Journal of the Indian Society of Agricultural Statistics
The Society publishes an International Peer Reviewed Journal called Journal of the Indian Society of Agricultural Statistics with ISSN 0019-6363. Three issues of the Journal (April, August and December) are published annually. The first volume of the Journal was released in 1948. The Journal devoted to the publication of original research papers on all aspects of Statistics and Computer Applications preferably with innovative applications in Agricultural Sciences or that have a potential application in Agricultural Sciences. The review articles of the topics of current interest are welcome. The Journal also accepts books, monographs and periodicals for review. Special issues on thematic areas of both national and international importance are also brought out.
Indian Society of Agricultural Statistics (ISAS)
On Comparison of Classical and Machine Learning Models for Forecasting Rabi Green Gram Production in Odisha
Green gram is one of the major pulse crop of Odisha grown mostly during the Rabi seasons. Rabi green gram production accounts for approximately 51 percent of the total pulse production in Odisha. Data on production (in ‘000 MT) of rabi green gram are collected for the period from 1970-71 to 2023-2024. The entire data is splitted into two sets: training data set and testing/Cross validated data set. The data for the period from 1970-71 to 2019 20 for building training data set and 2020-21 to 2023-24 to be used as test data set/ cross validation data set. Classical Auto Regressive Integrated Moving Average (ARIMA) model and Artificial Neural Network (ANN) model is built for the training set data. Both the built-in models are compared on basis of Mean Absolute Percentage Error MAPE and Akaike Information Criteria Corrected (AICc) for the training set and test set data. The model performing better on basis of MAPE and AICc is selected for obtaining final forecast values of rabi green gram production. ARIMA (2, 0, 0) fits best which after performing the BDS test for the residuals shows that both linearity and non-linearity is found in the data in ratio of 7:1. So hybrid ARIMA-ANN model is prescribed to be fitted with appropriate weightage to selected best fit ARIMA and ANN models. Out of the suitable NNAR models, NNAR (2, 3) model to be the best fit. So the hybrid ARIMA-ANN model is fitted with weightage of 0.875 to the selected best fit ARIMA (2,0,0) model and 0.125 to the selected best fit NNAR (2,3) model. The model fit statistics for the hybrid ARIMA-ANN model is found to be lower in case of training data set and testing data set. The forecast values of rabi green gram production obtained from the fitted hybrid ARIMA-ANN model are found to be decreasing in future years. Thus, it could be concluded that the advanced machine learning model definitely improves the accuracy of forecasting.
: Akaike Information Criteria Corrected; Forecasting; Hybrid ARIMA-ANN; linearity; Non- linearity.
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A Hybrid Grey Bayesian Rolling Model for Forecasting Annual Rural Unemployment Rate in West Bengal
In this article, a reliable forecasting framework is developed for predicting the annual rural unemployment rate (UR) in West Bengal, India, where time series data are scarce and noisy. Accurate unemployment forecasts are crucial for targeted policymaking, especially in rural regions facing socio-economic vulnerabilities. The paper evaluates the performance of advanced grey system models in addressing data limitations and improving predictive accuracy. The study utilizes annual rural UR estimates for various age groups and gender from 2017–18 to 2023–24, derived from the Periodic Labour Force Survey (PLFS). Three forecasting models are developed: Grey Bayesian Model, Rolling Grey Model, and a hybrid Rolling Grey Bayesian Model. The models are compared against traditional moving averages using Relative Mean Absolute Percentage Error (RMAPE). Parameter estimation in the Bayesian models is implemented through MCMC simulations using WinBUGS. The results indicate that the Rolling Grey and Rolling Grey Bayesian models consistently outperform traditional and standalone grey models, with RMAPE values as low as 8.40%. These models show superior predictive capability, particularly for the 15–29 age group and female workforce categories. The dynamic updating mechanism of the rolling models effectively handles short time series and chaotic labour market data. This study demonstrates the novel integration of grey modelling with Bayesian inference and rolling mechanisms for rural labour market forecasting. It contributes a data-efficient, adaptive forecasting approach suited for regions with limited labour statistics, offering practical value for policymakers in developing economies.
Unemployment rate; Grey systems; Rolling grey model; Bayesian grey model; Forecasting; PLFS.
Some Series of Cyclic Partially Balanced Designs
Some methods of constructions of cyclic balanced incomplete block (BIB) designs, group divisible designs and rectangular designs are described. As a particular case, cyclic solution of the BIB designwith parameters: 45, 11, 5, 99, 1 v r k b λ = = = = = , and listed as H51 in the table of Hall (1998), is obtained.The present solution is different from the earlier solutions reported in Dey (1986) and Hall (1998).
Cyclic solution; Balanced incomplete block design; Group divisible design; Dihedral group; Galois field
A Pioneering Approach to Population Mean Estimation Leveraging Ranked Set Successive Sampling
Ranked set sampling has been observed to be an efficient sampling method that has gained popularity in recent years. In the present work, the concept of ranked set sampling has been extended to successive sampling, which results in a novel sampling scheme called Ranked Set Successive Sampling (RSSS). The theoretical foundation of RSSS has been discussed and estimation methodologies has been modified to be applicable in RSSS environment for the estimation of population mean. RSSS has been considered for two successive occasions, and the estimators are proposed for estimating the population mean at current (second) occasion in two-occasion RSSS. The performance of the proposed estimators are examined through simulation-based evaluation including the real data applications as well as simulated populations. The simulation results shows that the concept of RSSS is practically feasible. The comparative analysis reveals that the proposed estimators under RSSS exhibit improved efficiency over the available similar successive sampling estimators, which may be applicable in various real-life situations.
Ranked Set Successive Sampling; Auxiliary variable; Mean squared error; Relative efficiency.
