Representative Farmers Research Articles

FACTORS INFLUENCING FARMERS PERCEPTION ON FERTILIZER SERVICES OFFERED AT RYTHU BHAROSA KENDRAS

The current study was carried out during the year 2022 and 2023 to knew the factors influencing farmers perception on fertilizer services offered at Rythu Bharosa Kendras in Rayalaseema region of Andhra Pradesh. A representative sample of 120 farmers in two districts of Andhra Pradesh i.e., Tirupati and Chittoor were selected for the study. Through pre-structed interview schedule, farmers perception was collected. Representative farmers were very satisfied about the physical location of the Rythu Bharosa Kendras with the highest mean score of 4.62. Respondents were very unsatisfied about in time availability of all the fertilizers in RBK with respect to cropping pattern with the lowest mean score of 1.86. 42.50% of the representative sample had moderate perception, 37.50% of them had poor perception and 20.00% of the sample farmers had good perception on fertilizer services offered at Rythu Bharosa Kendra. Impact of farmers socio-economic factors like education, major occupation, farming experience, farm size, annual income and farmers age on their perception over fertilizer services offered at Rythu Bharosa Kendras were analysed. It was noticed that only farming experience and major occupation have significant impact on their perception over fertilizer services offered at Rythu Bharosa Kendras.

Unveiling Peru’s Agricultural Diversity: Navigating Historical and Future Trends in a Post-COVID-19 Context

Over a comprehensive 5-year assessment, and extrapolating it prospectively until 2025, a thorough examination was conducted of productive agrobiodiversity in nine rural agricultural districts across Peru. The present study involved in-depth interviews with 180 representative farmers of the Coast, Highlands, and Jungle natural regions. Employing the Shannon–Weiner diversity index and the Margalef species richness index, the dynamics within years and across different zones were analyzed. Utilizing quadratic trend models, we assessed the frequency of each crop, aiming for the optimal fit concerning absolute deviation from the mean, mean squared deviation, and mean absolute percentage error. These findings revealed five distinct crop types—tuberous, fruits, cereals, legumes, and roots—distributed across 25 diverse families. Looking ahead to 2025, our projections indicated positive trends in 15 families and negative trends in 9 crop families. The nuanced mathematical distinctions observed in crop management decisions varied significantly depending on the specific area and year, underscoring the importance of localized considerations in agricultural planning.

DAMPAK IMPLEMENTASI KEBIJAKAN PENGGUNAAN KARTU TANI TERHADAP EFEKTIVTAS DISTRIBUSI PUPUK BERSUBSIDI DI KABUPATEN SUMBAWA BARAT

Subsidized fertilizers played a crucial role in supporting agricultural productivity and food security in Indonesia. However, the distribution of subsidized fertilizers has faced challenges in terms of efficiency and transparency. One of the causes of these issues lies in the problems of a market structure that tends to be oligopolistic and the existence of conspiracies among interests that prioritize personal and group interests, as well as the unavailability of fertilizers at the beginning of the planting season. To address this problem, the Indonesian government introduced the Farmer Card policy as a tool to identify farmers receiving subsidized fertilizers. This research aimed to analyze the impact of the implementation of the Farmer Card on the effectiveness of subsidized fertilizer distribution in West Sumbawa Regency. This study used a quantitative analysis method by collecting data from farmers receiving subsidized fertilizers, fertilizer agents, and stakeholders in West Sumbawa Regency. The data included farmers' understanding of the Farmer Card and its implementation, as well as the subsidized fertilizer redemption process. The research sample was taken using purposive sampling technique with 100 samples from all representative farmers receiving the farmer card in each district. Multiple linear regression analysis was used to measure the relationship between the use of the Farmer Card and the effectiveness of subsidized fertilizer distribution. The research results showed that the implementation of the Farmer Card had a positive impact on the effectiveness of subsidized fertilizer distribution, amounting to 25.70%. Farmers using the Farmer Card tended to receive subsidized fertilizer more efficiently, gain better access to fertilizers, and utilize fertilizers more effectively. Additionally, the redemption of fertilizers through the Farmer Card increased agricultural production and farmers' income.

