scholarly journals The Agricultural Model Intercomparison and Improvement Project: Phase I Activities by a Global Community of Science

2015 ◽  
pp. 3-24 ◽  
Author(s):  
Cynthia Rosenzweig ◽  
James W. Jones ◽  
Jerry L. Hatfield ◽  
John M. Antle ◽  
Alexander C. Ruane ◽  
...  
Keyword(s):  
Phase I ◽  
Model Intercomparison ◽  
Global Community ◽  
Improvement Project

Simulação de cenários agrícolas futuros para algodoeiro com base em projeções de mudanças climáticas

Agrometeoros ◽  
2020 ◽  
Vol 27 (1) ◽  
Author(s):  
Stefany Amanda Quilles Fava ◽  
Evandro Henrique Figueiredo Moura da Silva ◽  
Luis Alberto Silva Antolin ◽  
Fábio Ricardo Marin
Keyword(s):  
Model Intercomparison ◽  
Improvement Project

Diante da importância econômica e social da produção de fibras no Brasil e no mundo, é relevante antever os possíveis impactos do clima futuro na produtividade de algodão em uma região onde a cultura é representativa. O presente estudo teve como objetivo simular cenários agrícolas futuros para a cultura do algodão, com base em projeções de mudanças climáticas, para o município de Barreiras, BA. Para isso, o modelo DSSAT/CROPGRO-COTTON foi calibrado com as características genéticas da cultivar CNPA ITA 90. A produtividade foi simulada para os últimos 30 anos (1980 - 2010), representando a produtividade no clima atual e, a fim de representar a produtividade em 2050, foram realizadas simulações para o período de 2040 - 2069 para seis cenários climáticos futuros gerados a partir da metodologia descrita pelo Agricultural Model Intercomparison and Improvement Project (AgMIP). A produtividade média nos cenários futuros variou de 4.652 kg ha-1 a 5.389 kg ha-1, apresentando um expressivo aumento nos seis cenários estudados, porém indicando maior risco climático para o cultivo do algodoeiro nesta região.

scholarly journals The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies

2013 ◽  
Vol 170 ◽  
pp. 166-182 ◽  
Author(s):  
C. Rosenzweig ◽  
J.W. Jones ◽  
J.L. Hatfield ◽  
A.C. Ruane ◽  
K.J. Boote ◽  
...  
Keyword(s):  
Model Intercomparison ◽  
Pilot Studies ◽  
Improvement Project

The Global Gridded Crop Model Intercomparison – an AgMIP activity

2021 ◽  
Author(s):  
Christoph Müller ◽  
Jonas Jägermeyr ◽  
the GGCMI team
Keyword(s):  
Crop Yields ◽  
Crop Model ◽  
Large Set ◽  
Phase 2 ◽  
Model Intercomparison ◽  
Output Data ◽  
Phase 3 ◽  
Area Index ◽  
Improvement Project ◽  
Entry Points

<p>The Global Gridded Crop Model Intercomparison was founded in 2012 as a joint activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP) and the InterSectoral Model Intercomparison Project (ISIMIP). Over these 10 years, GGCMI has attracted contributions from many international crop modeling groups and has generated large global agricultural data sets in different model simulation phases. Input data comprise gridded management data for agricultural systems that can be used in combination with climate data that are provided by ISIMIP. Annual output data include crop yields and other variables of plants and soil status for irrigated and purely rainfed production systems for different field crops at 0.5 degree spatial resolution, covering the whole land surface, where crop production is feasible. All data are made publicly available. While Phase 1 of GGCMI was focused on the historical period<sup>[1,2]</sup>, aiming at model evaluation<sup>[3]</sup>, Phase 2 generated an unprecedented large data set of systematic disturbances along the CO2 (C), Temperature (T), Water (W) and Nitrogen (N) dimension<sup>[4]</sup>. A major outcome of Phase 2 is a very large set of emulators<sup>[5]</sup> that allows for lightweight, flexible and comprehensive crop yield projections and analyses. With analyses of Phase 2 still forming, Phase 3 was started in collaboration with ISIMIP’s Phase 3, providing new future projections for a range of CMIP6 climate change projections and different management scenarios. Crop models do not only provide outputs on crop yields but also on various processes, such as evapotranspiration, leaf area index, phenology and soil dynamics that allow for broader analyses. GGCMI is a collaborative effort and always open to new contributors. Given the amount and complexity of in- and output data, we welcome proposals for new studies and data analyses. In this presentation we’re providing an overview of the GGCMI activities and exemplify possible entry points for collaboration.</p><p><sup>[1] http://dx.doi.org/10.5194/gmd-8-261-2015</sup></p><p><sup>[2] http://dx.doi.org/10.1038/s41597-019-0023-8</sup></p><p><sup>[3] http://dx.doi.org/10.5194/gmd-10-1403-2017</sup></p><p><sup>[4] http://dx.doi.org/10.5194/gmd-13-2315-2020</sup></p><p><sup>[5] http://dx.doi.org/10.5194/gmd-13-3995-2020</sup></p>

scholarly journals The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0)

2015 ◽  
Vol 8 (2) ◽  
pp. 261-277 ◽  
Author(s):  
J. Elliott ◽  
C. Müller ◽  
D. Deryng ◽  
J. Chryssanthacopoulos ◽  
K. J. Boote ◽  
...  
Keyword(s):  
Land Surface ◽  
Input Data ◽  
Large Scale ◽  
Phase 1 ◽  
Climate Impact ◽  
Crop Model ◽  
Surface Fluxes ◽  
Historical Period ◽  
Model Intercomparison ◽  
Improvement Project

Abstract. We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project includes global simulations of yields, phenologies, and many land-surface fluxes using 12–15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of large-scale climate extremes from the historical record.

