Combined simulation and optimization framework for irrigation scheduling in agriculture fields

Abstract

In the context of growing evidence of climate change and the fact that agriculture uses about 70% of all the water available for irrigation in semi-arid areas, there is an increasing probability of water scarcity scenarios. Water irrigation optimization is, therefore, one of the main goals of researchers and stakeholders involved in irrigated agriculture. Irrigation scheduling is often conducted based on simple water requirement calculations without accounting for the strong link between water movement in the root zone, soil–water–crop productivity and irrigation expenses. In this work, we present a combined simulation and optimization framework aimed at estimating irrigation parameters that maximize the crop net margin. The simulation component couples the movement of water in a variably saturated porous media driven by irrigation with crop water uptake and crop yields. The optimization component assures maximum gain with minimum cost of crop production during a growing season. An application of the method demonstrates that an optimal solution exists and substantially differs from traditional methods. In contrast to traditional methods, results show that the optimal irrigation scheduling solution prevents water logging and provides a more constant value of water content during the entire growing season within the root zone. As a result, in this case, the crop net margin cost exhibits a substantial increase with respect to the traditional method. The optimal irrigation scheduling solution is also shown to strongly depend on the particular soil hydraulic properties of the given field site.

Conjunctive Water Management for Agriculture With Groundwater Salinity

“…Even after recharging, the water-table is assumed to not reach the root zone or cause soil salination from capillary rise; (d) salts are perfectly mixed in the aquifer at the end of the planning horizon (see Appendix B) without salt precipitation onto or dissolution from aquifer materials. The salinity relationships in this model could be modified by using regression to predict groundwater salinity changes (Lefkoff & Gorelick, 1990b) or applying models such as HYDRUS to simulate the movement of water and salt transport (Fontanet et al, 2022); (e) the effects of salinity on crop yield is simplified, especially for long-lasting harm to perennial crops. Maas and Grattan (1999) and Grattan (2002) can be applied to model salinity's effects on more crops in California; (f) the same blended mix of groundwater and surface water is applied to both perennial and annual crops.…”

High-technology agriculture using the internet of things and cloud computing for an efficient Smart Irrigation System

“…The smallest farming properties dominate most Moroccan agriculture. On another side, the fact that their agricultural returns are related to the climate, low and erratic rainfall from one year to another, and even within a year, results in poor crop quality and large financial losses owing to poor productivity [7][8][9].…”