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Related Experiment Video

Updated: Jun 24, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

Ajay Sharma¹, Joginder Kumar¹, Mandeep Redhu²

¹Department of Mathematics and Statistics, CCS, Haryana Agricultural University, Hisar, Haryana, India.

Scientific Reports

|June 1, 2024

Summary

This summary is machine-generated.

Discriminant function analysis accurately predicts rice yield using historical data and weather information. Forecasting one month before harvest offers valuable insights for agricultural planning and decision-making.

Keywords:

Estimation Multivariate analysis RMSE and MAPE Rice yield Weather parameters

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Area of Science:

Agricultural Science
Statistical Modeling
Crop Forecasting

Background:

Accurate rice yield prediction is crucial for food security and economic stability.
Traditional methods often lack the precision needed for effective agricultural planning.
Integrating meteorological data with statistical models can enhance yield forecasting accuracy.

Purpose of the Study:

To develop and compare multivariate predictive models for rice yield.
To identify the most effective statistical technique for forecasting crop yield in Haryana.
To determine the optimal time for rice yield prediction prior to harvest.

Main Methods:

Application of stepwise multiple regression, discriminant function analysis, and logistic regression.
Utilizing time series data of rice yield (1980-2021) and fortnightly meteorological data.
Classification of crop yield data into two and three classes for analysis.

Main Results:

Discriminant function analysis demonstrated superior accuracy in predicting rice yield compared to logistic regression.
Evaluation metrics included Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation, and Mean Absolute Percentage Error.
The study identified one month prior to harvest as the optimal forecasting window.

Conclusions:

Discriminant function analysis is a highly effective tool for accurate rice yield prediction.
Integrating weather data and statistical modeling enhances agricultural planning capabilities.
Timely and accurate yield forecasts support informed decision-making in agriculture.