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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 Variable selection for classification and discrimination of Indian Mustard (Brassica juncea) genotypes for yield and oil content(CCSHAU, Hisar, 2019-07-10) Godara, Poonam; Hooda, BKThe present study deals with the problem of variable selection for classification and discrimination of Indian Mustard (Brassica juncea) genotypes for yield and oil content. The study used secondary data on 310 Indian mustard genotypes obtained from Oilseeds section of the department of Genetics and Plant Breeding, CCS HAU, Hisar. The experiment was conducted during rabi season of 2015-16. Five variable selection methods (Univariate Two-Sample t-test, Rao´s F test for Additional Information, STEPDISC Procedure (backward and forward) using Wilk´s Lambda criterion and Random Forests Algorithm) for classification and discrimination were compared using Monte Carlo simulation. Performance of the methods was assessed in terms of leave one out cross validation error for classification. Comparing the performance of various methods affecting seed yield for samples of equal sizes in scheme I, Rao's F test, Wilkˊs lambda (Backward) and Wilkˊs lambda (Forward) were found better than others. In scheme II, the most suitable methods affecting oil content with least leave one out cross validation error rate were Wilkˊs lambda (Backward) and Wilkˊs lambda (Forward). Based on results of the scheme I and II, Wilk´s Lambda (backward and forward) were found most suitable method for classification affecting the seed yield and oil content significantly. In scheme I using leave one out cross validation error rate four important variables for discrimination affecting the seed yield per plants were secondary branches, primary branches, days to maturity and siliqua number on main shoot with least error of rate of 21.72 per cent. The important variables for discrimination which significantly affected the oil content were siliqua length, Secondary branches, primary branches and days to maturity with least error rate of 33.90 per cent. Secondary branches, siliqua number on main shoot, seeds per siliqua and 1000 seed weight were found to be important variables in scheme III with least error rate of 27.68 per cent. Three characters which discriminate the groups having low seed yield and high seed yield were 1000 seed weight, siliqua length and seeds per siliqua, while siliqua length 1000 seed weight and primary branches were found the most discriminating variables affecting oil content. Using the correlation between variables and discriminant score, the most important variables affecting the seed yield were secondary branches, primary branches and days to maturity. The three most important variables discriminating between oil content were siliqua length, secondary branches and seeds per siliqua. Most important variables discriminating between low seed yield with low oil content and high seed yield with high oil content groups were secondary branches, primary branches and siliqua number of main shoot. The variable, number of secondary branches have been found to be the most important for classification and discrimination of Indian mustard genotypes for seed yield and oil content.ThesisItem Open Access Prediction of monthly onion arrivals and prices in major Indian markets using Support vector machine and ANN models(CCSHAU, 2019) Bharti, Deepa; Hooda, B.K.In the present study, growth rates in area, production, productivity and export of onion in India and world have been studied during 1980-2016. Support Vector Machine (SVM) and Artificial Neural Network (ANN) models were also developed and compared for monthly arrivals and prices of onion in major Indian Markets for the period 1990 to 2016. Study revealed a significant increase in area, production, productivity and export of onion in India during the period 1980-2016 with compound growth rates of 5.13, 6.72, 1.47and 7.62 per cent per annum. While significant increase with compound growth rates 3.54, 4.21 and 0.18 per cent per annum was also observed in area, production and productivity at the world level for the period 1980 to 2016. SVM was found to be the better predictor for onion arrivals in Delhi, Kolkata and Bengaluru markets. While, ANN performed better for the Mumbai and Jaipur market. Also for onion prices of selected market, SVM models were better predictors in three markets prices, while ANN models were fitted on two markets. In Kolkata, Bengaluru and Mumbai markets prices, lower value of RMSE and MAPE were found using SVM model than ANN model. For the Jaipur and Delhi market, least value of RMSE and MAPE were found for ANN model. Hence SVM models outperformed ANN models.ThesisItem Open Access Prediction of wheat yield using Artificial Neural Network and Fuzzy time series models in Eastern agro climatic zone of Haryana(CCSHAU, 2019) Sindhu, Abhishek; Hooda, B.K.This study deals with the prediction of wheat yield using Artificial Neural Network and Fuzzy time series models in Eastern agro climatic zone of Haryana. It also includes the algorithms for the model development and computations. ANN and fuzzy time series models for wheat yield prediction in Eastern agro climatic zone of Haryana have been developed using meteorological parameters. The predicted yield obtained by the fuzzy time series model have been compared with that of the Artificial neural network model along with the actual wheat yield, and the results are found encouraging. Best fitted architecture for Artificial Neural network was selected based on goodness of fit statistic criterion for wheat yield prediction