scholarly journals Pre Harvest Forecasting of Kharif Rice Yield Using Weather Parameters for Strategic Decision Making in Agriculture

2020 ◽  
pp. 162-170
Author(s):  
Y. A. Garde ◽  
V. S. Thorat ◽  
R. R. Pisal ◽  
V. T. Shinde
Keyword(s):  
Decision Making ◽  
Crop Yield ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Crop Production ◽  
Function Analysis ◽  
Strategic Decision ◽  
Weather Data ◽  
Strategic Decision Making ◽  
Yield Forecast

In the recent year, pre harvest crop yield forecasting has been a topic of interest for producers, policy makers, government and agricultural related organizations. Pre harvest crop forecasting is important for national food security. Construction of appropriate yield forecast promotes the output of scenario analyses of crop production at a farm level, which enables suitable tactical and strategic decision making by the farmer. Indeed, considerable benefits apply when seasonal forecasting of crop performance is applied across the whole value chain in crop production. Timely and accurate yield forecast is essential for crop production, marketing, storage and transportation decisions as well as for managing the risk associated with these activities. In present manuscript efforts were made for development of pre harvest forecast models by using different statistical approaches viz. multiple linear regression (MLR), discriminant function analysis and ordinal logistic regression. The study utilized the crop yield data and corresponding weekly weather data of last 30 years (1985-2014). The model development was carried out at 35th and 36th SMW (Standard Meteorological Week) for getting forecast well in advance of actual harvesting of the field crop. The study revealed that method of discriminant function analysis gave best pre harvest forecast as compare to remaining developed models. It was observed high value of Adj. R2= 0.94, low value of RMSE= 164.24 and MAPE= 5.30. The model can be used in different crop for reliable and dependable forecast and these forecasts have significant value in agricultural planning and policy making.

scholarly journals Use of discriminant function analysis for forecasting crop yield

MAUSAM ◽  
2022 ◽  
Vol 63 (3) ◽  
pp. 455-458
Author(s):  
RANJANA AGRAWAL ◽  
CHANDRA HAS ◽  
KAUSTAV ADITYA
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Time Series Data ◽  
Function Analysis ◽  
Wheat Yield ◽  
Quadratic Function ◽  
Qualitative Assessment ◽  
Weather Data ◽  
Series Data ◽  
Yield Forecast

The present paper deals with use of discriminant function analysis for developing wheat yield forecast model for Kanpur (India). Discriminant function analysis is a technique of obtaining linear/Quadratic function which discriminates the best among populations and as such, provides qualitative assessment of the probable yield. In this study, quantitative forecasts of yield have been obtained using multiple regression technique taking regressors as weather scores obtained through discriminant function analysis. Time series data of 30 years (1971-2000) have been divided into three categories: congenial, normal and adverse, based on yield distribution. Taking these three groups as three populations, discriminant function analysis has been carried out. Discriminant scores obtained from this have been used as regressors in the modelling. Various strategies of using weekly weather data have been proposed. The models have been used to forecast yield in the subsequent three years 2000-01 to 2002-03 (which were not included in model development). The approach provided reliable yield forecast about two months before harvest.

scholarly journals Bayesian discriminant function analysis based forecasting of crop yield in Kanpur district of Uttar Pradesh

2021 ◽  
Vol 21 (4) ◽  
pp. 462-467
Author(s):  
Vandita Kumari ◽  
Kaustav Aditya ◽  
Hukum Chandra ◽  
Amarender Kumar
Keyword(s):  
Crop Yield ◽  
Discriminant Function ◽  
Bayesian Approach ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Uttar Pradesh ◽  
Weather Data ◽  
Percentage Error ◽  
Yield Data ◽  
Analysis Technique

Discriminant function analysis technique using Bayesian approach has been attempted for wheat forecasting in Kanpur district of Uttar Pradesh, India both qualitatively and quantitatively. Crop yield data and weekly weather data on temperature (maximum and minimum), relative humidity (maximum and minimum), rainfall for 16 weeks of the crop cultivation have been used in the study. These data have been utilized for model fitting and validation. Crop years were divided into two and three groups based on the de-trended yield. Crop yield forecast models have been developed using posterior probabilities calculated through Bayesian approach in stepwise discriminant function analysis along with year as regressors for different weeks. Suitable strategy has been used to solve the problem of number of variables more than number of data points. Performance of the models obtained at different weeks was compared using Adjusted R2, PRESS (Predicted error sum of square), number of misclassifications. Forecasts were evaluated using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. The result shows that the model based on three groups case perform better. The performance of the proposed Bayesian discriminant function analysis technique approach was better as compared to existing discriminant function analysis score based approach both qualitatively and quantitatively.

