Nonlinear modelling of sheep and goat populations in India
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Abstract
The objective of this paper was to study the trend in population of sheep and goat populations during 1951 to 2012 in India. The data were compiled from various issues of BAHS (Basic Animal Husbandry Statistics) for the period 1951 to 2012. Different nonlinear growth models such as Parabolic/Sikka, Brody, Brody modified, Wood, Logistic and Gompertz models were fitted to the census data of sheep and goat population. The goodness of fit of the models was tested by Coefficient of determination (R2), Adjusted coefficient of determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE) and Akaike Information Criteria (AIC). The populations of sheep and goat in India during the year 1951 were 39.10 million and 47.20 million numbers respectively and reached 135.17 million and 65.06 million respectively in the year 2012. Based on the various measures of goodness of fit we observed that the Parabolic/Sikka model was the best fitted model for studying the pattern in the populations of sheep and goat in India. This model has been used to project the sheep and goat population in India during 2020, 2025 and 2030. If the present pattern of growth continued in near future then the projected sheep population will be 102.37 million numbers whereas goat population will be 151.57 million numbers in the year 2030. The present study will provide the pattern in which the changes have been observed in sheep and goat populations in India during 1951 to 2012.
Article Details
Article Details
Adjusted R2, AIC, Durbin Watson test, MAE, MSE, Nonlinear models
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