Abstract
A prediction function is developed for sugarcane yield using preplant soil nutrition levels, cultivar, and soil type. A tree regression approach is used because the resulting function encompasses the complexity of response between yield, multiple nutrients and other factors, while handling large amounts of data and providing information useful in the development of fertilizer and other production recommendations. Data collected from 148 control plots of experiments performed on commercial fields in the Everglades Agricultural Area of Florida are used to illustrate the method.
Keywords
Binary tree, Soil testing, Florida Everglades Agricultural Area
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Portier, Kenneth M. and Anderson, David L.
(1995).
"USING TREE REGRESSION TO IDENTIFY NUTRITIONAL AND ENVIRONMENTAL FACTORS AFFECTING SUGARCANE PRODUCTION,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1342
USING TREE REGRESSION TO IDENTIFY NUTRITIONAL AND ENVIRONMENTAL FACTORS AFFECTING SUGARCANE PRODUCTION
A prediction function is developed for sugarcane yield using preplant soil nutrition levels, cultivar, and soil type. A tree regression approach is used because the resulting function encompasses the complexity of response between yield, multiple nutrients and other factors, while handling large amounts of data and providing information useful in the development of fertilizer and other production recommendations. Data collected from 148 control plots of experiments performed on commercial fields in the Everglades Agricultural Area of Florida are used to illustrate the method.
