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PEMODELAN REGRESI LOGISTIK PENGGUNAAN REDUKTAN HERBISIDA PADA BUDIDAYA KOPI DI PAGARALAM

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Penilaian

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Coffee is one of the sub-sectors of the plantation industry that has an important role in improving the economy, both at local and national levels. The province of South Sumatra is one of the largest coffee producers in Indonesia, and one of the coffee-producing districts/cities in South Sumatra is Pagaralam. The frequent use of herbicides by coffee farmers in weed control can have a negative impact on product quality and the environment. Reductant as a reducing agent for herbicide residues can be an alternative in overcoming this problem. This study aims to obtain a binary logistic regression model regarding the use of herbicide reductants by coffee farmers in Pagaralam. Logistic regression modeling using stepwise method. This study involved 166 coffee farmers, consisting of 81 farmers who used reductants and 85 farmers who did not use reductants. There were 10 independent variables observed to determine the characteristics of farmers' decisions in the use of herbicide reductants. Tree age , organic fertilizer , and land productivity are variables that have a significant effect on the use of herbicide reductants. An increase in one category of tree age and organic fertilizer, as well as a decrease in one category of land productivity, can lead to an increase probability in the use of reductants by coffee farmers.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207004991	T83486	T834862022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T834862022

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2022

Collation

xii, 59 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

519.530 7

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Matematika statistikal deskriptif
Jurusan Matematika

Specific Detail Info

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Statement of Responsibility

SEPTA

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