On the use of statistical models to predict crop yield responses to climate change
▶ Statistical models are able to reproduce many of the key features of process-based model responses to warming and precipitation changes. ▶ Time-series models are better suited for predicting response to precipitation than temperature, whereas panel or cross-section models are better suited for tem...
Uloženo v:
| Vydáno v: | Agricultural and forest meteorology Ročník 150; číslo 11; s. 1443 - 1452 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
15.10.2010
[Oxford]: Elsevier Science Ltd Elsevier |
| Témata: | |
| ISSN: | 0168-1923, 1873-2240 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | ▶ Statistical models are able to reproduce many of the key features of process-based model responses to warming and precipitation changes. ▶ Time-series models are better suited for predicting response to precipitation than temperature, whereas panel or cross-section models are better suited for temperature. ▶ The skill of statistical models that use growing season average temperature and precipitation improves as the spatial scale of analysis becomes broader.
Predicting the potential effects of climate change on crop yields requires a model of how crops respond to weather. As predictions from different models often disagree, understanding the sources of this divergence is central to building a more robust picture of climate change's likely impacts. A common approach is to use statistical models trained on historical yields and some simplified measurements of weather, such as growing season average temperature and precipitation. Although the general strengths and weaknesses of statistical models are widely understood, there has been little systematic evaluation of their performance relative to other methods. Here we use a perfect model approach to examine the ability of statistical models to predict yield responses to changes in mean temperature and precipitation, as simulated by a process-based crop model. The CERES-Maize model was first used to simulate historical maize yield variability at nearly 200 sites in Sub-Saharan Africa, as well as the impacts of hypothetical future scenarios of 2
°C warming and 20% precipitation reduction. Statistical models of three types (time series, panel, and cross-sectional models) were then trained on the simulated historical variability and used to predict the responses to the future climate changes. The agreement between the process-based and statistical models’ predictions was then assessed as a measure of how well statistical models can capture crop responses to warming or precipitation changes. The performance of statistical models differed by climate variable and spatial scale, with time-series statistical models ably reproducing site-specific yield response to precipitation change, but performing less well for temperature responses. In contrast, statistical models that relied on information from multiple sites, namely panel and cross-sectional models, were better at predicting responses to temperature change than precipitation change. The models based on multiple sites were also much less sensitive to the length of historical period used for training. For all three statistical approaches, the performance improved when individual sites were first aggregated to country-level averages. Results suggest that statistical models, as compared to CERES-Maize, represent a useful if imperfect tool for projecting future yield responses, with their usefulness higher at broader spatial scales. It is also at these broader scales that climate projections are most available and reliable, and therefore statistical models are likely to continue to play an important role in anticipating future impacts of climate change. |
|---|---|
| AbstractList | Statistical models are able to reproduce many of the key features of process-based model responses to warming and precipitation changes. Time-series models are better suited for predicting response to precipitation than temperature, whereas panel or cross-section models are better suited for temperature. The skill of statistical models that use growing season average temperature and precipitation improves as the spatial scale of analysis becomes broader. Predicting the potential effects of climate change on crop yields requires a model of how crops respond to weather. As predictions from different models often disagree, understanding the sources of this divergence is central to building a more robust picture of climate change's likely impacts. A common approach is to use statistical models trained on historical yields and some simplified measurements of weather, such as growing season average temperature and precipitation. Although the general strengths and weaknesses of statistical models are widely understood, there has been little systematic evaluation of their performance relative to other methods. Here we use a perfect model approach to examine the ability of statistical models to predict yield responses to changes in mean temperature and precipitation, as simulated by a process-based crop model. The CERES-Maize model was first used to simulate historical maize yield variability at nearly 200 sites in Sub-Saharan Africa, as well as the impacts of hypothetical future scenarios of 2 degree warming and 20% precipitation reduction. Statistical models of three types (time series, panel, and cross-sectional models) were then trained on the simulated historical variability and used to predict the responses to the future climate changes. The agreement between the process-based and statistical models' predictions was then assessed as a measure of how well statistical models can capture crop responses to warming or precipitation changes. The performance of statistical models differed by climate variable and spatial scale, with time-series statistical models ably reproducing site-specific yield response to precipitation change, but performing less well for temperature responses. In contrast, statistical models that relied on information from multiple sites, namely panel and cross-sectional models, were better at predicting responses to temperature change than precipitation change. The models based on multiple sites were also much less sensitive to the length of historical period used for training. For all three statistical approaches, the performance improved when individual sites were first aggregated to country-level averages. Results suggest that statistical models, as compared to CERES-Maize, represent a useful if imperfect tool for projecting future yield responses, with their usefulness higher at broader spatial scales. It is also at these broader scales that climate projections are most available and reliable, and therefore statistical models are likely to continue to play an important role in anticipating future impacts of