Ranjit Kumar Paul

• PH.D
• Researcher at Indian Agricultural Statistics Research Institute

The Seasonal Autoregressive Integrated Moving Average (SARIMA) model has gained popularity since its inception due to its ability to forecast seasonality. Usually, the SARIMA model captures the seasonality but does not consider the effect of the exogenous variable(s) in the seasonality process. Hence, this study aims to empirically introduce and im...

Forecasting agricultural commodity prices has been a long‐standing challenge for researchers and policymakers. The diverse behaviors exhibited by price of different commodities, ranging from the high volatility, nonlinearity, and complexity of vegetables to the lower volatility and linear patterns of cereals. This different pattern necessitates the...

In the lush tea estates of Northeast India, the growth of the tea plant (Camellia sinensis L.) hinges on the intricate dance of soil properties. This present study delves into the soil characteristics of sixteen tea estates, situated spanning the verdant landscapes of Assam and the enchanting Darjeeling hills in West Bengal. We meticulously analyze...

The unexpected fluctuation of prices of agricultural commodities may have impactful repercussions on the producers. The price volatility can be modeled by applying time series analysis. A time series consists of linear and non-linear components. The linear component can be modeled by the autoregressive integrated moving average (ARIMA) methodology....

The forecasting of time series continues to be a prominent area of interest among researchers exploring advanced learning techniques. In recent times, deep recurrent neural networks, particularly long short-term memory (LSTM) models, have demonstrated exceptional forecasting capabilities compared to other neural network architectures. To tackle the...

Price volatility in agricultural commodities adds uncertainty for farmers, traders, and other stakeholders in the agricultural supply chain. Sudden price changes can disrupt income predictions, making it difficult to plan and access financing. This affects not only the farming community but also rural economies and livelihood of people involved in...

The present study proposes trivariate-autoregressive moving average�generalized autoregressive conditional heteroscedasticity (TV-ARMA–GARCH) type Vine Copula model. The algorithm for generating a one-step-ahead forecast has been developed based on simulation. Detailed analysis of price volatility for three major vegetable crops across three geo...

This study provides insights into the trends and composition of India’s agricultural exports, their competitiveness, export destinations, and potential opportunities for expansion. Nonetheless, the country faces several challenges related to quality standards, sanitary and phytosanitary measures, and non-tariff barriers to enhance its standing in t...

Oilseed prices are inherently volatile and uncertain, making accurate predictions is important for the stakeholders. In time series forecasting, fuzzy techniques have proven effective for managing complex and uncertain datasets. This study introduces an innovative approach to predicting oilseeds prices by developing intuitionistic fuzzy based machi...

Time series prediction often faces challenges due to hidden patterns and noise within the data. This paper presented a novel algorithm that combines wavelet decomposition with long short-term memory (LSTM) networks, providing a distinct method for handling these challenges. The study considered the monthly rainfall data (mm) of India from January 1...

Accurate prediction of agricultural prices is crucial due to their complex and nonlinear nature. Due to the perishable nature of TOP (Tomato, Onion and Potato) vegetable produce, price fluctuates based on supply and demand. It is necessary to forecast harvest period TOP prices, so growers can make informed production decisions and also farmers can...

This study explores the dynamics and performance of different time series models, such as stochastic, machine learning and deep learning models. The need to investigate different techniques in the process of selecting the best model for forecasting area and yield of cereal crops in India and thereby yielding efficient predictions is demonstrated in...

Climate change has a considerable influence on agricultural output, raising farmers’ production risk. Nevertheless, the risk can be mitigated by selecting stable genotypes. In countries such as India, where significant proportions of farmers are smallholders or operate on marginal land, the minimization of risk is of paramount importance. Existing...

Managing the high volatility in food prices is a significant policy challenge in food-deficit developing countries. High food prices affect food consumption, especially for the poor, who spend a sizable proportion of their income on food. On the other hand, producers benefit from the rising prices but not necessarily from the high price volatility....

Time series modeling of the price of agricultural commodities has immense importance in the Indian agricultural landscape. Volatility is an intrinsic property of time series. If positive and negative shocks of the same scale have differing effects on it, it is said to be asymmetric. The volatility of any time series is said to have long-term persis...

