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Open Access
FORECASTING THE ARRIVALS AND PRICES OF OILEEDS IN CHHATTISGARH
(Indira Gandhi Krishi Vishwavidyalaya, Raipur, 2020) Rathore, Anil Kumar; Singh, A.K.; Chandrakar, G.; Koshta, A.K.; Sengar, R.S.
Oilseed crops play a crucial role in the economy of India under agricultural sector which has diverse area under oilseed crops. In the State of Chhattisgarh, the oilseed crops, namely, groundnut, sunflower, niger, sesame, soybean, linseed, mustard-rapeseed are grown in its different parts. For the development of agriculture it is quite necessary that farmers get good prices of their products in the agricultural markets. If there could be some forecast of arrivals and prices in the agricultural markets, the farmer would be benefitted in terms of selling its products. In Chhattisgarh such study has been done for cereals and pulses, but not for oilseeds so it has been attempted to fill up this gap through this study. With the help of forecasting of arrivals and prices, farmers of this State could find the forecast for the specific month in which they get high and remunerative price of their produce. To get a good idea of the arrivals and prices of oilseeds varying over the time, it is necessary to study the time series patterns of arrivals and prices over the years in major markets of Chhattisgarh. Using the datacollected in this study, different linear, non-linear and time series models are fitted for both variables in these markets, and best model based forecastswere made to fulfil the requirements of planners and farmers.
Open Access
DEVELOPMENT OF YIELD PREDICTION MODELS FOR OILSEEDS BASED ON WEATHER PARAMETERS IN INDIA
(Indira Gandhi Krishi Vishwavidyalaya, Raipur, 2019) SURENDRA, A.R.; Pandey, K.K.; Shukla, S.; Verma, Praveen; Umesh
Oilseeds cultivation is undertaken across the country in about 26.2 million hectares, of which 72% is confined to rainfed farming. The diverse agro-ecological conditions in the country are favourable for growing nine annual oilseed crops, which include seven edible oilseeds (groundnut, rapeseed & mustard, soybean, sunflower, sesame, safflower and niger) and two non-edible oilseeds (castor and linseed). Any changes in weather parameters might affect the oilseeds yield. So, the crop yield prediction based on weather parameters will help farmers, policy makers and administrators to manage activities. The present study examines the development of yield prediction models for oilseeds based on weather parameters in india. The study was undertaken based on secondary data, the data on yield of total oilseeds in india for 48 years data from 1970-71 to 2017-18 has been collected from www.indiastat.com. Weather data for five weather parameters (Max & Min Temperature, Rainfall, RH1 and RH2) were collected from India Meteorological Department (IMD), Pune and www.indiawaterportal.org. Four models were developed for oilseeds yield namely LASSO Stepwise Regression, Models based on weather indices (MWI), Models using composite weather variables (CWV) and Artificial Neural Networks (ANN). A comparison study of the results obtained from LASSO, MWI, CWVand ANNis also performed. The results were compared using the R2, RMSE statistic and percentage prediction error. The R2& RMSE of the models varied between 0.72&66.66, 0.69 &79.39, 0.74 &62.45 and 0.81 &56.99 respectively. The average percentage prediction error for LASSO, MWI, CWV and ANN models on the test set were found to be 5.99, 7.20, 5.55and 5.18 respectively. The results indicate that the ANN model had a higher R2 value, lower RMSE value and a lower percentage prediction error when compared to other models. The ranking of the model reveals that Artificial Neural Networks (ANN) was the best performing model followed by models using composite weather variables,LASSO Stepwise Regression model and models based on weather indices. The reason behind the good performance of ANN model is that, in a real sense, neural networks are one of the best solutions in search for a few agriculture problems, especially when it comes to predict crop yield. A conclusion has been made that ANN has better explained yield variability rather than other methods. Undeniably, the application of ANN to precision agriculture plays a crucial role in future evaluation of the concept of precision agriculture as a sustainable means of meeting crop yield demands. However, further research about the ANN impacts towards crop yield production must be conducted to ensure sustainability of future needs.
