{"title":"Implications of climate-smart agriculture technology adoption on women's productivity and food security in Tanzania","authors":"Mkupete Jaah Mkupete, Jorge Davalos","doi":"10.1111/agec.12874","DOIUrl":null,"url":null,"abstract":"<p>Gender gaps in productivity and food security persist in the face of climate change, necessitating effective strategies for empowering women and reducing their vulnerability. This study examines the gender-specific impacts of climate-smart agriculture (CSA) adoption on productivity, food security, and resilience to climate shocks in Tanzania. Using panel data from the World Bank's Tanzanian Living Standard Measurement Survey (LSMS) spanning 2008–2013, we employ a multinomial switching regression model (MSRM) approach to identify the effects of CSA adoption on agricultural outcomes. Our findings reveal that CSA non-adoption exacerbates the gender gap in yields and food security, favoring men. However, CSA adoption leads to more equitable outcomes, bridging the gender gap and improving productivity and food security for both men and women. Additionally, adopters of CSA techniques exhibit greater resilience to climate shocks, experiencing smaller yield declines during periods of low rainfall. This study contributes to the literature by providing empirical evidence on the gendered impacts of CSA adoption, testing the risk reduction capacity of CSA technologies, and addressing the limited research on Tanzania. The findings emphasize the importance of gender-responsive CSA policies in promoting agricultural resilience and food security in the face of climate change.</p>","PeriodicalId":50837,"journal":{"name":"Agricultural Economics","volume":"56 2","pages":"247-267"},"PeriodicalIF":4.5000,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural Economics","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/agec.12874","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ECONOMICS & POLICY","Score":null,"Total":0}
引用次数: 0
Abstract
Gender gaps in productivity and food security persist in the face of climate change, necessitating effective strategies for empowering women and reducing their vulnerability. This study examines the gender-specific impacts of climate-smart agriculture (CSA) adoption on productivity, food security, and resilience to climate shocks in Tanzania. Using panel data from the World Bank's Tanzanian Living Standard Measurement Survey (LSMS) spanning 2008–2013, we employ a multinomial switching regression model (MSRM) approach to identify the effects of CSA adoption on agricultural outcomes. Our findings reveal that CSA non-adoption exacerbates the gender gap in yields and food security, favoring men. However, CSA adoption leads to more equitable outcomes, bridging the gender gap and improving productivity and food security for both men and women. Additionally, adopters of CSA techniques exhibit greater resilience to climate shocks, experiencing smaller yield declines during periods of low rainfall. This study contributes to the literature by providing empirical evidence on the gendered impacts of CSA adoption, testing the risk reduction capacity of CSA technologies, and addressing the limited research on Tanzania. The findings emphasize the importance of gender-responsive CSA policies in promoting agricultural resilience and food security in the face of climate change.
期刊介绍:
Agricultural Economics aims to disseminate the most important research results and policy analyses in our discipline, from all regions of the world. Topical coverage ranges from consumption and nutrition to land use and the environment, at every scale of analysis from households to markets and the macro-economy. Applicable methodologies include econometric estimation and statistical hypothesis testing, optimization and simulation models, descriptive reviews and policy analyses. We particularly encourage submission of empirical work that can be replicated and tested by others.