Are high-throughput root phenotyping platforms suitable for informing root system architecture models with genotype-specific parameters? An evaluation based on the root model ArchiSimple and a small panel of wheat cultivars

Given the difficulties in accessing plant roots in situ, high-throughput root phenotyping (HTRP) platforms under controlled conditions have been developed to meet the growing demand for characterizing root system architecture (RSA) for genetic analyses. However, a proper evaluation of their capacity to provide the same estimates for strictly identical root traits across platforms has never been achieved. In this study, we performed such an evaluation based on six major parameters of the RSA model ArchiSimple, using a diversity panel of 14 bread wheat cultivars in two HTRP platforms that had different growth media and non-destructive imaging systems together with a conventional set-up that had a solid growth medium and destructive sampling. Significant effects of the experimental set-up were found for all the parameters and no significant correlations across the diversity panel among the three set-ups could be detected. Differences in temperature, irradiance, and/or the medium in which the plants were growing might partly explain both the differences in the parameter values across the experiments as well as the genotype × set-up interactions. Furthermore, the values and the rankings across genotypes of only a subset of parameters were conserved between contrasting growth stages. As the parameters chosen for our analysis are root traits that have strong impacts on RSA and are close to parameters used in a majority of RSA models, our results highlight the need to carefully consider both developmental and environmental drivers in root phenomics studies.

Storage protein activator controls grain protein accumulation in bread wheat in a nitrogen dependent manner

The expression of cereal grain storage protein (GSP) genes is controlled by a complex network of transcription factors (TFs). Storage protein activator (SPA) is a major TF acting in this network but its specific function in wheat (Triticum aestivum L.) remains to be determined. Here we generated an RNAi line in which expression of the three SPA homoeologs was reduced. In this line and its null segregant we analyzed GSP accumulation and expression of GSP and regulatory TF genes under two regimes of nitrogen availability. We show that down regulation of SPA decreases grain protein concentration at maturity under low but not high nitrogen supply. Under low nitrogen supply, the decrease in SPA expression also caused a reduction in the total quantity of GSP per grain and in the ratio of GSP to albumin-globulins, without significantly affecting GSP composition. The slight reduction in GSP gene expression measured in the SPA RNAi line under low nitrogen supply did not entirely account for the more significant decrease in GSP accumulation, suggesting that SPA regulates additional levels of GSP synthesis. Our results demonstrate a clear role of SPA in the regulation of grain nitrogen metabolism when nitrogen is a limiting resource.

Wheat DOF transcription factors TaSAD and WPBF regulate glutenin gene expression in cooperation with SPA

Grain storage proteins (GSPs) quantity and composition determine the end-use value of wheat flour. GSPs consists of low-molecular-weight glutenins (LMW-GS), high-molecular-weight glutenins (HMW-GS) and gliadins. GSP gene expression is controlled by a complex network of DNA-protein and protein-protein interactions, which coordinate the tissue-specific protein expression during grain development. The regulatory network has been most extensively studied in barley, particularly the two transcription factors (TFs) of the DNA binding with One Finger (DOF) family, barley Prolamin-box Binding Factor (BPBF) and Scutellum and Aleurone-expressed DOF (SAD). They activate hordein synthesis by binding to the Prolamin box, a motif in the hordein promoter. The BPBF ortholog previously identified in wheat, WPBF, has a transcriptional activity in expression of some GSP genes. Here, the wheat ortholog of SAD, named TaSAD, was identified. The binding of TaSAD to GSP gene promoter sequences in vitro and its transcriptional activity in vivo were investigated. In electrophoretic mobility shift assays, recombinant TaSAD and WPBF proteins bound to cis-motifs like those located on HMW-GS and LMW-GS gene promoters known to bind DOF TFs. We showed by transient expression assays in wheat endosperms that TaSAD and WPBF activate GSP gene expression. Moreover, co-bombardment of Storage Protein Activator (SPA) with WPBF or TaSAD had an additive effect on the expression of GSP genes, possibly through conserved cooperative protein-protein interactions.

