Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade

Evidence for increasing global wheat yield potential

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 +/- 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges

Do maize crop models catch the impact of future [CO2] on maize yield and water use?

Do maize crop models catch the impact of future [CO2] on maize yield and water use?. iCROPM2016 International Crop Modelling Symposiu

Data format for model in- and output

A common format for model input variables and model output variables has been defined to be distributed to modellers participating in the model inter-comparison and improvement. The aim of common formats is to support the communication between the modellers, those providing empirical data of the experiments and those analysing the simulation results. The input format facilitates the model application in a way that each cropping-system to be modelled will be defined in the same way. Data will be delivered in EXCEL sheets with sub-tables for each block of inputs. Tables are mostly organized in a way that allows export and sequential read-in by the models. The common output format enables effective processing of results estimating model performance indicators

Analysis of recent changes in maximum and minimum temperatures in Pakistan

16 páginas, 8 figuras.© 2015 Elsevier B.V.. Data from 37 weather stations with records of maximum and minimum temperatures (Tmax and Tmin hereafter) were used to analyse trends in both variables at a monthly, seasonal and annual resolution. Sen's slope and Mann-Kendall statistical tests were applied to calculate the sign and slopes of trends and their statistical significance. A correlation analysis was also performed to study possible relationships between temperatures and certain teleconnection patterns with an influence on Northern Hemisphere temperatures: the North Atlantic Oscillation (NAO), Arctic Oscillation (AO), El Niño-Southern Oscillation (ENSO), and North Sea Caspian Pattern (NCP). The study reveals that Tmax has significantly increased (in over 30% of sites) in the pre-monsoon season and yearly. The sharpest increases were observed in March. Tmin clearly showed positive trends in the pre-monsoon season and at the annual scale. It is also worth noting a cooling trend in the northern areas during the study period. Tmax increased faster than Tmin in the northern areas in all the seasons studied and at annual resolution, while the opposite occurred in the rest of the country (except in the pre-monsoon season).The highest correlation coefficients between patterns and Tmax and Tmin were seen in the months of the pre-monsoon season: with NAO from January to March; with ENSO in May and with NCP in the late pre-monsoon season (May). AO was the pattern with the lowest relationships with temperatures. These results could have a significant influence on agriculture and water resources in Pakistan if these trends are maintained in the future.This research was supported by an Alexander von Humboldt Foundation Fellowship to Muhammad Anjum Iqbal.Peer Reviewe

Impacts and adaptation of European crop production systems to climate change

The studies on anthropogenic climate change performed in the last decade over Europe show consis- Q2 tent projections of increases in temperature and different patterns of precipitation with widespread increases in northern Europe and decreases over parts of southern and eastern Europe. The development in national grain yields of wheat in the period 1961¿2006 for countries in Europe shows that yields in northern Europe are limited by cool temperatures, whereas yields in southern Europe are limited by high temperatures and low rainfall. Yields increased considerably during the period 1970¿1990 in all countries due to improved technologies with the highest absolute increases in western and central Europe. In many countries and in recent years there is a tendency towards yield stagnation and increased yield variability. Some of these trends may have been influenced by the recent climatic changes over Europe. A set of qualitative and quantitative questionnaires on perceived risks and foreseen impacts of climate and climate change on agriculture in Europe was distributed to agro-climatic and agronomy experts in 26 countries. Europe was divided into 13 Environmental Zones (EZ). In total, we had 50 individual responses for specific EZ. The questionnaires provided both country and EZ specific information on the: (1) main vulnerabilities of crops and cropping systems under present climate; (2) estimates of climate change impacts on the production of nine selected crops; (3) possible adaptation options as well as (4) adaptation observed so far. In addition we focused on the overall awareness and presence of warning and decision support systems with relevance for adaptation to climate change. The results show that farmers across Europe are currently adapting to climate change, in particular in terms of changing timing of cultivation and selecting other crop species and cultivars. The responses in the questionnaires show a surprisingly high proportion of negative expectations concerning the impacts of climate change on crops and crop production throughout Europe, even in the cool temperate northern European countries. The expected impacts, both positive and negative, are just as large in northern Europe as in the Mediterranean countries, and this is largely linked with the possibilities for effective adaptation to maintain current yields. The most negative effects were found for the continental climate in the Pannonian zone, which includes Hungary, Serbia, Bulgaria and Romania. This region will suffer from increased incidents of heat waves and droughts without possibilities for effectively shifting crop cultivation to other parts of the years. A wide range of adaptation options exists in most European regions to mitigate many of the negative impacts of climate change on crop production in Europe. However, considering all effects of climate change and possibilties for adaptation, impacts are still mostly negative in wide regions across Europe.JRC.H.4-Monitoring Agricultural Resource

Risk assessent and foreseen impacts on agriculture

During 2006, COST action 734 (CLIVAGRI - Impacts of Climate Change and Variability on European Agriculture) was launched thanks to the coordinated activity of 15 EU countries. The main objective of the Action is the evaluation of possible impacts from climate change and variability on agriculture and the assessment of critical thresholds for various European areas. In order to gather information on perceived risks and foreseen impacts of climate change on agriculture in Europe we designed a set of qualitative and quantitative questionnaires that were distributed to leading experts in 26 EU countries. The results show that the farmer across Europe are currently adapting to climate change, in particular in terms of changing timining of cultivation and selecting other crops species and cultivars.JRC.G.3-Monitoring agricultural resource

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters, and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters, and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate-change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.201