both crop safety and consumer health. These findings emphasize the urgent need for routine microbiological monitoring, improved water management practices, and the implementation of targeted intervention strategies to effectively reduce microbial contamination levels within irrigation systems across the study areas.

Purpose This study investigates the impact of ad hoc government payments—specifically the Market Facilitation Program (MFP) and Coronavirus Food Assistance Program (CFAP)—and Farm Bill safety net payments—Agricultural Risk Coverage (ARC) and Price Loss Coverage (PLC)—on non-real estate agricultural loan delinquencies in the United States. The goal is to evaluate the relative effectiveness of these payments in alleviating financial stress in the agricultural sector. Design/methodology/approach We use a state-level panel dataset covering the years 2015–2022 and apply linear fixed effects models to estimate the marginal effect of each payment type on total non-real estate farm debt and delinquency rates. Robustness is assessed using dynamic panel models and Lewbel's IV estimator to address potential endogeneity. Findings ARC and CFAP payments are significantly associated with reductions in short-term loan delinquencies (30–89 days past due). ARC payments also increase total operating debt, suggesting improved liquidity. PLC payments reduce longer-term delinquencies (90+ days past due), while MFP payments increase total debt but do not reduce delinquencies, indicating weaker effectiveness. Originality/value This is the first study to jointly evaluate the effects of ARC, PLC, MFP, and CFAP payments on non-real estate farm debt outcomes using actual payment timing and amounts. It offers novel empirical insights into the financial efficacy of government support programs in agriculture.

Summer droughts are becoming increasingly severe under climate change, posing significant threats to global food security and ecosystem stability. While multivariate time series (MTS) analysis has emerged as a powerful tool for environmental modeling, it suffers from two limitations: (1) failure to account for temporal volatility patterns, and (2) difficulty in capturing non-stationary relationships among meteorological variables. Therefore, we introduce an innovative goal-oriented adaptive autoregressive integration system, i.e., Multi-Variate Time Series Former (MVformer) by integrating three modules: (1) an Adaptive Sampling Autoregressive Prediction (ASAP) module that dynamically balances teacher forcing and autoregression; (2) a volatility neural network capturing nonlinear temporal dependencies; and (3) extreme clustering for automated pattern discovery. MVformer first processes MTS through ASAP using causal attention and sliding windows for enhanced long-term forecasting, then fuses predictions with historical data into high-dimensional features for density-based extremal clustering to detect droughts. We validate MVformer based on meteorological data from 2,415 Chinese monitoring stations. Experiments show MVformer achieves optimal prediction accuracy (MSE: 0.617, MAE: 0.402, MAPE: 21.945%) and clustering quality (Inertia: 0.004, Silhouette Score: 0.424, Calinski-Harabasz: 767.442, Dunn index: 0.072). In summary, this study provides a robust predictive model for climate monitoring, drought early warning, and agricultural risk management.

The agricultural sector in developing countries is highly susceptible to significant losses due to weather variability and seasonal risks. Existing premium calculation methods often rely on homogeneous risk assumptions, which fail to account for claim patterns that are highly dependent on agricultural seasonality. This limitation often leads to mispriced premiums, deterring farmer participation in crucial insurance schemes. To address this, our study proposes and analyzes a compound cyclic Poisson model designed to estimate agricultural insurance premiums under weather-dependent shocks. The model explicitly integrates seasonal variations in claim frequency and severity, aligning premium calculation with actual agricultural risk profiles. Our approach uses a quantitative, stochastic modeling method based on a compound cyclic Poisson process, which effectively captures cyclical claim patterns that correspond with planting and harvesting seasons. As a case study, the research was conducted in South Sulawesi province, an ideal representation of an agrarian region with high weather risk intensity. The weather index used in this study combines rainfall and temperature indicators to better represent climate-induced risks. Through simulations, we found that the insurance premium, derived from our model, ranges from IDR 36,796 during low weather index conditions to IDR 328,713 during high weather index conditions, approximately 20-80% below the fixed AUTP market premium of IDR 180,000. This flexible pricing range allows farmers to choose the most suitable policy for their risk level and empowers insurance companies to set fair and financially sustainable premiums, ultimately encouraging broader participation in agricultural insurance. The originality of this study lies in the integration of a compound cyclic Poisson process to model seasonal claim dynamics in agricultural insurance. This approach contributes to the literature by providing a stochastic framework that bridges theoretical modelling and practical premium calibration under real world weather variability.

