An Effective Multiple Linear Regression-Based Forecasting Model for Demand-Based Constructive Farming

Demand planning plays a very strategic role in improving the performance of every business, as the planning for a whole lot of other activities depends on the accuracy and validity of this exercise. The field of agriculture is not an exception; demand forecasting plays an important role in this area...

Full description

Saved in:
Bibliographic Details
Published in:International journal of web-based learning and teaching technologies 2020-04, Vol.15 (2), p.1-18
Main Authors: Balaji Prabhu B.V, Dakshayini, M
Format: Article
Language:English
Subjects:
Agricultural industry
Agricultural Production
Agriculture
Analysis
Commodities
Crop production
Demand
Economic forecasting
Farmers
Farming
Food
Forecasting
Foreign Countries
Mapping
Multiple Regression Analysis
Planning
Prediction
Regression analysis
Regression models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Demand planning plays a very strategic role in improving the performance of every business, as the planning for a whole lot of other activities depends on the accuracy and validity of this exercise. The field of agriculture is not an exception; demand forecasting plays an important role in this area also, where a farmer can plan for the crop production according to the demand in future. Hence, a system which could forecasts the demand for day-to-day food harvests and assists the farmers in planning the crop production accordingly may lead to beneficial farming business. This paper would experiment by forecasting the demand using multiple linear regression (EMLR-DF) for different food commodities and implements the model to assists the farmers in demand based constructive farming. Implementation results have proved the effectiveness of the proposed system in educating the farmers in producing the yields mapping to the demand. Implementation and comparison results have proved the proposed EMLR-DF is more effective and accurate.
ISSN:1548-1093
1548-1107
DOI:10.4018/IJWLTT.2020040101