Decision-making method for maize irrigation in supplementary irrigation areas based on the DSSAT model and a genetic algorithm

Wang, Yue; Jiang, Kongtao; Shen, Hongzheng; Wang, Nan; Liu, Ruizhe; Wu, Jiujiang

doi:10.1016/j.agwat.2023.108231

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…The formula for dry matter accumulation in the DSSAT model [ 38 ] is as follows:

where ∆TOT is the increment of daily dry matter mass of crop (t/hm 2 ); PARCE is photosynthetically active radiation conversion rate (g/MJ). IPAR is the amount of photosynthetically active radiation intercepted (MJ/hm 2 ); TOT is the total dry matter (t/hm 2 ); SWDF is a water stress factor affecting dry matter accumulation.…”

Section: Methodsmentioning

confidence: 99%

“…This novel method provided insights into the estimation of grain protein content and overall yield for corn. Wan et al [ 20 ] harnessed the DSSAT model to inform and improve irrigation strategies for summer maize, introducing a method to guide irrigation timing by comparing cumulative rainfall with cumulative evapotranspiration (ETc) data. Song et al [ 21 ] focused on refining the CERES-Maize model’s capacity to simulate maize growth under water-limited conditions.…”

Section: Introductionmentioning

confidence: 99%

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Study on Modeling and Evaluating Alfalfa Yield and Optimal Water Use Efficiency in the Agro-Pastoral Ecotone of Northern China

Miao,

Wang,

et al. 2024

Plants

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The agro-pastoral ecotone in northern China is the main production area of agriculture and animal husbandry, in which agricultural development relies entirely on groundwater. Due to the increasing water consumption of groundwater year by year, groundwater resources are becoming increasingly scarce. The substantial water demand and low germination rate in the first year are the main characteristics of alfalfa (Medicago sativa L.) yield in the agro-pastoral ecotone in northern China. Due to unscientific irrigation, water resources are seriously wasted, which restricts the development of local agriculture and animal husbandry. The study constructed the Dssat-Forages-Alfalfa model and used soil water content, leaf area index, and yield data collected with in situ observation experiments in 2022 and 2023 to calibrate and validate the parameters. The study found ARE < 10%, ENRMS < 15%, and R2 ≥ 0.85. The model simulation accuracy was acceptable. The study revealed that the water consumption at the surface soil layer (0–20 cm) was more than 6~12% and 13~31% than that at the 20–40 cm and 40–60 cm soil layers, respectively. The study showed when the irrigation quota was 30 mm, the annual yield of alfalfa (Medicago sativa L.) (7435 kg/ha) was consistent with that of the irrigation quota of 33 mm, and increased by 3.99% to 5.34% and 6.86% to 10.67% compared with that of irrigation quotas of 27 mm and 24 mm, respectively. To ensure the germination rate of alfalfa (Medicago sativa L.), it is recommended to control the initial soil water content at 0.8 θfc~1.0 θfc, with an irrigation quota of 30 mm, which was the best scheme for water-use efficiency and economic yield. The study aimed to provide technological support for the rational utilization of groundwater and the scientific improvement of alfalfa yield in the agro-pastoral ecotone in northern China.

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“…The formula for dry matter accumulation in the DSSAT model [ 38 ] is as follows:

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on Modeling and Evaluating Alfalfa Yield and Optimal Water Use Efficiency in the Agro-Pastoral Ecotone of Northern China

Miao,

Wang,

et al. 2024

Plants

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“…Genetic algorithms are also emerging in the field of computer systems [28][29][30][31]. At the same time, genetic algorithms are also popular in agriculture [32][33][34]. Researchers have also used genetic algorithms in materials science [35,36].…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