ABSTRACT This study investigates the time‐varying impacts of WTI crude oil (WTI), natural gas, and EU Allowance (EUA) futures on U.S. wheat, corn, and soybean markets using thermal optimal path (TOP) analysis and time‐varying parameter vector autoregression (TVP‐VAR) models. The findings reveal dynamic heterogeneity in price transmission mechanisms between energy and agricultural markets. WTI crude oil affects grain prices through its impact on biofuel demand and shipping costs, while natural gas directly influences wheat markets via its effect on fertilizer production costs. EUA futures exhibit long‐term policy effects with weaker short‐term impacts. Empirical results demonstrate that extreme events significantly amplify the transmission of energy shocks to grain markets, with notable differences in sensitivity to energy price fluctuations across grain varieties. The study provides empirical evidence for agricultural risk management, energy policy formulation, and climate governance, suggesting a future exploration of long‐term policy shocks on energy‐agriculture linkages.

Agricultural production is highly susceptible to weather-related uncertainties, which are expected to increase due to climate change. While most studies address production risks, market risks are often overlooked despite their growing impact on farm income. Crop diversification is a strategy to reduce both production and market risks. This study investigates how temporal and spatial diversification strategies influence farm incomes and risk exposure across different arable farm types in Eastern Germany. A stochastic bio-economic farm model (MODAM) was implemented to optimize decision-making. The farm model integrates yield variability from a crop growth model and market volatility through Monte Carlo simulations of crop and fertilizer prices. Nine showcase farms were analyzed under three diversification strategies: temporal diversification, subfield division, and strip cropping, against narrow rotations with sole cropping. All diversification strategies included (among other crops) soybean cultivation. The model assessed two climate scenarios (1990–2020 and 2020–2060) and two policy environments: the CAP 2023 area payment system and a novel premium, paid based on the field perimeter, promoting smaller field units. Soybean integration into cereal dominated cropping systems was limited under temporal and subfield diversification but increased with strip cropping. Expected gross margins improved under future climate conditions compared to historical conditions across all strategies. Diversification consistently reduced economic risk relative to narrow rotations and sole cropping, with subfield division and strip cropping showing the most substantial effects. Strip cropping reduced economic risk but involved higher trade-offs. Subfield division significantly reduced economic risk without sacrificing gross margins, especially under risk-averse behavioral preferences and future climate scenarios. A modest perimeter-based payment (1.5 €/100 m length of field edge) replacing area-based premiums helped maintain gross margins under strip cropping while significantly reducing the conditional-Value-at-Risk. Spatial diversification like subfield division and strip cropping, are effective in mitigating farm income risks under climatic and market uncertainty. Policy instruments such as perimeter-based payments can enhance these effects. • Diversification effects depend on farm type, climate, and risk preferences. • Sole cropping shows highest tail risk, esp. for large vulnerable farms. • Smaller field units result in lower risk without gross margin loss despite higher labor demand. • Strip cropping reduces conditional Value-at-Risk, needing policy support for economic viability.

The objective is to scientifically substantiate the importance of the digital economy as a key factor in the strategic transformation of the agro-industrial complex of Kazakhstan in the con text of Industry 4.0, aimed at increasing productivity, strengthening food security, and enhancing competitiveness. Methods include the abstract-logical method for generalizing theoretical provisions of agricultural digitalization and identifying development patterns, comparative analysis of time series for monitoring changes in indicators for 2010–2024, and system synthesis for integrating economic, technological, and institutional components into a holistic model of digital transformation. Results based on panel data for 2010–2024 examine issues of technological modernization in agriculture based on new digital competencies and innovative solutions. International experience in implementing high-tech management mechanisms is systematized. A comprehensive assessment of the level of digital development of the agro-industrial complex of the republic is provided. Despite an increase in gross output to 8.3 trillion tenge, the share of technological services remains critically low (0.17%). The potential of precision agriculture tools, the Internet of Things (IoT), and Big Data technologies is demonstrated. Prospects of the “service digitalization” model, which al lows small and medium-sized farms to reduce costs through IT outsourcing, are substantiated. Financial and economic practices of sector transformation are analyzed, including leasing of next generation equipment, preferential lending, and digital insurance instruments for agricultural risks. Conclusions show that the integration of information technologies optimizes the balance of supply and demand and ensures transparency of value chains. It is proven that long-term stability of agroindustrial production is impossible without forming a new effective concept of agricultural education and investment in human capital