climate change. Predicting the potential effects of climate change on crop yields requires a model of how crops respond to weather. As predictions from different models often disagree, understanding the sources of this divergence is central to building a more robust picture of climate change's likely impacts. A common approach is to use statistical models trained on historical yields and some simplified measurements of weather, such as growing season average temperature and precipitation. Although the general strengths and weaknesses of statistical models are widely understood, there has been little systematic evaluation of their performance relative to other methods. Here we use a perfect model approach to examine the ability of statistical models to predict yield responses to changes in mean temperature and precipitation, as simulated by a process-based crop model. The CERES-Maize model was first used to simulate historical maize yield variability at nearly 200 sites in Sub-Saharan Africa, as well as the impacts of hypothetical future scenarios of 2°C warming and 20% precipitation reduction. Statistical models of three types (time series, panel, and cross-sectional models) were then trained on the simulated historical variability and used to predict the responses to the future climate changes. The agreement between the process-based and statistical models' predictions was then assessed as a measure of how well statistical models can capture crop responses to warming or precipitation changes. The performance of statistical models differed by climate variable and spatial scale, with time-series statistical models ably reproducing site-specific yield response to precipitation change, but performing less well for temperature responses. In contrast, statistical models that relied on information from multiple sites, namely panel and cross-sectional models, were better at predicting responses to temperature change than precipitation change. The models based on multiple sites were also much less sensitive to the length of historical period used for training. For all three statistical approaches, the performance improved when individual sites were first aggregated to country-level averages. Results suggest that statistical models, as compared to CERES-Maize, represent a useful if imperfect tool for projecting future yield responses, with their usefulness higher at broader spatial scales. It is also at these broader scales that climate projections are most available and reliable, and therefore statistical models are likely to continue to play an important role in anticipating future impacts of climate change. ▶ Statistical models are able to reproduce many of the key features of process-based model responses to warming and precipitation changes. ▶ Time-series models are better suited for predicting response to precipitation than temperature, whereas panel or cross-section models are better suited for temperature. ▶ The skill of statistical models that use growing season average temperature and precipitation improves as the spatial scale of analysis becomes broader. Predicting the potential effects of climate change on crop yields requires a model of how crops respond to weather. As predictions from different models often disagree, understanding the sources of this divergence is central to building a more robust picture of climate change's likely impacts. A common approach is to use statistical models trained on historical yields and some simplified measurements of weather, such as growing season average temperature and precipitation. Although the general strengths and weaknesses of statistical models are widely understood, there has been little systematic evaluation of their performance relative to other methods. Here we use a perfect model approach to examine the ability of statistical models to predict yield responses to changes in mean temperature and precipitation, as simulated by a process-based crop model. The CERES-Maize model was first used to simulate historical maize yield variability at nearly 200 sites in Sub-Saharan Africa, as well as the impacts of hypothetical future scenarios of 2 °C warming and 20% precipitation reduction. Statistical models of three types (time series, panel, and cross-sectional models) were then trained on the simulated historical variability and used to predict the responses to the future climate changes. The agreement between the process-based and statistical models’ predictions was then assessed as a measure of how well statistical models can capture crop responses to warming or precipitation changes. The performance of statistical models differed by climate variable and spatial scale, with time-series statistical models ably reproducing site-specific yield response to precipitation change, but performing less well for temperature responses. In contrast, statistical models that relied on information from multiple sites, namely panel and cross-sectional models, were better at predicting responses to temperature change than precipitation change. The models based on multiple sites were also much less sensitive to the length of historical period used for training. For all three statistical approaches, the performance improved when individual sites were first aggregated to country-level averages. Results suggest that statistical models, as compared to CERES-Maize, represent a useful if imperfect tool for projecting future yield responses, with their usefulness higher at broader spatial scales. It is also at these broader scales that climate projections are most available and reliable, and therefore statistical models are likely to continue to play an important role in anticipating future impacts of climate change. |
| Author | Lobell, David B. Burke, Marshall B. |
| Author_xml | – sequence: 1 givenname: David B. surname: Lobell fullname: Lobell, David B. email: dlobell@stanford.edu, dlobell@stanfordalumni.org organization: Department of Environmental Earth System Science, Stanford University, Stanford, CA 94305, United States – sequence: 2 givenname: Marshall B. surname: Burke fullname: Burke, Marshall B. organization: Program on Food Security and Environment, Stanford University, Stanford, CA 94305, United States |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23303393$$DView record in Pascal Francis |
| BookMark | eNqNkc1u3CAURlGVSJ0kfYawqdqNpxcYG7zoIor6J0WKlDRrhK-vE0Ye4wITKW9f3Em76KLpCgnOx4XznbCjKUzE2LmAtQDRfNiu3X0cQtxRXksou6DXAOYVWwmjVSXlBo7YqpCmEq1Ur9lJSlsAIbVuV-zmeuL5gfg-EQ8DT9lln7JHN_Jd6GlMPAc-R-o9Zo4xzPzJ09jzSGkOU6Jf5zj6ncvE8cFN93TGjgc3JnrzvJ6yu8-fvl9-ra6uv3y7vLiqsFYyV73sQaNopKAWTL9pjRuo7zabRnfOIOCgGyFqo9ANfSdc2zVYEKgbxFboTp2yd4d75xh-7Cllu_MJaRzdRGGfrJGyLj8G8V8kSGhkId__kywWawMN1G1B3z6jLhVdQ3QT-mTnWFzEJyuVAqVaVbiPB67YSynSYNEvlsOUo_OjFWCXHu3W_unRLj1a0Lb0WPL6r_zvES8nzw_JwYUFKa-7u118gDDFIDSFuDgQpWh69BRtQk8TlrojYbZ98C9O-QnV58ha |
