Defining agro-ecological regions for field crops in variable target production environments: A case study on mungbean in the northern grains region of Australia

•The APSIM model was used to characterise mungbean growing areas of Australia.•The model characterised four drought target production environments (TPE).•Percentile ranks of simulated seasonal yield defined seven agro-ecoregions (AER).•Each AER had geographically contiguous locations having similar...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Agricultural and forest meteorology 2014-08, Vol.194, p.207-217
Hauptverfasser: Chauhan, Y.S., Rachaputi, Rao C.N.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 217
container_issue
container_start_page 207
container_title Agricultural and forest meteorology
container_volume 194
creator Chauhan, Y.S.
Rachaputi, Rao C.N.
description •The APSIM model was used to characterise mungbean growing areas of Australia.•The model characterised four drought target production environments (TPE).•Percentile ranks of simulated seasonal yield defined seven agro-ecoregions (AER).•Each AER had geographically contiguous locations having similar frequencies of TPE.•Locations within each AER could be used as selection environments for different TPE. The northern grains region (NGR) of Australia, which includes the state of Queensland and the northern half of the New South Wales, has highly variable climate leading to heightened production risk for all rainfed crops. Characterisation of the production environment of this region can assist in exploration of potential opportunities for reducing this risk. In this case study on mungbean (Vigna radiata L. Wilczek.) we demonstrate how this region could be characterised using the Agricultural Production Systems sIMulator (APSIM) model. The model was first evaluated for variety Crystal grown widely in the region, and then applied to simulate a water stress index (the daily supply and demand ratio) and yield from 1889 to 2012 at 28 locations. The model was run using location specific as well as three generic soils of 136, 166 and 204mm plant available water holding capacities (PAWC). Two complementary characterisations were performed using the simulated output, one based on clustering of supply demand ratio averaged for every 100°Cd to and from flowering, and another on clustering of percentile rankings of seasonal yield variation at different locations. Clustering of supply demand ratio revealed four drought patterns (i.e., target production environments) which commenced at different times from flowering. Seasonal frequencies of these drought patterns, which differed due to major location effects and relatively smaller soil effects, accounted for significant (∼84%) variation in simulated yield. Clustering of percentile ranks corresponding to simulated yield in different seasons identified seven meaningful yield clusters. Location memberships of these yield clusters were geographically contiguous and were only slightly influenced for the lowest PAWC generic soil. All locations within these yield clusters showed a tendency to have similar seasonal drought patterns and their frequencies. Locations within different yield clusters could therefore be considered as part of distinct agro-ecoregions. These model defined agro-ecoregions could be used as selection environm
doi_str_mv 10.1016/j.agrformet.2014.04.007
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1642230388</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0168192314001075</els_id><sourcerecordid>1627972607</sourcerecordid><originalsourceid>FETCH-LOGICAL-c465t-b2bf372b17da79839ed26ef725a9127a8e71523744403b55f0b57bf16cf4c3803</originalsourceid><addsrcrecordid>eNqNkcuO1DAQRSMEEs3AN1AbJDZp_EjihF1reEojsYG15Tjl4FZiN2WnpfkbPhU3PZotSCXVok7dq6pbVa8523PGu3fHvZnJRVox7wXjzZ6VYupJteO9krUQDXta7QrZ13wQ8nn1IqUjY1woNeyq3x_Q-eDDDEUl1mjjEmdvzQKEs48hQZEG53GZwFI8JfABzoa8GReEbGjGDCeK02ZzwQHD2VMMK4ac3sMBrEkIKW_TPZTpuoV5RBMuIvknQohUGgWYyfjidfWE6OCwpUxm8eZl9cyZJeGrh35T_fj08fvtl_ru2-evt4e72jZdm-tRjE4qMXI1GTX0csBJdOiUaM1QTjU9Kt4KqZqmYXJsW8fGVo2Od9Y1VvZM3lRvr7rlmF8bpqxXnywuiwkYt6R51wghmez7_0CFGpTomCqouqLldykROn0ivxq615zpS3z6qB_j05f4NCv1d_PNg4lJJQ5HJlifHtdF37aCC164w5XD8pyzR9LJegwWJ09os56i_6fXH7Y0t5E</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1627972607</pqid></control><display><type>article</type><title>Defining agro-ecological regions for field crops in variable target production environments: A case study on mungbean in the northern grains region of Australia</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Chauhan, Y.S. ; Rachaputi, Rao C.N.</creator><creatorcontrib>Chauhan, Y.S. ; Rachaputi, Rao C.N.</creatorcontrib><description>•The APSIM model was used to characterise mungbean growing areas of Australia.•The model characterised four drought target production environments (TPE).•Percentile ranks of simulated seasonal yield defined seven agro-ecoregions (AER).•Each AER had geographically contiguous locations having similar frequencies of TPE.