Applying Grey System Theory for MSW Forecasting in Data-Limited Urban Environments: A Case Study of Shillong, Meghalaya
Rapid urbanization, population growth, and shifting consumption patterns have made municipal solid waste (MSW) management a pressing environmental issue. Accurate projections of waste generation are critical for planning collection, transportation, treatment, and disposal systems. However, conventional forecasting methods typically require large datasets, which are often unavailable in developing cities. This study applies the Grey System Theory GM(1,1) model to forecast MSW generation in Shillong, Meghalaya, using annual data from the Shillong Municipal Board for 2021–2025. GM(1,1) was chosen for its capacity to produce reliable forecasts with limited data. Parameters were estimated using the least squares method, and model performance was assessed using MAE, MAPE, and RMSE. The model demonstrated strong accuracy with a MAPE of 1.28%, MAE of 1123.76 MT, and RMSE of 1096.66 MT. Forecasts indicate that annual MSW generation in Shillong will rise from about 75,998 MT in 2026 to 98,227 MT in 2035, an increase from 208 TPD to 269 TPD. This upward trend underscores the need for upgraded waste management infrastructure and strategic long-term planning. The findings confirm that GM(1,1) is an effective forecasting tool for data-scarce urban settings and offers practical insights for sustainable waste management in Shillong
Municipal Solid Waste; Grey System Theory; GM(1,1); Forecasting; Shillong; Waste Management.
Study of Some Generalized Classes of Estimators in Probability Proportional to Size Sampling : An Empirical Study
The aim of this study is to propose three novel generalized classes of estimators under probability proportional to size Employing a first-order approximation ( ), the expressions for the bias and mean squared error ( sampling scheme. ) are derived. Actual dataset is used in the quantitative assessment to evaluate the competency of the new proposed class of estimators. From the outcome of numerical study, it is advocated that the proposed estimators gives the least and utmost precent relative efficiency (PRE) relative to other estimators
Probability proportional to size ( ); Bias; ; Empirical Study; .
AVR-AgDb: A Plant-Based Antiviral Repository to Minimize the Detrimental Effects of Frequent Viral Outbreaks and the Next Pandemic
There is an imperative need to investigate less toxic antiviral phytomolecules than nucleic acid analogs, enzyme inhibitors, or synthetic molecules as antiviral therapeutics. Agricultural crops and their secondary metabolites have been well known for their healing and immunity-boosting properties throughout the globe since human civilization. A recent global pandemic, COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has become one of the most devastating challenges for humans, compounded by increasing diffusion due to migration, global travel, and urbanization. Even before this, about >650K annual deaths had been due to seasonal viral flu disease alone. Dramatic mutations in viruses and the time needed in vaccine development have made the world realize nutrition-immunity intervention as a pragmatic preventive approach for most human viral diseases. In the telecommunications era, an efficient, user-friendly database for searching antiviral plants and their constituents is a prerequisite. Such a database will also facilitate post-COVID economic resilience in the agricultural system. It may also accelerate the research and development of eco-friendly antiviral molecules from natural resources. It further provides classified antiviral molecules with their specific names and structure, their modes of action, and model organisms studied, along with respective literature references. This information can be accessed or retrieved plant wise, viral disease-wise and chemical compound class/sub-class-wise using a URL (https://avragdb.abrl.in/). It comprehensively comprehends about >350 molecules with antiviral properties from >500 plants of medicinal and agricultural importance. The database could be useful for phytotherapy research and spreading the accountability of plant-based antiviral formulations.
Database, Virus; Viral diseases; Plant-based; Antiviral compounds; COVID-19; AVR-AgDb.
The aim of this paper is to introduce hierarchical clustering for bivariate circular or toroidal data. Here a mixture model approach is used as for model based clustering. Here, the clustering is performed in two stages, the first stage being based on the marginal distribution of one of the circular variables and the second stage being based on the conditional distribution of the other variable given the former. In particular, two types of such mixture models are constructed. A real life application on gene data is made to illustrate the use of the proposed approach. Comparison of the two models is also done based on this example.
Akaike information criterion (AIC); Bayesian information criterion (BIC); Bivariate von-Mises distribution; Expectation-Maximization algorithm; Hierrachical; Clustering.
Randomized Response Techniques: Its Development in Six Decades
Randomized response techniques (RR) are used to collect data relating to sensitive, personal, and /or stigmatizing characteristics. The direct method (DR) of interview cannot be applied in such situations because the respondents generally do not reveal the true information due to the social stigma and /or fear. So, the respondents often provide socially approved or untrue responses. As a result, DR suffers from inaccurate data and a high rate of nonresponse. To overcome such difficulties, Warner (1965) introduced an ingenious method of data collection, known as the randomized response (RR) technique, where respondents need not provide the true response but only to provide the outcome of a random experiment. Respondents’ privacy is maintained, as the interviewer will not identify whether the submitted response is true or masked. A chronological development of the RR techniques for the last six decades is provided, starting with the pioneering RR model provided by Warner. Detailed developments of the RR models for the qualitative and quantitative characteristics are provided along with the applications for the complex survey designs. Methods of estimation of the population characteristics from the multiple randomized response techniques are presented. The methods of estimations of the proportions of overlaps of multiple characteristics are presented. The optional randomized response techniques (ORT) are also discussed. Finally, some of the nonrandomized response techniques (NRR) and the measures of privacy protection are presented.
Crosswise model; Complex survey; Direct response; Forced RR Model; Maximum likelihood; Multiple randomized response, Non randomized response; Parallel model; Triangular Model; Primary protection.
