Response of wheat growth,grain yield and water use to elevated CO2 under a Free‐Air CO2 Enrichment (FACE) experiment and modelling in a semi‐arid environment

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.     

Determining the Causes of Spatial and Temporal Variability of Wheat Yields at Sub-field Scale Using a New Method of Upscaling a Crop Model

Wheat yields in the Mediterranean climate of Western and Southern Australia are often limited by water. Our measurements on a 70 ha growers field showed linear relationships between grain yield and the plant available soil water storage capacity (PAWc) of the top 100 cm of the soil profile. PAWc was linearly related to apparent soil electrical conductivity measured by proximal sensing using electromagnetic induction (EM38). The APSIM wheat model also employs PAWc as one of the systems parameters and simulated linear relationships between PAWc and yield. These relationships were used to transform an EM38-derived PAWc map of the field into yield maps for three major season types (dry, medium and wet) and nitrogen (N) fertiliser management scenarios. The results indicated that the main cause of temporal and spatial yield variability within the field was due to interactions of seasonal rainfall, PAWc and N fertiliser applications. Spatial variability was low in low rainfall years when yields across the field were low and the higher soil water storage capacity sites were often underutilised. With adequate N, spatial variability increased with seasonal rainfall as sites with higher PAWc conserved more water in wet seasons to give higher yield response than sites with low PAWc. The higher yield response of high PAWc sites to rainfall gave rise to larger temporal variability compared with sites with low PAWc. Provision of adequate N is required for the water limited yield potential to be expressed and this increased both spatial and temporal variability. Sites with low PAWc performed poorly irrespective of rainfall and N application. PAWc is inherently low on deep coarse sands; these sites should be considered for a change in land use. Elsewhere, strategic management interventions should aim to improve PAWc through sub-soil amelioration and deep root growth to increase the capital asset of the farm. The resulting increase in yields will occur in favourable seasons and with adequate fertiliser provisions. The largest grain yield response to water and N will be obtained on sites with the highest PAWc and it is at those sites that the greatest profits from fertiliser use could be achieved in wet seasons.     

Post‐heading heat stress and yield impact in winter wheat of China

Wheat is sensitive to high temperatures, but the spatial and temporal variability of high temperature and its impact on yield are often not known. An analysis of historical climate and yield data was undertaken to characterize the spatial and temporal variability of heat stress between heading and maturity and its impact on wheat grain yield in China. Several heat stress indices were developed to quantify heat intensity, frequency, and duration between heading and maturity based on measured maximum temperature records of the last 50 years from 166 stations in the main wheat‐growing region of China. Surprisingly, heat stress between heading and maturity was more severe in the generally cooler northern wheat‐growing regions than the generally warmer southern regions of China, because of the delayed time of heading with low temperatures during the earlier growing season and the exposure of the post‐heading phase into the warmer part of the year. Heat stress between heading and maturity has increased in the last decades in most of the main winter wheat production areas of China, but the rate was higher in the south than in the north. The correlation between measured grain yields and post‐heading heat stress and average temperature were statistically significant in the entire wheat‐producing region, and explained about 29% of the observed spatial and temporal yield variability. A heat stress index considering the duration and intensity of heat between heading and maturity was required to describe the correlation of heat stress and yield variability. Because heat stress is a major cause of yield loss and the number of heat events is projected to increase in the future, quantifying the future impact of heat stress on wheat production and developing appropriate adaptation and mitigation strategies are critical for developing food security policies in China and elsewhere.     

Testing the responses of four wheat crop models to heat stress at anthesis and grain filling

Higher temperatures caused by future climate change will bring more frequent heat stress events and pose an increasing risk to global wheat production. Crop models have been widely used to simulate future crop productivity but are rarely tested with observed heat stress experimental datasets. Four wheat models (DSSAT‐CERES‐Wheat, DSSAT‐Nwheat, APSIM‐Wheat, and WheatGrow) were evaluated with 4 years of environment‐controlled phytotron experimental datasets with two wheat cultivars under heat stress at anthesis and grain filling stages. Heat stress at anthesis reduced observed grain numbers per unit area and individual grain size, while heat stress during grain filling mainly decreased the size of the individual grains. The observed impact of heat stress on grain filling duration, total aboveground biomass, grain yield, and grain protein concentration (GPC) varied depending on cultivar and accumulated heat stress. For every unit increase of heat degree days (HDD, degree days over 30 °C), grain filling duration was reduced by 0.30–0.60%, total aboveground biomass was reduced by 0.37–0.43%, and grain yield was reduced by 1.0–1.6%, but GPC was increased by 0.50% for cv Yangmai16 and 0.80% for cv Xumai30. The tested crop simulation models could reproduce some of the observed reductions in grain filling duration, final total aboveground biomass, and grain yield, as well as the observed increase in GPC due to heat stress. Most of the crop models tended to reproduce heat stress impacts better during grain filling than at anthesis. Some of the tested models require improvements in the response to heat stress during grain filling, but all models need improvements in simulating heat stress effects on grain set during anthesis. The observed significant genetic variability in the response of wheat to heat stress needs to be considered through cultivar parameters in future simulation studies.     

A potato model intercomparison across varying climates and productivity levels

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low‐input (Chinoli, Bolivia and Gisozi, Burundi)‐ and high‐input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low‐ vs. high‐input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100‐ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.     