The extreme 2016 wheat yield failure in France

France suffered, in 2016, the most extreme wheat yield decline in recent history, with some districts losing 55% yield. To attribute causes, we combined the largest coherent detailed wheat field experimental dataset with statistical and crop model techniques, climate information, and yield physiology. The 2016 yield was composed of up to 40% fewer grains that were up to 30% lighter than expected across eight research stations in France. The flowering stage was affected by prolonged cloud cover and heavy rainfall when 31% of the loss in grain yield was incurred from reduced solar radiation and 19% from floret damage. Grain filling was also affected as 26% of grain yield loss was caused by soil anoxia, 11% by fungal foliar diseases, and 10% by ear blight. Compounding climate effects caused the extreme yield decline. The likelihood of these compound factors recurring under future climate change is estimated to change with a higher frequency of extremely low wheat yields.

Minimally invasive laparo-thoracoscopic Ivor-Lewis esophagectomy with semi-mechanical triangular anastomosis: Short-term outcomes of 114 consecutive patients

INTRODUCTION

Several surgical teams have developed so-called minimally invasive esophagectomy techniques with the intention of decreasing post-operative complications. The goal of this report is to determine the feasibility, reproducibility, morbidity and mortality of esophagectomy and intrathoracic anastomosis via thoracoscopy.

METHODS

This retrospective series included 114 consecutive non-selected patients who underwent Lewis Santy type esophagectomy between 2016 and 2020. The procedure was performed via abdominal laparoscopy, thoracoscopy with the patient in a supine position, without selective intubation, with intra-thoracic semi-mechanical triangular esophagogastric anastomosis.

RESULTS

Mean patient age was 62.8years. Conversion from laparoscopy to laparotomy was required in three patients (2.6%); no patient required conversion from thoracoscopy to thoracotomy. A semi-mechanical triangular esophagogastric anastomosis was successfully performed in all patients. Median duration of hospital stay was 16 (8-116) days. Mortality was 2.6%; 34 patients (29.8%) had major complications, 55 (48%) had a respiratory complication. The leakage rate was 12.3%; most were type I. Only 5.2% required an additional procedure. There was no mortality.

CONCLUSION

The analysis of this consecutive series found that this operative technique was reproducible and reliable. These results need to be confirmed by other studies. Pulmonary morbidity was high and remains the main challenge in this type of surgery.

Neglecting acclimation of photosynthesis under drought can cause significant errors in predicting leaf photosynthesis in wheat

Extreme climatic events, such as heat waves, cold snaps and drought spells, related to global climate change, have become more frequent and intense in recent years. Acclimation of plant physiological processes to changes in environmental conditions is a key component of plant adaptation to climate change. We assessed the temperature response of leaf photosynthetic parameters in wheat grown under contrasting water regimes and growth temperatures (T_growth ). Two independent experiments were conducted under controlled conditions. In Experiment 1, two wheat genotypes were subjected to well-watered or drought-stressed treatments; in Experiment 2, the two water regimes combined with high, medium and low T_growth were imposed on one genotype. Parameters of a biochemical C₃ -photosynthesis model were estimated at six leaf temperatures for each factor combination. Photosynthesis acclimated more to drought than to T_growth . Drought affected photosynthesis by lowering its optimum temperature (T_opt ) and the values at T_opt of light-saturated net photosynthesis, stomatal conductance, mesophyll conductance, the maximum rate of electron transport (J_max ) and the maximum rate of carboxylation by Rubisco (V_cmax ). T_opt for V_cmax was up to 40°C under well-watered conditions but 24-34°C under drought. The decrease in photosynthesis under drought varied among T_growth but was similar between genotypes. The temperature response of photosynthetic quantum yield under drought was partly attributed to photorespiration but more to alternative electron transport. All these changes in biochemical parameters could not be fully explained by the changed leaf nitrogen content. Further model analysis showed that both diffusional and biochemical parameters of photosynthesis and their thermal sensitivity acclimate little to T_growth , but acclimate considerably to drought and the combination of drought and T_growth . The commonly used modelling approaches, which typically consider the response of diffusional parameters, but ignore acclimation responses of biochemical parameters to drought and T_growth , strongly overestimate leaf photosynthesis under variable temperature and drought.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Drought exerts a greater influence than growth temperature on the temperature response of leaf day respiration in wheat (Triticum aestivum)