Lam,

Lee

2024

Mathematics

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As a famous population-based metaheuristic algorithm, a genetic algorithm can be used to overcome optimization complexities. A genetic algorithm adopts probabilistic transition rules and is suitable for parallelism, which makes this algorithm attractive in many areas, including the logistics and supply chain sector. To obtain a comprehensive understanding of the development in this area, this paper presents a bibliometric analysis on the application of a genetic algorithm in logistics and supply chains using data from 1991 to 2024 from the Web of Science database. The authors found a growing trend in the number of publications and citations over the years. This paper serves as an important reference to researchers by highlighting important research areas, such as multi-objective optimization, metaheuristics, sustainability issues in logistics, and machine learning integration. This bibliometric analysis also underlines the importance of Non-Dominated Sorting Genetic Algorithm II (NSGA-II), sustainability, machine learning, and variable neighborhood search in the application of a genetic algorithm in logistics and supply chains in the near future. The integration of a genetic algorithm with machine learning is also a potential research gap to be filled to overcome the limitations of genetic algorithms, such as the long computational time, difficulties in obtaining optimal solutions, and convergence issues for application in logistics and supply chains.

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“…The conventional approach to irrigation scheduling relies on analyzing average multiyear meteorological data, often derived from long-term rainfall records or a representative Agronomy 2023, 13, 2849 2 of 12 hydrological year [10,11]. While the conventional approach is practical and convenient, it tends to overlook the influence of random rainfall patterns.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Irrigation Strategy Based on Stochastic Rainfall and Evapotranspiration

Long,

Wang,

et al. 2023

Agronomy

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The North China Plain plays a pivotal role in China’s crop production, contributing to 30% of the maize yield. Nevertheless, summer maize in this region faces challenges due to climatic constraints characterized by concurrent high temperatures and rainfall during the growing season, resulting in a relatively high evapotranspiration rate. In this study, we explored eight soil moisture-based threshold irrigation strategies, consisting of two upper limits and four lower limits, along with a rainfed mode (E). The upper and lower irrigation limits are expressed as a percentage of the field’s water-holding capacity (sfc). For the four full irrigation modes (A1, A2, A3, A4), the lower limits were set at 0.6 sfc, 0.6 sfc, 0.5 sfc, and 0.5 sfc, respectively. The upper limits were defined at two levels: 0.8 sfc for A1 and A2 and sfc for A3 and A4. Similarly, for the four deficit irrigation modes (B1, B2, B3, B4), the lower limits were established at 0.4 sfc, 0.4 sfc, 0.3 sfc, and 0.3 sfc, respectively, with the upper limits set at two levels: 0.8 sfc for B1 and B2 and the full sfc for B3 and B4. To investigate the impact of rainfall and potential evapotranspiration on these irrigation modes under long-term fluctuations, we employed a stochastic framework that probabilistically linked rainfall events and irrigation applications. The Monte Carlo method was employed to simulate a long-term series (4000a) of rainfall parameters and evapotranspiration using 62 years of meteorological data from the Xinxiang region, situated in the southern part of the North China Plain. Results showed that the relative yield and net irrigation water requirement of summer maize decreased with decreasing irrigation lower limits. Additionally, the interannual variation of rainfall parameters and evapotranspiration during the growing season were remarkable, which led to the lowest relative yield of the rainfed mode (E) aligned with a larger interannual difference. According to the simulation results, mode A4 (irrigation lower limit equals 0.5 sfc, irrigation upper limit equals 0.8 sfc) could be adopted for adequate water resources. Conversely, mode B2 is more suitable for a lack of water resources.

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Decision-making method for maize irrigation in supplementary irrigation areas based on the DSSAT model and a genetic algorithm

Cited by 5 publications

References 28 publications

Study on Modeling and Evaluating Alfalfa Yield and Optimal Water Use Efficiency in the Agro-Pastoral Ecotone of Northern China

Study on Modeling and Evaluating Alfalfa Yield and Optimal Water Use Efficiency in the Agro-Pastoral Ecotone of Northern China

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

Simulation of Irrigation Strategy Based on Stochastic Rainfall and Evapotranspiration

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