Determinants of Climate Smart Agriculture Technology Adoption in the Drought Prone Districts of Malawi using a Multivariate Probit Analysis

Maguza-Tembo, Francis; Edriss, Abdi-Khalil; Mangisoni, Julius

doi:10.9734/ajaees/2017/32489

Cited by 21 publications

(27 citation statements)

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“…Farmers are more likely to adopt CSATs (WS 1 S 1 ) if they own the land and the topography is flat as shown in the case of Bac Lieu province. These results support the previous works of Kassie and Holden (2007) and Maguza-Tembo et al (2017), who found that tenants are less likely to apply new technologies on rented plots because of the absence of security of tenure in the farm. In the same way, the quality of the soil and the weather conditions affect the farmers' adoption decisions.…”

Section: Determinants Of Adoption Of Climate-smart Agricultural Technsupporting

confidence: 91%

“…Fortunately, the costs and benefits of potential and priority CSATs have been determined in various studies conducted in Vietnam (Nghia et al, 2015). Farmers often build sustainable agricultural production systems that are resilient to different climate-related conditions and other shocks by using a combination of various CSATs (Maguza-Tembo et al, 2017). Unfortunately, of the few studies that have attempted to identify the determinants of farmer adoption of more than one CSAT (Di Falco and Veronesi, 2013;Parvathi and Waibel, 2015;Teklewold et al, 2013Teklewold et al, , 2017, none of them have been conducted in Vietnam.…”

Section: Climate-smart Agriculture Technologiesmentioning

confidence: 99%

“…A review of the literature shows that there are many categories for grouping the factors affecting farmer's adoption of new technologies (Kassie et al, 2013;Maguza-Tembo et al, 2017;Mwangi and Kariuki, 2015;Rejesus et al, 2011;Teklewold et al, 2013Teklewold et al, , 2017) based on the current technologies being investigated, the location and the researcher's reference or even suitability to clients' needs (Bonabana-Wabbi, 2002). In this study, the categorization is based on the CSATs being investigated and literature reviews.…”

Section: Counterfactual Analysismentioning

confidence: 99%

“…In this study, the categorization is based on the CSATs being investigated and literature reviews. These factors include householdspecific characteristics (gender, age, education level of respondents, number of family workers and experience in rice cultivation) (Bonabana-Wabbi, 2002;Chander and Thangavelu, 2004; Climate-smart agriculture technologies Lavison, 2013;Leavy and Smith, 2010;Mignouna et al, 2011;Obisesan, 2014;Omonona et al, 2006;Teklewold et al, 2017); economic factors (rice income, off-farm income, tropical livestock unit and area of rice farms) (Ellis and Freeman, 2004;Gabre-Madhin and Haggblade, 2004;Harper et al, 1990;Katengeza et al, 2012;Lowenberg-DeBoer, 2000;Maguza-Tembo et al, 2017;Mignouna et al, 2011;Parvathi and Waibel, 2015;Reardon et al, 2007;Uaiene et al, 2009;Yaron et al, 1992); market factors (distance from household to input and product markets) (Jansen et al, 2006;Wollni et al, 2010); institutional factors (access to agricultural extension and credit, access to climate information, confidence on the know-how of extension workers and membership in social/agricultural groups) (Maguza-Tembo et al, 2017;Mignouna et al, 2011;Mwangi and Kariuki, 2015;Teklewold et al, 2017); climate-related shocks (rainfall satisfaction index, waterlogging, drought stress, and pest and disease stress) (Jansen et al, 2006;Kassie et al, 2013;Teklewold et al, 2013); farm characteristics (land tenure, slope of plots and soil fertility); and attitudes (self-assessments of likelihood of willingness to try new technologies and attitude toward risk) (Arslan et al, 2017;Besley, 1995;Kassie et al, 2013;…”

Section: Counterfactual Analysismentioning

confidence: 99%

“…Farmers have a vast array of alternative technologies that they can use either singly or in combination to deal with various environmental conditions (Dorfman, 1996;Teklewold et al, 2017). Due, however, to differences in cultures, awareness, resource endowments, objectives, preferences and socio-economic backgrounds, farmers differ in their willingness to adopt new technologies (Maguza-Tembo et al, 2017). Farmers may modify or combine different CSATs with other practices and technologies to address their specific conditions and strategy.…”

Section: Introductionmentioning

confidence: 99%

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Determinants of adoption of climate-smart agriculture technologies in rice production in Vietnam

