Farm Characteristics Research Articles

From typology to criteria considered by farmers: what explains agroecological practice implementation in North-Sudanian Burkina Faso?

Cropping systems in the North-Sudanian zone of Burkina Faso face significant challenges related to poor yields, declining soil fertility and harsh climatic conditions. Together these necessitate a shift toward more sustainable farming practices. Agroecology aims to enhance yields while minimizing environmental harm through the use of ecological functions and has been promoted by researchers and farmers’ organizations as a solution. However, its implementation remains limited. This study investigated the criteria farmers consider when implementing agroecological practices at the farm level and how these criteria and their implementation are influenced by farm characteristics. Data collection methods included the serious game TAKIT, together with baseline and complementary household surveys (108 farmers each). Farm diversity was analyzed using a statistical typology. The influence of farm types, farm structural variables and the village location on (1) whether or not agroecological practices were implemented and (2) the criteria considered by farmers was explored. Four distinct farm types were identified: low resource endowed farms relying on off-farm income, low resource endowed farms relying on livestock income, medium resource endowed farms relying on agricultural and livestock income, and high resource endowed farms with diverse sources of income. There were no significant differences in the implementation of agroecological practices across farm types. Crop rotations were the most frequently implemented practice (by 91% of the study farmers), while the 2-by-2 line intercropping of sorghum-cowpea was the least implemented (9% of farmers). Implementation of zai pits varied significantly between villages, with farmers in Nagreonkoudogo more likely to use them than those in Tanvousse, due to differing soil characteristics. Farmers considered several criteria when deciding whether to implement agroecological practices, including the ability to improve yield and preserve soil. Constraints to their implementation included a lack of knowledge and their high labor requirements. These criteria did not differ across farm types, likely because they stem from shared environmental constraints or conditions. The study highlights the complexity of agroecological transitions in sub-Saharan Africa, and illustrates the need to adequately consider contextual conditions. The co-design of new practices, and the redesign of existing ones, should align with criteria considered by farmers.

Assessing Commercial Sugarcane Irrigators’ Intentions to Adapt Water-Use Behaviour in Response to Climate Variability in South Africa

The scarcity of water resources in South Africa remains a considerable challenge for water users. This study evaluated the impact of climate variability on the adaptive water-use behaviour of sugarcane producers by identifying the factors influencing their adaptation decisions. A survey, the Theory of Planned Behaviour (TPB), and structural equation modelling (SEM) were used to achieve this objective. The study involved 54 sugarcane producers from the Impala Irrigation Scheme, selected through random sampling. Socio-economic profiles revealed a largely male, older demographic with varied education levels and farm characteristics. Results indicated that attitude (β = 0.349, p < 0.1) and subjective norms (β = 0.281, p < 0.05) significantly influenced farmers’ intentions to adapt, while perceived behavioural control had no significant effect (β = 0.051, p > 0.1). These findings suggest that improving farmers’ attitudes and strengthening social support systems can enhance their intentions to adopt adaptive strategies. However, the model’s explanatory power (R² = 0.276) suggests that other unexamined factors may also influence farmers’ adaptive intentions, highlighting the need for further research. Overall, our findings suggest that interventions targeting attitudes, social support, and resource access can improve adaptive behaviours.

Factors influencing the expressed willingness to transition from collection to cultivation of non-timber forest products: The case of Caesalpinia spinosa in southern Ecuador

Planting trees with additional non-timber products (NTFPs) (such as fruits) in agricultural lands is emerging as an important strategy in Forest Landscape Restoration (FLR) to enhance the provision of ecosystem services and improve habitat quality. In many cases, the planting of NTFP trees follows a transition from collecting NTFPs in natural forests to integrating the respective trees into agricultural farms with ecological and socio-economic implications. Therefore, understanding the current practice and challenges of current NTFP collection and willingness for cultivation is crucial to effectively use such land-use trajectories for FLR. In this study, we investigate the case of Caesalpinia spinosa (tara), whose fruits provide an emerging NTFP in southern Ecuador. Using household data from 125 households currently collecting tara fruits, we first describe the relatively young production system and then use a Heckman two-step econometric model to identify the factors influencing the expressed willingness to cultivate tara trees on farms and the area to be cultivated. The econometric model results show that the characteristics of tara collection and trade (gender of collectors, distance to the collection site, and management practices) are important determinants of tara cultivation. However, it's the household labor availability and farm characteristics that play a significant role in impacting the willingness to cultivate. The area to be cultivated was mainly influenced by household labor and economic characteristics, and tara collection and trade (management practices) characteristics. Our findings help to understand NTFP-related land-use transitions, thus supporting the design of appropriate and specific policy interventions for the sustainable development of NTFPs.

