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  • Open Access


    Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

    Rab Nawaz Bashir1, Imran Sarwar Bajwa2, Muhammad Waseem Iqbal3,*, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1915-1930, 2023, DOI:10.32604/iasc.2023.030844

    Abstract Soil salinity is a serious land degradation issue in agriculture. It is a major threat to agriculture productivity. Extra irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction (LF) of irrigation water. For the leaching process to be effective, the LF of irrigation water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration (ET) rate. The relationship between environmental conditions and ET rate is hard to be defined by a linear relationship and data-driven Machine learning (ML)… More >

  • Open Access


    Model Identification and Control of Evapotranspiration for Irrigation Water Optimization

    Wafa Difallah1,2,*, Fateh Bounaama2, Belkacem Draoui2, Khelifa Benahmed3, Abdelkader Laaboudi4

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1749-1767, 2022, DOI:10.32604/cmc.2022.019071

    Abstract Water conservation starts from rationalizing irrigation, as it is the largest consumer of this vital source. Following the critical and urgent nature of this issue, several works have been proposed. The idea of most researchers is to develop irrigation management systems to meet the water needs of plants with optimal use of this resource. In fact, irrigation water requirement is only the amount of water that must be applied to compensate the evapotranspiration loss. Penman-Monteith equation is the most common formula to evaluate reference evapotranspiration, but it requires many factors that cannot be available in many cases. This leads to… More >

  • Open Access


    A PSO-XGBoost Model for Estimating Daily Reference Evapotranspiration in the Solar Greenhouse

    Jingxin Yu1,3, Wengang Zheng1,*, Linlin Xu3, Lili Zhangzhong1, Geng Zhang2, Feifei Shan1

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 989-1003, 2020, DOI:10.32604/iasc.2020.010130

    Abstract Accurate estimation of reference evapotranspiration (ET0) is a critical prerequisite for the development of agricultural water management strategies. It is challenging to estimate the ET0 of a solar greenhouse because of its unique environmental variations. Based on the idea of ensemble learning, this paper proposed a novel ET0i estimation model named PSO-XGBoost, which took eXtreme Gradient Boosting (XGBoost) as the main regression model and used Particle Swarm Optimization (PSO) algorithm to optimize the parameters of XGBoost. Using the meteorological and soil moisture data during the two-crop planting process as the experimental data, and taking ET0i calculated based on the improved… More >

  • Open Access


    Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with Grey Wolf Optimization Algorithm

    Xianghui Lu1, Junliang Fan2, Lifeng Wu1,*, Jianhua Dong3

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 699-723, 2020, DOI:10.32604/cmes.2020.011004

    Abstract It is important for regional water resources management to know the agricultural water consumption information several months in advance. Forecasting reference evapotranspiration (ET0) in the next few months is important for irrigation and reservoir management. Studies on forecasting of multiple-month ahead ET0 using machine learning models have not been reported yet. Besides, machine learning models such as the XGBoost model has multiple parameters that need to be tuned, and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution. This study investigated the performance of the hybrid extreme gradient boosting (XGBoost) model… More >

  • Open Access


    Yield and quality of forage maize (Zea mays L.) with different levels of subsurface drip irrigation and plant density

    Yescas CP1, MA Segura C1, L Martínez C2, VP Álvarez R1, JA Montemayor T1, JA Orozco V1, JE Frías R1

    Phyton-International Journal of Experimental Botany, Vol.84, No.2, pp. 272-279, 2015, DOI:10.32604/phyton.2015.84.272

    Abstract The scarcity of water in arid and semiarid regions of the world is a problem that every day increases by climate change. The subsurface drip irrigation (SDI) and changes in population density of plants are alternatives that can be used to make a sustainable use of water. Therefore, the objectives of this study were to determine the combination that allows for an increased corn performance and efficient use of water without losing the quality of forage. Three different irrigation levels were applied through a system of a SDI at three different densities of forage maize plants in an arid region.… More >

  • Open Access


    Evapotranspiration and energy balance measurements over a soybean field in the semiarid sowthwestern region of Buenos Aires province (Argentina)

    Cargnel MD1, AL Orchansky2, RE Brevedan2, SS Baioni2, MN Fioretti2

    Phyton-International Journal of Experimental Botany, Vol.86, pp. 181-189, 2017, DOI:10.32604/phyton.2017.86.181

    Abstract Two field experiments were carried out in a semiarid region of Argentina over a soybean (Glycine max L. Merrill) field. The sites of study were San Adolfo (39˚ 23’ S, 62˚ 22’ W, 22 m.a.s.l.) and Nueva Roma (38˚ 29’ S, 62˚ 39’ W, 70 m.a.s.l.). Soybeans were planted on Jan 4 (San Adolfo) and Nov 27 (Nueva Roma) in 0.75 m wide rows and at 400000 pl/ha during two consecutive growing seasons. Energy balance and evapotranspiration (ET) were estimated during the reproductive stages from full bloom (R2) to full maturity (R8). In Nueva Roma ET or latent heat flux… More >

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