Application of XGBoost Regression in Maize Yield Prediction

Artificial Intelligence (AI) is the human-like intelligence imbued in machines so that they can perform tasks that normally require human intelligence. Machine learning is an AI technique which carries on the concepts of predictive analytics with one important distinction: the AI system can make assumptions, test hypotheses, and learn independently. XGBoost, Extreme gradient boosting, is a popular machine-learning library for regression tasks. It implements the gradient-boosting decision tree algorithm, which combines several feeble decision trees to produce a robust predictive model. In Boosted Trees, boosting is the process of transforming poor learners into strong learners. It is an ensemble method; a weak learner is a classifier with a low correlation with classification, whereas a strong learner has a high correlation. Maize is a staple food in Kenya and having it in sufficient amounts in the country assures the farmers' food security and economic stability. Crop yield measures the seeds or grains produced by a particular plot of land. Typically, it is expressed in kilograms per hectare, bushels per acre, or sacks per acre. This study predicted maize yield in Uasin Gishu, a county in Kenya, using XGBOOST regression algorithm of machine learning. The regression model used the mixed-methods research design, the survey employed well-structured questionnaires comprising of quantitative and qualitative variables, directly administered to selected representative farmers from 30 clustered wards. The questionnaire comprised 30 variables related to maize production from 900 randomly selected maize farmers distributed across 30 wards. XGBOOST machine learning regression model was fitted, and it could predict maize yield and identify the top features or variables that affect maize yield. The model was evaluated using regression metrics Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), which values were 0.4563, 0.2082, 25.2700 and 0.3532, respectively. This algorithm was recommended for maize yield prediction.

Random Forest Regression in Maize Yield Prediction

Artificial Intelligence is the discipline of making computers behave without explicit programming. Machine learning is a subset of artificial Intelligence that enables machines to learn autonomously from previous data without explicit programming. The purpose of machine learning in agriculture is to increase crop yield and quality in the agricultural sector. It is driven by the emergence of big data technologies and high-performance computation, which provide new opportunities to unravel, quantify, and comprehend data-intensive agricultural operational processes. Random Forest is an ensemble technique that reduces the result's overfitting. This algorithm is primarily utilized for forecasting. It generates a forest with numerous trees. The random forest classifier predicts that the model's accuracy will increase as the number of trees in the forest increases. All through the training phase, multiple decision trees are constructed. It generates subsets of data from randomly selected training samples with replacement. Each data subset is employed to train decision trees. It utilizes multiple trees to reduce the possibility of overfitting. Maize is a staple food in Kenya and having it in sufficient amounts in the country assures the farmers' food security and economic stability. This study predicted maize yield in the Kenyan county of Uasin Gishu using the machine learning algorithm Random Forest regression. The regression model employed a mixed-methods research design, and the survey employed well-structured questionnaires containing quantitative and qualitative variables, which were directly administered to 30 clustered wards' representative farmers. The questionnaire encompassed 30 maize production-related variables from 900 randomly selected maize producers in 30 wards. The model was able to identify important variables from the dataset and predicted maize yield. The prediction evaluation used machine learning regression metrics, Root Mean Squared error-RMSE=0.52199, Mean Squared Error-MSE =0.27248, and Mean Absolute Error-MAE = 0.471722. The model predicted maize yield and indicated the contribution of each variable to the overall prediction.

Soil related environmental considerations of farmers in the Great Hungarian Plain

Climate change and associated environmental changes are the major sustainability challenges facing the world today. The selection of appropriate agrotechnological elements is required not only to increase the quality and quantity of food produced but as well as reducing the costs of the farmers and protecting the environment for future generations. In the Great Hungarian Plain, the adoption of recent environmentally friendly technologies is still not sufficient and the region faces various environmental challenges. This study aimed to analyse the economic and environmental consciousness of farmers in the selection of 3 agrotechnological elements (soil protective cultivation, soil reclamation/conditioning, manure application). A pilot study was conducted on 5 representative farmers, in which they were interviewed, and questionnaires were designed to critically analyse farmers’ perceptions of the environment based on the opinions of 106 respondents. The result show that farmers are aware of the environmental impacts of selected technologies in their farms. They highlighted some of the environmental challenges they are experiencing including drought, secondary salinization, and unfavourable soil properties. However, they showed more economic than environmental consciousness, as they suggested that it was important to be familiar with economic issues and conditions to ensure higher income. The level of willingness to introduce or apply soil protective cultivation, soil reclamation/conditioning, and manure differed from low to high.