Improving Pneumococcal Vaccination Rates in Rheumatology Patients by Using Best Practice Alerts in the Electronic Health Records

2020 ◽  
pp. jrheum.200806
Author(s):  
Heena Sheth ◽  
Vera Grimes ◽  
Diana Rudge ◽  
Brandon Ayers ◽  
Larry Moreland ◽  
...  
Keyword(s):  
Phase I ◽  
Immunosuppressive Therapy ◽  
Phase Ii ◽  
Best Practice ◽  
Pneumococcal Vaccination ◽  
Study Phase ◽  
Post Intervention ◽  
Improvement Project ◽  
Vaccination Rates ◽  
To Receive

Objective To improve pneumococcal vaccination (PV) rates among rheumatology clinic patients on immunosuppressive therapy in the outpatient settings. Methods This quality improvement project was based on the pre-post-intervention design. Phase I of the project targeted rheumatoid arthritis patients from thirteen rheumatology clinics (1/2013 to 7/2015) on immunosuppressive therapy to receive pneumococcal polysaccharide vaccine (PPSV23). In Phase II study (1/2016-10/2017), all patients on immunosuppressive medications irrespective of diagnosis were targeted to receive PPSV23 and the pneumococcal conjugate vaccine (PCV13). The Best Practice Alert (BPA)s for both PVs were developed based on CDC guidelines which appeared on electronic medical records for eligible patient at the time of assessment by the medical assistant. The BPA was designed to inform the vaccination status and enable physician to order PV or document refusal or deferral reasons. Education regarding vaccine guidelines, the BPAs, vaccination process, and regular feedback of results were important project interventions. The vaccination rates during pre-post intervention for each study phase were compared using Chi square test. Results During Phase I, PPSV23 vaccination rates improved from 27.9% pre-intervention rate to 61.5% (p<0.0001). During Phase II, 77% of patients had received either PPSV23 or PCV13 compared to 49.6% of patients in the pre-intervention period (p<0.0001). The documentation rates (vaccine received, ordered, patient refusal and deferral reasons) increased significantly in both phases. Conclusion Electronic identification of vaccine eligibility and implementation of BPAs with capabilities to order and document significantly improved PV rates. The process has potential for self-sustainability and generalizability.

scholarly journals Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments

2018 ◽  
Vol 376 (2119) ◽  
pp. 20160455 ◽  
Author(s):  
Cynthia Rosenzweig ◽  
Alex C. Ruane ◽  
John Antle ◽  
Joshua Elliott ◽  
Muhammad Ashfaq ◽  
...  
Keyword(s):  
Regional Economics ◽  
Coupled Model ◽  
Maize Yield ◽  
Climate Impacts ◽  
Proof Of Concept ◽  
Model Intercomparison ◽  
Improvement Project ◽  
Crop Models ◽  
Warming World ◽  
Global Economics

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there are mixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO 2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate. This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

scholarly journals The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO<sub>2</sub>, temperature, water, and nitrogen levels (protocol version 1.0)

2019 ◽  
Author(s):  
James Franke ◽  
Christoph Müller ◽  
Joshua Elliott ◽  
Alex C. Ruane ◽  
Jonas Jagermeyr ◽  
...  
Keyword(s):  
Climate Change ◽  
Phase Ii ◽  
Crop Model ◽  
Assessment Tools ◽  
Nonlinear Dependence ◽  
Model Intercomparison ◽  
Improvement Project ◽  
Crop Models ◽  
Growing Season Length ◽  
Temperature Water

Abstract. Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase II experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase II experimental protocol and its simulation data archive. Twelve crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (``CTWN'') for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase II archive. For example, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that indicates yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions, but is largest in high-latitude regions where crops may be grown in the future.

scholarly journals The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO<sub>2</sub>, temperature, water, and nitrogen levels (protocol version 1.0)

2020 ◽  
Vol 13 (5) ◽  
pp. 2315-2336 ◽  
Author(s):  
James A. Franke ◽  
Christoph Müller ◽  
Joshua Elliott ◽  
Alex C. Ruane ◽  
Jonas Jägermeyr ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Model ◽  
Assessment Tools ◽  
Nonlinear Dependence ◽  
Phase 2 ◽  
Model Intercomparison ◽  
Improvement Project ◽  
Crop Models ◽  
Growing Season Length ◽  
Temperature Water

Abstract. Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

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