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Chaudhary Charan Singh Haryana Agricultural University, Hisar
Chaudhary Charan Singh Haryana Agricultural University popularly known as HAU, is one of Asia's biggest agricultural universities, located at Hisar in the Indian state of Haryana. It is named after India's seventh Prime Minister, Chaudhary Charan Singh. It is a leader in agricultural research in India and contributed significantly to Green Revolution and White Revolution in India in the 1960s and 70s. It has a very large campus and has several research centres throughout the state. It won the Indian Council of Agricultural Research's Award for the Best Institute in 1997. HAU was initially a campus of Punjab Agricultural University, Ludhiana. After the formation of Haryana in 1966, it became an autonomous institution on February 2, 1970 through a Presidential Ordinance, later ratified as Haryana and Punjab Agricultural Universities Act, 1970, passed by the Lok Sabha on March 29, 1970. A. L. Fletcher, the first Vice-Chancellor of the university, was instrumental in its initial growth.
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ThesisItem Open Access A comparative study of forecast models for sugarcane yield prediction of Haryana(CCSHAU, Hisar, 2022-11) Sanjeev; Bhardwaj, NitinCrop yield prediction is one of the most difficult issues in precision agriculture, and numerous models have been proposed. Because agricultural production is affected by a variety of factors such as climate, weather, soil, fertilizer, and seed variety. The most commonly used features in these models are temperature, rainfall, and soil type. Crop yield forecasting plays an important role for decision-makers at the national and regional levels. An accurate crop yield forecast model can help farmers decide what to plant and when to plant. Furthermore, as agricultural trade expanded and transportation infrastructure improved, farmers adopted a more business-like mindset and stopped viewing themselves as subsistence units. The study developed and compared the accuracy of sugarcane yield prediction models such as ARIMA, ARIMAX, ANN, NARX and Hybrid (ARIMA-ANN, ARIMAX-ANN) for the Karnal, Ambala, Kurukshetra, Yamunanagar, Panipat districts and Haryana as whole. The development of various models made use of time series data on sugarcane yields as well as fortnightly weather data on average maximum temperature, average minimum temperature, and accumulated rainfall over the crop period for Karnal, Ambala and Haryana from 1966–1967 to 2014–15, Kurukshetra, Yamunanagar, and Panipat from 1972–1973 to 2014–15. The yield data period from2015-16 to 2019-20 has been used to check the validity of the fitted models for sugarcane yield. The statistical modeling approaches viz., stepwise multiple linear regression, ARIMA, ARIMAX, ANN, NARX and Hybrid (ARIMA-ANN, ARIMAX-ANN) were applied for the study. ARIMAX and NARX models were developed to predict sugarcane yield for selected districts and Haryana using weather variable selected from stepwise multiple linear regression. Finally, forecast performance(s) of the fitted models were observed in terms of percent relative deviation, root mean square error and mean absolute percentage error of sugarcane yield forecasts from observed yield(s). Hybrid (ARIMA-ANN, ARIMAX-ANN) models performed well with lower error metrics as compared to the other fitted models. Five-steps ahead forecast figures i.e. 2015-16 to 2019-20 favored the use of Hybrid models to obtain sugarcane yield forecasts in all selected districts and Haryana under study. Empirical evidence from this study confirms that the Hybrid models can produce reliable forecasts. Therefore, developed forecast models are capable of providing reliable estimates of sugarcane yield well in advance while yield estimates given by state department were obtained quite later.ThesisItem Open Access Volatility forecast models of prices and arrivals of tomato in APMC markets of Haryana(CCSHAU, Hisar, 2023-01) Pushpa; JoginderThe majority of agricultural time series data are nonlinear, nonstationary and leptokurtic in nature. Thus, one of the most difficult areas of time series forecasting is agricultural price forecasting. Accurate forecasting assists both farmers and policymakers in making good decisions. According to the literature, each of the forecasting models has its own set of limitations. In the current study, forecasting performance of SARIMA, GARCH, ANN, Hybrid (SARIMA-GARCH and SARIMA-ANN) and multivariate time series (VAR and VARMA) models has been compared for monthly prices and arrivals of tomato in selected markets of Haryana. The purpose of the study is to give short term forecast of prices and arrivals of tomato with various forecast horizons such as one, three, six, nine and twelve months. Based on empirical results of the study, it is found that ANN models outperformed the others models for all horizon except one month ahead based on performance measures like MAPE and SEP. It is observed that Hybrid (SARIAM-ANN) models do not enhance the forecasting performance. The hybrid (SARIMA-GARCH) model outperforms the individual SARIMA and GARCH models in forecasting the prices and arrivals of tomato. It can be seen that the residuals obtained from linear SARIMA models contain the appropriate ARCH effect. The results of multivariate time series reveal that VARMA model outperforms the VAR model based on minimum values of forecasting performance measures such as MAPE and SEP.ThesisItem Open Access Modeling of tuberculosis through structural equations and