Prediction of Urban Unemployment Rate in India using Grey Model Pradip Basak, Mrinmoy Ray, Kanchan Sinha and Anuja A.R. Urban Unemployment Rate (UR) is a crucial indicator representing the livelihood of people in India. In India, the quarterly estimates of urban UR in the Current Weekly Status (CWS) are released by National Statistics Office (NSO) through Periodic Labour Force Survey (PLFS). At present, the urban UR estimates are available in India from the quarter April-June 2018 to January-March 2023 at the state and national level. Accurate forecasting of the UR is essential for early identification of the socio-economic problems so that timely and targeted intervention, and proper policy planning can be done to reduce the same. Time series methodology utilised so far for the forecasting of UR require monthly or quarterly data of sufficient length. Therefore, the usual methods of forecasting of UR may not yield reliable forecast in this type of small time series as the assumption on the data requirement will be violated. As a superiority to conventional statistical models, grey models require very limited data to build a forecast model (Deng, 1989). In this article, application of grey model has been considered on the quarterly estimates of urban UR for forecasting the unemployment in urban India. The Grey model shows excellent performance in forecasting the urban UR at the national level and at the state level, it shows good performances for most of the states.

  Nonlinear Statistical Modelling of Area and Production of Apple Crop of Kashmir Valley Uzma Majeed, Nageena Nazir, S.A. Mir, Immad A. Shah and Ishfaq A. Bhat The current study was conducted to compare the performance of five nonlinear growth models, namely, Monomolecular, Logistics, Gompertz,Richards, and Weibull for studying the area and production of the apple crop in Kashmir. Long-term data for the past 45 years pertaining to the area and production of apple was obtained from the Directorate of Horticulture. Run test and Shapiro-Wilk tests were employed to examine the assumptions of ?independence? and ?normality? of error terms, respectively. Levenberg-Marquardt (LM) iterative method was used to estimate the parameters. The best model was chosen based on various model adequacy test criteria including MAE, RMSE, MSE, and R2. Therefore, Richards and Weibull models were found to be the best fit for the area and production of apple, respectively. The area and production of the apple crop were forecasted for a few years using the best fit models.

  Polygonal Association Scheme and PBIB(3) Designs in Two Replicates Seema Jaggi, Cini Varghese and Ashutosh Dalal Partially Balanced Incomplete Block (PBIB) designs are a well-known class of incomplete block designs useful in agricultural research which are based on concept of association schemes. Here, a three-associate class polygonal association scheme has been defined. A method of constructing PBIB(3) designs based on polygonal association scheme has been described. The designs btained by this method require only two replications and hence reduce the requirement of experimental material. Further, the efficiency of these designs has also been worked out and is found to be quite high.

  A New Two Auxiliary Calibration Estimator of the Population Total in Two Stage Sampling Design using Nonlinear Constraints Pathi Devendra Kumar, Kaustav Aditya, Tauqueer Ahmad, Ankur Biswas and Surya Prakash Tripathi In this study, a two auxiliary calibration estimator is proposed under two stage sampling design using a nonlinear constraint with the assumption of availability of population level auxiliaries at the cluster level and the size of the clusters were assumed unknown. The performance of the proposed estimator was evaluated through a simulation study. The empirical result shows that the developed estimator was performing better than the existing estimators under two stage sampling design when population level auxiliary information were available at the cluster level.

  Comparative Study of ARIMA, SARIMA and Hybrid (ARIMA + ANN and SARIMA + ANN) Models for Wholesale Monthly Average Price of Tomato and Onion Sanjeev, Pushpa, Vikram, Preeti and Pawan Kumar Time series price forecasting is an important area of forecasting in which past observations of the same variable are collected and analysed to develop a model describing the underlying relationship. In this paper, to compare the forecasting performance of ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal Autoregressive Integrated Moving Average) hybrid (ARIMA + ANN (Artificial Neuron Network) and SARIMA +ANN) techniques for all India wholesale monthly average price time series of tomato and onion crop. The ARIMA and SARIMA techniques are used to capture the linear pattern of data. The ANN technique is used to capture the nonlinear patterns of the residuals obtain from ARIMA and SARIMA techniques. Empirical results indicate that hybrid (SARIMA + ANN) technique is effective way to improve the forecasting performance for price series of tomato and onion crop on the basis of least value of error measure such as RD (%), RMSE, MAPE and MAE.

  A Computational Approach for Estimation of a Finite Population Mean under Two-Phase Sampling in Presence of Two Auxiliary Variables Manish Kumar, Sarvesh Kumar Dubey, V.N. Rai and B.V.S. Sisodia In this paper, a transformed class of ratio-cum-product estimators has been developed for estimating the mean of a finite population using two auxiliary variables in two-phase sampling. The mathematical expressions for bias and mean square error (MSE) of the proposed class, as well as for the other pre-existing estimators, have been obtained to the first order of approximation. Some of the existing estimators are shown to be the members of the proposed class. The proposed class of estimators has been compared with the other existing estimators using the MSE criterion. The theoretical results have been empirically validated by using real population datasets, and also by conducting a simulation study.

  Generalized-Type Calibration Estimator of Population Mean in Stratified Random Sampling Manoj Kumar Chaudhary, Basant Kumar Ray and G.K. Vishwakarma In the present paper, we have suggested a generalized-type calibration estimator for estimating the population mean in stratified random sampling. We have pioneered out the generalized-type calibration estimator using chi-square type distance function subject to the calibration constraints based on a single auxiliary variable. The new set of stratum weights is chosen such that the chi-square type distance would be minimized under the given calibration constraints. One can derive a number of existing calibration estimators and some new calibration estimators of the mean of a stratified population using the suggested generalized-type calibration estimator. Some special cases of the suggested generalized-type calibration estimator have been discussed in detail. A simulation study has also been carried out to strengthen the performance of the suggested generalized-type calibration estimator.

  Digitalization of Agricultural Education in Northern India: Accessibility, Use and Effectiveness Rajni Jain, Pavithra S, Arthy Ashok, Anshu Bharadwaj, Richa Sachan and Ranjit Kumar Paul This study investigates the availability, awareness, and utilization of ICT (Information and Communication Technology) infrastructure and software tools among agricultural students in Northern India. The research is based on a comprehensive primary survey conducted among students enrolled in State Agricultural Universities (SAUs). We computed indices for ICT awareness, availability, and usage and categorized students into low, medium, and high ICT usage groups for further analysis. Additionally, we examined the adoption of various e-resources tailored to the field of agriculture, such as Agridaksh and Agropedia. Our findings reveal a direct correlation between ICT awareness and availability with its actual usage among students. Several factors including educational level, gender, social background, family context, and ICT awareness were found to influence ICT usage among students. Analysis of student perceptions regarding the effectiveness of ICT resources in various academic activities indicates that over 75 percent of students consider ICT use in the learning process as a time-saving measure. Furthermore, nearly 66 percent agree that ICT enhances efficiency and accuracy in their work. More than half of the students reported improved understanding of concepts, enhanced academic performance, increased placement and employment opportunities, and greater prospects for higher education through the utilization of various ICT resources. However, students perceived a relatively lower impact of ICT resources on placement and employment creation. These results underscore the potential of digital tools in agricultural education and emphasize the need to bridge the existing gap in access, awareness, and usage of ICT tools in State Agricultural Universities in Northern India.