Typology of Grasscutter (Thryonomys swinderianus) Farming in Southern Benin Two Decades after Its Domestication

Aims: The aim of the study was to provide information on the current characteristics of grasscutter farms in Benin to identify areas for improvement. Study Design: A survey was conducted among 100 grasscutter farmers in southern Benin, identifying three groups of farmers. Place and Duration of Study: Data were collected between August and October 2023 in the departments of Mono, Couffo, Atlantique, Ouémé, Plateau and Zou in southern Benin. Methodology: Data collection were carried out through a retrospective survey using the snowball sampling method. During the survey phase, farmers were selected based on the accessibility of their farms and their willingness to participate in the questionnaire. Data were analyzed using SAS software, and a multiple component analysis (MCA) was performed to identify the different groups of grasscutter farmers. Results: Three groups of farmers were identified. Women were absent in Group 1, less present in Group 2 (8.20%), and more represented in Group 3 (23.53%). The most common occupations among respondents across all three groups were in agriculture and livestock farming. However, farmers were less frequent in Group 3 (82.34%) compared to the other groups (100%) (p<0.05). Ground-enclosure farming was only reported in Group 1 (76.47%). In the other groups, farming is done on tiers in compartmentalized enclosures. The smallest herd size was observed in Group 3 (p<0.05). All respondents in groups 1 and 2 fed their animals with a mixture of forage and commercial feed, while 64.71% did so in Group 3 (p<0.05). Panicum maximum, Pennisetum purpureum, and Moringa oleifera are the most commonly used forages in grasscutter feeding. Grasscutters are sold live by all farmers in Groups 1 and 2 and by 88.24% in Group 3 (p<0.05). The age at the sale is similar in Groups 1 (4.70 months) and 2 (4.15 months) and higher in Group 3 (7.28 months) (p<0.001). The most frequent constraints in all three groups were difficulties in accessing forage and lack of financial resources. Conclusion: Harmonizing grasscutter farming practices could be a lever to promote grasscutter farming in Benin.

Determinants of rice production in Bantaeng Regency, Indonesia: In search of innovative sustainable farm management practices

Over the past five years, rice production in Bantaeng Regency has averaged 5.2 tons per hectare, considerably below Bantaeng Regency's 2020 goal of 10.0 tons. Low rice yield internationally and domestically has prompted many studies to assess the effect of farmer and farm characteristics, and input factors on rice production. These include education, agricultural income, farmer cooperatives, production inputs, and financing. In response to this issue, the present study examined determinants of rice production in Bantaeng Regency, Indonesia. A representative sample of 185 rice farmers was randomly selected for structured interviews. The data analysis was carried out by employing the Binary Logistics Regression Analysis (BLRA). We used the BLRA to regress thirteen independent variables (land area, seed application, urea fertilizer, NPK fertilizer, insecticides, herbicides, farm labor, education length, education level, farmer age, experience in farming, landowner status, and farming distance) to examine their effects on rice production. As a result, all independent variables simultaneously had a statistically significant effect on rice yield. Then, the partial testing of thirteen variables revealed that five variables—urea fertilizer, NPK fertilizer, pesticide, labor, and farmer age—had a positive and significant impact on rice production. In contrast, the seed application variable has a statistically negative effect on rice yield, while the other variables do not. Based on the results of this study, we recommend five innovative rice farm management strategies to enhance rice production in the future. Firstly, the farmer should expand their cropped area and increase the use of urea fertilizer, insecticides, and herbicides, labor and seed reduction is expected to boost rice yield immediately. Secondly, governments must ensure farmers' access to urea and NPK fertilizers, pesticides, and certified seeds in the future. Thirdly, policymakers must regulate the high costs and limited availability of chemical fertilizers and pesticides, including insecticides and herbicides. The policymaker would guarantee farmers adequate access to these resources. Fourthly, the farmers should use certified seeds with great output potential and apply the right amount. Fifthly, the rural labor replacement policy regulates rural wages and pay to handle the expected farm labor shortage.

