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Toward a Weather Model for Predicting the Marketable Pod Yield of Vegetable Soybean
principal components regression
partial least squares regression
relative prediction error
Summary This study aims to develop a weather model for predicting the marketable pod yield of vegetable soybean in South Taiwan. A total of 74 observations on the marketable pod yield of Kaohsiung No. 5, a soybean variety bred specially for vegetable usage, were obtained in a series of sowing date × harvest date field experiments conducted at the Kaohsiung District Agricultural Improvement Station located in Pingtung City during the years of 1998 through 2000. Meanwhile, daily mean air temperature, sunshine hour, solar radiation and precipitation were measured during the time of field experiments. To manage the huge meteorological data, the whole growing period of each crop was divided into two stages (vegetative growth stage and reproductive growth stage), or three stages(vegetative growth stage, flowering-podding stage, and pod-filling stage). Thereby the duration of each growth stage and the mean of each of the four meteorological variables over each growth stage were taken as the candidate predictors for the marketable pod yield. The data was split into two portions, i.e., the 42 earlier observations were used as training data set to develop the models, and the rest 32 observations were reserved as test data set to check the prediction accuracy of the fitted models. Second-degree polynomial models in the mentioned predictors were fitted to the training data. The methods of ridge regression, principal components regression, and partial least squares regression were employed to cope with the collinearity among the predictor variables. The result shows that the models fitted with the whole growing period being divided into three stages were not able to predict the marketable pod yield in the test data, although their fitness to the training data were rather satisfactory. Among the three fitting procedures, ridge regression failed to give any model with reasonable prediction ability. With the whole growing period being divided into two stages, both principal components regression and partial least squares regression gave models with not only very good fitness to the training data but also faily high accuracy in predicting the marketable yield of the test data. The correlation coefficient between the predicted marketable pod yield and its observed value was as large as 0.85, and the maximum of the relative prediction error was as small as 22％.
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