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 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 Getting Rid of Dichotomous Sex Estimations: Why Logistic Regression Should be Preferred Over Discriminant Function Analysis

2020 ◽  
Vol 65 (5) ◽  
pp. 1685-1691
Author(s):  
Bjørn Peare Bartholdy ◽  
Elena Sandoval ◽  
Menno L. P. Hoogland ◽  
Sarah A. Schrader
Keyword(s):  
Logistic Regression ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis

scholarly journals Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

2015 ◽  
Vol XL-7/W3 ◽  
pp. 39-44 ◽  
Author(s):  
A. Kolotii ◽  
N. Kussul ◽  
A. Shelestov ◽  
S. Skakun ◽  
B. Yailymov ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Biophysical Model ◽  
Wheat Crop ◽  
Biophysical Parameters ◽  
Model Based ◽  
Winter Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

scholarly journals A comparison of three statistical methods for analysing extinction threat status

2013 ◽  
Vol 41 (1) ◽  
pp. 37-44 ◽  
Author(s):  
HEATHER R. TAFT ◽  
DEREK A. ROFF ◽  
ATTE KOMONEN ◽  
JANNE S. KOTIAHO
Keyword(s):  
Logistic Regression ◽  
Life History ◽  
Statistical Methods ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Statistical Models ◽  
Function Analysis ◽  
Regression Trees ◽  
Threat Status ◽  
Life History Characteristics

SUMMARYThe International Union for Conservation of Nature (IUCN) Red List provides a globally-recognized evaluation of the conservation status of species, with the aim of catalysing appropriate conservation action. However, in some parts of the world, species data may be lacking or insufficient to predict risk status. If species with shared ecological or life history characteristics also tend to share their risk of extinction, then ecological or life history characteristics may be used to predict which species may be at risk, although perhaps not yet classified as such by the IUCN. Statistical models may be a means to determine whether there are non-threatened or unclassified species that share the characteristics of threatened species, however there are no data on which model might be most appropriate or whether multiple models should be used. In this paper, three types of statistical models, namely regression trees, logistic regression and discriminant function analysis are compared using data on the ecological characteristics of Finnish lepidopterans (butterflies and moths). Overall, logistic regression performed slightly better than discriminant function analysis in predicting species status, and both outperformed regression trees. Uncertainty in species classification suggests that multiple analyses should be performed and particular attention devoted to those species for which the methods disagree. Such standard statistical methods may be a valuable additional tool in assessing the likely threat status of a species where there is a paucity of abundance data.

Predicting Public Procurement Irregularity: An Application of Neural Networks

2018 ◽  
Vol 15 (1) ◽  
pp. 141-154 ◽  
Author(s):  
Ting Sun ◽  
Leonardo J. Sales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logistic Regression ◽  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Prediction Models ◽  
Function Analysis ◽  
Public Procurement ◽  
Predictive Performance ◽  
Z Scores

ABSTRACT Using the data describing the characteristics of contractors provided by the Comptroller General of the Union, Brazil (CGU), this paper mainly implements two artificial neural networks, traditional neural network (TNN) and deep neural network (DNN), to develop prediction models of public procurement irregularities designed for the initial screening of contractors. This is the first application of DNN in the context of government auditing. To examine the effectiveness of DNN, the authors compare its predictive performance to TNN and two other algorithms (logistic regression and discriminant function analysis) and find that DNN significantly outperforms TNN and other algorithms in terms of accuracy, precision, F-scores, AUC, and other metrics, as suggested by the high Z-scores of the Z-tests. Although TNN has a higher recall than DNN, the difference of recall between TNN and DNN is insignificant. Logistic regression and discriminant function analysis achieve the highest recall scores, but their Z-scores are much lower than those of other metrics. Therefore, DNN generally performs more accurately than other approaches and meets the requirement of the CGU for an early alarm system.

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.

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