Agricultural Risk Research Articles

Farmers’ Awareness and Approach to Agricultural Insurance as a Risk Mitigation Strategy

This study aims to explore the development of agriculture insurance from farmers’ perspective in a developing country such as Albania and to identify the reasons why farmers choose to be insured or not. Moreover, the paper aims to evaluate the willingness of farmers to choose agricultural insurance as the best method to reduce agricultural risks based on the offer of the insurance market in the agricultural sector. For this purpose, the study employs primary data gathered from 452 questionnaires distributed in four regions with the highest agriculture contribution and the highest number of farmers in Albania (Tirana, Dibër, Korçë and Fier). The results indicate that Albanian farmers, despite being aware of the various risks involved in agricultural production, opt not to purchase insurance policies and instead choose to self-insure. Analysis shows that this choice is influenced by factors such as lack of information, price, and trust in insurance providers. Moreover, the study points out that farmers are willing to pay a low premium that does not match the price set by insurance companies for this service.

Deciphering Farm Pond Adoption Dynamics and Operational Efficiency in Drought-Prone Regions of Maharashtra, India

The study delves the dynamics of farm pond adoption and operational efficiency in the Vidarbha and Marathwada regions of Maharashtra during 2022-23. Through a combination of quantitative and qualitative methods, data were collected from 160 beneficiaries and 160 non-beneficiaries, employing focus group discussions and structured interviews. Agricultural risks were examined, revealing a significant impact on farm income. Challenges such as low ground water table (MS 0.978), high temperatures (MS 0.966), and least evapotranspiration (MS 0.759) affected farmers' livelihoods, underscoring the need for a multidisciplinary approach to address these issues. The study highlights factors influencing farm pond adoption, with high awareness (96.56%) contrasting with lower adoption rates (51.56%). Adopters cited increased water access (22.81%), and relief from water scarcity (20%) as motives. Non-adopters faced barriers like financial constraints (21.88%) and land shortage (18.75%). Farm pond characteristics were comprehensively explored, revealing variations in size, structure, year of adoption, and irrigation sources. Maintenance activities were examined, showcasing key areas for pond health. Regular inspection (31.88%), vegetation management (17.50%), sediment removal (25.63%), and erosion prevention (19.38 %) emerged as crucial tasks, with variations between the regions. Financial aspects of pond construction and maintenance were elucidated, indicating a need of subsidies. Despite widespread awareness of farm pond benefits, adoption rates remained constrained by financial limitations, land availability, and awareness gaps. The study underscores the importance of targeted interventions, including financial aid, technical guidance, and capacity-building. These efforts are essential for promoting widespread adoption, enhancing agricultural productivity, water management, and the resilience of farming communities in both regions.

Climate risk and green total factor productivity in agriculture: The moderating role of climate policy uncertainty.

In light of the escalating global warming and the escalating frequency of extreme weather events, the agricultural sector, being a fundamental and pivotal industry worldwide, is encountering substantial challenges due to climate change. Using Chinese provincial panel data for 2000-2021, this paper utilizes a two-way fixed-effect model to investigate the impact of Climate Risk (CR) on green total factor productivity in agriculture (AGTFP), with China's climate policy uncertainty (CPU) being introduced as a moderating variable within the research framework to scrutinize its influence in this context. The findings reveal a noteworthy adverse effect of CR on AGTFP, further exacerbated by CPU. Heterogeneity analysis results show that there is a clear regional variation in the effect of CR on AGTFP across different Chinese regions, with CR significantly inhibiting AGTFP development in the northern regions and provinces in major grain producing regions. Consequently, there is a pressing necessity to bolster the establishment of climate change monitoring infrastructures, devise tailored climate adaptation strategies at a regional level, and enhance the clarity and predictability of climate policies to fortify the resilience and sustainability of agricultural production systems.