Producer Willingness to Accept Incentive Levels for Cover Crop Adoption in the Southern Great Plains

Abstract The purpose of this study is to analyze agricultural producers’ willingness to adopt regenerative cover crop practices in their operation and the effects of producer and farm characteristics on willingness to accept (WTA) values. The paper utilizes the double-bounded contingent valuation method to analyze survey responses submitted by producers and non-operating landowners in the Texas and Oklahoma portions of the Southern Great Plains. Results showed an average WTA of $26.38/acre for producers to adopt cover crops and that programs aimed at increasing adoption rates may require more substantial investment compared to those focused on continuity with current adopters.

How Does Herbicide Resistance Change Farmer’s Weed Management Decisions? Evidence from the Roundup Ready Experiment

Adoption of diverse weed management practices is viewed as essential for slowing the spread of herbicide-resistant (HR) weeds. Yet, adoption of diverse tactics has remained low, while there has been explosive growth of resistant weeds. This study analyzes U.S.-farm-level data to identify factors affecting adoption of diverse weed management practices. This study uses directed acyclic graphs (DAGs) to consider how practice adoption is influenced by different causal pathways between farmer and farm characteristics and farmer awareness of and concern over HR weeds. This study then uses multiple regression analysis to estimate the direct and indirect pathways that influence practice adoption. Respondents relied more heavily on herbicide-based weed control methods than on mechanical or cultural methods. Concern over herbicide resistance increased the number of practices farmers adopted and the percentage of acres where farmers implemented these practices. Practice adoption was negatively associated with increasing levels of farmer risk aversion. Technological optimism—belief that new herbicides would soon be developed to counter HR weeds—discouraged diverse herbicide use practices that combat resistance, but encouraged use of some non-chemical weed control methods. Perceived weed dispersal externalities (from weed mobility) led to more diverse weed management, running counter to hypotheses that greater mobility reduces incentives for individual resistance management.

Determinants of adoption of automation and robotics technology in the agriculture sector–A mixed methods, narrative, interpretive knowledge synthesis

Automation and robotics technologies in agriculture promise to increase productivity with a smaller environmental footprint. However, adoption of agri-innovations is rarely a simple decision. The decision to adopt is determined by numerous factors. Employing a mixed methods narrative, interpretive knowledge synthesis, we review 72 unique studies between 2017–2021, and conduct a thematic analysis. Noting the innate complexity of agriculture, we identify 13 determinants of adoption of automation and robotic technologies in agriculture: data; farm characteristics and surrounding physical environment; farmer characteristics; policy and regulation; labour’s absorptive capacity; social elements; interoperability; standards; access to information; operational benefits; public infrastructure; technological characteristics; and uncertainty and risk. We conclude with seven observations. First, while automation and robotics are promising agri-innovations, they will not be appropriate or beneficial for all farms. There are other forms of agricultural innovation, and their uptake likely will always vary even within the same commodity and region. Second, taking a reductive approach to understanding adoption of agri-innovations may hinder the transformation to sustainable agriculture production systems; it is important to understand the role of complexity in shaping the dynamic interplay among determinants. Third, public infrastructure is more than just the Internet, yet there was little reference to other forms of public infrastructure in the dataset. Fourth, while many papers argue public policy is important for increasing the adoption of these innovations, few provide concrete policy suggestions or scalable examples. Fifth, trust and transparency are central to adoption. Technology developers need to take farmers concerns and needs seriously. Sixth, technology developers must offer practical solutions to real problems. Seventh, automation and robotics encompasses many technologies, and yet no standard or consistent terminology exists. This makes communication about these innovations more difficult. We propose a typology under the rubric of data-driven agricultural technologies.