Community-Based Approach for Agricultural Water Management of Summer Crops

Globalization, population change, and rural-to-urban movement are the main causes of the enormous issues faced by rural communities. Every growing nation, including the Kurdistan Region of Iraq (KRI), has a sizable proportion of rural farmers among its populace. This study was done in the Sulaimani Governorate, which was divided into six main districts namely; Garmian, Penjwin, Halabja, Chamchamal, Sharazur, and Raniya, and aimed to compare the groundwater level with summer crop water demand. The weighted Sum Method (WSM) and a Normalized Difference Vegetation Index map (NDVI) were used to analyze and illustrate the current water demand status for summer crops. The study employed a participatory research design, utilizing a purposive sampling method to select 60 representative farmers, 25 key decision-makers, representatives from Non-Governmental Organizations (NGOs), and experts in the field of water management. Data was collected through the use of focus group discussions (FGDs) and Key Informant Interviews (KIIs) to gather information from the selected participants. This study is innovative in its attempt to establish a correlation between the yield of summer crops and the availability of groundwater. As a conclusion for this study, the results suggest that the level of groundwater plays a significant role in determining the production of summer crops. Additionally, the sustainable development of various regions within the Sulaimani Governorate is influenced by a complex interplay of environmental, economic, and social factors. Future work will be focus on doing a detail research regarding time series for ground water level (GWL) vs. crop production vs. NDVI.

Impact of Brown Plant Hopper (Bph) Management Training on Boro Rice Cultivation In Serajganj District

A study was conducted under the project areas of Tarash Upazila at Sirajganj district. The objectives were to assess the improvement of farmers’ knowledge and perception on different factors of BPH incidence and its management to examine the economic impact on rice and to determine the profitability of Boro rice cultivation among the different groups of farms. Out of 850 samples listed, 170 representative farmers among which 16 target, 54 trained and 100 non-trained farmers were selected respectively by stratified random sampling technique. Rice plants are affected by 20-33 major insect pests. Among them, BPH is considered as most damaging one. Eighty eight, 83 and 93% target, trained and non trained farmers reported that the project needs to continue while 100% farmers of each group reported that the project is beneficial to control BPH to increase rice production. Eighty seven, 56 and 51 % farmers of the target, trained and nontrained group used double nozzle for spraying insecticides to control BPH in their field. In boro season, 2073 kg/ha and 1209 kg/ha yield loss of BRRI dhan29 were found before and after the project, respectively. The lower yield loss was due to proper management taken by the farmers who got training and acquired sufficient knowledge for controlling BPH. The study revealed that 37% higher cost and 47% higher time is required by single nozzle sprayer than double nozzle sprayer. Therefore, farmers of all categories prefer double nozzle sprayer as it is good for health and saves money and time. They also reported that use of double nozzle sprayer is more profitable than single nozzle sprayer. Benefit cost analysis indicated that the gross return, net return and BCR were found higher in case of the target farmers and these were Tk.171107/ha, Tk.73735/ha and 1.76 respectively, but the cost of production per kg was lower (Tk.10.27) than those of the other two groups due to more knowledge gathered by the target farmers on management practices for rice production. Partial budgeting analysis indicated that the double nozzle users were more benefited by Tk 7287/ha than the single nozzle users for boro rice cultivation. Thus, after the project the farmers of all categories of the project area were socio-economically benefited learning how to use double nozzle for controlling BPH.
 Bangladesh J. Agril. Res. 46(2): 211-226, June 2021

Multi-Level Determinants of Acceptance in Centralized Pesticide Delivery among Farmers: Evidence from Huangshan City, China

