Use of discriminant function analysis for forecasting crop yield

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.

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

Use of ordinal logistic regression in 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.

How the Titles of Popular Songs have Changed over the Last 60 Years

Billboard magazine has been keeping track of the 100 hottest (most popular) songs of the year since 1958. Lists of the Hot 100 titles from 1960 to 2019 (6001 titles) were used to study the way in which popular song titles changed over time. Based on significant polynomial regression trends and significant results from a discriminant function analysis, it is concluded that there were three main phases in titles (early, middle, and late) and that these phases differ in predictable manners in terms of stylistic features such as length, abstraction, activity, and the use of the word “love”. Early phase titles are longer, more concrete, more passive, and they do not use the word “love” often; middle phase titles are of medium length, more abstract, of medium activation, and use the word “love” frequently. Titles of the last phase are shorter, more concrete, more active, and do not often employ the word love. A possible factor contributing to these differences is the rise in popularity of rock and roll and hip-hop respectively and their different periods of ascendency.

Padrões morfológicos na venação alar de espécies de Tetragona Lepeletier & Serville, 1828 do grupo clavipes (Hymenoptera: Apidae: Meliponini)

Tetragona Lepeletier & Serville, 1828 (Hymenoptera: Apidae) is a genus of stingless bees widely distributed in Brazil. It has 15 species distributed in the Neotropics, from Mexico to Uruguay, nine of which are found in Brazil. However, Tetragona elongata (Lepeletier & Serville, 1828), a species known only from the Southeast region and which had been synonymized with Tetragona clavipes (Fabricius, 1804), was revalidated without any justification. The aim of this study was to test whether the morphometrics analysis of the wings is efficient in the diagnosis of the species of this genus, in addition to testing the validity of the revalidation mentioned above. This technique was applied by accessing the right forewings of 660 workers of T. clavipes, T. elongata e T. quadrangula (Lepeletier, 1836), from five Brazilian collections. For the geometric morphometric analysis, 12 landmarks were selected. The software MorphoJ version 1.6 was used to do Discriminant Function analysis (1000 replications) and Canonical Variation Analysis (CVA). Between T. clavipes and T. elongata, there was a 100% variance between species (canonical variation analysis), suggesting that it may be an indication of speciation. Even though T. elongata has been revalidated, it still has overlapped with T. clavipes, which indicates to be the same species. Taxonomic studies are needed to synonymize them.

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

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.

​Discrimination of Five Gobid Species (Family: Gobiidae) based on Otolith Morphometry

Background: The fishes of family Gobiidae are one of the least studied fishes, especially for otolith structure. The otoliths were possess species specific features. Hence, traits of sagittal otolith of gobid species studied. Methods: Five gobid species Odontamblyopus roseus (Valenciennes, 1837), Trypauchen vagina (Bloch and Schneider, 1801), Glossogobius giuris (Hamilton, 1822), Parachaeturichthys polynema (Bleeker, 1853) and Boleophthalmus dussumieri (Valenciennes, 1837) were investigated by three methods including morphological, shape indices and step wise discriminant function analysis (SDFA). Result: Interrelationship between shape indices investigated, at 95% level of confidence (P less than 0.05), revealed that perimeter of P. polynema and area of T. vagina have isometric growth with their length (b = 3.0071, 2.90, respectively) and otolith area of B. dussumieri have positive allometric growth (b = 4.23077). SDFA, based on otolith morphometry, discriminated species up with 97.18% accuracy. Hence, the results of present investigation can be used for discrimination of the species and as a tool in predicting fish size from the otoliths and in calculating the biomass of these less studied fish species.

Factors that Differentiate Winning and Losing in Men’s University Basketball

The objective of this study is to analyse the game-related statistics that differentiate winning and losing teams, according to the finale game scores in a men’s university basketball league. Samples were gathered from the archival data of the 2019–2020 regular season of the league. Sixteen game-related statistics were analysed: two- and three-point field-goals (both successful and unsuccessful), free-throws (both successful and unsuccessful), defensive and offensive rebounds, assists, steals, turnover, blocks, second-chance points, fast break points, fouls committed and received. The data were clustered into different game types based on the final outcome point differences: all games, balanced games (11 points and below) and unbalanced games (12 points and above). Discriminant function analysis was conducted to identify the performance indicators that classify winning and losing games. The results revealed that winning and losing in balanced games were discriminated by successful two-point field goals, unsuccessful two-point field goals, unsuccessful three-point field goals, successful free-throws, assists, steals, blocks, second-chance points, fast-break points, fouls committed, and fouls received. For unbalanced games, winning and losing were distinguished by successful two-point field goals, successful three-point field goals, successful free-throws, unsuccessful free-throws, defensive rebounds, blocks, fast-break points, and fouls received. In conclusion, offensive and defensive indices are critical to winning and losing in university-level basketball.