Climate-smart agricultural practices and its implication in Ethiopia: a systematic review

Purpose The purpose of this paper is to assess the adoption of climate-smart agriculture (CSA) and its implication on improving the farming household food security status, their resilience and livelihood risk management of farmers. Design/methodology/approach This systematic review has followed procedures to accomplish the review, including literature searches, screening studies, data extraction, synthesis and presentation of the data. Findings Based on the result of the review, the determinants of CSA adoption can be categorized into five categories, including demographic factors (age, sex, family size, dependency ratio, education), economic factors (land size, household income, livestock ownership), institutional factors (extension services, training access, credit services, farm input, market distance), environmental factors (agroecology, change in precipitation, slope of land) and social factors (cooperatives membership, farmers perception). The result also shows that applying CSA practices has an indispensable role on increasing productivity, food security, income, building resilient livelihoods, minimizing production risk and alleviating poverty. This concluded CSA practice has a multidimensional role in the livelihood of agrarian population like Ethiopia, yet its adoption was constrained by several factors. Originality/value This review mainly emphasizes on the most commonly practiced CSA strategies that are examined by different scholars.

Production Risk of Hybrid Maize in Sugihmanik Village, Tanggungharjo Subdistrict, Grobogan Regency (Case Study: Faisal Tani Store)

This study aims to examine the impact of production factors on hybrid corn production and to assess the level of production risk in Sugihmanik Village, Tanggungharjo District, Grobogan Regency (Case Study: Faisal Tani Store). The research adopts a quantitative approach with a sample of 69 hybrid corn farmers. The data analysis method utilized the Cobb-Douglas production function with multiple linear regression and coefficient of variation analysis. Data processing was conducted using SPSS 25. The results indicated that capital, land area, and labor factors collectively influence hybrid corn production. In addition, the land area and labor variables were found to have a significant effect on hybrid corn production, while the capital variable did not show a significant effect. The coefficient of variation (CV) for production risk in hybrid corn farming in Sugihmanik Village, Tanggungharjo District, Grobogan Regency (Case Study: Faisal Tani Store) was 0.33, indicating a low level of production risk.

Temporal and spatial patterns of extreme heat on wheat in China under climate change scenarios

Revealing the spatial-temporal pattern of extreme heat on staple crops is crucial for proposing adaptation strategies to mitigate climate change-related agricultural risks. Studies in this field generally focus on the reproductive stage and rely on a single-staged threshold temperature to construct extreme heat indicators, which particularly neglect the vegetative stage of wheat. Therefore, to measure the extreme heat risks more scientifically across the entire life cycle of wheat, our study defines a new comprehensive extreme heat index (CEHI) that considers specific thresholds in both the reproductive and vegetative stages. In general, under three climate scenarios (RCP2.6, RCP4.5, and RCP8.5), approximately 20 % of the wheat-planting regions in China, especially in winter wheat regions such as the North China Plain, the Sichuan Basin, and the Xinjiang Tarim Basin, are projected to face high levels of extreme heat. Meanwhile, from 2010 to 2099, the average growth rates of extreme heat in China under RCP2.6, RCP4.5, and RCP8.5 scenarios are approximately 0.08, 0.06, and 0.1, respectively. By the century's end, the proportion of wheat-planting regions experiencing high and very high levels (CEHI≥0.4) of extreme heat is projected to increase from 18.0 %, 17.9 %, and 18.4 % to 21.4 %, 25.1 %, and 28.9 % under RCP2.6, RCP4.5, and RCP8.5 scenarios. Among them, RCP8.5 has the highest extreme heat severity on wheat in China, followed by RCP4.5, while RCP2.6 has minimal severity. Under the RCP8.5 scenario, the proportions of very high, high, moderate, low, and very low levels of extreme heat are 3.4 %, 18.5 %, 16.7 %, 14.9 %, and 46.5 %, respectively. Meanwhile, our study also emphasizes that although higher-latitude spring wheat regions will experience a significantly increasing trend in extreme heat, this may not spell long-term damage to wheat. Therefore, with consideration of varied temperature sensitivities across wheat growth stages, our study indicates that CEHI serves as an effective method to comprehensively and scientifically assess extreme heat on wheat. Furthermore, based on the regional and varietal differences in extreme heat under climate change, our study highlights the importance of developing region- and variety-specific policies to ensure the sustainability of wheat.

