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ThesisItem Open Access Trend analysis of area, production and trade of major agricultural crops in BRICS countries(CCSHAU, Hisar, 2022-07-22) Sowmya, Ravada; JoginderThe present study was carried out with the objectives: firstly, to identify the trends of area, production and trade of wheat, maize and sugarcane in BRICS countries by using various linear and non linear models. Secondly, to evaluate the contribution of BRICS countries to global pool. For this, we have discussed various linear and non-linear models such as quadratic, cubic, logarithmic, logistic, Gompertz and monomolecular models. The data for area, production and trade of selected crops for the period 1961 to 2019 have been collected from FAOSTAT. The parameters of the selected models were estimated using Levenberg - Marquardt‟s iterative method of non-linear regression. Based on various performance measures such as R2 , RMSE and MAE, best models were fitted among the selected models. Based on these performance measures, we found that cubic and logistic models followed by Gompertz model were well fitted for area, production and trade as compared to other models. Also, the contribution of BRICS countries in area, production, imports and exports of wheat is 24, 27, 46 and 12 percent respectively. In case of maize, the BRICS contributed 28, 24, 25 and 24 percent in area, production, imports and exports respectively and in case of sugarcane, the contribution of BRICS countries is 48, 49, 29 and 64 percent in area, production, imports and exports respectively.ThesisItem Open Access Comparative study of stability measures for rice (Oryza sativa) genotypes(CCS HAU, Hisar, 2023-07) Bisht, Prashant; Vinay KumarThe study aimed to compare existing parametric and non-parametric stability measures for rice genotypes and propose a modified measure based on entropy-based TOPSIS. Experimental data from thirty-six rice genotypes evaluated at two locations over two cropping seasons were analyzed to evaluate genotype-environment interaction. The results revealed strong positive correlations between stability measures such as Wricke's ecovalence and Shukla's stability variance, indicating their similarity in stability ranking. Additionally, significant positive correlations were found between mean yield and specific stability measures, suggesting their effectiveness in selecting genotypes with high yield potential. The proposed modified measure, incorporating entropy weights and four parametric measures, provided a comprehensive assessment of stability. The study emphasized the importance of considering both stability and yield in genotype selection, and the findings contribute to a better understanding of stability analysis in rice cultivation.ThesisItem Open Access An Economic Analysis of Carrot Cultivation in Haryana and Karnataka(CCS HAU HISAR, 2022-08) M S, Manasa; Papang.S, JanailinThe present study was undertaken to know the growth rate, instability and contribution of area and yield on the production of carrot. The study also focussed on the economic analysis of carrot cultivation, resource use efficiency in carrot production and constraints faced by farmers in production and marketing of the carrot crop. The study is based on both primary and secondary data. For collection of primary data, a multistage sampling method was used which includes a sample of 40 farmers cultivating carrot from Haryana and 40 farmers cultivating carrot from Karnataka. The secondary data was collected for a period of 20 years from 2001 to 2020.The growth rate was estimated using compound annual growth rate, instability is measured using CDVI, contribution of area and yield on production is studied using decomposition analysis. The economics of carrot cultivation was calculated using various cost concepts as devised by CACP and B:C Ratio. Resource-use efficiency was measured by employing Cobb-Douglas production function and for constraints analysis Garrett’s ranking technique was employed. Results revealed that the growth rate in area and production carrot in India as well as in Haryana and Karnataka have increased with 7.62, 14.72, 0.81 and 7.57, 4.47, 0.46 per cent per year, respectively while productivity have decreased by -0.24, -0.24 and -0.38 per cent per year, respectively. The instability in area and production of carrot showed a higher degree of instability compared to productivity. The decomposition analysis showed that the area effect and yield effect were found to be responsible for increase in production of carrot in India as well as in Haryana. Whereas, in Karnataka, the yield effect was found to be responsible for increase in production of carrot, while area