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The increasing acidification of soil due to pollution and agricultural management practices is a growing problem worldwide, where food production is already under threat by climate change, more frequent droughts, and soil nutrient depletion. Soil acidification is quantified by pH measurements and is a primary metric for soil health. High soil acidity is a constraint on the production of grains and other crops because it decreases the bioavailability of important plant nutrients while increasing soil toxicity arising from an imbalance of essential soil elements. Field pH can be estimated by colour test kits which are very cost-effective and particularly suitable for developing countries where laboratory services are not available or fail to provide timely results. Because the pH test kit is based on visual colour matching between a colour card scale and a soil sample in solution, there are epistemic uncertainties, such as variability in expert opinion, differences in colour vision, measurement error, instrumentation, and changes in daylight spectral content. In this study, expert human observers were compared in experiments conducted using a standard pH test kit under a range of environmental conditions. A significant difference in uncertainty in colour discrimination was evident between male and female experts, whereas changes in daylight conditions had lower impact on the variance of pH estimates. In a group of subject matter experts, the male standard error (0.35 pH) was 57% higher on average over the range of pH values (pH = 4 → 10) compared to females (0.22 pH). This error was largest (70%) in the low pH 4 to 6.5 range, which is a critical range for successful amelioration of soil acidification. The results suggest that historical database measurements may have hitherto unrecognised uncertainties that affect confidence intervals for experimental data that in turn will have an impact on predictive models and policy development.

International audience; Plant responses to rising atmospheric carbon dioxide (CO2) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO2 concentrations. Recent concerted efforts have narrowed the uncertainties in CO2-induced crop responses so that climate change impact simulations omitting CO2 can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO2 and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.Understanding of the effects of elevated CO2 on crops has improved sufficiently that modelling future climatic effects on agriculture should eliminate 'no CO2' simulations. Further advancement in the estimation of the effects can be realized by studying a wider variety of crop species under a wider range of growing conditions, improving the representation of responses to climate extremes in crop models and simulating additional crop physiological processes related to nutritional quality.

Understanding sources of uncertainty in climate–crop modelling is critical for informing adaptation strategies for cropping systems. An understanding of the major sources of uncertainty in yield change is needed to develop strategies to reduce the total uncertainty. Here, we simulated rain-fed wheat cropping at four representative locations in China and Australia using eight crop models, 32 global climate models (GCMs) and two climate downscaling methods, to investigate sources of uncertainty in yield response to climate change. We partitioned the total uncertainty into sources caused by GCMs, crop models, climate scenarios and the interactions between these three. Generally, the contributions to uncertainty were broadly similar in the two downscaling methods. The dominant source of uncertainty is GCMs in Australia, whereas in China it is crop models. This difference is largely due to uncertainty in GCM-projected future rainfall change across locations. Our findings highlight the site-specific sources of uncertainty, which should be one step towards understanding uncertainties for more robust climate–crop modelling. Understanding major sources of uncertainty in yield change facilitates adaptation strategies for cropping systems. Using eight crop models, 32 global climate models and two climate downscaling methods, it is shown that their relative contribution to uncertainty in climate–crop modelling depends on location.

A method is described for integrating crop modelling and production economics to quantify optimum applications of multiple nutrients and yield gaps. The method is demonstrated for crop production in the high‐rainfall zone of southern Australia. Data from a biophysical crop model were used to overcome the persistent problem of inadequate experimental data. The Mitscherlich function was expanded to accommodate four variable inputs – nitrogen, phosphorus, potassium and sulphur – and the expansion path was used to determine the economic optimum application of all four nutrients. Modelling revealed the state‐contingent yield potential and the extent to which unrealised yield could be explained by profit‐maximising behaviour and risk‐aversion by growers. If growers and their advisors were guided by the methods described, they would be better equipped to assess crop nutrient demands and limitations, predict yield potential, additional profit and the risks associated with high input systems in a variable climate. If scientists were more aware of the extra profits and the risks involved (as well as the quantitative relationships between inputs and outputs) when thinking about what to produce and how to do so, they would be more circumspect about the net benefits to be obtained from closing yield gaps.

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO concentrations (e[CO ]), there is a risk that too much early growth might deplete soil water and lead to more severe terminal drought stress in environments where production relies on stored soil water content. If this is the case, the incorporation of such a trait in wheat breeding programs might have unintended negative consequences in the future, especially in dry years. We used selected data from cultivars with proven expression of high and low early vigour from the Australian Grains Free Air CO Enrichment (AGFACE) facility, and complemented this analysis with simulation results from two crop growth models which differ in the modelling of leaf area development and crop water use. Grain yield responses to e[CO ] were lower in the high early vigour group compared to the low early vigour group, and although these differences were not significant, they were corroborated by simulation model results. However, the simulated lower response with high early vigour lines was not caused by an earlier or greater depletion of soil water under e[CO ] and the mechanisms responsible appear to be related to an earlier saturation of the radiation intercepted. Whether this is the case in the field needs to be further investigated. In addition, there was some evidence that the timing of the drought stress during crop growth influenced the effect of e[CO ] regardless of the early vigour trait. There is a need for FACE investigations of the value of traits for drought adaptation to be conducted under more severe drought conditions and variable timing of drought stress, a risky but necessary endeavour.

The stimulatory effect of elevated [CO2 ] (e[CO2 ]) on crop production in future climates is likely to be cancelled out by predicted increases in average temperatures. This effect may become stronger through more frequent and severe heat waves, which are predicted to increase in most climate change scenarios. Whilst the growth and yield response of some legumes grown under the interactive effect of e[CO2 ] and heat waves has been studied, little is known about how N2 fixation and overall N metabolism is affected by this combination. To address these knowledge gaps, two lentil genotypes were grown under ambient [CO2 ] (a[CO2 ], ~400 µmol·mol-1 ) and e[CO2 ] (~550 µmol·mol-1 ) in the Australian Grains Free Air CO2 Enrichment facility and exposed to a simulated heat wave (3-day periods of high temperatures ~40 °C) at flat pod stage. Nodulation and concentrations of water-soluble carbohydrates (WSC), total free amino acids, N and N2 fixation were assessed following the imposition of the heat wave until crop maturity. Elevated [CO2 ] stimulated N2 fixation so that total N2 fixation in e[CO2 ]-grown plants was always higher than in a[CO2 ], non-stressed control plants. Heat wave triggered a significant decrease in active nodules and WSC concentrations, but e[CO2 ] had the opposite effect. Leaf N remobilization and grain N improved under interaction of e[CO2 ] and heat wave. These results suggested that larger WSC pools and nodulation under e[CO2 ] can support post-heat wave recovery of N2 fixation. Elevated [CO2 ]-induced accelerated leaf N remobilisation might contribute to restore grain N concentration following a heat wave.