We assessed how the temperature response of leaf day respiration (R_d ) in wheat responded to contrasting water regimes and growth temperatures. In Experiment 1, well-watered and drought-stressed conditions were imposed on two genotypes; in Experiment 2, the two water regimes combined with high (HT), medium (MT) and low (LT) growth temperatures were imposed on one of the genotypes. R_d was estimated from simultaneous gas exchange and chlorophyll fluorescence measurements at six leaf temperatures (T_leaf ) for each treatment, using the Yin method for nonphotorespiratory conditions and the nonrectangular hyperbolic fitting method for photorespiratory conditions. The two genotypes responded similarly to growth and measurement conditions. Estimates of R_d for nonphotorespiratory conditions were generally higher than those for photorespiratory conditions, but their responses to T_leaf were similar. Under well-watered conditions, R_d and its sensitivity to T_leaf slightly acclimated to LT, but did not acclimate to HT. Temperature sensitivities of R_d were considerably suppressed by drought, and the suppression varied among growth temperatures. Thus, it is necessary to quantify interactions between drought and growth temperature for reliably modelling R_d under climate change. Our study also demonstrated that the Kok method, one of the currently popular methods for estimating R_d , underestimated R_d significantly.

Global wheat production could benefit from closing the genetic yield gap

Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Y_ig) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Y_ig, covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Y_ig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Y_ig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management.

Sensitivities to temperature and evaporative demand in wheat relatives

There is potential sources of alleles and genes currently locked into wheat-related species that could be introduced into wheat breeding programs for current and future hot and dry climates. However, neither the intra- nor the inter-specific diversity of the responses of leaf growth and transpiration to temperature and evaporative demand have been investigated in a large diversity of wheat-related species. By analysing 12 groups of wheat-related sub-species, we questioned the n-dimensional structure of the genetic diversity for traits linked to plant vegetative structures and development, leaf expansion and transpiration together with their responses to "non-stressing" range of temperature and evaporative demand. In addition to provide new insight on how genome type, ploidy level, phylogeny and breeding pressure together structure this genetic diversity, this study provides new mathematical formalisms and the associated parameters of trait responses in the large genetic diversity of wheat-related species. This potentially allow crop models predicting the impact of this diversity on yield, and indicate potential sources of varietal improvement for modern wheat germplasms, through interspecific crosses.

Importance of the description of light interception in crop growth models

Canopy light interception determines the amount of energy captured by a crop, and is thus critical to modeling crop growth and yield, and may substantially contribute to the prediction uncertainty of crop growth models (CGMs). We thus analyzed the canopy light interception models of the 26 wheat (Triticum aestivum) CGMs used by the Agricultural Model Intercomparison and Improvement Project (AgMIP). Twenty-one CGMs assume that the light extinction coefficient (K) is constant, varying from 0.37 to 0.80 depending on the model. The other models take into account the illumination conditions and assume either that all green surfaces in the canopy have the same inclination angle (θ) or that θ distribution follows a spherical distribution. These assumptions have not yet been evaluated due to a lack of experimental data. Therefore, we conducted a field experiment with five cultivars with contrasting leaf stature sown at normal and double row spacing, and analyzed θ distribution in the canopies from three-dimensional canopy reconstructions. In all the canopies, θ distribution was well represented by an ellipsoidal distribution. We thus carried out an intercomparison between the light interception models of the AgMIP-Wheat CGMs ensemble and a physically based K model with ellipsoidal leaf angle distribution and canopy clumping (KellC). Results showed that the KellC model outperformed current approaches under most illumination conditions and that the uncertainty in simulated wheat growth and final grain yield due to light models could be as high as 45%. Therefore, our results call for an overhaul of light interception models in CGMs.

