Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment
The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed...
Gespeichert in:
| Veröffentlicht in: | International journal of biometeorology 2018-08, Vol.62 (8), p.1543-1556 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1556 |
|---|---|
| container_issue | 8 |
| container_start_page | 1543 |
| container_title | International journal of biometeorology |
| container_volume | 62 |
| creator | Lashkari, A. Salehnia, N. Asadi, S. Paymard, P. Zare, H. Bannayan, M. |
| description | The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000–2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (
d
) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values,
r
, and
d
values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAI
max
were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (
r
2
≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data. |
| doi_str_mv | 10.1007/s00484-018-1555-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2036789387</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2036310372</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-7b1cb8648f9086128769fc120aab7de2632b6544be492e72a4d8e3998ac2092c3</originalsourceid><addsrcrecordid>eNp1kctKxTAQhoMoejz6AG4k4MZNdZKmTboU8QaCG12HtJlqtCc9Jq2XtzfHegFBCAQy33wz5Cdkj8ERA5DHEUAokQFTGSuKIntbIzMmcp4xXoh1MgPgkEnG1RbZjvERUo8q5SbZ4pUUIIHPyNPZi-lGM7je076l1rUtBvQDvQ_OWrQ0GOdb03XUmsFEHCJt-zC9purrA5qBvjvsLF0GtK75NDlPTTpJQdG_uND7RXLukI1kirj7dc_J3fnZ7elldn1zcXV6cp01ueRDJmvW1KoUqq1AlWl7WVZtwzgYU0uLvMx5XRZC1CgqjpIbYRXmVaVMw6HiTT4nh5N3GfrnEeOgFy422HXGYz9GzSEvpapyJRN68Ad97Mfg03afVM4grZQoNlFN6GMM2OplcAsT3jUDvUpCT0nolIReJaHfUs_-l3msF2h_Or6_PgF8AmIq-XsMv6P_t34AA6GUQQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2036310372</pqid></control><display><type>article</type><title>Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment</title><source>SpringerLink Journals - AutoHoldings</source><creator>Lashkari, A. ; Salehnia, N. ; Asadi, S. ; Paymard, P. ; Zare, H. ; Bannayan, M.</creator><creatorcontrib>Lashkari, A. ; Salehnia, N. ; Asadi, S. ; Paymard, P. ; Zare, H. ; Bannayan, M.</creatorcontrib><description>The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000–2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (
d
) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values,
r
, and
d
values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAI
max
were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (
r
2
≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.</description><identifier>ISSN: 0020-7128</identifier><identifier>EISSN: 1432-1254</identifier><identifier>DOI: 10.1007/s00484-018-1555-x</identifier><identifier>PMID: 29740702</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agricultural production ; Animal Physiology ; Arid environments ; Aridity ; Artificial neural networks ; Biological and Medical Physics ; Biophysics ; Cereal crops ; Computer simulation ; Crop growth ; Crop yield ; Daily precipitation ; Data integration ; Datasets ; Dry matter ; Earth and Environmental Science ; Environment ; Environmental Health ; Grain ; Ground stations ; Hydrologic data ; Leaf area ; Leaf area index ; Meteorology ; Missing data ; Neural networks ; Original Paper ; Plant Physiology ; Precipitation ; Precipitation data ; Rainfall ; Rainfall estimation ; Remote sensing ; Root-mean-square errors ; Solar radiation ; Temperature data ; Tropical rainfall ; Tropical Rainfall Measuring Mission (TRMM) ; Wheat ; Wheat yield</subject><ispartof>International journal of biometeorology, 2018-08, Vol.62 (8), p.1543-1556</ispartof><rights>ISB 2018</rights><rights>International Journal of Biometeorology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-7b1cb8648f9086128769fc120aab7de2632b6544be492e72a4d8e3998ac2092c3</citedby><cites>FETCH-LOGICAL-c372t-7b1cb8648f9086128769fc120aab7de2632b6544be492e72a4d8e3998ac2092c3</cites><orcidid>0000-0002-0395-581X</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/s00484-018-1555-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00484-018-1555-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29740702$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lashkari, A.