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 Pre-harvest forecast of pigeon-pea yield using discriminant function analysis of weather variables

MAUSAM ◽  
2021 ◽  
Vol 67 (3) ◽  
pp. 577-582
Author(s):  
R. R. YADAV ◽  
B. V. S. SISODIA ◽  
SUNIL KUMAR
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Statistical Models ◽  
Time Series Data ◽  
Function Analysis ◽  
Uttar Pradesh ◽  
Pigeon Pea ◽  
Series Data ◽  
Maximum Temperature ◽  
Weather Variables

In the present paper, an application of discriminant function analysis of weather variables (minimum & maximum temperature, Rainfall, Rainy days, Relative humidity 7 hr & 14 hr, Sunshine hour and Wind velocity )for developing suitable statistical models to forecast pigeon-pea yield in Faizabad district of Eastern Uttar Pradesh has been demonstrated. Time series data on pigeon-pea yield for 22 years (1990-91 to 2011-12) have been divided into three groups, viz., congenial, normal, and adverse based on de-trended yield distribution. Considering these groups as three populations, discriminant function analysis using weekly data on eight weather variables in different forms has been carried out. The sets of discriminant scores obtained from such analysis have been used as regressor variables along with time trend variable and pigeon-pea yield as regressand in development of statistical models. In all nine models have been developed. The forecast yield of pigeon-pea have been obtained from these models for the year 2009-10, 2010-11 and 2011-12, which were not included in the development of the models. The model 4 and 9 have been found to be most appropriate on the basis of R2adj, percent deviation of forecast, percent root mean square error (%RMSE) and percent standard error (PSE) for the reliable forecast of pigeon-pea yield about two and half months before the crop harvest.

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 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.

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.

scholarly journals Description of Echolocation Call Parameters for Urban Bats in Vietnam as a Step Towards a More Integrated Acoustic Monitoring of Urban Wildlife in Southeast Asia

Diversity ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Long Kim Pham ◽  
Bang Van Tran ◽  
Quy Tan Le ◽  
Trung Thanh Nguyen ◽  
Christian C. Voigt
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Quantitative Description ◽  
Southeast Asian ◽  
Asian Countries ◽  
Echolocation Call ◽  
Echolocation Calls ◽  
Pilot Work ◽  
Southeast Asian Countries

This study is the first step towards more systematic monitoring of urban bat fauna in Vietnam and other Southeast Asian countries by collecting bat echolocation call parameters in Ho Chi Minh and Tra Vinh cities. We captured urban bats and then recorded echolocation calls after releasing in a tent. Additional bat’s echolocation calls from the free-flying bats were recorded at the site where we captured bat. We used the obtained echolocation call parameters for a discriminant function analysis to test the accuracy of classifying these species based on their echolocation call parameters. Data from this pilot work revealed a low level of diversity for the studied bat assemblages. Additionally, the discriminant function analysis successfully classified bats to four bat species with an accuracy of >87.4%. On average, species assignments were correct for all calls from Taphozous melanopogon (100% success rate), for 70% of calls from Pipistrellus javanicus, for 80.8% of calls from Myotis hasseltii and 67.3% of calls from Scotophilus kuhlii. Our study comprises the first quantitative description of echolocation call parameters for urban bats of Vietnam. The success in classifying urban bats based on their echolocation call parameters provides a promising baseline for monitoring the effect of urbanization on bat assemblages in Vietnam and potentially also other Southeast Asian countries.

Mineralogy of Egyptian Bentonitic Clays I: Discriminant Function Analysis

2012 ◽  
Vol 60 (4) ◽  
pp. 387-404 ◽  
Author(s):  
Mohamed Agha ◽  
Ray E. Ferrell ◽  
George F. Hart
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis

Using discriminant function analysis to identify Holocene tephras based on magnetite composition: a case study from the Sunwapta Pass area, Jasper National Park

1986 ◽  
Vol 23 (6) ◽  
pp. 804-812 ◽  
Author(s):  
A. B. Beaudoin ◽  
R. H. King
Keyword(s):  
National Parks ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
National Park ◽  
Function Analysis ◽  
Classification Schemes ◽  
Jasper National Park ◽  
Multivariate Method ◽  
Reference Samples

The magnetite composition from three sets of samples of Mazama, St. Helens set Y, and Bridge River tephras from Jasper and Banff national parks are used to test whether discriminant function analysis can unambiguously distinguish these tephras. The multivariate method is found to be very sensitive to the change in reference samples. St. Helens set Y tephra is clearly distinguished. However, discrimination between Mazama and Bridge River tephras is less distinct. A set of unknown tephras from the Sunwapta Pass area was used to test the classification schemes. Unknown tephras are assigned to different tephra types depending on which reference tephra set is used in the discriminant function analysis.

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