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Coconut yield modeling using principal component analysis and backward selection techniques applied to the leaf nutrients as predictor variables. / Jeffrey Gupit Agot.

By: Material type: TextTextLanguage: English Publication details: 2006Description: 80 leavesSubject(s): Dissertation note: Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2006 Abstract: Eight mineral nutrients (nitrogen, phosphorus, potassium, magnesium, calcium, chlorine, sodium and boron) and copra yield of coconut as influenced by six different fertilization practices were measured by the Davao Research Center ? Philippine Coconut Authority researchers in a study entitled ?growth and yield of young coconut palms s affected by annual and intermittent fertilizer application?. From these data, regression analysis was done to model copra yield per tree for each of the six fertilization treatments. Coconut yield was the independent variable while the eight mineral nutrients were the dependent variables. Four treatments, namely no fertilization, 1 year fertilization and 1 year cut, 2 years? fertilization and 1 year cut, 2 years? fertilization, 1 year fertilization were subjected to Principal Component Analysis (PCAn) before backward selection technique was applied. Two treatments, annual fertilization and 1 year fertilization and 2 years cut, were directly subjected to backward regression technique. All regression coefficients were significant at α =0.05. The overall model for each treatment is significant at α=0.05. the coefficient of determination ranged from 0.775 to 0.955. N, Cl, and yield were highest when the palm was fertilized yearly as well as when fertilizer was applied for 2 years? fertilization with 1 year cut. The level of N, Cl and yield was lowest with no fertilization. The yield of palms given annual fertilization and 2 years? fertilization and 1 year cut was higher than the rest of the treatments. Precision of the six models were improved after using PCAn and backward selection techniques to the original variables.
List(s) this item appears in: BS Applied Mathematics
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Thesis Thesis University Library General Reference Preservation Copy LG993.5 2005 A64 A34 (Browse shelf(Opens below)) Not For Loan 3UPML00011729
Thesis Thesis University Library Archives and Records Preservation Copy LG993.5 2006 A64 A34 (Browse shelf(Opens below)) Not For Loan 3UPML00031661

College of Science and Mathematics

Thesis (BS Applied Mathematics) -- University of the Philippines Mindanao, 2006

Eight mineral nutrients (nitrogen, phosphorus, potassium, magnesium, calcium, chlorine, sodium and boron) and copra yield of coconut as influenced by six different fertilization practices were measured by the Davao Research Center ? Philippine Coconut Authority researchers in a study entitled ?growth and yield of young coconut palms s affected by annual and intermittent fertilizer application?. From these data, regression analysis was done to model copra yield per tree for each of the six fertilization treatments. Coconut yield was the independent variable while the eight mineral nutrients were the dependent variables. Four treatments, namely no fertilization, 1 year fertilization and 1 year cut, 2 years? fertilization and 1 year cut, 2 years? fertilization, 1 year fertilization were subjected to Principal Component Analysis (PCAn) before backward selection technique was applied. Two treatments, annual fertilization and 1 year fertilization and 2 years cut, were directly subjected to backward regression technique. All regression coefficients were significant at α =0.05. The overall model for each treatment is significant at α=0.05. the coefficient of determination ranged from 0.775 to 0.955. N, Cl, and yield were highest when the palm was fertilized yearly as well as when fertilizer was applied for 2 years? fertilization with 1 year cut. The level of N, Cl and yield was lowest with no fertilization. The yield of palms given annual fertilization and 2 years? fertilization and 1 year cut was higher than the rest of the treatments. Precision of the six models were improved after using PCAn and backward selection techniques to the original variables.

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