Using appropriate experimental design and modeling approach is important for predicting fruit yield of strawberry (Fragaria virginiana Mill.). A field study with Taoyuen 3 (T3) strawberry, a hybrid of Fragaria virginiana and Fragaria chiloensis (L.) Mill., was conducted at Taitung County, Taiwan, using a central composite design (CCD) to optimize its fruit yield. Response surface methodology (RSM), based on a three‐variable and five‐level CCD, was employed to obtain an optimal combination of porphyritic andesite (X1: 20 g kg−1), arbuscular mycorrhizal fungi (X2: 9 g kg−1), and soil pH levels (X3: 6.5) for predicting yields and maximum fruit productivity (10.3 g fruit−1). The data obtained in this study were fitted to a second‐order polynomial using multiple regression analysis. The observed data were in close agreement with the predicted value (two‐tailed independent t test with p = 0.923) based on the model. Our data indicate that RSM is a reliable approach to develop a model for predicting the fruit yields of the T3 strawberry in net houses.
Djulis (Chenopodium formosanum Koidz.), a cereal plant native to Taiwan, was used for wine making, insect repellant, and health promotion. Therefore, using appropriate experimental design and modeling approach is of importance to predict the effect of the interaction among nitrogen (N), phosphorus (P), and potassium (K) on djulis yields. In this study, a mixture design approach was applied to investigate the effects of interactions among nitrogen, phosphorus and potassium on the grain yields of djulis plants. Based on a three-variable including N, P, and K with each maximum of them was of 200 g (6 m2)-1, a mixture design approach was employed by 42 experiments in 14 study plots to obtain an optimal combination of N : P : K= 100 : 200 : 100 (g / 6 m2)-1) to predict an optimal dry djulis yield of 52.80 g / plant in this study. This study revealed that the linear mixture of N, P, and K and the interaction of P and K had marked effects on the yields of djulis. Moreover, the results were fitted to a quadratic polynomial equation using a multiple regression analysis. Our data showed this mixture design is a reliable approach to develop a model that can be used to predict the djulis yields
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