Long season, winter-type canola cultivars have the potential for significantly higher yields than short-season, spring-type canola, yet until recently, breeding of new canola cultivars in Australia has focussed on spring-type canola. This has been to accommodate the typically drier, warmer conditions across the Australian cropping belt where long-season varieties do not perform well due to delayed flowering and risk of water-stress during grain fill. However, as cropping continues to expand into the Australian High Rainfall Zone (500–900 mm, HRZ), breeders have become increasingly interested in developing winter-spring crosses (not yet commercially available) which have an intermediate phenology between that of a spring and winter-type canola. As the vernalisation requirement for these crosses is lower than winter-type canola, the areas where such cultivars can be grown profitably in Australia is potentially much wider than for winter-type canola. Field experimentation and crop simulation studies across the potential cropping region of southern Australia were used to determine the yield potential of these winter-spring canola crosses compared with currently available spring-type and winter-type cultivars. Our analysis showed that the four winter-spring crosses evaluated had a range of vernalisation requirements which were between the small requirements of spring-types and the large requirement of winter-types. In this study the Catchment Analysis Tool (CAT) spatial modelling framework was used to determine the expected canola yields of four cultivars across the entire cropping region of southern Australia. These cultivars were the spring-type 45Y88CL, winter-type Hyola® 970CL and two winter-spring crosses K50057 and K50058 with vernalisation requirements at the higher end and the lower end of the range of winter-spring crosses, respectively.The potential benefit of some increase in the vernalisation requirement, based on the area currently sown to canola, was an additional 381 M tonnes per year (based on 50-year average) of canola, if K50058 was sown in areas where it proved superior to 45Y88CL. At the 5-year average canola price of $486 t−1, this would provide an additional AUD 185 Mil/annum for the industry. In general, the modelled yield advantage from canola cultivars with increased vernalisation requirement was greater in the areas of southern Australia that had milder climates and higher rainfall. The value to the Australian canola industry of substituting spring cultivars (e.g. 45Y88CL) with winter x spring (K50057) or winter (Hyola970CL) cultivars where they had a yield advantage, was AUD 82.8 M and AUD 29.2 M, respectively.

Wheat 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 CO2 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 disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., B.L received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat. A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an AgreenSkills fellowship under grant agreement no. PCOFUND‐GA‐2010‐267196. PM, A.M., D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). L.X. and Y.Z. were supported by the National High‐Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493 and 31561143003). R.R. received support from the German Ministry for Research and Education (BMBF) through project SPACES‐LLL. Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat programme [BB/P016855/1]. M.J. and J.E.O. were supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour. for his support. R.P.R., T.P., and F.T. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM) and from the Academy of Finland through the projects NORFASYS (decision nos. 268277 and 292944) and PLUMES (decision nos. 277403 and 292836). K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. and G.J.F. acknowledge support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research Development Corporation, Australia. P.K.A.'s work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.. B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1).

Atmospheric carbon dioxide concentrations [CO2] are increasing steadily. Some reports have shown that root growth in grain crops is mostly stimulated in the topsoil rather than evenly throughout the soil profile by e[CO2], which is not optimal for crops grown in semi-arid environments with strong reliance on stored water. An experiment was conducted during the 2014 and 2015 growing seasons with two lentil (Lens culinaris) genotypes grown under Free Air CO2 Enrichment (FACE) in which root growth was observed non-destructively with mini-rhizotrons approximately every 2–3 weeks. Root growth was not always statistically increased by e[CO2] and not consistently between depths and genotypes. In 2014, root growth in the top 15 cm of the soil profile (topsoil) was indeed increased by e[CO2], but increases at lower depths (30–45 cm) later in the season were greater than in the topsoil. In 2015, e[CO2] only increased root length in the topsoil for one genotype, potentially reflecting the lack of plant available soil water between 30–60 cm until recharged by irrigation during grain filling. Our limited data to compare responses to e[CO2] showed that root length increases in the topsoil were correlated with a lower yield response to e[CO2]. The increase in yield response was rather correlated with increases in root growth below 30 cm depth.

The yield of rain-fed crops needs to increase to meet the requirements of a growing human population. One way to achieve this is to store more rain in the root zone of soils and increase its use by crops. This will be difficult where poorly structured, dense clay subsoils occur. These subsoils limit crop yields by reducing root growth, and the uptake of soil water and nutrients. Reduced root growth results from physical factors, such as low porosity restricting the movement of air and water, high soil strength and temporal water logging which impedes the roots. Chemical factors such as alkalinity, salinity, and sodicity may further restrict root growth in neutral pH and alkaline clay subsoils. This review will focus on the challenging task of improving structure in these dense clay subsoils and to overcome physicochemical constraints in the subsoil, in order to increase crop yields. Early attempts to ameliorate these subsoils using tillage practices, and amendments in some cases, were rarely successful. A more recent and successful approach has been to deep-band nutrient-rich organic amendments in the subsoil, and to follow with a field crop. This increased root growth in the subsoil, and was often associated with improved subsoil structure. It was able to produce substantial and prolonged increases in crop production. A key benefit was an increase in plant available water in the subsoil and the utilization of this water by the crop. The impact of the practice depends on the amount and distribution of rainfall, and this results in increased financial risk for the farm business. Specialized machinery will need to be developed to handle the amendments, while the form, rate of incorporation, and placement depth of amendments will need to be optimized to reduce high upfront costs of the practice. Refereed/Peer-reviewed

Frost can significantly reduce production of field crops grown in Mediterranean-type environments, where significant economic losses for Australian wheat occur annually. If non-destructive sensors could make rapid, spatial assessment of frost damage, this could limit economic losses through timely management decisions. This paper reports on a methodology for imposing frost treatments to wheat under field conditions and the utility of canopy reflectance data for detecting early crop damage. Purpose-built chambers using stepped additions of dry ice allowed for a range of frost scenarios to be tested when applied at wheat heading and anthesis. For frost treatments applied at anthesis, grain number and yield were reduced by 8.8% and 7.2%, respectively, for every degree Celsius below zero (down to −4°C). This effect was additive over two consecutive nights. For cold load equivalent, there was a 2.2% and 1.9% reduction in grain number and yield, respectively, per °C hr (below 0°C). For wheat, spectral indices PRI and NDVI (reflectance) and FRF_G and SFR_G (fluorescence) showed significant relationships, with cold load applied for heading treatments. Next steps include targeting frost intensities equivalent to cold loads between 20 and 80°C hr and testing the utility of these proposed indices in a commercial paddock setting.

