Open AccessProduction Risk with Feasible Generalized Least Square
Author(s) -
Kanis Fatama Ferdushi,
Kamrul Hossain,
Anton Abdulbasah Kamil
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012109
Subject(s) - production (economics) , quadratic equation , stratified sampling , extension (predicate logic) , mathematics , sampling (signal processing) , square (algebra) , fertilizer , unit (ring theory) , square root , statistics , mathematical optimization , agricultural engineering , econometrics , computer science , economics , engineering , microeconomics , agronomy , geometry , mathematics education , filter (signal processing) , programming language , computer vision , biology
This study investigates production risk. A multistage stratified random sampling technique was adopted to select sampling unit. In between Cobb Douglas and Linear quadratic model, the linear quadratic model had been picked through feasible generalized least square method. The numerical model, we utilize the information from rice cultivating in Bangladesh. The results show that uneven socioeconomic and farm-specific inputs are creating risk in rice production. Input variables such as area, labour, and fertilizer and managerial factors, for example, experience, schooling, contact with extension, training, natural calamity, member and status indicated a significant impact on rice productions uncertainty. This indicated that both input and managerial factors were important for the rice production.