| CODEN | AFMEEB |
| CitedBy_id | crossref_primary_10_1016_j_enpol_2018_10_016 crossref_primary_10_1080_03650340_2020_1819531 crossref_primary_10_1002_joc_6792 crossref_primary_10_1007_s11442_013_1029_3 crossref_primary_10_1016_j_jclepro_2020_122333 crossref_primary_10_3390_rs14061474 crossref_primary_10_1007_s00122_021_03773_7 crossref_primary_10_3389_fpls_2021_749854 crossref_primary_10_1371_journal_pone_0305762 crossref_primary_10_3390_econometrics9020024 crossref_primary_10_3390_rs16010069 crossref_primary_10_3390_jimaging4040052 crossref_primary_10_1016_j_agrformet_2024_110242 crossref_primary_10_1016_j_sajb_2023_03_059 crossref_primary_10_1111_pce_13466 crossref_primary_10_1080_23322039_2024_2345437 crossref_primary_10_3389_fsufs_2023_1101717 crossref_primary_10_1109_JSEN_2024_3488085 crossref_primary_10_3354_cr01307 crossref_primary_10_1016_j_ecolmodel_2025_111132 crossref_primary_10_1186_s40066_018_0228_7 crossref_primary_10_1016_j_eja_2025_127519 crossref_primary_10_1016_j_agrformet_2015_10_004 crossref_primary_10_1038_s41598_021_93061_7 crossref_primary_10_1016_j_agrformet_2015_10_005 crossref_primary_10_1016_j_agrformet_2016_12_010 crossref_primary_10_1175_WCAS_D_17_0036_1 crossref_primary_10_1002_ece3_782 crossref_primary_10_3390_app11167352 crossref_primary_10_1016_j_agrformet_2020_108154 crossref_primary_10_1007_s11707_017_0665_9 crossref_primary_10_1007_s10584_017_2054_5 crossref_primary_10_3390_su11236659 crossref_primary_10_3389_frai_2021_647999 crossref_primary_10_1088_1748_9326_aa6cb9 crossref_primary_10_1088_1748_9326_aae159 crossref_primary_10_1016_j_worlddev_2017_02_008 crossref_primary_10_3390_agriculture8120197 crossref_primary_10_1016_j_worlddev_2024_106683 crossref_primary_10_1080_02626667_2024_2377353 crossref_primary_10_3390_land11081293 crossref_primary_10_1016_j_agrformet_2021_108340 crossref_primary_10_1016_j_envc_2025_101095 crossref_primary_10_1016_j_jclepro_2019_117648 crossref_primary_10_1016_j_agsy_2010_12_006 crossref_primary_10_1007_s12571_023_01351_x crossref_primary_10_2166_wcc_2021_030 crossref_primary_10_3390_atmos4040365 crossref_primary_10_3390_rs14132975 crossref_primary_10_3390_cli5030054 crossref_primary_10_15544_mts_2015_13 crossref_primary_10_1016_j_agrformet_2014_01_013 crossref_primary_10_1016_j_econmod_2017_02_005 crossref_primary_10_1016_j_fcr_2019_02_005 crossref_primary_10_1094_CCHEM_07_14_0146_R crossref_primary_10_1139_cjps_2017_0135 crossref_primary_10_1016_j_ecolecon_2015_11_016 crossref_primary_10_1016_j_agee_2015_04_006 crossref_primary_10_1016_j_eja_2024_127254 crossref_primary_10_1016_j_eja_2024_127496 crossref_primary_10_1016_S2095_3119_20_63244_0 crossref_primary_10_3389_frai_2024_1312115 crossref_primary_10_1007_s40031_024_01029_8 crossref_primary_10_1002_agj2_21198 crossref_primary_10_3390_rs16173217 crossref_primary_10_1016_j_ecolind_2019_105991 crossref_primary_10_1093_jeea_jvab054 crossref_primary_10_1371_journal_pone_0191217 crossref_primary_10_3390_agriculture13122214 crossref_primary_10_1038_s41893_020_0560_3 crossref_primary_10_1093_jxb_erac503 crossref_primary_10_1016_j_gloplacha_2013_08_010 crossref_primary_10_1016_j_agsy_2024_104140 crossref_primary_10_1016_j_jag_2022_102823 crossref_primary_10_3389_fsufs_2023_1246347 crossref_primary_10_1016_j_eja_2018_05_006 crossref_primary_10_3390_rs13183582 crossref_primary_10_1016_j_jclepro_2020_123651 crossref_primary_10_1371_journal_pone_0236757 crossref_primary_10_2134_jeq2017_09_0374 crossref_primary_10_38124_ijisrt_25may118 crossref_primary_10_1016_j_wace_2021_100369 crossref_primary_10_1007_s00484_017_1470_6 crossref_primary_10_3389_fpls_2022_975976 crossref_primary_10_1093_hr_uhae144 crossref_primary_10_1007_s11356_021_13313_x crossref_primary_10_1007_s42106_019_00038_8 crossref_primary_10_1007_s42106_021_00179_9 crossref_primary_10_1016_j_agrformet_2012_04_007 crossref_primary_10_1016_j_eja_2023_127066 crossref_primary_10_1016_j_carbpol_2013_06_005 crossref_primary_10_3390_rs13122395 crossref_primary_10_1016_j_agwat_2022_107881 crossref_primary_10_1016_j_jclepro_2023_136543 crossref_primary_10_1002_2014MS000311 crossref_primary_10_1002_fes3_275 crossref_primary_10_1016_j_jue_2015_04_004 crossref_primary_10_1016_j_compag_2025_110847 crossref_primary_10_1142_S2010007814500110 crossref_primary_10_1017_S001447971300015X crossref_primary_10_1007_s12517_022_09654_7 crossref_primary_10_1007_s12571_013_0256_x crossref_primary_10_1016_j_agee_2018_09_006 crossref_primary_10_1007_s00704_022_04291_2 crossref_primary_10_1016_j_agrformet_2021_108369 crossref_primary_10_1016_j_ecolind_2021_108517 crossref_primary_10_1007_s11027_022_10000_1 crossref_primary_10_1016_j_eja_2019_125992 crossref_primary_10_1080_10106049_2022_2071476 crossref_primary_10_1016_j_fcr_2015_10_013 crossref_primary_10_1080_10920277_2023_2182792 crossref_primary_10_1007_s10113_015_0860_8 crossref_primary_10_1093_pcp_pcx141 crossref_primary_10_1016_j_fcr_2023_109044 crossref_primary_10_1109_LGRS_2022_3211444 crossref_primary_10_30910_turkjans_1656274 crossref_primary_10_3390_plants11151925 crossref_primary_10_1111_gcb_12660 crossref_primary_10_1016_j_scitotenv_2023_167265 crossref_primary_10_5194_esd_12_1371_2021 crossref_primary_10_1016_j_jclepro_2019_117853 crossref_primary_10_1111_gcb_12412 crossref_primary_10_3390_microorganisms10020279 crossref_primary_10_1016_j_agrformet_2019_107828 crossref_primary_10_1007_s41324_020_00346_6 crossref_primary_10_1038_s41586_025_09085_w crossref_primary_10_3390_e23111546 crossref_primary_10_3390_rs17030491 crossref_primary_10_1016_j_agrformet_2015_03_018 crossref_primary_10_1016_j_envsoft_2024_106119 crossref_primary_10_1038_srep33160 crossref_primary_10_1002_eco_2500 crossref_primary_10_1016_j_eja_2013_02_010 crossref_primary_10_1139_gen_2020_0175 crossref_primary_10_2139_ssrn_4222020 crossref_primary_10_3390_land10121389 crossref_primary_10_1007_s10113_014_0685_x crossref_primary_10_1016_j_gloplacha_2018_01_011 crossref_primary_10_4028_www_scientific_net_AMM_239_240_1165 crossref_primary_10_1080_17421772_2020_1754448 crossref_primary_10_1016_j_fcr_2023_109034 crossref_primary_10_1016_j_telpol_2022_102370 crossref_primary_10_1111_gcb_12325 crossref_primary_10_1016_j_agrformet_2016_12_022 crossref_primary_10_1016_j_compag_2019_01_011 crossref_primary_10_2478_picbe_2025_0111 crossref_primary_10_1016_j_scitotenv_2018_06_198 crossref_primary_10_3390_w11010038 crossref_primary_10_1016_j_scitotenv_2019_135355 crossref_primary_10_1007_s00382_012_1299_y crossref_primary_10_1007_s13132_023_01713_y crossref_primary_10_1007_s10668_022_02517_x crossref_primary_10_1088_1748_9326_abadcb crossref_primary_10_2135_cropsci2012_09_0545 crossref_primary_10_1016_j_gloenvcha_2022_102548 crossref_primary_10_3390_agronomy13030704 crossref_primary_10_1016_j_envdev_2016_09_001 crossref_primary_10_1016_j_plaphy_2023_107762 crossref_primary_10_5194_esd_9_119_2018 crossref_primary_10_1016_j_jeconom_2021_03_010 