Pre-harvest forecast of kharif rice yield using PCA and MLR technique in Navsari district of Gujarat

In this paper Principal Components (PC) and Multiple Linear Regression (MLR) Technique were used for development of pre-harvest model for rice yield in the Navsari district of south Gujarat. The weather indices were developed and utilized for development of pre-harvest forecast models. The data of r...

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Bibliographic Details
Published inJournal of agrometeorology Vol. 21; no. 3; pp. 336 - 343
Main Authors Banakara, K B, Pandya, H R, Garde, Y A
Format Journal Article
LanguageEnglish
Published Anand Association of Agrometeorologist 01.09.2019
Association of agrometeorologists
Subjects
Agricultural production
Agriculture
Cereal crops
Crop growth
Crop yield
Crops
Food
forecast
Harvest
Harvesting
Humidity
MLR techniques
PCA
Principal components analysis
Rice
Rice yield
Variables
Weather
Weather index
Weather indices
Wheat
Asia
India
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Summary:In this paper Principal Components (PC) and Multiple Linear Regression (MLR) Technique were used for development of pre-harvest model for rice yield in the Navsari district of south Gujarat. The weather indices were developed and utilized for development of pre-harvest forecast models. The data of rice yield and weather parameters from 1990 to 2012 were utilized. The cross validation of the developed forecast model were confirmed using data of the years 2013 to 2016. It was observed that value of Adj. R2 varied from 89 to 96. The appropriate forecast model was selected based on high value of Adj. R2. Based on the outcomes in Navsari district, MLR techniques found to be better than PCA for pre harvest forecasting of rice crop yield. The Model-2 found competent to forecast rice yield in Navsari district before eight weeks of actual harvest of crop (37th SMW) i.e during reproductive stage of the crop growth period.
ISSN:0972-1665
2583-2980
DOI:10.54386/jam.v21i3.256