Open Access
EMPIRICAL STUDY OF POST-GRADUATE STUDENTS OF COLLEGE OF AGRICULTURE, RAIPUR TOWARDS ENTREPRENEURSHIP
(Indira Gandhi Krishi Vishwavidyalaya, Raipur, 2019) M. D., MANJUNATHA; Shukla, Sindhu; Ramole, Sweta; Choudhary, Vijay; Khan, M.A.
The present study is an attempt to assess the empirical study of post-graduate students of college of agriculture, raipur towards entrepreneurship. To assess the disposition a five point likert scale questions used, namely Strongly Agree (SA), Agree (A), Undecided (UD), Disagree (DA), and Strongly Disagree (SDA). Out of the total respondents, 43.2% were female and 56.8% were male, 65.0% belonged to the nuclear family and 35.0% to the joint family. 13.5% of respondents were from a big family (> 7 members), 21.4% from a small family (up to 4 members) and 65.0% from a medium family (5 to 7).About74.1 % belonged to the rural background, while 25.9% of them had an urban background. Personal entrepreneurship capacity shows that 34.21% of the respondents were strongly agreed that they knew the methods for finding out what the market requirements and 33.46% of respondents agreed to comprehend the kinds of issuers that meet an entrepreneur when it came to marketing an idea, while 39.47 % of respondents agreed heavily that they could create an idea. As the sensitization of respondents towards entrepreneurship is concerned, it is observed that 29.32% of the respondents have agreed and 34.96% were undecided that they clearly followed or assisted friends who have started the enterprise. Studying entrepreneurial concepts, 39.85% of the respondents were found strongly agreed and whereas, 22.18% of the respondents were just agreed that entrepreneurship improves individual and social growth. While, 35.34% of respondents were agreed of starting a new enterprise from an idea and 19.17% respondents agreed that entrepreneurship should be done within an existing enterprise. Tendencies for becoming self-employed v/s being an employee indicate that 47.37% of the respondents agreed that family / friends are self-employed, followed by 45.49 % of respondents agreed that having own business is the most suitable option for them. While, 47.37% of respondents agreed that they have an idea that can be a business opportunity and have the possibility for self-fulfillment respectively. The data with regard to overall empirical study of entrepreneurship of respondents that majority 45.10% of respondents had a favor for agricultural entrepreneurship, while 20.8 and 34.10% respondents had a high favor and unfavor for agricultural entrepreneurship respectively. In the spearman’s rank correlation assessment of the 8 variables under research, only two factors, namely family type, family size of postgraduate students had a negative and significant correlation, gender, family background, academic performance, land holding size and postgraduate family annual income, had a positive and significant correlation with their agricultural entrepreneurship index.
