Journal of Irrigation and Drainage Engineering

Among the goals set by the United Nations 2030 agenda for sustainable development to address the economic component of sustainable development goal (SDG) target 6.4, More Crop Per Drop happens to be the universal motto for using water more efficiently in the agricultural sector. A fool-proof, transparent, integrated approach across different governing sectors is essential to optimally allocate the limited available resources amid the competing agricultural activities. This study has attempted to integrate a linear programming model on a geospatial platform to develop a spatial decision support and management system for integrated command area management. The study shows that the geospatial approach enhances the visualization of conventional optimization outputs under different irrigation efficiencies and fixed water allocation conditions. The geomatic model highlights water-stressed regions with 45% and optimal cropping patterns at 57% irrigation efficiency. The spatial optimization technique suggests geographic distribution of optimal cropping patterns to attain more agricultural productivity per unit of water.

Sedimentation in a drain system has been recognized as one of the major causes of nuisance flooding produced by localized storms in Southeast Texas. The objective of the study is to assess the sediment load from runoff under different hydrological and road conditions to implement design guidance for a proper self-cleaning drain system. A diffusion wave model is developed to simulate runoff hydrodynamics on the roadway with curb inlets. An empirical sediment load equation is developed as a function of rainfall intensities, road conditions, and field sediment characteristics. The required flow velocity to erode the cohesive deposited sediment, and transport the deposited sediment incorporating the sediment from runoff in the drainpipe has been investigated to implement design guidance for a self-cleaning drain system. Regional hydrological conditions and field sediment characteristics are critical to design an effective drainage system for improving coastal resilience under severe weather conditions.

Plastic film mulching in the field changes the soil structure and the normal distribution of soil water. Residual plastic film (RPF) in the soil causes serious environmental problems to the agricultural ecosystem. In order to determine the role of biochar in mitigating the negative effects of RPF pollution in the soil on agricultural production, in this work, we conducted a pot experiment without biochar and RPF as the control treatment. Four levels were set for biochar: no biochar ($0\mathrm{t}/{\mathrm{hm}}^2$), low biochar ($2.5\mathrm{t}/{\mathrm{hm}}^2$), medium biochar ($7.5\mathrm{t}/{\mathrm{hm}}^2$), and high biochar ($15\mathrm{t}/{\mathrm{hm}}^2$). Besides, four levels were set for the RPF: 0, 80, 320, and $720\mathrm{kg}/{\mathrm{hm}}^2$. The results showed that medium biochar could improve the soil environment of the RPF, the saturation water content in the soil was increased by 4.5%–11.4%, and the root activity was increased by 6.3%–13.4%. In addition, the root density of flue-cured tobacco was also significantly increased. Medium and high biochar had a significant effect on higher residual plastic film (320 and $720\mathrm{kg}/{\mathrm{hm}}^2$). It can alleviate soil tillage obstacles caused by plastic film residue, improve soil water-holding and water-releasing capacity, and optimize tobacco plant growth environment. This study provides evidence for the application of biochar as a soil ameliorant in residual film soil. This suggests that we should do more research on the environmental impact of RPF pollution on agroecosystems.

Three sets of drip emitter samples that were used in agricultural farms for 3–5 years were examined using a Computed Tomography (CT) scanner. The 2D slices and 3D images obtained were processed using Dragonfly 2020.1 software. Clogging material that was deposited gradually over the years on the emitter geometry was segmented using three different methods: (1) intensity thresholding, (2) machine learning (ML), and (3) deep learning (DL). The DL method not only delivered a more precise estimation of the quantity of clogging material, but also eased the segmentation process. Various measurements of emitter geometry, covering flow path and outlet areas, were taken and compared for three sample emitters. Clogging material got deposited predominantly on the outlet areas for all three samples, irrespective of their different usage times and emitter geometries. Efforts to optimize the design of emitters against clogging need to take this finding into consideration. Sample Emitter 2 with distinctly narrower flow path and smoother curved flow boundaries was found to have the least deposition of clogging material on its surface. Further study with a larger data set is required to establish a definite relationship between the geometric features and clogging intensity of drip emitters.

Climate change affects water resources quality and quantity and water consumption and crop yield through changes in the amount of precipitation and its pattern, temperature, evapotranspiration, erosion, and pollution wash off in a watershed. In this study, a simulation-optimization model was developed by coupling the soil and water assessment tool (SWAT) and nondominated sorting differential evolution (NSDE) algorithm to develop strategies for mitigation and adaptation to climate change. The multiobjective model was applied to maximize agricultural revenue and minimize nutrient discharge to the Minab reservoir. The calibrated and verified model was utilized to estimate future (2021–2099) variables. The annual precipitation and reservoir inflow will decrease by 14% and 46%, respectively. Climate change analysis indicated that the precipitation temporal pattern will change to early fall or late spring. The results of optimization showed that the agricultural revenue increased by 37%, and total nitrogen (TN) load decreased by 70%, compared to the current condition. Also, applying climate change to the optimum strategies could reduce total revenue and TN load by 57% and 41%, respectively.