Implications of crop yield distributions for multiperil crop insurance rating in Ghana: a lasso model application

PurposeThe objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda.Design/methodology/approachThis study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model.FindingsIt is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields.Research limitations/implicationsThe primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions.Originality/valueThis is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.

Risk identification and mitigation among rice farmers in Pakistan: a fuzzy-AHP multi-criteria decision-making approach

Rice farming in developing countries contributes significantly to economic growth, yet it confronts diverse risks. Strengthening its resilience necessitates systematically identifying and prioritizing these risks, allowing for optimized resource allocation. The majority of published literature, however, focuses primarily on individual risks rather than comparing or ranking them according to their importance. This regime needs to be more helpful for managers and policy-makers in achieving effective management. As a result, frequent management failures are causing substantial economic losses and threatening food security in the most populous regions of the world. This study addresses these existing research gaps by systematically identifying and prioritizing these risks, aligning with the United Nations Sustainable Development Goal (SDG) 2, viz., Zero Hunger, ultimately contributing to enhanced risk management and developing countries’ rice farming sector resilience. Data was obtained from Pakistan through a purposefully designed questionnaire and sourced from a pool of 412 respondents representing a spectrum of stakeholders selected by the snowball technique. Multi-criteria decision analysis (MCDA) was employed to deconstruct and comprehend the data statistically. A key observation is the recognition of the main risks within rice farming, notably revolving around resource-related and external environmental risks. These risks emphasize the critical management of sub-risks of water management and pest and disease. This study also identified several risk sub-factors that are critical but have yet to be extensively discussed in the literature. Furthermore, survey respondents expressed low levels of risk perception and suggested strengthening the management system through policy reforms. To enhance the resilience of rice farming, stakeholders must engage in effective risk communication, capacity building, and policy implementation. Therefore, more comprehensive integrated risk management interventions are urgently needed to address rice farming risks for achieving SDG 2.

Mitigasi Penyebab Risiko Usahatani Kopi Arabika (Studi Kasus pada Kelompok Tani Hutan Giri Senang, Kampung Legok Nyenang, Kecamatan Cilengkrang, Kabupaten Bandung)

The Giri Senang Forest Farmer Group (KTH) has experienced a significant decline in arabica coffee production over the past few years. This indicates the existence of risks that need to be addressed immediately. The research was conducted with the aim of identifying the causes of risks in arabica coffee farming, understanding the mitigation actions that have been taken by KTH Giri Senang, and proposing mitigation actions that can be implemented to overcome these problems. The research was carried out using descriptive qualitative analysis with a case study research technique. The causes of risk were identified using a Fishbone Diagram and analyzed with the Failure Mode and Effect Analysis (FMEA) method. Based on the results of the research, there are 7 priority risk causes out of 26 total causes: unfavorable weather, leaf rust disease, insufficient or excessive rainfall, leaf spot disease, coffee berry borer pests, weather changes due to the La Nina-El Nino phenomenon, and lack of pruning. Mitigation actions that have been taken include making a holding pond, using pesticides, traps, pest control agents, harvesting spoils, and sanitation to eradicate pests and diseases, as well as forming a Pest Control Team. Proposed mitigation actions that can be implemented include organizing agricultural insurance, subsidizing insurance premiums, making holes or roraks, switching to organic farming, using spare harvest bags, collaborating with educational or research institutions, holding discussion forums for farmers, and creating video tutorials and educational content as alternative extension activities.

