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ThesisItem Open Access Modelling wheat yield based on weather parameters at its phenological stages(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-08) Mariya, Merlin J.; Shukla, A.K.Crop yield prediction is an important aspect for a developing country like India in such a way that it helps decision makers, frame policies and strategies related to distribution, marketing and storage of agricultural products which ultimately lead to the sustainable growth and development of the country. The agricultural sector is severely affected by short term weather fluctuations and long term climate variations. Weather variability during important growth stages of a crop can result in uncontrolled crop yield variations. Wheat (Triticum aestivum) is one of the most widely grown cereal crops and an important staple food next to rice in India. The present study attempted to develop wheat yield prediction models for Udham Singh Nagar district of Uttarakhand state based on weather parameters during different growth stages of wheat. Maximum temperature, Minimum temperature, Relative Humidity A.M, Relative Humidity P.M, Total rainfall, Sunshine hours, Wind velocity and Evapotranspiration were the weather parameters considered for the study. Statistical and soft computing techniques namely Multiple Linear Regression, Artificial Neural Network and Ridge Regression were employed in the study using R software and SPSS software package. Correlations between rabi wheat yield and weather parameters during different growth stages of wheat were also analysed. The following conclusions were drawn from the study: •Correlation between rabi wheat yield and maximum temperature during the Dough stage was found to be positive but there was a negative correlation in the case of minimum temperature during the Dough stage. •Rabi wheat yield was found to be negatively correlated with minimum temperature during the milking stage whereas, rabi wheat yield was found to be positively correlated with morning relative humidity during the tillering stage and evening relative humidity during the Crown Root Initiation stage. •MLR-W (MLR model developed by using weather parameters at different growth stages of wheat used directly as predictors) model could perform better than the other two models developed using MLR method •ANN-WI (ANN model developed by using weather indices as predictors) model could perform better than the other two models developed using ANN. •RR-D (Ridge Regression model developed by using deviations of weather parameters from optimum value during important growth stages of wheat as predictors) model could perform better than the other two models developed using Ridge Regression •Evaluation based on statistical indices and error percentage during validation revealed that, ANN-WI (ANN model developed using unweighted and weighted weather indices as predictors, R2 = 0.96) out performed MLR-W model and RR-D model. •Crop yield prediction models based on weather parameters during important growth stages of the crop could provide reliable results.ThesisItem Open Access Assessment of trends, variability and climatic change for the district Lahaul And Spiti of Himachal Pradesh(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-06) Singh, Gaurav; Ahmad, HaseenThe present investigation was carried out to study the weather parameters viz. precipitation, maximum temperature and minimum temperature for the district Lahaul and Spiti of Himachal Pradesh over the period of 102 years (1901-2002). The data were collected from Indian Water Portal website www.indianwaterportal.org. The study's objectives were to look at trends, variability, and distribution fitting for weather parameters on an annual, seasonal, and monthly basis, and to use Microsoft Excel and IBM SPSS software to calculate different measures of statistics such as mean, skewness, kurtosis, standard deviation etc. to test the normality of weather parameters. The values of shows that the weather parameters did not follow normal distribution. A comparative study was made among the weather parameters by fitting a trend line by method of least squares to obtained best fitted straight line trend. Then studied the best fit distributions by using Easy Fit 5.5 Software. The study showed significant and insignificant increasing trends for winter and monsoon season’s precipitation, and insignificant increasing and decreasing trends for pre-monsoon and post-monsoon seasons precipitation, whereas annual & monthly precipitation showed insignificant increasing trends. For maximum and minimum temperatures, premonsoon seasons showed significant increasing trends while winter, monsoon and postmonsoon seasons showed significant decreasing trends. Annual maximum and minimum temperatures showed significant increasing trends. Monthly maximum and minimum temperatures also showed significant increasing trends.ThesisItem Open Access Some statistical and soft computing models for crop yield and weather forecasting(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-08) Bhatt, Neha; Shukla, A.K.Crop yield forecasting and weather forecasting are two important aspects for developing economy so that adequate planning exercise is undertaken for sustainable growth and overall development of the country. However ,such forecast studies need to be done on continuing basis and for different agro-climatic zones due to visible effect of changing climatic conditions and weather shifts at different locations and area . Therefore, the present study was undertaken for forecasting the yield of two major crops viz. Rice and Wheat and to forecast the eight weather parameters namely Maximum temperature, Minimum temperature, Humidity A.M, Humidity P.M, Rainfall, Sunshine Hrs, Wind speed and Evapotranspiration the data for the last 20 years (w.e.f 2000- to 2020) of yield and eight weather parameters were obtained from G.B. Pant University of Agriculture and Technology ,Pantnagar, District Udham Singh Nagar, Uttarakhand, India. Statistical and soft computing techniques namely ETS ,TBATS, ARIMA, MLR, FIS ,ANN and ARIMAX were used to develop the forecasting models using R and MATLAB softwares. Correlations between yields of Rice and Wheat with the weather parameters were also calculated. On the basis of the present study following conclusions were drawn: Rice Yield was positively significantly correlated with Minimum Temperature, Humidity P.M., Rainfall, Number of Rainy Days and Sunshine Hours during the cropping season . Wheat yield was positively significantly correlated with Maximum Temperature , Minimum Temperature, Rainfall, Number of Rainy Days and Sunshine Hours during the cropping season . ARIMA model was found to be the best fit monthly weather prediction model for Maximum Temperature, Minimum Temperature, Humidity A.M., Humidity P.M., Sunshine Hours, Wind Velocity and Evapotranspiration as compared to ETS ,TBATS and ARIMA models. ANN model was found to be the best fit daily weather prediction model for Maximum Temperature, Minimum Temperature, Humidity A.M., Humidity P.M., Sunshine Hours and Evapotranspiration as compared to ETS and ARIMA models. For Rice yield prediction on the basis of seasonal average of weather data, various models were developed using ANN, FIS and ARIMAX techniques and it was found that ARIMAX-RY-II( ARIMAX-RICE YIELD – Based on 5 weather parameters) was the best fit model for Rice yield prediction. For Wheat yield prediction on the basis of seasonal average of weather data, various models were developed using ANN, FIS and ARIMAX techniques and it was found that FIS-WY (FIS- WHEAT YIELD) was the best fit model for Wheat yield prediction. For predicting the Rice yield on the basis of monthly weather data during the rice crop season , different MLR models were developed on the basis of different combinations of weather parameters and the best fit MLR-RY-A model with R 2 = 0.62 was selected It was found that November Maximum Temperature, September Minimum Temperature, July Humidity A.M., August Humidity A.M., August Sunshine Hours , November Sunshine Hours, July Maximum Temperature, October Humidity A.M. , November Humidity A.M. , September Rainfall and September Evapotranspiration could be used as best predictors for estimating the rice yield. For predicting the Wheat yield on the basis of monthly weather data during the Wheat crop season , different MLR models were developed on the basis of different combinations of weather parameters and the best fit MLRWY- A model with R 2 = 0.45 was selected . It was found that October Maximum Temperature March Maximum Temperature, March Humidity A.M., November Humidity P.M., April Sunshine Hours and April Wind Speed could be used as best predictors for estimating the wheat yield.ThesisItem Open Access Time series analysis of milk yield data and climatic effect on milk yield(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-06) Navneet Kaur; Vinod KumarThe Present study is concerned with the time series analysis of milk yield data and climatic, breed and fodder effect on milk yield. Major findings of the study are: 1. The milk yield from Crossbred cows, Sahiwal cows and Murrah buffaloes follow Burr, Kumaraswamy and General Preto distribution respectively. 2. The Milk Yield data of Crossbred and Sahiwal cows and Murrah buffaloes exhibit downward trend and are non-stationary. The stationarity is achieved by trend and seasonal differencing of the data. The ARIMA (0,1,1) (1,1,1)12 for Crossbred cows, ARIMA (1,1,0) (0,1,1)12 for Sahiwal cows and ARIMA (0,1,1) (2,1,0)12 for Murrah buffaloes are found to be the best fit model. The forecasting results showed good agreement between observed and predicted values. 3. The effect of climatic factors on Milk Yield performance is estimated using Multiple Linear Regression analysis using the Best model variables selection method. 4. Breed of the dairy animals is found to have a significant effect on average milk production, whereas seasons do not have a significant effect on average milk production in dairy animals. 5. The average milk production of the crossbredsand Sahiwal cows and Murrah buffaloes was higher during the first season (January-April) when dairy animals were fed a diet containing green fodder consisting of barley (Jau) and barseem. The results of the study are expected to provide useful information to dairy researchers and statisticians for future policy planning in the study area.ThesisItem Open Access Bayesian analysis of wheat productivity data(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-09) Maitreya; Vinod KumarThesisItem Open Access A statistical study on prevalence of anemia in adolescent girls(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-11) Chauhan, Chaya; Shukla, A.K.Present work deals with the statistical analysis of anthropometric parameters and sociodemographic data of adolescent girls to check the prevalence of Anemia in the Rural and Tribal area of Udham Singh Nagar District of Uttarakhand. The study is based on secondary data related to anthropometric and socio-demographic parameters of 520 adolescent girls (170 Rural and 350 Tribal). The study was conducted by categorizing the whole dataset into two categories according to their age i.e. Early adolescent girls & Late adolescent girls. Major conclusions of the study are: 1. On basis of distribution patterns of the seven parameters viz. Weight, Height, Waist Circumference, Hip Circumference, Waist to Hip Ratio, BMI & Haemoglobin obtained using EasyFit 5.6 Professional software, it was concluded that probability distribution of only one parameter i.e. Height in the case of Rural Late Adolescent Girls category follows Normal Distribution. Other than it, all of the parameters in different categories of Rural and Tribal Adolescent Girls follow Non-Normal distribution. 2. On the basis of Karl Pearson’s Correlation Coefficient and Chi-square test of Independence relationship between various anthropometric parameters and socio demographic parameters was found. Only one anthropometric parameter i.e. Waist to Height Ratio was negatively correlated with Haemoglobin. It was also found that Haemoglobin depends on sociodemographic parameters viz. No. of Family members, No. of siblings, Age at the time of Menarche, Standard of Living Score, Food Habit, No. of Home prepared meals taken in a day, Menarche Achieved, Menstrual Flow, Avoiding Food during Menstrual Cycle, Nutrition Education on Anemia, Fasting Frequency and Socio-economic class. 