ESTIMATION AND MODELLING OF POTATO WATER FOOTPRINT USING MACHINE LEARNING APPROACH IN NILE DELTA, EGYPT

Abstract

According to Egyptian sustainable development strategy 2030, the promotion of food security, nutrition, and sustainable agricultural growth. Irrigated agriculture has been enhanced food security however, it increased the pressure on water resources especially with the huge food demand due to population growth and socio-economic development. Integrated water management means water management in terms of the amount of water and the time in which the water is added so that there is no water loss or any increase in costs, and the integrated water management helps to add the required quantities of water at the right time. The water footprint of crops is one of the means of integrated water management, as it helps in saving water quantities to obtain the highest efficiency of water use in countries, as well as to obtain the highest return from one cubic meter of water. Water footprint (WF), an indicator that includes both direct and indirect use of water, is a measure of how much fresh water a product consumes during its life cycle. Its components are green, blue and gray water. The amount of rainwater absorbed by plants but not the runoff is referred to as the green WF. The amount of water from rivers, reservoirs and groundwater is represented by the blue WF. The concept of the water footprint as a volumetric indicator of water use can be estimated, by dividing the actual evapotranspiration of a plant by the yield of the crop. Crop evapotranspiration is essential in water