Farm characteristics determine why a large share of organically produced wine is not labelled as organic

Abstract Organic agricultural production is increasing globally and is of high policy relevance, particularly in Europe. Various measures incentivize farmers to adopt organic practices, such as direct payments and labelling. We here address a rarely considered aspect of organic production, that not all organic producers in Europe opt for organic labelling for their products at the point of sale. We investigate the discrepancy between organic production and labelling in Swiss viticulture. Out of 115 grapevine growers who adhere to organic principles in production, 43.5% do not use organic labelling when marketing their wines. We find that especially farms using alternative labelling strategies (e.g. for fungus-resistant varieties), smaller farms and farms less specialized in viticulture to be more likely to forgo organic labelling. We draw conclusions for policy and science. For example, our findings show that there may be, for some crops, more organic products on shelves than indicated from sales statistics of organically labelled products. The use of statistics that indicate the share of sales of organic products for specific products and food sales at large may thus be misleading.

Effects of Smart Farming on the Productivity of Korean Dairy Farms: A Case Study of Robotic Milking Systems

The Korean agricultural sector faces increasing challenges such as an aging population, labor shortages, and the liberalization of agricultural markets. To overcome these challenges, the Korean government has striven to enhance the competitiveness of agriculture by introducing AI-based technologies to the agricultural sector, labeling this as smart farming. This study estimates farm-level benefits of adopting smart farming technologies, robotic milking systems, in Korean dairy farms. The benefits are estimated by comparing the productivity (i.e., the savings of labor input, increased calf production, and increased milk production) of adopting and non-adopting farms. Our study uses the propensity score matching method to address potential problems from confounding factors, sample selection bias, and the small number of adopters. Our results show that farms that adopted robotic milking systems produced 0.10 to 0.11 more calves per year than farms that did not adopt the system. The adopters also increased milk production by 2.44 kg to 2.88 kg per head/day, while reducing labor input by 0.15 to 0.30 per head/week. However, the reduced labor input was not statistically significant. When the analysis was extended to regard the farm characteristics, the labor input became significant from small and family-run farms. We also found that the increase in the number of calves produced per head was statically significant from small farms, family-run farms, and farms with successors. The increased milk production per head was statistically significant from large farms, farms employing hired workers, and farms with successors. Our findings suggest that the Korean government continue promoting smart farming technologies such as the robotic milking system to increase the adoption rate. The findings can also provide useful information about target markets of this technology, which can be used to increase the adoption rate and ultimately enhance the sustainability and competitiveness of the Korean dairy industry.

The hidden role of buffalo trade network in bovine epidemic spreading.

Animal movements are a key factor in the spread of pathogens. Consequently, network analysis of animal movements is a well-developed and well-studied field. The relationships between animals facilitate the diffusion of infectious agents and, in particular, shared environments and close interactions can facilitate cross-species transmission. Cattle are often the focus of these studies since they are among the most widely distributed and traded species globally. This remains true for Italy as well, but with an important additional consideration. Indeed, another important productive reality in the peninsula is buffalo farming. These farms have an interesting characteristic: approximately two-thirds of them also rear cattle. This coexistence between cattle and buffalo could have an impact on the diffusion of pathogens. Given that buffalo farms are often overlooked in the literature, the primary goal of this work is to investigate the potential consequences of omitting buffalo from cattle network analyses. To investigate this impact, we will focus on Q fever, a disease that can infect both species and is present on the Italian territory and for which the impact of the buffalo population has not been thoroughly studied, and simulate its spread to the farms of both species through compartmental models. Our analysis reveals that despite the significant difference in network sizes, the unique characteristic of Italian buffalo farms makes the buffalo network essential for a comprehensive understanding of bovine disease dynamics in Italy.

