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Nutrient modeling for a semi-intensive IMC pond: an MS-Excel approach
Semi-intensive Indian Major Carp (IMC) culture was practised in polythene lined dugout ponds at the Aquacultural Farm of Indian Institute of Technology, Kharagpur, West Bengal for 3 consecutive years at three different stocking densities (S.D), viz., 20,000, 35,000 and 50,000 numbers of fingerlings...
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Published in: | Water science and technology 2017-11, Vol.76 (9-10), p.2858-2866 |
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description | Semi-intensive Indian Major Carp (IMC) culture was practised in polythene lined dugout ponds at the Aquacultural Farm of Indian Institute of Technology, Kharagpur, West Bengal for 3 consecutive years at three different stocking densities (S.D), viz., 20,000, 35,000 and 50,000 numbers of fingerlings per hectare of water spread area. Fingerlings of Catla, Rohu and Mrigal were raised at a stocking ratio of 4:3:3. Total ammonia nitrogen (TAN) value along with other fishpond water quality parameters was monitored at 1 day intervals to ensure a good water ecosystem for a better fish growth. Water exchange was carried out before the TAN reached the critical limit. Field data on TAN obtained from the cultured fishponds stocked with three different stocking densities were used to study the dynamics of TAN. A developed model used to study the nutrient dynamics in shrimp pond was used to validate the observed data in the IMC pond ecosystem. Two years of observed TAN data were used to calibrate the spreadsheet model and the same model was validated using the third year observed data. The manual calibration based on the trial and error process of parameters adjustments was used and several simulations were performed by changing the model parameters. After adjustment of each parameter, the simulated and measured values of the water quality parameters were compared to judge the improvement in the model prediction. Forward finite difference discretization method was used in a MS-Excel spreadsheet to calibrate and validate the model for obtaining the TAN levels during the culture period. Observed data from the cultured fishponds of three different S.D were used to standardize 13 model parameters. The efficiency of the developed spreadsheet model was found to be more than 90% for the TAN estimation in the IMC cultured fishponds. |
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Fingerlings of Catla, Rohu and Mrigal were raised at a stocking ratio of 4:3:3. Total ammonia nitrogen (TAN) value along with other fishpond water quality parameters was monitored at 1 day intervals to ensure a good water ecosystem for a better fish growth. Water exchange was carried out before the TAN reached the critical limit. Field data on TAN obtained from the cultured fishponds stocked with three different stocking densities were used to study the dynamics of TAN. A developed model used to study the nutrient dynamics in shrimp pond was used to validate the observed data in the IMC pond ecosystem. Two years of observed TAN data were used to calibrate the spreadsheet model and the same model was validated using the third year observed data. The manual calibration based on the trial and error process of parameters adjustments was used and several simulations were performed by changing the model parameters. After adjustment of each parameter, the simulated and measured values of the water quality parameters were compared to judge the improvement in the model prediction. Forward finite difference discretization method was used in a MS-Excel spreadsheet to calibrate and validate the model for obtaining the TAN levels during the culture period. Observed data from the cultured fishponds of three different S.D were used to standardize 13 model parameters. The efficiency of the developed spreadsheet model was found to be more than 90% for the TAN estimation in the IMC cultured fishponds.