An Integrated IDOCRIW-MCDM Framework for the Evaluation and Selection of Indian Mustard (Brassica juncea) Genotypes
Identifying the best-performing genotypes of Indian mustard (Brassica juncea) across multiple agronomic criteria is rarely straightforward, as no single genotype tends to excel across the board. This study developed a systematic approach to this challenge, drawing on two independent datasets of genotypes information recorded under well-defined evaluation conditions. Rather than relying on intuition, this study builds a structured decision framework using three well-established Multi-Criteria Decision-Making (MCDM) methods: the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), ViseKriterijumsaOptimizacija I KompromisnoResenje (VIKOR), and Complex Proportional Assessment (COPRAS). Since neither dataset provides predefined criterion weights, the Integrated Determination of Objective Criteria Weights (IDOCRIW) method is applied to derive them objectively from the data itself. For the first dataset, TOPSIS and COPRAS arrive at identical rankings (ρ = 1.000), while VIKOR diverges sharply (ρ = −1.000), reflecting its compromise-based logic. Results obtained that genotype A5 (IC212031) consistently emerges as the top performer. For the second dataset, VIKOR and COPRAS both placed A17 (NRC YS 5-2) at the top and A25 (Anuradha) at the bottom, an agreement at both extremes that highlights the reliability and robustness of the proposed framework
Multi-criteria decision making (MCDM); TOPSIS; VIKOR; COPRAS; Genotypes; IDOCRIW
A Comprehensive Study of Mean Estimators using Two Auxiliary Variables in Survey Sampling
This paper proposes a class of estimators for the finite population mean by utilizing auxiliary information from two correlated variables and under simple random sampling without replacement (SRSWOR). The bias and mean squared error (MSE) of the estimators are derived using first order approximation. Theoretical comparisons establish conditions under which the proposed estimators outperform traditional estimators such as the unbiased, ratio, product, and difference estimators. An empirical study further confirms that the proposed estimators achieve lower MSE and higher relative efficiency, demonstrating improved precision when dual auxiliary information is incorporated in finite population sampling.
: Bias; Mean square error; Ratio estimator; Auxiliary variable; Study variable.
A Trait Pyramiding Approach for Development of Non-Parametric Stability Models in Chrysanthemum (Chrysanthemum Morifolium) Crop Varietal Release
Plant breeding forms a vital part of horticulture, contributing significantly to the creation of new varieties with desirable traits. Diverse breeding methodologies have been adopted to achieve improvements in yield, biotic stress resistance, nutritional attributes, and adaptability to varying environments. In this pursuit, stability models are developed to release a promising line as a variety at the end of the evaluation trials. However, regular approach results in various stable line for various traits, making difficult for a holistic recommendation. Thus, through trait pyramiding concept, non-parametric stability models are developed to identify stable line(s) across all traits. This concept is demonstrated using various yield attributing traits of 15 Chrysanthemum lines evaluated at ICAR-Indian Institute of Horticultural Research, Bengaluru during 2023 to 2025. R-codes were developed for the analysis. Based on the Non-parametric stability models of Venugopalan indices, it was found that IIHR 4-8 and IIHR 2-16 are stable across all traits evaluated such as flower diameter (cm), number of flowers/plant, flower stalk length (cm), plant height (cm), plant spread (cm). These stable lines will be utilized effectively to release a variety holistically combining all desirable traits in crop hybridization trials, emphasizes the need to construct a non-parametric index for assessing the stability of a set of lines collectively based on various traits evaluated over multiple seasons or years in an experimental setup
Chrysanthemum; Non-parametric index; R-codes; Stability models; Trait pyramiding.
On the Unification of Auxiliary Information Estimators in Survey Sampling
The use of auxiliary information has led to the development of a large number of estimators for improving the efficiency of population mean estimation in survey sampling. This study establishes a unified theoretical framework by demonstrating that many ratio-type, product-type, exponential, logarithmic, and regression-type estimators proposed in recent decades are special cases of the pioneering general class of estimators introduced by Srivastava (1971) and the wider class proposed by Singh and Upadhyaya (1986). Analytical expressions of mean squared error are presented, and it is shown that the minimum mean squared error achievable under Srivastava’s class is equal to that of the classical linear regression estimator, while the Singh and Upadhyaya class provides a broader framework with additional flexibility. A recently proposed log-ratio estimator is examined and shown to require continuity-based patching. The results confirm the fundamental role of the Srivastava, and Singh and Upadhyaya classes as the underlying unifying structure for auxiliary information estimators and provide practical guidance for selecting efficient estimators in finite and large population settings.
