2021
DOI: 10.54386/jam.v23i1.94
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Rice yield forecasting using agro-meteorological variables: A multivariate approach

Abstract: The weather variables impact the crop differently throughout the various stages of development. The weather effect on crop yield thus can be determined not only by the magnitude of weather variables but also on the variability of weather over crop season. Crop yield forecasting methods incorporating weather information provide a better prediction of yield accounting the relative effects of each weather component. Regression analysis is the most frequently used statistical technique for investigating and… Show more

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Cited by 7 publications
(3 citation statements)
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“…Nain et al [20] had suggested the Principle Component Analysis (PCA) address the problem of multicollinearity. The inclusion of PCs obtained from meteorological data as predictor variables improves yield forecast accuracy.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Nain et al [20] had suggested the Principle Component Analysis (PCA) address the problem of multicollinearity. The inclusion of PCs obtained from meteorological data as predictor variables improves yield forecast accuracy.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The study determined the predominance of various meteorological data on the yield of the crop [10]. Crop yield is mostly affected by technological changes and weather variability [11,12]. It can be assumed that the technological factors will increase yield smoothly through time and therefore, year or other parameters of time can be used to study the overall effect of technology on crop yield [13,14,15].…”
Section: Introductionmentioning
confidence: 99%
“…Using machine learning, agricultural productivity has been estimated using decision trees, linear regression, lasso regression, and ridge regression. Nain et al, (2021) have derived prediction models for rice yield in Karnal district, Haryana. Specifically, the performance of multiple linear regression, principal component analysis, and discriminant function analysis to be evaluate the most effective method among these approaches for accurately predicting rice yield before harvest in the specified geographical area.…”
mentioning
confidence: 99%