Joint Regression Analysis and Incorporation of Environmental Variables in Stochastic Frontier Production Function: An Application to Experimental Data of Winter Rye
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Springer Berlin Heidelberg
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This chapter joins the main properties of two specific regression techniques, joint-regression analysis (JRA) and stochastic frontier approach (SFA) in the analysis of experimental data sets from a breeding program of winter rye (Secale cereale L.), conducted in Poland, Research Center for Cultivar Testing de Słupia Wielka, over the period 1997–1998. With JRA, a meta-model, based on several linear regressions, had been estimated in order to analyze multilocation trials of winter rye production and to select the best cultivars (more productive) for a related stratum (locality/genotype). With SFA, another regression model had been investigated to predict production rankings of cultivars, through individual efficiency estimates. These measures resulted from a stochastic production frontier on experimental data of production and different climate conditions. Both techniques show similar dominant cultivars for the same environments.
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1. Pereira, D. G.; Sampaio, A. (2013). Joint Regression Analysis and Incorporation of Environmental Variables in Stochastic Frontier Production Function: An Application to Experimental Data of Winter Rye. In Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications, da Silva, J.L.; Caeiro, F.; Natário, I.; Braumann, C.A.; Esquível, M.L.; Mexia, J. (Eds.), Springer, Berlin, pp. 325-336, Print ISBN: 978-3-642-34903-4, Online ISBN: 978-3-642-34904-1, URL: http://link.springer.com/chapter/10.1007/978-3-642-34904-1_34