STATISTICAL APPRAISAL OF AREA, PRODUCTION AND PRODUCTIVITY OF BENGAL GRAM CROP IN KALABURAGI DISTRICT OF KARNATAKA
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Date
2021-12-26, 2021-12-26
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University of Agricultural Sciences GKVK, Bangalore
University of Agricultural Sciences GKVK, Bangalore
University of Agricultural Sciences GKVK, Bangalore
Abstract
An attempt was made to analyse the trends in area, production, and productivity of the Bengal gram crop by considering the data of 25 years (1995-96 to 2019-20) in the Kalaburagi district of Karnataka state. The models used for trend analysis includes linear, quadratic, cubic, exponential, and log-logistic. The model with the lowest MAPE was chosen as the best fit. The cubic model was the best-fitting model for Bengal gram area and productivity, whereas the linear model found better fitted for production. The study also conducted to forecast the production of Bengal gram in the district for the next five years using the ARIMA model by considering the data of 50 years (1970-71 to 2019-20) out of which 47 years were considered for training the model and the remaining 3 years for testing purpose. The results revealed that ARIMA (0,1,1) with a drift model better fitted for the data and showed an increasing trend in the production of Bengal gram for the next 5 years. Further, an attempt was made to analyse the structural change in area and productivity of Bengal gram. Structural change was seen in 2007-08 and 2015-16 for area and productivity respectively. The difference between the pre and post-break periods established mean yields was found to be statistically significant in both area and productivity.
An attempt was made to analyse the trends in area, production, and productivity of the Bengal gram crop by considering the data of 25 years (1995-96 to 2019-20) in the Kalaburagi district of Karnataka state. The models used for trend analysis includes linear, quadratic, cubic, exponential, and log-logistic. The model with the lowest MAPE was chosen as the best fit. The cubic model was the best-fitting model for Bengal gram area and productivity, whereas the linear model found better fitted for production. The study also conducted to forecast the production of Bengal gram in the district for the next five years using the ARIMA model by considering the data of 50 years (1970-71 to 2019-20) out of which 47 years were considered for training the model and the remaining 3 years for testing purpose. The results revealed that ARIMA (0,1,1) with a drift model better fitted for the data and showed an increasing trend in the production of Bengal gram for the next 5 years. Further, an attempt was made to analyse the structural change in area and productivity of Bengal gram. Structural change was seen in 2007-08 and 2015-16 for area and productivity respectively. The difference between the pre and post-break periods established mean yields was found to be statistically significant in both area and productivity.
An attempt was made to analyse the trends in area, production, and productivity of the Bengal gram crop by considering the data of 25 years (1995-96 to 2019-20) in the Kalaburagi district of Karnataka state. The models used for trend analysis includes linear, quadratic, cubic, exponential, and log-logistic. The model with the lowest MAPE was chosen as the best fit. The cubic model was the best-fitting model for Bengal gram area and productivity, whereas the linear model found better fitted for production. The study also conducted to forecast the production of Bengal gram in the district for the next five years using the ARIMA model by considering the data of 50 years (1970-71 to 2019-20) out of which 47 years were considered for training the model and the remaining 3 years for testing purpose. The results revealed that ARIMA (0,1,1) with a drift model better fitted for the data and showed an increasing trend in the production of Bengal gram for the next 5 years. Further, an attempt was made to analyse the structural change in area and productivity of Bengal gram. Structural change was seen in 2007-08 and 2015-16 for area and productivity respectively. The difference between the pre and post-break periods established mean yields was found to be statistically significant in both area and productivity.