Analysis of Data from Crop Protection Experiments Using Generalized Linear Model: the Case of Parthenium

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Addis Abeba university


Among many weeds that cause crop loss, parthenium was found to be the most terrible one according to some exploratory studies. The problem of parthenium is not only that it cause very sever crop loss, but also it cause health problems to human and animal beings. Control of Parthenium by Farmers ,cultural and labour intensive, caused farmers to suffer from skin allergy, itching, fever, and asthma. This study tried to popularize different Generalized linear models for modeling agricultural data which is used for describing the data sufficiently well and then identify the natural relationship between different variables for further analayis as well as applications. Generalized linear models (GLMs) are used to do regression modeling for non-normal data with a minimum of extra complication compared with normal linear regression. One of the available programs that is important in current statistical practice is the GLM procedure in the SAS software package. The study is based on the result of a parthenium and other species count data, secondary data,obtained from Ethiopian instisute of Agricultre research. Descriptive statistics supported by graphical presentations have been discussed to show the dominance of parthenium on other species per plot area. Furthermore, to evaluate the probability of a plot or a quadrant to be free of parthenium, models form GLM family are applied to the data using SAS software. Based on the parameter estimates, fitted models were formulated, parameters are interpreted and comparison of fitted models conducted. In this model fitting process, an attempt was made to alleviate a confusion of which model to which data. The logit and probit model fitting gives similar results for the same data as expected and the choice of one model cannot be made based on AIC, because the AIC for both models is the same. The poisson regression model fit is found to be inadequate for two different variables, as its Deviance value is far from one. The Negative Binomial Model gives a better fit and its Deviance shows the model is adequate for the same data used for poisson regression. The multinomial logit model for parthenium infestation in five categories as dependent variable and the sum of all other species gives a better result, as infestation level increase i.e as the severity of parthenium infestation increase, the number of the sum of other species gets low which in turn means that the probability of getting other species gets very low



the Case of Parthenium