Adoption Determinants of Adapted Climate Smart Agriculture Technologies Among Smallholder Farmers in Machakos, Makueni, and Kitui Counties of Kenya

Muriithi, Lydia N.; Onyari, Charles N.; Mogaka, Hezron; Gichimu, Bernard M.; Gatumo, Geoffrey N.; Kwena, Kizito

doi:10.4314/jae.v25i2.7

Cited by 13 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Moronge and Nyamweya (2019), farm size influences adoption as large land gives space to experiment and practice of innovations. The findings agree with those of Muriithi et al, (2021) who indicated that farmers with larger farms, had a higher likelihood of adopting intercropping and crop rotation than those with smaller farms. The findings further reveal a negative association between farm size and uptake of cereal-legume intercrop and pasture cropping .The findings agree with those by Llones and Suwanmaneepong (2021) that increasing farm size will more likely increase input usage under conventional production among nonadopters thus reducing chances of adopting new innovations.…”

Section: Support Required By Farming Households To Increase Uptake Of...supporting

confidence: 90%

“…Generally, education is believed to increase a farmer's awareness and understanding of new agricultural innovations. Muriithi et al (2021) and Zakaria et al (2020) argue that some technologies are knowledge intensive and require basic education to facilitate use and adoption. Therefore this finding challenges those of (Muriithi et al, 2021) and other earlier empirical evidences (Tokede et al, 2020;Zakaria et al, 2020) that show a positive association between education level and farmers' decision to adopt new innovations.…”

Section: Support Required By Farming Households To Increase Uptake Of...mentioning

confidence: 99%

See 1 more Smart Citation

Socioeconomic Factors Influencing Uptake of Regenerative Agriculture Technologies in the Dry-lands of Embu County, Kenya

Otara¹,

Mogaka²,

Ndirangu³

et al. 2023

jae

View full text Add to dashboard Cite

This study evaluated socioeconomic factors influencing the uptake of regenerative agriculture technologies in the dry lands of Embu County. Semi-structured questionnaires were administered to 400 farm households. Multivariate Probit model (MVP) and percentage were used to analyse the data. The findings of the study indicate that several socioeconomic factors including farming experience, farm size, main occupation, off-farm activities, age, gender, marital status and education level influenced the uptake of various regenerative agriculture technologies. Government and other inventors should take these factors into consideration while making decisions and formulating policies to support the dissemination and uptake of agricultural innovations.

show abstract

Section: Support Required By Farming Households To Increase Uptake Of...supporting

confidence: 90%

Section: Support Required By Farming Households To Increase Uptake Of...mentioning

confidence: 99%

Socioeconomic Factors Influencing Uptake of Regenerative Agriculture Technologies in the Dry-lands of Embu County, Kenya

Otara¹,

Mogaka²,

Ndirangu³

et al. 2023

jae

View full text Add to dashboard Cite

show abstract

“…Educated people are also in better position to understand the benefits of cowpea storage both from economic and health perspective and hence tend to use the technology more. This corroborates Muriithi et al, 2021) who found education level to affect adoption of climate smart agriculture technology among smallholder farmers in Machakos, Makueni, and Kitui Counties of Kenya. This is contrary to the assertions of Moussa et.…”

