Evaluation of Evapotranspiration Prediction for Cassava Crop Using Artificial Neural Network Models and Empirical Models over Cross River Basin in Nigeria

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.