effect and interaction effect were responsible for decrease in production. In Haryana, the per hectare total cost of cultivation of carrot worked out was ₹219528. Farmers received a net return of ₹185238 with a B:C ratio of 1.84 per hectare. Similarly, the farmers growing carrot in Karnataka obtained a net returns ₹218941 per hectare and total cost of cultivation worked out was ₹358886 per hectare with a B:C ratio of 1.61.The returns to scale from carrot production in Haryana was found to be 1.03 and resource use efficiency of different inputs used in production was revealed as positive and greater than one indicating their under-utilization, except for the irrigation where it found to be positive and less than one indicating its over-utilization. The returns to scale from carrot production in Karnataka was found to be 0.54 and resource-use efficiency of different inputs used in production was revealed as positive and greater than one indicating their under-utilization, except for the input plant protection chemicals and human labour where they found to be positive and less than one indicating their over-utilization. The major constraints faced by farmers were, high cost of labour, scarcity of labour, attack of pest and diseases, high fluctuation in prices and lack of cooperative marketing system in village.ThesisItem Open Access An economic analysis of production and marketing of sweet corn in Sonepat district of Haryana(CCSHAU,HiSAR, 2021-09) Panday, Raj Ratan; Parminder SinghThe present investigation was done to estimate the costs and returns, marketing costs, margins, price spread, marketing efficiency, and constraints in sweet corn production in Sonepat district of Haryana. The selection of Sonepat was done based on highest acreage under sweet corn in Haryana. A sample of 80 farmers was taken for study from four villages of one block. As a result of study total cost of cultivation was found to be ₹ 53731.52 acre-1 and yield was 64.13 quintal acre-1. Gross returns were ₹ 99466.61 acre-1 and net returns were ₹ 45735.08 acre-1. Small farmers had highest cost of cultivation i.e., ₹ 54411.63 acre-1, while medium farmers had highest return i.e., ₹ 102418.97 acre-1. Overall, five marketing channels of sweet corn were identified. Channel-I (processing unit based) had highest net price received by farmers, while channel five (direct retailer based) had highest marketing efficiency. In production constraints, high cost of seed was found to be most severe followed problem of stray animals. In marketing constraints, problem of malpractice at marketplace was most severe followed by fluctuation of price however, high cost of transportation and low no. of processing facilities was also a problem. Overall, sweet corn is reliable crop with a potential to generate some good return and there is a need to make farmers aware about it.ThesisItem Open Access Estimation of compound growth rates for acreage, production, and productivity of major vegetable crops in Haryana – a nonlinear growth model approach(CCSHAU, Hisar, 2021-09) Vikash Kumar Sumit; Manoj KumarThe study on computation of compound annual growth rate (CAGR) of major vegetable crops i.e., Potato, Onion, Tomato, Radish, Carrot and Cabbage & Cauliflower in Haryana was conducted using nonlinear growth models for the period 1992-93 to 2018-19. Logistic, Gompertz and Monomolecular growth mod-els were used in the study. The initial values for different models were obtained using method of three selected points, method of partial sums and Yule’s method in RStudio software. The SPSS software was used to get the final parameters of above models which uses Levenberg-Marquardt algorithm. Residual analysis and goodness of fit tests was used to decide the best fitted model. The Monomolecular model was best fitted for area and production of Potato, Onion, Tomato and Radish. The productivity of Tomato and Radish were fitted using Logistic model. Gompertz and Monomolecular models were fitted for Po-tato and Onion productivity, respectively. The area and productivity of Carrot were fitted using Logistic model and its production using Gompertz model. For Cabbage & Cauliflower, Gompertz, Monomolec-ular, and Logistic were fitted for its area, production, and productivity, respectively. The area, produc-tion, and productivity of Potato recorded average CAGR of 3.94%, 6.25%, and 2.31 %, respectively. Onion had average CAGR of 8.63%, 10.43%, and 1.70% for its area, production, and productivity. The area and production of remaining crops had positive average CAGR (less than 12%), while productivity for same had negative growth rate (greater than -1.50%) except Tomato (0.31%).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.