The global distribution of tobacco (Nicotiana tabacum), a crop of major economic importance, and its soil-borne pest root-knot nematodes (Meloidogyne spp.), which cause substantial yield losses worldwide, is strongly influenced by climatic and soil edaphic factors. Identifying regions of spatial overlap between tobacco suitability and nematode occurrence is essential for assessing vulnerability to pest-induced yield losses under changing environmental conditions. In this study, species distribution modelling was applied to quantify current and projected global suitability for Nicotiana tabacum and Meloidogyne spp. using the Maximum Entropy (MaxEnt) modelling across baseline (1970–2000), mid-century (2021–2040), and late-century (2081–2100) periods under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585). Models were developed using global occurrence records and predictor sets comprising 19 bioclimatic variables, elevation, and soil edaphic properties, including soil texture, soil organic carbon, and soil pH, evaluated both jointly and independently to disentangle their relative contributions. Model performance was high for Meloidogyne spp. (training AUC up to 0.93) and moderate for N. tabacum (AUC ≈ 0.78), indicating reliable discrimination of suitable habitats. Baseline projections revealed extensive suitability overlap in major tobacco-producing regions such as South and Southeast Asia, sub-Saharan Africa, and parts of South America. Soil edaphic factors emerged as primary determinants of nematode suitability, whereas tobacco distribution was more strongly constrained by temperature and precipitation seasonality. Future projections indicated progressive contraction and fragmentation of tobacco-suitable areas, accompanied by pronounced spatial restructuring of nematode suitability and an expansion of high-risk overlap zones under intermediate and high-emission scenarios. These results demonstrate that climate change may intensify crop–pest interactions in specific regions, emphasizing the importance of integrating climatic and soil constraints into long-term agricultural risk assessment and management strategies.

ABSTRACT Smallholder sweet potato farmers in Papua New Guinea's Eastern Highlands Province (EHP) face challenges as they navigate social, economic, demographic, and environmental change. While they routinely adapt to weather variability and shifting markets, population growth is placing additional strain on an already finely balanced farming system. This study shows that farmers' decisions are shaped not only by agricultural considerations but also by the interconnectedness of social, economic, and biophysical factors, highlighting the risks of addressing single issues in isolation. Although agricultural intensification may be a necessary response to population pressures, it can come at a cost. Moving to a higher‐input approach can unsettle the smallholder farmer's system and risk undermining their resilience.

Seaweed farming, a crucial part of the blue economy in Sorsogon, Philippines, operates in a dynamic and frequently unpredictable environment. This environment is vulnerable to various factors that can affect its economic viability and long-term sustainability. These challenges include fluctuating market prices, environmental risks like climate change and disease outbreaks (such as the “ice-ice” disease), and operational inefficiencies. For small-scale farmers, these risks can be particularly devastating, threatening their livelihoods and hindering the sector’s growth. This study analyzed seaweed farming operations and risks using Risk Management Theory. Data from a survey of 115 farmers in Sorsogon City and Castilla, along with interviews with stakeholders, and experts, revealed a sector dominated by small-scale and medium-scale ventures, primarily cultivating Kappaphycus alvarezii through fixed-bottom long lines. While all farmers accessed government propagules, only 20% received direct financial support, highlighting a liquidity gap. Environmental hazards, particularly typhoons and “ice-ice” disease, drove yield volatility. To mitigate these risks, the study recommends Risk Avoidance through the mandatory conversion of unviable farming systems, and Risk Reduction via the distribution of climate-resilient genotypes. These findings provide a strategic roadmap for policy interventions aimed at transforming Sorsogon’s seaweed industry from a subsistence activity into a resilient commercial enterprise.

Localized climate evidence for agricultural risk assessment remains scarce in northern Ethiopia. This study integrates long-term station observations with CHIRPS v3 and ERA5 to extend climate analysis in Tigray to 2024 and to link sub-regional rainfall and temperature variability directly to agricultural risk. Data from six meteorological stations in south Tigray were combined with high-resolution gridded rainfall and temperature datasets. Long-term seasonal and annual trends and variability in rainfall and temperature were assessed using standard non-parametric trend and rainfall variability analysis methods. Rainfall anomalies identified severe dry years (1984, 2002, 2015, 2022) and wet years (1998, 2006, 2010), highlighting pronounced interannual variability. Regional aggregates revealed statistically significant warming across Tmax, Tmin, and Tmean, with Sen’s slopes ranging from + 0.018 to + 0.046 °C yr⁻¹. Warming was strongest in Tmin, implying reduced night-time cooling and narrowing diurnal ranges. Between 1981 and 2008, temperatures rose steadily, briefly cooled during 2009–2011, then accelerated markedly through the 2010–2020s. Precipitation Concentration Index (PCI) results suggested increasing intra-annual rainfall concentration in some areas, with more rain concentrated in fewer events. Overall, the results indicate clear warming trends with spatially and temporally variable rainfall changes, carrying important implications for rain-fed agriculture. Improved observational networks, combined with downscaled projections, and expanded use of agro-climatic metrics, are critical for robust climate risk assessment and adaptation planning.

