Achal Lama

• PhD
• Scientist 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...

This app is designed to analyze time series data, test stationarity and linearity, and apply the best forecasting model (ARIMA, ANN, or SVR).

Accurate rainfall forecasting is crucial in disaster management, agriculture, and hydrological resource planning. Traditional methods for rainfall prediction often face challenges in capturing the complex and nonlinear relationships inherent in meteorological data. In this paper, we proposed a novel multiscale ensemble approach for rainfall forecas...

Efficient nitrogen (N) management plays a critical role in optimizing rice yield and ensuring economic sustainability. This study evaluates the impact of variable nitrogen levels on the yield dynamics and economic viability of transplanted rice (TPR) and direct seeded rice (DSR). Field experiments were conducted in research farm of ICAR-IARI, New D...

Aims: This study investigates the determinants of formal agricultural credit flow at the district level in India, using region-wise fixed effects models. Study Design: Panel data regression technique. Place and Duration of Study: Data from districts across Indian states from 2000 to 2021 were analyzed. Methodology: Districts categorized into high,...

In this research paper, machine learning techniques were applied to a multivariate meteorological time series data for estimating the wheat yield of five districts of Punjab. Wheat yield data and weather parameters over 34 years were collected from the study area and the model was developed using stepwise multi-linear regression (SMLR), artificial...

Agricultural credit plays a vital role in supporting Indian agriculture, and to enhance farmers’ access to formal credit, several reforms have been introduced. This study examines the response of agricultural credit to policy reforms using data on outstanding agricultural credit from scheduled commercial banks in the southern region during 1976-202...

Accurate potato price forecasting is crucial for managing market volatility, optimizing supply chains, and improving decision-making for farmers and policymakers. This study compares the forecasting performance of six models: autoregressive integrated moving average (ARIMA), recurrent neural network (RNN), gated recurrent unit (GRU), long short-ter...

In this study, we proposed a forecasting model for non-linear and non-stationary potato prices by integrating singular spectrum analysis (SSA) with a time delay neural network (TDNN). The price series was initially decomposed into independent components using SSA. Each component was then individually forecasted using TDNN, and the final forecast wa...

Forecasting rainfall is crucial for countries like India where farming is the livelihood for around half of the population and rainfall is their most important water source. The intensity of rainfall varies for different seasons and is not spread evenly across the country. Over the years, different researchers used various statistical models for ra...

Wheat crops are highly affected by the influence of weather parameters. Thus, there is a need to develop and validate weather-based models using machine learning for its reliable prediction. Wheat yield and weather data during the crop growing period were collected from IARI, New Delhi, Hisar, Amritsar, Ludhiana and Patiala. The yield prediction mo...

Wheat crop is highly affected by the influence of weather parameter, adverse weather drastically reduce wheat yield. Thus, there is need to develop and validate weather-based models using machine learning for its reliable prediction at multiple stages. Wheat yield and weather data during crop growing period were collected from IARI, New Delhi, Hisa...

The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tac...

Please cite this article as: A. Lama, S. Ray, T. Biswas et al., Python code for modeling ARIMA-LSTM architecture with random forest algorithm, Software Impacts (2024), doi: https://doi. Abstract: Over conventional statistical models, machine learning mechanisms are establishing themselves as a potential area for modeling and forecasting complex tim...

Nitrogen responses vary under diverse agronomic management practices, influencing vegetation indices (VIs) and productivity across different ecological conditions. However, the proper quantification of these responses under various crop establishment methods with varied nitrogen levels is rarely studied. Therefore, a field experiment was conducted...

This study utilizes time series analysis and machine learning techniques to model and forecast rainfall patterns across different seasons in India. The statistical models, i.e., autoregressive integrated moving average (ARIMA) and state space model and machine learning models, i.e., Support Vector Machine, Artificial Neural Network and Random Fores...

Machine learning mechanism is establishing itself as a promising area for modelling and forecasting complex time series over conventional statistical models. In this article, focus has been made on presenting a machine learning algorithm with special attention to deep learning model in form of a potential alternative to statistical models such as A...

An attempt was made to study the humic acid (HA) quality and clay humus complex in order to generate valuable information regarding soil carbon (C) and recalcitrant carbon variations under conservation agriculture (CA) practices. It is worthwhile to mention that CA has got wider acceptance among researchers and farmers nowadays. A field experiment...

As crop yield is determined by numerous input parameters , it is important to identify the most important variables/parameters and eliminate those that may reduce the accuracy of the prediction models. The feature selection algorithms assist in selecting only those relevant features for the prediction algorithms. Instead of a complete set of featur...

Time series modelling utilizes previous values to forecast the future values. Exponential smoothing is one of the approaches to make forecast as well as to smooth the time series data. Among the various exponential smoothing model, Single Exponential Smoothing (SES) is the most popular model in time series due its simplicity of understanding and im...