Auxiliary information; General class of estimators; Regression estimator, Mean squared error; Survey sampling
Covariate Allocation in Completely Randomized Designs under Capsule–Based Cost Constraints
The incorporation of covariates into Completely Randomized Designs (CRDs) enhances inferential precision by accounting for systematic variation among experimental units, yet in practical contexts, structural and economic constraints often limit the freedom of covariate assignment. This paper presents a unified framework for optimal covariate allocation in CRDs under a capsule–based cost structure, where a capsule represents a subset of plots constrained to share identical covariate values. Such a formulation transforms the classical design problem into a discrete optimization setting that explicitly captures the trade–off between information gain and economic expenditure. For the single–covariate case, it is established that balanced two–capsule partitions achieve the theoretical upper bound on information while minimizing cost, thereby yielding a greedy allocation strategy that is provably D–optimal under the proposed cost model. For the multiple–covariate case, the same two–capsule allocation principle is adopted as far as possible, with higher capsule splits introduced only when necessary to maintain orthogonality or budget feasibility. If the available budget permits each treatment to employ its optimal capsule configuration, the resulting design achieves D–optimality for the covariate parameters; otherwise, partial implementation continues to yield near–optimal efficiency. The framework assumes capsule cost depends solely on its size, ensuring analytical tractability while realistically modelling cost escalation. The proposed methodology unifies theoretical optimality with practical feasibility and can be naturally extended to Randomized Block, Latin Square, and Balanced Incomplete Block Designs under similar capsule–based constraints. Dutta, G., P. Das, and N.K. Mandal. (2009). “Optimum Covariate Designs in Partially Balanced Incomplete Block (PBIB) Design Set-Ups.” Journal of Statistical Planning and Inference, 139, 2823-35. Dutta, G., P. Das, and N.K. Mandal. (2010). “Optimum Covariate Designs in Binary Proper Equi-Replicate Block Design Set-up.” Discrete Mathematics, 310, 1037-49. Dutta, G., and Bikas Kumar Sinha. 2017a. “Nearly Optimal Covariate Designs—Part i.” Communications in Statistics - Theory and Methods, 46(19), 9691-702. https://doi.org/10.1080/03610926.2 016.1217020. Dutta, G., and Bikas Kumar Sinha. 2017b. “Nearly Optimal Covariate Designs—Part II.” Communications in Statistics - Theory and Methods, 46(19), 9703-25. https://doi.org/10.1080/03610926.20 16.1217021. Harville, David A. (1974). “Nearly Optimal Allocation Procedures.” Journal of the American Statistical Association, 69(346), 501-7. Hore, Samrat, Anup Dewanji, and Aditya Chatterjee. (2014). “Design Issues Related to Allocation of Experimental Units with Known Covariates into Two Treatment Groups.” Journal of Statistical Planning and Inference, 155, 117-26. Rao, P.S.S.N.V.P., S.B. Rao, G.M. Saha, and B.K. Sinha. (2003). “Optimal Designs for Covariates’ Models and Mixed Orthogonal Arrays.” Electronic Notes in Discrete Mathematics, 15, 155-58. Saha Ray, R., and G. Dutta. (2019). “Optimal and Efficient Treatment Control Design in the Heteroscedastic One-Way Layout with Covariates.” Statistics, 53(2), 459-69. https://doi.org/10.1080/02 331888.2019.1573821. Sapam, S., G. Dutta, and B.K. Sinha. (2024). “Optimal Covariate Allocation in Asymmetrical Factorial Designs: A Comparative Analysis of Design Choices.” International Journal of Statistical Sciences, 24(2 Special), 49-66. Sapam, S., G. Dutta, and B.K. Sinha. (2025). “Optimal Covariate Allocation in Symmetric 32 Factorial Designs: A Comparative Study of Efficiency and Treatment Contrast Estimation.” Unpublished manuscript. Sarkar, B., A. Majumder, and G. Dutta. (2025). “Optimizing Covariate Allocation in Factorial Designs: Advancing d-Optimality with Multiple Covariates.” Unpublished manuscript. Sinha, B.K., and P.S.S.N.V.P. Rao. (2019). “Some Aspects of Optimal Covariate Designs in Factorial Experiments.” Statistics and Applications, 17, 77-86. Sinha, B.K., P.S.S.N.V.P. Rao, T. Mathew, and S.B. Rao. (2014). “A New Class of Optimal Designs in the Presence of a Quantitative Covariate.” International Journal of Statistical Sciences, 14, 1-15. Troya, L.J. 1982a. “Cyclic Designs for a Covariate Model.” Journal of Statistical Planning and Inference, 7, 49-75. Troya, L.J. 1982b. “Optimal Designs for Covariate Models.” Journal of Statistical Planning and Inference, 6, 373-419.
Completely Randomized Design; Covariate design; Capsule costs; Optimal allocation; Information matrix; D-optimality.
Modified Sampling Methodology for Estimation of Area and Production of Horticultural Crops
Reliable estimation of horticultural crop area and production is crucial for agricultural planning in India. The earlier Crop Estimation Survey-Fruits & Vegetables (CES-F&V) methodology developed by ICAR–IASRI was discontinued due to operational complexity and high cost, necessitating an alternative approach. This study presents a modified sampling methodology developed under the CHAMAN programme for integrated assessment of fruits and vegetables. Field testing across Maharashtra, Andhra Pradesh, Tamil Nadu, and Himachal Pradesh generated district-level estimates of area, production, and yield. The results showed close agreement with official statistics of the Department of Agriculture & Farmers Welfare, with acceptable precision levels. A significant reduction in sample size, from 150–200 to 80 villages per district, improved cost-effectiveness and operational efficiency without compromising accuracy. The methodology demonstrates strong potential for large-scale implementation, including the use of model-based approaches in low-productivity districts.
Horticulture; Sampling design, Area and production estimation; Stratified multistage sampling; CHAMAN; Field testing.
Pan-Genomics in Fruit Crops: Current Progress, Challenges and Future Directions
Pan-genomics captures the full spectrum of genetic diversity within plant species and overcomes the limitations of single reference genomes. By integrating core, dispensable, and unique genomic components, pan-genomic approaches enable comprehensive detection of structural variants, presence–absence variations, and lineage-specific genes that underpin phenotypic diversity, domestication, and environmental adaptation. In plant and horticultural crops, particularly tropical and subtropical fruit species characterized by high heterozygosity, polyploidy, and clonal propagation, pan genomics has revealed extensive hidden variation influencing fruit quality, disease resistance, and stress tolerance that was previously inaccessible using traditional genomics. This review synthesizes the concepts, methodological advances, computational tools, and recent progress in plant pan-genomics, with a focused emphasis on tropical and subtropical fruit crops. We discuss pan-genome construction strategies, graph-based representations, and emerging resources, alongside key applications in trait discovery, evolutionary analysis, and molecular breeding. Current challenges, including limited accession diversity, incomplete functional annotation, and computational complexity, are critically examined. Finally, we highlight future directions toward population-scale, graph-based, and multi-omics–integrated pan-genomes, underscoring their potential to accelerate precision breeding and support the development of climate-resilient, high-quality fruit crop varieties.
Pan-genomics; Tropical and subtropical fruit crops; Structural variation; Presence–absence variation (PAV); Graph-based genomes; Crop wild relatives; Molecular breeding; Genomic diversity; Long-read sequencing; Climate-resilient horticulture.