Tran

Rañola

Sander³

et al. 2019

IJCCSM

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Purpose In recent years, climate-smart agriculture (CSA) was introduced to Vietnam to enhance farmers’ resilience and adaptation to climate change. Among the climate-smart agricultural technologies (CSATs) introduced were water-saving techniques and improved stress tolerant varieties. This study aims to examine the determinants of farmers’ adoption of these technologies and the effects of their adoption on net rice income (NRI) in three provinces as follows: Thai Binh (North), Ha Tinh (Central) and Bac Lieu (South). Design/methodology/approach Determinants of adoption of CSATs and the adoption effects on NRI are analyzed by using a multinomial endogenous switching regression framework. Findings The results showed that gender, age, number of family workers, climate-related factors, farm characteristics, distance to markets, access to climate information, confidence on the know-how of extension workers, membership in social/agricultural groups and attitude toward risk were the major factors affecting the decision to adopt CSATs. However, the effects of these factors on the adoption of CSATs varied across three provinces. These technologies when adopted tend to increase NRI but the increase is much greater when these are combined. Practical implications It is important to consider first the appropriateness of the CSA packages to the specific conditions of the target areas before they are promoted. It is also necessary to enhance the technical capacity of local extension workers and provide farmers more training on CSATs. Originality/value This study is the first attempt to identify key determinants of adoption of CSATs either singly or in combination and the adoption effects on NRI in Vietnam.

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Section: Determinants Of Adoption Of Climate-smart Agricultural Technsupporting

confidence: 91%

Section: Climate-smart Agriculture Technologiesmentioning

confidence: 99%

Section: Counterfactual Analysismentioning

confidence: 99%

Section: Counterfactual Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Determinants of adoption of climate-smart agriculture technologies in rice production in Vietnam

Tran

Rañola

Sander³

et al. 2019

IJCCSM

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Improving targeting of farmers for enrollment in agri‐environmental programs

Duke

Johnston

Shober

et al. 2022

Applied Eco Perspectives Pol

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Agricultural cost-share research and planning tend to focus on one program at a time, and hence overlook additional efficiencies that might be obtained by considering the possibility that enrollment decisions are related across different programs. Models of multipleprogram participation decisions enable these relationships to be considered as part of conditional enrollment predictions, providing more complete and accurate understanding of enrollment behavior. Analysis of data from farmer surveys in Maryland and Ohio show complementary drivers across program participation. Results are consistent with economies of scale and/or scope among different agri-environmental programs.The data also show the gains in prediction accuracy when the model accounts for participation in other programs, thereby enabling improved targeting and program design. For instance, enrollment in commoditytype programs causes a much larger marginal increase (12.6%) in the probability of Maryland cover crops participation than does the increase from Conservation Reserve Program enrollment (4.4%).

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Unlocking Climate Finance Potential for Climate Adaptation: Case of Climate Smart Agricultural Financing in Sub Saharan Africa

Mungai

Ndiritu

Silva

2020

African Handbook of Climate Change Adaptation

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Climate change has emerged as one of the greatest challenges faced by the world today. Adverse impacts of climate change are visible across sectors like agriculture and other natural resources due to increasing average temperature and changing weather patterns. Africa constitutes around 13% of the global population but contributes the least (around 2%) to greenhouse gas (GHG) emissions globally. Concerning the global climate vulnerability index, Africa is most impacted (around 21%) by climate change and its’ population is most vulnerable to climate sensitivity and fragility of the continent’s natural environment and increasingly erratic weather patterns, low adoption of climate-resilient technologies, and high dependence on environment-based livelihoods. Hence, Africa needs to adopt low carbon and climate-resilient development to address climate-related issues and to have sustainable development. In line with the low carbon/climate-resilient development agenda, 53 countries (except Libya) have submitted Nationally Determined Contribution (NDC) and have set ambitious targets under NDC and Sustainable Development Goals. A quick analysis of the NDCs and various studies indicates the enormity of the financing needs. According to Climate Invetsment Funds (CFI), Sub-Saharan Africa will require an estimated USD222 billion for climate resilience investments to reach its NDCs. One of the critical stakeholders to play a key role in meeting the financing needs of climate-smart agriculture (CSA) related targets is the private sector. There is around 98% gap in financing for CSA. Even though substantial climate finance potential exists in selected countries for the private sector, there are certain challenges and barriers like financial, policy, lack of awareness, and low provision for climate funding in the national budget.

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Determinants of Climate Smart Agriculture Technology Adoption in the Drought Prone Districts of Malawi using a Multivariate Probit Analysis

Cited by 21 publications

References 0 publications

Determinants of adoption of climate-smart agriculture technologies in rice production in Vietnam

Determinants of adoption of climate-smart agriculture technologies in rice production in Vietnam

Improving targeting of farmers for enrollment in agri‐environmental programs

Unlocking Climate Finance Potential for Climate Adaptation: Case of Climate Smart Agricultural Financing in Sub Saharan Africa

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