Increased biomass and yield of plants grown under elevated [CO2 ] often corresponds to decreased grain N concentration ([N]), diminishing nutritional quality of crops. Legumes through their symbiotic N2 fixation may be better able to maintain biomass [N] and grain [N] under elevated [CO2 ], provided N2 fixation is stimulated by elevated [CO2 ] in line with growth and yield. In Mediterranean-type agroecosystems, N2 fixation may be impaired by drought, and it is unclear whether elevated [CO2 ] stimulation of N2 fixation can overcome this impact in dry years. To address this question, we grew lentil under two [CO2 ] (ambient ~400 ppm and elevated ~550 ppm) levels in a free-air CO2 enrichment facility over two growing seasons sharply contrasting in rainfall. Elevated [CO2 ] stimulated N2 fixation through greater nodule number (+27%), mass (+18%), and specific fixation activity (+17%), and this stimulation was greater in the high than in the low rainfall/dry season. Elevated [CO2 ] depressed grain [N] (-4%) in the dry season. In contrast, grain [N] increased (+3%) in the high rainfall season under elevated [CO2 ], as a consequence of greater post-flowering N2 fixation. Our results suggest that the benefit for N2 fixation from elevated [CO2 ] is high as long as there is enough soil water to continue N2 fixation during grain filling.

This study investigated crop water use of wheat grown in a dryland Mediterranean-type environment under elevated atmospheric CO2 concentrations ([CO2]). Two related cultivars, contrasting in agronomic features (cvs. Scout and Yitpi; Scout has good early vigour and high transpiration efficiency), were grown under ambient [CO2] (a[CO2], ∼400 μmol mol−1) and elevated [CO2] (e[CO2], ∼550 μmol mol−1) in the Australian Grains Free Air CO2 Enrichment (AGFACE) facility for two growing seasons. Each year, an irrigation treatment (rainfed versus irrigated) was imposed within the CO2-treatments. Normalised difference vegetation index (as surrogate for canopy cover) and root length in the upper (0 cm–32 cm) and deeper (33 cm–64 cm) soil layers were measured at stem-elongation and anthesis. Elevated [CO2] stimulated root length of wheat in both upper and deeper soil layers, and this stimulation was modified by cultivars and irrigation regimes. Across cultivars and all treatments, water use, biomass and grain yield were positively associated with root length in the deeper soil layer but not with root length in the upper soil layer. The ‘CO2 fertilisation effect’ on biomass and grain yield was of similar magnitude under both irrigated and rainfed conditions. Although e[CO2] did not increase canopy cover in these experiments, the CO2 effect on water use depended on cultivars and irrigation regimes. Despite greater e[CO2]-induced stimulation of tillers and spikes, the cv. Scout did not receive more biomass or grain yield benefit from the ‘CO2 fertilisation effect’ compared to cv. Yitpi.

Climate change is recognised to alter the distribution of rainfall and increase temperature and atmospheric CO2 concentration [CO2] and pose a formidable challenge to the sustainability of various cropping industries around the world. Additionally, in specific regions, like China, and for particular crops, like sugarcane, the likely effects of climate change are not straightforward. This is because of non-linearity of the interacting climatic factors on crop growth and yield. In our study, the APSIM-Sugarcane model was used to examine the likely response of sugarcane in future climate scenarios. Statistically downscaled climate data based on 28 global climate models (GCMs) under RCP4.5 and RCP8.5 scenarios were used to generate the change in climate in southern China. The results show that the model well reproduced observations for biomass dry matter (DM), biomass fresh matter (FM), sugar yields (S) and leaf area index (LAI), with values for the index of agreement of 0.65, 0.71, 0.84 and 0.70, respectively. The values of RMSE were relatively low with 7.10 t ha−1 for DM, 18.87 t ha−1 for FM, 0.84 t ha−1 for S and 1.03 m2 m−2 for LAI. On average, the ensemble of downscaled GCM projections showed a small increase in radiation and rainfall in the future at the four locations considered, with significantly increased temperature. Sugarcane yields in southern China appeared to be positively affected under future climate and [CO2] changes. Overall, DM was projected to increase by 5.6 and 6.4 and 6.6 t ha−1 for RCP4.5 in 2030s, 2060s and 2090s relative to 1961–2010, respectively. However, RCP8.5 had less promotion compared to RCP4.5 on DM. Similar increased trends for three future time periods could be found in FM and S. Our results showed that the largest percentage change in S occurred at high latitude locations (e.g., Hezhou), with mean values 28.1% and 39.4% for RCP4.5 and RCP8.5 in 2030s, 44.2% and 23.5% in 2060s, and 41.1% and 45.5% for in 2090s, respectively. In addition, our multiple linear regression analyses showed that the changes in radiation, rainfall and temperature together with elevated [CO2] could explain more than 70% of sugarcane yields change across four locations. Across all locations, increases in sugarcane yields were strongly correlated (P = .001) with each degree (Celsius) increase in future temperature and per mm increase in future rainfall. For example, the DM, FM and S increased 7.8–14.2, 16.6–36.1 and 2.7–6.1 kg ha−1 mm−1 responding to rainfall, respectively. Although uncertainties in our study on the impact of climate change on sugarcane might arise from the choice of crop model and GCMs, the results would be pivotal for developing high-yield adaptive strategies as well as informing policy makers to improve sugarcane productivity in China.