Forecasting of tropical cyclones helps the coastal communities to prepare and minimize the damage. Despite numerous studies, the inherent complexity and non-linear nature of cyclones pose challenges for achieving accurate forecasts. The versatility and effectiveness of ensemble forecasting techniques make them well-suited for cyclonic data. This st...

Volatility is a matter of concern for time series modeling. It provides valuable insights into the fluctuation and stability of concerning variables over time. Volatility patterns in historical data can provide valuable information for predicting future behaviour. Nonlinear time series models such as the autoregressive conditional heteroscedastic (...

Artificial Intelligence (AI) research in agriculture is examined in this study using bibliometric analysis on a dataset of 439 articles from the SCOPUS Elsevier database. The findings reveal a notable surge in AI research in agriculture, especially during and after the Covid-19 pandemic. The terms, 'cloud analysis' highlights central themes like ma...

Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guide...

Accurate prediction of time series data is crucial for informed decision-making and economic development. However, predicting noisy time series data is a challenging task due to their irregularity and complex trends. In the past, several attempts have been made to model complex time series data using both stochastic and machine learning techniques....

This paper deals with study of exposure of Karnataka state to climate change for a period 1979-2019. The Mann Whitney Pettit’s homogeneity test (MWP) was analysed for 240 data sets for monthly data of minimum (MTmin) and maximum temperature (MT max) across ten agro climatic zones) to estimate the year of structural break or year of shift in mean mo...

Due to the changing climate and frequent occurrence of extreme events, farmers face significant challenges. Precise rainfall prediction is necessary for proper crop planning. The presence of nonlinearity and chaotic structure in the historical rainfall series distorts the performances of the usual prediction models. In the present study, algorithm...

In a normal situation onion prices vary in a highly unprecedented way in India. So, it is worth noticing the effect of an uncertain situation on onion prices. In this article an efficient Artificial Intelligence (AI) tool, i.e., Support Vector Regression (SVR) has been used to predict the price fluctuation of onion over the lockdown period, unlock...

Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of predictive models. Algorithms based on a combination of wavelet with deep learning, machine learni...

Big data analysis, with its capability to process large and diverse datasets, offers innovative solutions for sustainable agricultural practices. By leveraging IoT devices for real-time data collection from fields, soil, plants, and machinery, coupled with cloud-based integration of additional information like weather data, analysts may derive impo...

The volatility in prices of agricultural commodities is a major concern for policymakers, researchers, and value chain participants, including farmers. The study examines the trend and pattern of agricultural price volatility and its seasonality using monthly data from January 2010 to December 2022. The fixed effects model has been used to decipher...

A primary survey during the year 2021-22 was carried out among the 240 farmers of Bidar and Gulbarga districts of North Eastern Transition Zone in Karnataka to study the farmer’s perception on climate change for the period 1979 to 2019 and validate their opinions with the change in the meteorological indicators. About 74 percent of farmers expresse...

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 indic...

In this study, we analysed the trend, composition and dynamics of India's comparative advantage in agricultural exports from 2001 to 2021 using data from the INTRACEN database for seven agricultural commodities , namely rice, crustaceans, bovine meat, cotton, pepper , cane sugar and tea. The contribution of agricultural exports to the agricultural...

One of the primary goals of time series (TS) modeling is to forecast future observations. Although point forecasts are the most common type of prediction, interval forecasts are more informative and are typically obtained as prediction intervals (PIs). For non-linear TS data, the ARCH model is one of the widely used models. The Sieve Bootstrap meth...

Efficient water management through farm pond technology is a great initiative by Krishi Bhagya Yojana scheme in 2015. Out of 240 sample farmers, about 180 farmers are adopters and 60 are non adopters of farm pond technology in Bidar and Gulbarga districts of Karnataka. Majority of farmers prefer farm pond of size 30 m × 30 m × 3 m as during Kharif...

Advancements in high-throughput technologies, genomics, transcriptomics, and metabolomics play an important role in obtaining biological information about living organisms. The field of computational biology and bioinformatics has experienced significant growth with the advent of high-throughput sequencing technologies and other high-throughput tec...