Determinants of coping strategies among agropastoralists in Kitui and Isiolo counties, Kenya

Agropastoralists in Kenya’s Arid and Semi-arid Lands (ASALs) face various shocks that threaten their livelihoods and expose them to significant health and economic risks. While existing studies often focus on climate-related shocks, they frequently overlook other challenges faced by these communities. A comprehensive understanding of how agropastoralists manage diverse shocks is essential to developing effective vulnerability reduction strategies. This study examined the major shocks experienced by agropastoral households in Kenya’s ASALs, their primary coping strategies, and the determinants informing the choice of the strategies adopted. Using cross-sectional data from 371 households in Isiolo (203) and Kitui (168) counties, this study applied descriptive analysis and multivariate probit (MVP) models to assess coping strategies and influencing factors. From 2016 to 2021, households faced shocks, such as droughts, livestock pests and diseases, crop pests, theft, and intercommunal conflicts. Regional variations were observed: crop pests were common in Kitui, whereas Isiolo experienced higher theft and conflict rates. Coping strategies include income diversification, asset sales, increased farm labor, and reduced food expenditure. Severe droughts and conflicts led to migration, while floods caused extreme measures such as withdrawing children from school. Key factors influencing coping strategies include household demographics, farm characteristics, and market proximity. Households with more adults, higher literacy, or greater income from crops and livestock are less likely to adopt costly strategies. This study recommends integrated interventions to enhance resilience to both climate and non-climate shocks.

Cocoa Farmers’ Perceptions of Drought and Adaptive Strategies in the Ghana–Togo Transboundary Cocoa Belt

This study investigated the perception of drought by cocoa farmers and explored the effectiveness of adaptive strategies (ASs) used in smallholding farms in the transboundary region between Ghana and Togo. Drought significantly threatens cocoa production in this region, affecting farmers’ livelihoods and cocoa supply chains. This study used a multistage sampling approach, which involved surveys with questionnaires administered to 330 cocoa farmers throughout the study area, along with on-site observations. Statistical analysis included binary logistic and Poisson regression models to explore the relationship between farmer socioeconomic characteristics and adaptation practices. The findings revealed that cocoa farmers in the region have a nuanced understanding of drought, attributed to changing climatic patterns and unsustainable land management practices such as deforestation. To mitigate its impacts, farmers employ a variety of ASs, including investment in farm management, soil management, and intercropping with crop diversification. Furthermore, socioeconomic factors, including age, formal education, household size, land tenure right, adaptation cost assessment, and an underestimation of self-efficacy, were shown to affect the choice in the AS. Among the ASs adopted, only farm management practices (weeding, pruning, fertilizer application, etc.) significantly improved yield. This study contributes to understanding drought as a critical issue for cocoa farmers and the adaptation practices used by smallholder cocoa farmers. Given that among the strategies adopted, only farm management practices, also known as good agricultural practices (GAPs), significantly improves yield, this study recommends well-designed and innovative packages of sustainable farm management based on farm and owner characteristics. These include irrigation schemes, timely soil fertilizer monitoring and supply, and the provision of drought-resistant varieties along with technical itineraries. Additional interventions require drought emergency responses, with other factors such as education and financial support mechanisms expected to improve farmers’ timely decision-making to adapt and improve cocoa production resilience to drought episodes in international transboundary regions with complex governance structures.

The adoption of cotton combine services and farm technical efficiency: evidence from Kazakhstan and Uzbekistan

PurposeThis study examines the adoption of cotton combine services and its impact on farm technical efficiency in Kazakhstan and Uzbekistan. The research aims to determine whether mechanisation influences productivity and economic output at the farm level.Design/methodology/approachUsing farm-level data from 511 cotton growers in Kazakhstan and Uzbekistan collected in 2019, this study employs stochastic frontier analysis to measure potential output and technical inefficiency among cotton farmers. The analysis includes a translog functional form to account for the use of cotton combine services and other farming variables.FindingsThe findings indicate that while mechanisation through cotton combines can potentially increase technical efficiency by optimising the harvesting process, the benefits are not uniformly experienced across all farms. Variations in farm characteristics, such as labour availability and existing agricultural practices, influence the efficiency of technology adoption. Institutional factors and historical legacies also play a significant role in the adoption and impact of mechanisation.Research limitations/implicationsThe study is based on cross-sectional data from 2019, and the findings may not capture longer-term trends or recent developments in mechanisation policies in the study countries.Originality/valueThis research provides a nuanced understanding of the conditions under which cotton combine services enhance or hinder technical efficiency. It highlights the necessity for carefully tailored policies for mechanisation, especially in Uzbekistan, where rural labour is abundant and predominantly female. The study contributes to the broader discourse on agricultural mechanisation in developing countries by focusing on the specific context of Central Asia.