Section: Drivers Of Adoption Of Pics Technology In the Study Areasupporting

confidence: 86%

Adoption of Purdue Improved Cowpea Storage in North-East Nigeria

Murtala¹,

Yakubu²,

Muhammad³

et al. 2021

JASD

View full text Add to dashboard Cite

The study explored the general adoption of Purdue improved cowpea storage (PICS) technology in the fragile north-eastern Nigeria. Multi-stage sampling approach was used to select 3,000 respondents from the study area. The primary data for this study were collected in 2020 through a field survey conducted by the researchers through the use of structured questionnaire. Both descriptive and inferential statistics (Propensity score matching [PSM] and Inverse probability weighted regression adjusted [IPWRA]) were used. ATBU-PICS project had increased the adoption by 8-25 PICS bags with a standard error of 11.30-14.11 using different matching algorithm. The adoption was found to be influenced positively by age, years of formal education, participation into ATBU-PICS, knowledge of PICS technology and farm size at P≤0.01, P≤0.01, P≤0.01, P≤0.01 and P≤0.05, respectively. Sex and farming experience negatively affected the adoption of PICS at P≤0.01 and P≤0.01, respectively. The study therefore, concluded that the age, years of formal education, participation into ATBU-PICS, knowledge of PICS technology and farm size were the factors that influenced adoption of PICS in the study area. It was recommended that more awareness creation is needed to generate more PICS technology knowledge so as to achieve further penetration and further project should be initiated to complement the previous efforts in the study area.

show abstract

“…Nonetheless, a higher percentage of farmers-33.3% in aza and 42.7% in Giyani-had no formal education, indicating lower levels of literacy study shows that there are more uneducated farmers in the study area. This should cate the need for a major intervention, as education is believed to improve an individ reasoning capability and increase the awareness of variable technologies that ca adopted [53]. There was high exposure and awareness of climate risks, which implies most farmers in the study areas experienced different agro-ecological zones and cli Table 2 presents the sampled smallholder maize farmers' decision to adopt CSA in Ga-Makanye, Gabaza, and Giyani.…”

Section: Smallholder Maize Farmers' Willingness To Adopt Csa In Ga-ma...mentioning

confidence: 99%

Adaptation through Climate-Smart Agriculture: Examining the Socioeconomic Factors Influencing the Willingness to Adopt Climate-Smart Agriculture among Smallholder Maize Farmers in the Limpopo Province, South Africa

Machete,

Senyolo,

Gidi

2024

Climate

View full text Add to dashboard Cite

Agriculture contributes to the South African economy, but this sector is highly vulnerable to climate change risks. Smallholder maize farmers are specifically susceptible to climate change impacts. The maize crop plays a crucial role in the country’s food security as is considered a staple food and feed. The study aimed at examining the socioeconomic factors influencing smallholder maize farmers’ willingness to adopt climate-smart agriculture in the Limpopo Province, South Africa. It was conducted in three different areas due to their specific agro-ecological zones. A multipurpose research design was used to gather data, and multistage random sampling was used to choose the study areas. Subsequently, 209 purposefully selected farmers were interviewed face-to-face using structured questionnaires and focus discussion groups. Descriptive results revealed that 81%, 67%, and 63% farmers in Ga-Makanye, Gabaza, and Giyani were willing to adopt CSA. Using the double-hurdle model, the t-test was significant at 1%, Prob > chi2 = 0. 0000, indicating a good model. At a 5% confidence level, education, crop diversification, and information about climate-smart agriculture (CSA) positively influenced adoption, while household size and agricultural experience negatively influenced it. It is recommended that the Department of Agriculture, Land Reform, and Rural Development provide CSA workshops and educational programs to farmers to enhance their knowledge and decision-making processes regarding adaptation strategies.

show abstract

Adoption Determinants of Adapted Climate Smart Agriculture Technologies Among Smallholder Farmers in Machakos, Makueni, and Kitui Counties of Kenya

Cited by 13 publications

References 15 publications

Socioeconomic Factors Influencing Uptake of Regenerative Agriculture Technologies in the Dry-lands of Embu County, Kenya

Socioeconomic Factors Influencing Uptake of Regenerative Agriculture Technologies in the Dry-lands of Embu County, Kenya

Adoption of Purdue Improved Cowpea Storage in North-East Nigeria

Adaptation through Climate-Smart Agriculture: Examining the Socioeconomic Factors Influencing the Willingness to Adopt Climate-Smart Agriculture among Smallholder Maize Farmers in the Limpopo Province, South Africa

Contact Info

Product

Resources

About