The effects of human activities are becoming clearer every year, with multiple reports of struggling and eroded ecosystems resulting in new threats of plant and animal extinctions throughout the world. It has been speculated that roadside tea-growing soils impact on metal dynamics from soil to tea plants and subsequently to tea infusion which may b...

Volatility is an important characteristic of time series. If the volatility of a series at any time epoch is affected by its distant counterpart, then it is known as long memory in volatility. The (FIGARCH) model is useful for addressing the long memory in volatility. In this paper, for empirical illustration, the daily modal spot price of mustard...

Ensemble forecasts from multiple models have gained enormous popularity as it provides a more efficient forecast as compared to the individual counterpart. The linear weighted combination method is most widely utilized for its simplicity and efficiency. Despite hard efforts by various researchers, two considerable challenges still exist: (1) System...

Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most s...

Seasonal production, weather abnormalities, and high perishability introduce a high degree of volatility to potato prices. Price volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it in a dissimilar way. GARCH is a symmetric model, and it cannot capture asymmetric price volatility. EGARCH, APARCH, and...

Vegetables are the staple food in our diets. Vegetable prices are difficult to forecast because they are influenced by a variety of factors, including weather, demand and supply chain, Government policies, etc. and exhibit volatile fluctuations. Marketing of vegetables is complex, especially because of their perishability, seasonality and bulkiness...

The onset of the novel Coronavirus (COVID-19) has impacted all sectors of society. To avoid the rapid spread of this virus, the Government of India imposed a nationwide lockdown in four phases. Lockdown, due to COVID-19 pandemic, resulted a decline in pollution in India in general and in dense cities in particular. Data on key air quality indicator...

The methods used for forecasting financial series are based on the concept that a pattern can be identified in the data and distinguished from randomness by smoothing past values. This smoothing process eliminates randomness from the data, enabling the inherent pattern to be used for forecasting. However, acquiring high frequency national accounts...

Four filters have been chosen namely 'haar', 'c6', 'la8', and 'bl14' (Kindly refer to 'wavelets' in 'CRAN' repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results...

Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. Th...

Helps to describe a data frame in hand. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>. Has been developed during PhD work of the maintainer

Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature...

A novel method for rainfall forecasting has been proposed using Multi Resolution Analysis (MRA). This approach decomposes annual rainfall series and long-term climate indices into component sub-series at different temporal scales, allowing for a more detailed analysis of the factors influencing annual rainfall. Multiple Linear Regression (MLR) is t...

To investigate the interdependence between Indian onion markets in terms of price volatility, the present study was conducted in four different vital onion markets in India, viz. Mumbai, Nashik, Delhi and Bengaluru. The long term monthly data, from March, 2003 to September, 2015 was collected from the website of agmarknet.nic.in. We have employed t...

In plant and animal breeding, sometimes observations are not independently distributed. There may exist a correlated relationship between the observations. In the presence of highly correlated observations, the classical premise of independence between observations is violated. Plant and animal breeders are particularly interested to study the gene...

Forecasting price volatility of agricultural commodities has immense importance nowadays. The use of traditional parametric model in capturing volatility in price series has been found to be inefficient. In this context, machine learning (ML) technique like support vector regression (SVR) may be applied to improve accuracy of forecasting. In the pr...

In this paper, stock price data has been predicted using several state-of-the-art methodologies such as stochastic models, machine learning techniques, and deep learning algorithms. An efficient decomposition method resonating with these Machine Intelligence (MI) models has been embedded with boosting ensemble method. Finally a Model Confidence S...

CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In th...

This study analysed the current scenario of India-EU trade, composition of trade, growth and instability of agricultural commodities during the period 1997-98 to 2021-22. India’s agricultural export to the EU was US $ 4.7 billion and imported US $ 1.3 billion from the EU in 2021-22. Netherlands, Italy and Germany were three most important EU member...

