Through stimulation of root growth, increasing atmospheric CO2 concentration (CO2) may facilitate access of crops to sub-soil water, which could potentially prolong physiological activity in dryland ...environments, particularly because crops are more water use efficient under elevated CO2 (eCO2). This study investigated the effect of drought in shallow soil versus sub-soil on agronomic and physiological responses of wheat to eCO2 in a glasshouse experiment. Wheat (Triticum aestivum L. cv. Yitpi) was grown in split-columns with the top (0-30 cm) and bottom (31-60 cm; 'sub-soil') soil layer hydraulically separated by a wax-coated, root-penetrable layer under ambient CO2 (aCO2, ∼400 μmol mol-1) or eCO2 (∼700 μmol mol-1) CO2. Drought was imposed from stem-elongation in either the top or bottom soil layer or both by withholding 33% of the irrigation, resulting in four water treatments (WW, WD, DW, DD; D = drought, W = well-watered, letters denote water treatment in top and bottom soil layer, respectively). Leaf gas exchange was measured weekly from stem-elongation until anthesis. Above-and belowground biomass, grain yield and yield components were evaluated at three developmental stages (stem-elongation, anthesis and maturity). Compared with aCO2, net assimilation rate was higher and stomatal conductance was lower under eCO2, resulting in greater intrinsic water use efficiency. Elevated CO2 stimulated both above- and belowground biomass as well as grain yield, however, this stimulation was greater under well-watered (WW) than drought (DD) throughout the whole soil profile. Imposition of drought in either or both soil layers decreased aboveground biomass and grain yield under both CO2 compared to the well-watered treatment. However, the greatest 'CO2 fertilisation effect' was observed when drought was imposed in the top soil layer only (DW), and this was associated with eCO2-stimulation of root growth especially in the well-watered bottom layer. We suggest that stimulation of belowground biomass under eCO2 will allow better access to sub-soil water during grain filling period, when additional water is converted into additional yield with high efficiency in Mediterranean-type dryland agro-ecosystems. If sufficient water is available in the sub-soil, eCO2 may help mitigating the effect of drying surface soil.
The impact of elevated CO2 (eCO2) 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 eCO2, but that nutrient uptake per unit water transpired is higher under eCO2 than under ambient CO2 (aCO2). This result suggests that transpiration‐driven mass flow of nutrients contributes to decreases in nutrient concentrations under eCO2, but cannot solely explain the overall decline.
Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different ...scales for measuring canopy N content (g times N times m-2) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r2 = 0.81; standard error (SE) = 0.41%; p < 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content (g times N times m-2) (r2 = 0.73; SE = 0.603; p < 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately.
A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel 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.
One way of estimating the projected impact of climate change on crops is to use crop models. There is a large variability in results of different crop models, but it has been observed that the mean or median of a multimodel ensemble (MME) often gives good agreement with observed data. We used empirical data from several MME studies, plus theoretical arguments, to better understand why and when the MME mean will be a good predictor. This should help modelers decide how to create and use MMEs for climate impact assessment.
The impact of elevated atmospheric CO2 (eCO2) on plants often includes a decrease in their nutrient status, including Ca and Mg, but the reasons for this decline have not been clearly identified. One ...of the proposed hypotheses is a decrease in transpiration-driven mass flow of nutrients due to decreased stomatal conductance. We used glasshouse and Free Air CO2 Enrichment (FACE) experiments with wheat to show that, in addition to decrease in transpiration rate, eCO2 decreased the concentrations of Ca and Mg in the xylem sap. This result suggests that uptake of nutrients is not only decreased by reduced transpiration-driven mass flow, but also by as yet unidentified mechanisms that lead to reduced concentrations in the xylem sap.
► Maximum crop yield response to elevated CO2 is needed for food security. ► Source–sink relations may determine CO2 response and provide selection targets. ► Crop nutrition traits must ensure food ...quality under elevated CO2. ► Elevated CO2 effects on antioxidants may affect crop stress tolerance.
The present overview paper reviews knowledge on plant metabolism under elevated atmospheric CO2 concentrations (eCO2) with regard to underpinning options for the management of crop production systems and the selection of crop traits beneficial for future conditions.
Better understanding of intra-specific variability in responses to eCO2 is of great importance to breed or select best possible genotypes for future conditions. Yield increases per 100μLL−1 increase in CO2 varied between none and over 30% among varieties of important crops. Carbon source–sink relationships are believed to play a major role in determining the ability of a plant to utilise eCO2 and avoid downward acclimation of photosynthesis upon prolonged eCO2 exposure. Corresponding traits (e.g. tillering capacity, stem carbohydrate storage capacity, or seed size and numbers) are currently under investigation in Free Air Carbon dioxide Enrichment (FACE) facilities, such as AGFACE (Australian Grains FACE).
The stimulatory effect of eCO2 on plant growth is dependent on adequate nutrient supply. For example, N concentrations in plant tissues generally decrease under eCO2, which in leaves is commonly related to a decrease in Rubisco concentration and activity, and therefore linked to photosynthetic downward acclimation. This effect is also of direct concern for food production where decreased N and protein content can have negative effects on product quality (e.g. grain protein). Plant nutrient metabolism appears to adjust to a new physiological equilibrium under eCO2 which limits the extent to which nutrient application can ameliorate the situation. What the control points are for an adjustment of plant N metabolism is unclear. Rubisco metabolism in leaves, N assimilation, N translocation or N uptake are all potential key steps that may be inhibited or downregulated under eCO2. To achieve the best possible growth response whilst maintaining product quality, it is important to understand plant nutrient metabolism under eCO2.