Highly toxic pesticides bring negative externalities to water pollution, which increase the demand for green pesticides that are low-toxic and high-efficiency. With the implementation of the unique the Centralized Pesticide Distribution (CPD) policy aiming to reduce the use of chemical pesticides in Huangshan, we try to explore the factors that affect farmers’ acceptance of CPD and thus change their pesticides selection. Based on the theory of farmers’ ecological rationality, we build a decision-making ecosystem on accepting CPD and assuming that farmers’ behavior is determined hierarchically by factors, then the Logistic-AISM model is used for empirical testing using data collected from 233 representative farmers. The results show that the proportion of agri-income and participation in cooperatives fundamentally affected choices for CPD acceptance through two middle-level indirect factor: government publicity and the availability of agri-information by changing farmers’ cognition on pesticides and production status. Thus, CPD may be improved by selling a richer variety of insecticides and implementing more publicity. Lessons from China inspire other intensive farming countries to promote green pesticides by: expanding the sales channel of green agri-products, playing the auxiliary role of cooperatives, and inventing more eco-friendly pesticides.

The impact of education and experience on profit of smallholder dairy farming at Batu City of Malang Raya, Indonesia

The research was conducted at Batu, City, Malang Raya of Indonesia. Study addressed to examine the farmer characteristics, dairy farming income, and the factors influencing on profit. The case study applied multistage sampling method to select 34 representative farmers which divided into three strata namely, stratum-1 (rearing <4 Animal Units), stratum-2 (owning 4-8 AU), and stratum-3 (controlling >8 AU). Primary data collection used survey method with structured questionnaire, whereas secondary data were available in related institutions and sources. Data analysis implemented descriptive and multiple regression technique. Results confirmed that farmers has experienced about 6-10 years in raising dairy farming and they has secondary school education. The profitable dairy farming was smallholder dairy farming that rearing more than 8 AU with daily income per Animal Unit of IDR 64,554 and structured with IDR 11,131 of revenue and IDR 47,577 of production cost. Farmer’s experience was positively explaining the smallholder dairy profit, and the high school education attainment was more likely to increase venture’s income. The farm return has positive and strong relationships with the more number of dairy cattle owned.

Socioeconomic Impact Assessment of Water Resources Conservation and Management to Protect Groundwater in Punjab, Pakistan

Water is the most important resource; it is utilized largely in agricultural production and is fundamental to ensuring global food security. This study aims to assess sustainable water management interventions and their impact on the farm economy. To increase water productivity, the most important adaptations that have been proposed are high-efficiency irrigation systems, drought-resistant varieties, the substitution of water-intensive crops with less water-demanding crops, the mulching of soil, zero tillage, and all on-farm operations that can save water, especially ground water. The recent analysis utilized farm survey data from 469 representative farmers along with secondary statistics. The data were collected via a multi-stage sampling technique to ensure the availability of representative farm populations based on a comprehensive site selection criterion. The TOA-MD model estimates the adoption rate of a proposed adaptation based on net farm returns. The impact of high-efficiency irrigation systems and the substitution of high delta crops for low delta crops had a positive impact on net farm returns and per capita income, and a negative impact on farm poverty in the study area. It is recommended that policymakers consult farmer representatives about agricultural and water-related issues so that all the policies can be implemented properly.

Economics of production and marketing of mandarin in Parbat and Baglung districts of Nepal