•Locations within each AER could be used as selection environments for different TPE. The northern grains region (NGR) of Australia, which includes the state of Queensland and the northern half of the New South Wales, has highly variable climate leading to heightened production risk for all rainfed crops. Characterisation of the production environment of this region can assist in exploration of potential opportunities for reducing this risk. In this case study on mungbean (Vigna radiata L. Wilczek.) we demonstrate how this region could be characterised using the Agricultural Production Systems sIMulator (APSIM) model. The model was first evaluated for variety Crystal grown widely in the region, and then applied to simulate a water stress index (the daily supply and demand ratio) and yield from 1889 to 2012 at 28 locations. The model was run using location specific as well as three generic soils of 136, 166 and 204mm plant available water holding capacities (PAWC). Two complementary characterisations were performed using the simulated output, one based on clustering of supply demand ratio averaged for every 100°Cd to and from flowering, and another on clustering of percentile rankings of seasonal yield variation at different locations. Clustering of supply demand ratio revealed four drought patterns (i.e., target production environments) which commenced at different times from flowering. Seasonal frequencies of these drought patterns, which differed due to major location effects and relatively smaller soil effects, accounted for significant (∼84%) variation in simulated yield. Clustering of percentile ranks corresponding to simulated yield in different seasons identified seven meaningful yield clusters. Location memberships of these yield clusters were geographically contiguous and were only slightly influenced for the lowest PAWC generic soil. All locations within these yield clusters showed a tendency to have similar seasonal drought patterns and their frequencies. Locations within different yield clusters could therefore be considered as part of distinct agro-ecoregions. These model defined agro-ecoregions could be used as selection environments for their dominant target production environment(s) to develop new genotypes and their agronomy for better adaptation and yield under variable climatic conditions.</description><identifier>ISSN: 0168-1923</identifier><identifier>EISSN: 1873-2240</identifier><identifier>DOI: 10.1016/j.agrformet.2014.04.007</identifier><identifier>CODEN: AFMEEB</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage ; Agronomy. Soil science and plant productions ; APSIM ; Biological and medical sciences ; Climate variability ; Clustering ; Clusters ; Computer simulation ; Droughts ; Flowering ; Fundamental and applied biological sciences. Psychology ; General agroecology ; General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping ; General agronomy. Plant production ; Generalities. Agricultural and farming systems. Agricultural development ; Mathematical models ; Modelling ; Risk ; Soils ; Target production environments ; Vigna radiata ; Vigna radiata L. Wilczek</subject><ispartof>Agricultural and forest meteorology, 2014-08, Vol.194, p.207-217</ispartof><rights>2014</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c465t-b2bf372b17da79839ed26ef725a9127a8e71523744403b55f0b57bf16cf4c3803</citedby><cites>FETCH-LOGICAL-c465t-b2bf372b17da79839ed26ef725a9127a8e71523744403b55f0b57bf16cf4c3803</cites><orcidid>0000-0002-0135-6362</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.agrformet.2014.04.007$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&amp;idt=28552121$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Chauhan, Y.S.</creatorcontrib><creatorcontrib>Rachaputi, Rao C.N.</creatorcontrib><title>Defining agro-ecological regions for field crops in variable target production environments: A case study on mungbean in the northern grains region of Australia</title><title>Agricultural and forest meteorology</title><description>•The APSIM model was used to characterise mungbean growing areas of Australia.•The model characterised four drought target production environments (TPE).•Percentile ranks of simulated seasonal yield defined seven agro-ecoregions (AER).•Each AER had geographically contiguous locations having similar frequencies of TPE.•Locations within each AER could be used as selection environments for different TPE. The northern grains region (NGR) of Australia, which includes the state of Queensland and the northern half of the New South Wales, has highly variable climate leading to heightened production risk for all rainfed crops. Characterisation of the production environment of this region can assist in exploration of potential opportunities for reducing this risk. In this case study on mungbean (Vigna radiata L. Wilczek.) we demonstrate how this region could be characterised using the Agricultural Production Systems sIMulator (APSIM) model. The model was first evaluated for variety Crystal grown widely in the region, and then applied to simulate a water stress index (the daily supply