Optimization of irrigation scheduling for spring wheat with mulching and limited irrigation water in an arid climate
Graphical abstract
The dynamic process of evaporation and transpiration was estimated from the dual crop coefficient model for crop evapotranspiration and field water balance model. The crop yield was estimated from the evapotranspiration processes with the crop water production model. The optimal irrigation scheduling for maximum yield with limited water supply can be achieved by the simulation-based optimization model using genetic algorithm.
Introduction
Water shortage is a frequent occurrence in arid and semiarid regions, which is a limiting factor for crop growth in these regions. Therefore, it is important to use limited water resources more efficiently to increase crop yield and water use efficiency (WUE = crop yield/evapotranspiration (ET)). At a cropland scale, this may be achieved by a combination of management (such as optimal irrigation scheduling) and agronomic (such as plastic or straw mulch) measures under a specific irrigation method.
Appropriate irrigation scheduling to allocate limited irrigation water properly in time and space is an effective way to balance water-saving and high crop yield by improving the marginal benefit produced by per unit water (Fereres and Soriano, 2007). For this purpose, appropriate optimization method or simulation-based optimization method (Singh, 2012, Singh, 2014a, Allam et al., 2016) can be used.
Optimization methods applied to deficit irrigation usually aims to maximize an objective function such as crop yield, net benefit, or WUE under several constraints (Garcia-Vila et al., 2009, Akhtar et al., 2013, Garg and Dadhich, 2014, Leite et al., 2015). Traditional optimization methods mainly include linear (Anwar and Clarke, 2001, Lu et al., 2011), nonlinear (Benli and Kodal, 2003, Ghahraman and Sepaskhah, 2004), and dynamic programming (Prasad et al., 2006, Jin et al., 2012). In recent years, some new methods have been used in irrigation scheduling, including genetic algorithms (GA) (Wu et al., 2007, Moghaddasi et al., 2010), an effective global random search method, and simulated annealing algorithms (Brown et al., 2010), a probabilistic technique for approximating the global optimum. However, pure optimization methods usually over-simplified the impact of irrigation on cropland ET, field water balance, and crop growth and yield, and can find the optimal irrigation amount in a growth stage or time interval that is not readily available for irrigation water management (Mao and Shang, 2014).
To solve this problem, simulation-based optimization methods were used in water resources allocation (Safavi et al., 2010, Sedki and Ouazar, 2011, Singh, 2014b) and irrigation scheduling (Shang, 2005, Shang and Mao, 2006, Soundharajan and Sudheer, 2009) for its advantages of determining the irrigation time accurately. The simulation-based optimization method integrates the superiority of simulation models in describing the ET process and crop yield and optimization algorithm in finding the optimal solution. Therefore, it is appropriate to be used in irrigation scheduling by fully considering the complex relations of irrigation with ET and crop yield.
Mulching is an important agronomic practice to improve WUE and crop yield for its effect of reducing soil water evaporation (E), conserving soil water, increasing soil temperature, and aiding in weed control (Allen et al., 1998, Igbadun et al., 2012). It was reported to be suitable for most crops, such as spring wheat (Li et al., 1999, Xie et al., 2005), maize (Li et al., 2013, Fan et al., 2016), and vegetables (Moreno and Moreno, 2008, Yaghi et al., 2013). However, some studies have also found that mulch would cause a higher ET than non-mulch because increased leaf area index (LAI) in mulching field significantly increased crop transpiration (T) (Xie et al., 2005, Chen et al., 2015). Consequently, inappropriate irrigation scheduling may cause a more severe water stress in some growth stages and result in a yield reduction for mulching cropland. Du et al. (2003) indicated that the mulch might even lead to a lower yield in cases of low initial soil water or long-time mulch in semiarid area. Therefore, the impact of mulching on ET and yield should be further studied.
Deficit irrigation, defined as the application of water below full irrigation (Fereres and Soriano, 2007), was also combined with mulch technology to reduce irrigation water use and to attain a higher crop yield. The effects of different irrigation scheduling and mulch on yield and WUE of various crops was investigated, such as wheat (Humphreys et al., 2011, Ram et al., 2013), tomato (Kere et al., 2003, Mukherjee et al., 2012), and other economic crops (Nayak et al., 2015, Kaur and Brar, 2016). Most of those studies designed the irrigation scheduling based on different irrigation frequency or irrigation amount of each application, which is usually sub-optimal irrigation water management and not applicable to cases with limited irrigation water.
Therefore, appropriate combination of optimal irrigation scheduling and mulching can be an effective way to increase crop yield and WUE in arid regions with limited water for irrigation. The main objectives of the current study were to develop a daily scale simulation-based optimization model for optimal irrigation scheduling to study optimal irrigation scheduling at different deficit irrigation scenarios for spring wheat (Triticum aestivum L.) in an arid region of Northwest China.
Section snippets
Simulation-based optimization method for irrigation scheduling of mulching cropland
Simulation-based optimization method applied to optimize crop irrigation scheduling with limited water and plastic mulch is composed of three parts, field water balance model, crop yield simulation model, and optimization method for irrigation scheduling (Fig. 1). First, for a specified irrigation scheduling (B2 in Fig. 1), the dynamic process of ET (B8) can be attained based on simulation of field water balance (B3-B8). Second, the relationship between crop ET and yield can be expressed by a
Field water balance model
Calibrated parameters for the field water balance model are given in Table 4. Results indicated that RMSEsws for M0 and M1 in the calibration period of 2014 were 15 mm and 17 mm, respectively (Table 5). While for the validation period of 2015, the RMSEsws for M0 and M1 were both about 21 mm, slightly greater than the RMSEsws of the calibration period. These values of RMSEsws are similar to results using different models for soil water simulation in cropland (Yang et al., 2012, Shang and Mao, 2014
Conclusions
We proposed a simulation-based optimization model for optimal irrigation scheduling of mulching crop aiming at maximum yield with limited irrigation water based on experiment data in 2014 and 2015. Based on the dual crop coefficient method, a field water balance model were used to simulate the processes of soil water evaporation, transpiration, and soil water content in the root zone for a given irrigation scheduling, and a crop-water production function was used to estimate the corresponding
Acknowledgments
The study was partially supported by National Natural Science Foundation of China (Grant Numbers 51279077 and 51679234). The authors would like to thank the editor and all the reviewers for their insightful comments and constructive suggestions.
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2023, Agricultural Water ManagementCitation Excerpt :The higher the TWC, the higher the grain yield (Table 6). However, the timing and amount of SI are determined by rainfall intensity (Wen et al., 2017). Previous studies showed that the results on the stages and timing of SI were different (Liu, 2021; Xu et al., 2018b; Xu et al., 2016; Xue et al., 2021), but irrespective of the rainfall intensity, the grain yield of wheat in the SI treatments improved significantly.