bayesian approach(CCSHAU, Hisar, 2023-01) Rohit Kundu; Sheoran, O.P.This study analyzed data on tuberculosis in India to identify latent variables and understand relationships between variables. A structural equation model (SEM) was used, but the initial model did not converge to a satisfactory solution. The model was revised and modified until it converged to an optimal solution with acceptable fit statistics. The Markov Chain Monte Carlo (MCMC) method was also used to identify change points in the number of notified cases of tuberculosis. The results showed an increase in TB cases in the 2000s, followed by two change points after 2010 when the government prioritized controlling the disease. However, the number of cases has continued to increase in recent years. The MCMC method and Gibbs sampler were found to be useful for analyzing epidemiological data with change points. The study also analyzed the prevalence of TB in India using data from the National Family Health Surveys from 2005-2006 and 2015-2016. The results showed that overall, the prevalence of TB did not significantly change between the two surveys. However, the gender gap in TB prevalence (difference in prevalence between males and females) did show a statistically significant decrease, mainly observed in rural areas and found to vary by religion and social group. The rural-urban gap in TB prevalence was most prominent among certain groups, including Muslims, individuals belonging to other religious groups, Scheduled Tribes, and those in the poorest wealth quintile. It is suggested that the decreasing trend in the gender gap may be due to an improvement in the socio-economic status of women and increased detection and reporting of TB cases among women.ThesisItem Open Access A study on impact of climate change and statistical models for pre-harvest forecast of wheat-yield in Haryana(CCSHAU, Hisar, 2023-05) Chetna; Monika DeviThis study aimed to improve the predictability of wheat yield in four districts of Haryana state using advanced statistical techniques. The best models for predicting weather variables were identified, and analyzed the impact of weather variables on crop yield during different growth stages. It was found that weather variables had varying effects on crop yield during different growth stages and across different districts. The observed positive effects of temperature on crop yield during the reproductive stages could be attributed to increased photosynthesis and growth rate of the crop, while the negative effects of temperature during the germination, milking, and harvesting stages could be due to increased plant stress and water loss. The study also found that the negative effects of rainfall on crop yield during certain growth stages could be attributed to waterlogging and soil compaction, while the positive effects of rainfall during certain growth stages could be due to increased soil moisture availability. The study developed models with high R2 values and low error values for predicting wheat yield in all four districts. Pre-harvest forecast models were developed to predict wheat yield before harvest in selected districts of Haryana, using discriminant function analysis and weekly meteorological variables. The models achieved high accuracy in correctly classifying the grouped cases in all districts, with varying effects of predictor variables and autocorrelation. The evaluation of various models for yield forecasting in different districts of Haryana State has yielded impressive results. Principal Component Analysis (PCA) was also utilized to investigate the impact of weather variables on the weather indices in various districts of Haryana State. The models showed a good fit with observed data and high accuracy in predicting yield, with different levels of complexity and performance depending on the district and the model used.ThesisItem Open Access Problems and prospects of flower crops in India(Chaudhary Charan Singh Haryana Agricultural University hisar, 2022-12) Ritu; Bhatia, Jitender KumarThe present study was carried out with the objectives to analyze the trends and growth in area, production and production and productivity of flowers in India, to examine costs involved, returns attained, various marketing channels, value added products from flowers and to identify various constraints in flower cultivation, marketing and export of flower crops in Haryana. The study was based on primary as well as secondary data. The time-series data related to area, production, productivity, export and import of flowers in different zones of India as well as in different zones of Haryana was collected for years 2001-21; the growth rate and trends were computed. The study has been restricted to three crops only i.e. marigold, rose and gladiolus due to availability of reasonable number of flower growers. The study pertains to two districts Sonipat and Gurugram of Haryana. From the selected districts, one block of Sonipat (Rai) and one block of Gurugram (Pataudi) were selected based on highest number of flower cultivators. For marketing data, Delhi flower markets were selected. The outcome of study revealed an increasing trend in the area and production and productivity with CGRs values of 7.86, 8.43 and 3.65 per cent, respectively. Whereas, in Haryana, the trend in area, production of cut flowers and productivity indicated declining trend (-1.04%, -4.64% and -4.54%) over the study period while production of loose flowers illustrated increasing trend with CGRs value of 1.35 per cent. The trends in export indicated declining trend (-2.76%), while