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Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia
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Background The profound impacts of climate change on the environment, economy, and society are anticipated to significantly influence smallholder farmers, whose livelihoods and traditional ways of life are inextricably intertwined with the natural environment. This comprehensive study delves into the multifaceted factors shaping the adoption of climate-smart agricultural practices and their subsequent effects on crop productivity among the small-scale farming communities in Nyimba District, situated within Zambia. Methods The study collected data from 194 smallholder farmer households across 12 villages in 4 agricultural camps of Nyimba District between June and July 2022. A logistic regression model was utilized to assess the factors influencing crop production and the adoption of climate-smart agricultural practices in response to climate change and variability within the study area. Furthermore, propensity score matching was performed to evaluate the impacts of adopting climate-smart agriculture by comparing adopter households with their non-adopter counterparts. Results The logit regression model’s findings from the research indicate that several factors influence smallholder farmer households’ adoption of climate-smart agricultural techniques and crop productivity. These factors include the farmer’s educational level, household size, fertilizer utilization, the household head’s age and gender, farming experience duration, livestock ownership status, annual household income, farmland size, the household head’s marital status, and access to climate-related information. Additionally, the propensity score matching analysis revealed that crop yields among smallholder farmers who adopted climate-smart agricultural practices were 20.20% higher compared to non-adopters. Furthermore, the analysis showed that implementing such practices in the study area led to a 21.50% increase in maize yields for adopter households relative to their non-adopter counterparts. Conclusion The findings of this research offer valuable insights to policymakers, guiding them in enhancing farmers’ climate change adaptation strategies and formulating relevant policies. Nevertheless, these initiatives and concerted efforts possess the potential to mitigate the detrimental impacts posed by climate change and climatic variability. This manuscript is an extract from my master of science degree in climate-smart agriculture from Haramaya University titled ‘Climate-Smart Practices: Effects of Agroforestry and Conservation Agriculture on Selected Physicochemical Properties of Soils and Crop Productivity among Smallholder Farmers, in Nyimba, Zambia.’ Please refer to this link; http://ir.haramaya.edu.et/hru/bitstream/handle/123456789/6402/PETROS%20CHAVULA.pdf?isAllowed=y&sequence=1
F1000 Research Ltd
Title: Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia
Description:
Background The profound impacts of climate change on the environment, economy, and society are anticipated to significantly influence smallholder farmers, whose livelihoods and traditional ways of life are inextricably intertwined with the natural environment.
This comprehensive study delves into the multifaceted factors shaping the adoption of climate-smart agricultural practices and their subsequent effects on crop productivity among the small-scale farming communities in Nyimba District, situated within Zambia.
Methods The study collected data from 194 smallholder farmer households across 12 villages in 4 agricultural camps of Nyimba District between June and July 2022.
A logistic regression model was utilized to assess the factors influencing crop production and the adoption of climate-smart agricultural practices in response to climate change and variability within the study area.
Furthermore, propensity score matching was performed to evaluate the impacts of adopting climate-smart agriculture by comparing adopter households with their non-adopter counterparts.
Results The logit regression model’s findings from the research indicate that several factors influence smallholder farmer households’ adoption of climate-smart agricultural techniques and crop productivity.
These factors include the farmer’s educational level, household size, fertilizer utilization, the household head’s age and gender, farming experience duration, livestock ownership status, annual household income, farmland size, the household head’s marital status, and access to climate-related information.
Additionally, the propensity score matching analysis revealed that crop yields among smallholder farmers who adopted climate-smart agricultural practices were 20.
20% higher compared to non-adopters.
Furthermore, the analysis showed that implementing such practices in the study area led to a 21.
50% increase in maize yields for adopter households relative to their non-adopter counterparts.
Conclusion The findings of this research offer valuable insights to policymakers, guiding them in enhancing farmers’ climate change adaptation strategies and formulating relevant policies.
Nevertheless, these initiatives and concerted efforts possess the potential to mitigate the detrimental impacts posed by climate change and climatic variability.
This manuscript is an extract from my master of science degree in climate-smart agriculture from Haramaya University titled ‘Climate-Smart Practices: Effects of Agroforestry and Conservation Agriculture on Selected Physicochemical Properties of Soils and Crop Productivity among Smallholder Farmers, in Nyimba, Zambia.
’ Please refer to this link; http://ir.
haramaya.
edu.
et/hru/bitstream/handle/123456789/6402/PETROS%20CHAVULA.
pdf?isAllowed=y&sequence=1.
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