The impact of elevated [CO2 ] (e[CO2 ]) on crops often includes a decrease in their nutrient concentrations where reduced transpiration-driven mass flow of nutrients has been suggested to play a role. We used two independent approaches, a free-air CO2 enrichment (FACE) experiment in the South Eastern wheat belt of Australia and a simulation study employing the agricultural production systems simulator (APSIM), to show that transpiration (mm) and nutrient uptake (g m-2 ) of nitrogen (N), potassium (K), sulfur (S), calcium (Ca), magnesium (Mg) and manganese (Mn) in wheat are correlated under e[CO2 ], but that nutrient uptake per unit water transpired is higher under e[CO2 ] than under ambient [CO2 ] (a[CO2 ]). This result suggests that transpiration-driven mass flow of nutrients contributes to decreases in nutrient concentrations under e[CO2 ], but cannot solely explain the overall decline.

Climate change (CC) presents a challenge for the sustainable development of wheat production systems in Australia. This study aimed to (1) quantify the impact of future CC on wheat grain yield for the period centred on 2030 from the perspectives of wheat phenology, water use and water use efficiency (WUE) and (2) evaluate the effectiveness of changing sowing times and cultivars in response to the expected impacts of future CC on wheat grain yield. The daily outputs of CSIRO Conformal-Cubic Atmospheric Model for baseline and future periods were used by a stochastic weather generator to derive changes in mean climate and in climate variability and to construct local climate scenarios, which were then coupled with a wheat crop model to achieve the two research aims. We considered three locations in New South Wales, Australia, six times of sowing (TOS) and three bread wheat (Triticum aestivum L.) cultivars in this study. Simulation results show that in 2030 (1) for impact analysis, wheat phenological events are expected to occur earlier and crop water use is expected to decrease across all cases (the combination of three locations, six TOS and three cultivars), wheat grain yield would increase or decrease depending on locations and TOS; and WUE would increase in most of the cases; (2) for adaptation considerations, the combination of TOS and cultivars with the highest yield varied across locations. Wheat growers at different locations will require different strategies in managing the negative impacts or taking the opportunities of future CC.

We compared Support Vector Machine (SVM) and Random Forest (RF) machine learning approaches with the widely used Jarvis-type phenomenological model for predicting stomatal conductance (gs) in wheat (Triticum aestivum L.) using historical measurements collected in the Australian Grains Free-Air CO2 Enrichment (AGFACE) facility. The machine learning-based methods produced greater accuracy than the Jarvis-type model in predicting gs from leaf age, atmospheric [CO2], photosynthetically active radiation, vapour pressure deficit, temperature, time of day, and soil water availability (i.e. phenological and environmental variables determining gs). The R2 was 0.76 for the Jarvis-type but 0.92 for SVM and 0.97 for RF machine learning-based models, with a calculated RMSE of 0.292 mol m−2 s−1 in the Jarvis-type compared to 0.129 mol m−2 s−1 in SVM and 0.081 mol m−2 s−1 in RF. The machine learning models, however, needed large datasets for training to achieve statistical significance, and do not offer the same opportunity to provide physiological insights through a statistically testable hypothesis. These results show that using the machine-learning based methods can achieve high prediction accuracy of gs that is especially important when incorporated into larger models, but their ability to extrapolate beyond observed data ranges will need to be assessed before they could be considered in place of the physical model.

The use of different downscaling methods (DSMs) to generate downscaled daily climate (DDC) data for assessing climate change impacts on wheat cropping systems was investigated. DDC data were generated from SRES A2 emission scenario simulations of seven global climate models (GCMs) using two different change factor methods, denoted as DTS and RSC, and two weather generator methods, LARS-WG (LWG) and NWAI-WG (NWG). The DDC data were used to drive the Agricultural Production Systems sIMulator (APSIM) wheat model. Significant differences in future changes in simulated crop growth and soil water balance were found between the four DSMs. For raw GCM output, multi-GCM mean changes by the mid-twenty-first century in annual mean temperature (AMT) ranged from +1.2 to +2.0 °C and in annual rainfall (AR) ranged from −11 to −5% across the 6 study sites. The DTS, LWG and NWG SDMs modified the raw GCM changes in both AMT and AR. As a result, climate change impacts on crop growth and soil water balance are not well correlated between DSMs. Since different DSMs give different impact results, we conclude that in addition to using multiple GCMs, selecting appropriate DSMs can be an important consideration in climate change impact assessments.

Atmospheric CO 2 concentrations ([CO 2 ]) are predicted to increase from current levels of about 400 ppm to reach 550 ppm by 2050. The direct benefits of elevated [CO 2 ] (e[CO 2 ]) to plant growth appear to be greater under low rainfall conditions, but there are few field (Free Air CO 2 Enrichment or FACE) experimental set-ups that directly address semi-arid conditions. The objectives of this study were to investigate the following research questions: 1) What are the effects of e[CO 2 ] on the growth and grain yield of lentil ( Lens culinaris ) grown under semi-arid conditions under FACE? 2) Does e[CO 2 ] decrease grain nitrogen in lentil? and 3) Is there genotypic variability in the response to e[CO 2 ] in lentil cultivars? Elevated [CO 2 ] increased yields by approximately 0.5 t ha −1 (relative increase ranging from 18 to 138%) by increasing both biomass accumulation (by 32%) and the harvest index (by up to 60%). However, the relative response of grain yield to e[CO 2 ] was not consistently greater under dry conditions and might depend on water availability post-flowering. Grain nitrogen concentration was significantly reduced by e[CO 2 ] under the conditions of this experiment. No differences were found between the cultivars selected in the response to elevated [CO 2 ] for grain yield or any other parameters observed despite well expressed genotypic variability in many traits of interest. Biomass accumulation from flowering to maturity was considerably increased by elevated [CO 2 ] (a 50% increase) which suggests that the indeterminate growth habit of lentils provides vegetative sinks in addition to reproductive sinks during the grain-filling period.

