Homestead farming has been the backbone agricultural economy of Kerala but the productivity of the homesteads in Kerala has yet to reach an acceptable level. In this paper the possibility of increasing the contribution of this sector through proper crop planning is sought. The optimum model was developed by using linear programming (LP) technique. The constraints included in the analysis were total area, intercropped area, investment amount and population of each enterprise. The optimum model reveal the scope of 22.83 per cent enhancement in net return as compared to net return from the existing plan. Sensitivity analysis of the optimum model revealed that further enhancement of net return in The agro-ecological region could be achieved by increasing the cropping intensity in the underutilized intercropped area and changing the binding enterprises.

The weather variables impact the crop differently throughout the various stages of development. The weather effect on crop yield thus can be determined not only by the magnitude of weather variables but also on the variability of weather over crop season. Crop yield forecasting methods incorporating weather information provide a better prediction of yield accounting the relative effects of each weather component. Regression analysis is the most frequently used statistical technique for investigating and modelling the relationship between variables. Building a multiple regression model is an iterative process. Usually several analyses are required for checking the data quality as well as for improvement in the model structure. The use and interpretation of multiple linear regression models depends on the estimates of individual regression coefficients. However, in some situations the problem of multicollinearity exists when there are near linear dependencies between/among the independent variables. The Principal Component Analysis (PCA) method has been proposed to address the problem of multicollinearity. Using principal component scores (PC) derived from weather variables as predictor variables helps to obtain better estimate the yield. The discriminant analysis is a multivariate technique involving the classification of separate sets of objects (or sets of observations) and assigning of new objects (or observations) to the groups defined previously. Forecasting of crop yield can also be done using discriminant analysis scores based on the weather variables as regressor.

An effort was made to investigate the behaviour of the area, production and productivity of tomato crop in the Haryana and India. For the purpose modelling and forecasting, linear trend, exponential trend, quadratic trend, S-curve trend, ARIMA modelling techniques were used and analysed the available information from 1991 to 2018. The results show that there will not be a significant increase in tomato productivity in Haryana, but it will raise yield in India. The total production of tomatoes in Haryana will be 1029 thousand tons by 2024 and the current production (2018-19) is 643.55 thousand tons and an increase of 4043 tons can be achieved in 2024 in India. It is noteworthy that although the area under tomatoes will increase in the near future in Haryana, but productivity remains the same. Productivity in India may increase in the coming years, although the area under cultivation remains the same.

Crop forecasting is a formidable challenge for every nation. The Government of India has developed a number of forecasting systems. The national and state governments need such pre-harvest forecasts for various policy decisions on storage, distribution, pricing, marketing, import-export and many more. In this paper, univariate forecasting models such as random walk, random walk with drift, moving average, simple exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are considered and analyzed for their efficiency for forecasting vegetable production in the Haryana state. The State annual data on vegetable production were divided into the training data set from 1966-67 to 2013-14 and the test data set from 2014-15 to 2018-19. Suitable models were selected on the basis of error analysis on the training data and a percent error deviation test on the test data. Model diagnostic checking was carried out on ACF and PACF in residual terms through runs above and below the median, runs up and down and Ljung-Box tests. It is inferred that ARIMA (2,1,1) was found to be optimal and that the forecast values for the years 2019-20 to 2023-24 were estimated on the basis of this model, which were 7.82,8.23,8.72,9.2 and 9.72 million tonnes for the year 2019-20 to 2023-24, respectively.  The significance of the mode is that we can forecast the values using this best fit model and forecast values are very important for the policymakers and other government agencies for proper policy decision regarding food security.

In literature, several ratio type estimators of population mean were proposed by statisticians but none of them made pair wise comparison of these estimators. In this paper an attempt has been made for pair wise efficiency comparison of the same and find out the different conditions on which one estimator performed better than the other. Depending on the structure of data used, the efficiency comparison of these estimators is varied in certain circumstances. In this study we have revealed the efficiency conditions of the existing ratio estimators, through pair wise comparisons and examine the relative performance of ratio estimators in terms of efficiency and unbiasedness empirically.