Implications of Farm Production and Farmers' Characteristics on Poverty Reduction in Benue Division, Cameroon

The main objective of this study was to determine the effect of farm production and Farmers' Characteristics on Poverty Reduction in the Benue division in the Northern region of Cameroon. Data were collected from 399 respondents using a self-administered questionnaire through a stratified sampling technique of 12 sub-divisions in the Benue division. We adopted a survey research design. To estimate our results, we used Multiple correspondent analysis (MCA) and the OLS technique. The results reveal that there is a positive relationship between farm production and poverty reduction in Benue state. An increase in farm production will lead to a 2.19% reduction in farmers' poverty in Benue and this result is significant at a 5% level of significance. Similarly, the farmer's characteristics reveal that gender, farmers' experience, and educational level also significantly affect poverty reduction. In this light, there is a need for the government to train and deploy more extension workers in the various sub-divisions and communities in Benue division, to increase farmers' access to these services. This can be done by imparting specific skills to produce dominant food crops and livestock in the Benue division through workshops and seminars.

Strategies of positive deviants in fodder conservation among smallholder dairy farming systems in highlands and midlands of Kenya.

Fodder scarcity and low quality affect the productivity of dairy cattle in Kenya yet only few smallholder dairy farmers show positive deviance in fodder conservation. Information on the strategies of such positive deviant (PD) farmers is scanty. The objective of this study was to determine strategies that distinguish PDs from peers (non-PDs) in fodder conservation among smallholder dairy farmers in highlands and midlands of Kenya. A cross-sectional survey was conducted among farms producing and conserving fodder in Tharaka Nithi County. Multistage sampling procedures was used; purposive sampling of the Sub-counties and locations, random selection of villages and proportional selection of 246 farms through snowball chain referral. Data were collected on demographic, socio-economic, farm characteristics and fodder conservation. Farms were categorized as PDs (n = 24) or non-PDs (n = 97). The PDs conserved fodder adequate to last dry season (about 3 months) and had milk yield 15kg/cow/day and above, while non-PDs do not qualify for both criteria. Chi-square and T-test were used to analyze categorical and continuous variables respectively. Logit regression was used to examine independent variables that influence probability of PDs. Farmers with high school and tertiary education who accessed extension services were PDs while non-PDs had non-formal and primary education and access extension less. Farms conserving hay and silage dominated PDs. Land in hectare (ha) and herd sizes in Tropical Livestock Unit (TLU) distinguished PDs (1.7ha and 9.0 TLU) from non-PDs (0.7ha and 3.3 TLU). The strategies that distinguished PDs from non-PDs were allocating land to fodder production, high milk yield kg/cow/day, large herd size and access to extension services. Increasing access to extension and resources (land) raised chances of PDs in fodder conservation. Knowledge of PDs strategies is important for fodder conservation, improve productivity and inform direction of future research.

An Ultra‐Short‐Term Wind Power Prediction Method Based on Spatiotemporal Characteristics Fusion

ABSTRACTAiming at the problem that the existing ultra‐short‐term wind power prediction methods lack consideration of the spatial correlation characteristics of wind farms, resulting in insufficient prediction accuracy, an ultra‐short‐term wind power prediction method based on spatiotemporal characteristics fusion is proposed in this article. First, the fluctuation difference of the time window of wind power is input into the K‐means clustering algorithm to cluster wind farms into several clusters based on power fluctuation similarity. Then, the principal component analysis algorithm is used to reduce the dimensionality of numerical weather prediction data combinations in different regions to reduce the impact of redundant information on modeling accuracy. Finally, a convolutional long‐short‐term memory neural network is designed to extract spatiotemporal features of wind power data and output prediction results. The experimental verification on 18 wind farms in a province in China shows that the proposed wind power prediction method has an average root mean square error of only 0.1257 and has certain applicability.