Elevated CO₂ stimulates crop yields but leads to lower tissue and grain nitrogen concentrations [N], raising concerns about grain quality in cereals. To test whether N fertiliser application above optimum growth requirements can alleviate the decline in tissue [N], wheat was grown in a Free Air CO₂ Enrichment facility in a low‐rainfall cropping system on high soil N. Crops were grown with and without addition of 50–60 kg N/ha in 12 growing environments created by supplemental irrigation and two sowing dates over 3 years. Elevated CO₂ increased yield and biomass (on average by 25%) and decreased biomass [N] (3%–9%) and grain [N] (5%). Nitrogen uptake was greater (20%) in crops grown under elevated CO₂. Additional N supply had no effect on yield and biomass, confirming high soil N. Small increases in [N] with N addition were insufficient to offset declines in grain [N] under elevated CO₂. Instead, N application increased the [N] in straw and decreased N harvest index. The results suggest that conventional addition of N does not mitigate grain [N] depression under elevated CO₂, and lend support to hypotheses that link decreases in crop [N] with biochemical limitations rather than N supply.

Maintaining grain quality of wheat under climate change is critical for human nutrition, end-use functional properties, as well as commodity value. This paper reviews the current knowledge of high temperature and elevated atmospheric CO2 on whole-grain and functional properties of wheat. It also considers the utility of contemporary crop models for investigating the impacts of climate change on wheat quality; and discusses opportunities for advancing model capability. Under elevated CO2 wheat yield can increase by up to 36%, but universally grain protein concentration decreases and a shift in composition translates to reduced functional properties. High temperature during the post-anthesis period of crops can cause a step change reduction in grain-set, grain size and milling yield. Numerous crop models including APSIM-Nwheat, CropSyst, Sirius, GLAM-HTS account for high CO2 effects through modification of RUE, TE or critical leaf-N concentration and high temperature by accelerated leaf senescence, grain number, potential grain weight or HI modifications. For grain quality, however, crop models are typically restricted to predicting average grain size and grain-N content (concentration), although the SiriusQuality model accounts for the major storage proteins, gliadin and glutenin. For protein composition, high temperature stress reduces the glutenin/gliadin ratio and limits the synthesis of the larger SDS-insoluble glutenin polymers which causes wheat dough to have weaker viscoelasticity properties. This link provides an opportunity to model high temperature effects on grain functional properties. Further development and testing, utilizing grain quality data from global FACE programmes will be particularly valuable for validating and enhancing the performance of such models. For whole-grain characteristics, a single-spike model approach, which accounts for intra-spike variation in assimilate deposition may provide an opportunity to predict grain size distribution and associated screenings percentage and milling yield. Taken together expanding the predictive capability of our crop models to grain quality is an important step in providing a powerful tool for developing adaptation strategies for combating the impacts of climate change to global crop production and grain quality.

Reducing the number of tillers per plant using a tiller inhibition (tin) gene has been considered as an important trait for wheat production in dryland environments. We used a spatial analysis approach with a daily time-step coupled radiation and transpiration efficiency model to simulate the impact of the reduced-tillering trait on wheat yield under different climate change scenarios across Australia's arable land. Our results show a small but consistent yield advantage of the reduced-tillering trait in the most water-limited environments both under current and likely future conditions. Our climate scenarios show that whilst elevated [CO2 ] (e[CO2 ]) alone might limit the area where the reduced-tillering trait is advantageous, the most likely climate scenario of e[CO2 ] combined with increased temperature and reduced rainfall consistently increased the area where restricted tillering has an advantage. Whilst long-term average yield advantages were small (ranged from 31 to 51 kg ha-1 year-1 ), across large dryland areas the value is large (potential cost-benefits ranged from Australian dollar 23 to 60 MIL/year). It seems therefore worthwhile to further explore this reduced-tillering trait in relation to a range of different environments and climates, because its benefits are likely to grow in future dry environments where wheat is grown around the world.

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, We thank the Agricultural Model Intercomparison and Improvement Project (AgMIP) for support. B.L., L.X., and Y.Z. were supported by the National Science Foundation for Distinguished Young Scholars (31725020), the National Natural Science Foundation of China (31801260, 51711520319, and 31611130182), the Natural Science Foundation of Jiangsu province (BK20180523), the 111 Project (B16026), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). S.A. and B.K. received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), and the CGIAR Research Program on Wheat. P.M, D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493, 31761143006, and 31561143003). R.R. acknowledges support from the German Federal Ministry for Research and Education (BMBF) through project “Limpopo Living Landscapes” project (SPACES program; grant number 01LL1304A). Rothamsted Research receives grant‐aided support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat project [BB/P016855/1]. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour for his support. K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. acknowledges support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources. P.K.A. was supported by the multiple donors contributing to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1). J.R.P. acknowledges the support of the Labex Agro (Agropolis no. 1501‐003). La. T.P. and F.T. received financial support from the Academy of Finland through the project PLUMES (decision nos. 277403 and 292836) and from Natural Resources Institute Finland through the project ClimSmartAgri.

Intercropping is considered by its advocates to be a sustainable, environmentally sound, and economically advantageous cropping system. Intercropping systems are complex, with non-uniform competition between the component species within the cropping cycle, typically leading to unequal relative yields making evaluation difficult. This paper is a review of the main existing metrics used in the scientific literature to assess intercropping systems. Their strengths and limitations are discussed. Robust metrics for characterising intercropping systems are proposed. A major limitation is that current metrics assume the same management level between intercropping and monocropping systems and do not consider differences in costs of production. Another drawback is that they assume the component crops in the mixture are of equal value. Moreover, in employing metrics, many studies have considered direct and private costs and benefits only, ignoring indirect and social costs and benefits of intercropping systems per se. Furthermore, production risk and growers’ risk preferences were often overlooked. In evaluating intercropping advantage using data from field trials, four metrics are recommended that collectively take into account all important differences in private costs and benefits between intercropping and monocropping systems, specifically the Land Equivalent Ratio, Yield Ratio, Value Ratio and Net Gross Margin.

