Joseph Manzvera, Kwabena Asomanin Anaman, Akwasi Mensah-Bonsu, Alfred Barimah
{"title":"津巴布韦玉米种植户对加强季节性天气和气候服务的偏好:选择实验分析","authors":"Joseph Manzvera, Kwabena Asomanin Anaman, Akwasi Mensah-Bonsu, Alfred Barimah","doi":"10.1002/met.70040","DOIUrl":null,"url":null,"abstract":"<p>Using a discrete choice experiment, this article analyzed maize farmers' stated preferences for seasonal weather forecast attributes in Zimbabwe. Specifically, the study assessed the most preferred attributes of modern seasonal weather forecasts to guide investment priorities. The mixed logit model, which accounts for taste heterogeneity, was employed to analyze the data. The results show that maize farmers place positive utility on downscaling forecasts to the village level, bundling with agronomic advisory information, and a long lead time of 6 months ahead of the onset of the rainy season. Farmers are willing to pay 1.40 United States dollars (US$) for downscaling seasonal forecasts to the village level, US$1.50 for bundling seasonal weather forecasts with agronomic information such as suitable crop varieties to grow, and US$1.80 for disseminating seasonal forecasts with 6 months lead time. The marginal willingness to pay estimates translate to US$368 million economic value of modern seasonal weather forecasts per annum for all maize farmers in Zimbabwe. These findings underscored the importance attached to seasonal weather forecasts by farmers as a valuable decision-support service. Therefore, this study presents a compelling case for increasing national resource allocation towards the production and delivery of location-specific seasonal weather with a six-month lead time and bundling the forecasts with agronomic advisory information. Co-production of seasonal weather forecasts and integrating them with indigenous seasonal weather forecasts, as well as disseminating forecasts via mobile applications, could also be explored in addition to radio stations and extension agents. Public–private partnerships with private-sector players, such as telecommunication companies, could help to digitalize seasonal weather forecast dissemination.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"32 2","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70040","citationCount":"0","resultStr":"{\"title\":\"Preferences for enhanced seasonal weather and climate services among maize farmers in Zimbabwe: A choice experiment analysis\",\"authors\":\"Joseph Manzvera, Kwabena Asomanin Anaman, Akwasi Mensah-Bonsu, Alfred Barimah\",\"doi\":\"10.1002/met.70040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Using a discrete choice experiment, this article analyzed maize farmers' stated preferences for seasonal weather forecast attributes in Zimbabwe. Specifically, the study assessed the most preferred attributes of modern seasonal weather forecasts to guide investment priorities. The mixed logit model, which accounts for taste heterogeneity, was employed to analyze the data. The results show that maize farmers place positive utility on downscaling forecasts to the village level, bundling with agronomic advisory information, and a long lead time of 6 months ahead of the onset of the rainy season. Farmers are willing to pay 1.40 United States dollars (US$) for downscaling seasonal forecasts to the village level, US$1.50 for bundling seasonal weather forecasts with agronomic information such as suitable crop varieties to grow, and US$1.80 for disseminating seasonal forecasts with 6 months lead time. The marginal willingness to pay estimates translate to US$368 million economic value of modern seasonal weather forecasts per annum for all maize farmers in Zimbabwe. These findings underscored the importance attached to seasonal weather forecasts by farmers as a valuable decision-support service. Therefore, this study presents a compelling case for increasing national resource allocation towards the production and delivery of location-specific seasonal weather with a six-month lead time and bundling the forecasts with agronomic advisory information. Co-production of seasonal weather forecasts and integrating them with indigenous seasonal weather forecasts, as well as disseminating forecasts via mobile applications, could also be explored in addition to radio stations and extension agents. Public–private partnerships with private-sector players, such as telecommunication companies, could help to digitalize seasonal weather forecast dissemination.</p>\",\"PeriodicalId\":49825,\"journal\":{\"name\":\"Meteorological Applications\",\"volume\":\"32 2\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70040\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Meteorological Applications\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/met.70040\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METEOROLOGY & ATMOSPHERIC SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meteorological Applications","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/met.70040","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METEOROLOGY & ATMOSPHERIC SCIENCES","Score":null,"Total":0}
Preferences for enhanced seasonal weather and climate services among maize farmers in Zimbabwe: A choice experiment analysis
Using a discrete choice experiment, this article analyzed maize farmers' stated preferences for seasonal weather forecast attributes in Zimbabwe. Specifically, the study assessed the most preferred attributes of modern seasonal weather forecasts to guide investment priorities. The mixed logit model, which accounts for taste heterogeneity, was employed to analyze the data. The results show that maize farmers place positive utility on downscaling forecasts to the village level, bundling with agronomic advisory information, and a long lead time of 6 months ahead of the onset of the rainy season. Farmers are willing to pay 1.40 United States dollars (US$) for downscaling seasonal forecasts to the village level, US$1.50 for bundling seasonal weather forecasts with agronomic information such as suitable crop varieties to grow, and US$1.80 for disseminating seasonal forecasts with 6 months lead time. The marginal willingness to pay estimates translate to US$368 million economic value of modern seasonal weather forecasts per annum for all maize farmers in Zimbabwe. These findings underscored the importance attached to seasonal weather forecasts by farmers as a valuable decision-support service. Therefore, this study presents a compelling case for increasing national resource allocation towards the production and delivery of location-specific seasonal weather with a six-month lead time and bundling the forecasts with agronomic advisory information. Co-production of seasonal weather forecasts and integrating them with indigenous seasonal weather forecasts, as well as disseminating forecasts via mobile applications, could also be explored in addition to radio stations and extension agents. Public–private partnerships with private-sector players, such as telecommunication companies, could help to digitalize seasonal weather forecast dissemination.
期刊介绍:
The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including:
applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits;
forecasting, warning and service delivery techniques and methods;
weather hazards, their analysis and prediction;
performance, verification and value of numerical models and forecasting services;
practical applications of ocean and climate models;
education and training.