Browsing by Author "Alatise, Michael"

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    Evaluation of Evapotranspiration Prediction for Cassava Crop Using Artificial Neural Network Models and Empirical Models over Cross River Basin in Nigeria
    (MDPI, 2025-01-01) Eludire, Oluwadamilare Oluwasegun; Faloye, Oluwaseun Temitope; Alatise, Michael; Ajayi, Ayodele Ebenezer; Oguntunde, Phillip G.; Badmus, Tayo; Fashina, Abayomi; Adeyeri, Oluwafemi E.; Olorunfemi, Idowu Ezekiel; Ogunrinde, Akinwale T.
    first_pagesettingsOrder Article Reprints Open AccessArticle Evaluation of Evapotranspiration Prediction for Cassava Crop Using Artificial Neural Network Models and Empirical Models over Cross River Basin in Nigeria by Oluwadamilare Oluwasegun Eludire 1,2,Oluwaseun Temitope Faloye 3,4,*ORCID,Michael Alatise 2,Ayodele Ebenezer Ajayi 2,4,5,Philip Oguntunde 2,Tayo Badmus 1,Abayomi Fashina 6,Oluwafemi E. Adeyeri 7,*ORCID,Idowu Ezekiel Olorunfemi 8ORCID andAkinwale T. Ogunrinde 9 1 Department of Agricultural and Bioresources Engineering, Faculty of Engineering and Technology, University of Calabar, Calabar PMB 1115, Nigeria 2 Department of Agricultural and Environmental Engineering, Federal University of Technology, Akure PMB 704, Nigeria 3 Department of Water Resources Management and Agrometeorology, Federal University, Oye-Ekiti PMB 373, Nigeria 4 Institute for Plant Nutrition and Soil Science, Christian Albrecht’s University zu Kiel, Hermann Rodewaldstr. 2, 24118 Kiel, Germany 5 Institute for Fourth Industrial Revolution, SE Bogoro Centre, Afe Babalola University, Ado Ekiti 360001, Nigeria 6 Department of Soil Science and Land Resources Management, Federal University, PMB 373, Oye-Ekiti 371104, Nigeria 7 School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong 8 Department of Civil Engineering, Lead City University Ibadan, Ibadan 200255, Nigeria 9 Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Beijing 100045, China * Authors to whom correspondence should be addressed. Water 2025, 17(1), 87; https://doi.org/10.3390/w17010087 Submission received: 28 August 2024 / Revised: 5 October 2024 / Accepted: 8 October 2024 / Published: 1 January 2025 (This article belongs to the Section Water, Agriculture and Aquaculture) Editorial Note: Due to an editorial processing error, this article was incorrectly included within the Special Issue Crop Evapotranspiration, Crop Irrigation and Water Savings upon publication. This article was removed from this Special Issue’s webpage on 14 February 2025 but remains within the regular issue in which it was originally published. The editorial office confirms that this article adhered to MDPI's standard editorial process (https://www.mdpi.com/editorial_process). Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract The accurate assessment of water availability throughout the cassava cropping season (the initial, developmental, mid-season, and late stages) is crucial for mitigating the impacts of climate change on crop production. Using the Mann–Kendall Test, we investigated the trends in rainfall and cassava crop evapotranspiration (ETc) within the Cross River basin in Nigeria. Reference evapotranspiration (ETo) was based on two approaches, namely Artificial Neural Network (ANN) modelling and three established empirical models—the Penman–Monteith (considered the standard method), Blaney–Morin–Nigeria (BMN), and Hargreaves–Samani (HAG) models. ANN predictions were performed by using inputs from BMN and HAG parameters, denoted as BMN-ANN and HAG-ANN, respectively. The results from the ANN models were compared to those obtained from the Penman–Monteith method. Remotely sensed meteorological data spanning 39 years (1979–2017) were acquired from the Climatic Research Unit (CRU) to estimate ETc, while cassava yield data were acquired from the International Institute of Tropical Agriculture (IITA), Ibadan. The study revealed a significant upward trend in cassava crop ETc over the study period. Additionally, the ANN models outperformed the empirical models in terms of prediction accuracy. The BMN-ANN model with a Tansig activation function and a 3-3-1 architecture (number of input neurons, hidden layers, and output neurons) achieved the highest performance, with a coefficient of determination (R2) of 0.9890, a root mean square error (RMSE) of 0.000056 mm/day, and a Willmott’s index of agreement (d) of 0.9960. There is a decreasing trend in cassava yield in the region and further analysis indicated potential average daily water deficits of approximately −1.1 mm/day during the developmental stage. These deficits could potentially hinder root biomass, yield, and overall cassava yield in the Cross River basin. Our findings highlight the effectiveness of ANN modelling for irrigation planning, especially in the face of a worsening climate change scenario.