Tree regression analysis to determine effects of soil variability on sugarcane yields

Approximately 15% of south Florida sugarcane (Saccharum spp.) is grown on h igh wafer table sandy soils that overlie limestone bedrock. This study dete rmined treatment and site-specific factors affecting sugarcane production o n these soils using a new statistical tool called free regression. Sugarcan e was grown in a 38-ha area for three seasons (1991, 1992, and 1993), Treat ments were subirrigation water table depth (0.45 vs. 0.70 m), N fertilizati on frequency (13 vs. 7 split applications for 3 yr at 224 kg N ha(-1) yr(-1 )), and Mg fertilizer rate (0 vs. 60 kg Mg ha(-1) yr(-1)), using a split-sp lit plot design. Soil was sampled from plots before each crop to determine pH,and soil test P, K, Ca, Mg, and Si. Depth to rock was determined with gr ound-penetrating radar. Three statistical techniques were used to examine d esign and the effect of soil factors on sugarcane yield: traditional simple correlations, general linear mixed-model analysis (GLM and MIXED), and a n ew technique, tree regression. Tree regression resulted in functions encomp assing the complexity of response between yield, soil nutrients, and other factors, while handling large amounts of data. The regression free identifi ed sugarcane yields ranging from 42.6 to 100.8 t ha(-1) grouped according t o conditions defined by soil Ca, crop, soil Mg, the P "intensity/capacity" ratio, and water table level. The strength of the general linear mixed-mode l approach was in inference testing, whereas the strength of free regressio n tree analysis is for prediction of covariate importance under broadly spa ced environments.