Leaf elongation response to blue light is mediated by stomatal-induced variations in transpiration in Festuca arundinacea

Reduced blue light irradiance is known to enhance leaf elongation rate (LER) in grasses, but the mechanisms involved have not yet been elucidated. We investigated whether leaf elongation response to reduced blue light could be mediated by stomata-induced variations of plant transpiration. Two experiments were carried out on tall fescue in order to monitor LER and transpiration under reduced blue light irradiance. Additionally, LER dynamics were compared with those observed in the response to vapour pressure deficit (VPD)-induced variations of transpiration. Finally, we developed a model of water flow within a tiller to simulate the observed short-term response of LER to various transpiration regimes. LER dramatically increased in response to blue light reduction and then reached new steady states, which remained higher than the control. Reduced blue light triggered a simultaneous stomatal closure which induced an immediate decrease of leaf transpiration. The hydraulic model of leaf elongation accurately predicted the LER response to blue light and VPD, resulting from an increase in the growth-induced water potential gradient in the leaf growth zone. Our results suggest that the blue light signal is sensed by stomata of expanded leaves and transduced to the leaf growth zone through the hydraulic architecture of the tiller.

Colon sparing resection versus extended colectomy for left-sided obstructing colon cancer with caecal ischaemia or perforation: a nationwide study from the French Surgical Association

AIM

It is not known whether patients with obstructive left colon cancer (OLCC) with caecal ischaemia or diastatic perforation (defined as a blowout of the caecal wall related to colonic overdistension) should undergo a (sub)total colectomy (STC) or an ileo-caecal resection with double-barrelled ileo-colostomy. We aimed to compare the results of these two strategies.

METHOD

From 2000 to 2015, 1220 patients with OLCC underwent surgery by clinicians who were members of the French Surgical Association. Of these cases, 201 (16%) were found to have caecal ischaemia or diastatic perforation intra-operatively: 174 patients (87%) underwent a STC (extended colectomy group) and 27 (13%) an ileo-caecal resection with double-end stoma (colon-sparing group). Outcomes were compared retrospectively.

RESULTS

In the extended colectomy group, 95 patients (55%) had primary anastomosis and 79 (45%) had a STC with an end ileostomy. In the colon-sparing group, 10 patients (37%) had simultaneous resection of their primary tumour with segmental colectomy and an anastomosis which was protected by a double-barrelled ileo-colostomy. The demographic data for the two groups were comparable. Median operative time was longer in the STC group (P = 0.0044). There was a decrease in postoperative mortality (7% vs 12%, P = 0.75) and overall morbidity (56% vs 67%, P = 0.37) including surgical (30% vs 40%, P = 0.29) and severe complications (17% vs 27%, P = 0.29) in the colon-sparing group, although these differences did not reach statistical significance. Cumulative morbidity included all surgical stages and the rate of permanent stoma was 66% and 37%, respectively, with no significant difference between the two groups. Overall survival and disease-free survival were similar between the two groups.

CONCLUSION

The colon-sparing strategy may represent a valid and safe alternative to STC in OLCC patients with caecal ischaemia or diastatic perforation.

Omics Data Reveal Putative Regulators of Einkorn Grain Protein Composition under Sulfur Deficiency

Understanding the molecular mechanisms controlling the accumulation of grain storage proteins in response to nitrogen (N) and sulfur (S) nutrition is essential to improve cereal grain nutritional and functional properties. Here, we studied the grain transcriptome and metabolome responses to postanthesis N and S supply for the diploid wheat einkorn (Triticum monococcum). During grain filling, 848 transcripts and 24 metabolites were differentially accumulated in response to N and S availability. The accumulation of total free amino acids per grain and the expression levels of 241 genes showed significant modifications during most of the grain filling period and were upregulated in response to S deficiency. Among them, 24 transcripts strongly responded to S deficiency and were identified in coexpression network analyses as potential coordinators of the grain response to N and S supply. Sulfate transporters and genes involved in sulfate and Met metabolism were upregulated, suggesting regulation of the pool of free amino acids and of the grain N-to-S ratio. Several genes highlighted in this study might limit the impact of S deficiency on the accumulation of grain storage proteins.

Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions

We propose new methods to predict genotype × environment interaction by selecting relevant environmental covariates and using an AMMI decomposition of the interaction. Farmers are asked to produce more efficiently and to reduce their inputs in the context of climate change. They have to face more and more limiting factors that can combine in numerous stress scenarios. One solution to this challenge is to develop varieties adapted to specific environmental stress scenarios. For this, plant breeders can use genomic predictions coupled with environmental characterization to identify promising combinations of genes in relation to stress covariates. One way to do it is to take into account the genetic similarity between varieties and the similarity between environments within a mixed model framework. Molecular markers and environmental covariates (EC) can be used to estimate relevant covariance matrices. In the present study, based on a multi-environment trial of 220 European elite winter bread wheat (Triticum aestivum L.) varieties phenotyped in 42 environments, we compared reference regression models potentially including ECs, and proposed alternative models to increase prediction accuracy. We showed that selecting a subset of ECs, and estimating covariance matrices using an AMMI decomposition to benefit from the information brought by the phenotypic records of the training set are promising approaches to better predict genotype-by-environment interactions (G ×  E). We found that using a different kinship for the main genetic effect and the G ×  E effect increased prediction accuracy. Our study also demonstrates that integrative stress indexes simulated by crop growth models are more efficient to capture G ×  E than climatic covariates.

Estimation of Plant and Canopy Architectural Traits Using the Digital Plant Phenotyping Platform

The extraction of desirable heritable traits for crop improvement from high-throughput phenotyping (HTP) observations remains challenging. We developed a modeling workflow named "Digital Plant Phenotyping Platform" (D3P), to access crop architectural traits from HTP observations. D3P couples the Architectural model of DEvelopment based on L-systems (ADEL) wheat (Triticum aestivum) model (ADEL-Wheat), which describes the time course of the three-dimensional architecture of wheat crops, with simulators of images acquired with HTP sensors. We demonstrated that a sequential assimilation of the green fraction derived from Red-Green-Blue images of the crop into D3P provides accurate estimates of five key parameters (phyllochron, lamina length of the first leaf, rate of elongation of leaf lamina, number of green leaves at the start of leaf senescence, and minimum number of green leaves) of the ADEL-Wheat model that drive the time course of green area index and the number of axes with more than three leaves at the end of the tillering period. However, leaf and tiller orientation and inclination characteristics were poorly estimated. D3P was also used to optimize the observational configuration. The results, obtained from in silico experiments conducted on wheat crops at several vegetative stages, showed that the accessible traits could be estimated accurately with observations made at 0° and 60° zenith view inclination with a temporal frequency of 100 °Cd (degree day). This illustrates the potential of the proposed holistic approach that integrates all the available information into a consistent system for interpretation. The potential benefits and limitations of the approach are further discussed.

Invited review: Intergovernmental Panel on Climate Change, agriculture, and food-A case of shifting cultivation and history

Since 1990, the Intergovernmental Panel on Climate Change (IPCC) has produced five Assessment Reports (ARs), in which agriculture as the production of food for humans via crops and livestock have featured in one form or another. A constructed database of the ca. 2,100 cited experiments and simulations in the five ARs was analyzed with respect to impacts on yields via crop type, region, and whether adaptation was included. Quantitative data on impacts and adaptation in livestock farming have been extremely scarce in the ARs. The main conclusions from impact and adaptation are that crop yields will decline, but that responses have large statistical variation. Mitigation assessments in the ARs have used both bottom-up and top-down methods but need better to link emissions and their mitigation with food production and security. Relevant policy options have become broader in later ARs and included more of the social and nonproduction aspects of food security. Our overall conclusion is that agriculture and food security, which are two of the most central, critical, and imminent issues in climate change, have been dealt with an unfocussed and inconsistent manner between the IPCC five ARs. This is partly a result of not only agriculture spanning two IPCC working groups but also the very strong focus on projections from computer crop simulation modeling. For the future, we suggest a need to examine interactions between themes such as crop resource use efficiencies and to include all production and nonproduction aspects of food security in future roles for integrated assessment models.