Comparatively little is known about mechanisms of potential changes in plant stress tolerance under eCO2. Defence metabolites such as antioxidants are, in part, directly linked to primary carbohydrate mechanism and so potentially impacted by eCO2. It is unknown whether photoprotective and antioxidative defence systems, key to plant stress tolerance, will be affected, and if so, whether the response will be strengthened or weakened by eCO2. Better understanding of underlying principles is particularly important because it is virtually impossible to test all possible stress factor combinations with eCO2 in realistic field settings.
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 (eCO2) 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 eCO2 (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 eCO2 was 24% at Horsham and 53% at Walpeup, with some treatment responses greater than 70%, depending on environment. Under supplemental irrigation, eCO2 stimulated yields at Horsham by 37% compared to 13% under rainfed conditions, showing that water limited growth and yield response to eCO2. Heat wave effects were ameliorated under eCO2 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 eCO2 late sowing and heat stressed treatments, when supplied with more water. There were no clear differences in cultivar response due to eCO2. Multiple regression showed that yield response to eCO2 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 eCO2 response. The large responses to eCO2 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.
•Machine learning (ML) produces high prediction accuracy of gs in wheat.•The ML models are especially important for interpolating datasets with good accuracy.•The ML models require large datasets for ...training to achieve statistical significance.
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.
Atmospheric CO2 concentrations have been increasing from about 280ppm to 400ppm from the pre-industrial era until now. If intraspecific variability in the response to elevated CO2 (eCO2) can be ...found, then it should be possible to select for greater responsiveness in crop breeding programs. Our experiment aimed to determine the effects of eCO2 on the yield, biomass, leaf and grain nitrogen content of a range of field pea (Pisum sativum L.) cultivars subjected to rainfed and supplemental irrigation conditions. Plants were grown under Free Air CO2 Enrichment (FACE) at the Australian Grains FACE facility in Horsham, Victoria, Australia under eCO2 (550ppm) or at ambient CO2 (390–400ppm) under rainfed conditions and supplemental irrigation during three seasons, 2010–2012. Yields were significantly increased by 26% under eCO2 due to an increase in the number of pods per area. Grain size, the number of grains per pod and the harvest index remained unaffected by eCO2. Grain nitrogen concentration (N) was slightly, but significantly, decreased by eCO2, but this was not consistent across cultivars under all water regimes. The dual purpose cultivar PBA Hayman consistently maintained grain N in response to eCO2 while the response in grain N in the cultivars Sturt and PBA Twilight depended on the irrigation treatment. While there was no evidence for consistent differences in seed yield response to eCO2 for the chosen cultivars, understanding the mechanisms for why some cultivars are able to maintain N under eCO2 would allow breeding programs to develop varieties resistant to decreases in N under eCO2.
•Two wheat cultivars were grown at elevated CO2, under six environmental conditions.•Elevated CO2 effect on grain protein, Zn, Mg and Na were varied with environmental conditions.•Found no clear ...relationship of relative effects of eCO2 on grain protein with any of the environmental indices tested.•Relative effects of eCO2 on grain yield and protein were strongly correlated.•These findings suggest that primary factor determine grain protein concentration under eCO2 is yield dilution.
Bread wheat (Triticum aestivum L. cv. Yitpi and cv. Janz) was grown under field conditions in the Australian Grains Free-Air CO2 Enrichment (AGFACE) facility. Ambient CO2 (aCO2, ∼384μmolmol−1) and elevated CO2 (eCO2, ∼550μmolmol−1) were combined with two soil water levels (rain-fed and irrigated) and two times of sowing (TOS) in three consecutive years to provide six environments (2007-TOS1, 2007-TOS2, 2008-TOS1, 2008-TOS2, 2009-TOS1, 2009-TOS2). Grain samples were assessed for a range of physical, nutritional and dough rheological properties. The effect of eCO2 on thousand grain weight (TGW) was significantly different in each growing environment: TGW was significantly increased under eCO2 only at 2007-TOS2 (by 5%), 2009-TOS1 (by 5%) and 2009-TOS2 (by 15%) but not significantly changed under other conditions. The magnitude of reduction of grain protein concentration at eCO2 differed among the growing environments but was highly correlated with the percentage yield stimulation under eCO2 (r2=0.91) suggesting that grain protein concentration under eCO2 was diluted by increased yield. Across all treatments, grain nutrient concentration was significantly reduced by eCO2 for Fe (3.9%, 6.2%), Cu (2.2%, 3.4%), Zn (5.9%, 5.7%), Ca (5.6%, 7.3%), Mg (5.6%, 5.8%), Na (21.2%, 30.4%), S (4.4%, 4.4%), P (4.1%, 3.2%) in cv. Yitpi and Janz, respectively. Effects of eCO2 on grain Zn, Mg and Na concentrations were dependent on the growing environment. Relative reduction of grain S, Fe, Mg, Zn, P at eCO2 were significantly correlated with grain yield stimulation at eCO2. Reductions of these nutrients under eCO2 were not fully explained by biomass dilution as the relationships differed for each nutrient. Under eCO2, flour yield of cv. Janz was increased but that of cv. Yitpi was not changed. Even though grain protein concentrations of both cultivars were similar at eCO2, bread volume as inferred indirectly by dough rheology parameters was 12% greater for cv. Janz (185±5cm3) than cv. Yitpi (162±4cm3) at eCO2. This disparity may be related to the compositional changes in wheat flour protein at eCO2, suggesting that future breeding and adaptation strategies to improve the grain quality under eCO2 should consider the prevailing hydro-thermal conditions.
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![Elevated atmospheric [CO2] ...](http://api.summon.serialssolutions.com/2.0.0/image/issn/XR4PS5YY4K/1354-1013/small "Elevated atmospheric [CO2] can dramatically increase wheat yields in semi-arid environments and buffer against heat waves")
