Abstract
Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.
Highlights
Rice is the staple food of almost all Sri Lankans. erefore, it is estimated that 2.7 million metric tons of rough rice is produced annually to satisfy the demand of the country [1]
Match; the lines have only two data points where a straight line is directly predicted as the trend line which goes through the data points. erefore, the Artificial Neural Networks (ANNs) model is not functioned well under data scarcity. is clearly justifies the analysis in the annual resolution combining both seasons as two datasets
Nonlinear complex relationships among various climatic factors and paddy yield were obtained for several districts in Sri Lanka using artificial neural networks
Summary
Rice is the staple food of almost all Sri Lankans. erefore, it is estimated that 2.7 million metric tons of rough rice (paddy) is produced annually to satisfy the demand (around 95%) of the country [1]. Erefore, it is estimated that 2.7 million metric tons of rough rice (paddy) is produced annually to satisfy the demand (around 95%) of the country [1]. Erefore, it is important none other than any other agricultural products in Sri Lanka. Paddy, as a crop, is one of the most affected cultivations in many countries due to the on-going climate variability [2, 3]. Is is mainly because of the water requirement for the paddy cultivation. Intense and excess rainfall can produce adverse effects, along with major flooding devastating vegetation, while crop yield reduces due to water shortage in drought climates. Rice cultivation is considered a Mathematical Problems in Engineering semiaquatic plant grown at a controlled supply of water. Rice cultivation is considered a Mathematical Problems in Engineering semiaquatic plant grown at a controlled supply of water. e source of water supply and degree of flooding are treated to be some environmental factors which determine the paddy harvest
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