</description><identifier>ISSN: 0273-1223</identifier><identifier>EISSN: 1996-9732</identifier><identifier>DOI: 10.2166/wst.2017.458</identifier><identifier>PMID: 29168726</identifier><language>eng</language><publisher>England: IWA Publishing</publisher><subject>Ammonia ; Aquaculture ; Calibration ; Carp ; Computer simulation ; Cyprinidae ; Cyprinus carpio ; Data ; Data processing ; Dynamics ; Ecological monitoring ; Ecosystems ; Environmental monitoring ; Fingerlings ; Finite difference method ; Fish ; Fish ponds ; Fishponds ; Freshwater fishes ; Hypophthalmichthys molitrix ; Marine crustaceans ; Mathematical models ; Mineral nutrients ; Modelling ; Nutrient dynamics ; Oreochromis niloticus ; Parameters ; Polyethylenes ; Ponds ; Process parameters ; Stocking ; Studies ; Water exchange ; Water quality</subject><ispartof>Water science and technology, 2017-11, Vol.76 (9-10), p.2858-2866</ispartof><rights>Copyright IWA Publishing Nov 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c314t-2d63a69995608696bc5a602e883eb588db9c56b057f6961f029fe5def12d2b153</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29168726$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ray, Lala I P</creatorcontrib><creatorcontrib>Mal, B C</creatorcontrib><creatorcontrib>Moulick, S</creatorcontrib><title>Nutrient modeling for a semi-intensive IMC pond: an MS-Excel approach</title><title>Water science and technology</title><addtitle>Water Sci Technol</addtitle><description>Semi-intensive Indian Major Carp (IMC) culture was practised in polythene lined dugout ponds at the Aquacultural Farm of Indian Institute of Technology, Kharagpur, West Bengal for 3 consecutive years at three different stocking densities (S.D), viz., 20,000, 35,000 and 50,000 numbers of fingerlings per hectare of water spread area. Fingerlings of Catla, Rohu and Mrigal were raised at a stocking ratio of 4:3:3. Total ammonia nitrogen (TAN) value along with other fishpond water quality parameters was monitored at 1 day intervals to ensure a good water ecosystem for a better fish growth. Water exchange was carried out before the TAN reached the critical limit. Field data on TAN obtained from the cultured fishponds stocked with three different stocking densities were used to study the dynamics of TAN. A developed model used to study the nutrient dynamics in shrimp pond was used to validate the observed data in the IMC pond ecosystem. Two years of observed TAN data were used to calibrate the spreadsheet model and the same model was validated using the third year observed data. The manual calibration based on the trial and error process of parameters adjustments was used and several simulations were performed by changing the model parameters. After adjustment of each parameter, the simulated and measured values of the water quality parameters were compared to judge the improvement in the model prediction. Forward finite difference discretization method was used in a MS-Excel spreadsheet to calibrate and validate the model for obtaining the TAN levels during the culture period. Observed data from the cultured fishponds of three different S.D were used to standardize 13 model parameters. The efficiency of the developed spreadsheet model was found to be more than 90% for the TAN estimation in the IMC cultured fishponds.</description><subject>Ammonia</subject><subject>Aquaculture</subject><subject>Calibration</subject><subject>Carp</subject><subject>Computer simulation</subject><subject>Cyprinidae</subject><subject>Cyprinus carpio</subject><subject>Data</subject><subject>Data processing</subject><subject>Dynamics</subject><subject>Ecological monitoring</subject><subject>Ecosystems</subject><subject>Environmental monitoring</subject><subject>Fingerlings</subject><subject>Finite difference method</subject><subject>Fish</subject><subject>Fish ponds</subject><subject>Fishponds</subject><subject>Freshwater fishes</subject><subject>Hypophthalmichthys molitrix</subject><subject>Marine crustaceans</subject><subject>Mathematical models</subject><subject>Mineral nutrients</subject><subject>Modelling</subject><subject>Nutrient dynamics</subject><subject>Oreochromis niloticus</subject><subject>Parameters</subject><subject>Polyethylenes</subject><subject>Ponds</subject><subject>Process parameters</subject><subject>Stocking</subject><subject>Studies</subject><subject>Water exchange</subject><subject>Water quality</subject><issn>0273-1223</issn><issn>1996-9732</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNpdkDtPwzAUhS0EoqWwMSNLLAyk-BG_2FBVoFILAzBbTuJAqsQJdsLj3-OqhYHpDvfTOUcfAKcYTQnm_Ooz9FOCsJimTO6BMVaKJ0pQsg_GiAiaYELoCByFsEYICZqiQzAiCnMpCB-D-cPQ-8q6HjZtYevKvcKy9dDAYJsqqVxvXag-LFysZrBrXXENjYOrp2T-ldsamq7zrcnfjsFBaepgT3Z3Al5u58-z-2T5eLeY3SyTnOK0T0jBqeFKKcaR5IpnOTMcESsltRmTsshUzniGmCjjF5eIqNKywpaYFCTDjE7AxTY31r4PNvS6qUIcUhtn2yForLiQHItURvT8H7puB-_iukgJLIhSEkfqckvlvg3B21J3vmqM_9YY6Y1eHfXqjV4d9Ub8bBc6ZI0t_uBfn_QH--NzQQ</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>Ray, Lala I P</creator><creator>Mal, B C</creator><creator>Moulick, S</creator><general>IWA Publishing</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7UA</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>H96</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L.G</scope><scope>L6V</scope><scope>M0S</scope><scope>M1P</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>7X8</scope></search><sort><creationdate>20171101</creationdate><title>Nutrient