Open Access
DEVELOPMENT OF YIELD PREDICTION MODELS FOR OILSEEDS BASED ON WEATHER PARAMETERS IN INDIA
(Indira Gandhi Krishi Vishwavidyalaya, Raipur, 2019) SURENDRA A R; Pandey, K.K.; Alivelu, K.; Shukla, S.; Verma, Praveen
Oilseeds cultivation is undertaken across the country in about 26.2 million hectares, of which 72% is confined to rainfed farming. The diverse agro-ecological conditions in the country are favourable for growing nine annual oilseed crops, which include seven edible oilseeds (groundnut, rapeseed & mustard, soybean, sunflower, sesame, safflower and niger) and two non-edible oilseeds (castor and linseed). Any changes in weather parameters might affect the oilseeds yield. So, the crop yield prediction based on weather parameters will help farmers, policy makers and administrators to manage activities. The present study examines the development of yield prediction models for oilseeds based on weather parameters in india. The study was undertaken based on secondary data, the data on yield of total oilseeds in india for 48 years data from 1970-71 to 2017-18 has been collected from www.indiastat.com. Weather data for five weather parameters (Max & Min Temperature, Rainfall, RH1 and RH2) were collected from India Meteorological Department (IMD), Pune and www.indiawaterportal.org. Four models were developed for oilseeds yield namely LASSO Stepwise Regression, Models based on weather indices (MWI), Models using composite weather variables (CWV) and Artificial Neural Networks (ANN). A comparison study of the results obtained from LASSO, MWI, CWVand ANNis also performed. The results were compared using the R2, RMSE statistic and percentage prediction error. The R2& RMSE of the models varied between 0.72&66.66, 0.69 &79.39, 0.74 &62.45 and 0.81 &56.99 respectively. The average percentage prediction error for LASSO, MWI, CWV and ANN models on the test set were found to be 5.99, 7.20, 5.55and 5.18 respectively. The results indicate that the ANN model had a higher R2 value, lower RMSE value and a lower percentage prediction error when compared to other models. The ranking of the model reveals that Artificial Neural Networks (ANN) was the best performing model followed by models using composite weather variables,LASSO Stepwise Regression model and models based on weather indices. The reason behind the good performance of ANN model is that, in a real sense, neural networks are one of the best solutions in search for a few agriculture problems, especially when it comes to predict crop yield. A conclusion has been made that ANN has better explained yield variability rather than other methods. Undeniably, the application of ANN to precision agriculture plays a crucial role in future evaluation of the concept of precision agriculture as a sustainable means of meeting crop yield demands. However, further research about the ANN impacts towards crop yield production must be conducted to ensure sustainability of future needs.
Open Access
EMPIRICAL STUDY OF POST-GRADUATE STUDENTS OF COLLEGE OF AGRICULTURE, RAIPUR TOWARDS ENTREPRENEURSHIP
(Indira Gandhi Krishi Vishwavidyalaya, Raipur, 2019) Majunatha, M.D.; Shukla, Sindhu; Ramole, Sweta; Choudhary, Vijay; Khan, M.A.
The present study is an attempt to assess the empirical study of post-graduate students of college of agriculture, raipur towards entrepreneurship. To assess the disposition a five point likert scale questions used, namely Strongly Agree (SA), Agree (A), Undecided (UD), Disagree (DA), and Strongly Disagree (SDA). Out of the total respondents, 43.2% were female and 56.8% were male, 65.0% belonged to the nuclear family and 35.0% to the joint family. 13.5% of respondents were from a big family (> 7 members), 21.4% from a small family (up to 4 members) and 65.0% from a medium family (5 to 7).About74.1 % belonged to the rural background, while 25.9% of them had an urban background. Personal entrepreneurship capacity shows that 34.21% of the respondents were strongly agreed that they knew the methods for finding out what the market requirements and 33.46% of respondents agreed to comprehend the kinds of issuers that meet an entrepreneur when it came to marketing an idea, while 39.47 % of respondents agreed heavily that they could create an idea. As the sensitization of respondents towards entrepreneurship is concerned, it is observed that 29.32% of the respondents have agreed and 34.96% were undecided that they clearly followed or assisted friends who have started the enterprise. Studying entrepreneurial concepts, 39.85% of the respondents were found strongly agreed and whereas, 22.18% of the respondents were just agreed that entrepreneurship improves individual and social growth. While, 35.34% of respondents were agreed of starting a new enterprise from an idea and 19.17% respondents agreed that entrepreneurship should be done within an existing enterprise. Tendencies for becoming self-employed v/s being an employee indicate that 47.37% of the respondents agreed that family / friends are self-employed, followed by 45.49 % of respondents agreed that having own business is the most suitable option for them. While, 47.37% of respondents agreed that they have an idea that can be a business opportunity and have the possibility for self-fulfillment respectively. The data with regard to overall empirical study of entrepreneurship of respondents that majority 45.10% of respondents had a favor for agricultural entrepreneurship, while 20.8 and 34.10% respondents had a high favor and unfavor for agricultural entrepreneurship respectively. In the spearman’s rank correlation assessment of the 8 variables under research, only two factors, namely family type, family size of postgraduate students had a negative and significant correlation, gender, family background, academic performance, land holding size and postgraduate family annual income, had a positive and significant correlation with their agricultural entrepreneurship index.