The level of risk of rice farming due to the flood disasters experienced by rice farmers on swamp Lebak lands in Pemulutan Subdistrict Ogan Ilir Regency

The level of risk of rice farming due to the flood disasters experienced by rice farmers on swamp Lebak lands in Pemulutan Subdistrict Ogan Ilir Regency

Farm Typology to Enhance Rice Technologies Adoption in Ethiopia

The aim of this study is to conduct a review of farm typology in order to improve the adoption of rice technologies in Ethiopia. Rice production has experienced significant growth in the country, with the cultivated area expanding from 33,820 hectares in 2013 to 60,000 hectares in 2022. However, the adoption level of rice technologies remains low, posing a challenge to meet the increasing demand that is currently being met through rice imports. To address this issue, the government has developed a national rice production strategy and flagship programs at the national level. Furthermore, understanding the characteristics of farmers is crucial for the successful implementation of these initiatives, as farmers are heterogeneous in terms of their resources, preferences, and objectives. Consequently, the adoption rate of rice technologies varies among farmers, leading to different demands for agricultural advisory services. To develop effective farm typologies, it is important to identify the key drivers of technology adoption, such as farm size, risk exposure, human capital, labor availability, credit access, and access to commodity markets. While there are various theories available for studying farm typology, the sustainable livelihood theory is particularly comprehensive in creating effective typologies. These theories employ both quantitative and qualitative approaches. In conclusion, conducting a farm typology study specific to rice cultivation is crucial for enhancing the adoption level of rice technologies.

Bracing for impact: how shifting precipitation extremes may influence physical climate risks in an uncertain future

As extreme precipitation intensifies under climate change, traditional risk models based on the ‘100-year return period’ concept are becoming inadequate in assessing real-world risks. In response, this nationwide study explores shifting extremes under non-stationary warming using high-resolution data across the contiguous United States. Results reveal pronounced variability in 100-year return levels, with Coastal and Southern regions displaying the highest baseline projections, and future spikes are anticipated in the Northeast, Ohio Valley, Northwest, and California. Exposure analysis indicates approximately 53 million residents currently reside in high-risk zones, potentially almost doubling and tripling under 2 °C and 4 °C warming. Drought frequency also rises, with over 37% of major farmland vulnerable to multi-year droughts, raising agricultural risks. Record 2023 sea surface temperature anomalies suggest an impending extreme El Niño event, demonstrating the need to account for natural climate variability. The insights gained aim to inform decision-makers in shaping adaptation strategies and enhancing the resilience of communities in response to evolving extremes.

DeepQC: A deep learning system for automatic quality control of in-situ soil moisture sensor time series data

Amidst changing climate, real-time soil moisture monitoring is vital for the development of in-season decision support tools to help farmers manage weather-related risks in agriculture. Precision Sustainable Agriculture (PSA) recently established a real-time soil moisture monitoring network across the central, Midwest, and eastern U.S., but continuous field-scale sensor observations often come with data gaps and anomalies. To maintain the data quality and continuity needed for developing decision tools, a quality control system is necessary.The International Soil Moisture Network (ISMN) introduced the Flagit module for anomaly detection in soil moisture time series observations. However, under certain conditions, Flagit's threshold and spectral based quality control approaches may underperform in identifying anomalies. Recently, deep learning methods have been successfully applied to detect time series anomalies in time series data in various disciplines. However, their use in agriculture for anomaly detection in time series datasets has not been yet investigated. This study focuses on developing a Bi-directional Long Short-Term Memory (LSTM) model, referred to as DeepQC, to identify anomalies in soil moisture time series data. Manual flagged PSA observations were used for training, validation, and testing the model, following an 80:10:10 split. The study then compared the DeepQC and Flagit-based estimates to assess their relative performance.Flagit correctly flagged 95.8 % of the correct observations and 50.3 % of the anomaly observations, indicating its limitations in identifying anomalies, particularly at sites consists of more than 30 % anomalies. On the other hand, the DeepQC correctly flagged 89.8 % of the correct observations and 99.5 % of the anomalies, with overall accuracy of 95.6 %, in significantly less time, demonstrating its superiority over Flagit approach. Importantly, the performance of the DeepQC remained consistent regardless of the number of anomalies in site observations. Given the promising results obtained with the DeepQC, future studies will focus on implementing and finetuning this model on national and global soil moisture networks.