Hybrid Deep Learning with Denoising Stacked Autoencoders for Agricultural Price Forecasting
The complex nature of agricultural price data, characterized by perishability, seasonality, and non-stationarity, often renders conventional forecasting models inadequate. To address these challenges, this study proposes two hybrid learning frameworks: Stacked Autoencoder-based Deep Learning (SAE-DL) and Denoising Stacked Autoencoder-based Deep Learning (DSAE-DL). These models represent a significant departure from existing deep learning (DL) models, such as Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), 1D Convolutional Neural Network (1D CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Unlike these standard models, which process raw input directly and often struggle with data volatility, our proposed hybrid approach integrates an autoencoder bottleneck to perform hierarchical feature extraction and dimensionality reduction prior to forecasting. The SAE-DL model focuses on capturing the underlying compressed representation of the price series, while the DSAE-DL introduces a stochastic denoising mechanism to specifically reconstruct core features from noisy data and outliers. Empirical analysis using a real-world onion price dataset reveals that the proposed DSAE-DL models, particularly the DSAE-RNN configuration, consistently outperform all other models across metrics, including RMSE, MAE, and MAPE. The Diebold-Mariano (DM) test further confirms that both proposed hybrid frameworks, SAE-DL and DSAE-DL, significantly improve prediction accuracy compared to standalone DL models. These findings highlight the superior robustness of hybrid autoencoder-based architectures in contending with market volatility.
Agricultural Price; Deep learning; Machine learning; Forecasting; Time series data
Improved Crop Yield Forecasting using Wavelet-Based Denoising and Artificial Neural Networks Framework
Agricultural crop yields are often non-stationary, noisy and influenced by multiple environmental factors, which limits the effectiveness of traditional statistical models. Advanced hybrid approaches that can capture both linear and non-linear patterns, along with multi-scale characteristics, are required for reliable forecasting. This study aimed to develop and evaluate a hybrid wavelet-based forecasting framework by integrating the Maximal Overlap Discrete Wavelet Transform (MODWT) with Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) models for predicting crop yield series of Total Pulses, Gram, and Tur in India. Annual yield data from 1950 to 2023 were analysed using a hybrid modelling framework. The MODWT was applied to decompose the original time series into multi-resolution components, followed by denoising through thresholding techniques. The decomposed components were then modelled using ANN and ARIMA. Optimal wavelet filters and decomposition levels were obtained based on predictive performance. The results showed that hybrid wavelet-based models significantly outperformed standalone ARIMA and ANN models. This study contributes to the literature by systematically optimizing wavelet filters and decomposition levels within a hybrid modelling framework and demonstrates the effectiveness of combining wavelet-based denoising with machine learning and statistical models for non-stationary agricultural time series data.
Hybrid model; Neural network; Pulse yield;Wavelets and volatility.
Fuzzy Classification Tree Modelling with Application in Agricultural Ergonomics
Classification in agricultural systems are quite useful for planning for which decision trees like Classification And Regression Trees (CART) can be used effectively. Additionally, data in reality involves fuzziness that necessitates development of CART which can handle them. As against crisp boundaries between which elements are members and non-members of a particular set, fuzzy set theory offers degree of membership anywhere between 0 and 1 to each set element. Fuzzy based CART has been dealt with in this study considering agricultural ergonomics data with response variable taking levels as presence/ absence of discomfort for labourers during farm operation. The associated variables were both categorical: farm machinery load, operation modes, percent aerobic capacity of farm labourers and continuous: difference between working/ resting heart rates, oxygen consumption during farm operation. The data was divided into training and test sets for model building and validation respectively. Membership function for each variable has been defined with the aid of linguistic variables, using which all the variables were fuzzified. The conventional CART was obtained and complexity parameter was used to prune the tree. The set of ‘if-then’ rules obtained from this CART formed the knowledge base for the fuzzy inference system (FIS) to build conventional Fuzzy CART. The inputs were given to FIS and the outputs for making decisions were defuzzified into crisp values indicating the degree of discomfort. For comparison, those values greater than 0.5 were taken as presence of discomfort, absence otherwise. As an improvement to this Fuzzy CART, the bias due to simultaneous selection of the split variable and the split point in CART was overcome by using separate selection procedures, while other steps remained the same. This proposed Fuzzy CART model was found to be outperforming the existing CART approaches viz., conventional CART, a certain modified CART (method obtained by earlier workers wherein separate selection procedures for split variable and split point as mentioned above were employed in conventional CART) and Fuzzy CART methods when the results were compared using the correct classification rate. Even though model based logistic regression outperformed the proposed Fuzzy CART, the latter has many advantages over the former. Thus it has been demonstrated that Fuzzy CART can be used as a viable alternative for classification purposes in agricultural domain.
ANOVA F statistic; Correct Classification Rate; Dalenius and Hodges stratification; Fuzzification; Logistic regression; Pearson’s Chi square statistic; Rule base; Split variable/split point selection; Triangular membership function.
Low Volatility Optimal Portfolio Selection: Financial Experiments with Mixture Designs
This article assesses the performance of various stock market portfolios using a space-filling mixture design and a model for the logratios in the portfolio allocation. Traditional portfolio analysis relies on historical correlations between various returns and adopts various optimization models. However, these methods rely excessively on the assumptions made and often tend to ignore the statistical variability in their optimization procedures. As a result, they may not perform well when implemented on independent future data. Additionally, the constraints and bounds imposed play a crucial role in determining the feasible region and hence the optimal value therein. By integrating a systematic simplex design approach for exploring component mixtures in our portfolio and applying a logratio transformation, we offer a robust framework for analyzing the portfolios and exploring how various portfolios perform relative to each other. An advantage of our method is that we are able to estimate the standard error for each portfolio, and this allows us to incorporate the consideration of volatilities into our decision making. Using publicly available stock market data, we demonstrate the effectiveness of our approach.