scholarly journals Use of ordinal logistic regression in crop yield forecasting

MAUSAM ◽  
2021 ◽  
Vol 67 (4) ◽  
pp. 913-918
Author(s):  
VANDITA KUMARI ◽  
RANJANA AGRAWAL ◽  
AMRENDER KUMAR
Keyword(s):  
Logistic Regression ◽  
Crop Yield ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Ordinal Logistic Regression ◽  
Percentage Error ◽  
Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

The performance of ordinal logistic regression and discriminant function analysis has been compared in crop yield forecasting of wheat crop for Kanpur district of Uttar Pradesh. Crop years were divided into two or three groups based on the detrended yield. Crop yield forecast models have been developed using probabilities obtained through ordinal logistic regression along with year as regressors and validated using subsequent years data. In discriminant function approach two types of models were developed, one using scores and another using posterior probabilities. Performance of the models obtained at different weeks was compared using Adj R2, PRESS (Predicted error sum of square), number of misclassifications and forecasts were compared using RMSE (Root Mean Square Error) and MAPE (Mean absolute percentage error) of forecast. Ordinal logistic regression based approach was found to be better than discriminant function analysis approach.  

scholarly journals A physical therapy decision-making tool for stratifying persons with Parkinson's disease into community exercise classes

2019 ◽  
Vol 9 (6) ◽  
Author(s):  
Emily E Borchers ◽  
Tara L McIsaac ◽  
Jennifer K Bazan-Wigle ◽  
Aaron J Elkins ◽  
Ralph C Bay ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Decision Making ◽  
Physical Therapy ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Therapy Decision ◽  
Clinical Measures ◽  
Class Assignment ◽  
Decision Making Tool

Aim: Physical therapy and exercise are considered essential components in the management of Parkinson's disease (PD). Using our retrospective data and years of experience in assigning persons with PD to multilevel group classes we propose a two-part physical therapy decision-making tool consisting of participant and exercise program considerations. Methods: Retrospective medical record review and therapist consensus identified evaluation considerations determined to aide clinical decision making. The ability of these variables (i.e., demographics, clinical characteristics, clinical measures cut-offs) to predict the class assignment decision of PD-specialized physical therapists was evaluated using discriminant function analysis. Results: Therapist-assigned groups differed significantly on all clinical measures (p < 0.001) which provided the categorical data required for discriminant analysis. Using all variables, the discriminant function analysis predicted class assignment of the therapists with 79% agreement. Conclusion: This proposed tool provides a framework that may guide the process for increasing access to multilevel group classes.

scholarly journals Different approaches on pre harvest forecasting of wheat yield

2015 ◽  
Vol 7 (2) ◽  
pp. 839-843
Author(s):  
Y. A. Garde ◽  
B. S. Dhekale ◽  
S. Singh
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Function Analysis ◽  
Wheat Yield ◽  
National Income ◽  
Uttar Pradesh ◽  
Forecast Model ◽  
Wheat Crop ◽  
Yield Forecast ◽  
Rabi Crops

Agriculture is backbone of Indian economy, contributing about 40 per cent towards the Gross National Product and provide livelihood to about 70 per cent of the population. According to the national income published in Economic survey 2014-15, by the CSO, the share of agriculture in total GDP is 18 percent in 2013-14. The Rabi crops data released by the Directorate of Economics and Statistics recently indicates that the total area coverage has declined; area under wheat has gone down by 2.9 per cent. Therefore needs to be do research to study weathersituation and effect on crop production. Pre harvest forecasting is true essence, is a branch of anticipatory sciences used for identifying and foretelling alternative feasible future. Crop yield forecast provided useful information to farmers, marketers, government agencies and other agencies. In this paper Multiple Linear Regression (MLR) Technique and discriminant function analysis were derived for estimating wheat productivity for the district of Varanasi in eastern Uttar Pradesh. The value of Adj. R2 varied from 0.63 to 0.94 in different models. It is observed that high value of Adj. R2 in the Model-2 which indicated that it is appropriate forecast model than other models, also the value of RMSE varied from minimum 1.17 to maximum 2.47. The study revealed that MLR techniques with incorporating technical and statistical indicators (Model 2) was found to be better for forecasting of wheat crop yield on the basis of both Adjusted R2 and RMSE values.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

Sign in / Sign up

Export Citation Format

Share Document