International Journal of Education and Management Studies

Monika Devi ¹, Vinay Mehla ¹

Affiliations
1 Assistant Scientist, Department of Agricultural Economics, CCS Haryana Agricultural University, Hisar, Haryana, IN

Abstract

India is the world's largest producer and consumer of a wide range of various pulses, predominately tropical and sub-tropical crops such as chickpea, black gram and green gram (mungbean) and others, which are high in protein, fibre, and vitamins, as well as high-quality carbohydrates, minerals, and vitamins. The study is attempted to examine trends and growth pattern of major pulses crops in India based on secondary data collected from various published sources for the period 1980-2020. The major pulses crops: Green gram, Black gram and Chickpea have been taken under consideration and data on area, production and yield of selected crops have been utilized at India level. Standard deviation (SD) and Coefficient of Variation (CV) were used to measure the variability in the collated data. The compound annual growth rates (CAGR) for area, production and yield were estimated and found to have best fitting with the data. The linear model was fitted to estimate the trends of area, production and yield of crop. The study revealed that highest average area in India was under chickpea followed by black gram and green gram and that production and yield follow the same pattern. In terms of area and production, black gram showed largest fluctuations, followed by chickpea and green gram. The most stable yield was found in chickpea among all selected crops,

Keywords

Black Gram, Chickpea, Green Gram, Growth, Pluses and Trends

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Chetna ¹, Pushpa ¹, Aditi ¹, Nisha ², Ajay Sharma ¹, Monika Devi ²

Affiliations
1 Department of Mathematics and Statistics, CCSHAU, Hisar, Haryana, IN
2 Department of Agricultural Economics, CCS HAU, Hisar, Haryana, IN

Abstract

Based on weather data from 1980-1981 to 2013-2014 for the districts of Hisar, Bhiwani, and Sirsa, as well as from 1997-1998 to 2011-2012 for the district of Fatehabad, the western zone of Haryana has generated zonal wheat yield models. Multiple linear regression and main component analysis were employed to achieve this objective. The models' validity was confirmed for the post-sample years 2014-15, 2015-16, 2016-17, 2017-18, and 2018-19. The overall results show a preference for using prediction equations based on principal component analysis for recording % deviations from real-time yields. The zonal weather models can forecast the district-level wheat production four to five weeks before crop harvest.

Keywords

multiple linear regression, Eigen values, PC components, higher loading displaying weather variables

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