and demand ratio) and yield from 1889 to 2012 at 28 locations. The model was run using location specific as well as three generic soils of 136, 166 and 204mm plant available water holding capacities (PAWC). Two complementary characterisations were performed using the simulated output, one based on clustering of supply demand ratio averaged for every 100°Cd to and from flowering, and another on clustering of percentile rankings of seasonal yield variation at different locations. Clustering of supply demand ratio revealed four drought patterns (i.e., target production environments) which commenced at different times from flowering. Seasonal frequencies of these drought patterns, which differed due to major location effects and relatively smaller soil effects, accounted for significant (∼84%) variation in simulated yield. Clustering of percentile ranks corresponding to simulated yield in different seasons identified seven meaningful yield clusters. Location memberships of these yield clusters were geographically contiguous and were only slightly influenced for the lowest PAWC generic soil. All locations within these yield clusters showed a tendency to have similar seasonal drought patterns and their frequencies. Locations within different yield clusters could therefore be considered as part of distinct agro-ecoregions. These model defined agro-ecoregions could be used as selection environments for their dominant target production environment(s) to develop new genotypes and their agronomy for better adaptation and yield under variable climatic conditions.</description><subject>Agricultural and forest climatology and meteorology. Irrigation. Drainage</subject><subject>Agronomy. Soil science and plant productions</subject><subject>APSIM</subject><subject>Biological and medical sciences</subject><subject>Climate variability</subject><subject>Clustering</subject><subject>Clusters</subject><subject>Computer simulation</subject><subject>Droughts</subject><subject>Flowering</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General agroecology</subject><subject>General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping</subject><subject>General agronomy. Plant production</subject><subject>Generalities. Agricultural and farming systems. Agricultural development</subject><subject>Mathematical models</subject><subject>Modelling</subject><subject>Risk</subject><subject>Soils</subject><subject>Target production environments</subject><subject>Vigna radiata</subject><subject>Vigna radiata L. Wilczek</subject><issn>0168-1923</issn><issn>1873-2240</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkcuO1DAQRSMEEs3AN1AbJDZp_EjihF1reEojsYG15Tjl4FZiN2WnpfkbPhU3PZotSCXVok7dq6pbVa8523PGu3fHvZnJRVox7wXjzZ6VYupJteO9krUQDXta7QrZ13wQ8nn1IqUjY1woNeyq3x_Q-eDDDEUl1mjjEmdvzQKEs48hQZEG53GZwFI8JfABzoa8GReEbGjGDCeK02ZzwQHD2VMMK4ac3sMBrEkIKW_TPZTpuoV5RBMuIvknQohUGgWYyfjidfWE6OCwpUxm8eZl9cyZJeGrh35T_fj08fvtl_ru2-evt4e72jZdm-tRjE4qMXI1GTX0csBJdOiUaM1QTjU9Kt4KqZqmYXJsW8fGVo2Od9Y1VvZM3lRvr7rlmF8bpqxXnywuiwkYt6R51wghmez7_0CFGpTomCqouqLldykROn0ivxq615zpS3z6qB_j05f4NCv1d_PNg4lJJQ5HJlifHtdF37aCC164w5XD8pyzR9LJegwWJ09os56i_6fXH7Y0t5E</recordid><startdate>20140815</startdate><enddate>20140815</enddate><creator>Chauhan, Y.S.</creator><creator>Rachaputi, Rao C.N.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>KL.</scope><scope>SOI</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-0135-6362</orcidid></search><sort><creationdate>20140815</creationdate><title>Defining agro-ecological regions for field crops in variable target production environments: A case study on mungbean in the northern grains region of Australia</title><author>Chauhan, Y.S. ; Rachaputi, Rao C.N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c465t-b2bf372b17da79839ed26ef725a9127a8e71523744403b55f0b57bf16cf4c3803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Agricultural and forest climatology and meteorology. Irrigation. Drainage</topic><topic>Agronomy. Soil science and plant productions</topic><topic>APSIM</topic><topic>Biological and medical sciences</topic><topic>Climate variability</topic><topic>Clustering</topic><topic>Clusters</topic><topic>Computer simulation</topic><topic>Droughts</topic><topic>Flowering</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General agroecology</topic><topic>General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping</topic><topic>General agronomy. Plant production</topic><topic>Generalities. Agricultural and farming systems. Agricultural development</topic><topic>Mathematical models</topic><topic>Modelling</topic><topic>Risk</topic><topic>Soils</topic><topic>Target production environments</topic><topic>Vigna radiata</topic><topic>Vigna radiata L. Wilczek</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chauhan, Y.S.</creatorcontrib><creatorcontrib>Rachaputi, Rao C.N.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Agricultural and forest meteorology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chauhan, Y.S.