Perceptions and Awareness of Pradhan Mantri Fasal Bima Yojana among Insured and Non-insured Farmers in Haryana, India: Enhancing Agricultural Risk Management

The research paper employed a sample of 600 households from Haryana, comprising 300 insured and 300 non-insured farmers, to ascertain their perceptions regarding crop insurance. Methodologically, the researcher applied tabular analysis alongside elementary statistical techniques such as measures of central tendency and percentages for data examination. The findings revealed a significant lack of awareness among non-insured farmers regarding crop insurance, with nearly half of them being uninformed. Conversely, among insured farmers, the primary motivation for obtaining crop insurance stemmed from either bank compulsion or the pursuit of financial security. Concerns regarding premium rates were evident, with a majority perceiving them to be excessive. Recommendations for the enhancement of customer satisfaction and the promotion of greater farmer participation included the proposition of tailored insurance offerings and the mitigation of concerns pertaining to premium rates

A methodological framework proposal for managing risk in small-scale farming through the integration of knowledge and data analytics

IntroductionClimate change and weather variability pose significant challenges to small-scale crop production systems, increasing the frequency and intensity of extreme weather events. In this context, data modeling becomes a crucial tool for risk management and promotes producer resilience during losses caused by adverse weather events, particularly within agricultural insurance. However, data modeling requires access to available data representing production system conditions and external risk factors. One of the main problems in the agricultural sector, especially in small-scale farming, is data scarcity, which acts as a barrier to effectively addressing these issues. Data scarcity limits understanding the local-level impacts of climate change and the design of adaptation or mitigation strategies to manage adverse events, directly impacting production system productivity. Integrating knowledge into data modeling is a proposed strategy to address the issue of data scarcity. However, despite different mechanisms for knowledge representation, a methodological framework to integrate knowledge into data modeling is lacking.MethodsThis paper proposes developing a methodological framework (MF) to guide the characterization, extraction, representation, and integration of knowledge into data modeling, supporting the application of data solutions for small farmers. The development of the MF encompasses three phases. The first phase involves identifying the information underlying the MF. To achieve this, elements such as the type of knowledge managed in agriculture, data structure types, knowledge extraction methods, and knowledge representation methods were identified using the systematic review framework proposed by Kitchemhan, considering their limitations and the tools employed. In the second phase of MF construction, the gathered information was utilized to design the process modeling of the MF using the Business Process Model and Notation (BPMN).Finally, in the third phase of MF development, an evaluation was conducted using the expert weighting method.ResultsAs a result, it was possible to theoretically verify that the proposed MF facilitates the integration of knowledge into data models. The MF serves as a foundation for establishing adaptation and mitigation strategies against adverse events stemming from climate variability and change in small-scale production systems, especially under conditions of data scarcity.DiscussionThe developed MF provides a structured approach to managing data scarcity in small-scale farming by effectively integrating knowledge into data modeling processes. This integration enhances the capacity to design and implement robust adaptation and mitigation strategies, thereby improving the resilience and productivity of small-scale crop production systems in the face of climate variability and change. Future research could focus on the practical application of this MF and its impact on small-scale farming practices, further validating its effectiveness and scalability.

Assessment of microbial communities in a dairy farm from a food safety perspective