crossref_primary_10_3390_rs16040701 crossref_primary_10_1002_joc_4481 crossref_primary_10_1007_s11356_020_10655_w crossref_primary_10_1016_j_agrformet_2019_107808 crossref_primary_10_1038_s41598_024_62027_w crossref_primary_10_3390_agronomy10060809 crossref_primary_10_1108_IJCCSM_11_2022_0139 crossref_primary_10_3390_rs16050863 crossref_primary_10_1007_s11442_023_2150_6 crossref_primary_10_1016_j_agrformet_2012_03_015 crossref_primary_10_1016_j_agsy_2015_12_002 crossref_primary_10_1016_j_scitotenv_2020_140473 crossref_primary_10_1016_j_agsy_2020_103008 crossref_primary_10_1007_s00704_022_04218_x crossref_primary_10_3390_atmos11101097 crossref_primary_10_1007_s00704_014_1093_3 crossref_primary_10_3390_rs16132417 crossref_primary_10_1007_s00704_020_03492_x crossref_primary_10_1016_j_jhydrol_2020_125097 crossref_primary_10_3390_su12031216 crossref_primary_10_5018_economics_ejournal_ja_2015_10 crossref_primary_10_1007_s00704_016_1779_9 crossref_primary_10_1016_j_agrformet_2015_08_256 crossref_primary_10_1175_JAMC_D_14_0147_1 crossref_primary_10_1002_ldr_3699 crossref_primary_10_3390_atmos11121350 crossref_primary_10_1038_ncomms15212 crossref_primary_10_1016_j_catena_2025_108743 crossref_primary_10_1016_j_compag_2023_107876 crossref_primary_10_1080_01431161_2020_1871102 crossref_primary_10_1007_s10584_014_1062_y crossref_primary_10_1016_j_compag_2022_107101 crossref_primary_10_1007_s00484_024_02763_w crossref_primary_10_1108_MEQ_10_2021_0241 crossref_primary_10_1088_1748_9326_aa518a crossref_primary_10_3390_cli12100161 crossref_primary_10_1007_s11356_022_20722_z crossref_primary_10_3389_fclim_2022_976427 crossref_primary_10_3390_rs12020236 crossref_primary_10_1016_j_compag_2021_106557 crossref_primary_10_1093_ajae_aas035 crossref_primary_10_3390_w15244215 crossref_primary_10_1038_s41598_017_01599_2 crossref_primary_10_1007_s10584_011_0338_8 crossref_primary_10_1080_1343943X_2018_1459752 crossref_primary_10_1371_journal_pone_0308736 crossref_primary_10_14720_aas_2019_114_1_14 crossref_primary_10_1016_j_agrformet_2020_107931 crossref_primary_10_1016_j_agsy_2016_10_002 crossref_primary_10_1016_j_fcr_2016_02_008 crossref_primary_10_1016_j_compag_2025_110140 crossref_primary_10_1007_s00122_025_04984_y crossref_primary_10_1016_j_ecolmodel_2017_02_009 crossref_primary_10_1007_s40003_024_00719_5 crossref_primary_10_3354_cr01605 crossref_primary_10_1093_ajae_aau122 crossref_primary_10_1016_j_agrformet_2012_09_011 crossref_primary_10_3390_agriculture10030058 crossref_primary_10_1016_j_fcr_2013_08_012 crossref_primary_10_1016_j_gloenvcha_2016_12_006 crossref_primary_10_5194_gmd_18_1287_2025 crossref_primary_10_1016_j_envsoft_2018_07_017 crossref_primary_10_1007_s00704_020_03504_w crossref_primary_10_1002_cli2_70019 crossref_primary_10_2134_agronj13_0321 crossref_primary_10_1016_j_ecolind_2020_106935 crossref_primary_10_1016_j_ancene_2017_05_002 crossref_primary_10_1080_23311932_2017_1309739 crossref_primary_10_3390_su12239890 crossref_primary_10_1007_s10584_012_0428_2 crossref_primary_10_1007_s40808_021_01199_0 crossref_primary_10_1016_j_agsy_2018_01_010 crossref_primary_10_1016_j_scitotenv_2020_138235 crossref_primary_10_1142_S2010007825500150 crossref_primary_10_1007_s40333_022_0021_0 crossref_primary_10_1007_s13593_013_0179_0 crossref_primary_10_1093_jssam_smu024 crossref_primary_10_1016_j_envsoft_2015_03_007 crossref_primary_10_1007_s11430_024_1477_2 crossref_primary_10_3390_rs14133005 crossref_primary_10_1016_j_scitotenv_2021_145474 crossref_primary_10_1016_j_rse_2025_114638 crossref_primary_10_3390_rs14246290 crossref_primary_10_1016_j_eja_2024_127426 crossref_primary_10_3390_plants14060906 crossref_primary_10_1016_j_agrformet_2023_109458 crossref_primary_10_1088_2515_7620_adac33 crossref_primary_10_3390_rs16132342 crossref_primary_10_1080_17565529_2024_2329465 crossref_primary_10_1016_j_agrformet_2022_109186 crossref_primary_10_3390_su16177339 crossref_primary_10_1016_j_eja_2016_09_015 crossref_primary_10_3390_s20185293 crossref_primary_10_3390_agriculture14071093 crossref_primary_10_3390_atmos16010034 crossref_primary_10_1038_s41598_025_13453_x crossref_primary_10_1016_j_agrformet_2014_11_003 crossref_primary_10_2139_ssrn_5198832 crossref_primary_10_1088_1748_9326_ab5ebb crossref_primary_10_1002_ird_2566 crossref_primary_10_1002_agj2_21412 crossref_primary_10_1016_j_jag_2020_102126 crossref_primary_10_1016_j_pce_2020_102866 crossref_primary_10_3389_fevo_2017_00051 crossref_primary_10_1016_j_jag_2025_104367 crossref_primary_10_1007_s10113_012_0332_3 crossref_primary_10_1007_s11027_016_9731_y crossref_primary_10_1007_s13351_014_4002_x crossref_primary_10_1088_1748_9326_ab66cb crossref_primary_10_1016_j_agsy_2016_12_006 crossref_primary_10_1007_s42106_023_00268_x crossref_primary_10_1088_1748_9326_10_4_045004 crossref_primary_10_1038_s41558_022_01376_8 crossref_primary_10_1111_gcb_12250 crossref_primary_10_1016_j_apgeog_2016_10_004 crossref_primary_10_1155_2020_9424327 crossref_primary_10_3390_su122310133 crossref_primary_10_1007_s00484_023_02544_x crossref_primary_10_1007_s12517_021_08432_1 crossref_primary_10_1016_j_jag_2020_102258 crossref_primary_10_1016_j_agrformet_2023_109674 crossref_primary_10_3389_fsufs_2020_00052 crossref_primary_10_1142_S2010007820500050 crossref_primary_10_1016_j_agwat_2014_08_007 crossref_primary_10_1016_j_jenvman_2020_111024 crossref_primary_10_3390_rs13122249 crossref_primary_10_1016_j_jaridenv_2020_104195 crossref_primary_10_1007_s00704_019_02924_7 crossref_primary_10_1016_j_scitotenv_2023_163288 crossref_primary_10_1016_j_cj_2017_01_004 crossref_primary_10_5194_hess_29_4341_2025 crossref_primary_10_1016_j_agsy_2016_12_017 crossref_primary_10_1088_2752_5295_adcbc9 crossref_primary_10_1016_j_agsy_2023_103633 crossref_primary_10_1038_s41598_021_88277_6 crossref_primary_10_1007_s10584_015_1428_9 crossref_primary_10_3389_fpls_2016_01262 crossref_primary_10_1016_j_atmosenv_2017_09_002 crossref_primary_10_1080_10106049_2022_2112301 crossref_primary_10_3390_atmos10070378 crossref_primary_10_1016_j_atech_2023_100224 crossref_primary_10_3390_su14116916 crossref_primary_10_1007_s10113_012_0388_0 crossref_primary_10_3390_su11226271 crossref_primary_10_1007_s42106_019_00052_w crossref_primary_10_1016_j_jag_2022_102861 crossref_primary_10_1002_agj2_20585 crossref_primary_10_1007_s11027_025_10199_9 crossref_primary_10_1111_agec_12315 crossref_primary_10_5194_hess_21_295_2017 crossref_primary_10_1111_jac_12590 crossref_primary_10_1371_journal_pone_0252067 crossref_primary_10_1016_j_agrformet_2023_109693 crossref_primary_10_1016_j_energy_2018_11_064 crossref_primary_10_3390_cli11100198 crossref_primary_10_1007_s10584_013_0822_4 crossref_primary_10_3390_cli6020041 crossref_primary_10_5194_bg_15_4301_2018 crossref_primary_10_1016_j_agwat_2023_108490 crossref_primary_10_1016_j_jclepro_2018_03_304 crossref_primary_10_1016_j_jhazmat_2021_127049 