7 research outputs found
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No full textNot AvailableAbstract: In this globalized world, management of food security in the developing countries like India where agriculture is dominated needs efficient and reliable price forecasting models more than ever. Forecasts of agricultural
prices are handy to the policymakers, agribusiness industries and farmers. In the present study, Functional Coefficient Autoregression (FCAR) has been applied for modeling and forecasting the monthly wholesale price of clean
coffee seeds in Hyderabad coffee consuming center using the data from Jan, 2001 to Sep, 2014. FCAR (2,2) model
was found suitable based on the minimum Average Prediction Error (APE) criterion. The FCAR model thus obtained
was compared with the Autoregressive Integrated Moving Average (ARIMA) model. Since the original series was
found to be nonstationary from Augmented Dickey-Fuller test (ADF statistic=-2.84, p=0.22), the differenced series
(ADF statistic=-4.20, p<0.01) was used and ARIMA (12,1,0) was found suitable. The FCAR model obtained was
compared with the ARIMA model with respect to forecast accuracy measures viz., Root Mean Square Error (RMSE)
and Mean Absolute Percentage Error (MAPE). The RMSE and MAPE for the FCAR (2,2) were found to be 17.16
and 4.41%, respectively, whereas for the ARIMA (12,1,0) models, 62.64 and 26.15%, respectively. The results
indicated that the FCAR model was efficient than the ARIMA model in forecasting the future prices.Not Availabl
Natural Farming Practices in India: Its Adoption and Impact on Crop Yield and Farmers’ Income
No full textNatural Farming (NF) is contemplated by its protagonist as one of the most potential crop cultivation methods to drastically cut down production costs by reducing dependence on market for purchase of critical inputs. Being considered as an agroecologically diverse farming practice, it brings hosts of ecological and social benefits, although, there are two school of thoughts- opposing each other on the efficacy of its practices. In order to better understand the practice followed in NF as well as the cost saving and income gain by the NF farmers, the study was undertaken in the states of Karnataka and Andhra Pradesh during January-June 2019 covering 55 and 124 NF-adopting farmers and 50 and 61 non- NF farmers in Karnataka and Andhra Pradesh, respectively. Though there are certain practices prescribed in natural farming, the most adopted practice is use of Jeevamritha, Beejamritha and other plant protection materials. Further, there is always scope for tweaking and innovation in these practices like Ghanajeevamritha, use of Azolla in paddy field or applying Jeevamritha through drip irrigation. Significant reduction in cost of cultivation of all the crops was observed. However, the effect on crop yield is not conclusive. NF-farmers in Karnataka harvested better yield in finger millet, but lower yield in paddy and sugarcane. While in Andhra Pradesh, yield advantage was visible in paddy. It was also observed that the NF-adopted farmers who applied farm yard manure harvested better crop yield than those who did not apply. Thus, natural farming may not look as yield enhancing farming practices, but definitely increases farmers’ income through cost reduction and long-term sustainability
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No full textNot AvailableAbstract : Forecasting is one of the core focuses of statisticians working in agricultural research. Obtaining timely as well as
accurate forecasts under all possible circumstances is the need of the hour. Most of the forecasting techniques make one or
the other assumptions limiting their applications. Vector Autoregression is one such widely used multivariate forecasting
technique where homoscedasticity of errors is assumed for estimation of parameters by ordinary least square (OLS) method.
This study proposes genetic algorithm (GA), a heuristic search algorithm, which does not make any such assumptions for
estimating the parameters under such situation. The developed methodology is empirically validated using simulated bivariate
vector autoregressive model of order 1 under heteroscedasticity. The relative error of parameter estimates and Mean Absolute
Percentage Error have shown that GA performs better than OLS estimation under heteroscedasticity. The proposed methodology
is also tested under homoscedasticity using bivariate data of fish landings. The results indicated that both GA and OLS are
equally efficient in estimating the parameters.Not Availabl
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No full textNot AvailableThis gives news about events, happening and achievements at NAARM, Hyderabad during April - June 2018Not Availabl
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No full textNot AvailableForecasts of agricultural prices are useful to the farmers, policymakers and agribusiness industries. In this globalized world, management of food security in the developing countries like India where agriculture is dominated needs efficient and reliable price forecasting models. In the present study, Vector Autoregression (VAR) has been applied for modeling and forecasting of monthly wholesale price of clean coffee seeds in different coffee consuming centers, viz. Bengaluru, Chennai and Hyderabad. Augmented Dickey-Fuller (ADF) test has been used for testing the stationarity of the time series. The appropriate VAR model is selected based on minimum Akaike Information Criterion (AIC). The VAR model obtained is compared with the Auto Regressive Integrated Moving Average (ARIMA) models with respect to forecast accuracy measures. The residuals of the fitted models were diagnosed for possible presence of autocorrelation and Autoregressive Conditional Heteroscedasticity (ARCH) effects.Not Availabl
Understanding Learner Behaviour in Online Courses through Learning Analytics
No full textNot AvailableBuilding an effective online course requires an understanding of learning analytics. The study assumes significance in the COVID 19 pandemic situation as there is a sudden surge in online courses. Analysis of the online course using the data generated from the Moodle Learning Management System (LMS), Google Forms and Google Analytics was carried out to understand the tenants of an effective online course. About 515 learners participated in the initial pre-training needs & expectations? survey and 472 learners gave feedback at the end, apart from the real-time data generated from LMS and Google Analytics during the course period. This case study analysed online learning behaviour and the supporting learning environment and suggest critical factors to be at the centre stage in the design and development of online courses; leads to the improved online learning experience and thus the quality of education. User needs, quality of resources and effectiveness of online courses are equally important in taking further online courses
