2019
DOI: 10.1080/00036846.2019.1602710
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Modelling the heterogeneous effects of stocking rate on dairy production: an application of unconditional quantile regression with fixed effects

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Cited by 14 publications
(15 citation statements)
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References 27 publications
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“…The coefficient of the business type variable in Table 2 is negative and statistically significant, whereas in Table 3 it is positive and statistically significant, suggesting that relative to owner-operators, share-milkers perform significantly better financially but not physically. The significant coefficient of the cattle breed variable in Table 2 suggests that, relative to farms with herds comprising other cattle breeds (e.g., Friesian, Jersey, and Ayrshire), farms with crossbred cows have greater dairy productivity, a finding that is consistent with previous studies (VanRaden and Sanders, 2003;Ma et al, 2019b). Tables 2 and 3 also show that both milking frequency (i.e., twice a day) and stocking rate contribute to higher dairy productivity and profitability.…”
Section: Short Communicationsupporting
confidence: 84%
“…The coefficient of the business type variable in Table 2 is negative and statistically significant, whereas in Table 3 it is positive and statistically significant, suggesting that relative to owner-operators, share-milkers perform significantly better financially but not physically. The significant coefficient of the cattle breed variable in Table 2 suggests that, relative to farms with herds comprising other cattle breeds (e.g., Friesian, Jersey, and Ayrshire), farms with crossbred cows have greater dairy productivity, a finding that is consistent with previous studies (VanRaden and Sanders, 2003;Ma et al, 2019b). Tables 2 and 3 also show that both milking frequency (i.e., twice a day) and stocking rate contribute to higher dairy productivity and profitability.…”
Section: Short Communicationsupporting
confidence: 84%
“…To explore this possibility, we also employ a quantile regression model. Although much of the previous research has used a conditional quantile regression (CQR) model, one major drawback of this approach is that the quantiles are defined conditional on the employed covariates, and it is impossible to add or delete control variables freely without redefining the quantiles (Firpo, Fortin, & Lemieux, ; Ma, Renwick, & Greig, ). A UQR model can overcome this drawback of CQR.…”
Section: Empirical Strategiesmentioning
confidence: 99%
“…2015; Ma et al . 2019, 2020). Given that the UQR model enables to overcome the weakness of the CQR model, and it provides a better estimate, the UQR model is used in the present study.…”
Section: Theoretical Framework and Econometric Approachmentioning
confidence: 99%