crossref_primary_10_1007_s00484_023_02458_8 crossref_primary_10_1007_s44279_025_00287_4 crossref_primary_10_1007_s12517_024_11921_8 crossref_primary_10_1007_s40710_021_00526_y crossref_primary_10_1177_1471082X14568248 crossref_primary_10_1016_S2095_3119_15_61095_4 crossref_primary_10_1029_2022EF003106 crossref_primary_10_1016_j_jenvman_2019_03_030 crossref_primary_10_1016_j_jeem_2016_06_005 crossref_primary_10_3390_jrfm17030107 crossref_primary_10_1016_j_compag_2020_105442 crossref_primary_10_1007_s00704_021_03799_3 crossref_primary_10_1016_j_fcr_2020_107988 crossref_primary_10_3390_agriculture12020130 crossref_primary_10_1016_j_fcr_2025_110069 crossref_primary_10_4018_IJACI_300799 crossref_primary_10_1016_j_strueco_2020_08_003 crossref_primary_10_1016_j_scitotenv_2016_12_158 crossref_primary_10_1007_s10479_022_04995_8 crossref_primary_10_1108_IJCCSM_01_2014_0005 crossref_primary_10_1016_j_agwat_2020_106692 crossref_primary_10_1016_j_scitotenv_2023_164502 crossref_primary_10_1016_j_techfore_2019_119711 crossref_primary_10_1016_j_jclepro_2019_06_251 crossref_primary_10_1186_s40066_015_0028_2 crossref_primary_10_1371_journal_pone_0184474 crossref_primary_10_3390_agronomy13020320 crossref_primary_10_1108_AFR_11_2017_0102 crossref_primary_10_3390_agronomy15030654 crossref_primary_10_1016_j_foodchem_2013_07_089 crossref_primary_10_3390_rs15010072 crossref_primary_10_1016_j_fcr_2020_107756 crossref_primary_10_1017_S0021859616000897 crossref_primary_10_3389_fpls_2022_895183 crossref_primary_10_1016_j_agsy_2019_02_009 crossref_primary_10_1016_j_eja_2017_06_012 crossref_primary_10_1016_j_agrformet_2022_109235 crossref_primary_10_1088_1748_9326_7_3_034032 crossref_primary_10_1007_s13351_017_6114_6 crossref_primary_10_1051_ctv_20153001029 crossref_primary_10_3390_horticulturae3010009 crossref_primary_10_1002_2017EF000687 crossref_primary_10_1016_j_rsase_2024_101371 crossref_primary_10_1111_gcb_15073 crossref_primary_10_3390_atmos11121291 crossref_primary_10_3390_agronomy10030426 crossref_primary_10_1111_grs_12412 crossref_primary_10_1007_s10584_015_1350_1 crossref_primary_10_3390_agriculture12122123 crossref_primary_10_1007_s00477_016_1215_9 crossref_primary_10_1155_2021_6646126 crossref_primary_10_3390_math9233058 crossref_primary_10_1016_j_compag_2024_109706 crossref_primary_10_1108_EJMBE_08_2023_0244 crossref_primary_10_1007_s12355_024_01461_6 crossref_primary_10_3390_agronomy11112245 crossref_primary_10_2480_agrmet_D_24_00033 crossref_primary_10_1016_j_ecoinf_2023_102235 crossref_primary_10_1016_j_agrformet_2017_02_033 crossref_primary_10_3390_earth3010004 crossref_primary_10_3390_computers13060137 crossref_primary_10_3390_rs12081232 crossref_primary_10_1080_22797254_2023_2294121 crossref_primary_10_1016_j_csag_2025_100065 crossref_primary_10_1016_j_jscs_2020_11_006 crossref_primary_10_1371_journal_pone_0259180 crossref_primary_10_1016_j_envsoft_2016_03_008 crossref_primary_10_3390_rs14102340 crossref_primary_10_3390_su16166849 crossref_primary_10_1175_JAMC_D_16_0258_1 crossref_primary_10_3390_agriculture15111179 crossref_primary_10_3390_agriculture13030618 crossref_primary_10_3390_su16114443 crossref_primary_10_1016_j_crm_2017_10_001 crossref_primary_10_1073_pnas_1222463110 crossref_primary_10_1371_journal_pone_0217148 crossref_primary_10_1016_j_agee_2012_04_026 crossref_primary_10_1007_s00704_015_1723_4 crossref_primary_10_1007_s10584_016_1652_y crossref_primary_10_1111_gcb_17556 crossref_primary_10_5194_hess_25_1827_2021 crossref_primary_10_1007_s00704_021_03635_8 crossref_primary_10_1016_j_seps_2023_101578 crossref_primary_10_1007_s10666_023_09927_9 crossref_primary_10_1111_agec_12180 crossref_primary_10_3389_ffgc_2023_1198186 crossref_primary_10_1016_j_jhydrol_2017_07_060 crossref_primary_10_1007_s43621_024_00745_x crossref_primary_10_1111_ajae_12446 crossref_primary_10_1016_j_ecolind_2017_12_047 crossref_primary_10_1038_s41598_025_02700_w crossref_primary_10_1016_j_agsy_2022_103384 crossref_primary_10_1088_1748_9326_aa6f23 crossref_primary_10_1016_j_landusepol_2022_106011 crossref_primary_10_1088_1748_9326_ad42b5 crossref_primary_10_1080_01621459_2017_1411268 crossref_primary_10_1016_j_eja_2017_05_002 crossref_primary_10_3390_plants12203548 crossref_primary_10_1080_10920277_2023_2176323 crossref_primary_10_2134_agronj14_0510 crossref_primary_10_1371_journal_pone_0137409 crossref_primary_10_1002_jsfa_11576 crossref_primary_10_3390_rs12071111 crossref_primary_10_1016_j_ifacol_2018_08_167 crossref_primary_10_3390_su151914204 crossref_primary_10_1007_s41685_022_00264_5 crossref_primary_10_3390_land8030049 crossref_primary_10_1093_erae_jbt025 crossref_primary_10_1007_s00704_016_1935_2 crossref_primary_10_1007_s10584_018_2150_1 crossref_primary_10_1007_s00704_024_04894_x crossref_primary_10_3390_ijgi11080433 crossref_primary_10_1007_s10584_014_1067_6 crossref_primary_10_1007_s40808_020_00932_5 crossref_primary_10_1080_01621459_2022_2123333 crossref_primary_10_1007_s10333_019_00755_w crossref_primary_10_1016_j_agwat_2020_106168 crossref_primary_10_1038_s41597_025_04650_4 crossref_primary_10_1016_j_agee_2014_06_014 crossref_primary_10_3897_popecon_7_e101500 crossref_primary_10_18278_wfp_4_2_9 crossref_primary_10_1007_s00704_024_05334_6 crossref_primary_10_1007_s10661_023_12055_2 crossref_primary_10_1007_s40808_020_00943_2 crossref_primary_10_1111_ajae_12549 crossref_primary_10_1371_journal_pone_0252335 crossref_primary_10_3390_agronomy10111645 crossref_primary_10_1016_j_ecolind_2019_01_059 crossref_primary_10_1016_j_heliyon_2024_e40359 crossref_primary_10_2134_agronj2017_01_0052 crossref_primary_10_1016_j_agee_2014_06_009 crossref_primary_10_1038_nclimate1916 crossref_primary_10_1029_2023EF004063 crossref_primary_10_1016_j_scitotenv_2017_06_211 crossref_primary_10_1088_1748_9326_ab422b crossref_primary_10_1088_1748_9326_ab25a1 crossref_primary_10_1088_1748_9326_10_12_124014 crossref_primary_10_1016_j_compag_2019_105031 crossref_primary_10_3390_agronomy15010098 crossref_primary_10_1007_s12524_022_01549_0 crossref_primary_10_1016_j_scitotenv_2017_07_017 crossref_primary_10_3390_agronomy9060316 crossref_primary_10_1016_j_agrformet_2018_06_009 crossref_primary_10_1016_j_jenvman_2025_126271 crossref_primary_10_1016_j_wace_2020_100271 crossref_primary_10_3390_land10121339 crossref_primary_10_1029_2019EF001316 crossref_primary_10_1088_1748_9326_10_11_115002 crossref_primary_10_1016_j_jenvman_2023_119162 crossref_primary_10_3390_w13243624 crossref_primary_10_1007_s10584_022_03356_5 crossref_primary_10_1007_s42106_022_00209_0 crossref_primary_10_3389_fsufs_2022_847892 crossref_primary_10_3390_agronomy9120833 crossref_primary_10_1061__ASCE_WR_1943_5452_0001175 crossref_primary_10_1007_s10668_019_00445_x crossref_primary_10_2478_picbe_2024_0130 crossref_primary_10_1007_s00704_023_04408_1 crossref_primary_10_1016_j_insmatheco_2020_11_003 crossref_primary_10_1016_j_scitotenv_2019_07_027 