and is used for prediction of wheat yield in Eastern agro climatic zone of Haryana using meteorological parameters. We found that Logsig transfer function was the best fitted neural network with five neurons in a single hidden layer. The values of R2, MSE, RMSE and MARD criterion were used to compare the performance of ANN and Fuzzy time series models. These criterions indicates that ANN model is slightly better than the fuzzy time series model for prediction of wheat yield in Eastern agro climatic zone of Haryana.ThesisItem Open Access Non-linear growth models for area, production and productivity of important foodgrains in Haryana(CCSHAU, 2019) Sanju; Hooda, B.K.The persent study was carried out with the objective to develop non-linear growth models for acreage, production and productivity of total foodgrains in Haryana. Season wise and crop wise best non-linear models for describing growth in area, production and productivity of foodgrains were also considered. We discussed different non-linear growth model and also determined the initial value for each parameter. Three different non-linear growth models viz. Logistic, Gompertz and Monomolecular was used for area, production and productivity of important foodgrains(Rice and Wheat) in Haryana for the period 1966 to 2015. The parameters were estimated using Levenberg - Marquardt’s iterative method of non-linear regression. Best model was selected based on goodness of fit statistics such R2, RMSE and MAE. The best model was used for forcasting of area, production and productivity of foodgrains for the period 2016 to 2020. We found that Logistic model was the best fitted growth model for area, production and productivity of total (kharif+rabi) foodgrains. We also observed that Logistic model is the best fitted growth model for area, production and productivity of kharif and rabi foodgrains in Haryana. Finally we concluded that none of the tried models was found suitable to fit for foodgrains area in Haryana. Logistic model was found suitable to fit for production as well as productivity of foodgrains grown in Haryana followed by Gompertz model.ThesisItem Open Access Pre harvest forecast models of rice yield based on weather variables for Eastern agro-climatic zone of Haryana using Discriminant function analysis(CCSHAU, 2019) Nain, Gurmeet; Bhardwaj, NitinThe present investigation entitled “Pre harvest forecast models of rice yield based on weather variables for Eastern agro-climatic zone of Haryana using Discriminant function analysis” consists of five chapters including summary and conclusion. The purpose of the study is to develop statistical models for studying the relationship between weather variables and crop yield and to develop different forecast models based on discriminant function analysis. Time series data on rice yield and weekly data from 22nd Standard Meteorological Week (SMW) to 41th SMW of on five weather variables viz., minimum temperature, maximum temperature, relative humidity, wind velocity and sun shine hour covering the period from 1996-1997 to 2016-2017 have been utilized for development of pre-harvest forecast model and the remaining two-year 2016-17 and 2017-18 yield data was used to validate the models. Statistical methodology using multiple regression and discriminant functions for developing pre-harvest forecast models has been described. The Model-1 is based on weather indices and rests are based on discriminant functions. The model 9 is proposed one. These models can be used to get the reliable forecast of rice yield about one and half months prior to the harvest. In all, nine models have been developed to study the relationship between crop yield and weather variables. The model-3(R2 =85.8%, Adjust R2=83.1%, PED =1.15 & 1.04 for 2016-17 & 2017-18 respectively) has been found to be the best for studying the relationship between crop yield and weather variables. Model 6 & Model 9 also exposes chances for better forecast. Therefore, these models also can be recommended for pre-harvest forecast of the rice yield in practice.ThesisItem Open Access Impact of Agricultural Price Policy on Oilseed and Pulse crop in Haryana(CCSHAU, 2019) Sandeep Kumar; Luhach, Ved PrakashThe present study was carried out with the objectives to analyse the trend in area, production and productivity, the seasonal variation in price and arrival, gap between FHP (Farm Harvest Prices) and MSP (Minimum Support Prices) and identify the constraints in production and marketing of rapeseed & mustard and chickpea. The study was based on primary as well as secondary data. Bhiwani and Mahendragarh districts were purposively selected based on the highest area under rapeseed & mustard and chickpea from Haryana, respectively. Two blocks namely Tosham and Kairu from Bhiwani district, Kanina and Mahendragarh from Mahendragarh district selected purposively. Further two villages of each selected block were selected randomly. From each village, 10 farmers were selected randomly and finally, 80 farmers of eight villages were interviewed to excerpt all desired information. The outcomes of study revealed an increasing trend in the area, production and productivity of rapeseed and mustard at the national level with CGRs values of 0.05, 1.89 and 1.67 per cent, respectively. Whereas, in Haryana, the trend in area indicated decreasing trend -0.31per cent, while production and productivity illustrated increasing trend with CGRs values of 2.00 and 3.87 per cent, respectively. In the study shown an increasing trend in the area, production and productivity of chickpea at the national level with CGRs values of 1.71, 2.84 and 1.11 per cent, respectively. Whereas, in