Gas exchange and water relations of spring wheat under full-season infrared warming

Wall, Gerard W.; Kimball, Bruce A.; White, Jeffrey W.; Ottman, Michael J.

doi:10.1111/j.1365-2486.2011.02399.x

Cited by 71 publications

(81 citation statements)

References 79 publications

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“…On the other hand, a major argument against weighting of climate models based on hindcast error is that it assumes that the response to future radiative forcing will be consistent with that in the historical period, which cannot be tested. Crop models on the other hand can at least be tested against field experiments that impose higher atmospheric CO 2 concentrations (Kimball et al 1995;Ewert et al 2002) and higher temperatures (Wall et al 2011) than are observed today.…”

Section: Assigning Different Weights To Each Model In a Multi-model Ementioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

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Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically Climatic Change (2016) 139:551-564 DOI 10.1007/s10584-016-1803-1 Highlights ?•Ensembles of crop models can improve uncertainty estimates, impact projections, and collaboration.•The climate modeling community has identified and studied many of the questions related to design and analysis of ensembles.•Much of the climate experience could be adapted to crop modeling. oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

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Section: Assigning Different Weights To Each Model In a Multi-model Ementioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

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“…Although many glasshouse and controlled-environment temperature experiments have been described, they are often not suitable for model testing as the heating of root systems in pots 9 and effects on micro-climate differ greatly from field conditions 10 . Detailed information on field experiments with a wide range of sowing dates and infrared heating recently became available for wheat 11,12 . Such experiments are well suited for testing the ability of crop models to quantify temperature responses under field conditions.…”

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confidence: 99%

“…7). As these opposing thermal regimes affect development, gas exchange and water relations of wheat 12 , it is important to consider in-season dynamics when determining grain yield. Many models simulated the dynamic effects on growth ( Supplementary Fig.…”

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confidence: 99%

Rising temperatures reduce global wheat production

et al. 2014

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Crop models are essential tools for assessing the threat of climate change to local and global food production 1 . Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature 2 . Here we systematically tested 30 di erent wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 • C to 32 • C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each • C of further temperature increase and become more variable over space and time.Understanding how different climate factors interact and impact food production 3 is essential when reaching decisions on how to adapt to the effects of climate change. To implement such strategies the contribution of various climate variables on crop yields need to be separated and quantified. For instance, a change in temperature will require a different adaptation strategy than a change in rainfall 4 . Temperature changes alone are reported to have potentially large negative impacts on crop production 5 , and hotspots-locations where plants suffer from high temperature stress-have been identified across the globe 6,7 . Crop simulation models are useful tools in climate impact studies as they deal with multiple climate factors and how they interact with various crop growth and yield formation processes that are sensitive to climate. These models have been applied in many studies, including the assessment of temperature impacts on crop production 1,8 . However, none of the crop models have been tested systematically against experiments at different temperatures in field conditions. Although many glasshouse and controlled-environment temperature experiments have been described, they are often not suitable for model testing as the heating of root systems in pots 9 and effects on micro-climate differ greatly from field conditions 10 . Detailed information on field experiments with a wide range of sowing dates and infrared heating recently became available for wheat 11,12 . Such experiments are well suited for testing the ability of crop models to quantify temperature responses under field conditions. Testing the temperature responses of crop models is particularly important for assessing the impact of climate change on wheat production, because the largest uncertainty in simulated impacts on yield arises from increasing temperatures 2 .In a 'Hot Serial Cereal' (HSC) well-irrigated and fertilized experiment with a single cultivar, the observed days after sowing (DAS) to maturity declined...

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“…The canopy temperature data were processed by dataloggers, which provided 0-10 V control signals to dimmers, which in turn modulated the output of the heaters {[Model FTE-1000 (1000 W, 240 V, 245 mm long x 60 mm wide)] mounted in reflector housings, so as to maintain the canopy temperatures of the Heated plots at 1.5°C warmer than those of the Reference plots during daytime and 3.0°C warmer at night. Additional experimental details are presented in Wall et al (2011Wall et al ( , 2013, Ottman et al (2012), White et al (2011White et al ( , 2012, and Kimball et al (2012Kimball et al ( , 2015Kimball et al ( , 2016. Solar radiation, air temperature, and wind speed were measured on a weather mast in the experimental field most of the time starting with the fall, 2007 planting.…”

Section: Field Experimentsmentioning

confidence: 99%

Wheat response to a wide range of temperatures, as determined from the Hot Serial Cereal (HSC) Experiment

Kimball

2018

ODJAR

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Abstract:Temperatures are warming on a global scale, a phenomenon that likely will affect future crop productivity. Crop growth models are useful tools to predict the likely effects of these global changes on agricultural productivity and to develop strategies to maximize the benefits and minimize the detriments of such changes. However, few such models have been tested at the higher temperatures expected in the future. Therefore, a "Hot Serial Cereal" experiment was conducted on wheat (Triticum aestivum L.), the world's foremost food and feed crop, in order to obtain a dataset appropriate for testing the high temperature performance of wheat growth models. The wheat (Cereal) was planted serially (Serial) about every six weeks for over two years at Maricopa, Arizona, USA, which experiences the whole range of temperatures at which plants grow on Earth. In addition, on six planting dates infrared heaters in a T-FACE (temperature free-air controlled enhancement) system (Hot) were deployed over one-third of the plots to warm the wheat by additional target 1.5°C during daytime and 3.0°C at night. Achieved average degrees of warming were 1.3 and 2.7°C for day and night. Overall, a dataset covering 27 differently treated wheat crops with three replicates each was obtained covering an air temperature range from -2 to 42°C. Herein, the management, soils, weather, physiology, phenology, growth, yield, quality, and other data are presented.

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Gas exchange and water relations of spring wheat under full-season infrared warming

Cited by 71 publications

References 79 publications

Lessons from climate modeling on the design and use of ensembles for crop modeling

Lessons from climate modeling on the design and use of ensembles for crop modeling

Rising temperatures reduce global wheat production

Wheat response to a wide range of temperatures, as determined from the Hot Serial Cereal (HSC) Experiment

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