Open Access
DEVELOPMENT OF YIELD PREDICTION MODELS FOR OILSEEDS BASED ON WEATHER PARAMETERS IN INDIA
(Indira Gandhi Krishi Vishwavidyalaya, Raipur, 2019) Surendra, A.R.; Pandey, K.K.; Alivelu, K.; Shukla, S.; Verma, Praveen; Umesh
Oilseeds cultivation is undertaken across the country in about 26.2 million hectares, of which 72% is confined to rainfed farming. The diverse agro-ecological conditions in the country are favourable for growing nine annual oilseed crops, which include seven edible oilseeds (groundnut, rapeseed & mustard, soybean, sunflower, sesame, safflower and niger) and two non-edible oilseeds (castor and linseed). Any changes in weather parameters might affect the oilseeds yield. So, the crop yield prediction based on weather parameters will help farmers, policy makers and administrators to manage activities. The present study examines the development of yield prediction models for oilseeds based on weather parameters in india. The study was undertaken based on secondary data, the data on yield of total oilseeds in india for 48 years data from 1970-71 to 2017-18 has been collected from www.indiastat.com. Weather data for five weather parameters (Max & Min Temperature, Rainfall, RH1 and RH2) were collected from India Meteorological Department (IMD), Pune and www.indiawaterportal.org. Four models were developed for oilseeds yield namely LASSO Stepwise Regression, Models based on weather indices (MWI), Models using composite weather variables (CWV) and Artificial Neural Networks (ANN). A comparison study of the results obtained from LASSO, MWI, CWVand ANNis also performed. The results were compared using the R2, RMSE statistic and percentage prediction error. The R2& RMSE of the models varied between 0.72&66.66, 0.69 &79.39, 0.74 &62.45 and 0.81 &56.99 respectively. The average percentage prediction error for LASSO, MWI, CWV and ANN models on the test set were found to be 5.99, 7.20, 5.55and 5.18 respectively. The results indicate that the ANN model had a higher R2 value, lower RMSE value and a lower percentage prediction error when compared to other models. The ranking of the model reveals that Artificial Neural Networks (ANN) was the best performing model followed by models using composite weather variables,LASSO Stepwise Regression model and models based on weather indices. The reason behind the good performance of ANN model is that, in a real sense, neural networks are one of the best solutions in search for a few agriculture problems, especially when it comes to predict crop yield. A conclusion has been made that ANN has better explained yield variability rather than other methods. Undeniably, the application of ANN to precision agriculture plays a crucial role in future evaluation of the concept of precision agriculture as a sustainable means of meeting crop yield demands. However, further research about the ANN impacts towards crop yield production must be conducted to ensure sustainability of future needs.
Open Access
IDENTIFYING APPROPRIATE STATISTICAL GROWTH MODELS FOR COMPUTATION OF COMPOUND GROWTH RATES OF MAJOR OILSEEDS AND PRINCIPAL CROPS OF CHHATTISGARH
(Indira Gandhi Krishi Vishwavidyalaya, Raipur, 2019) Kashyap, Umesh Kumar; Lakhera, M.L.; Sarada, Ch.; Chandrakar, G.; Choudhary, V.K.; Mehta, Nandan
Oilseed crops occupy a predominant place in an Indian economy next to food grains. In this study, an attempt made to identifying appropriate statistical models for computation of compound growth rate of major oilseeds and principals crops in Chhattisgarh State using non linear statistical models with a view to study trends of major oilseed crops and principal crops of Chhattisgarh; fitting appropriate statistical growth models for area, production and yield of major oilseeds crops and principal crops and Computation of compound growth rate based on suitable growth model for area, production and yield. The study was based on time series data pertaining to the area, production and productivity of oilseeds, cereals and pulses for the period of 20 years (i.e. 1998-99 to 2017-18) is obtained from department of agriculture, Chhattisgarh. Attempts have been made using four statistical growth models Monomolecular, Logistic, Gompertz and Exponential models. The best model selection is based on criteria like lowest MSE, RMSE, AIC, and BIC, calculated through the Statistical Software “R: The R Project for Statistical Computing”. After identifying best model compound growth rate (CGR) also calculated for oilseeds, cereals and pulses crops.