3. On the basis of Z test used for comparison of various anthropometric parameters and Haemoglobin for Rural and Tribal adolescent girls, it was concluded that there is a significant difference between the Weights of Rural and Tribal Early Adolescent Girls.. A significant difference was found between Weight, Height, Waist Circumference, Hip circumference, Waist to Height Ratio and Haemoglobin in case of Rural and Tribal Late adolescent girls. A significant difference was also observed in prevalence of Anemia in Rural and Tribal Late adolescent girls. 4. On the basis of Multiple Linear Regression (MLR) and Binomial Logistic Regression (BLR) models developed for prediction of Haemoglobin and BMI, it was concluded that Multiple Linear Regression provides better result than Binomial Logistic Regression model for estimating Haemoglobin and BMI in case of Rural Late Adolescent Girls, Tribal Early and Late Adolescent Girls categories. However, BLR model was found better than MLR for estimating Haemoglobin in case of Rural Early Adolescent Girls. The findings of the study are expected to provide useful information to the nutritionist and health experts for future policy planning in the study area.ThesisItem Open Access A statistical study of temperature forecasting data(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-12) Abhishek, S.S.; Bhardwaj, S.B.In our daily life, we often use some forecasting techniques to predict weather, temperature, economy, etc. Based on these forecasting results, we can prevent damages to occur or get benefits from the forecasting activities. In fact, an event in the real-world can be affected by many factors. The more the facts we consider, the higher the forecasting accuracy rate. Moreover, the length of each interval in the universe of discourse also affects the forecasting results. The present research work entitled ‘A statistical study of temperature forecasting data’ has been undertaken to compute errors empirically by considering the actual temperature along with corresponding 36 months forecasted observations. Moreover, an attempt has been made to measure the forecast accuracy with actual maximum and minimum temperatures with corresponding hypothetical temperatures of last 36 months using Theil’s U-statistic. A low value of Theil’s statistic is revealing that there is a greater forecasting accuracy for both the actual minimum and maximum temperatures and the corresponding hypothetical forecasted observations. In the final segment of this study, identification of the best fitted model using exponential smoothing method through coefficient of determination (R2), mean absolute error (MAE) and mean absolute percent error (MAPE) has also been done. The model Holtwinters’ additive model was found to be best fitted model as it has exhibits highest R2 and lowest MAE and MAPE values. The study was carried out for Pantnagar, U. S Nagar, Uttarakhand using time series data from 2017 to 2019.ThesisItem Open Access Bayesian estimation of the survival characteristics for weighted Xgamma distribution(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-10) Jai Prakash; Vinod KumarIn present study a weighted version of xgamma distribution is introduced and studied, which is a life time distribution. We work out its length biased version and distributional properties. The Maximum Likelihood estimates of the parameter ϴ have been obtained by means of Newton Rapson method. With the help of Tierney and Kadane method of approximation, we have obtained Bayes estimators of ϴ, Survival function, failure rate function and Mean time to failure under two priors namely gamma and uniform. The result obtained have been illustrated by means of several randomly generated data sets from the proposed distribution each replicated 10000 times. The Bayes risks have been evaluated by SELF. A real life data set has also been used to estabilish its utility. It is concluded that gamma prior is superior to uniform prior for finding the Bayes estimates of the parameter ϴ, Survival function, failure rate function and Mean time to failure of the length biased version of the proposed distribution.ThesisItem Open Access Statistical investigation of the effects of fertilizers on rice crop characteristics(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-02) Rawat, Manish; Vinod KumarThe aim of the study was to analyse the effect of fertilizers on Rice crop characteristics and its impact on grain yield. The data were collected from Norman E. Borlaug Crop Research Centre, G.B.P.U.A&T. Pantnagar, Rice Agronomy block. The experiment was laid out in Randomise Block Design with four replications and readings were taken timely as on the critical stages of the crop. The results of the study revealed that all crop characteristics were positively correlated with grain yield. Also multiple regression equation was obtained by the help of multiple regression models for predicting grain yield. Effectiveness of fertilizer treatments were tested by ANOVA and ANCOVA, it was found that different fertilizer combinations are significant with grain yield with p=0.000. Thus, the results obtained through ANOVA and ANCOVA are similar. Then ANOVA and ANCOVA were carried out for all five crop characteristics viz. Height, Test Weight, Shoot Number, Panicles number and Spikelet number. It was found that the treatments (different fertilizer combinations) are significant for all five characters with p=0.000. DMRT was also applied to compare the treatments pair-wise. Path coefficient analysis was performed to find direct and indirect effects of crop characteristics on grain yield. Results revealed that Test weight, Panicle number and Number of spikelets had direct positive effect on grain yield while Height and Shoot number had negative direct effect on grain yield.