Microbial communities associated with dairy farm operations have a significant influence on food safety, dairy product quality, and animal health. This study aimed to create a microbial mapping at a dairy farm to learn about their bacterial diversity, distribution, and potential dissemination pathways. The investigation included the detection of key zoonotic pathogens, enumeration of Staphylococcus aureus and Escherichia coli as indicators of typical bacterial loads in a dairy production environment, and a microbiome analysis using metagenomics. A total of 160 samples (environmental, udder swabs, feed, feces, raw milk, and water) were collected during winter (N = 80) and spring (N = 80). In winter, Cronobacter spp. were detected in four feed and two water samples; L. monocytogenes was identified in two samples, one from feces and one from a cattle mat; E. coli O157:H7 was found in two feed samples. On the other hand, during spring, Cronobacter spp. were present in four feed samples and one hallway drain, with only one feed sample testing positive for E. coli O157:H7, while L. monocytogenes was absent during the spring season. Regarding microbial counts, there was no significant difference between the two seasons (p = 0.068) for S. aureus; however, a significant difference (p = 0.025) was observed for E. coli. Environmental microbiome analysis showed the presence of Proteobacteria (46.0 %) and Firmicutes (27.2 %) as the dominant phyla during both seasons. Moraxellaceae (11.8 %) and Pseudomonadaceae (10.62 %) were notable during winter, while Lactobacillaceae (13.0 %) and Enterobacteriaceae (12.6 %) were prominent during spring. These findings offer valuable insights into microbial distribution within a dairy farm and potential risks to animal and human health through environmental cross-contamination.

A comprehensive evaluation of the contamination scenario and water quality in the gas fields of north-east region, Bangladesh

Gas fields generate a significant volume of produced water, disposed in the vicinity of gas fields in Bangladesh after processing. It may have a variety of effects on ecology and the environment. This study was conducted to assess the contamination scenario and quality of produced and discharged water from gas fields in northeastern Bangladesh. The physicochemical analyses for this study were performed using standard procedures. Based on the outcomes of the analyzed samples, the current research employs a variety of indexing and statistical approaches to investigate the overall status of the studied water. The physiochemical analysis revealed high electrical conductivity (EC), total dissolved solid (TDS), salinity, and Na contents in both produced and discharged water. No severe cases have been identified, certain metals, such as Fe, Ni, and Cd, have been detected at levels high enough to impact specific index values in some cases. The results of the weighted contamination index (WCI) indicated mild to considerable pollution in the gas field region. The average score of potential ecological risk (PER) reflects minimal ecological risk. The heavy metal toxicity load (HTML) reveals negligible metal pollution in the studied water. The agricultural risk indices displayed increased sodium concentrations and EC, resulting in salinity and sodium risks. The magnesium absorption ratio is within the allowable range. In addition, the average heavy metal pollution index (HMPI) value demonstrates that the produced and discharged water is unsuitable for drinking. The entropy-based water quality index (EWQI) is below the threshold limit (<100) for all samples, indicating satisfactory water quality. This study is an early effort to evaluate the quality of wastewater produced and discharged from gas fields in Bangladesh. The findings of this research will provide valuable insights for future researchers and regulators in effectively managing and mitigating pollution from wastewater.

Agrochemical use in Farming Practices and Risk Factors for Kidney Disease among Dwellers of Rural Farming Communities in South-West Nigeria.

The outlook of chronic kidney disease (CKD) in low-and-medium-income-countries is poor. Modern farming practices in Nigeria are becoming increasingly associated with the use of herbicides and pesticides. The study aimed to evaluate agrochemical use in farming practices and risk factors for kidney disease among dwellers of rural farming communities in South-West Nigeria. This was a cross-sectional survey of adult dwellers of an agrarian rural farming community in South-West Nigeria. Participants provided information on demographics, lifestyles, knowledge, and awareness of risk factors for kidney disease, and the use of agrochemicals in farming practice. Anthropometric measurements and blood pressure values were obtained while blood and spot urine were collected for random blood glucose, serum creatinine, urinalysis, and albumin-creatinine ratio. A total of 572 rural dwellers were enrolled in the study, with a mean age of 49.9 ± 17.5 years while 39.3% were male and 88.9% engaged in active farming. The prevalence of traditional risk factors for kidney disease was hypertension (24.3%), Diabetes mellitus (2.8%), cigarette smoking (7.5%), alcohol intake (20.8%) and herbal consumption (57.1%). The use of pesticides/herbicides was reported in 69.9%, while 25.3% did not use protective gear during its use. Proteinuria, hematuria, and reduced eGFR were observed in 29.8%, 6.1%, and 33.9% of participants, respectively. This study showed a high prevalence of herbicides and pesticide use and traditional risk factors for kidney disease, in addition to the high prevalence of markers of kidney damage among the dwellers of rural farming settlements in South-West Nigeria.

