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Influence of Climatic Variables on Soybean Yield in Yogyakarta, Indonesia
Thesis Abstract:
The second and fourth degree multiple regression were used on the data to obtain the effect of climatic variables independently on soybean yield at different stages of crop development. Likewise, the selected crop stages method, using the stepwise regression procedure was used to establish a general prediction model for soybean yield. An attempt was made to compare results obtained from the stepwise regression method with the results of the second and fourth degree multiple regression technique.
Results obtained in the second and fourth degree multiple regression showed that only maximum temperature and sunshine duration equation model for the first growing season were significant at five percent level of significance.
On the other hand, the stepwise regression model identified only the most significant climatic variables that affected the yield of soybean. Results showed that for the soybean growing seaso, out of 12 climatic variables entered, only six variables were considered which explains 99.79 percent of the variation of the soybean yield, which was significant at one percent level.
The second and fourth degree regression model appeared to be sufficiently reliable for practical purposes. However, the selected crop stages method using the stepwise regression procedure gave a relatively better result than that of the second and fourth degree multiple regression in predicting the yield of soybean.