modeling for a semi-intensive IMC pond: an MS-Excel approach</title><author>Ray, Lala I P ; Mal, B C ; Moulick, S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c314t-2d63a69995608696bc5a602e883eb588db9c56b057f6961f029fe5def12d2b153</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Ammonia</topic><topic>Aquaculture</topic><topic>Calibration</topic><topic>Carp</topic><topic>Computer simulation</topic><topic>Cyprinidae</topic><topic>Cyprinus carpio</topic><topic>Data</topic><topic>Data processing</topic><topic>Dynamics</topic><topic>Ecological monitoring</topic><topic>Ecosystems</topic><topic>Environmental monitoring</topic><topic>Fingerlings</topic><topic>Finite difference method</topic><topic>Fish</topic><topic>Fish ponds</topic><topic>Fishponds</topic><topic>Freshwater fishes</topic><topic>Hypophthalmichthys molitrix</topic><topic>Marine crustaceans</topic><topic>Mathematical models</topic><topic>Mineral nutrients</topic><topic>Modelling</topic><topic>Nutrient dynamics</topic><topic>Oreochromis niloticus</topic><topic>Parameters</topic><topic>Polyethylenes</topic><topic>Ponds</topic><topic>Process parameters</topic><topic>Stocking</topic><topic>Studies</topic><topic>Water exchange</topic><topic>Water quality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ray, Lala I P</creatorcontrib><creatorcontrib>Mal, B C</creatorcontrib><creatorcontrib>Moulick, S</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Engineering Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>MEDLINE - Academic</collection><jtitle>Water science and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ray, Lala I P</au><au>Mal, B C</au><au>Moulick, S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nutrient modeling for a semi-intensive IMC pond: an MS-Excel approach</atitle><jtitle>Water science and technology</jtitle><addtitle>Water Sci Technol</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>76</volume><issue>9-10</issue><spage>2858</spage><epage>2866</epage><pages>2858-2866</pages><issn>0273-1223</issn><eissn>1996-9732</eissn><abstract>Semi-intensive Indian Major Carp (IMC) culture was practised in polythene lined dugout ponds at the Aquacultural Farm of Indian Institute of Technology, Kharagpur, West Bengal for 3 consecutive years at three different stocking densities (S.D), viz., 20,000, 35,000 and 50,000 numbers of fingerlings per hectare of water spread area. Fingerlings of Catla, Rohu and Mrigal were raised at a stocking ratio of 4:3:3. Total ammonia nitrogen (TAN) value along with other fishpond water quality parameters was monitored at 1 day intervals to ensure a good water ecosystem for a better fish growth. Water exchange was carried out before the TAN reached the critical limit. Field data on TAN obtained from the cultured fishponds stocked with three different stocking densities were used to study the dynamics of TAN. A developed model used to study the nutrient dynamics in shrimp pond was used to validate the observed data in the IMC pond ecosystem. Two years of observed TAN data were used to calibrate the spreadsheet model and the same model was validated using the third year observed data. The manual calibration based on the trial and error process of parameters adjustments was used and several simulations were performed by changing the model parameters. After adjustment of each parameter, the simulated and measured values of the water quality parameters were compared to judge the improvement in the model prediction. Forward finite difference discretization method was used in a MS-Excel spreadsheet to calibrate and validate the model for obtaining the TAN levels during the culture period. Observed data from the cultured fishponds of three different S.D were used to standardize 13 model parameters. The efficiency of the developed spreadsheet model was found to be more than 90% for the TAN estimation in the IMC cultured fishponds.</abstract><cop>England</cop><pub>IWA Publishing</pub><pmid>29168726</pmid><doi>10.2166/wst.2017.458</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Ammonia Aquaculture Calibration Carp Computer simulation Cyprinidae Cyprinus carpio Data Data processing Dynamics Ecological monitoring Ecosystems Environmental monitoring Fingerlings Finite difference method Fish Fish ponds Fishponds Freshwater fishes Hypophthalmichthys molitrix Marine crustaceans Mathematical models Mineral nutrients Modelling Nutrient dynamics Oreochromis niloticus Parameters Polyethylenes Ponds Process parameters Stocking Studies Water exchange Water quality |
title | Nutrient modeling for a semi-intensive IMC pond: an MS-Excel approach |
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