import illustrated increasing trend (12.02%). The results of direction of trade of export of flowers from India through Markov value chain resulted that USA was the most reliable country with high probability of retention (0.6217). Per acre total cost of cultivation in French and African marigold worked out was ₹ 65948.48 and ₹ 45495.37, respectively. The corresponding figures for rose were 132874.91for 1st year and ₹ 123884.7 for 2nd year and for gladiolus it was ₹ 318096.63 for 1st year and ₹ 82960.16 for 2nd year. Further, the net returns for French and African marigold were ₹ 177651.52 and ₹ 128504.63, respectively. The corresponding figures for rose were ₹164620.36 for 1st year and ₹ 404866.99 for 2nd year and for gladiolus were ₹170595.87 in 1st year and ₹ 405732.34 in 2nd year. It was found that channel-I was the most efficient among all the marketing channels in disposal of flowers. While considering marketing of value-added products then found that processor’s margin was highest and marketing efficiency was highest among shortest marketing channels for marketing of all floricultural products. Attack of insects-pests, high input prices were major cultivation problems, while transportation cost and high commission charges were major marketing constraints and lack of lack of exporting agencies, coordination among flower growers and exporters and lack of role of FPO’s dealing with flower crops were major export problems faced by farmers in the study area.ThesisItem Open Access Resampling Techniques For Evaluating G × E Interaction In Oilseed Crops(Chaudhary Charan Singh Haryana Agricultural University hisar, 2022-09) Deepankar; Hooda, B. KIn multi-environment trials (METs), a set of genotypes is grown simultaneously in different set of environments. The major objective of METs is identification of genotypes which consistently perform across a wider range of environments. To assess the stability of genotype, in literature there exists various parametric and non-parametric measures. But researcher faces conundrum of choosing appropriate stability measure before moving to main objective of MET. To ease researcher in this dilemma, we developed majority approaches where the results of various parametric and nonparametric stability measure were combined. Under majority approaches, we evaluated i) rank sum of parametric and non-parametric stability measures, ii) modal approach, iii) A new weighted-normalized index and iv) a composite measure using TOPSIS algorithm. A statistical distribution is a mathematical function that describes how the results of an experimental trial are likely to occur at random. The stability measures are a complex function of observed values therefore it is difficult to develop theoretical framework to predict their sampling distribution. Hence bootstrap technique has been used to determine their sampling distributions of stability measures. In METs, for studying GEI, additive main effects and multiplicative interactions (AMMI) and genotype and genotype x environment interaction (GGE) models are frequently used by researchers. In both models after removing the additive effect, singular value decomposition is used to partition genotype x environment interaction into ordered sum of multiplicative terms. Researchers usually retain first two multiplicative terms for biplot analysis without giving much thought in checking the significance of multiplicative terms for retention. The resampling techniques such as bootstrap and cross-validation have been used to test the significance of the multiplicative terms by approximating p value for each multiplicative term. Only those multiplicative terms have been retained in model which are found to be significant i.e., p value < 0.05 or 0.01.ThesisItem Open Access Bayesian Estimation for Some Lifetime Models under Different Loss Functions(Chaudhary Charan Singh Haryana Agricultural University hisar, 2023-01) Pavitra Kumari; Vinay KumarThe life testing experiments are carried out to obtain the lifetime data on patients for survival analysis and to study the reliability of electrical, electronic and mechanical systems, information theory, artificial intelligence, etc. This thesis deals with the classical and Bayesian estimation methods for the generalized of lifetime models. We consider four distinct loss functions, namely, square error loss function, entropy loss function, precautionary loss function, Linex loss function and type II censoring in this thesis. Type II censoring has the significant advantage that you know in advance how many failure times your test will yield. Generalizations of univariate lifetime distributions are often of interest to serve for real life phenomena. These generalized lifetime distributions are very useful in many fields such as medicine, physics, engineering and biology. We consider three distinct lifetime models, namely, Lomax, Rayleigh Lomax and IPBH lifetime model and developed statistical inferences for the associated model parameters and reliability characteristics from both the classical and Bayesian estimation perspectives in Chapter 4. Lomax distribution is one of the well-known univariate distributions that is considered as an alternative to the exponential, gamma and Weibull distributions for heavy tailed data. In this thesis, we introduce a generalization of the Lomax distribution called Rayleigh Lomax (RL) distribution. This distribution provides great fit in modelling wide range of real data sets. It is a very flexible distribution that is related to some of the useful univariate distributions such as exponential, Weibull