Nexus among rice production and environmental factors in the coastal region of Bangladesh: a stochastic modeling approach for future forecasting
Sustainable rice production in the coastal region of Bangladesh is threatened due to various climatic impacts. The study investigated the current scenario of rice production and identified the linkage among different input variables related to rice production such as seed, urea, pesticide, etc. in t...
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Veröffentlicht in: | Modeling earth systems and environment 2021-06, Vol.7 (2), p.1121-1131 |
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description | Sustainable rice production in the coastal region of Bangladesh is threatened due to various climatic impacts. The study investigated the current scenario of rice production and identified the linkage among different input variables related to rice production such as seed, urea, pesticide, etc. in three districts of Patuakhali, Khulna, and Cox’s Bazar, using a log–log model and panel data analysis. The Stochastic Model of multiple regression analysis helped to identify the most important input variables. The input factors found common in all three study areas were seed. The log–log model was used to identify how climatic factors influenced these input variables. Identifying such factors would help to ensure sustainable agriculture production in the future. Seed cost showed a negative elasticity in Patuakhali, while urea cost showed a negative elasticity in Khulna region. Time series forecasting of rice production up to 2030 in the study areas was examined using the best fitted ARIMA model. While rice production showed an increasing trend in two regions, it did not show a satisfactory upward trend in Kutubdia, which is mostly related to their mismanagement of fertilizer use and irrigation water scarcity. The analysis from the research indicated that an area-specific adaptation plan is required to sustain the agricultural production, as the input variables for rice production were not found similar for three different study districts where a specific attention needs to be given. Moreover, to address the nonexistence of quality panel data for rice production, measures have been introduced which includes an online data collection system using a mobile application. |
doi_str_mv | 10.1007/s40808-020-00969-6 |
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Nazrul ; Rahman, Abir ; Alam, Md. Sajadul ; Ali, Mirza Shawkat ; Akter, Dilruba</creator><creatorcontrib>Jakariya, Md ; Sarker, Sazzadur Rahman ; Sayem, Sheikh Mohammad ; Saad, Saman ; Islam, Md. Nazrul ; Rahman, Abir ; Alam, Md. Sajadul ; Ali, Mirza Shawkat ; Akter, Dilruba</creatorcontrib><description>Sustainable rice production in the coastal region of Bangladesh is threatened due to various climatic impacts. The study investigated the current scenario of rice production and identified the linkage among different input variables related to rice production such as seed, urea, pesticide, etc. in three districts of Patuakhali, Khulna, and Cox’s Bazar, using a log–log model and panel data analysis. The Stochastic Model of multiple regression analysis helped to identify the most important input variables. The input factors found common in all three study areas were seed. The log–log model was used to identify how climatic factors influenced these input variables. Identifying such factors would help to ensure sustainable agriculture production in the future. Seed cost showed a negative elasticity in Patuakhali, while urea cost showed a negative elasticity in Khulna region. Time series forecasting of rice production up to 2030 in the study areas was examined using the best fitted ARIMA model. While rice production showed an increasing trend in two regions, it did not show a satisfactory upward trend in Kutubdia, which is mostly related to their mismanagement of fertilizer use and irrigation water scarcity. The analysis from the research indicated that an area-specific adaptation plan is required to sustain the agricultural production, as the input variables for rice production were not found similar for three different study districts where a specific attention needs to be given. Moreover, to address the nonexistence of quality panel data for rice production, measures have been introduced which includes an online data collection system using a mobile application.