The General Crop Estimation Survey (GCES) scheme requires a large number of Crop Cutting Experiments (CCEs) to be conducted to get a reliable estimate below the district level. However, conducting a large number of CCEs imposes a financial burden on Govt. agencies. Additionally, largescale surveys like GCES often result in many outlier observations in the CCE data. To address this issue,this study was conducted to estimate the yield rate of cotton with a relatively fewer number of CCEs than the GCES scheme using the proposed Outlier Robust Geographically Weighted Regression (ORGWR) approach. Validation of the proposed methodology was done using the real CCE dataset of Amravati district for the 2012-13 agriculture year in Maharashtra. In this approach,the number of CCEs conducted for GCES scheme was reduced, and then this reduced number of the CCEs can be predicted using the proposed ORGWR approach. The predicted CCEs and the incomplete CCEs data are then combined to form a complete dataset. This complete dataset is used to calculate the crop yield accurately. The study conducted a comparison between the ORGWR approach and GCES methodology for estimating the average yield of cotton. The results showed that the ORGWR approach, when used with a lesser number of CCEs, yielded estimates that were almost equivalent to those obtained using the GCES methodology with the complete dataset. Moreover, the standard error of the estimate was reliable, indicating the validity of the results.

  The forecasting of rainfall is said to be the most difficult of other hydrological processes due to sudden changes in atmospheric processes. The rainfall directly and indirectly influences agriculture and allied sectors. Sudden changes in rainfall or an uneven distribution of rainfall can lead to crop loss. In order to avoid such problems and take the necessary precautions, it is mandatory to forecast the rainfall using various models with maximum precision. In this study, rainfall for northern, central and southern Kerala, India, was predicted using an artificial neural network (ANN) with a multi-layer perceptron (MLP) feed-forward neural network and an extreme learning machine (ELM) neural network. The monthly rainfall data was collected for a period of 39 years (1982?2020) from the regional agricultural research stations (RARS), Pilicode and Pattambi, for the northern and central zones of Kerala, respectively, whereas for the southern zone of Kerala, data was collected from RARS, Vellayani, for a period of 36 years (1985?2020). For the rainfall data collected from three different zones of Kerala, the MLP and ELM were applied. The comparison and validation of MLP and ELM models was done based on the error values of mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results indicated that for three different zones in Kerala, ANN with MLP showed better performance in forecasting rainfall compared to the ELM model. The best-selected model was also used for forecasting the next 5 years rainfall in each zone of Kerala.

  In the present communication, some results on robustness of generalized efficiency balanced (GEB) block designs with equal block sizes (binary and non-binary) are presented. The necessary & sufficient conditions are obtained for robustness as per criteria 1 (Ghosh, 1982) when the missing observations appear in the following patterns: (i) t (≥1) observations of a design pertaining to the same treatment are missing and (ii) all observations in a block are missing but the residual design still becomes connected. Sufficient conditions for robustness according to Criteria 1 are examined in each type of GEB designs. Another criterion (Criteria 2) for robustness of a block design is the small amount loss of efficiency values of the residual design after deletion of t (≥1) observations. The declining trend of efficiency values after deletion of treatments one by one are presented graphically for measuring the robustness of designs under study.

  Tomato plays a vital role in Karnataka?s agro-processing and food industries, contributing significantly to the state?s economy. Even though tomato production in Karnataka is substantial, the state?s market is characterized by price volatility. Tomato prices can undergo drastic fluctuations within short time periods, posing severe challenges to the farmers and consumers. To address these problems, time series models, such as Exponential Smoothing, ARIMA, SARIMA, BATS and TBATS have been implemented to forecast the tomato prices in Kolar market of Karnataka state using monthly wholesale prices data from the year 2010 to 2022. Among the applied models, BATS showed superior performance in terms of model validation criteria such as Root Mean Square Error and Mean Absolute Percentage Error. Keywords: Tomato price; Exponential Smoothing; ARIMA; SARIMA; BATS and TBATS.

