Identifying the causal impact of＇ some intervention challenging when one is faced with correlated binary end-points in observational studies is a challenging task, and it is even more The statistical literature on an...Identifying the causal impact of＇ some intervention challenging when one is faced with correlated binary end-points in observational studies is a challenging task, and it is even more The statistical literature on analyzing such data is well documented. Dependence between observations from the same study subject in correlated data renders invalid the usual chi-square tests of independence and inflates the variance ofparameter estimates. Disaggregated approaches such as hierarchical linear models which are able to adjust for individual level covariate：s are favoured in the analysis of such data, thereby gaining power over aggregated and individual-level analyses. In this article the authors, therefore, address the issue of analyzing correlated data with dichotomous end-points by using hierarchical logistic regression, a generalization of the standard logistic regression model for independent outcomes.展开更多
Variable Rate Technology （VRT） takes within-field variability into consideration and aims to match resource application to crop requirement. Even though Texas is the most important cotton producing state in the US, ...Variable Rate Technology （VRT） takes within-field variability into consideration and aims to match resource application to crop requirement. Even though Texas is the most important cotton producing state in the US, the rate of VRT adoption is very low here. Hence, analyzing the factors influencing the adoption and providing a regional estimate of the impact of VRT adoption on cotton yield is very important. This study used the 2009 Southern Cotton Precision Farming Survey to analyze the farm and farmer characteristics affecting the adoption of VRT among Texas cotton farmers and to empirically estimate the impact of adoption of VRT on cotton yield in Texas. A two-stage least square procedure with a logistic regression model in the first stage and a multiple linear regression model in the second stage was used to analyze the data. The study revealed that there are significant regional differences in adoption pattern within the state of Texas; and the farmers from the coastal region, where there is higher within-field variability, were more likely to adopt VRT compared to other regions. Younger farmers, farmers managing larger farms, and farmers who use computers for farming operations were more likely to adopt VRT. The results also showed that, on an average, the adoption of VRT does not lead to significant yield improvements for cotton in Texas. Since the impact of VRT adoption on yield is not significant, the source of economic advantage of VRT adoption in Texas may be the reduction of input cost.展开更多
文摘Identifying the causal impact of＇ some intervention challenging when one is faced with correlated binary end-points in observational studies is a challenging task, and it is even more The statistical literature on analyzing such data is well documented. Dependence between observations from the same study subject in correlated data renders invalid the usual chi-square tests of independence and inflates the variance ofparameter estimates. Disaggregated approaches such as hierarchical linear models which are able to adjust for individual level covariate：s are favoured in the analysis of such data, thereby gaining power over aggregated and individual-level analyses. In this article the authors, therefore, address the issue of analyzing correlated data with dichotomous end-points by using hierarchical logistic regression, a generalization of the standard logistic regression model for independent outcomes.
文摘Variable Rate Technology （VRT） takes within-field variability into consideration and aims to match resource application to crop requirement. Even though Texas is the most important cotton producing state in the US, the rate of VRT adoption is very low here. Hence, analyzing the factors influencing the adoption and providing a regional estimate of the impact of VRT adoption on cotton yield is very important. This study used the 2009 Southern Cotton Precision Farming Survey to analyze the farm and farmer characteristics affecting the adoption of VRT among Texas cotton farmers and to empirically estimate the impact of adoption of VRT on cotton yield in Texas. A two-stage least square procedure with a logistic regression model in the first stage and a multiple linear regression model in the second stage was used to analyze the data. The study revealed that there are significant regional differences in adoption pattern within the state of Texas; and the farmers from the coastal region, where there is higher within-field variability, were more likely to adopt VRT compared to other regions. Younger farmers, farmers managing larger farms, and farmers who use computers for farming operations were more likely to adopt VRT. The results also showed that, on an average, the adoption of VRT does not lead to significant yield improvements for cotton in Texas. Since the impact of VRT adoption on yield is not significant, the source of economic advantage of VRT adoption in Texas may be the reduction of input cost.