This study was conducted in mandarin growing areas of Parbat and Baglung districts of Gandaki Province where total of 67 representative farmers and 20 traders were selected as sample, in which 36 farmers were from Parbat and 31 farmers were from Baglung district. The average cost of Mandarin production per ropani (19.66 ropani= 1 hactare) was NRs. 17220 highest contributor being labor cost with 43.82 percent of share. Average return and profit of Mandarin farming per ropani was NRs. 48,978 and NRs. 31,757 respectively with B:C ratio of 2.93. Average return, profit and B:C ratio was found to be significantly higher in Parbat than Baglung. The Cobb-Douglas production function of Mandarin farming shows, output of Mandarin farming is significantly and positively affected by expenditure on labor, land rent, and cost of nutrients with the increasing return to scale (1.097). Producer-Pre-harvest contractor–Wholesaler-Retailor-Consumer was most prominent marketing channel with 55 percent share of total sold volume. Inadequate irrigation facility was the most prominent production problem, while poor marketing facility and access to market information were the most prominent marketing problems faced by farmers. Difficulty in transportation was recognized as the most prominent marketing problems faced by mandarin traders. Average marketing margin and producers’ share were NRs. 45.95 and 57.41 percent respectively both of which were highest in Parbat. In Parbat, harvesting is carried in January and February, which serves as off-season production in domestic markets, which ensures higher per unit price for respective district’s output among two districts.

Adoption of potato varieties in West and Kellem Wollega Zones, Ethiopia

Potato (Solanum tuberosum L.) is one of the possible food security crops which provide high yield and quality product in short period of time. Due to the lack of clearly known best varieties of it, its adoption to farmers is very less. The present study was conducted to assess the type of potato farmers prefer, adoption of released potato varieties and its management practices in west and Kellem Wollega Zones, Ethiopia. Survey was carried out in Ayira, Yubdo, Hawa Gelan, Dale Wabara and Dale Sadi woreda where four kebeles were purposively selected based on the potato farming potential. Open and close ended interview questions were generated for 384 selected representative farmers. Data was analyzed by SPSS software. The result showed that, 97.6% of the farmers have willing to farm potato. 47.3% and 22.7% of them experienced to farm local potato (land race) and released potato varieties, respectively. Farmers use landrace potato due to less awareness to released potato and accessibility of local potato. 70.1% of farmers responded there is no adoption of released potato in the area. Factors hindering potato farming in the study area are potato disease and lack of released potato. The least method used by farmers is use of resistant potato. Generally, there is scarcity of released potato seeds indicating that there is no its adoption in the study site. This problem is enforcing farmers to use local potato varieties which may not resist above stated hindering factors and make farmers to face food insecurity problems and economic reduction. Therefore, improving locally existing potato or attracting the improved potato varieties from elsewhere to the zones may be a solution of its adoption.

Assessing effects of rainfall on farming activities as the predictor of climate changes in Sadi Chanka District of Kellem Wolega, Oromia, Ethiopia

Abstract This study was carried out in Sadi Chanka District (Ethiopia) to assess effects of rainfall on farming activities as the predictor of climate change. This study used a mixed approach. Purposive sampling technique was used to select representative kebeles. Elkofale, Keto Shan and Komba were chosen and 128 farmers from each kebeles were involved in a survey. A simple random sampling technique was used to select representative farmers of the study area. In this study, interview, focus group discussion, questionnaires and site observation were employed as data collection tools. SPSS Version 20 was used for data analysis. The majority of the respondent farmers revealed that their productivity status had fluctuated patterns due to untimely rainfall and scarcity of water occasionally. The present study confirmed that choices of agricultural activities by farmers were linked with the change in climate and irregular distribution of rainfall patterns. The ecology of farmland in Sadi Chanka has been degraded critically. This work had also clearly demonstrated the existence of farmers' choices of agricultural activities, including coffee production, poultry production and production of fruits like mango and papaya. Community as whole and concerned governmental sectors should be alerted on participatory ecological management and climate change mitigation.

Adoption Choice of Risk Management Tools in Agricultural Production under Climate Change