Haryana, the trend in area and production indicated decreasing trend -8.29 per cent and -7.79 per cent while productivity illustrated increasing trend with CGR values of 0.46 per cent, respectively. Seasonal analysis resulted that the rapeseed & mustard and chickpea arrivals in the selected markets were higher in the months of March to June (Peak period) and lower in the months of February to October to February (Lean period). The inverse relationship was found between price and arrivals of rapeseed & mustard and chickpea in the selected markets. Gap between FHP and MSP resulted that in mostly cases FHP is higher than MSP because higher demand than supplies does not allow the market prices to fall below MSP. In recent year, market prices ruled higher than MSP. The major problems faced by the farmer in the production, marketing of rapeseed & mustard and chickpea in Bhiwani district were inadequate irrigation facilities 74.17 per cent followed by lack of adoption of plant protection measures 70.00 per cent and marketing constraints were wide fluctuation in prices 72.50 per cent followed by remunerative prices 61.67 per cent. In Mahendragarh district major production constraint of rapeseed & mustard and chickpea were inadequate knowledge of recommended packages and practices 61.67 per cent followed by big inadequate irrigation facilities 58.33 per cent, lack of adoption of plant protection measures 55.83 per cent and marketing constraints were was remunerative prices 62.50per cent followed by wide fluctuation in prices 60.00 per cent, large number of intermediaries in marketing process 56.67per cent.ThesisItem Open Access A Comparative study of ratio estimators of population mean using simulation techniques(CCSHAU, 2019) Rawat, Pooja; Manoj KumarThe ratio estimators are often employed to estimate the population mean of a study variable with the help of an auxiliary variable which are positively correlated with each other. In the present study, different ratio estimators available in literature were reviewed by using simulation technique. Two different populations were generated using bivariate normal distribution. From the generated population samples of various sizes were drawn using simple random sampling without replacement method for different correlation coefficient. Different ratio estimators were compared with respect to bias, mean square error and relative efficiency using simulation techniques. To describe the sampling distribution of different ratio estimators, skewness and kurtosis measures were computed.ThesisItem Open Access Computation of annual growth rate using nonlinear growth model for major fruits in Haryana(CCSHAU, 2019) Panghal, Pradeep; Manoj KumarIn the present study annual growth rates and average annual growth rates were computed using best fitted non-linear model for the major fruits of Haryana i.e. Guava, Citrus and Mango. On the basis of goodness of fit criteria (R2, MSE, RMSE and MAE), the best fitted model was chosen. And it was concluded that Logistic model was best fitted for almost all data sets of area and production of fruits in Hisar, Kurukshetra and Yamunanagar district as well as in Haryana state. The average annual growth rates for Mango, Guava and Citrus fruits in Haryana are 3.99%, 6.47%, 9.64% and for production it was 7.28%, 10.20%, 14.90% during the period 1990-91 to 2015-16.ThesisItem Open Access Transfer Function Modeling for Cotton Yield Prediction in Haryana(CCSHAU, 2019) Naveen; Verma, UrmilCrop yield models are abstract presentation of interaction of the crop with its environment and can range from simple correlation of yield with a finite number of variables to the complex statistical models with predictive end. The pre-harvest forecasts are useful to farmers to decide in advance their future prospects and course of action. The study has been categorized in two parts i.e. development of regression based weather-yield models and transfer function models for cotton yield prediction in Hisar, Sirsa, Bhiwani and Fatehabad districts of Haryana. Firstly, the multiple linear regression was used to develop zonal yield models for obtaining cotton yield estimates in the districts under consideration. Linear time-trend was obtained using cotton yield data of the period 1980-81 to 2009-10. The zonal models were fitted by taking DOA yield as dependent variable and fortnightly weather variables along with trend yield/crop condition term as regressors. The validity of fitted models have been checked for the post-sample years 2010-11 to 2014-15. Secondly, the ARIMA models with alternative combinations of weather variables were tried for fitting transfer function models. The fortnightly weather variables selected on the basis of stepwise regression method (viz., RH4, RF3, RF7, SSH4 and SSH7 over the crop growth period) were utilized as input series for fitting TF models. TF (2,1,0) model with RH4 and RF7 for Hisar and TF (0,1,1) model with SSH4 and SSH7 for Sirsa and Fatehabad districts respectively, were finalized to obtain the district-level cotton yield estimates for the post sample period. The performance(s) of the contending models were observed in terms of average absolute percent deviations and RMSEs. Transfer Function models consistently showed the superiority over regression based weather-yield models in capturing lower percent deviations in all time regimes. The results showed that the district-level cotton yield(s) prediction gives good agreement with DOA yield estimates. The average absolute percent deviations of post-sample period estimates falling between 5- 11 percent favour the use of TF models for cotton yield prediction in Haryana.