Wheat production will be impacted by increasing concentration of atmospheric CO2 [CO2 ], which is expected to rise from about 400 μmol mol(-1) in 2015 to 550 μmol mol(-1) by 2050. Changes to plant physiology and crop responses from elevated [CO2 ] (e[CO2 ]) are well documented for some environments, but field-level responses in dryland Mediterranean environments with terminal drought and heat waves are scarce. The Australian Grains Free Air CO2 Enrichment facility was established to compare wheat (Triticum aestivum) growth and yield under ambient (~370 μmol(-1) in 2007) and e[CO2 ] (550 μmol(-1) ) in semi-arid environments. Experiments were undertaken at two dryland sites (Horsham and Walpeup) across three years with two cultivars, two sowing times and two irrigation treatments. Mean yield stimulation due to e[CO2 ] was 24% at Horsham and 53% at Walpeup, with some treatment responses greater than 70%, depending on environment. Under supplemental irrigation, e[CO2 ] stimulated yields at Horsham by 37% compared to 13% under rainfed conditions, showing that water limited growth and yield response to e[CO2 ]. Heat wave effects were ameliorated under e[CO2 ] as shown by reductions of 31% and 54% in screenings and 10% and 12% larger kernels (Horsham and Walpeup). Greatest yield stimulations occurred in the e[CO2 ] late sowing and heat stressed treatments, when supplied with more water. There were no clear differences in cultivar response due to e[CO2 ]. Multiple regression showed that yield response to e[CO2 ] depended on temperatures and water availability before and after anthesis. Thus, timing of temperature and water and the crop's ability to translocate carbohydrates to the grain postanthesis were all important in determining the e[CO2 ] response. The large responses to e[CO2 ] under dryland conditions have not been previously reported and underscore the need for field level research to provide mechanistic understanding for adapting crops to a changing climate.

The level of soil organic carbon (SOC) that is attained under agriculture largely depends upon rates of carbon input and its decomposition under various agronomic practises such as stubble (crop residue) management and fertiliser application. In this study, we used the APSIM-Wheat and APSIM-Agpasture models to simulate changes in SOC in a range of crop and pasture management systems across nine locations in eastern Australia. We explored the extent to which various crop and pasture management options affect changes in SOC from a sub-tropical to a temperate environment. Specifically, we examined how nitrogen fertilisation, stubble management and stocking rate affect SOC and what strategies might be employed by farmers to increase SOC sequestration across eastern Australia. We modelled a continuous cropping regime, a continuously grazed pasture and a mixed cropping and pasture rotation. Under continuous cropping higher nitrogen application and higher amounts of stubble incorporation increased the SOC levels at all locations. At Roma, the northern-most site, there was little additional gain in SOC from increasing N above 70 kg N ha− 1 whereas most other sites showed benefits above 70 kg N ha− 1. The biggest factor in boosting SOC under cropping was the level of stubble incorporation. At all but one site, continuously grazed pasture generally resulted in SOC increases over the 60 years. However, increasing stocking rate decreased the rates of SOC changes at all sites. Crop-pasture rotations show that the impacts of even 4 years of pasture is likely to be significant in reducing declining SOC at low nitrogen application during cropping phases. N fertilisation and stubble incorporation reduced the impact of stocking rate by reducing the decline in SOC. The difference in SOC changes between nine sites across eastern Australia was largely described by mean temperature and rainfall but high temperature strongly interacted with management practises (stocking rate, N application and residue incorporation) to reduce the sequestration of C despite favourable rainfall. Our results indicate that a mean annual temperature higher than about 20 °C can switch a soil from net sink into a net source of atmospheric CO2 if other factors affecting soil carbon changes such as stubble incorporation, stocking rate and site rainfall are constant.

We developed a photoperiod-corrected thermal model that can predict wheat phenology based solely on the combination of photoperiod (Ppd) and vernalisation (Vrn) alleles to identify the phenological suitability of germplasm across the cropping region in southern Australia. More than 200 wheat genotypes that vary in combinations of Ppd and Vrn alleles were grown at 17 locations spanning 11° Latitude, thus providing a wide range in temperature and daylength gradients. The phenological sensitivities of a genotype to varying basic temperature, photoperiod and vernalisation requirement was adjusted via optimisation to minimise the least square difference between the measured and predicted dates of both terminal spike (TS) and flowering (AN). The model predicted dates of TS and AN to within 5 days of the field values. Information was used to identify the alleles required to achieve a wheat ideotype defined in a previous study. The optimum allelic combinations required to target the optimum flowering period for different locations when sown on different dates were also identified. The use of allelic based phenological models has the potential to reduce the costs to breeding programs and accelerate the release of better adapted germplasm to new and changing environments.

The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewith include one hard red spring wheat cultivar (Yecora Rojo) sown approximately every six weeks from December to August for a two-year period for a total of 11 planting dates out of the 15 of the entire HSC experiment. The treatments were chosen to avoid any effect of frost on grain yields. On late fall, winter and early spring plantings temperature free-air controlled enhancement (T-FACE) apparatus utilizing infrared heaters with supplemental irrigation were used to increase air temperature by 1.3°C/2.7°C (day/night) with conditions equivalent to raising air temperature at constant relative humidity (i.e. as expected with global warming) during the whole crop growth cycle. Experimental data include local daily weather data, soil characteristics and initial conditions, detailed crop measurements taken at three growth stages during the growth cycle, and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models. Data access via doi 10.7910/DVN/M9ZT0F

International audience; A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2‐6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters; average bias, model effect variance, environment effect variance and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models, and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

The response of wheat crops to elevated CO2 (eCO2 ) was measured and modelled with the Australian Grains Free-Air CO2 Enrichment experiment, located at Horsham, Australia. Treatments included CO2 by water, N and temperature. The location represents a semi-arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha-1 and 1600 to 3900 kg ha-1 , respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO2 (from 365 to 550 μmol mol-1 CO2 ) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm (P = 0.10) by eCO2 , increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM-Wheat, APSIM-Nwheat, CAT-Wheat, CROPSYST, OLEARY-CONNOR and SALUS) in simulating crop responses to eCO2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency (RUE) or transpiration efficiency (TE) was not critical to define the overall response to eCO2 . However, under irrigation, the effect of late sowing on response to eCO2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO2 , water and temperature is required to resolve these model discrepancies.