The long term impact of climate change is likely to affect food security and therefore prompts the agricultural sector to be more resilient to production and market risk, and uncertainties caused by these changes in climate. Choosing from a wide range of risk management tools can help farmers manage uncertainties and adapt to climate change. However, the choice of adoption of these risk management tools are greatly influenced by a number of factors. An empirical study was conducted to estimate the factors influencing the adoption choice of agricultural risk management tools by farmers in Akwa Ibom State, Nigeria. Multistage sampling procedure was employed to select the representative farmers for the study. Information were elicited from farmers using questionnaires. Factors influencing the adoption of crop diversification, insurance and contract farming were analyzed using multinomial logit approach. Empirical results revealed that the decision to adopt crop diversification and insurance were inversely and significantly affected by age of the farmers. Results further showed that farmers decision to adopt crop diversification and contract farming as risk management tools were positively and significantly influenced by the size of farmland at (p<0.05). Findings also showed that access to weather information and frequency of extension contact significantly influenced farmers decision to adopt crop insurance and crop diversification respectively at 10% level. Result indicates that farmers decision to adopt crop diversification as a risk management tool was positively affected by frequency of extension contact. Promoting efficient and effective extension service delivery, access to weather and climate information and human capital development would be sensible policy options.

Market access and resource endowment define the soil fertility status of smallholder farming systems of South‐Kivu, DR Congo

AbstractThis study verified the inter‐related effect of ‘market distance’, defined as walking time, ‘farm typology’, defined as resource endowment, and ‘site’, defined as geographic location with contrasting agro‐ecologies, as well as farmers’ indigenous knowledge on soil fertility variability in smallholder farming systems in two distinct regions (Bushumba, Mushinga) of South‐Kivu, DR Congo. A total of 384 soil samples were selected from representative farmers’ fields and analysed for soil pH, soil organic carbon (SOC) content and quality, as well as nutrient contents, using midDRIFTS (mid‐infrared diffuse reflectance Fourier transform spectroscopy) and wet chemistry analyses. MidDRIFTS was also used to calculate SOC stability indexes as SOC quality proxies. ‘Market distance’ and ‘farm typology’ were key determinants of soil fertility variability, both with contrasting trends in Bushumba and Mushinga. Decreasing soil fertility with increasing market distance was noted across all farm typologies. ‘Farm typology’ was related to exchangeable calcium and magnesium, while ‘site’ resulted in a difference of plant available phosphorus. SOC quality indexes were related to ‘site’, interacting with ‘market distance’. A ‘market distance’ effect became obvious in the medium wealthy and poor farms of Mushinga, where a lower SOC quality in remote fields plots was noted with increasing market distance. In agreement with farmers’ indigenous knowledge, soil fertility levels were higher in deep than shallow soils, which were reflected in higher nutrient stocks in deep soils receiving organic amendments. Our results inferred that soil fertility variability across smallholder farms must consider various inter‐related determinants as basis for site‐specific fertility management interventions.

Nutrient balances in smallholder farms in northern Ethiopia

AbstractDepletion of soil nutrient is a major impediment for food production in sub‐Saharan Africa, but little is known about nutrient dynamics at the farm level. Therefore, the objective of this study was to evaluate nutrient management on a range of contrasting farms by estimating balances of N, P and K. Representative farmers of three wealth classes (rich, medium and poor) were selected from two different agro‐ecological regions (Alaje and Raya Azebo) in northern Ethiopia. The MonQI model based on data obtained from farm questionnaires was used for processing and analysis of partial nutrient balances (input from mineral and organic fertilizer minus output with crop and straw) and full balances (all input sources minus output with crop, erosion, leaching and gaseous losses). Partial N, P and K balances were 25.6, 6.3 and 7.8 kg ha−1 in Alaje and −7.1, 10.6 and −6.7 kg ha−1 in Raya Azebo. Full N, P and K balances were −26.2, 6.7 and 2.9 kg ha−1 in Alaje and −17.9, 11.1 and −5.3 kg ha−1 in Raya Azebo. Partial N and K and full K balances were different (p < .05) between the regions. Farms owned by rich and poor farmers differed in full N balances in Alaje and in partial N balances in Raya Azebo (p < .05). With the use of more fertilizer, N losses to the environment also increased. Generally, soil stocks of N and K are further declining. There is a need for socio‐economic policies to introduce effective nutrient management on the farms and to provide poorer farmers' access to fertilizer.

