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Forecasting Model of Wheat Yield in Relation to Rainfall Variability in North Africa Countries

Soliman, Ibrahim (2019): Forecasting Model of Wheat Yield in Relation to Rainfall Variability in North Africa Countries. Published in: International Journal of Food and Beverage Manufacturing and Business Models (IJFBMBM) , Vol. 2, No. 4 (July 2019)


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This study investigated the effect of rainfall variations on wheat yield in Morocco, was a case study of North African countries because it cultivates the largest arable and rainfed acreage in North Africa. The data covered the period 2004–2015 from 12 stations for weather forecasting. The estimated coefficient of variation of wheat yield ranged between 79.5% and 38.0%, as it increased in poor-rain years and in the regions of annual rainfall less than 350 mm. the high correlation between the number of rainy days and the annual rainfall in mm, implied to introduce only the later as an explanatory variable in the forecasting model of wheat grain yield. The double-log form was the best fitted model for such model. The wheat yield showed higher significant response to spring months rain fall changes than the annual rainfall. The double-log form of the monthly rainfall model to forecast wheat yield. The estimated elasticity coefficient showed that a 10% increase in March, April and May rainfall would result in 5.9%, -0.1% and 0.2% change in wheat yield, respectively. The estimated response of wheat farm price to grain yield showed that a 10% increase in wheat yield would decrease the farm gate price by 4.1% implying that It is a supply-oriented market. Recommended Policies include generating a national buffer stock and a regional strategic stock of wheat to compensate the negative impacts of rainfall fluctuation and drought years, based upon an integrated program among North African countries. To provide a supplementary water source to secure sufficient water for irrigation. The areas less than 300mm rainfall should be allocated for livestock feeds with economical range management.

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