Analysis of the benefits to wheat yield from assimilates stored prior to grain filling in a range of environments*

Grain yields of rainfed agriculture in Australia are often low and vary substantially from season to season. Assimilates stored prior to grain filling have been identified as important contributors to grain yield in such environments, but quantifying their benefit has been hampered by inadequate methods and large seasonal variability. APSIM-Nwheat is a crop system simulation model, consisting of modules that incorporate aspects of soil water, nitrogen (N), crop residues, crop growth and development. Model outputs were compared with detailed measurements of N fertilizer experiments on loamy soils at three locations in southern New South Wales, Australia. The field measurements allowed the routine for remobilization of assimilates stored prior to grain filling in the model to be tested for the first time and simulations showed close agreement with observed data. Analysing system components indicated that with increasing yield, both the observed and simulated absolute amount of remobilization generally increased while the relative contribution to grain yield decreased. The simulated relative contribution of assimilates stored prior to grain filling to grain yield also decreased with increasing availability of water after anthesis. The model, linked to long-term historical weather records was used to analyse yield benefits from assimilates stored prior to grain filling under rainfed conditions at a range of locations in the main wheat growing areas of Australia. Simulation results highlighted that in each of these locations assimilates stored prior to grain filling often contributed a significant proportion to grain yield. The simulated contribution of assimilates stored prior to grain filling to grain yield can amount to several tonnes per hectare, however, it varied substantially from 5–90% of grain yield depending on seasonal rainfall amount and distribution, N supply, crop growth and seasonal water use. High N application often reduced the proportion of water available after anthesis and decreased the relative contribution of remobilization to grain yield as long as grain yields increased, particularly on soils with greater water-holding capacity. Increasing the capacity or potential to accumulate pre-grain filling assimilates for later remobilization by 20% increased yields by a maximum of 12% in moderate seasons with terminal droughts, but had little effect in poor or very good seasons in which factors that affect the amount of carbohydrates stored rather than the storage capacity itself appeared to limit grain yield. These factors were, little growth due to water or N deficit in the weeks prior to and shortly after anthesis (when most of the assimilates accumulate for later remobilization), poor sink demand of grains due to low grain number as a result of little pre-anthesis growth or high photosynthetic rate during grain filling. Increasing the potential storage capacity for remobilization is expected to increase grain yield especially under conditions of terminal drought.     

Analysis of water- and nitrogen-use efficiency of wheat in a Mediterranean climate

Water-use efficiency (WUE [g grain yield m^–2 mm^–1 ET]) and nitrogen-use efficiency (NUE [ g grain yield g^–1 N_applied]) are important measures that can affect the productivity of crops in different environmental systems. However, measurement and interpretation of WUE and NUE in the field are often hampered by the high degree of complexity of these systems due to season-to-season variability in rainfall, the variation in crop responses to soil types and to agronomic management. To be able to guide agronomic practice, experimentally-derived measurements of WUE and NUE need to be extrapolated across time and space through appropriate modelling. To illustrate this approach, the Agricultural Production Systems Simulator (APSIM), which has been rigorously tested for wheat (Triticum aestivum L.) in a Mediterranean environment, was used to estimate and analyse the WUE and NUE of wheat crops in the Mediterranean-climatic region of the central Western Australian agricultural zone. The APSIM model was run for three locations (average annual rainfall of 461 mm [high rainfall zone], 386 mm [medium] and 310 mm [low]) and two soil types that had contrasting plant-available water-holding capacities in the rooting zone (sand: 55 mm, clay soil: 109 mm). Simulations were carried out with historical weather records (82–87 years) assuming current crop management and cultivars. The modelling analyses highlighted the inherently high degree of seasonal variability in yield, WUE and NUE of wheat, depending on soil type, N fertiliser input, rainfall amount and, in particular, rainfall distribution. The clay soil tended to be more productive in terms of grain yield, WUE and NUE in the high and medium rainfall zones, but less productive in most years in the low rainfall zone. The sandy soil was less productive in the high rainfall zone due to the high nitrate leaching potential of this soil type, but more productive than the clay in the low rainfall zone due to poorer pre-anthesis growth and less water use, less water loss by soil evaporation and relatively more water use in the post-anthesis phase. When a wheat crop was sown early on clay soil in the low rainfall zone, it yielded as high as in the other rainfall zones in seasons when rainfall was above average or there was a good store of water in the soil prior to sowing. The simulations confirmed findings from a limited number of field experiments and extended these findings both qualitatively and quantitatively across soil types, rainfall regions and crop management options. Furthermore, by using long-term historical weather records, the simulations extended the findings across the wide range of climatic scenarios experienced in mediterranean-climatic regions.     

Multimodel ensembles of wheat growth: many models are better than one

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC. The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.     

Hot spots of wheat yield decline with rising temperatures

Many of the irrigated spring wheat regions in the world are also regions with high poverty. The impacts of temperature increase on wheat yield in regions of high poverty are uncertain. A grain yield–temperature response function combined with a quantification of model uncertainty was constructed using a multimodel ensemble from two key irrigated spring wheat areas (India and Sudan) and applied to all irrigated spring wheat regions in the world. Southern Indian and southern Pakistani wheat‐growing regions with large yield reductions from increasing temperatures coincided with high poverty headcounts, indicating these areas as future food security ‘hot spots’. The multimodel simulations produced a linear absolute decline of yields with increasing temperature, with uncertainty varying with reference temperature at a location. As a consequence of the linear absolute yield decline, the relative yield reductions are larger in low‐yielding environments (e.g., high reference temperature areas in southern India, southern Pakistan and all Sudan wheat‐growing regions) and farmers in these regions will be hit hardest by increasing temperatures. However, as absolute yield declines are about the same in low‐ and high‐yielding regions, the contributed deficit to national production caused by increasing temperatures is higher in high‐yielding environments (e.g., northern India) because these environments contribute more to national wheat production. Although Sudan could potentially grow more wheat if irrigation is available, grain yields would be low due to high reference temperatures, with future increases in temperature further limiting production.