</au><au>Rachaputi, Rao C.N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Defining agro-ecological regions for field crops in variable target production environments: A case study on mungbean in the northern grains region of Australia</atitle><jtitle>Agricultural and forest meteorology</jtitle><date>2014-08-15</date><risdate>2014</risdate><volume>194</volume><spage>207</spage><epage>217</epage><pages>207-217</pages><issn>0168-1923</issn><eissn>1873-2240</eissn><coden>AFMEEB</coden><abstract>•The APSIM model was used to characterise mungbean growing areas of Australia.•The model characterised four drought target production environments (TPE).•Percentile ranks of simulated seasonal yield defined seven agro-ecoregions (AER).•Each AER had geographically contiguous locations having similar frequencies of TPE.•Locations within each AER could be used as selection environments for different TPE. The northern grains region (NGR) of Australia, which includes the state of Queensland and the northern half of the New South Wales, has highly variable climate leading to heightened production risk for all rainfed crops. Characterisation of the production environment of this region can assist in exploration of potential opportunities for reducing this risk. In this case study on mungbean (Vigna radiata L. Wilczek.) we demonstrate how this region could be characterised using the Agricultural Production Systems sIMulator (APSIM) model. The model was first evaluated for variety Crystal grown widely in the region, and then applied to simulate a water stress index (the daily supply and demand ratio) and yield from 1889 to 2012 at 28 locations. The model was run using location specific as well as three generic soils of 136, 166 and 204mm plant available water holding capacities (PAWC). Two complementary characterisations were performed using the simulated output, one based on clustering of supply demand ratio averaged for every 100°Cd to and from flowering, and another on clustering of percentile rankings of seasonal yield variation at different locations. Clustering of supply demand ratio revealed four drought patterns (i.e., target production environments) which commenced at different times from flowering. Seasonal frequencies of these drought patterns, which differed due to major location effects and relatively smaller soil effects, accounted for significant (∼84%) variation in simulated yield. Clustering of percentile ranks corresponding to simulated yield in different seasons identified seven meaningful yield clusters. Location memberships of these yield clusters were geographically contiguous and were only slightly influenced for the lowest PAWC generic soil. All locations within these yield clusters showed a tendency to have similar seasonal drought patterns and their frequencies. Locations within different yield clusters could therefore be considered as part of distinct agro-ecoregions. These model defined agro-ecoregions could be used as selection environments for their dominant target production environment(s) to develop new genotypes and their agronomy for better adaptation and yield under variable climatic conditions.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.agrformet.2014.04.007</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-0135-6362</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0168-1923
ispartof Agricultural and forest meteorology, 2014-08, Vol.194, p.207-217
issn 0168-1923
1873-2240
language eng
recordid cdi_proquest_miscellaneous_1642230388
source Elsevier ScienceDirect Journals Complete
subjects Agricultural and forest climatology and meteorology. Irrigation. Drainage
Agronomy. Soil science and plant productions
APSIM
Biological and medical sciences
Climate variability
Clustering
Clusters
Computer simulation
Droughts
Flowering
Fundamental and applied biological sciences. Psychology
General agroecology
General agroecology. Agricultural and farming systems. Agricultural development. Rural area planning. Landscaping
General agronomy. Plant production
Generalities. Agricultural and farming systems. Agricultural development
Mathematical models
Modelling
Risk
Soils
Target production environments
Vigna radiata
Vigna radiata L. Wilczek
title Defining agro-ecological regions for field crops in variable target production environments: A case study on mungbean in the northern grains region of Australia
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T04%3A09%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Defining%20agro-ecological%20regions%20for%20field%20crops%20in%20variable%20target%20production%20environments:%20A%20case%20study%20on%20mungbean%20in%20the%20northern%20grains%20region%20of%20Australia&rft.jtitle=Agricultural%20and%20forest%20meteorology&rft.au=Chauhan,%20Y.S.&rft.date=2014-08-15&rft.volume=194&rft.spage=207&rft.epage=217&rft.pages=207-217&rft.issn=0168-1923&rft.eissn=1873-2240&rft.coden=AFMEEB&rft_id=info:doi/10.1016/j.agrformet.2014.04.007&rft_dat=%3Cproquest_cross%3E1627972607%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1627972607&rft_id=info:pmid/&rft_els_id=S0168192314001075&rfr_iscdi=true