Rice yield prediction in lower Gangetic Plain of India through multivariate approach and multiple regression analysis

Biswas, Ria; Bhattacharyya, B. B.

doi:10.54386/jam.v21i1.214

Cited by 3 publications

(1 citation statement)

References 1 publication

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“…The Model-1A which was developed through MLR for 13 week has least value of RMSE so it was considered as suitable model for pre-harvest forecasting of groundnut yield. The similar results were found by Parmar et al (2004) and Rajegowda et al (2014) for groundnut yield forecasting and Biswas and Bhattacharya (2019) in sugarcane.…”

Section: Resultssupporting

confidence: 86%

Forecasting of groundnut yield using meteorological variables

Bhimani¹,

Gundaniya²,

Darji³

2022

GJoEE

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Agriculture is one of the most significant economic sectors in India, which is strongly dependent on climatic conditions. Groundnut is one of the major oilseed crops of Gujarat and Junagadh has highest production of groundnut in Gujarat. For effective, forward-looking, and current planning, especially in agriculture, which is fraught with uncertainty, reliable and timely forecasts are essential. Therefore, effective yield forecast of such important oilseed crop is necessary for future planning and policy making. In the present investigation, the time series data of groundnut yield and weather parameters of 29 years Junagadh (1991-92 to 2019-20) were used. The week wise weather indices were generated using correlation between de-trend yield and weekly weather variables. Multiple Linear Regression (MLR) and Discriminant Function Analysis were used to develop yield forecasting model using weather indices before three week and one week before harvest, respectively. These models were compared using Coefficient of multiple determination (Adj. R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The study found that model-1A, developed using MLR technique have high value of adj. R2(78.8%) and low RMSE value (671.72). Multiple linear regression (MLR) was found to be more accurate than the discriminant function analysis approach.

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Section: Resultssupporting

confidence: 86%

Forecasting of groundnut yield using meteorological variables

Bhimani¹,

Gundaniya²,

Darji³

2022

GJoEE

View full text Add to dashboard Cite

show abstract

Algorithms for forecasting cotton yield based on climatic parameters in Brazil

Aparecido

Meneses

Souza

et al. 2020

Archives of Agronomy and Soil Science

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Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression

Guo

Xiang

et al. 2021

Agronomy

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Rice yield is not only influenced by factors of varieties and managements, but also by environmental factors. In this study, agronomic trait data of rice and climate data in eastern China were collected, and rice yields were predicted using a variety of algorithms, including the non-linear tool of feed-forward backpropagation neural networks (FFBN) and the linear model of partial least squares regression (PLSR). The results showed that both the agronomic traits and the climate data were significantly related with rice yield. The PLSR model showed that covariates occurred among the parameters, and modifications should be considered for climate data-based modelling. The FFBN model demonstrated better prediction performance than that of PLSR, in which the relation coefficient (R2) and root mean square error (RMSE) were 0.611 vs. 0.374 and 0.578 vs. 0.865 ton/ha using climate data, respectively; and 0.742 vs. 0.689 and 0.556 vs. 0.608 using agronomic trait data, respectively. When using fused data the R2 and RMSE improved to 0.843 vs. 0.746 and 0.440 vs. 0.549, respectively. The optimum architecture of the FFBN consisted of one hidden layer with 29 neurons. Therefore, the FFBN algorithm is an effective option for the prediction of rice yield in complex systems of rice production.

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Rice yield prediction in lower Gangetic Plain of India through multivariate approach and multiple regression analysis

Cited by 3 publications

References 1 publication

Forecasting of groundnut yield using meteorological variables

Forecasting of groundnut yield using meteorological variables

Algorithms for forecasting cotton yield based on climatic parameters in Brazil

Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression

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