crossref_primary_10_3390_stats8020030 crossref_primary_10_1111_grs_12220 crossref_primary_10_1111_gcb_14822 crossref_primary_10_3354_cr01057 crossref_primary_10_3390_su17020474 crossref_primary_10_1016_j_jhydrol_2019_124524 crossref_primary_10_1080_01431161_2025_2459215 crossref_primary_10_3390_su12176784 crossref_primary_10_1007_s42452_019_0912_7 crossref_primary_10_1111_gcb_13738 crossref_primary_10_1017_S0021859614000392 crossref_primary_10_3390_su14073721 crossref_primary_10_1016_j_accre_2022_08_007 crossref_primary_10_1016_j_njas_2017_02_002 crossref_primary_10_1007_s00376_014_4161_9 crossref_primary_10_1111_aab_12402 crossref_primary_10_1088_1748_9326_ab93fc crossref_primary_10_3390_su8070670 crossref_primary_10_1016_j_jia_2025_04_038 crossref_primary_10_1016_j_agrformet_2017_12_256 crossref_primary_10_1109_JSTARS_2014_2357584 crossref_primary_10_3390_su15043485 crossref_primary_10_3390_su14159366 crossref_primary_10_3390_su14159246 crossref_primary_10_1002_agj2_20709 crossref_primary_10_1088_1748_9326_abede6 crossref_primary_10_1007_s10584_025_03868_w crossref_primary_10_1002_agr_21348 crossref_primary_10_1007_s00704_014_1179_y crossref_primary_10_1016_j_eja_2021_126335 crossref_primary_10_1515_biol_2022_0507 crossref_primary_10_2480_agrmet_D_24_00009 crossref_primary_10_1007_s00484_018_1583_6 crossref_primary_10_1002_joc_4975 crossref_primary_10_1108_AFR_08_2017_0064 crossref_primary_10_1002_joc_5820 crossref_primary_10_1016_j_compag_2017_09_024 crossref_primary_10_1088_2515_7620_ac814c crossref_primary_10_1016_j_agrformet_2017_05_008 crossref_primary_10_3390_agronomy12051205 crossref_primary_10_1002_jsfa_70124 crossref_primary_10_1002_wcc_498 crossref_primary_10_48130_grares_0025_0003 crossref_primary_10_1111_pbi_70019 crossref_primary_10_1016_j_agrformet_2012_05_013 crossref_primary_10_1007_s00477_014_0871_x crossref_primary_10_1007_s00704_020_03256_7 crossref_primary_10_1007_s12517_024_11871_1 crossref_primary_10_1038_s44264_025_00067_z crossref_primary_10_1016_j_agrformet_2025_110567 crossref_primary_10_1016_j_agrformet_2025_110687 crossref_primary_10_1007_s10584_017_1941_0 crossref_primary_10_1007_s11442_022_1938_0 crossref_primary_10_1007_s13351_019_8143_9 crossref_primary_10_1016_j_ecoinf_2025_103011 crossref_primary_10_1080_23311932_2019_1581457 crossref_primary_10_3390_su9081412 crossref_primary_10_1016_j_compag_2020_105880 crossref_primary_10_1155_2019_2767018 crossref_primary_10_1016_j_eja_2022_126569 crossref_primary_10_1016_j_eja_2023_126868 crossref_primary_10_3390_rs12111819 crossref_primary_10_3390_su15043068 crossref_primary_10_1016_j_agsy_2014_12_003 crossref_primary_10_1088_2515_7620_ab67f0 crossref_primary_10_1016_j_foodpol_2022_102304 crossref_primary_10_1002_jsfa_12020 crossref_primary_10_1007_s11111_016_0263_x crossref_primary_10_1371_journal_pone_0112785 crossref_primary_10_3354_cr01138 crossref_primary_10_1079_ab_2025_0011 crossref_primary_10_1007_s11769_023_1345_1 crossref_primary_10_1016_j_eja_2023_126956 crossref_primary_10_3389_fpls_2016_00729 crossref_primary_10_1080_15427528_2022_2145591 crossref_primary_10_2134_agronj2015_0482 crossref_primary_10_1007_s00704_013_1031_9 crossref_primary_10_1080_10095020_2021_1936656 crossref_primary_10_1007_s13753_012_0010_6 crossref_primary_10_1108_CAER_11_2020_0275 crossref_primary_10_1007_s00704_023_04573_3 crossref_primary_10_1016_j_agwat_2023_108140 crossref_primary_10_1016_j_compag_2020_105554 crossref_primary_10_1007_s00704_021_03848_x crossref_primary_10_3390_agronomy10101585 crossref_primary_10_1016_j_crope_2023_07_002 crossref_primary_10_1016_j_ecolecon_2020_106875 crossref_primary_10_1088_1748_9326_aaf8be crossref_primary_10_1093_jxb_erac146 crossref_primary_10_1371_journal_pone_0156571 crossref_primary_10_1111_grs_12163 crossref_primary_10_1088_1748_9326_ac1fbb crossref_primary_10_3390_su151310237 crossref_primary_10_1016_j_envsoft_2019_104562 crossref_primary_10_5194_hess_25_551_2021 crossref_primary_10_17660_ActaHortic_2016_1123_8 crossref_primary_10_1016_j_cj_2023_06_008 crossref_primary_10_1080_23322039_2024_2421894 crossref_primary_10_1088_1748_9326_11_12_123001 crossref_primary_10_1360_SSTe_2024_0018 crossref_primary_10_3389_fpls_2019_00809 crossref_primary_10_5194_esd_12_151_2021 crossref_primary_10_1016_j_compag_2020_105667 crossref_primary_10_1016_j_scitotenv_2021_148090 crossref_primary_10_3390_ijerph17249241 crossref_primary_10_1016_j_agwat_2014_03_006 crossref_primary_10_3390_soilsystems5040058 crossref_primary_10_3390_agriculture11060550 |
| ContentType | Journal Article |
| Copyright | 2010 Elsevier B.V. 2015 INIST-CNRS |
| Copyright_xml | – notice: 2010 Elsevier B.V. – notice: 2015 INIST-CNRS |
| DBID | FBQ AAYXX CITATION IQODW 7SU 8FD C1K FR3 H8D KR7 L7M 7S9 L.6 7ST 7TG 7U6 7UA KL. SOI |
| DOI | 10.1016/j.agrformet.2010.07.008 |
| DatabaseName | AGRIS CrossRef Pascal-Francis Environmental Engineering Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database Aerospace Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace AGRICOLA AGRICOLA - Academic Environment Abstracts Meteorological & Geoastrophysical Abstracts Sustainability Science Abstracts Water Resources Abstracts Meteorological & Geoastrophysical Abstracts - Academic Environment Abstracts |
| DatabaseTitle | CrossRef Aerospace Database Civil Engineering Abstracts Technology Research Database Environmental Engineering Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Environmental Sciences and Pollution Management AGRICOLA AGRICOLA - Academic Meteorological & Geoastrophysical Abstracts Environment Abstracts Meteorological & Geoastrophysical Abstracts - Academic Sustainability Science Abstracts Water Resources Abstracts |
| DatabaseTitleList | Aerospace Database AGRICOLA Meteorological & Geoastrophysical Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Meteorology & Climatology Agriculture |
| EISSN | 1873-2240 |
| EndPage | 1452 |
| ExternalDocumentID | 23303393 10_1016_j_agrformet_2010_07_008 US201301891706 S0168192310001978 |
| GeographicLocations | Sub-Saharan Africa Africa |
| GeographicLocations_xml | – name: Sub-Saharan Africa – name: Africa |
| GroupedDBID | --K --M .~1 0R~ 1B1 1RT 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JM 9JN AABNK AABVA AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AAQXK AATLK AAXUO ABEFU ABFNM ABGRD ABJNI ABLJU ABMAC ABQEM ABQYD ABXDB ABYKQ ACDAQ ACGFS ACIUM ACLVX ACRLP ACSBN ADBBV ADEZE ADMUD ADQTV AEBSH AEKER AENEX AEQOU AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHHHB AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BLXMC CBWCG CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HLV HMA HVGLF HZ~ IHE IMUCA J1W KOM LW9 LY3 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAB SDF SDG SDP SEP SES SEW SPC SPCBC SSA SSE SSZ T5K WH7 WUQ Y6R ZMT ~02 ~G- ~KM ABPIF ABPTK FBQ AAHBH AATTM AAXKI AAYWO AAYXX ABUFD ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFPUW AIIUN AKBMS AKRWK AKYEP ANKPU CITATION EFKBS ~HD AFJKZ AGCQF AGQPQ AGRNS AIGII APXCP BNPGV IQODW SSH 7SU 8FD C1K FR3 H8D KR7 L7M 7S9 L.6 7ST 7TG 7U6 7UA KL. SOI |
