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Determining soil fertility using principal componen regression analysis of oil palm plantation in West Sulawesi, Indonesia
Pasaribu U.S.a, Nurhayati N.b, Ilmi N.F.F.a, Sari K.N.a
a Statistics Research Division, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Indonesia
b Department of Mathematics, Faculty of Mathematics and Natural Sciences, Jenderal Soedirman University, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.Palm oil is one of the most popular vegetable oils in the world besides soybean oil. Nowadays, more than 80% of the world’s palm oil supply comes from Indonesia and Malaysia. As the largest supplier, Indonesia must be improved and maintained its productivity with good management, such as maintaining soil fertilization to produce the best oil palm. This activity requires analysis of the soil chemical factors and soil conditions (fertile or not) from oil palm plantations, which involves a large amount of data. Principal component regression is an appropriate statistical method to solve the problem. In this method, the coefficient of the regression model is the coefficient of the principal components (PCs). Dimension reduction is performed on the predictor variables that have a high correlation so that the PCs have an insignificant correlation. The data are obtained by measuring soil samples around the trees from oil palm plantation in West Sulawesi, Indonesia. The data consist of sixteen variables and thirty-six observations (0-20 cm) for each variable. There are three PCs that become predictors of the regression model with information absorption rates reaching 78%, i.e., some macronutrients (Potassium oxide, Potassium, Calcium, and Cation exchange capacity), soil acidity and organic properties (Carbon and Nitrogen). Furthermore, the accuracy of the estimation value in this logistic regression model reaches 90% by using stepwise backward method.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Carbon and nitrogen,Cation exchange capacities,Dimension reduction,Logistic Regression modeling,Oil palm plantations,Predictor variables,Principal component regression,Principal Components[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]This research was supported by P3MI 2019 Institut Teknologi Bandung.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1742-6596/1494/1/012013[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]