Topic discovery is the innovation towards extracting the underlying semantic structure from large collection of unstructured text. It is a convenient way to analyze unclassified text into topic clusters that can be utilized in classification of documents. A topic containsa set of words that frequently occurs together and defines the complete text i...

Conservation agriculture (CA) coupled with integrated crop management (ICM)-practices based on a whole-farm approach could preserve the agroecosystems to achieve the soil-related Sustainable Development Goals (SDGs). Hence, eight conventional and CA-based ICM practices have been evaluated for 6 years in a direct seeded rice-zero till wheat rotation...

Parameters of the EXPARMA model can be estimated using this package. The user is provided with the best fitted EXPARMA model for the data set under consideration.

This article presents the state of art review in the domain of weather based pre-harvest crop yield forecasting models. To begin with, it discusses the various approaches evolved over time for construction of weather indices from weather variables. Owning to complex relationship between crop yield and weather variables, various models have been pro...

This study proposes a novel approach for multi-step time series forecasting using a stacked long-short term memory (LSTM) sequence-to-sequence autoencoder (LSTM-SAE) to handle the volatility of edible oil prices in the Indian market. The approach was implemented on Ruchi Soya Ltd. stock price dataset and compared with other deep learning models lik...

Text Annotation is the process of adding metadata in the text and used in various tasks like natural language processing (NLP) and machine learning models. Named entity recognition (NER) is one of the interesting and challenging tasks of NLP and is being used extensively in many domains. The application of NER will also be useful in handling docume...

Agricultural commodity price is very volatile in nature due to its nonlinearity and nonstationary character. The volatility behaviour of the commodity price creates a lot of problems for both producer and consumer. The steady forecast of the price may reduce the problems and increase the profit for the stakeholders. In this study, an ensemble hybri...

An effective multiplication of dragon fruit by cladode cuttings is most preferred for large scale production of quality planting materials. Therefore, an experiment was carried out under poly house, Faculty of Horticulture, BCKV, Mohanpur, Nadia, West Bengal, India during the months of May-September., 2019 in Factorial Randomized Block Design with...

Accurate and timely price information and forecasting help in making efficient plans and strategies. Non-linearity and non-stationarity behaviour of price data create problems in price forecasting. In this paper, variational mode decomposition (VMD) based optimised genetic algorithm (GA) hybrid machine learning (ML) models have been proposed. The V...

This paper introduces a novel hybrid approach, combining machine learning algorithms with feature selection, for efficient modelling and forecasting of complex phenomenon governed by multifactorial and nonlinear behaviours, such as crop yield. We have attempted to harness the benefits of the soft computing algorithm multivariate adaptive regression...

Regression analysis is one of the most commonly employed statistical tool for analyzing the “cause and effect” relationship. The regression coefficients are tested to find if they are affecting the dependent variable and the sign and the values of the coefficients help us to know how and by much they are affecting the dependent variables. However,...

Wheat being highly affected by the weather, adverse weather drastically reduces the wheat yield. Model was developed for multi stage wheat yield prediction by stepwise multi linear regression (SMLR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO) and hybrid machine learning LASSO-SVR and SMLR-SVR techniques...

For the last two years, countries around the globe have been suffering and severely affected by the Covid-19 pandemic due to the novel coronavirus. Researchers from various disciplines are conducting research and publishing number of articles related to this virus and its effects. Furthermore, articles related to Covid-19 are being continuously pub...

Weather indices are formed from weather variables in this package. The users can input any number of weather variables recorded over any number of weeks. This package has no restriction on the number of weeks and weather variables to be taken as input.The details of the method can be seen (i)'Joint effects of weather variables on rice yields' by R....

The VMDML package helps to fit the Variational Mode Decomposition based different machine learning models. It will also provide you with accuracy measures along with an option to select the proportion of training and testing data sets. Users can choose among the available choices of parameters of variational mode decomposition for fitting the ML mo...

False smut of rice which was considered a minor disease of rice is presently spreading in most of the rice growing areas of the world causing reduction in yield and quality of the produce. West Bengal is the largest producer with largest area under rice in India. No survey on severity of rice false smut disease in West Bengal has been conducted. Th...

Eight ICM practices were evaluated for six years consecutively; wherein, ICM1&2- ˈbusiness-as-usualˈ (conventional transplanted rice fb flatbed wheat), ICM3&4- conventional direct seeded rice (DSR) fb furrow irrigated raised-bed wheat without residues, ICM5&6- conservation agriculture (CA)-based zero tilled (ZT) DSR fb ZT wheat with the wheat and r...