Financial Experiments; Mixture designs; Portfolio optimization; Space-filling Designs. AMS Subject Classifications: 62K20, 62K99, 62P05
Application of Advanced Statistical Techniques for Horticultural Crops Research: Present Status and Future Prospects
Statistical methods play a vital role in different areas of horticultural crops research right from planning of any research experiment till selection of appropriate tool for data analysis to unravel many of the hidden results. They are central to horticultural research, enabling robust analysis of complex research issues entailing multi-disciplinary fields such as crop improvement, production, protection, post-harvest management, climate resilience, and socio-economic studies. The present communication, while delineating several of such multidisciplinary research outcomes, it also provides a road map for its potential application in emerging areas of research. Further, detailed descriptions of the statistical methodologies employed are referenced to the original sources for readers seeking methodological specifics.
: Carbon Sequestration; Crop Modelling; Crop-varietal release; Horticulture; Machine learning models; Mass transfer kinetics.
Beyond Calories: Understanding India’s Changing Nutritional Landscape
India has achieved substantial progress in agricultural production and food availability over recent decades. However, a steady decline in reported per capita calorie intake across successive National Sample Survey (NSS) rounds has raised concerns, particularly as calorie intake remains a key indicator in global hunger assessments. This apparent paradox has led to questions about whether declining calorie consumption reflects worsening nutritional status or broader structural changes in the population. This paper examines trends in calorie intake alongside shifts in dietary patterns, Recommended Dietary Allowances (RDA), minimum dietary energy requirements (MDER), and evidence from national surveys such as NNMB and NFHS. Over time, RDA revisions indicate reduced energy requirements due to declining physical activity, urbanization, and mechanization. Concurrently, diets have diversified, moving away from cereal heavy consumption toward increased intake of milk, fats, fruits, and animal-source foods. Data show a reduction in the proportion of households consuming below 1800 kcal per capita per day. Moreover, a notable share of individuals reporting lower calorie intake exhibit overweight, obesity, hypertension, and dyslipidaemia, suggesting that lower intake may reflect conscious dietary moderation rather than deprivation. Anthropometric trends further support this, with improvements in adult BMI profiles and reductions in child undernutrition. The findings indicate that declining calorie intake in India should not be interpreted in isolation as evidence of rising hunger. Instead, it reflects changing energy needs, dietary transitions, and health-driven behavioural shifts. Calorie-based indicators alone are inadequate; a multidimensional approach incorporating dietary quality, anthropometry, metabolic health, and access is essential for accurately assessing nutritional status.
: Body Mass Index; Calorie intake; Global Hunger Index; Minimum dietary energy requirement; Recommended Dietary Allowance.
A Quantitative Analysis of the Research Impact of India with Globe in Health and Nutrition: Facets and Way Forward
As per the recent report of the Global Hunger Index (GHI), India falls into the serious category. GHI uses the composite index of four indicators, namely undernourishment, child stunting, child wasting, and Under-five Mortality Rate. Hence, it becomes imperative to understand these four dimensions that are closely related to health and nutrition. The present study uses a quantitative approach for a deeper understanding of the research impact of India with Globe in the field of Health and Nutrition. While assessing the impact, we relied on the top 100 cited papers in the field, and applied prompt-based theme clustering to reveal the domains of primary and secondary features. Our analysis provides the demonstrative evidence that the level of evidence and ratings of studies in health and nutrition from India resonate well with the global standards. A set of five and twelve themes emerged from the analysis, respectively for the Globe and India across four population settings. More such studies are required to explore the unexplored terrain in health and nutrition, especially the cognitive and social determinants.
Health and nutrition; analysis; clustering; themes; research impact; publish or perish; cited.
Artificial Neural Networks Models for the Classification of Genotypes for Rice Yield
Artificial neural networks, or ANNs, are widely used in many scientific fields like classification, image processing, identification, and prediction. This research was done at the SKUAST Jammu for classification of 140 rice varieties on the basis of their yield. Classification was made in terms of 9 morphological characters such as yield per plant, number of days for 50percent flowering, number of days for full flowering, plant height, number of effective tillers per plant, panicle length, grain length, grain width and ratio of grain length & grain width. The yield per plant was grouped into three categories and used as the response variable, while the remaining traits served as predictor variables. The rice genotypes were then classified according to neural networks (NN) methods Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The ability measures of classification such as Accuracy Rate, Kappa Statistics, Average Precision, Average Recall and F1 Score were used for testing samples. It is observed that RBF NN performed better than MLP NN for different classes of yield on the basis of classification ability measures. The variable effective number of tillers per plant found out to be important variable as per RBF NN whereas plant height was important variable in case of MLP NN.
Classification; Rice; Artificial Neural Network; Multilayer Perceptron; Radial basis Function; Classification ability measures.
Some Competing Accelerated Life Testing Models with a Bayesian Perspective*
Several competing accelerated life testing models have been studied for modeling accelerated failure times by many authors (see Meeker and Escobar, 1998). This article briefly reviews several aspects and properties of some competing accelerated life testing models namely the log-normal, the inverse Gaussian and the gamma models which are widely recognized in reliability theory. In these situations, modeling of the mean time to failure is modeled as a function of the stress variable, while assuming other parameter(s) to be constant. In this paper we considered the modeling of coefficient of variation also as a function of the stress variable, that has not been considered in the literature, in the best knowledge of the authors. We propose a Bayesian approach for finding a point estimate as well as credible interval estimate for the coefficient of variation under the assumption that the data are drawn from one of the above mentioned distributions
: Inverse Gaussian Model; Log-normal Model; Gamma Model; Coefficient of Variation; Bayesian Approach
Sequence Alignment and Mapping Algorithms used in Big Data Bioinformatics
The rapid evolution of next-generation sequencing (NGS) technologies has transformed genomics into a data-intensive science, generating unprecedented volumes of sequencing data and creating major computational challenges for efficient read mapping and alignment. This review focuses on recent advances in mapping and alignment algorithmic strategies that have emerged in response to increasing sequencing throughput, longer read lengths, and the growing demand for rapid and accurate genomic analyses. Special emphasis is placed on lightweight and sketch-based approaches, including MinHash and HyperLogLog, which enable efficient similarity estimation and large-scale dataset comparison, particularly in metagenomic applications. Key indexing and compression techniques such as the Burrows–Wheeler Transform (BWT), FM-Index, and Locality Sensitive Hashing (LSH) are critically discussed for their roles in reducing memory requirements and accelerating alignment while maintaining high sensitivity and tolerance to mismatches. Alignment tools are compared based on essential performance metrics, including mapping sensitivity, proportion of appropriately paired reads, computational time, memory usage, and robustness in handling tandem repeat-rich reads. This review synthesizes recent methodological innovations aimed at addressing big-data challenges in bioinformatics and provides insights to support the development of next-generation hybrid aligners that integrate emerging sketching and indexing paradigms for scalable and accurate genomic analysis.