Harnessing the potential yields of evergreen perennial crops like tea (Camellia sinensis L.) essentially requires the application of optimum doses of nutrients based on the soil test reports. In the present study, the soil pH, organic carbon (OC), available potassium as K2O (AK), and available sulphur (AS) of 7300 soil samples from 115 tea estates...

In general, statistical models for estimation of heritability follow certain assumptions, i.e. random components including the error follow a normal distribution and are identically independently distributed. But in the practical situation, sometimes these assumptions are violated. Thus, from the perspective of plant and animal breeding programs, e...

Farmer Producer Organisations (FPOs) have shown to be a beacon of hope for the millions of farmers across India. The present study was conducted in four randomly selected districts of West Bengal namely, Birbhum, Murshidabad, Purba Bardhaman and Nadia. Five high performing and five low performing FPOs were selected from the study area. Further, two...

It is imperative to find suitable strategies to utilize the native soil phosphorus (P), as natural rock phosphate deposits are at a verge of depletion. We explored two such cost-effective and eco-friendly strategies for native soil P solubilization: silicon (Si)-rich agro-wastes (as Si source) and phosphate solubilizing microorganism (PSM). An incu...

A field study was conducted from 0 to 360 days to investigate the effect of tea pruning litter biochar (TPLBC) on the accumulation of major micronutrients (Cu, Mn, and Zn) in soil, their uptake by tea plant (clone: S.3 A/3) and level of contamination in soil due to TPLBC. To evaluate the level of contamination due to TPLBC, a soil pollution assessm...

Insect pest and weather relations analysed using statistical models empower crop pest management through their capacity to forewarn abundance or their damage during season of crop cultivation. Field datasets of eight seasons (2010–2017) of Maharashtra (India) used to study the influence of weather factors lagged by one week on soybean semilooper (C...

The study was conducted to assess the stability of Farmer Producer Organizations (FPOs) and the factors contributing to the stability of FPOs in West Bengal during 2020. Using random sampling procedure, data were collected from 120 farmer members from ten FPOs from four districts of the state namely Birbhum, Murshidabad, Purba Bardhaman and Nadia t...

The COVID-19 pandemic has impacted almost all the sectors including agriculture in the country. The present paper investigates the impact of COVID-19 induced lockdown on both wholesale and retail prices of major pulses in India. The daily wholesale and retail price data on five major pulses namely Lentil, Moong, Arhar, Urad and Gram are collected f...

Agricultural commodity prices, particularly the prices of perishable commodities, are volatile. The interdependency of market prices of agricultural commodities makes it difficult for accurate modelling. In the present study, two variants of multivariate generalized auto-regressive conditional heteroscedastic models, namely DCC and BEKK, have been...

Background Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around...

Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monit...

Purpose – The present study provides evidence on export advantages of horticultural commodities based on competitiveness, trade balance and seasonality dimensions. Design/methodology/approach – The study delineated horticultural commodities in terms of comparative advantage, examined temporal shifts in export advantages (mapping) and estimated seas...

Influence of weather variables on occurrence of spiders in pigeon pea across locations of seven agro-climatic zones of India was studied in addition to development of forecast models with their comparisons on performance. Considering the non-normal and nonlinear nature of time series data of spiders, non-parametric techniques were applied with deve...

Citation: Paul, R.K.; Vennila, S.; Yeasin, M.; Yadav, S.K.; Nisar, S.; Paul, A.K.; Gupta, A.; Malathi, S.; Jyosthna, M.K.; Kavitha, Z.; et al. Wavelet Decomposition and Machine Learning Technique for Predicting Occurrence of Spiders in Pigeon Pea.

It has been observed that most of the agricultural time series data in general and price data in particular are non-linear, non-stationary, non-normal and heteroscedastic in nature. Therefore, application of usual linear and nonlinear parametric models like Autoregressive integrated moving average (ARIMA), Generalized autoregressive conditional het...

The objective of present study was to investigate the efficiency of Autoregressive fractionally integrated moving average model with exogenous input (ARFIMAX) in forecasting price of Indian mustard [Brassica juncea (L.) Czern. & Coss]. The daily modal price and arrival data of mustard for two major markets of India, viz. Bharatpur and Agra were col...

