Crop yield simulation optimization using precision irrigation and subsurface water retention technology

• Published in: Environmental modelling & software : with environment data news 2019-09, Vol.119, p.433-444
• Main Authors: Roy, Proteek Chandan, Guber, Andrey, Abouali, Mohammad, Nejadhashemi, A. Pouyan, Deb, Kalyanmoy, Smucker, Alvin J.M.
• Format: Article
• Language: English
• Subjects: Agricultural production; Agricultural technology; Computer applications; Computer programs; Computer simulation; Coordination; Corn; Crop growth; Crop production; Crop yield; Crops; DSSAT; Flow simulation; HYDRUS-2D; Irrigation; Irrigation rate; Irrigation scheduling; Irrigation water; Maximization; Membranes; Multi-objective optimization; Multiple objective analysis; Nutrient flow; Nutrients; Optimization; Rain water; Rainfall; Retention; Sandy soils; Schedules; Software; Soil water; Subsurface water; Sustainable agriculture; Two dimensional models; Water conservation; Water consumption; Water use; Water use efficiency
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2019.07.006

Maximizing crop production with minimal resources such as water and energy is the primary focus of sustainable agriculture. Subsurface water retention technology (SWRT) is a stable approach that preserves water in sandy soils using water saving membranes. An optimal use of SWRT depends on its shape, location and other factors. In order to predict crop yield for different irrigation schedule, we require at least two computational processes: (i) a crop growth modeling process and (ii) a water and nutrient permeation process through soil to the root system. Validation of software parameters to suit properties of specific field becomes increasingly hard since they involve a coordination with field data and coordination between two software. In this paper, we propose a computationally fast approach that utilizes HYDRUS-2D software for water and nutrient flow simulation and DSSAT crop simulation software with an evolutionary multi-objective optimization (EMO) procedure in a coordinated manner to minimize water utilization and maximize crop yield prediction. Our proposed method consists of training one-dimensional crop model (DSSAT) on data generated by two dimensional model calibrates and validates (HYDRUS-2D), that accounts for water accumulation in the SWRT membranes. Then we used DSSAT model to find the best irrigation schedules for maximizing crop yield with the highest plant water use efficiency (Tambussi et al., 2007; Blum, 2009) using for the EMO methodology. The optimization procedure minimizes water usage with the help of rainfall water and increases corn yield prediction as much as six times compare to a non-optimized and random irrigation schedule without any SWRT membrane. Our framework also demonstrates an integration of latest computing software and hardware technologies synergistically to facilitate better crop production with minimal water requirement. •Precision irrigation using subsurface water retention technology (SWRT) is optimized•Water and nutrient mobility are simulated using HYDRUS-2D software•HYDRUS-2D’s computational complexity is alleviated using a calibration procedure of DSSAT software which is fast•A multi-objective optimization method is employed to obtain optimal irrigation practices for minimum water usage and maximize crop growth.•This paper depicts how recent computational intelligence methods can be utilized to integrate two irrigation-based simulation software with weather and soil characteristics to obtain two important goa