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This work uses a contingent valuation and a factor analysis to respectively measure pastoralists' willingness to pay (WTP) and characterize their profile in the context of Senegal. Using primary data on 300 pastoralists, our results show that 50% of the respondents are ready to pay at least 3000 CFA (around 6 USD) to insure against forage shortage...
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Introduction:
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... In 1998In , 2010In , 2020, June forecasts of extremely warm west Pacific SSTs correctly indicated OND droughts (Figure 2b) that led to widespread livestock loss and plummeting livestock prices. Index-Based Livestock Insurance is another promising intervention strategy that targets pastoralists and agro-pastoralists who face some of the most-extreme risks from drought (Syll, 2021). Climate forecasts (Figure 3b) might be combined with Predictive Livestock Early Warning Systems (Matere et al., 2020) to improve predictions of forage conditions. ...
... In November 2022, at COP27, the UN Secretary-General unveiled the "Early Warnings for All Plan" (WMO, 2022) which provides $3.1 billion USD to support EWS in developing countries. The plan supports four disaster-risk reduction (Syll, 2021) pillars: (a) Disaster-risk knowledge, (b) Observations and Forecasting, (c) Preparedness and response, and (d) Dissemination and communication. EWS "are a proven, effective, and feasible climate adaptation measure, that save lives, and provide a tenfold return on investment," (Global Commission on Adaptation, 2019) which have been recognized by the IPCC as a key adaptation strategy (Pörtner et al., 2022). ...
This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization “Early Warning for All” Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations.
Poultry and pig farmers in the West Region of Cameroon inherently face risks. Notwithstanding, no insurance company offers indemnity insurance to cover these risks. This study investigates the premiums poultry and pig farmers are willing to pay for insurance and the determinants of these premiums. A quantitative design was employed, involving a sample of 484 poultry and pig farmers selected through cluster and snowball sampling techniques from the Mifi, Koung-Khi, Bamboutos, and Upper-Plateau Divisions. Primary data were collected using structured questionnaires (of which 430 questionnaires were retrieved), and quantitative analyses were conducted using the Chi-Square, Logistic Regression, and Integrated Value Mapping Tests. This study revealed that most farmers (40.7%) are willing to pay $79 (50,000CFA) for indemnity insurance. For poultry farmers, household and flock size are significant determinants. Production factors influenced the premiums Original Research Article Oben et al.; Asian J. 115 they are willing to pay for insurance than socioeconomic factors, with a predictive power/explanatory power of 33.9% and 9.5%, respectively. The Integrated Value Mapping (IVM) combining the predictive effects of both components was 38%, implying that 62% variability was not explained, as there are other factors to reckon with. For pig farmers, years of farming experience, annual farming income, division, household and flock size are significant determinants of the premium farmers are willing to pay for insurance. Socioeconomic factors predicted their willingness to subscribe to insurance almost at the same degree as production factors, with a predictive power/explanatory of 61.6% and 62.4%, respectively. The Integrated Value Mapping (IVM) combining the predictive effects of both components was 74.3%, implying that 25.7% variability was not explained as there are other factors to reckon with. This study recommends that the government and development partners should establish premium-subsidised indemnity insurance initiatives, especially for small-scale farmers.
Poultry and pig farmers in the West Region of Cameroon inherently face risks. Notwithstanding, no insurance company offers indemnity insurance to cover these risks. This study investigates the premiums poultry and pig farmers are willing to pay for insurance and the determinants of these premiums. A quantitative design was employed, involving a sample of 484 poultry and pig farmers selected through cluster and snowball sampling techniques from the Mifi, Koung-Khi, Bamboutos, and Upper-Plateau Divisions. Primary data were collected using structured questionnaires (of which 430 questionnaires were retrieved), and quantitative analyses were conducted using the Chi-Square, Logistic Regression, and Integrated Value Mapping Tests. This study revealed that most farmers (40.7%) are willing to pay $79 (50,000CFA) for indemnity insurance. For poultry farmers, household and flock size are significant determinants. Production factors influenced the premiums Original Research Article Oben et al.; Asian J. 115 they are willing to pay for insurance than socioeconomic factors, with a predictive power/explanatory power of 33.9% and 9.5%, respectively. The Integrated Value Mapping (IVM) combining the predictive effects of both components was 38%, implying that 62% variability was not explained, as there are other factors to reckon with. For pig farmers, years of farming experience, annual farming income, division, household and flock size are significant determinants of the premium farmers are willing to pay for insurance. Socioeconomic factors predicted their willingness to subscribe to insurance almost at the same degree as production factors, with a predictive power/explanatory of 61.6% and 62.4%, respectively. The Integrated Value Mapping (IVM) combining the predictive effects of both components was 74.3%, implying that 25.7% variability was not explained as there are other factors to reckon with. This study recommends that the government and development partners should establish premium-subsidised indemnity insurance initiatives, especially for small-scale farmers.
