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In India, the agriculture is main profession for more than sixty percent of the population. The stakeholders of agriculture in India, facing plenty of problems that leads the people of the country to shift their profession and lets them migrate towards urban area. So, need of implementing technology in agriculture is must in future days because as population is increasing in exponential form as the result huge requirement of food and agricultural product. The data analytics will play a significant role in agricultural dataset for implementing prediction and recommendation system in the sector. Yield is one of the factors to be considered in the agriculture that determines the wellness and prosperity of the farmer. In this paper deals with prediction system to predict yield of areca nut product in Puttur taluk, Dakshin Kannda District, Karnataka state in India. The time series data analytics model known as Auto Regressive Integrated Moving Average (ARIMA) model is used for yield prediction system. The research is mainly focused on forecasting of areca nut production for next four or five years in Puttur taluk. It compares various ARIMA models with performance criteria and selects best model for prediction purpose. The diagnostic check is carried out to test the system performance After the prediction, then the actual values and predicted values are compared and presented in the form graphical representation. [ABSTRACT FROM AUTHOR]

Agriculture in India today is facing acute problems from production to marketing aspects. Hence, it is needed to supplement the income of rural folk and the Indian farmers should find an alternate means of livelihood. Since, agriculture is the main occupation of the people in India there is a need to think of allied income generation strategies with agriculture, agro tourism is one such approach. This approach has got lot of potential by utilizing the local resources to its fullest potential. It also has the capacity to generate a marginal or supplementary source of income and also creates employment opportunities to the farmers. Hence it acts as an alternative means and ways of livelihood security to the farmers. The study was conducted in Ramanagara district of Karnataka with an objective of analyzing the impact of agro tourism on livelihood security of farmers. This study aims to provide much needed empirical data about livelihood security of farmers in agro tourism centres. Results from a survey of 32 farmers of the selected study area indicated that majority (59.38 %) of the farmers belonged to the medium category of overall livelihood security followed by 28.12 per cent of the farmers belonging to the low and 12.50 per cent of the farmers belonging to the high category of livelihood security. The mean livelihood score under different factors influencing the livelihood security of farmers in agro tourism is also presented in this paper. The findings highlights the agro tourism can be effectively implemented, if the idea is promoted and widely adopted, it not only enhances livelihood security of the farmers but it also serves as a value addition and can accelerate further economic growth of the country. [ABSTRACT FROM AUTHOR]

The present study was carried out in Srinivaspura taluk which has the highest area under tamarind in Kolar district of Karnataka. The study focused on assessment of costs and returns of tamarind production, documentation of harvesting and post-harvest practices, and analysis of marketing of tamarind in Srinivaspura taluk. The primary data related to tamarind production, harvesting, post-harvest practices and marketing of tamarind was collected from 50 sample farmers in Srinivasapura taluk. To achieve the specific objectives of the study, descriptive statistics including simple averages and percentages was adopted. The study found that the total cost of tamarind cultivation per acre was Rs.47,881/-, the average gross returns realized was Rs.89,744/- and the average net returns amounted to Rs.41,863/-. The benefit-cost ratio of Rs.1.87 indicates that tamarind production generated reasonably good returns under rainfed conditions. In the study area, traditional method of harvesting and post-harvest handling was followed which involved manual labour at all stages of harvesting, drying, deseeding, bagging, and preparing the tamarind for the market. Majority (62 %) of the farmers sold their produce to pre-harvest contractors. Immediate cash payment by the pre-harvest contractors, farm gate sales, and easy disposal of small quantities of produce were the major reasons for farmers to choose pre-harvest contractors for selling tamarind. Based on the findings of the study, it can be concluded that adoption of new technology in production and management practices of tamarind results in more yield and would fetch additional income to farmers. The farmers skilled in processing and retailing can opt for value addition by adopting hygienic process at all stages of harvesting and marketing, and can also indulge in selling branded packages of tamarind to realize higher prices in potential markets. [ABSTRACT FROM AUTHOR]

Sorghum is an important dual-purpose crop of India grown for food and fodder. Prevailing weather conditions during the crop growth period determine the yield of sorghum. Hence, the crop yield forecasting models based on weather parameters will be an appropriate option for policymakers and researchers to develop sustainable cropping strategies. In the present study, six multivariate weather-based models viz., least absolute shrinkage and selection operator (LASSO), elastic net (ENET), principal component analysis (PCA) in combination with stepwise multiple linear regression (SMLR), artificial neural network (ANN) alone and in combination with PCA and ridge regression model are examined by fixing 90% of the data for calibration and remaining dataset for validation to forecast rabi sorghum yield for different districts of Karnataka. The R2 and root mean square error (RMSE) during calibration ranged between 0.42 to 0.98 and 30.48 to 304.17 kg ha−1, respectively, without actual evapotranspiration (AET) whereas, these evaluation parameters varied from 0.38 to 0.99 and 19.84 to 308.79 kg ha−1, respectively with AET inclusion. During validation, the RMSE and nRMSE (normalized root mean square error) varied between 88.99 to 1265.03 kg ha−1 and 4.49 to 96.84%, respectively without AET and including AET as one of the weather variable RMSE and nRMSE were 63.48 to 1172.01 kg ha−1 and 4.16 to 92.56%, respectively. The performance of six multivariate models revealed that LASSO was the best model followed by ENET compared to PCA_SMLR, ANN, PCA_ANN and ridge regression models because of reduced overfitting through penalisation of regression coefficient. Thus, it can be concluded that LASSO and ENET weather-based models can be effectively utilized for the district level forecast of sorghum yield. [ABSTRACT FROM AUTHOR]