Abstract
The Ceylon Journal of Science is a peer-reviewed journal published quarterly by the University of Peradeniya, Sri Lanka in March, June, September and December. It is aimed at publishing high quality research articles on topics related to different disciplines in Science. The journal accepts original research articles, book reviews, reviews and mini-reviews, short communications, opinions, research notes, and commentaries and notes. The journal strictly adheres to publication ethics as emphasized by the Committee on Publication Ethics (COPE). The Journal has its own website.The Ceylon Journal of Science is indexed in Sri Lanka Journals Online (SLJOL), Directory of Open Access Journals (DOAJ), Google Scholar and Zoological Records. According to the Google Scholar; H5-Index: 12H5-Median: 15According to the Exaly (1970 – 2021); Impact Factor: 0.6 (top 19%)Extended IF: 0.6 (top 19%) H-Index: 8 (top 28%)Citations/paper: 1.42
Highlights
Sustainable growth of paddy, which is the widely cultivated crop in Sri Lanka that provides the staple food of the majority of Sri Lankans regardless of income level, is crucial for the country’s economic health and prosperity
Several regression techniques have been used to model the relationship between climatic factors and paddy production in the North-Western province of Sri Lanka that makes a significant contribution to the total harvest of the country
The objective of this study is to explore comparatively the significance of regression analysis techniques for paddy yield estimation and to find a unique forecasting model for the North-Western province of Sri Lanka
Summary
Sustainable growth of paddy, which is the widely cultivated crop in Sri Lanka that provides the staple food of the majority of Sri Lankans regardless of income level, is crucial for the country’s economic health and prosperity. Dammalage, De Silva and Satirapod (2017) using a model based on satellite remote sensing data for estimating the pre-harvest rice yield in the Kurunegala district of Sri Lanka from 2007 to 2016, showed that seasonal rice yield could be predicted one month ahead of harvesting with an average overall accuracy of 92%. They revealed that the extent of cultivated paddy lands needs to be identified accurately as some of the paddy fields considered in their study had not been cultivated regularly during each season. They considered only three climatic factors namely the rainfall, minimum and maximum atmospheric temperatures, to show that the predicted and actual paddy yields are correlated with the very factors
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