</creatorcontrib><creatorcontrib>Salehnia, N.</creatorcontrib><creatorcontrib>Asadi, S.</creatorcontrib><creatorcontrib>Paymard, P.</creatorcontrib><creatorcontrib>Zare, H.</creatorcontrib><creatorcontrib>Bannayan, M.</creatorcontrib><title>Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment</title><title>International journal of biometeorology</title><addtitle>Int J Biometeorol</addtitle><addtitle>Int J Biometeorol</addtitle><description>The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000–2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (
d
) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values,
r
, and
d
values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAI
max
were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (
r
2
≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.</description><subject>Agricultural production</subject><subject>Animal Physiology</subject><subject>Arid environments</subject><subject>Aridity</subject><subject>Artificial neural networks</subject><subject>Biological and Medical Physics</subject><subject>Biophysics</subject><subject>Cereal crops</subject><subject>Computer simulation</subject><subject>Crop growth</subject><subject>Crop yield</subject><subject>Daily precipitation</subject><subject>Data integration</subject><subject>Datasets</subject><subject>Dry matter</subject><subject>Earth and Environmental Science</subject><subject>Environment</subject><subject>Environmental Health</subject><subject>Grain</subject><subject>Ground stations</subject><subject>Hydrologic data</subject><subject>Leaf area</subject><subject>Leaf area index</subject><subject>Meteorology</subject><subject>Missing data</subject><subject>Neural networks</subject><subject>Original Paper</subject><subject>Plant Physiology</subject><subject>Precipitation</subject><subject>Precipitation data</subject><subject>Rainfall</subject><subject>Rainfall estimation</subject><subject>Remote sensing</subject><subject>Root-mean-square errors</subject><subject>Solar radiation</subject><subject>Temperature data</subject><subject>Tropical rainfall</subject><subject>Tropical Rainfall Measuring Mission (TRMM)</subject><subject>Wheat</subject><subject>Wheat yield</subject><issn>0020-7128</issn><issn>1432-1254</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kctKxTAQhoMoejz6AG4k4MZNdZKmTboU8QaCG12HtJlqtCc9Jq2XtzfHegFBCAQy33wz5Cdkj8ERA5DHEUAokQFTGSuKIntbIzMmcp4xXoh1MgPgkEnG1RbZjvERUo8q5SbZ4pUUIIHPyNPZi-lGM7je076l1rUtBvQDvQ_OWrQ0GOdb03XUmsFEHCJt-zC9purrA5qBvjvsLF0GtK75NDlPTTpJQdG_uND7RXLukI1kirj7dc_J3fnZ7elldn1zcXV6cp01ueRDJmvW1KoUqq1AlWl7WVZtwzgYU0uLvMx5XRZC1CgqjpIbYRXmVaVMw6HiTT4nh5N3GfrnEeOgFy422HXGYz9GzSEvpapyJRN68Ad97Mfg03afVM4grZQoNlFN6GMM2OplcAsT3jUDvUpCT0nolIReJaHfUs_-l3msF2h_Or6_PgF8AmIq-XsMv6P_t34AA6GUQQ</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Lashkari, A.</creator><creator>Salehnia, N.</creator><creator>Asadi, S.</creator><creator>Paymard, P.</creator><creator>Zare, H.</creator><creator>Bannayan, M.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88F</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KL.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M1Q</scope><scope>M2P</scope><scope>M7P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-0395-581X</orcidid></search><sort><creationdate>20180801</creationdate><title>Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment</title><author>Lashkari, A. ; Salehnia, N. ; Asadi, S. ; Paymard, P. ; Zare, H. ; Bannayan, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-7b1cb8648f9086128769fc120aab7de2632b6544be492e72a4d8e3998ac2092c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Agricultural production</topic><topic>Animal Physiology</topic><topic>Arid environments</topic><topic>Aridity</topic><topic>Artificial neural networks</topic><topic>Biological and Medical Physics</topic><topic>Biophysics</topic><topic>Cereal crops</topic><topic>Computer simulation</topic><topic>Crop growth</topic><topic>Crop yield</topic><topic>Daily precipitation</topic><topic>Data integration</topic><topic>Datasets</topic><topic>Dry