| ID | FETCH-LOGICAL-c532t-d2d07c1621e908d498afedb4467ba8c0cf7611583cafdb1a9b6c8af056cc917b3 |
| ISICitedReferencesCount | 676 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000283022900006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0168-1923 |
| IngestDate | Tue Oct 07 09:53:34 EDT 2025 Sun Nov 09 10:18:40 EST 2025 Tue Oct 07 09:43:42 EDT 2025 Mon Jul 21 09:12:04 EDT 2025 Tue Nov 18 21:05:49 EST 2025 Sat Nov 29 06:45:09 EST 2025 Wed Dec 27 19:06:00 EST 2023 Fri Feb 23 02:31:38 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Keywords | Maize Marksim CERES-Maize Africa Biometeorology Monocotyledones Vegetals Zea mays Use Prediction C4-Type Forecast model Modeling Cereal crop Dynamical climatology Climate change Gramineae Statistical model Angiospermae Spermatophyta Yield Simulation model Cultivated plant |
| Language | English |
| License | https://www.elsevier.com/tdm/userlicense/1.0 CC BY 4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c532t-d2d07c1621e908d498afedb4467ba8c0cf7611583cafdb1a9b6c8af056cc917b3 |
| Notes | http://dx.doi.org/10.1016/j.agrformet.2010.07.008 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1685806059 |
| PQPubID | 23500 |
| PageCount | 10 |
| ParticipantIDs | proquest_miscellaneous_822519201 proquest_miscellaneous_822502062 proquest_miscellaneous_1685806059 pascalfrancis_primary_23303393 crossref_citationtrail_10_1016_j_agrformet_2010_07_008 crossref_primary_10_1016_j_agrformet_2010_07_008 fao_agris_US201301891706 elsevier_sciencedirect_doi_10_1016_j_agrformet_2010_07_008 |
| PublicationCentury | 2000 |
| PublicationDate | 2010-10-15 |
| PublicationDateYYYYMMDD | 2010-10-15 |
| PublicationDate_xml | – month: 10 year: 2010 text: 2010-10-15 day: 15 |
| PublicationDecade | 2010 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam |
| PublicationTitle | Agricultural and forest meteorology |
| PublicationYear | 2010 |
| Publisher | Elsevier B.V [Oxford]: Elsevier Science Ltd Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: [Oxford]: Elsevier Science Ltd – name: Elsevier |
| References | Beven (bib0015) 2002; 458 Tebaldi, Knutti (bib0150) 2007; 365 Lobell, Field (bib0100) 2007; 2 Peng, Huang, Sheehy, Laza, Visperas, Zhong, Centeno, Khush, Cassman (bib0120) 2004; 101 Addiscott, Tuck (bib0005) 2001; 52 Hansen, Indeje (bib0040) 2004; 125 Schlenker, Roberts (bib0135) 2009; 106 Sheehy, Mitchell, Ferrer (bib0140) 2006; 98 Tao, Yokozawa, Zhang (bib0145) 2009; 149 Lobell, Ortiz-Monasterio (bib0105) 2007; 99 White (bib0165) 2009; 129 Iizumi, Yokozawa, Nishimori (bib0060) 2009; 149 Dinar, Somé, Hassan, Mendelsohn, Benhin (bib0030) 2008 Jones, Thornton (bib0070) 2000 Jones, Thornton (bib0075) 2003; 13 Schlenker, Lobell (bib0130) 2010 Landau, Mitchell, Barnett, Colls, Craigon, Payne (bib0085) 2000; 101 Porter, Semenov (bib0125) 2005; 360 Hastie, Tibshirani, Friedman (bib0050) 2001 Challinor, Wheeler, Slingo, Craufurd, Grimes (bib0020) 2005; 44 Iglesias, Rosenzweig, Pereira (bib0055) 2000; 10 Christensen, Hewitson, Busuioc, Chen, Gao, Held, Jones, Kolli, Kwon, Laprise, Rueda, Mearns, Menéndez, Räisänen, Rinke, Sarr, Whetton (bib0025) 2007 Murphy, Sexton, Barnett, Jones, Webb, Collins, Stainforth (bib0115) 2004; 430 Gijsman, Thornton, Hoogenboom (bib0035) 2007; 56 Kurukulasuriya, Mendelsohn, Hassan, Benhin, Deressa, Diop, Eid, Fosu, Gbetibouo, Jain (bib0080) 2006; 20 Batjes (bib0010) 1995 Wang (bib0160) 2005; 25 Hansen, Jones (bib0045) 2000; 65 Lobell, Burke, Tebaldi, Mastrandrea, Falcon, Naylor (bib0110) 2008; 319 Thornton, Jones, Alagarswamy, Andresen (bib0155) 2009; 19 (bib0095) 2009 Jones, Hoogenboom, Porter, Boote, Batchelor, Hunt, Wilkens, Singh, Gijsman, Ritchie (bib0065) 2003; 18 Lobell, Burke (bib0090) 2008; 3 |
| References_xml | – year: 2010 ident: bib0130 article-title: Robust negative impacts of climate change on African agriculture publication-title: Environmental Research Letters – volume: 25 start-page: 739 year: 2005 end-page: 753 ident: bib0160 article-title: Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment publication-title: Climate Dynamics – year: 2001 ident: bib0050 article-title: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics – volume: 18 start-page: 235 year: 2003 end-page: 265 ident: bib0065 article-title: The DSSAT cropping system model publication-title: European Journal of Agronomy – volume: 20 start-page: 367 year: 2006 ident: bib0080 article-title: Will African Agriculture Survive Climate Change? publication-title: The World Bank Economic Review – volume: 106 start-page: 15594 year: 2009 end-page: 15598 ident: bib0135 article-title: Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change publication-title: Proceedings of the National Academy of Sciences – volume: 52 start-page: 129 year: 2001 end-page: 138 ident: bib0005 article-title: Non-linearity and error in modelling soil processes publication-title: European Journal of Soil Science – volume: 3 start-page: 034007 year: 2008 ident: bib0090 article-title: Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation publication-title: Environmental Research Letters – volume: 360 start-page: 2021 year: 2005 end-page: 2035 ident: bib0125 article-title: Crop responses to climatic variation publication-title: Philosophical Transactions of the Royal Society B-Biological Sciences – volume: 99 start-page: 469 year: 2007 end-page: 477 ident: bib0105 article-title: Impacts of day versus night temperatures on spring wheat yields: a comparison of empirical and CERES model predictions in three locations publication-title: Agronomy Journal – volume: 365 start-page: 2053 year: 2007 end-page: 2075 ident: bib0150 article-title: The use of the multi-model ensemble in probabilistic climate projections publication-title: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences – year: 1995 ident: bib0010 article-title: A Homogenized Soil Data File for Global Environmental Research: A Subset of FAO, ISRIC and NCRS Profiles (Version 1.0) – volume: 101 start-page: 151 year: 2000 end-page: 166 ident: bib0085 article-title: A parsimonious, multiple-regression model of wheat yield response to environment publication-title: Agricultural and Forest Meteorology – volume: 101 start-page: 9971 year: 2004 end-page: 9975 ident: bib0120 article-title: Rice yields decline with higher night temperature from global warming publication-title: Proceedings of the National Academy of Sciences of the United States of America – year: 2008 ident: bib0030 article-title: Climate Change and Agriculture in Africa: Impact Assessment and Adaptation Strategies – volume: 319 start-page: 607 year: 2008 end-page: 610 ident: bib0110 article-title: Prioritizing climate change adaptation needs for food security