Indian Crop Insurance Schemes: Gender Performance Analysis

Crop insurance schemes are pivotal in mitigating agricultural risks, especially in rural economies like India. However, the gender dimension of participation in these schemes remains largely overlooked. This research paper delves into the gender disparities in participation rates, coverage, and benefits accrued from Indian crop insurance schemes, using six years of data from the Pradhan Mantri Fasal Bima Yojana website. The researcher employed few descriptive statistics like mean, Standard Deviation (SD), coefficient of variation (CV), and Skewness for data analysis purpose. Besides Compound Annual Growth Rate (CAGR) has been used to determine the growth rate of farmers’ participation. The analysis reveals significant gender gaps in participation, with male farmers constituting a higher percentage of participants than their female counterparts across the Kharif ( In India, the Kharif season is when the monsoon crops are grown and harvested throughout the harvesting season of India, typically from June to October )and Rabi (Rabi season is when crops are usually sown in the middle of November and harvested in April or May) seasons. While male participation was declining, female participation remained relatively stable but at lower levels. Gender-disaggregated participation highlighted challenges faced by gender-minority farmers (Here Gender Minority farmers used for Transgender Farmers). Practical suggestions include gender-sensitive outreach programs, tailored policy interventions, and improvements in accessibility for female farmers. This research contributes to the discourse on gender equality in agriculture, providing insights for policymakers and practitioners aiming to design inclusive agricultural policies and programs. Addressing these disparities can lead to greater gender equality, enhancing the sustainability and resilience of agrarian economies like India.

African Swine Fever Disease Risk Assessment Using Multi-Criteria Decision Analysis: An Input for GIS-Based Risk Mapping

African Swine Fever (ASF) has severely impacted the Philippines' pig industry, caused socioeconomic losses, and affected income, supply, demand, and prices. There needs to be more understanding regarding the risk factors associated with ASF introduction into pig farms and the level of risk each farm faces. Accordingly, using a quantitative research method, this study used the Analytical Hierarchy Process (AHP) to categorize ASF risk factors into biosecurity and spatial risk factors. Twenty-five (25) respondents were selected using a purposive participatory approach to rank the importance level of each risk factor as per the two risk factor categories. The AHP analysis revealed that the highest-risk biosecurity factors are the "absence of protocols for changing clothes, separate entry and exit, disinfection of objects, restriction on food introduction, and external individuals accessing the farms." In the spatial risk factor ranking, the analysis showed that the “distance to pig farms utilizing swill feeding" was the highest risk factor, indicating its significant contribution to the overall risk of farms. A farm risk assessment was also performed based on the AHP results and the level of compliance of each farm on the different risk factors. The study was conducted on selected pig farms in the municipality of Echague by evaluating their compliance with the identified risk factors and determining the level of risk they posed. The risk assessment results for African swine fever on farms reveal a concerning scenario. With 70% of farms assessed as "high risk" in terms of biosecurity and 74% classified as "medium risk" in spatial vulnerability, the overall assessment indicates that 84% of farms are at a moderate risk level. This suggests a widespread need for improved biosecurity practices and disease monitoring to prevent the introduction and spread of African swine fever. The 16% of farms deemed "high risk" pose a significant threat, requiring immediate action to prevent disease outbreaks. These findings emphasize the importance of implementing stringent biosecurity measures, enhancing surveillance efforts, and raising awareness to safeguard the pig industry from the devastating impacts of the ASF disease, like the rising cost of meat and pork-based commodities. Furthermore, these show the importance of considering the biosecurity and spatial risk factors for a more comprehensive risk assessment. The AHP ranking and risk assessment process is crucial in developing a GIS-based risk mapping and surveillance system. This offers government authorities a valuable decision-making tool to proactively prevent the introduction of African swine fever (ASF) and mitigate the necessity for widespread culling of pigs. By implementing targeted interventions informed by the study results, the government can work towards safeguarding the pig industry.