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Forecasting future observations is one of the main goals of time series modeling. Point forecasts are the most common type of prediction, but interval predictions are more informative, usually obtained as prediction intervals (PIs). The conventional Box-Jenkins PIs perform well when the underlying distributional assumptions hold. To circumvent such adherence, resampling methods such as residual based Bootstrap were one of the approaches for constructing PIs, but these are only suitable when the order of the process is known. Sieve Bootstrap method has the advantage of requiring no such prior knowledge. In this work, two new Sieve Bootstrap methods have been proposed. The performance of the proposed methods for constructing PIs have been compared with the existing Bootstrap methods. One of the proposed methods, namely, predictive residual based rescaled Sieve Bootstrap (PRSB) has been found to outperform the other proposed predictive residual based Sieve Bootstrap (PSB) and also the existing methods considered. PSB method has been found to be superior to Box-Jenkins and existing Sieve Bootstrap methods. The proposed methods are not only an improvement over the existing predictive residual based Bootstrap methods in AR(p) set up but also have been extended to be applicable in ARMA(p, q) situations as well. [ABSTRACT FROM AUTHOR]

This study focuses on enhancing classification tree (CT) methodologies, commonly used in diverse planning sectors, by integrating a genetic algorithm (GA) to overcome inherent biases in traditional approaches. Classification and Regression Tree (CART), a widely used tree-based method, often shows a selection bias toward variables with more split options. This occurs due to the simultaneous selection of both split variables and points within the conventional CART approach. To address this issue, the study introduces an alternative approach that combines CART with GA optimization. This hybrid method aims to reduce the selection bias in variable splitting. The effectiveness of this new GA-based CART technique was tested using an agricultural ergonomics dataset from the ICAR-Indian Agricultural Research Institute in New Delhi. A comparative analysis was conducted between the new GA-based CART approach, the standard CART method, and an existing GA-based CART variant. The results demonstrated that the proposed GA-integrated CART approach outperformed the other methods. This indicates its potential as a more accurate and unbiased tool in classification methodologies, especially when dealing with complex, real-world datasets. [ABSTRACT FROM AUTHOR]

Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable choice as they may not adequately account for the complex spatial and temporal dependencies integrated within the data. This demands some data-driven techniques that can handle the intrinsic patterns such as non-linearity, non-stationarity, and non-normality. Space–Time Autoregressive Moving Average (STARMA) models were highly known for its ability to capture both spatial and temporal dependencies, offering a comprehensive framework for analyzing complex datasets. Spatial Weight Matrix (SWM) developed by the STARMA model helps in integrating the spatial effects of the neighboring sites. The study employed a novel dataset consisting of annual rainfall measurements spanning over 50 (1970–2019) years from 119 different locations (grid of 0.25 × 0.25 degree resolution) of West Bengal, a state of India. These extensive datasets were split into testing and training groups that enable the better understanding of the rainfall patterns at a granular level. The study findings demonstrated a notable improvement in forecasting accuracy by the STARMA model that can exhibit promising implications for agricultural management and planning, particularly in regions vulnerable to climate variability. [ABSTRACT FROM AUTHOR]

Zinc (Zn) deficiency in soil is a serious constraint affecting the yield and nutritional quality of wheat and, in turn, human health. Zn fertilization for enhancing its density in grains is a prominent technological solution for the problem. Accordingly, the present study (pot experiment) was undertaken to (i) assess the impacts of different Zn fertilization technologies on yield, concentrations of Zn, phytic acid (PA), iron (Fe) and also the bioavailability of Zn in grains and (ii) determine the optimised Zn fertilization technology that balances all the above attributes. To achieve this, six Zn fertilization technologies, namely, soil fertilization alone, combined soil and foliar fertilization at maximum tillering, jointing, flowering, dough stages and also foliar fertilization alone were tested and compared with control (no Zn) in forty different soil series representing two distinct soil orders, Inceptisols and Alfisols. Results showed that relative effectiveness of different Zn fertilization technologies varied for the crop attributes studied. Soil + foliar fertilization was superior in increasing grain yield (10–13% over the control). Moreover, for an optimum balance among all the tested attributes including bioavailability of Zn to human, foliar Zn fertilization at later crop growth stage (i.e. dough) combined with soil fertilization was the best. It was found that biofortified wheat grains obtained through Zn fertilization, on an average, could supply about 1.5 times more bioavailable Zn than the normal grains. Therefore, the outcomes of this study can provide a guideline for sustainable and quality wheat production, which will help address the malnutrition challenge. [ABSTRACT FROM AUTHOR]

In developing nations such as India, malnutrition is a major nutritional and health challenge. Biofortification has the potential to be an effective instrument in India's attempts to combat malnutrition. Expert opinion must be used to evaluate the factors related to the promotion, distribution and adoption of biofortified crops. The analytical hierarchy process (AHP) is one of the most often employed decision-making methods. However, conventional AHP is incapable of identifying ambiguity in human judgements. Fuzzy AHP has already been devised to overcome this limitation. Fuzzy AHP necessitates information in pairwise comparisons, which is not always easy to gather. In this context, the Fuzzy AHP technique based on the genetic algorithm has been proposed, which can compute the priority weight without using a pairwise comparison matrix by directly dealing with expert-provided data. The proposed approach has been illustrated using the opinions of 1600 farmers from Odisha, India. [ABSTRACT FROM AUTHOR]