in 2030 publication-title: Science – year: 2007 ident: bib0025 article-title: Regional climate projections publication-title: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change – volume: 125 start-page: 143 year: 2004 end-page: 157 ident: bib0040 article-title: Linking dynamic seasonal climate forecasts with crop simulation for maize yield prediction in semi-arid Kenya publication-title: Agricultural and Forest Meteorology – start-page: 445 year: 2000 end-page: 453 ident: bib0070 article-title: MarkSim Software to generate daily weather data for Latin America and Africa publication-title: American Society of Agronomy – volume: 56 start-page: 85 year: 2007 end-page: 100 ident: bib0035 article-title: Using the WISE database to parameterize soil inputs for crop simulation models publication-title: Computers and Electronics in Agriculture – volume: 149 start-page: 831 year: 2009 end-page: 850 ident: bib0145 article-title: Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis publication-title: Agricultural and Forest Meteorology – volume: 129 start-page: 547 year: 2009 end-page: 548 ident: bib0165 article-title: Comments on a report of regression-based evidence for impact of recent climate change on winter wheat yields publication-title: Agriculture, Ecosystems and Environment – volume: 2 year: 2007 ident: bib0100 article-title: Global scale climate–crop yield relationships and the impacts of recent warming publication-title: Environmental Research Letters – volume: 65 start-page: 43 year: 2000 end-page: 72 ident: bib0045 article-title: Scaling-up crop models for climate variability applications publication-title: Agricultural Systems – volume: 458 start-page: 2465 year: 2002 end-page: 2484 ident: bib0015 article-title: Towards a coherent philosophy for modelling the environment publication-title: Proceedings of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences – volume: 149 start-page: 333 year: 2009 end-page: 348 ident: bib0060 article-title: Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: application of a Bayesian approach publication-title: Agricultural and Forest Meteorology – year: 2009 ident: bib0095 publication-title: Climate Change and Food Security: Adapting Agriculture to a Warmer World – volume: 19 start-page: 54 year: 2009 end-page: 65 ident: bib0155 article-title: Spatial variation of crop yield response to climate change in East Africa publication-title: Global Environmental Change – volume: 98 start-page: 151 year: 2006 end-page: 156 ident: bib0140 article-title: Decline in rice grain yields with temperature: models and correlations can give different estimates publication-title: Field Crops Research – volume: 13 start-page: 51 year: 2003 end-page: 59 ident: bib0075 article-title: The potential impacts of climate change on maize production in Africa and Latin America in 2055 publication-title: Global Environmental Change-Human and Policy Dimensions – volume: 10 start-page: 69 year: 2000 end-page: 80 ident: bib0055 article-title: Agricultural impacts of climate change in Spain: developing tools for a spatial analysis publication-title: Global Environmental Change – volume: 44 start-page: 516 year: 2005 end-page: 531 ident: bib0020 article-title: Simulation of crop yields using ERA-40: limits to skill and nonstationarity in weather–yield relationships publication-title: Journal of Applied Meteorology – volume: 430 start-page: 768 year: 2004 end-page: 772 ident: bib0115 article-title: Quantification of modelling uncertainties in a large ensemble of climate change simulations publication-title: Nature |
| SSID | ssj0012779 |
| Score | 2.5402343 |
| Snippet | ▶ Statistical models are able to reproduce many of the key features of process-based model responses to warming and precipitation changes. ▶ Time-series models... Predicting the potential effects of climate change on crop yields requires a model of how crops respond to weather. As predictions from different models often... Statistical models are able to reproduce many of the key features of process-based model responses to warming and precipitation changes. Time-series models are... |
| SourceID | proquest pascalfrancis crossref fao elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1443 |
| SubjectTerms | accuracy Africa Agricultural and forest climatology and meteorology. Irrigation. Drainage Agronomy. Soil science and plant productions air temperature Biological and medical sciences CERES-Maize Climate Climate change Computer simulation corn crop models Crops Fundamental and applied biological sciences. Psychology General agronomy. Plant production grain yield Maize Marksim Mathematical models meteorological parameters Panels Precipitation prediction spatial scale Statistical analysis statistical models Sub-Saharan Africa temporal variation time series analysis weather Zea mays |
| Title | On the use of statistical models to predict crop yield responses to climate change |
| URI | https://dx.doi.org/10.1016/j.agrformet.2010.07.008 https://www.proquest.com/docview/1685806059 https://www.proquest.com/docview/822502062 https://www.proquest.com/docview/822519201 |
| Volume | 150 |
| WOSCitedRecordID | wos000283022900006&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-2240 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0012779 issn: 0168-1923 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLbYxgM8IBighctkJLSXKihO0sTmrUydAHWtBC3qm5U4zrRpSkraovHvOSd2klZjFB54iVrfevk-28c-N0LexpHIYZ8WbhYI7oYizFzOU-HWsbyYp_K4DqX0bRSPx3w-F41Gd1mnE4iLgt_ciMV_hRrKAGx0nf0HuNtBoQBeA-jwBNjh-VfAT4zh4tpc0qPDUB2LGb1EMOtNHdFhUaF-ZtXD_F29n2jE1quMsayJ-KCuL0GS1dYteFOAHVxUXbQOvHQHoRc2FsxErctq645-VDZKjdpwvsvv_GFtTYLO4VSNyVxslb18QEMOzzXul819ZASHUGFchtsF1YSSbZjDegtUIQcuC02wWrtWYtnGvtvU3lrTzfXC1bvkovak0CtrkIexJ3m3jTWq-_FEns1GIzkdzqcni-8uJhhDRbzNtrJHDvy4L2ABPBh8Gs4_tyonPzaBGZtftGUM-NvPvkuU2cuTEm1skyVgm5v8KLe2-lp-mT4mj-zBgw4MYZ6Qe7o4JA87OPUhcc47EOkJPa1JUL97Sr5MCgq0okArWuZ0g1bU0IquSmppRZFWtKYVbWmF9ZZW1NDqGZmdDaenH12bj8NV_cBfuZmfebFikc-08HgWCp7kOkthWsdpwhVO7AgOGDxQSZ6lLBFppKAJiNhKCRanwXOyX5SFPiI05aGnWMo0T_OwzxNoleoQBlGx4v1UOSRq_lqpbLB6zJlyLRurxCvZYiIRE-mhIQV3iNd2XJh4Lbu7vG-wk1bsNOKkBAbu7nwEaGPd5VLOvvpoCMC4wKBUDjneokD7ffwA5MZABA5503BCwqKOmrqk0OV6KRlmhfAiOPo4hN7RBiT7Ppz1Iv_PTYDKHnuxe5SX5EE3wV-R_VW11q_JffUD2FQd28nyC-mb3FA |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=On+the+use+of+statistical+models+to+predict+crop+yield+responses+to+climate+change&rft.jtitle=Agricultural+and+forest+meteorology&rft.au=Lobell%2C+David+B&rft.au=Burke%2C+Marshall+B&rft.date=2010-10-15&rft.issn=0168-1923&rft.volume=150&rft.issue=11+p.1443-1452&rft.spage=1443&rft.epage=1452&rft_id=info:doi/10.1016%2Fj.agrformet.2010.07.008&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0168-1923&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0168-1923&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0168-1923&client=summon |