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...

This is a repository copy of Similar estimates of temperature impacts on global wheat yield by three independent methods.

Nendel 38 | Jørgen Eivind Olesen 37 | Taru Palosuo 44 | John R. Porter 42,45,46 | Eckart Priesack 39 | Dominique Ripoche 47 | Mikhail A. Semenov 48 | Claudio Stöckle 17 | Pierre Stratonovitch 48 | Thilo Streck 33 | Iwan Supit 49 | Fulu Tao 50,44 | Marijn Van der Velde 51 | Daniel Wallach 52 | Enli Wang 53 | Heidi Webber 30,38 AbstractWheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable 156 |

The development of crop varieties that are better suited to new climatic conditions is vital for future food production 1,2 . Increases in mean temperature accelerate crop development, resulting in shorter crop durations and reduced time to accumulate biomass and yield 3,4 . The process of breeding, delivery and adoption (BDA) of new maize varieties can take up to 30 years. Here, we assess for the first time the implications of warming during the BDA process by using five bias-corrected global climate models and four representative concentration pathways with realistic scenarios of maize BDA times in Africa. The results show that the projected di erence in temperature between the start and end of the maize BDA cycle results in shorter crop durations that are outside current variability. Both adaptation and mitigation can reduce duration loss. In particular, climate projections have the potential to provide target elevated temperatures for breeding. Whilst options for reducing BDA time are highly context dependent, common threads include improved recording and sharing of data across regions for the whole BDA cycle, streamlining of regulation, and capacity building. Finally, we show that the results have implications for maize across the tropics, where similar shortening of duration is projected.By 2050 the majority of African countries will have significant experience of novel climates 1 . However, precise information as to when novel climates will occur has not been available until the recent development of techniques to identify the time of emergence of climate change signals 5,6 . These techniques quantify the signal of a change in climate relative to the background 'noise' of current climate variability. Metrics that capture the response of crops to single or multiple aspects of weather or climate (crop-climate indices 7 ) are another tool that has been developed intensively in recent years. Alongside crop yield modelling, these techniques now enable assessments of the projected times at which climate change will alter crop productivity. These alterations are mediated through both crop growth (that is, photosynthesis and biomass accumulation) and development (phenological and morphological responses).We use seven crop-climate indices (Supplementary Table S2) to identify when heat stress, drought stress and crop duration (that is, time from germination to maturity) become systematically and significantly outside the ranges at present experienced by maize cultivation in sub-Saharan Africa. Crop breeders have long been aware of the need to develop new crop varieties that are suited to future climates, particularly with respect to heat and drought stress 8,9 . Heat stress impacts are evident in our analysis. However, heat stress indices are not sufficiently constrained at present (that is, uncertainty in their values is too great) for detection of a climate change signal; only the signal in crop duration changes exceeded the noise of climate variability and thus showed a time of emergence within this century (see...

Although northern peatlands cover only 3% of the land surface, their thick peat deposits contain an estimated onethird of the world's soil organic carbon (SOC). Under a changing climate the potential of peatlands to continue sequestering carbon is unknown. This paper presents an analysis of 6 years of total carbon balance of an almost intact Atlantic blanket bog in Glencar, County Kerry, Ireland. The three components of the measured carbon balance were: the land-atmosphere fluxes of carbon dioxide (CO 2 ) and methane (CH 4 ) and the flux of dissolved organic carbon (DOC) exported in a stream draining the peatland. The 6 years C balance was computed from 6 years (2003-2008) of measurements of meteorological and eddy-covariance CO 2 fluxes, periodic chamber measurements of CH 4 fluxes over 3.5 years, and 2 years of continuous DOC flux measurements. Over the 6 years, the mean annual carbon was À29.7 AE 30.6 (AE 1 SD) g C m À2 yr À1 with its components as follows: carbon in CO 2 was a sink of À47.8 AE 30.0 g C m À2 yr À1 ; carbon in CH 4 was a source of 4.1 AE 0.5 g C m À2 yr À1 and the carbon exported as stream DOC was a source of 14.0 AE 1.6 g C m À2 yr À1 . For 2 out of the 6 years, the site was a source of carbon with the sum of CH 4 and DOC flux exceeding the carbon sequestered as CO 2 . The average C balance for the 6 years corresponds to an average annual growth rate of the peatland surface of 1.3 mm yr À1 .

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects.The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available.The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components:Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk?Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output.Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.

This paper represents the first continuous dissolved organic carbon (DOC) record, measured in a stream draining an Atlantic blanket bog in South West Ireland for the calendar year 2007. At 30-min intervals, the DOC concentration was automatically measured using an in-stream spectroanalyser whose variation compared well with laboratory analysed samples taken by a 24-bottle auto-sampler. The concentration of DOC ranged from 2.7 to 11.5 mg L -1 with higher values during the summer and lower values during the winter. A simple linear regression model of DOC concentration versus air temperature of the previous day was found, suggesting that temperature more than discharge was controlling the DOC concentration in the stream. The change in DOC concentration with storm events showed two patterns: (1) in the colder period: the DOC concentration seemed to be independent of changes in stream flow; (2) in the warmer period: the DOC concentration was found to rise with increases in stream flow on some occasions and to decrease with increasing stream flow on other occasions. The annual export of DOC for 2007 was 14.1 (±1.5) g C m -2 . This value was calculated using stream discharge data that were determined by continuously recorded measurements of stream height. The flux of DOC calculated with the 30-min sampling was compared with that calculated based on lower sampling frequencies. We found that sampling frequency of weekly or monthly were adequate to calculate the annual flux of DOC in our study site in 2007.

Journal articleIFPRI3; ISIEPTDP