Open Access
DEVELOPMENT OF R-PACKAGE FOR CONSTRUCTION OF HADAMARD MATRICES AND ASSOCIATED BALANCED INCOMPLETE BLOCK DESIGNS
(Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.), 2019) REVANASIDDESHA, B B; Chaure, N. K.; Dhandapani, A.; Pandey, Devesh; Tegar, Ajay; Lakhera, M. L.
Hadamard Matrices are of great importance in both Mathematics and Statistics. They are useful in many divergence fields such as Telecommunication, space research (Seberry 2007), cryptographic algorithms, construction of Design of Experiments such as Balance Incomplete Block Designs, Group Divisible Design, Fractional Factorial Design, Balance Repeated Replications, and also used in Mixed Orthogonal Arrays. There are few existing utilities which can generate or make Hadamard matrices in statistical software such as SAS, matlab with limited coverage. R, an open source statistical computing environment, has become popular for data analysis. In this present study, construction of Hadamard matrices using R was taken up. HadamardR, an R-Package was designed and developed. In package HadamardR, the construction methods of Hadamard matrices were reviewed initially and 10 methods were selected for implementation. For each construction method, after analysing the conditions required on input, pseudo-codes were prepared and common functionalities across methods were noted. This package is tested up to permissible order up to 5000, means total number of Hadamard matrices is 1250. Out of 1250, for 45 orders construction methods are unknown. Out of remaining 1205 orders, HadamardR can generate 1158 (96%) orders. Symmetric Balanced Incomplete Block Designs (BIBD) can be constructed from Hadamard matrices and R code for analysis of BIBD by both intra-block and inter-block analysis were also presented along with an example. The complete documentation of R-package HadamardR was developed and it is expected that the documentation would be useful for users of the package.
Open Access
FORECASTING OF ARRIVALS AND PRICES OF PULSES IN CHHATTISGARH A STATISTICAL APPROACH
(Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.), 2018) GUPTA, AKHILESH KUMAR; Singh, A.K.; Rao, V. Srinivasa; Lakhera, M.L.; Koshta, A.K.; Sharma, M.L.
Chhattisgarh is the state carved out of erstwhile Madhya Pradesh in 2000. The pulses are the second most important crop after rice as staple diet. The economies of many farmers of Chhattisgarh depend on it through agricultural marketing (mandi) system. Thus, the forecasting of arrivals and prices of agricultural pulse produce of farmers can help farmers to know the exact month in which they can get fair prices of their produce in the mandi. This study attempts to fulfil this requirement of farmers. Using monthly time series data, from April 2009 to December 2017, of arrivals and prices of pulses (viz. chickpea, pigeon pea, black/green gram, lathyrus, lentil, pea) in the markets (mandis) of Chhattisgarh, collected from the website of Chhattisgarh State Marketing (mandi) Board, http://cg.nic.in/agrimandi/, different linear, non linear and time series models were fitted and best model was identified on the basis model selection criteria like highest R2, and lowest RMSE, MAE and MAPE through the Statistical Software “R: The R project for Statistical Computing”. The predicted values from the best fitted models were found to be very close to the actual values. The ARIMA models were found to be the best fitted models in general. Therefore, using the finally identified best fitted models, forecasts of arrivals and prices of pulses for future 24 months i.e. from January 2018 to December 2019, were made.

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