   Semi-Latin rectangles represent row-column designs where each row-column intersection contains the same number of experimental units, denoted as k>1. Additionally, each treatment appears an equal number of times in each row (Nr, say) and in each column (Nc, say) (Nr>=1 and Nc>=1 may or may not be same). Partially Balanced Semi-Latin rectangles (PBSLR) constitute a subset of Semi-Latin rectangles (SLR), serving as generalizations of Latin squares and Semi-Latin squares (SLS). These designs find utility in various agricultural and industrial experiments, particularly situations where one effect is considered a column effect and the other a row effect, with the intersection (block/cell) accommodating precisely four units. This article introduces two methods for constructing PBSLR designs with a block size of 4. Also, R package has been developed for generating the designs. Keywords: Semi Latin Rectangle; Partially Balanced Semi-Latin rectangles; Canonical efficiency factor; Average efficiency factor.

   The demand for energy and associated services to meet sustainable agricultural and economic growth and improve human health and lifestyle is increasing day by day. Hence, there is a need for systematic and scientific prediction of solar and other renewable sources of energy to meet these requirements. The main purpose of this study is to propose a hybrid Space-Time Autoregressive Moving Average Artificial Neural Network (STARMA-ANN) model for the precise and accurate forecasting of solar radiation for better planning and policy making. This approach has been implemented at seven geographical locations of Bihar in India. Spatial weight matrices have been used to describe all seven geographical locations and incorporated into the STARMA model to reflect the spatial and temporal correlation. To deal with nonlinear dynamics in the spatiotemporal data, ANN technique has been applied on residuals of the fitted STARMA model. The results have demonstrated that the proposed hybrid model performs better prediction accuracy than using conventional STARMA model, especially for spatiotemporal data with nonlinear characteristics of solar radiation. Keywords: Solar radiation; STARMA; ANN; Hybrid model; Spatial weight matrix; Forecasting

   In this paper we propose a ratio-type exponential estimator for estimating population mean of the study variable under Ranked set sampling when auxiliary information is known. The bias and Mean square error of the proposed estimator has been derived up to the first degree of approximation. A simulation study has been carried out to judge the performance of the newly proposed estimator along with existing estimators. It is obtained that the proposed estimator is more efficient as compared to the competing estimators. Keywords: Ranked Set Sampling; Bias; Mean square error; Relative efficiency; Simulation.

  This study focuses on assessing tree species diversity and evaluating the efficacy of both linear and non-linear regression models for predicting tree volume. The research was conducted in the slopes of the Pir Panjal range within the Shopian Forest Division, situated in the South-Western Himalayan Region of Kashmir. Data on Pinus wallichiana stands, including diameter at breast height (D) and tree height (H), were meticulously collected using appropriate measurement instruments. Employing a multi-stage sampling technique, ten plots of uniform size (10m x 10m) were selected across 20 blocks, and subsequently, 25 trees were randomly chosen from each plot. Six different linear and non-linear regression equations were fitted to the data, and the most suitable equation was identified for volume estimation. To evaluate the performance of the fitted regression models, metrics such as R-squared (R?), adjusted R?, root-mean-square error (RMSE), and Theil?s U statistic were employed. Additionally, validation procedures involved using the half-split approach and the Chow test. Upon analysis, it was determined that when employing diameter (D) and considering the joint effect of diameter and height (D2H (I)) as independent variables, the linear model (V=-1.41+15.12D) and power model (V=2.301 I0.502), quadratic model (V=1.8726+0.663 I- 0.011 I?) with highest R?, lowest RMSE and Theil?s U statistic respectively, emerged as the most suitable for volume estimation, demonstrating superior accuracy compared to alternative models. Consequently, our findings extend beyond academic inquiry, offering practical implications for sustainable forest management and planning in the Western Himalayan Regions of Kashmir. By providing robust volume estimation models tailored to Pinus wallichiana stands, our study equips forest managers and policymakers with essential tools for informed decision-making. Keywords: Regression equations; Tree volume; Height-diameter relationship; Pinus wallichiana; Temperate forests; Regression plots; Forest inventory.

