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      Farmers’ Adaptation Strategies to Climate Change and Their Implications in the Zou Department of South Benin

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      Environments
      MDPI AG

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          Abstract

          Climate change is a global phenomenon. Its impact on agricultural activities in developing countries has increased dramatically. Understanding how farmers perceive climate change and how they adapt to it is very important to the implementation of adequate policies for agricultural and food security. This paper aims to contribute to an understanding of farmers’ adaptation choices, determinants of the adaptation choices and the long-term implications of the adaptation choices. Data were collected from 120 respondents in the Zou Department of Benin. A binary logit model was used to analyze the factors influencing household decisions to adapt to climate change. Multinomial logistic regression analysis was estimated to analyze the factors influencing households’ choice of adaptation strategies to climate change. The results show that farmers have a developed perception of climate change. These changes are translated by rainfall disturbances (rainfall delays, early cessation, bad rainfall distribution etc.), shortening of the small dry season, increasing of temperature and sometimes, violent winds. The survey reveals that Benin farmers adopt many strategies in response to climate change. These strategies include “Crop–livestock diversification and other good practices (mulching, organic fertilizer),” “Use of improved varieties, chemical fertilizers and pesticides,” “Agroforestry and perennial plantation” and “Diversification of income-generating activities.” The findings also reveal that most of the respondents use these strategies in combination. From the binary logit model, we know that “farming experience” and “educational level of household head” positively influence adaptation decisions. The result of the multinomial logit analysis shows that farming experience, educational level, farm size and gender have a significant impact on climate change adaptation strategies. Based on in-depth analysis of each strategy, we identify crop diversification and agroforestry as being the most promising strategies with benefits for farmers, the environment and future generations.

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          Most cited references22

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          Resilience in Agriculture through Crop Diversification: Adaptive Management for Environmental Change

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            Is rainfall really changing? Farmers’ perceptions, meteorological data, and policy implications

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              Crop diversification and livelihoods of smallholder farmers in Zimbabwe: adaptive management for environmental change

              This paper demonstrates how crop diversification impacts on two outcomes of climate smart agriculture; increased productivity (legume and cereal crop productivity) and enhanced resilience (household income, food security, and nutrition) in rural Zimbabwe. Using data from over 500 smallholder farmers, we jointly estimate crop diversification and each of the outcome variables within a conditional (recursive) mixed process framework that corrects for selectivity bias arising due to the voluntary nature of crop diversification. We find that crop diversification depends on the land size, farming experience, asset wealth, location, access to agricultural extension services, information on output prices, low transportation costs and general information access. Our results also indicate that an increase in the rate of adoption improves crop productivity, income, food security and nutrition at household level. Overall, our results are indicative of the importance of crop diversification as a viable climate smart agriculture practice that significantly enhances crop productivity and consequently resilience in rural smallholder farming systems. We, therefore, recommend wider adoption of diversified cropping systems notably those currently less diversified for greater adaptation to the ever-changing climate.
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                Author and article information

                Journal
                Environments
                Environments
                MDPI AG
                2076-3298
                January 2018
                January 21 2018
                : 5
                : 1
                : 15
                Article
                10.3390/environments5010015
                20ae5bbd-c1e8-44ca-a308-b97dd0de0141
                © 2018

                https://creativecommons.org/licenses/by/4.0/

                History

                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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