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Research Article
Effect of Climate-smart Agricultural Practices on Productivity and Income of Smallholder Maize Farmers: Micro-level Evidence from Botswana
Issue:
Volume 10, Issue 2, April 2025
Pages:
46-57
Received:
3 March 2025
Accepted:
19 March 2025
Published:
31 March 2025
Abstract: Climate change presents considerable obstacles to agricultural productivity in Sub-Saharan Africa., resulting in low yields and reduced farmers’ income. Climate-smart agricultural (CSA) practices offer a viable pathway to address these challenges through their triple benefits: enhanced productivity, increased income, and reduction of greenhouse gas emissions. This study examines the effect of adopting four interdependent CSA practices (crop rotation, use of improved seeds, application of inorganic fertilizers, and maize-legume diversification) and their combinations on productivity and income. Using recent cross-sectional data from 384 maize farmers in North East District, Botswana, the study utilizes a multinomial endogenous switching regression model to correct for selection bias and endogeneity caused by both observable and unobservable factors. The results show that adoption decisions are shaped by variables such as education, farm size, farming experience, livestock ownership, membership in groups, access to extension services, market access, and land tenure systems. Notably, adopting all four CSA practices results in a productivity increase of 3.56 units and a significant income gain of 3,691.17 Botswana Pula. These results suggest that farmers experience the greatest improvements in productivity and income when they adopt a comprehensive set of CSA practices. Building on the findings, the paper recommends that both government and non-governmental organizations promote the adoption of these practices by offering innovative extension services. These services would help farmers gain a better understanding of the advantages of alternative climate-smart agricultural practices.
Abstract: Climate change presents considerable obstacles to agricultural productivity in Sub-Saharan Africa., resulting in low yields and reduced farmers’ income. Climate-smart agricultural (CSA) practices offer a viable pathway to address these challenges through their triple benefits: enhanced productivity, increased income, and reduction of greenhouse gas...
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Research Article
Costs and Return Analysis of Irrigated Rice Production Among Small Scale Farmers in Birnin Kebbi Local Government Area of Kebbi State
Issue:
Volume 10, Issue 2, April 2025
Pages:
58-66
Received:
2 March 2025
Accepted:
18 March 2025
Published:
10 April 2025
Abstract: This study investigates the costs and returns of irrigated rice production among small-scale farmers in the Birnin Kebbi Local Government Area of Kebbi State, Nigeria. Data were collected using a structured questionnaire, and a purposive multi-stage sampling method was employed to select 120 respondents from four major rice-producing villages: Ambursa, Dukku, Gulumbe, and Zauro. Descriptive statistics, multiple regression analysis, and gross margin analysis were used to analyze the data. The findings on socio-economic characteristics revealed that the respondents had an average age of 40 years, with 90% having a household size of 11 members. Most farmers (60%) had some form of education and possessed an average of 25 years of farming experience. The average farm size was 1.18 hectares, and 76.7% of respondents inherited their land. Furthermore, 77.5% of respondents had no extension contact in the previous season, and most were not part of any cooperative. Multiple regression analysis showed that significant factors influencing rice output were age, farming experience, farm size, and credit access. The study found that the total cost of production was ₦148,844.70 per hectare, while the gross return was ₦391,017.30 per hectare, resulting in a gross margin of ₦248,172.30 per hectare. Major constraints included lack of government support (17.3%), high fuel costs (16.2%), and poor market structure (15.3%). The study recommends increased government intervention in the form of subsidized farm inputs, improved extension services, and market access to enhance farmers’ productivity and profitability.
Abstract: This study investigates the costs and returns of irrigated rice production among small-scale farmers in the Birnin Kebbi Local Government Area of Kebbi State, Nigeria. Data were collected using a structured questionnaire, and a purposive multi-stage sampling method was employed to select 120 respondents from four major rice-producing villages: Ambu...
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Research Article
Modeling and Forecasting Bean Production in Mozambique: Challenges and Implications for Food Security and SDG 2
Filipe Mahaluca*
,
Faizal Carsane,
Alfeu Vilanculos
Issue:
Volume 10, Issue 2, April 2025
Pages:
67-85
Received:
1 September 2024
Accepted:
18 September 2024
Published:
29 April 2025
DOI:
10.11648/j.ijae.20251002.13
Downloads:
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Abstract: This study examines the effectiveness of ARIMA and LSTM models in forecasting bean production in Mozambique, using data from 2002 to 2022. The analysis reveals that the limited sample size, comprising only 21 years of data, significantly impacts the accuracy of both models, as reflected in high MAPE values. The ARIMA(1,1,1) model demonstrates robustness with the lowest RMSE among the ARIMA models, but the LSTM model, despite its challenges, shows superior capability in capturing nonlinear patterns, resulting in a lower average MAPE. Forecasts for the period from 2023 to 2030 suggest stable bean production with slight annual variations, although the wide confidence intervals highlight the inherent uncertainty in these predictions. This study underscores the importance of improving forecasting models to better guide agricultural planning and policy-making, particularly in the context of Mozambique's food insecurity challenges and the global objectives of SDG 2. The results emphasize the need for more extensive data collection and the inclusion of additional variables to enhance the accuracy of future forecasts, contributing to the reduction of food insecurity and the achievement of sustainable development goals in Mozambique.
Abstract: This study examines the effectiveness of ARIMA and LSTM models in forecasting bean production in Mozambique, using data from 2002 to 2022. The analysis reveals that the limited sample size, comprising only 21 years of data, significantly impacts the accuracy of both models, as reflected in high MAPE values. The ARIMA(1,1,1) model demonstrates robus...
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