Extreme weather events (frost and heat shock), already a significant challenge for grain producers, are predicted to increase under future climate scenarios. This paper reviews the current knowledge on the impacts of extreme heat (heat shock) and frost on crop production and how these impacts are incorporated into contemporary process-based crop models. Heat shock and frost result in a range of physiological impacts on wheat. Based on the literature we conclude that the greatest impacts on production from frost are associated with sterility and the abortion of formed grains around anthesis. While the greatest yield impact from heat shock are reduced grain number (sterility and abortion of grains) during anthesis to early grain filling; as well as the reduced duration of grain filling. Crop models generally did not consider the non-linear response in grain yield from a heat shock or frost event due to these key physiological impacts. While frost damage was incorporated into a number of models through winterkill functions, seedling death or advanced senescence, only the STICS model incorporated a potential decrease in grain number around anthesis. In contrast, heat shock was rarely considered within crop models, with only two examples found in the literature; (1) APSIM-Nwheat which incorporated accelerated senescence in response to extreme heat and (2) MONICA which incorporated a reduction in grain number and yield. We propose a conceptual model for the change in grain number and therefore yield in response to both a frost and heat shock event. We discuss the potential use of daily maximum/minimum temperatures, canopy temperature and heat/frost loads for determining crop response in the models. As well as identifying the need for a greater understanding on how the duration of temperature extremes impact on yield, as well as the cumulative effects of multiple heat/frost events and the interactions with other abiotic stresses including drought.

Wheat production is expected to be affected by climate change through changing components of the crop water balance such as rainfall, evapotranspiration (ET), runoff and drainage. We used the Agricultural Production Systems Simulator (APSIM)-wheat model to simulate the potential impact of climate change on field water balance, ET and water use efficiency (WUE) under the SRES A2 emissions scenario. We ran APSIM with daily climate data statistically downscaled from 18 Global Circulation Models (GCMs). Twelve soil types of varying plant available water holding capacity (PAWC) at six sites across semi-arid southeastern Australia were considered. Biases in the GCM-simulated climate data were bias-corrected against observations for the 1961–1999 baseline period. However, biases in the APSIM output data relative to APSIM simulations forced with climate observations remained. A secondary bias correction was therefore performed on the APSIM outputs. Bias-corrected APSIM outputs for a future period (2021–2040) were compared with APSIM outputs generated using observations for the baseline period to obtain future changes. The results show that effective rainfall was decreased over all sites due to decreased growing season rainfall. ET was decreased through reduced soil evaporation and crop transpiration. There were no significant changes in runoff at any site. The variation in deep drainage between sites was much greater than for runoff, ranging from less than a few millimetres at the drier sites to over 100 mm at the wetter. However, in general, the averaged drainage over different soil types were not significantly different between the baseline (1961–1999) and future period of 2021–2040 (P > 0.05). For the wetter sites, the variations in the future changes in drainage and runoff between the 18 GCMs were larger than those of the drier sites. At the dry sites, the variation in drainage decreased as PAWC increased. Overall, water use efficiency based on transpiration (WUE_T) and ET (WUE_ET) increased by 1.1 to 1.6 and 0.7 to 1.3 kg ha−1 mm−1, respectively, over the baseline historical climate. Significant relationships between changes in wheat yield and PAWC were only seen at three sites. At the dry sites, the impact of a future climate under a soil of high PAWC was less than that under one of low PAWC. Conversely, the opposite response was seen at two wetter sites, highlighting the importance of PAWC and rainfall in determining the interactive response of crops to primary components of the water balance.

The impact of elevated [CO

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.

Climate change threatens global wheat production and food security, including the wheat industry in Australia. Many studies have examined the impacts of changes in local climate on wheat yield per hectare, but there has been no assessment of changes in land area available for production due to changing climate. It is also unclear how total wheat production would change under future climate when autonomous adaptation options are adopted. We applied species distribution models to investigate future changes in areas climatically suitable for growing wheat in Australia. A crop model was used to assess wheat yield per hectare in these areas. Our results show that there is an overall tendency for a decrease in the areas suitable for growing wheat and a decline in the yield of the northeast Australian wheat belt. This results in reduced national wheat production although future climate change may benefit South Australia and Victoria. These projected outcomes infer that similar wheat-growing regions of the globe might also experience decreases in wheat production. Some cropping adaptation measures increase wheat yield per hectare and provide significant mitigation of the negative effects of climate change on national wheat production by 2041-2060. However, any positive effects will be insufficient to prevent a likely decline in production under a high CO2 emission scenario by 2081-2100 due to increasing losses in suitable wheat-growing areas. Therefore, additional adaptation strategies along with investment in wheat production are needed to maintain Australian agricultural production and enhance global food security. This scenario analysis provides a foundation towards understanding changes in Australia's wheat cropping systems, which will assist in developing adaptation strategies to mitigate climate change impacts on global wheat production.

Higher transpiration efficiency (TE) has been proposed as a mechanism to increase crop yields in dry environments where water availability usually limits yield. The application of a coupled radiation and TE simulation model shows wheat yield advantage of a high-TE cultivar (cv. Drysdale) over its almost identical low-TE parent line (Hartog), from about -7 to 558 kg/ha (mean 187 kg/ha) over the rainfed cropping region in Australia (221-1,351 mm annual rainfall), under the present-day climate. The smallest absolute yield response occurred in the more extreme drier and wetter areas of the wheat belt. However, under elevated CO

Climate change (CC) presents a challenge for the sustainable development of wheat production systems in Australia. This study aimed to (1) quantify the impact of future CC on wheat grain yield for the period centred on 2030 from the perspectives of wheat phenology, water use and water use efficiency (WUE) and (2) evaluate the effectiveness of changing sowing times and cultivars in response to the expected impacts of future CC on wheat grain yield. The daily outputs of CSIRO Conformal-Cubic Atmospheric Model for baseline and future periods were used by a stochastic weather generator to derive changes in mean climate and in climate variability and to construct local climate scenarios, which were then coupled with a wheat crop model to achieve the two research aims. We considered three locations in New South Wales, Australia, six times of sowing (TOS) and three bread wheat (Triticum aestivum L.) cultivars in this study. Simulation results show that in 2030 (1) for impact analysis, wheat phenological events are expected to occur earlier and crop water use is expected to decrease across all cases (the combination of three locations, six TOS and three cultivars), wheat grain yield would increase or decrease depending on locations and TOS; and WUE would increase in most of the cases; (2) for adaptation considerations, the combination of TOS and cultivars with the highest yield varied across locations. Wheat growers at different locations will require different strategies in managing the negative impacts or taking the opportunities of future CC.