Climate-driven freeze-thaw (FT) cycles amplify the combined toxicity of polystyrene nanoplastics (PS) and tributyl phosphate (TBP) in crops. TBP is a common plasticizer. Our multiomics study reveals that PS and TBP form complexes via van der Waals forces, enhancing PS uptake in rye roots. Coexposure induces severe oxidative stress (H2O2: 1.35-, 4.71-fold → 9.04-fold), suppresses photosynthesis, and activates antioxidant defenses, with FT conditions intensifying these effects. TBP restructures the root endophytic microbiome, enriching TBP-degrading bacteria (Acidovorax, Massilia). Transcriptomic analysis identifies jasmonic and abscisic acid (ABA) signaling pathways as central coordinators of plant defense through reactive oxygen species (ROS) scavenging and metabolic reprogramming. These findings demonstrate that FT cycles exacerbate NPs-plasticizer toxicity through three interconnected mechanisms: physicochemical complex formation, root microbiome remodeling, and hormonal signaling crosstalk. The study provides crucial mechanistic insights for assessing climate-pollution risks in cold-region agriculture, highlighting the need to consider pollutant interactions under dynamic environmental conditions.

This paper evaluates the effectiveness of Insurance-plus-Futures (IPF) programs in hedging grain price risk in China’s corn industry. Using data from four representative IPF models, the study assesses farmer income protection and hedging performance in futures markets. Results show that IPF models significantly mitigate income volatility, with the IPF-plus-Bank model offering the highest compensation rate. However, hedging effectiveness varies across models due to differences in market correlation and volatility. Notably, the integrated IPF-plus-Bank-and-Order model achieves the most robust risk-hedging efficiency by effectively anchoring basis risk. The findings highlight the importance of product design, pricing accuracy, and market infrastructure in enhancing agricultural risk management. The study offers policy insights for improving the scalability and efficiency of agricultural financial innovation.

The Philippines is one of the world’s most vulnerable countries to climate risks. It is exposed to strong typhoons, storm surges, prolonged monsoon rains and consequently, flooding, and extreme drought. To exacerbate this vulnerability, the country has a population of 109 million, 24% are poor, the country is archipelagic, its water resources are fragile, and with a very limited food production areas competing with rapidly expanding human settlements. In cognizant of these conditions, an interdisciplinary team from the University of the Philippines Los Banos, conducted an R&D program entitled “Smarter Approaches to Reinvigorate Agriculture as an Industry” or Project SARAi funded by the Philippines’ Department of Science and Technology (DOST) to address climate risks in agriculture. The products of this 10-year program include a Community-Level SARAI Enhanced Agriculture Monitoring System, or CL-SEAMS, which utilizes space technology and GIS, designed for crop and fisheries monitoring and forecasting at the local government levels (LGUs). The team has pilot tested the protocols in at least 3 municipalities and in the sugar industry sector. Capacity of the system also includes being able to determine areas damaged by extreme events in a shorter period of time (3-5 days vs. months previously). Hence, responses by the state are faster and less expensive. With projected climate extremes, this can enhance the capacity of the communities to adapt to changing weather and climate. Examples of SARAI digital tools include mobile applications for pest and disease identification and management (SPIDTech) to cover 20 crops; BanaTech, for determining harvest times for bananas; and Caphe, for coffee, to name a few. These digital tools are perceived to be beyond capacity at the local level. This paper will present the models of agriculture extension tested and adopted by SARAI to ensure that results of science, technology, and innovations empower farming communities nationwide.