   Water is an important natural resource considered as basic need for all living things around the world. The volume of pure water present in the Earth is regulated by the amount of rainfall received over the years. Sudden climatic changes are observed in throughout the world which led to flood, drought and uneven rainfall over the years. In this study, SARIMA and SARIMA-GARCH models are applied for forecasting rainfall in different zones of Kerala. The presence of heteroscedasticity in residuals obtained from SARIMA model was identified using ARCH-LM test and it was eliminated by applying SARIMA-GARCH model to the same. The ARCH-LM test results confirmed the presence of heteroscedasticity in residuals. The comparison of models used for predicting rainfall revealed that hybrid SARIMA-GARCH model is more efficient in projecting future values of rainfall in the northern and southern zones of Kerala whereas SARIMA model is showing more accuracy in the central zone of Kerala even in the presence of heteroscedasticity of residuals. The comparison of rainfall forecasted in different zones of Kerala clearly indicated that rainfall is higher in the northern zone whereas lower in the southern zone. In the northern and central zones, the rainfall showed a peak from June to September and almost negligible rainfall from December to February. The outperformed model in each zones of Kerala was applied for projection of future rainfall for next 5 years (2021-2025). Compare to previous years, the rainfall in the northern and central zones is expected to decrease whereas in southern zone of Kerala, rainfall will be almost same. Keywords: ARCH-LM test; Heteroscedasticity; Rainfall; Residual; SARIMA; SARIMA-GARCH

   Agriculture is an unorganized sector and it is a very high drudgery prone profession due to the absence of awareness and entry of advanced agricultural technologies. Many farming activities and operations are carried out by farm workers in the agriculture sector, where they are subject to very poor working conditions which can lead to physical and mental stress. Workers in agriculture experience musculoskeletal disorders in various body parts throughout various agricultural operations. These problems are caused by heavy work loads, repetitive movements, uncomfortable postures, prolonged periods of time spent in neutral or unsupported positions and the use of conventional equipment and implements that are not ergonomically built. The ergonomic risk factors present in the workplace can be examined using various evaluation instruments to ascertain the workers abilities and drawbacks. The work-related risks are measured using the scientifically validated posture assessment instruments. Rapid entire body assessment (REBA) is a technique that assess a person?s complete body including wrists, fore arms, elbows, shoulders, neck, trunk, back, legs and knees. It uses a systematic process to find the postural musculoskeletal disorders and risks associated with the job tasks. A mobile application is developed to assess the agricultural activities on the basis of physiological analysis and postural analysis using REBA ergonomics technique. The application evaluates the drudgery involved in the agricultural work by using physical assessment followed by postural ergonomics methods and provide recommendation of correct posture to avoid developing musculoskeletal disorders. Keywords: Drudgery; Postural assessment; REBA; Mobile application.

  In domains such as the stock market and manufacturing, there?s a growing demand for faster and more accurate data distribution identification methods due to the rapid generation of vast volumes of data, highlighting the need for enhanced real-time decision-making capabilities. Traditional methods of identifying data distributions often rely on manual inspection, limited statistical tests and time-consuming analysis, leading to inefficiencies and inaccuracies in classification. In this scenario, the presented research offers a novel approach leveraging Deep Learning (DL) models to automate the process. The presented methodology also enables faster and more accurate identification of data distributions by the generation of synthetic data points and training of the DL model for identifying different distribution types. The primary objective of this study is to develop a DL model that categorizes data points into specific distributions based on an input dataset. Moreover, for model training and evaluation, a total of 1000 datasets are generated, each comprising 1000 data points. The study considers five distributions (Normal, Uniform, Exponential, Log-normal and Beta distribution), with 200 datasets generated (with randomly selected parameters) for each distribution. In the study, the DL model is trained first, and later, the model is evaluated on a separate test (unseen) dataset. Then, its performance in classifying the distributions is assessed based on metrics such as accuracy and loss. The study results demonstrate the effectiveness of the proposed approach in accurately classifying the distribution of data points, providing valuable insights into the application of DL for distribution classification tasks. The proposed method enhances scalability, robustness and efficiency by harnessing the power of convolutional neural networks and advanced preprocessing techniques. Keywords: Distribution identification; Deep learning; Data distribution classification; Convolutional neural networks; Fast distribution detection

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