Precision agriculture: Evaluating a numerical model for optimal irrigation scheduling
Agriculture stands without a doubt as the largest consumer of water, particularly evident in arid regions where up to 85% of fresh water is used for irrigation, often with inefficient practices (Er-Raki et al., 2021). With the rapid growth of the global population and escalating demands for food production, a shift in irrigation practices needs to happen to achieve both optimal crop yields and foster sustainable development. Ideally, irrigation should be applied in the right amount, at the right time and in the right place.
Understanding the temporal and spatial distribution of water in soil is a key factor for the effective use of water resources in agriculture. Currently, many technologies that can quantify flow processes and soil-water status are being used. These technologies usually measure soil moisture directly in situ, which produces very accurate results, but their use is limited by their reach and operation costs. An alternative lies in models, with two primary approaches — volume balance models and dynamic models. Volume balance models work on the principle of describing the relationships between inputs, outputs, and changes in the accumulated volume in the system. These models are very popular because they are relatively simple and require few input parameters. However, more detailed description of the interactions between various factors such as water flow, evaporation and heat transport are provided by dynamic models (Panigrahi and Panda, 2003).
In an upcoming project, our objective is to employ a numerical model that combines hydrodynamical and thermodynamical approaches for short-term predictions of soil water content and temperature based on weather forecasts. This model applies the finite element method to solve the Richards equation, accounting for phase changes due to evaporation and freezing.
A drawback of this model lies in the number of needed input parameters. Detailed soil analysis will have to be conducted to determine the retention curve as well as other soil hydraulic and thermal properties. Meteorological data including air temperature, relative humidity, wind speed and solar radiation will be obtained from weekly weather forecasts. A week after the simulation a comparison between modeled soil moisture and temperature with on-site measured data will be conducted. Subsequently, the accuracy of the weather forecast will be evaluated to determine the impact of any inaccuracies on the model results. This assessment aims to validate the reliability of the model and to analyze the extent to which uncertainties in weather predictions may influence the precision of soil moisture and temperature forecasts.
Overall, the goal of this project is to determine the reliability and effectiveness of the numerical model in forecasting soil moisture and temperature and subsequently lay the groundwork for developing optimal irrigation scheduling strategies.
References:
Er-Raki, S., Ezzahar, J., Merlin, O., Amazirh, A., Hssaine, B.A., Kharrou, M.H., Khabba, S., Chehbouni, A., 2021. Performance of the HYDRUS-1D model for water balance components assessment of irrigated winter wheat under different water managements in semi-arid region of Morocco. Agricultural Water Management 244, 106546. https://doi.org/10.1016/j.agwat.2020.106546
Panigrahi, B., Panda, S.N., 2003. Field test of a soil water balance simulation model. Agricultural Water Management 58, 223–240. https://doi.org/10.1016/S0378-3774(02)00082-3