Cointegration among the prices of different commodities plays a pivotal role in the price decision mechanism. In this study, we have attempted to improve the existing time delay neural network (TDNN) by incorporating the error correction term (ECT) as an auxiliary information in the model. The R package "ECTTDNN" has been developed for carrying out...

Crop yield forecast is valuable to many players in the agri-food chain, including agronomists, farmers, policymakers and merchants of commodities. Machine learning may be used to estimate crop yields, as well as to decide what crops to sow and what to do during the growing season. In present study Machine learning techniques such as Random Forest R...

Sugarcane industry is of crucial importance to the South Asian countries. These countries depend heavily on agriculture and the sugarcane industry has immense potential to contribute towards its economic development. Hence, the precise and timely forecast of sugarcane production is of concern for farmers, policy makers and other stakeholders. In th...

Timely and accurate price forecasting is one of challenges in agriculture. It helps both producer and consumer to make the efficient plan. The inherent nonstationarity and nonlinearity in price data makes problem in forecasting. A single forecasting model may not be able to tackle nonstationarity and nonlinearity, simultaneously. With this context,...

n this study we focused on grass root level response of credit in terms its growth to the various policy reforms at regional level by identifying the structural breaks in the agricultural credit series. The report also documents the determinants of institutional credit to agriculture, models for forecasting agricultural credit and estimation proces...

Due to the semi-perishable nature of sweet potato the price fluctuation occur based on demand and supply. Hence, it becomes necessary to precisely forecast market price of sweet potato. Price forecasting of sweet potato was carried out for six selected states in India using time series monthly market price, collected from AGMARKNET price portal fro...

Rainfall being a complex phenomenon governed by various meteorological parameters is difficult to model and forecast with high precision. For hilly regions such as state of Sikkim and adjoining areas of West Bengal, rainfall acts as lifeline. Several parametric models such as seasonal autoregressive integrated moving average (SARIMA) and exponentia...

Abstract Field experiments were conducted to evaluate eight different integrated crop management (ICM) modules for 5 years in a maize-wheat rotation (MWR); wherein, ICM1&2-ˈbusiness-as-usualˈ (conventional flatbed maize and wheat, ICM3&4-conventional raised bed (CTRB) maize and wheat without residues, ICM5&6-conservation agriculture (CA)-based zero...

We have evaluated eight different integrated crop management (ICM) modules for five years in a maize-wheat rotation (M WR ); wherein, ICM 1&2 - ˈbusiness-as-usualˈ (conventional flatbed maize and wheat, ICM 3&4 - conventional raised bed (CT RB ) maize and wheat without residues, ICM 5&6 - conservation agriculture (CA)-based zero till (ZT) flatbed m...

As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd foreca...

As agriculture is the backbone of the Indian economy, Government needs a reliable forecast of crop yield for planning new schemes. The most extensively used technique for forecasting crop yield is regression analysis. The significance of parameters is one of the major problems of regression analysis. Non-significant parameters lead to absurd foreca...

To provide agricultural information and the latest updates to the farmers, the Ministry of Agriculture and Farmers Welfare, Government of India launched a helpline service “Kisan Call Center (KCC)” in 2004. A brief summary of each query is recorded in the KCC database for later reference and use. This repository enables us to analyze, interpret and...

Irrigation has always played a dominant role in agricultural development in India through drought-proofing and enhancing productivity. The present study assesses the inter-regional disparities in the development of irrigation infrastructure and public irrigation investment in India using secondary data collected from various sources from 1992 to 2...

Application of Ensemble Empirical Mode Decomposition and its variant based Support Vector regression model for univariate time series forecasting. For method details see Das (2020).<http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

A random variable can take very large or very small values known as extreme values. In some instances, the researcher’s interest lies mainly on these extreme values like maximum (or minimum) temperature, maximum (or minimum) amount of precipitations, maximum level of flood water, maximum (or minimum) wind speed, maximum (or minimum) level of diseas...

Smooth Transition Autoregressive (STAR) models are employed to describe cyclical data. As estimation of parameters of STAR using nonlinear methods was time-consuming, Genetic algorithm (GA), a powerful optimization procedure was applied for the same. Further, optimal one step and two step ahead forecasts along with their forecast error variances ar...

Since studies on biochar stability in agricultural soils are very limited, the microbial biomass carbon and soil enzyme activity influenced by biochar addition to field condition remains uncertain. Results of this study revealed that microbial biomass carbon and different soil enzyme activity were significantly influenced by both manure alone and c...

Different integrated crop management (ICM) modules have been developed to enhance the productivity and profitability of the rice-wheat rotation (RWR) of the upper Indo-Gangetic Plains (IGPs). As the available options are used quite often singly or with the few combinations, hence in the present study, eight ICM modules have been evaluated; wherein,...

