Growth trends and forecasting of fish production in Assam, India using ARIMA model

Yadav, Anil Kumar; Das, Kishore Kumar; Das, P. C.; Raman, Rohan Kumar; Kumar, Jeetendra; Das, Basanta Kumar

doi:10.31018/jans.v12i3.2353

Cited by 12 publications

(11 citation statements)

References 16 publications

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“…The forecasted fish production would be from 336.97 to 358.21 thousand metric tonnes for during years 2019-20 to 2022-23 in Assam state. The study noticed that actual and forecast values were closer (13) .…”

Section: Forecasting Of Fish and Fish Seed Productionmentioning

confidence: 80%

“…The Auto-Regressive Integrated Moving Average (ARIMA) methodology using for forecasting of fish production in Assam (1980-81 to 2014-15) (13) . The most common method is to check stationary by examining the graph or time plot of the data.…”

Section: Forecasting Of Fish and Fish Seed Production In Chhattisgarhmentioning

confidence: 99%

“…At the identification stage, one or more models were chosen which seem to provide statistically adequate representations of the available data. Identified best suitable model for the state's fish production was ARIMA (1,1,0) based on values of the model selection criterion (13) . An excellent discussion of model selection and different phases of time series approach for forecasting was made (14) .…”

Section: Identification Of the Modelmentioning

confidence: 99%

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Trend Analysis and Forecasting of Fish and Fish Seed Production in Chhattisgarh, India

Sonvanee¹,

Devi²,

Mohale³

2021

IJED

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Objectives: to study the trend and forecasting of fish & fish seed production in Chhattisgarh, India. Methods: The time-series data on fish production (in metric tonnes) and fish seed production (in lakhs) in Chhattisgarh for a period of 18 years from 2003-04 to 2020-21 was done. The Compound Growth Rates (CGR) of fish and fish seed production in Chhattisgarh was estimated by fitting a log-linear model. ARIMA model was applied for forecasting which includes the identification of model through coding under which p, d, q and estimated the parameters of model, diagnostic checking with respect to reliability of model based on Ljung-Box statistics and forecasting was made as step. This model also called Box-Jenkins model. Findings: In the study, the CGR of fish & fish seed production were found to be (10.72 per cent) and (12.62 per cent) positively significant in Chhattisgarh, which were proved by R 2 = 0.93 & 0.89 respectively. Forecasted fish production would be ranging from the minimum 628417.90 metric tonnes in 2021-22 to the maximum 857323.80 metric tonnes in 2025-26. Forecasted fish seed production would be ranging from 31267.17 lakhs to 44053.01 lakhs for the years from 2021-22 to 2025-26. Application/ Novelty: The actual and forecasted fish & fish seed production was in increasing trend, but it was largely shared in total production by limited farmers in Chhattisgarh. The study reported that directly supported in decisionmaking to the policy-maker, researcher, development agencies and extension agencies for increasing large number of person involved in fisheries sector, availability of per capita fish and estimated demand of fish seed.

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Section: Forecasting Of Fish and Fish Seed Productionmentioning

confidence: 80%

Section: Forecasting Of Fish and Fish Seed Production In Chhattisgarhmentioning

confidence: 99%

Section: Identification Of the Modelmentioning

confidence: 99%

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Trend Analysis and Forecasting of Fish and Fish Seed Production in Chhattisgarh, India

Sonvanee¹,

Devi²,

Mohale³

2021

IJED

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“…From the viewpoint of the analysis of models for time-series forecasting, many studies used the Autoregressive Integrated Moving Average (ARIMA), e.g., [4,22,28,38,50], and the Seasonal version (SARIMA), e.g., [3,37] to forecast fish landings but without considering the spatial distribution of the resources. Authors in [44] integrated chlorophyll concentration, derived from remote sensing satellite, and sea surface temperature images to generate a fishery forecast.…”

Section: Fishing Activities Forecastmentioning

confidence: 99%

From multiple aspect trajectories to predictive analysis: a case study on fishing vessels in the Northern Adriatic sea

et al. 2022

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In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task.

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“…The fisheries sub-sector is important for ensuring livelihood and nutritional security in the North-eastern Indian state of Assam, where reportedly over 90% people consume fish (Das et al, 2017(Das et al, , 2018Yadav et al, 2020)…”

Section: Introductionmentioning

confidence: 99%

Effect of extreme climatic events on fish seed production in Lower Brahmaputra Valley, Assam, India: Constraint analysis and adaptive strategies

Bhattacharjya

Yadav

Debnath

et al. 2021

Aquatic Ecosystem Health &Amp; Management

Self Cite

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The climate of Brahmaputra valley (located in the North-eastern Indian state of Assam) witnesses considerable changes with sub-tropical rain-storm atmosphere and has four well-defined seasons (monsoon, post-monsoon, winter and pre-monsoon) in a year. The state is experiencing extreme climatic events like erratic rainfall patterns (causing abnormally high floods) and drought-like situations in recent years, which adversely affects fish seed production thereby affecting pond aquaculture and fisheries enhancements in open-waters of the region. The present paper describes the major constraints faced by fish seed producers of the region because of extreme climatic events (abnormally high floods and prolonged dry spells) based on field studies in 27 fish seed production farms (randomly selected) located in three districts of Lower Brahmaputra Valley Zone of Assam, India. Primary data on different aspects of constraints faced by the fish seed producers because of extreme climatic events were collected and analysed. Rank Based Quotient values were calculated, based on ranks given by the fish seed producers for each constraint to prioritize them. Dominant constraint related to abnormally high floods faced by the fish seed producers was ‘loss of brood stock’ (Rank based quotient = 99.1) whereas the least important constraint was ‘entry of weeds/macrophytes’ during floods (Rank Based Quotient = 25.5). Constraints related to drought-like situations ranged from the least important ones like ‘low milt production of male brood fishes’ that had the lowest value (12.5) to the over-arching constraint of ‘inadequate water depth in fish ponds’ during pre-monsoon and prolonged dry spells (Rank Based Quotient = 100.0). Adaptive strategies developed by fish seed producers of the study region to overcome the major constraints have been outlined in the paper. The need for developing improved risk management at macro-level through appropriate policy support to fish seed producers in the fish-deficit region and suggested policy support measures for ensuring sustainability are discussed.

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Growth trends and forecasting of fish production in Assam, India using ARIMA model

Cited by 12 publications

References 16 publications

Trend Analysis and Forecasting of Fish and Fish Seed Production in Chhattisgarh, India

Trend Analysis and Forecasting of Fish and Fish Seed Production in Chhattisgarh, India

From multiple aspect trajectories to predictive analysis: a case study on fishing vessels in the Northern Adriatic sea

Effect of extreme climatic events on fish seed production in Lower Brahmaputra Valley, Assam, India: Constraint analysis and adaptive strategies

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