Ultra-Short-Term Wind Farm Power Prediction Considering Correlation of Wind Power Fluctuation

Accurate ultra-short-term power prediction for wind farms is challenging under rapid wind speed fluctuations, complicating production planning and power balancing. This paper proposes a new method considering spatial and temporal correlations of wind fluctuations among adjacent wind farms. The method first calculates the time difference between power fluctuations based on wind speed, direction, and relative positions, determining the prior information period. The variational Bayesian model is then used to extract implicit relationships between power fluctuations of adjacent wind farms, enabling power prediction during the prior information period. Finally, the non-prior information period is predicted to complete the ultra-short-term power prediction. Using measured data from three wind farms in Fujian Province, compared to other models, the method demonstrates improved accuracy by effectively leveraging the power fluctuation characteristics of adjacent wind farms, and it has a certain amount of generalizability.

UNDERSTANDING DAIRY SMALLHOLDERS’ INTENTION TO PARTICIPATE IN THE CONTROL AND ERADICATION OF BOVINE BRUCELLOSIS

<p><strong>Background:</strong> Brucellosis (<em>Brucella abortus</em>) is a disease that affects cattle and has a significant economic impact on the livelihood of farmers in developing countries. <strong>Objective:</strong> The research aimed to understand the dairy smallholders’ intention to participate in the national campaign to control and eradicate bovine brucellosis in Aculco, State of Mexico. <strong>Methodology:</strong> The study was conducted with 196 farmers who were already engaged in the campaign. The reasoned action approach (RAA) was used as a theoretical framework, and Spearman rank-order correlations were used to analyze the data. <strong>Results:</strong> The results showed that farmers firmly intended to participate in the campaign in the next 12 months. The intention was associated with farmers’ years of experience as milk producers and perception of the importance and usefulness of the campaign in addition to farm characteristics. Farmers’ intention was also related to positive beliefs (drivers), such as participation in the campaign, preventing the disease in the herd, enabling access to government support and subsidies, and avoiding spreading the disease to people. Other social referents such as veterinarians, farmers, and farmers’ sons also played an important role in farmers’ intentions. A couple of significant constraints were identified: the lack of financial resources, communication between farmers and personnel in charge of the campaign. <strong>Implications: </strong>The study provided insights to improve extension activities towards small-scale dairy farmers to participate in the national campaign of control and eradication of bovine brucellosis.<strong> </strong><strong>Conclusion:</strong> the three components of the RAA (attitude, perceived norms, and perceived behavioral control) were strong predictors of farmers’ intention to participate in the brucellosis eradication campaign. They enabled the factors influencing farmers’ intentions to be explored more deeply.</p>

Determinants of adoption of climate resilient agricultural solutions

CONTEXTClimate change poses significant challenges to agricultural productivity, crop yield and overall resilience of farming system, particularly in mountainous regions. In a mountainous coutry like Nepal, where agriculture is a crucial livelihood for a substantial population, the Climate Resilient Agricultural (CRA) solutions are imperative. These solutions aim to enhance the sustainability of water, reduce reliance on pesticides and chemical fertilizers, and increase farm productivity and income. However, understanding the factors influencing farmers' decisions to adopt or reject such solutions remains a critical knowledge gap. OBJECTIVESThis study investigates the socio-psychological and socio-demographic determinants affecting the adoption of climate-resilient agricultural solutions. METHODSThis study uses the theory of planned behavior and a mixed-method approach to conduct household surveys with farmers, Key Informant Interviews with experts and implementing partners, and Focus Group Discussions with farmers' groups. Responses to behavior-related questions were analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM), and the factors influencing the farmers' adoption behavior were identified using binary logistic regression. RESULTS AND CONCLUSIONSThe result based on a limited sample size indicates that farm characteristics and farmers' perceived behavior control significantly influence the adoption and continuation behavior of CRA solutions. In conclusion, fostering awareness, capacity building, and skill development and establishing both market and non-market incentives are essential for improving the adoption of new solutions for climate resilience in agriculture, however, the in-depth research is recommended to better understand the adoption behavior within specific farming systems. SIGNIFICANCEThe findings of this research offer valuable insights for policymakers and development practitioners striving to promote the widespread adoption of CRA solutions in farming communities. It will help to develop targeted strategies and policies that effectively support the integration of these solutions into agricultural practices, ultimately enhancing the resilience of the farming system in the face of climate change.