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 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.

Comprehensive global climate impact assessment for crop yields

2020 ◽  
Author(s):  
Christoph Müller ◽  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Crop Yields ◽  
Global Climate ◽  
Model Development ◽  
Climate Impact ◽  
Crop Model ◽  
Model Intercomparison ◽  
Crop Models ◽  
Climate Impact Assessment

&lt;p&gt;Climate change impacts on agriculture are subject to large uncertainties from a variety of sources. One of the most important sources of uncertainty is the uncertainty in the realization of climate change itself. In the absence of clear climate mitigation strategies and substantial uncertainties on population growth, economic development, technology and lifestyles, a very broad set of greenhouse gas emission scenarios has been developed to inform climate modeling. Climate models often differ in the spatial patterns of projected changes in particular with respect to changes in precipitation. The Coupled Model Intercomparison Project (CMIP5, CMIP6) provides a broad range of future climate change projections.&lt;/p&gt;&lt;p&gt;Crop models are often applied at selected sites or with global coverage, as in the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Global crop model applications have been shown to have some skill, but also add additional uncertainty, given that many processes cannot be calibrated properly for the lack of suitable reference data and because management information is largely absent (M&amp;#252;ller et al., 2017).&lt;/p&gt;&lt;p&gt;However, already the computational power required to compute the comprehensive set of climate projections prohibits such applications. Instead, typically, small and largely random selections of climate scenarios are used to project impacts, such as agricultural crop yields. McSweeney and Jones (2016) find that a selection of 5 climate models as often applied, is insufficient to cover the range of projections in all regions.&lt;/p&gt;&lt;p&gt;Here we present initial results of a comprehensive global climate impact assessment for crop yields that explores the full range of the CMIP6 climate projection archive. For this, we use a set of 9 global gridded crop model emulators (Franke et al., 2019b) that were trained on a very large systematic input sensitivity analysis with up to 1404 global-coverage, 31-year simulation data sets per crop and crop model (Franke et al., 2019a). The training domain includes variations in atmospheric carbon dioxide (CO2) concentrations (4 levels from 360 ppm to 810 ppm), air temperature (7 levels from -1 to +6&amp;#176;C), water supply (8 levels from -50 to +30% and full irrigation), nitrogen fertilization (3 levels from 10 to 200 kgN/ha) and adaptation (2 levels: none and regained growing seasons) and thus represents an unprecedented rich data base for emulator training. The emulators, in form of grid-cell specific regression models with 27 coefficients, are computationally light-weight and can thus be applied to the full CMIP6 data archive.&lt;/p&gt;&lt;p&gt;We here present first results from this analysis, breaking down the different sources of uncertainty (emission concentration pathways, climate model, crop model). Results will help to interpret crop model simulations in general: the unstructured reduction of the uncertainty space from selecting a small number of climate scenarios by e.g. first availability and/or individual crop models has so far hampered to quantify the uncertainty in crop model projections.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Franke (2019a) Geoscientific Model Development Discuss, 2019:1-30.&lt;/p&gt;&lt;p&gt;Franke (2019b)&amp;#160; Geoscientific Model Development, submitted&lt;/p&gt;&lt;p&gt;McSweeney &amp; Jones, (2016) Climate Services, 1:24-29.&lt;/p&gt;&lt;p&gt;M&amp;#252;ller (2017) Geoscientific Model Development, 10:1403-1422.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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.

scholarly journals The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)

2020 ◽  
Vol 13 (9) ◽  
pp. 3995-4018
Author(s):  
James A. Franke ◽  
Christoph Müller ◽  
Joshua Elliott ◽  
Alex C. Ruane ◽  
Jonas Jägermeyr ◽  
...  
Keyword(s):  
Crop Yields ◽  
Crop Model ◽  
Climate Impacts ◽  
Future Climate ◽  
Yield Response ◽  
Phase 2 ◽  
Crop Models ◽  
Growing Seasons ◽  
Nitrogen Inputs ◽  
Temperature Water

Abstract. Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

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.

Idalopirdin bei Alzheimererkrankung: nicht besser als Plazebo

2018 ◽  
Vol 86 (08) ◽  
pp. 456-457
Keyword(s):  
Phase 2 ◽  
Phase 3 ◽  
Alzheimer Demenz

Die Blockade von Serotoninrezeptoren, insbesondere des Serotonin-Rezeptortyps 5-HT6, als Zusatztherapie in Kombination mit Cholinesterasehemmer, hat in experimentellen Versuchen sowie in einer Phase-2-Studie positive Effekte bei Demenz gezeigt. Im Rahmen eines Phase-3 Entwicklungsprogramms wurde nun die Effektivität des selektiven Serotoninrezeptor-Antagonisten Idalopirdin bei leichter bis mittelschwerer Alzheimer Demenz geprüft.

