Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis

• Main Authors: Joginder Kumar, Monika Devi, Deepika Verma, D.P. Malik, Ajay Sharma
• Format: Article
• Language: English
• Published: Elsevier 2021-09-01
• Series: Journal of Agriculture and Food Research
• Subjects: Discriminant function analysis; Fortnight; Rice yield; Pre-harvest forecast and meteorological variables
• Online Access: http://www.sciencedirect.com/science/article/pii/S266615432100096X

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.