Agriculture is increasingly exposed to systemic risks driven by climate change, including extreme weather events, yield instability, and market fluctuations. Effective risk zoning is therefore crucial for actuarially fair and sustainable agricultural insurance. However, in multi-branch systems such as Türkiye’s Agricultural Insurance Pool (TARSIM), different insurance lines such as Crop, Greenhouse, Livestock, and Income Protection, employ distinct criteria and coding schemes, often resulting in inconsistent regional classifications. This study introduces a novel Machine Learning-Based Cross-Branch Consistency Framework to evaluate and harmonize agricultural risk zoning across these branches. Using official 2025 TARSIM datasets covering 71,902 localities, ordinal risk codes (A–Z) were encoded and analyzed through correlation statistics, clustering, and supervised learning. The results revealed strong interdependencies among high-intensity perils such as hail and storm across open-field and greenhouse insurance lines (r > 0.98), and high spatial synchrony among staple crop risks (r ≈ 0.99), confirming the existence of shared systemic exposure. K-Means clustering identified five statistically robust regional risk archetypes that transcend administrative boundaries and branch definitions. A novel Cross-Branch Consistency Index (CBCI) was developed to quantify inter-branch alignment, highlighting regions of both coherence and discrepancy in current zoning logic. Supervised learning validation using Random Forest achieved over 95% classification accuracy, demonstrating the reliability and reproducibility of the ML-derived zones. The findings support a transition from fragmented, expert-driven zoning to an integrated, data-driven risk management framework. The proposed methodology enhances actuarial fairness, transparency, and policy efficiency, providing a scalable foundation for modernizing state-supported agricultural insurance systems and advancing climate-resilient risk governance.

ALS is a neurodegenerative disorder with an unknown etiology for 90%-95% of cases. Several environmental and occupational exposures have been investigated although a prospective study examining the association of "ever living/working on a farm" (farming exposure) and insecticide exposure with risk of ALS mortality in women is lacking. Within the Women's Health Initiative (WHI), a nested case-control study was conducted among 93,676 women (n = 151 ALS death cases, n = 1,496 matched controls) from the WHI Observational Study. This included a questionnaire about farming and insecticide exposure history 1 year after baseline. Conditional logistic regression models adjusted for education, smoking, and physical activity estimated risk of ALS death. One-third of cases and 21% of controls reported farming exposure. Cases had higher odds of farming exposure than controls (adjusted odds ratio [aOR] = 1.59, 95% CI: 1.09-2.31) and was highest at durations of 15-19 (aOR = 1.81, 95% CI: 1.02-3.21) and ≥20 years (aOR = 2.32, 95% CI: 1.10-4.87) compared to no exposure. Tests for interaction revealed that women with farming and smoking exposure had higher odds of ALS death (aOR = 2.10, 95% CI: 1.26-3.51, p = 0.0045). Little evidence was noted for increased insecticide exposure and ALS mortality risk, although power was limited. Among post-menopausal women with 25+ years follow-up, a significant association was noted between farming exposure and risk of ALS death. This increased risk was highest among those who ever smoked. Future studies should include biomarkers of exposure and large cohorts of men and women with occupational and residential histories.

The rise of harsh weather conditions has made the crop yield vulnerable to fire and variable losses. To address this issue, this article proposes a real-time fire monitoring system which is appropriate for modern smart agriculture. This system utilizes cloud computing technology, Internet of Things (IoT) sensors, telemetry technology, and embedded systems technology to monitor the status in the fields in real time every second. The system has three layers. The first is the IoT device layer, which is composed of flame and smoke sensors, a raspberry pi 3 B+, and a network gateway. The second is the ThingsBoard cloud layer, which is used for efficient processing of large amounts of information. The third is the telemetry layer, which is used for aggregation of the information collected. One of the advantages of the system is the use of a customized aggregation algorithm, which uses sensor information and sends the results in JavaScript object notation (JSON) format using the message queuing telemetry transport (MQTT) protocol. The system was tested in the fields and performed well. It recorded an accuracy of 96.1% in detecting fire in 50 tests, with the rate of false alarms being below 2.8%. It is also clear from the tests that the system can differentiate between true and false alarms. The proposed system sends information to the cloud every two seconds, with an average response time of below 300 milliseconds. The results show that the monitoring system has a reliability rate of over 98%. The results also demonstrate that the Raspberry Pi used in this study had a stable and reasonable central processing unit (CPU) and memory usage rate. Compared to other previously proposed prototypes, the proposed system for monitoring has the advantage of incorporating resource telemetry and fire detection within the same IoT and cloud computing environment, a notable improvement towards sustainable agriculture and food security, as well as the mitigation of agricultural risks.