Rainfall data from 1979-2019 was analysed across the agro climatic zones in transition region of Karnataka. Rainfall Anomaly Index and Mean deviation methods are used to identify drought years and wet years over both spatial and temporal on time scale. The North Eastern Transition Zone can be considered as most vulnerable zone as it recorded for ab...

In the present paper, horizontal and vertical integration was carried out on the wholesale and retail prices of wheat in the major markets of India. On confirming cointegration between the wholesale and retail prices of wheat in all needs, the vector error correction model (VECM) was applied to find the speed of adjustment in the corresponding pric...

The study undertaken to analyse the growth rate performance of area, production, productivity of selected crops in Karnataka from year 1997 to 2019. At state level, it was found that, the productivity of cereals showed positive growth with 1.22 percent. The area under maize increased by 5.30 percent by displacing Jowar, Bajra, minor millets. The ri...

  The Bayesian model was applied for analyzing the first lactation in Karan Fries cattle. First lactation data of production (305-day or less milk yield and daily milk yield) were collected from the history-cum pedigree sheet and daily milk yield registers of the division of Dairy Cattle Breeding (DCB), National Dairy Research Institute (NDRI),...

Effect of tea pruning litter biochar (TPLBC) on arsenic (As), cadmium (Cd) and chromium (Cr) content in made tea and successive tea infusions were investigated in a greenhouse experiment with two tea cultivars (TV23 and S.3A/3). Made tea prepared fromTV23 and S.3A/3 clone, a decrease in the concentration of As, Cd, and Cr by 36.73%, 16.22%, 13.96%,...

The study has examined productivity instability of major crops for 20 years by considering first period as 1998-99 to 2008-09 and second period as 2009-10 to 2018-19 at the district level in Karnataka state. Instability categories were grouped as low < 15%, medium 15-30% ,High >30%. A low value index indicates the high stability in crop productivit...

National Highway may have damaging effect on soil chemical properties. In this study, a scale and assessment of selected chemical properties of tea growing soils with increasing sampling distance from National Highway have been documented. Top and sub soils from ten tea estates surrounding the National Highway of Dibrugrah and Tinsukia districts at...

The study was carried out for ten Agro climatic zones in Karnataka state in India. The temperature and rainfall data were used for analysis from 1979-2019 which is about 40 years. Understanding spatiotemporal rainfall pattern, Rainfall Anomaly Index which is drought indicator technique was used to classify the positive and negative severities in ra...

Accurate forecasting of various phenomenon has got crucial importance in the scenario of Indian agriculture as this helps farmers, policy-makers and government to acquire informed decisions. Agricultural time series datasets are mostly nonlinear, nonstationary, non-normal and heteroscedastic in nature. Though the stochastic model like autoregressiv...

Plant disease has long been one of the major threats to world food security due to reduction in the crop yield and quality. Accurate and precise diagnosis of plant diseases has been a significant challenge. Cost-effective automated computational systems for disease diagnosis would facilitate advancements in agriculture. The objective of this paper...

In recent years, deep learning techniques have become very popular in the field of image recognition and classification. Image-based diagnosis of diseases in crops using deep learning techniques has become trendy in the current scientific community. In this study, a deep convolutional neural network (CNN) model has been developed to identify the im...

Price of onion shows a high degree of volatility. Price volatility is said to be asymmetric when it is affected by positive and negative shocks of same magnitude with different degree. Asymmetric volatility can be captured by asymmetric GARCH type of model such as EGARCH, APARCH and GJR-GARCH. Weekly modal price of onion for Delhi, Lasalgaon and Be...

Presence of long memory in climatic variables is frequently observed. The trend assessment becomes difficult in the presence of long-memory as the usual methods are not capable to take care of this property during trend estimation. In order to estimate the trend in presence of long memory, the non-parametric wavelet method has become popular in the...

The performance of wavelet-based hybrid models using different combinations of wavelet filters was compared to that of other conventional models to model volatility in the onion prices and arrivals at the Lasalgaon market of Maharashtra, which is known to be one of the largest markets in terms of arrivals. Monthly data of more than twenty-three yea...