An Empirical Economic Assessment of Impacts of Climate Change on Agriculture in Zambia

This report assesses the economic impacts of climate change on agriculture in Zambia, using the Ricardian method. A multiple linear regression model with net revenue per hectare as response variable has been fitted with climate, hydrological, soil, and socioeconomic variables as explanatory variables. There is one main cropping season in Zambia, lasting from November to April. Crop production in this period depends solely on rains. Considering crop progression in three stages-germination, growing, and maturing, which require different amounts of water and temperature-the climate variables included in the model are long-term averages of the temperature and wetness index for the periods November to December, January to February, and March to April. Assuming a nonlinear relationship of farm revenue with the climate variables, quadratic terms for climate variables were also included in the model. The results indicate that most socioeconomic variables are not significant, whereas some climate variables and the corresponding quadratic variables are significant in the model. Further findings are that an increase in the November-December mean temperature and a decrease in the January-February mean rainfall have negative impacts on net farm revenue, whereas an increase in the January-February mean temperature and mean annual runoff has a positive impact.

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Bibliographic Details
Main Author: Jain, Suman
Language:English
Published: World Bank, Washington, DC 2007-07
Subjects:ADVERSE IMPACTS, AGRICULTURAL INPUTS, AGRICULTURAL LAND, AGRICULTURAL PRACTICES, AGRICULTURAL PRODUCTION, AGRICULTURAL PRODUCTIVITY, ANTHROPOGENIC EMISSIONS, ATMOSPHERE, CARBON, CARBON DIOXIDE, CASSAVA, CLIMATE, CLIMATE CHANGE, CLIMATE CHANGE RESEARCH, CLIMATE CHANGES, CLIMATE FORECASTS, CLIMATE VARIABILITY, CLIMATE VARIABLES, CLIMATE ZONES, CLIMATES, CLIMATIC RESEARCH, CLIMATIC RESEARCH UNIT, CLIMATOLOGY, CLOUDS, COMMERCIAL FARMERS, COTTON, CROP, CROP FORECASTING, CROP PRODUCTION, CROP VARIETIES, CROPPING, CROPS, CULTIVATION, DESERTIFICATION, DROUGHT, DRY SEASON, DRY SEASONS, ECOLOGICAL ZONE, ECOLOGICAL ZONES, ECONOMIC IMPACTS, FAO, FARM, FARM HOUSEHOLDS, FARM PRODUCE, FARMER, FARMERS, FARMING, FARMING SYSTEMS, FARMS, FERTILIZATION, FERTILIZER, FISHERIES, FLOODS, FOOD SECURITY, FRAMEWORK CONVENTION ON CLIMATE CHANGE, GDP, GLOBAL ENVIRONMENT, GLOBAL ENVIRONMENT FACILITY, GLOBAL WARMING, GRAIN, GRAZING, GREENHOUSE GAS, GREENHOUSE GAS EMISSIONS, GREENHOUSE GASES, GROUNDNUTS, GROWING SEASON, HARVESTING, HECTARES OF LAND, HYDROLOGICAL DATA, HYDROLOGY, HYDROMETEOROLOGY, IRRIGATION, LABOR FORCE, LAND USE, LAND VALUE, LANDS, MAIZE, MAIZE PRODUCTION, MARGINAL REVENUE, METEOROLOGICAL DATA, METEOROLOGICAL STATIONS, MILLET, NATURAL RESOURCES, PADDY, PLANTING, POLLUTANTS, PRECIPITATION, PRODUCE, RAINFALL, RAINFED AGRICULTURE, RANGELAND, RICE, RUNOFF, SATELLITES, SEA, SEAS, SEED, SEED BANKS, SEED COTTON, SEEDS, SMALL-SCALE FARMERS, SOCIOECONOMIC VARIABLES, SOIL, SOIL TYPE, SOIL TYPES, SOILS, SORGHUM, SOYA BEANS, SUNFLOWER, SURFACE TEMPERATURE, SWEET POTATOES, TEMPERATURE, TEMPERATURE CHANGE, TOBACCO, TOTAL REVENUE, TROPICAL CLIMATE, VEGETATION, WATER HARVESTING, WATER RESOURCES, WATER SHORTAGES, WEATHER, WET SEASON, WHEAT,
Online Access:http://documents.worldbank.org/curated/en/2007/07/8003696/empirical-economic-assessment-impacts-climate-change-agriculture-zambia
https://hdl.handle.net/10986/7478
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Summary:This report assesses the economic impacts of climate change on agriculture in Zambia, using the Ricardian method. A multiple linear regression model with net revenue per hectare as response variable has been fitted with climate, hydrological, soil, and socioeconomic variables as explanatory variables. There is one main cropping season in Zambia, lasting from November to April. Crop production in this period depends solely on rains. Considering crop progression in three stages-germination, growing, and maturing, which require different amounts of water and temperature-the climate variables included in the model are long-term averages of the temperature and wetness index for the periods November to December, January to February, and March to April. Assuming a nonlinear relationship of farm revenue with the climate variables, quadratic terms for climate variables were also included in the model. The results indicate that most socioeconomic variables are not significant, whereas some climate variables and the corresponding quadratic variables are significant in the model. Further findings are that an increase in the November-December mean temperature and a decrease in the January-February mean rainfall have negative impacts on net farm revenue, whereas an increase in the January-February mean temperature and mean annual runoff has a positive impact.