PENERAPAN MODEL QUANTUM LEARNING UNTUK MENINGKATKAN HASIL BELAJAR AUTOCAD SISWA KELAS X TEKNIK PEMESINAN SMK NEGERI 1 STABAT

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Abdul Hasan Saragih
Keyword(s):  
Positive Response ◽  
Phase 1 ◽  
First Grade ◽  
Learning Model ◽  
Second Phase ◽  
Phase 2 ◽  
Phase 3 ◽  
The Third ◽  
Third Phase ◽  
Quantum Learning

This classroom research was conducted on the autocad instructions to the first grade of mechinary class of SMK Negeri 1 Stabat aiming at : (1) improving the student’ archievementon autocad instructional to the student of mechinary architecture class of SMK Negeri 1 Stabat, (2) applying Quantum Learning Model to the students of mechinary class of SMK Negeri 1 Stabat, arising the positive response to autocad subject by applying Quantum Learning Model of the students of mechinary class of SMK Negeri 1 Stabat. The result shows that (1) by applying quantum learning model, the students’ achievement improves significantly. The improvement ofthe achievement of the 34 students is very satisfactory; on the first phase, 27 students passed (70.59%), 10 students failed (29.41%). On the second phase 27 students (79.41%) passed and 7 students (20.59%) failed. On the third phase 30 students (88.24%) passed and 4 students (11.76%) failed. The application of quantum learning model in SMK Negeri 1 Stabat proved satisfying. This was visible from the activeness of the students from phase 1 to 3. The activeness average of the students was 74.31% on phase 1,81.35% on phase 2, and 83.63% on phase 3. (3) The application of the quantum learning model on teaching autocad was very positively welcome by the students of mechinary class of SMK Negeri 1 Stabat. On phase 1 the improvement was 81.53% . It improved to 86.15% on phase 3. Therefore, The improvement ofstudent’ response can be categorized good.

The Efficacy and Safety of FMX101, Minocycline Foam 4%, for the Treatment of Acne Vulgaris: A Pooled Analysis of Phase 2 and Phase 3 Studies

2017 ◽  
Vol 1 ◽  
pp. s49
Author(s):  
Linda Stein Gold ◽  
Sunil Dhawan ◽  
Jonathan Weiss ◽  
Zoe D Draelos ◽  
Herman Ellman
Keyword(s):  
Acne Vulgaris ◽  
Pooled Analysis ◽  
Phase 2 ◽  
Efficacy And Safety ◽  
Phase 3

Abstract Not Available

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.

Clinical Drug Development for the Treatment of Pulmonary Arterial Hypertension: Collaboration With the Pharmaceutical Industry and Regulatory Agencies

2010 ◽  
Vol 9 (4) ◽  
pp. 214-219
Author(s):  
Robyn J. Barst
Keyword(s):  
Clinical Trials ◽  
Pulmonary Arterial Hypertension ◽  
Drug Development ◽  
Phase 1 ◽  
Preclinical Studies ◽  
Phase 2 ◽  
Phase 3 ◽  
Preclinical Testing ◽  
New Drug ◽  
Pulmonary Arterial

Drug development is the entire process of introducing a new drug to the market. It involves drug discovery, screening, preclinical testing, an Investigational New Drug (IND) application in the US or a Clinical Trial Application (CTA) in the EU, phase 1–3 clinical trials, a New Drug Application (NDA), Food and Drug Administration (FDA) review and approval, and postapproval studies required for continuing safety evaluation. Preclinical testing assesses safety and biologic activity, phase 1 determines safety and dosage, phase 2 evaluates efficacy and side effects, and phase 3 confirms efficacy and monitors adverse effects in a larger number of patients. Postapproval studies provide additional postmarketing data. On average, it takes 15 years from preclinical studies to regulatory approval by the FDA: about 3.5–6.5 years for preclinical, 1–1.5 years for phase 1, 2 years for phase 2, 3–3.5 years for phase 3, and 1.5–2.5 years for filing the NDA and completing the FDA review process. Of approximately 5000 compounds evaluated in preclinical studies, about 5 compounds enter clinical trials, and 1 compound is approved (Tufts Center for the Study of Drug Development, 2011). Most drug development programs include approximately 35–40 phase 1 studies, 15 phase 2 studies, and 3–5 pivotal trials with more than 5000 patients enrolled. Thus, to produce safe and effective drugs in a regulated environment is a highly complex process. Against this backdrop, what is the best way to develop drugs for pulmonary arterial hypertension (PAH), an orphan disease often rapidly fatal within several years of diagnosis and in which spontaneous regression does not occur?

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