Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat.

KEY MESSAGE: The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

Early vigour in wheat: Could it lead to more severe terminal drought stress under elevated atmospheric [CO2 ] and semi-arid conditions?

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 CO2 concentrations (e[CO2 ]), 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 programmes 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 CO2 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[CO2 ] 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[CO2 ] 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[CO2 ] 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.

A reduced-tillering trait shows small but important yield gains in dryland wheat production.

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.

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

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

Climate change impact and adaptation for wheat protein.

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.

Water availability moderates N2 fixation benefit from elevated [CO2 ]: A 2-year free-air CO2 enrichment study on lentil (Lens culinaris MEDIK.) in a water limited agroecosystem.

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.

Benefits of increasing transpiration efficiency in wheat under elevated CO2 for rainfed regions.

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 CO2 conditions, the response of Drysdale was much greater overall, ranging from 51 to 886 kg/ha (mean 284 kg/ha) with the greatest response in the higher rainfall areas. Changes in simulated TE under elevated CO2 conditions are seen across Australia with notable increased areas of higher TE under a drier climate in Western Australia, Queensland and parts of New South Wales and Victoria. This improved efficiency is subtly deceptive, with highest yields not necessarily directly correlated with highest TE. Nevertheless, the advantage of Drysdale over Hartog is clear with the benefit of the trait advantage attributed to TE ranging from 102% to 118% (mean 109%). The potential annual cost-benefits of this increased genetic TE trait across the wheat growing areas of Australia (5 year average of area planted to wheat) totaled AUD 631 MIL (5-year average wheat price of AUD/260 t) with an average of 187 kg/ha under the present climate. The benefit to an individual farmer will depend on location but elevated CO2 raises this nation-wide benefit to AUD 796 MIL in a 2°C warmer climate, slightly lower (AUD 715 MIL) if rainfall is also reduced by 20%.

Australian wheat production expected to decrease by the late 21st century.

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.

Multimodel ensembles improve predictions of crop-environment-management interactions.

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.

The relationship between transpiration and nutrient uptake in wheat changes under elevated atmospheric CO2.

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.

Effectiveness of time of sowing and cultivar choice for managing climate change: wheat crop phenology and water use efficiency.

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.

Elevated atmospheric [CO2 ] can dramatically increase wheat yields in semi-arid environments and buffer against heat waves.

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.

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.

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.

Pathways to identify and reduce uncertainties in agricultural climate impact assessments.

Both climate and impact models are essential for understanding and quantifying the impact of climate change on agricultural productivity. Multi-model ensembles have highlighted considerable uncertainties in these assessments, yet a systematic approach to quantify these uncertainties is lacking. We propose a standardized approach to attribute uncertainties in multi-model ensemble studies, based on insights from the Agricultural Model Intercomparison and Improvement Project. We find that crop model processes are the primary source of uncertainty in agricultural projections (over 50%), excluding unquantified hidden uncertainty that is not explicitly measured within the analyses. We propose multidimensional pathways to reduce uncertainty in climate change impact assessments.

Identification of agro-physiological traits of lentil that reduce risks of drought.

Ideotype breeding is an essential approach for selection of desired combination of plant traits for testing in crop growth model for potential yield gain in specific environments and management practices. Here we parameterized plant traits for untested lentil cultivars for the APSIM-lentil model in phenology, biomass, and seed yield. We then tested these against independent data and applied the model in an extrapolated analysis (i) to assess the impact of drought on productivity across different rainfall environments; (ii) to identify impactful plant traits and (iii) to design new lentil ideotypes with a combination of desirable traits that mitigate the impact of drought, in the context of various agronomic practices across a wide range of production environments. Desirable phenological and physiological traits related to yield were identified with RUE having the greatest effect on yield followed by HI rate. Leaf size significantly affected seed yield (p< 0.05) more than phenological phases. The physiological traits were integrated into four ideotype designs applied to two baseline cultivars (PBA Hallmark XT and PBA Jumbo2) providing eight ideotypes. We identified a combination of genetic traits that promises a yield advantage of around 10% against our current cultivars PBA Hallmark XT and PBA Jumbo2. Under drought conditions, our ideotypes achieved 5 to 25% yield advantages without stubble and 20 to 40% yield advantages with stubble residues. This shows the importance of genetic screening under realistic production conditions (e.g., stubble retention in particular environments). Such screening is aided by the employment of biophysical models that incorporate both genetic and agronomic variables that focus on successful traits in combination, to reduce the impact of drought in the development of new cultivars for various environments. Stubble retention was found to be a major agronomic contributor to high yield in water-limiting environments and this contribution declined with increasing growing season rainfall. In mid- and high-rainfall environments, the key drivers of yield were time of sowing, physiological traits and soil type. Overall, the agronomic practices, namely, early sowing, residue retention and narrow row spacing deceased the impact of drought when combined with improved physiological traits of the ideotypes based on long term climate data.

Does Elevated [CO2] Only Increase Root Growth in the Topsoil? A FACE Study with Lentil in a Semi-Arid Environment.

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.

Sources of uncertainty for wheat yield projections under future climate are site-specific.

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.