Naive Regression Growth Models for Prediction of Peppermint Yield Production
In this study, three novel regression models are introduced for estimating and forecasting peppermint yield production. Several indices of the goodness of fit are used to assess the quality of the suggested models. The proposed models for yield production are compared to current regression models that are well-known. Primary data from the Banki block of the Barabanki District of Uttar Pradesh State in India was used to validate the efficiency conditions for the suggested models to outperform the competition models. The empirical results suggest that the proposed models for estimating and predicting peppermint yield production are more efficient than competing estimators.
Al-Kassie, G.A.M. (2010). The role of peppermint (Mentha Piperita) on performance in broiler diets, Agriculture and Biology Journal of North America, 1(5), 1009–1013.
Dharmaraja S., Jain V., Anjoy P. and Chandra H. (2020) Empirical Analysis for Crop Yield Forecasting in India, Agricultural Research, 9(1), 132–138.
Draper, N.R. and Smith, H., Applied Regression Analysis, 3rd Ed., John Wiley & Sons, 1998.
Gujarati, D. N. And Sangeetha, Basic Econometrics, 4th Ed, Tata McGraw-Hill, 2007.
Guo Y., Xiang H., Li Z., Ma F. and Du C. (2021). Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression, Agronomy, 11(282), 1–11.
Gupta R.P., Rai V.N., Kumar S. and Snehdeep (2021). Statistical models for wheat yield using linear regression model based on meteorological parameters, Journal of Pharmacognosy and Phytochemistry, 10(2), 44–46.
Haque H. N., Azad N. K., Jha R. N. and Singh S. N. (1988). Optimum Size and Shape of Plots for Wheat, Annals of Agricultural Research, 9(2), 165–170.
Kaplan, S. and Gurcan, E.K. (2018). Comparison of growth curves using non-linear regression function in Japanese quail, Journal of Applied Animal Research, 46(1), 112–117.
Kumar, S., Suresh, R., Singh, V. and Singh, A. K. (2011). Economic Analysis of Menthol Mint Cultivation in Uttar Pradesh: A Case Study of Barabanki District, Agricultural Economics Research Review, 24(2), 345–350.
Kumar, R., Upadhyaya, R.K., Venkatesha, K.T., Padalia, R.C., Tiwari, A.K. and Singh, S. (2019). Performance of Different Parts of Planting Materials and Plant Geometry on Oil yield and Suckers Production of Menthol-mint (Mentha Arvensis L.) During Winter Season, International Journal of Current Microbiology and Applied Sciences, 8(1), 1261–1266.
Lavanya M., and Parameswari R. (2020). A Multiple Linear Regressions Model for Crop Prediction with Adam Optimizer and Neural Network Mlraonn, International Journal of Advanced Computer Science and Applications, 11(4), 253–257.
Misra, G.C., Shukla, A.K. and Yadav, S.K. (2009). A comparison of regression methods for improved estimation in sampling, Journal of Reliability and Statistical Studies, 2(2), 85–90.
Misra, G.C., Yadav, S.K., Shukla, A.K. and Bahadur, R. (2010). Use of a non-linear model for improved estimation in cluster sampling, Journal of Reliability and Statistical Studies, 3(2), 73–78.
Montgomery D.C., Peck E.A. and Vining G.C., Introduction to Linear Regression Analysis, 5th Ed., Wiley, 2012.
Murugan R., Thomas F.S., GeethaShree G., Glory S. and Shilpa A. (2020). Linear Regression Approach to Predict Crop Yield, 9(1), 40–44.
Nimase R. G., Kandalkar Y. B. and Bangar Y. C. (2018). Non-linear modelling for estimation of growth curve parameters in Madgyal sheep, Journal of Entomology and Zoology Studies, 6(2), 463–465.
Pardarshi Kisan Seva Yojna, Agriculture Department, Uttar Pradesh, http://upagripardarshi.gov.in/Index.aspx
Ratkowsky D.A., Non-Linear Regression Modeling, Marcel Dekker, New York, 1983.
Ratkowsky D.A., Hand Book of Non-Linear Regression Models, Marcel Dekker, New York, 1989.
Riazoshams H., Midi H. and Ghilagaber G., Robust Nonlinear Regression: with Applications using R, 1st Ed, Wiley, 2019.
Satoh D. (2019). Model selection among growth curve models that have the same number of parameters, Cogent Mathematics & Statistics, 6, 1–17.
Scarneciu, C. C., Sangeorzan, L., Rus, H., Scarneciu, V. D., Varciu, M. S., Andreescu, O. and Scarneciu, I. (2017). Comparison of linear and non-linear regression analysis to determine Pulmonary Pressure in Hyperthyroidism, Pakistan Journal of Medical Sciences, 33(1), 111–120.
Singh, N., Singh, P.K. and Kumar S.S. (2018). Growth Rate of Wheat Crop in Azamgarh Division of Eastern Uttar Pradesh, India, International Journal of Current Microbiology and Applied Sciences, 7(3), 3348–3352.
Smith, H.F. (1938). An empirical law describing heterogeneity in the yields of agricultural crops, Journal of Agricultural Science, 28, 1–23.
Wen Y., Liu K., Liu H., Cao H., Mao H., Dong X. and Yin Z. (2019). Comparison of nine growth curve models to describe growth of partridges (Alectoris Chukar), Journal of Applied Animal Research, 47(1), 195–200.
Young P., and Ord J. (1989). Model selection and estimation for technology growth curves, International Journal of Forecasting, 5, 501–513.
Zhao Z., Li Sh., Huang H., Li Ch., Wang Q. and Xue L. (2015). Comparative Study on Growth and Developmental Model of Indigenous Chicken Breeds in China, Open Journal of Animal Sciences, 5, 219–223.