Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees

Tissue culture data is non-linear in nature. Decision tree algorithms stand out in revealing the non-linear interactions and relationships between the predictors and responses. Classification and regression tree (CART), chi squared automatic interaction detector (CHAID) and exhaustive CHAID are the common decision tree algorithms. These three models were employed to predict and optimize the effect of minor mineral nutrients on shoot cultures of Corylus avellana L. cultivars. H3BO3, CuSO4 center dot 5H(2)O, MnSO4 center dot H2O, Na2MoO4 center dot 2H(2)O and Zn(NO3)(2)center dot 6H(2)O were tested in a range of 0.5 x to 4 x Driver and Kuniyuki (DKW) medium within a RSM optimal design. NiSO4 center dot 6H(2)O was also an input within the design with varying levels of 0 to 6 A mu M. Shoot quality and length were affected by genotype, B and Mo amounts. Multiplication rate depended on genotype, B, Zn and Cu levels. Callus formation was affected by genotype and B. Leaf size depended on genotype, Zn and Mn concentrations. Cu was a significant predictor of leaf color and Ni slightly improved SPAD readings (chlorophyll content). CART in general outperformed CHAID and exhaustive CHAID in terms of the predictive performance. Both CHAID and exhaustive CHAID failed to generate a tree model for a leaf size response. The optimal minor nutrients for hazelnuts based on the predictions of the CART algorithm were suggested to be: B 2.3 x DKW, Cu 0.5x, Mn 0.5x, 2 x Mo and Zn 2x.