</description><identifier>ISSN: 2363-6203</identifier><identifier>EISSN: 2363-6211</identifier><identifier>DOI: 10.1007/s40808-020-00969-6</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Agricultural economics ; Agricultural production ; Applications programs ; Autoregressive models ; Chemistry and Earth Sciences ; Coastal zone ; Computer Science ; Crop production ; Data analysis ; Data collection ; Earth and Environmental Science ; Earth Sciences ; Earth System Sciences ; Economic forecasting ; Ecosystems ; Elasticity ; Environment ; Environmental factors ; Fertilizers ; Irrigation water ; Longitudinal studies ; Math. Appl. in Environmental Science ; Mathematical Applications in the Physical Sciences ; Mobile computing ; Multiple regression analysis ; Original Article ; Pesticides ; Physics ; Regression analysis ; Regression models ; Rice ; Statistical analysis ; Statistics for Engineering ; Stochastic models ; Sustainability ; Sustainable agriculture ; Sustainable production ; Urea ; Water scarcity</subject><ispartof>Modeling earth systems and environment, 2021-06, Vol.7 (2), p.1121-1131</ispartof><rights>Springer Nature Switzerland AG 2020</rights><rights>Springer Nature Switzerland AG 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-e0b6fac9a05449987905e0f938af11a40730b4784a1e7bb2db16258f052b4dd93</citedby><cites>FETCH-LOGICAL-c319t-e0b6fac9a05449987905e0f938af11a40730b4784a1e7bb2db16258f052b4dd93</cites><orcidid>0000-0003-2677-5245</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40808-020-00969-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40808-020-00969-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Jakariya, Md</creatorcontrib><creatorcontrib>Sarker, Sazzadur Rahman</creatorcontrib><creatorcontrib>Sayem, Sheikh Mohammad</creatorcontrib><creatorcontrib>Saad, Saman</creatorcontrib><creatorcontrib>Islam, Md. Nazrul</creatorcontrib><creatorcontrib>Rahman, Abir</creatorcontrib><creatorcontrib>Alam, Md. Sajadul</creatorcontrib><creatorcontrib>Ali, Mirza Shawkat</creatorcontrib><creatorcontrib>Akter, Dilruba</creatorcontrib><title>Nexus among rice production and environmental factors in the coastal region of Bangladesh: a stochastic modeling approach for future forecasting</title><title>Modeling earth systems and environment</title><addtitle>Model. Earth Syst. Environ</addtitle><description>Sustainable rice production in the coastal region of Bangladesh is threatened due to various climatic impacts. The study investigated the current scenario of rice production and identified the linkage among different input variables related to rice production such as seed, urea, pesticide, etc. in three districts of Patuakhali, Khulna, and Cox’s Bazar, using a log–log model and panel data analysis. The Stochastic Model of multiple regression analysis helped to identify the most important input variables. The input factors found common in all three study areas were seed. The log–log model was used to identify how climatic factors influenced these input variables. Identifying such factors would help to ensure sustainable agriculture production in the future. Seed cost showed a negative elasticity in Patuakhali, while urea cost showed a negative elasticity in Khulna region. Time series forecasting of rice production up to 2030 in the study areas was examined using the best fitted ARIMA model. While rice production showed an increasing trend in two regions, it did not show a satisfactory upward trend in Kutubdia, which is mostly related to their mismanagement of fertilizer use and irrigation water scarcity. The analysis from the research indicated that an area-specific adaptation plan is required to sustain the agricultural production, as the input variables for rice production were not found similar for three different study districts where a specific attention needs to be given. Moreover, to address the nonexistence of quality panel data for rice production, measures have been introduced which includes an online data collection system using a mobile application.</description><subject>Agricultural economics</subject><subject>Agricultural production</subject><subject>Applications programs</subject><subject>Autoregressive models</subject><subject>Chemistry and Earth Sciences</subject><subject>Coastal zone</subject><subject>Computer Science</subject><subject>Crop production</subject><subject>Data analysis</subject><subject>Data collection</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth System Sciences</subject><subject>Economic forecasting</subject><subject>Ecosystems</subject><subject>Elasticity</subject><subject>Environment</subject><subject>Environmental factors</subject><subject>Fertilizers</subject><subject>Irrigation water</subject><subject>Longitudinal studies</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical Applications in the Physical Sciences</subject><subject>Mobile computing</subject><subject>Multiple regression analysis</subject><subject>Original Article</subject><subject>Pesticides</subject><subject>Physics</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Rice</subject><subject>Statistical analysis</subject><subject>Statistics for Engineering</subject><subject>Stochastic models</subject><subject>Sustainability</subject><subject>Sustainable agriculture</subject><subject>Sustainable production</subject><subject>Urea</subject><subject>Water scarcity</subject><issn>2363-6203</issn><issn>2363-6211</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE1OwzAQhSMEElXpBVhZYh0Yx4kTs4OKP6mCDawtxxknqVq72AmCW3BkHIpgx2pGo-_Nm3lJckrhnAKUFyGHCqoUMkgBBBcpP0hmGeMs5Rmlh789sONkEcIaACjPOBdilnw-4vsYiNo62xLfayQ775pRD72zRNmGoH3rvbNbtIPaEKP04HwgvSVDh0Q7Faaxx3binSHXyrYb1WDoLokiYXC6i0ivydY1uOmjidpFB6U7YpwnZhxGj1OLeuJse5IcGbUJuPip8-Tl9uZ5eZ-unu4ellerVDMqhhSh5vEYoaDIcyGqUkCBYASrlKFU5VAyqPOyyhXFsq6zpo4vF5WBIqvzphFsnpzt98ZrXkcMg1y70dtoKbOClrxkVZFHKttT2rsQPBq58_1W-Q9JQU7hy334MoYvv8OXPIrYXhQibFv0f6v_UX0BhE2Jug</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>Jakariya, Md</creator><creator>Sarker, Sazzadur Rahman</creator><creator>Sayem, Sheikh Mohammad</creator><creator>Saad, Saman</creator><creator>Islam, Md. 