matter</topic><topic>Earth and Environmental Science</topic><topic>Environment</topic><topic>Environmental Health</topic><topic>Grain</topic><topic>Ground stations</topic><topic>Hydrologic data</topic><topic>Leaf area</topic><topic>Leaf area index</topic><topic>Meteorology</topic><topic>Missing data</topic><topic>Neural networks</topic><topic>Original Paper</topic><topic>Plant Physiology</topic><topic>Precipitation</topic><topic>Precipitation data</topic><topic>Rainfall</topic><topic>Rainfall estimation</topic><topic>Remote sensing</topic><topic>Root-mean-square errors</topic><topic>Solar radiation</topic><topic>Temperature data</topic><topic>Tropical rainfall</topic><topic>Tropical Rainfall Measuring Mission (TRMM)</topic><topic>Wheat</topic><topic>Wheat yield</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lashkari, A.</creatorcontrib><creatorcontrib>Salehnia, N.</creatorcontrib><creatorcontrib>Asadi, S.</creatorcontrib><creatorcontrib>Paymard, P.</creatorcontrib><creatorcontrib>Zare, H.</creatorcontrib><creatorcontrib>Bannayan, M.</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>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 Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Military Database</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Environmental Science 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>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>International journal of biometeorology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lashkari, A.</au><au>Salehnia, N.</au><au>Asadi, S.</au><au>Paymard, P.</au><au>Zare, H.</au><au>Bannayan, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment</atitle><jtitle>International journal of biometeorology</jtitle><stitle>Int J Biometeorol</stitle><addtitle>Int J Biometeorol</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>62</volume><issue>8</issue><spage>1543</spage><epage>1556</epage><pages>1543-1556</pages><issn>0020-7128</issn><eissn>1432-1254</eissn><abstract>The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000–2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (
d
) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values,
r
, and
d
values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAI
max
were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (
r
2
≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>29740702</pmid><doi>10.1007/s00484-018-1555-x</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-0395-581X</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0020-7128 |
| ispartof | International journal of biometeorology, 2018-08, Vol.62 (8), p.1543-1556 |
| issn | 0020-7128 1432-1254 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2036789387 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Agricultural production Animal Physiology Arid environments Aridity Artificial neural networks Biological and Medical Physics Biophysics Cereal crops Computer simulation Crop growth Crop yield Daily precipitation Data integration Datasets Dry matter Earth and Environmental Science Environment Environmental Health Grain Ground stations Hydrologic data Leaf area Leaf area index Meteorology Missing data Neural networks Original Paper Plant Physiology Precipitation Precipitation data Rainfall Rainfall estimation Remote sensing Root-mean-square errors Solar radiation Temperature data Tropical rainfall Tropical Rainfall Measuring Mission (TRMM) Wheat Wheat yield |
| title | Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T16%3A48%3A47IST&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=Evaluation%20of%20different%20gridded%20rainfall%20datasets%20for%20rainfed%20wheat%20yield%20prediction%20in%20an%20arid%20environment&rft.jtitle=International%20journal%20of%20biometeorology&rft.au=Lashkari,%20A.&rft.date=2018-08-01&rft.volume=62&rft.issue=8&rft.spage=1543&rft.epage=1556&rft.pages=1543-1556&rft.issn=0020-7128&rft.eissn=1432-1254&rft_id=info:doi/10.1007/s00484-018-1555-x&rft_dat=%3Cproquest_cross%3E2036310372%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=2036310372&rft_id=info:pmid/29740702&rfr_iscdi=true |