Effect of Contract Farming on the Income and Risk of Pistachio and Saffron Producers in Iran

Contract farming is among the institutional arrangements essential for agricultural products. In most cases, these contracts lead to increased incomes and welfare of farmers. However, this type of business has not been taken seriously in Iran. This study aimed to analyze the effect of contract farming on the income and risk of the farmer’s income. To this end, pistachio and saffron farmers in the Yazd Province, in central Iran, were considered in the harvest year of 2017-2018. We divided the farmers into three homogeneous groups, each with different input consumption levels and production per hectare. We calibrated the cost functions of representative farmers in each group. These functions were employed to develop an ideal formal contract model and relational contract model. According to the results, formal contracts and relational contracts increased income and relational contracts reduced the risk to farmers when they were compatible with the incentive constraints of the farmer. Policymakers should provide favorable conditions for the development of contract farming. For instance, they can present contract farming to farmers and encourage agricultural enterprises to participate in contracts by facilitating access to credit and other investment incentives.

The Status of Insect Pests Prevailing in Stored Wheat Grain under Traditional Storages of Cheha District of Gurage Zone of South Central Ethiopia

Aim: To assess the status of the insect pests of stored wheat grains in Cheha destrict of Gurage Zone of Southern Ethiopia.
 Study Design: Peasant associations were selected purposefully based on intensity of wheat production, such that those peasant association growing wheat predominantly were selected for the survey, while villages, representative farmers and their storage systems were selected randomly using a nested design.
 Place and Duration of Study: Survey was conducted between 1, July to 30, December 2019 with the interval of one month in major wheat growing peasant associations of Cheha district of south central Ethiopia.
 Methodology: The assessment on abundance was made from of half kilogram of wheat grain sample taken from 135 randomly selected farmer’s storages of three peasant associations using taxonomic keys of books related to stored product insects.
 Results: Eight major species of insect pests consisting of four primary pests and four secondary pests belonging to five families with in two insect orders were documented. Of these pests recorded, Sitophilus oryzae, Sitophilus zeamais, Sitotroga cereallella, Tribolium castaneum and Tribolium confusum, respectively were the most prevalent and frequently occurring as they appeared between 12.74 and 33.78 individuals per 100 g of grain and as they occurred in the range between 77.78 and 92.26% per 100 g of sample wheat grain collected from the survey sites, respectively. These were followed by Cryptolestes ferrugineus, Cryptolestes pusillus and Rhyzopertha dominica which occurred between 7.26 and 10.74 individuals per 100 g of grain and in ranges between 51.85 and 66.67% per 100 g of sample wheat grain collected.
 Conclusion: The traditional methods and practices used by farmers were inefficient for sufficient wheat grains protection against insect pests in storages; this implies for the urgent need for designing management strategies for more effective and sustainable methods of control.

Framing vulnerability and coffee farmers’ behaviour in the context of climate change adaptation in Nicaragua

Abstract This paper analyses coffee producer’s vulnerability and adaptive capacity to climate change in Nicaragua. By its geographical position, Nicaragua is one of the countries most affected by climate change, and coffee production is expected to vastly shrink in some critical areas, suitability being reduced by up to 40% in the country. This paper analyses farmer’s perceptions and vulnerability indicators to find which indicators are linked to farmers’ perceived capacity to adapt to climate change, paying special attention to the issue of whether farmers perceive they have any capacity at all to adapt. The analysis was conducted through a survey to 212 representative farmers jointly with an analysis of vulnerability indicators. A Heckman selection model was estimated to jointly analyse the probability of being able to cope with climate change and the level of adaptive capacity that farmers perceive. We have simulated different policy scenarios considering the sustainable development goals of United Nations in terms of poverty reduction and education concerns. We also analysed the effects of specific programs on education about climate change awareness. Finally, we extend our analysis to a geographical evaluation of the farmer’s perceived vulnerability. The analysis shows that aspects such as farm size or education levels are relevant for modulating farmers’ perceptions on their own adaptive capacity. Large farm managers find themselves more often able to cope with climate change impacts though they find their capacity to be limited. Farmers that could not rely on rainfall water for their plantations also reported being less able to cope with climate change impacts. Poverty was also found to be correlated to perceptions, as regions lower proportions of inhabitants under poverty levels showed higher levels of confidence in adaptive capacity.