Conservation agriculture (CA)-based practices have been promoted and recouped, as they hold the potential to enhance farm profits besides a consistent improvement in soil properties. A 7 years' field experiment consisting of three crop establishment practices viz., zero-till flatbed (ZTFB), permanent beds (PNB), conventional system (CT) along with...

The collective utilization of biochar and organic manure represents the profit to plants and nutrient cycling. In this experiment, the maize (stalk and cob) biomass was pyrolyzed at 600 °C and morpho-mineralogically characterized. The scanning electron microscope (SEM) image represented cross-linked pores and feathery plate–like layer construction...

ECTTDNN: Cointegration Based Time Delay Neural Network Model. This cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.

The researchers can use this package to fit Empirical Mode Decomposition and Artificial Neural Network based hybrid model for nonlinear and non stationary time series data. It will also provide you with accuracy measures along with an option to select the proportion of training and testing data sets.User can get to choose appropriate lag with tunin...

Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.

Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.

EMDSVRhybrid: Hybrid Machine Learning Model. Researchers can fit Empirical Mode Decomposition and Support Vector Regression based hybrid model for nonlinear and non stationary time series data using this package.

Generalized Autoregressive Conditional Heteroscedastic (GARCH) model has gained popularity since its inception due to its ability to forecast volatility. Usually, GARCH model captures the volatility based on its past volatility and past squared residuals, but does not consider the effect of exogenous variable(s) on the volatility process owing to i...

Conservation agriculture (CA)-based practices have been promoted and recouped, as they hold the potential to enhance farm profits besides a consistent improvement in soil properties. The CA-based crop establishment practices (CEP) along with adequate fertilizer inputs in the diversified maize-chickpea rotation (MCR) could be a profitable choice to...

Price information is a crucial market information for a farmer. The price instability and uncertainty pose a significant challenge to decision makers in making proper production and marketing plans to minimize risk. Agricultural price series cannot be modelled and predicted accurately by traditional econometric models owing to its nonlinearity an...

n the present study exogenous variable is incorporated in the long memory model to give better forecast of time series. Autoregressive Fractionally Integrated Moving Average- Generalized Autoregressive Conditional Heteroscedastic (ARFIMA-GARCH) and Autoregressive Fractionally Integrated Moving Average with exogenous variable- Generalized Autoregres...

Conservation agriculture (CA) is being advocated as an alternative to conventional tillage based systems, as it not only holds the potential to enhance soil biological properties, but could also sustain production in the long-run. The impact of long-term tillage and nutrient management on soil biological properties, crops performance, yield and ret...

Millets are the major substitute for cereals such as rice and wheat. For developing country like India, millets hold immense importance as the cost of production is low and has high nutritional values. Various policy interventions are made by government of India from time to time to popularise its consumption and production. Few major policy interv...

Modelling and forecasting of volatility has attracted the attention of researchers for decades now. Agricultural commodity prices are characteristically fluctuating. In this paper volatile price series of spices namely Black pepper, Cardamom and Cumin are modelled and forecasted using family of Generalised Autoregressive Conditional Heteroscedastic...

time series analysis and forecasting is one of the challenging issues of statistical modelling. modelling of price and forecasting is a vital matter of concern for both the farming community and policy makers, especially in agriculture. many practical agricultural data, principally commodity price data shows the typical feature of long memory proce...

The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variables) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304-4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.

in the present investigation an attempt has been made to forecast and understand the volatility transmission in onion prices for three vital markets in Maharashtra, viz. lasalgaon, Pune and Nagpur. The AriMAX-GArch model was employed to estimate mean and volatility among the different markets and also examined the nature of dynamic correlation usin...

Nowadays, the availability of huge computing facilities makes it easy to adopt a complex algorithm for various problem-solving. As a result, the Bayesian method of parameter estimation, which is based on Bayes’ theorem given by Thomas Bayes, gains its popularity in recent times. Time series modeling and forecasting is an important aspect of modelin...

Forecasting of Potato Price Using Improved Long Memory Model

Exogenous variable is incorporated in the existing long memory model to improve the modeling performance and forecasting accuracy of the model.

The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) <http://www.jstor.org/stable/3532933> has been used to estimate the bivariate time ser...

Over the last few decades, India has seen an incessant increase of tractor use as well as expansion in its domestic tractor manufacturing industry, in spite of comparatively slow wage growth and a slow decline in the employment share of the agricultural sector. If the present situation is to be accounted, arguably as much as 90% of the country’s fa...

Volatility is a common phenomenon which can be observed in the financial market. Volatility is often considered to be same as risk, but the truth is, risk deals with only negative shocks whereas volatility takes care of both negative and positive shocks. It is important to model and forecast volatility efficiently as it involves a large domain of s...