and Rayleigh distributions. Moreover, this distribution can also be transformed to a lifetime distribution which is applicable in many situations. For example, we obtain the inverse estimation and confidence intervals. In present study apply AIC and BIC to detect the changes in parameters of the RL distribution. The performance of these approaches is studied through simulations and applications to real data sets. The statistical software R is used for computation throughout the thesis. Finally, a complete list of references and other literature surveys are given at the end of the thesis as a bibliography.ThesisItem Open Access Small Area Estimation of Wheat Yield Using Remote Sensing Data in Hisar and Sirsa Districts of Haryana(CCSHAU, Hisar, 2021-09) Muhammed Jaslam, P K; Manoj KumarThis study focuses on estimating the wheat yield by using the direct and indirect small area estimation techniques at block levels in Hisar and Sirsa districts of Haryana also to develop suitable crop yield models for wheat using satellite spectral data. The simple average of the yield recorded in the villages within the block is the usual estimator for block wheat yield, which is unstable and most of the block level estimates have large CVs. Direct small area estimation techniques such as post stratified and GREG estimation are used to get a precise estimate of wheat yield. Implicit models such as synthetic and composite estimation, as well as explicit models such as unit level and Area level SAE, are used in the indirect small area estimation technique. Furthermore, area level SAE model was developed for a total of 42 blocks in 6 districts of western zone of Haryana. The CV percent value of the block level estimate computed using all small area estimation is lower in comparison to the usual estimate. In the post-stratified direct, synthetic, and composite estimation methods used, the CV values of the composite estimators were found to be less in comparison to post stratified direct and synthetic estimators. In agreement to the basic theory, we obtained good estimation results using the unit level SAE model. Furthermore, using the Robust method of the unit level SAE model to reduce the effect of outliers boosted precision level. This study demonstrated that having a closely related auxiliary variable at the area level (SAE at the area level - Class 3 & 4) can provide a comparable level of precision to a unit level model. Since multicollinearity was detected between the predictor variables for crop yield modelling, we investigated ways in which the simple linear model can be improved by replacing plain least squares fitting with some alternative fitting procedures, such as stepwise regression, ridge regression, LASSO, principal component regression, and partial least square regression. The PLS regression model is found to be the best method (in terms of R2 and RMSE) for predicting block level yields using remote sensing data in western zone of HaryanaThesisItem Open Access Support Vector Machine and Artificial Neural Network Models for Classification of Wheat and Mustard Genotypes(CCSHAU, Hisar, 2021-10) Mujahid Khan; Hooda, B. K.The aim of this study was to classify the wheat and mustard genotypes using discriminant analyses, artificial neural networks and support vector machine models. The secondary data of 302 wheat and 870 mustard genotypes for 14 morphological variables were used. The class variable grain yield was categorized into 3 classes in wheat dataset, which makes it a multiclass problem. While, mustard genotypes were categorized into binary classes on the basis of grain yield, oil content and combined variables. In discriminant analyses, the performance of regularized discriminant analysis was higher than that of linear and quadratic discriminant analyses for both the datasets. Out of the three artificial neural network (ANN) models used for wheat dataset, training accuracy of resilient propagation was higher whereas less satisfactory results were obtained for radial basis function (RBF) network as compared to multi-layer perceptron (MLP) networks. But in mustard dataset, the training accuracies were notably high and testing accuracies were at par for RBF neural networks as compared to MLP networks. Out of the six kernels used for support vector machine (SVM) classification, RBF kernel outperformed all other kernel functions for both the datasets. Then the outputs of SVM paradigm with six kernels were combined in an Ensemble with Weighted Accuracy (EWA) model. The ensemble model provided high prediction accuracies for both the datasets in comparison to individual kernel classifiers. The particle swarm optimization (PSO) technique has set more suitable parameters, provided higher classification accuracy in both the datasets. The ensemble model outperformed the others with 95.1% training accuracy followed by resilient propagation neural networks (94.7%) and PSO optimized SVM (94.2%) for wheat genotypes. While for testing data set, the EWA model and PSO optimized SVM performed well with 94.9% accuracy. The classification of mustard genotypes was found better with the grain yield as class variable followed by oil content. The ensemble model outperformed the other classifiers with 93.5% training accuracy followed by PSO optimized SVM (92.6%) and RBF neural networks (91.9%) for mustard genotypes. Whereas for testing dataset, highest accuracy of 92.6% was achieved with PSO optimized SVM followed by all neural network models (90.7%). The lowest accuracies were obtained with linear discriminant analysis for both the datasets.