All data are available via DOI http://doi.org/10.7910/DVN/ECSFZG, he data set contains a portion of the International Heat Stress Genotype Experiment (IHSGE) data used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat crop models and quantify the impact of heat on global wheat yield productivity. It includes two spring wheat cultivars grown during two consecutive winter cropping cycles at hot, irrigated, and low latitude sites in Mexico (Ciudad Obregon and Tlaltizapan), Egypt (Aswan), India (Dharwar), the Sudan (Wad Medani), and Bangladesh (Dinajpur). Experiments in Mexico included normal (November-December) and late (January-March) sowing dates. Data include local daily weather data, soil characteristics and initial soil conditions, crop measurements (anthesis and maturity dates, anthesis and final total above ground biomass, final grain yields and yields components), and cultivar information. Simulations include both daily in-season and end-of-season results from 30 wheat models.

Grain number (GN) is determined by spike growth rate (SGR), fruiting efficiency (FE) and the duration of spike growth period (Ds). However, these three traits are not independent of each other and therefore quantifying their relative contribution to GN is important for improving yield potential. This study aimed to model GN as a function of SGR, FE and Ds in bread wheat and investigate the relative importance of these three traits in determining GN. A large number of commercial varieties, elite wheat breeding lines, and a Multiparent Advanced Generation InterCross population were evaluated for GN, SGR, Ds, and FE in six Australian environments in 2014 and 2015. The model explained 43–98% of the variation in GN in individual environments and 88% of the variation in GN across the six environments. The percentage of GN variance explained by the model was significantly higher than those explained by correlating GN to individual traits. The relative importance of the contribution of the three traits to GN was different and ranked as SGR > FE > Ds. The negative correlation between FE and spike dry weight at anthesis (SDWa) appears to be spurious, suggesting that there was no causality between high FE and low SDWa and vice versa. Thus, FE can be used as an independent trait to increase GN in addition to SGR and Ds. We also investigated if the wheat crop had enough source to fill the increased sink size in the southern Australian high rainfall environment. The source-sink balance analysis indicates that the available source, consisting of concurrent photosynthetic assimilate and stored water-soluble carbohydrate at anthesis, was sufficient to fill the established sink but no surplus assimilates were available to fill a larger sink in southwestern Australia. However, the opportunity to increase yield potential by further increasing grain number might exist in south-eastern Australia environments.

Fundamental changes in farming systems occurred throughout the medium-rainfall zone of southern Australia during the late 1990s. Pulse and canola crops replaced pastures and long-fallowing, and minimal-tillage practices were increasingly adopted. An experiment was established in 1998 to examine long-term effects of these changes on crop productivity and soil fertility. Nine rotation–tillage treatments based on 3- and 6-year cycles were compared by using wheat (Triticum aestivum) as a bioassay crop over 2001–17. Seasonal conditions during the study ranged from the Millennium Drought to the top rainfall decile. Averaged across the 17 seasons, wheat yields were significantly lower in rotations based on continuous wheat (1.73t/ha) or 3 years of perennial lucerne (Medicago sativa) (1.93t/ha) and highest after traditional long-fallow (2.92t/ha) or vetch (Vicia sativa) green manure (2.57t/ha). Wheat yields following a pulse (2.23t/ha) or canola (Brassica napus) (2.21t/ha) were intermediate. Whereas rotation effects varied with seasonal rainfall, there was a long-term trend for relative yields in continuous wheat rotations to decline and those following a green manure or fallow to differ increasingly from other treatments. Compared across the same rotation (canola–wheat–pulse), average wheat yields under no-tillage (2.09t/ha) were significant lower (Ppulse) in the rotation via their effect on soil mineral nitrogen (N). Lowest protein was recorded in continuous wheat and highest where at least one-third of the rotation contained a non-pulse legume, i.e. vetch green manure, lucerne or annual medic (Medicago truncatula). Soil-borne cereal fungal pathogens and nematodes generally had little effect on grain yields. Soil organic carbon (SOC) and total N stocks declined in all treatments over time, except in the lucerne and green manure treatments, where total N was maintained. Greatest declines in SOC and total N occurred in the fallow treatment. Results from the first 20 years of experimentation suggest that differences in grain yield (and protein) of wheat were most likely the result of treatment (rotation and tillage practice) effects on soil nitrate and water (growing-season rainfall and fallow storage) rather than disease. We found no evidence that current farming systems based on use of pulses, canola and reduced tillage are less able to maintain intervening cereal yields than traditional systems. On the contrary, practices such as long-fallowing are expected to have increasing negative influence on productivity in the longer term via decreased soil C and N fertility.

We tested the hypothesis that lengthening the duration between the terminal spikelet stage of development and anthesis (referred to here as the construction-phase duration, CPD) will increase yield per unit area in wheat (Triticum aestivum L.). Field experiments were undertaken at 17 sites across the high-rainfall zone of south-eastern and Western Australia in 2014 and 2015. In total, 205 wheat genotypes were grown. Genetic material included a set of near-isogenic lines (NILs) varying in photoperiod and vernalisation alleles; commercial wheat cultivars and breeding lines; and lines selected from the Multiparent Advanced Generation Inter Cross population. As such, this is the only comprehensive dataset in which the effect of variation in CPD on grain yield in field plots has been evaluated in diverse field environments. Within an optimum anthesis window of 10 days, longer CPD significantly increased grain yield by >11% at two sites and tended to increase grain yield at another 11 sites (not statistically significant). The average yield increase across these sites was 5.5%. There was no consistent trend whereby a specific yield component was responsible for the increase across sites. We suggest that CPD can be extended by genetic selection and by sacrificing some of the vegetative period without any detriment to grain yield. We also found that CPD is increased by extending the duration from sowing to flowering through earlier sowing, which may be associated with increased yields in some environments. We conclude that, for the same anthesis date, a longer CPD may be beneficial in moderately favourable rainfed environments with a relatively even distribution of rainfall. We explore the basis of these relationships and implications for growers and plant breeders.

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security. The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.