Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis
The present study is focused to develop pre-harvest forecast models for rice yield based on meteorological parameters in Haryana. The discriminant function analysis (DFA) technique has been used for forecasting the rice yield. For this study, fortnightly weather data from 1980–81 to 2018–19 on five...
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doaj-b957367ff18640cda4f0a4ba0815e9822021-08-26T04:36:04ZengElsevierJournal of Agriculture and Food Research2666-15432021-09-015100194Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysisJoginder Kumar0Monika Devi1Deepika Verma2D.P. Malik3Ajay Sharma4Department of Mathematics & Statistics, CCS Haryana Agricultural University, Hisar, India; Corresponding author.Department of Mathematics & Statistics, CCS Haryana Agricultural University, Hisar, IndiaDepartment of Biochemistry, All India Institute of Medical Sciences, New Delhi, IndiaDepartment of Agricultural Economics, CCS Haryana Agricultural University, Hisar, IndiaDepartment of Mathematics & Statistics, CCS Haryana Agricultural University, Hisar, IndiaThe present study is focused to develop pre-harvest forecast models for rice yield based on meteorological parameters in Haryana. The discriminant function analysis (DFA) technique has been used for forecasting the rice yield. For this study, fortnightly weather data from 1980–81 to 2018–19 on five weather variables namely maximum temperature, minimum temperature, average relative humidity, sunshine hours and accumulated rainfall have been used. The data for period from 1980–81 to 2014-15 have been utilized for model building and subsequent four years data for the period 2015–16 to 2018-19 have been used for model validation. The residuals obtained after fitting regression model by taking rice yield (dependent variable) and year (independent variable) using data for the period 1980–2015 have been used to categorize rice yield into two/three groups. Various performance measures have been used to measure the performance of the developed models at different fortnights. The results based on various performance measures indicated that 21st fortnight (29 September–13 October) or one month before harvest is the best time for forecasting the rice yield. The findings of the present study may be helpful for policy planners and various stakeholders to take appropriate decisions to make arrangement for procurement, distribution, storage, trade in domestic and international markets as well as for managing the proper inventory in advance.http://www.sciencedirect.com/science/article/pii/S266615432100096XDiscriminant function analysisFortnightRice yieldPre-harvest forecast and meteorological variables |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Joginder Kumar Monika Devi Deepika Verma D.P. Malik Ajay Sharma |
spellingShingle |
Joginder Kumar Monika Devi Deepika Verma D.P. Malik Ajay Sharma Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis Journal of Agriculture and Food Research Discriminant function analysis Fortnight Rice yield Pre-harvest forecast and meteorological variables |
author_facet |
Joginder Kumar Monika Devi Deepika Verma D.P. Malik Ajay Sharma |
author_sort |
Joginder Kumar |
title |
Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis |
title_short |
Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis |
title_full |
Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis |
title_fullStr |
Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis |
title_full_unstemmed |
Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis |
title_sort |
pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis |
publisher |
Elsevier |
series |
Journal of Agriculture and Food Research |
issn |
2666-1543 |
publishDate |
2021-09-01 |
description |
The present study is focused to develop pre-harvest forecast models for rice yield based on meteorological parameters in Haryana. The discriminant function analysis (DFA) technique has been used for forecasting the rice yield. For this study, fortnightly weather data from 1980–81 to 2018–19 on five weather variables namely maximum temperature, minimum temperature, average relative humidity, sunshine hours and accumulated rainfall have been used. The data for period from 1980–81 to 2014-15 have been utilized for model building and subsequent four years data for the period 2015–16 to 2018-19 have been used for model validation. The residuals obtained after fitting regression model by taking rice yield (dependent variable) and year (independent variable) using data for the period 1980–2015 have been used to categorize rice yield into two/three groups. Various performance measures have been used to measure the performance of the developed models at different fortnights. The results based on various performance measures indicated that 21st fortnight (29 September–13 October) or one month before harvest is the best time for forecasting the rice yield. The findings of the present study may be helpful for policy planners and various stakeholders to take appropriate decisions to make arrangement for procurement, distribution, storage, trade in domestic and international markets as well as for managing the proper inventory in advance. |
topic |
Discriminant function analysis Fortnight Rice yield Pre-harvest forecast and meteorological variables |
url |
http://www.sciencedirect.com/science/article/pii/S266615432100096X |
work_keys_str_mv |
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