BWT; FM-Index; HyperLogLog; MinHash and LSH
Multi-objective Crop Plan for Optimal Groundwater using Non-dominated Sorting Genetic Algorithms
Agriculture plays an exceedingly pivotal role in the future growth and development of a nation, relying heavily on its water and land resources. To achieve maximum agricultural productivity, it is crucial to ensure timely and adequate supply of both land and water for irrigated farming. This necessitates precise planning and execution of water resource management, coupled with the incorporation of modern technologies to optimize the utilization of available resources. A significant task in economic and sustainable agricultural decision-making involves optimizing resource constraints within a given planting period. Considering the socioeconomic conditions, the present study tackles a multi-objective optimization problem to identify an optimal crop plan that simultaneously maximizes profit while minimizing groundwater usage. In single-objective optimization problems, determining the superior solution was straightforward by comparing objective function values, whereas in multi-objective optimization, dominance criteria are employed for determination. The study focuses on the genetic algorithm based NSGA-II and NSGA-III algorithms, comparing their performance and effectiveness through result analysis. Additionally, graphical comparisons are presented to showcase the Pareto fronts. The findings indicate that NSGA-III is a more viable tool for addressing optimal crop planning problems compared to NSGA-II. problem. ARPN Journal of Engineering and Applied Sciences, 11, 4079-4086. Kuo, S.F., Merkley, G.P., and Liu, C.W. (2000). Decision support for irrigation project planning using a genetic algorithm. Agricultural Water Management, 45(3), 243-266. 275 Vafaeinejad, A. (2016). Cropping Pattern Optimization by Using of TOPSIS and Genetic Algorithm Based on the Capabilities of GIS. Iranian journal of Ecohydrology, 3(1), 69-82. Van Veldhuizen, D.A., and Lamont, G.B. (2000). On measuring multiobjective evolutionary Lalehzari, R., Boroomand Nasab, S., Moazed, H., and Haghighi, A. (2016). Multiobjective management of water allocation to algorithm performance. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00, 1, 204-211, IEEE. sustainable irrigation planning and optimal cropping pattern. Journal of Irrigation and Drainage Engineering, 142(1), 05015008. Mansourifar, M., Almassi, M., Borghaee, A.M., and Moghadassi, R. (2013). Optimization crops pattern in variable field ownership. World Applied Sciences Journal, 21(4), 492-497. Marko, O., Pavlović, D., Crnojević, V., and Deb, K. (2019). Optimisation of crop configuration using NSGA-III with categorical genetic operators. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. Márquez, A.L., Baños, R., Gil, C., Montoya, M.G., Manzano‐Agugliaro, F., and Montoya, F.G. (2011). Multi‐objective crop planning using pareto‐based evolutionary algorithms. Agricultural Economics, 42(6), 649-656. Mwiya, R.M., Zhang, Z., Zheng, C., and Wang, C. (2020). Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty. Sustainability, 12(18), 7694. Nath, K., Jain, R., Marwaha, S., Roy, H.S. and Arora, A. (2020). Identification of optimal crop plan using nature inspired metaheuristic algorithms. Indian Journal of Agricultural Sciences, 90(8), 1587-92. Olakulehin, O.J., and Omidiora, E.O. (2014). A genetic algorithm approach to maximize crop yields and sustain soil fertility. Net Journal of Agricultural Science, 2(3), 94-103. Oluwole, A.A., Oludayo, O.O., and Josiah, A. (2014). A comparative study of state-of-the-art evolutionary multi-objective algorithms for optimal crop-mix planning. International Journal of Agricultural Science and Technology, 2(1), 1-9. Pal, B.B., Chakraborti, D., and Biswas, P. (2009). A genetic algorithm based hybrid goal programming approach to land allocation problem for optimal cropping plan in agricultural system. In International Conference on Industrial and Information Systems (ICIIS). Raju, K.S., Vasan, A., Gupta, P., Ganesan, K., and Mathur, H. (2012). Multi-objective differential evolution application to irrigation planning. ISH Journal of Hydraulic engineering, 18(1), 54-64. Sadati, S.K., Speelman, S., Sabouhi, M., Gitizadeh, M., and Ghahraman, B. (2014). Optimal irrigation water allocation using a genetic algorithm under various weather conditions. Water, 6(10), 3068 3084. Srinivas, N., and Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248. Sarker, R., and Ray, T. (2009). An improved evolutionary algorithm for solving multi-objective crop planning models. Computers and electronics in agriculture, 68(2), 191-199. Sarma, A.K., Misra, R., and Chandramouli, V. (2006). Application of genetic algorithm to Opsearch, 43(3), 320-329. determine optimal cropping pattern. problem. ARPN Journal of Engineering and Applied Sciences, 11, 4079-4086. Kuo, S.F., Merkley, G.P., and Liu, C.W. (2000). Decision support for irrigation project planning using a genetic algorithm. Agricultural Water Management, 45(3), 243-266. 275 Vafaeinejad, A. (2016). Cropping Pattern Optimization by Using of TOPSIS and Genetic Algorithm Based on the Capabilities of GIS. Iranian journal of Ecohydrology, 3(1), 69-82. Van Veldhuizen, D.A., and Lamont, G.B. (2000). On measuring multiobjective evolutionary Lalehzari, R., Boroomand Nasab, S., Moazed, H., and Haghighi, A. (2016). Multiobjective management of water allocation to algorithm performance. In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00, 1, 204-211, IEEE. sustainable irrigation planning and optimal cropping pattern. Journal of Irrigation and Drainage Engineering, 142(1), 05015008. Mansourifar, M., Almassi, M., Borghaee, A.M., and Moghadassi, R. (2013). Optimization crops pattern in variable field ownership. World Applied Sciences Journal, 21(4), 492-497. Marko, O., Pavlović, D., Crnojević, V., and Deb, K. (2019). Optimisation of crop configuration using NSGA-III with categorical genetic operators. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. Márquez, A.L., Baños, R., Gil, C., Montoya, M.G., Manzano‐Agugliaro, F., and Montoya, F.G. (2011). Multi‐objective crop planning using pareto‐based evolutionary algorithms. Agricultural Economics, 42(6), 649-656. Mwiya, R.M., Zhang, Z., Zheng, C., and Wang, C. (2020). Comparison of Approaches for Irrigation Scheduling Using AquaCrop and NSGA-III Models under Climate Uncertainty. Sustainability, 12(18), 7694. Nath, K., Jain, R., Marwaha, S., Roy, H.S. and Arora, A. (2020). Identification of optimal crop plan using nature inspired metaheuristic algorithms. Indian Journal of Agricultural Sciences, 90(8), 1587-92. Olakulehin, O.J., and Omidiora, E.O. (2014). A genetic algorithm approach to maximize crop yields and sustain soil fertility. Net Journal of Agricultural Science, 2(3), 94-103. Oluwole, A.A., Oludayo, O.O., and Josiah, A. (2014). A comparative study of state-of-the-art evolutionary multi-objective algorithms for optimal crop-mix planning. International Journal of Agricultural Science and Technology, 2(1), 1-9. Pal, B.B., Chakraborti, D., and Biswas, P. (2009). A genetic algorithm based hybrid goal programming approach to land allocation problem for optimal cropping plan in agricultural system. In International Conference on Industrial and Information Systems (ICIIS). Raju, K.S., Vasan, A., Gupta, P., Ganesan, K., and Mathur, H. (2012). Multi-objective differential evolution application to irrigation planning. ISH Journal of Hydraulic engineering, 18(1), 54-64. Sadati, S.K., Speelman, S., Sabouhi, M., Gitizadeh, M., and Ghahraman, B. (2014). Optimal irrigation water allocation using a genetic algorithm under various weather conditions. Water, 6(10), 3068 3084. Srinivas, N., and Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248. Sarker, R., and Ray, T. (2009). An improved evolutionary algorithm for solving multi-objective crop planning models. Computers and electronics in agriculture, 68(2), 191-199. Sarma, A.K., Misra, R., and Chandramouli, V. (2006). Application of genetic algorithm to Opsearch, 43(3), 320-329. determine optimal cropping pattern.
Crop planning; Genetic algorithm; NSGA; Optimization; Pareto optimal.solution.
A New Series of Optimal General Efficiency Balance (GEB) Designs with Lesser Number of Experimental Units
An attempt has been made to construct a new series of binary GEB block designs with lesser number of experimental units. The designs are developed by using group divisible (GD) designs. The canonical efficiency factors (CEF) of each design are listed along with A, D and E optimality efficiency values. The designs require much lesser experimental units than existing BIB designs or variance balanced (VB) designs for a particular number of treatments. This article also deals with optimality criteria of balanced block designs. A list of GEB designs with their corresponding A, D and E optimal efficiency values are presented with lesser number of experimental units (n). The list shows that some GEB designs have more than 90% either A, or D or E efficiency values, which may confirm that only traditional parameters (v, b, and k) are not always acceptable for selection of a better cost-effective design.
BIB Designs; General Efficiency Balance (GEB) Block Designs; Group Divisible (GD) design; Optimality of Block Designs; Variance Balanced Designs.
Calibration-Enhanced Estimation of Population Mean with Multi-Auxiliary Information in Stratified Sampling
Calibration estimation plays an important role in improving the accuracy of population parameter estimates using available auxiliary information. In this paper, we propose two calibration estimators of the population mean using multi-auxiliary variables under stratified random sampling. These suggested calibration estimators incorporate the coefficients of variation of multi-auxiliary variables. The performance of the recommended estimators was compared with the estimators given by Rao et al. (2012) and Garg and Pachori (2020) through a simulation study in R software. The results showed that the recommended estimators are more effective and precise than the existing estimators under a stratified random sampling scheme.
Auxiliary variable; Calibration estimation; Coefficient of variation; Mean; Mean Squared Error; Stratified sampling.
Applications of Neutrosophic Statistics in Analysing Systematic Random Sample Survey Data
Systematic random sampling method has been extensively used to select samples when populations are defined across time and space and when sampling frame is not determined. This method is preferred for its ease of implementation and simple procedure, low budget considerations, convenience of sample selection and also, even when sampling frame is not available. In this paper, we aim to present different types of systematic sampling methods for the estimation of population mean/total. For illustration purpose, we have considered the real data examples from the practical applications and have analyzed them using neutrosophic logic, which takes into account the indeterminate or imprecise or missing data information.
Neutrosophic logic and math; Linear systematic sample; Circular systematic sample; Survey data analysis.