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Appl. in Environmental Science</topic><topic>Mathematical Applications in the Physical Sciences</topic><topic>Mobile computing</topic><topic>Multiple regression analysis</topic><topic>Original Article</topic><topic>Pesticides</topic><topic>Physics</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Rice</topic><topic>Statistical analysis</topic><topic>Statistics for Engineering</topic><topic>Stochastic models</topic><topic>Sustainability</topic><topic>Sustainable agriculture</topic><topic>Sustainable production</topic><topic>Urea</topic><topic>Water scarcity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jakariya, Md</creatorcontrib><creatorcontrib>Sarker, Sazzadur Rahman</creatorcontrib><creatorcontrib>Sayem, Sheikh Mohammad</creatorcontrib><creatorcontrib>Saad, Saman</creatorcontrib><creatorcontrib>Islam, Md. Nazrul</creatorcontrib><creatorcontrib>Rahman, Abir</creatorcontrib><creatorcontrib>Alam, Md. 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Nazrul</au><au>Rahman, Abir</au><au>Alam, Md. Sajadul</au><au>Ali, Mirza Shawkat</au><au>Akter, Dilruba</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nexus among rice production and environmental factors in the coastal region of Bangladesh: a stochastic modeling approach for future forecasting</atitle><jtitle>Modeling earth systems and environment</jtitle><stitle>Model. Earth Syst. Environ</stitle><date>2021-06-01</date><risdate>2021</risdate><volume>7</volume><issue>2</issue><spage>1121</spage><epage>1131</epage><pages>1121-1131</pages><issn>2363-6203</issn><eissn>2363-6211</eissn><abstract>Sustainable rice production in the coastal region of Bangladesh is threatened due to various climatic impacts. The study investigated the current scenario of rice production and identified the linkage among different input variables related to rice production such as seed, urea, pesticide, etc. in three districts of Patuakhali, Khulna, and Cox’s Bazar, using a log–log model and panel data analysis. The Stochastic Model of multiple regression analysis helped to identify the most important input variables. The input factors found common in all three study areas were seed. The log–log model was used to identify how climatic factors influenced these input variables. Identifying such factors would help to ensure sustainable agriculture production in the future. Seed cost showed a negative elasticity in Patuakhali, while urea cost showed a negative elasticity in Khulna region. Time series forecasting of rice production up to 2030 in the study areas was examined using the best fitted ARIMA model. While rice production showed an increasing trend in two regions, it did not show a satisfactory upward trend in Kutubdia, which is mostly related to their mismanagement of fertilizer use and irrigation water scarcity. The analysis from the research indicated that an area-specific adaptation plan is required to sustain the agricultural production, as the input variables for rice production were not found similar for three different study districts where a specific attention needs to be given. Moreover, to address the nonexistence of quality panel data for rice production, measures have been introduced which includes an online data collection system using a mobile application.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s40808-020-00969-6</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-2677-5245</orcidid></addata></record> |
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subjects | Agricultural economics Agricultural production Applications programs Autoregressive models Chemistry and Earth Sciences Coastal zone Computer Science Crop production Data analysis Data collection Earth and Environmental Science Earth Sciences Earth System Sciences Economic forecasting Ecosystems Elasticity Environment Environmental factors Fertilizers Irrigation water Longitudinal studies Math. Appl. in Environmental Science Mathematical Applications in the Physical Sciences Mobile computing Multiple regression analysis Original Article Pesticides Physics Regression analysis Regression models Rice Statistical analysis Statistics for Engineering Stochastic models Sustainability Sustainable agriculture Sustainable production Urea Water scarcity |
title | Nexus among rice production and environmental factors in the coastal region of Bangladesh: a stochastic modeling approach for future forecasting |
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