Proteomic Data Integration Highlights Central Actors Involved in Einkorn (Triticum monococcum ssp. monococcum) Grain Filling in Relation to Grain Storage Protein Composition

Albumins and globulins (AGs) of wheat endosperm represent about 20% of total grain proteins. Some of these physiologically active proteins can influence the synthesis of storage proteins (SPs) (gliadins and glutenins) and consequently, rheological properties of wheat flour and processing. To identify such AGs, data, (published by Bonnot et al., 2017) concerning abundance in 352 AGs and in the different seed SPs during grain filling and in response to different nitrogen (N) and sulfur (S) supply, were integrated with mixOmics R package. Relationships between AGs and SPs were first unraveled using the unsupervised method sparse Partial Least Square, also known as Projection to Latent Structure (sPLS). Then, data were integrated using a supervised approach taking into account the nutrition and the grain developmental stage. We used the block.splda procedure also referred to as DIABLO (Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies). These approaches led to the identification of discriminant and highly correlated features from the two datasets (AGs and SPs) which are not necessarily differentially expressed during seed development or in response to N or S supply. Eighteen AGs were correlated with the quantity of SPs per grain. A statistical validation of these proteins by genetic association analysis confirmed that 5 out of this AG set were robust candidate proteins able to modulate the seed SP synthesis. In conclusion, this latter result confirmed that the integrative strategy is an adequate way to reduce the number of potentially relevant AGs for further functional validation.

Thirty-day mortality after emergency surgery for obstructing colon cancer: survey and dedicated score from the French Surgical Association

AIM

The aim was to define risk factors for postoperative mortality in patients undergoing emergency surgery for obstructing colon cancer (OCC) and to propose a dedicated score.

METHOD

From 2000 to 2015, 2325 patients were treated for OCC in French surgical centres by members of the French National Surgical Association. A multivariate analysis was performed for variables with P value ≤ 0.20 in the univariate analysis for 30-day mortality. Predictive performance was assessed by the area under the receiver operating characteristic curve.

RESULTS

A total of 1983 patients were included. Thirty-day postoperative mortality was 7%. Multivariate analysis found five significant independent risk factors: age ≥ 75 (P = 0.013), American Society of Anesthesiologists (ASA) score ≥ III (P = 0.027), pulmonary comorbidity (P = 0.0002), right-sided cancer (P = 0.047) and haemodynamic failure (P < 0.0001). The odds ratio for risk of postoperative death was 3.42 with one factor, 5.80 with two factors, 15.73 with three factors, 29.23 with four factors and 77.25 with five factors. The discriminating capacity in predicting 30-day postoperative mortality was 0.80.

CONCLUSION

Thirty-day postoperative mortality after emergency surgery for OCC is correlated with age, ASA score, pulmonary comorbidity, site of tumour and haemodynamic failure, with a specific score ranging from 0 to 5.

Experimental and modeling evidence of carbon limitation of leaf appearance rate for spring and winter wheat

Accurate predictions of the timing of physiological stages and the development rate are crucial for predicting crop performance under field conditions. Plant development is controlled by the leaf appearance rate (LAR) and our understanding of how LAR responds to environmental factors is still limited. Here, we tested the hypothesis that carbon availability may account for the effects of irradiance, photoperiod, atmospheric CO2 concentration, and ontogeny on LAR. We conducted three experiments in growth chambers to quantify and disentangle these effects for both winter and spring wheat cultivars. Variations of LAR observed between environmental scenarios were well explained by the supply/demand ratio for carbon, quantified using the photothermal quotient. We therefore developed an ecophysiological model based on the photothermal quotient that accounts for the effects of temperature, irradiance, photoperiod, and ontogeny on LAR. Comparisons of observed leaf stages and LAR with simulations from our model, from a linear thermal-time model, and from a segmented linear thermal-time model corrected for sowing date showed that our model can simulate the observed changes in LAR in the field with the lowest error. Our findings demonstrate that a hypothesis-driven approach that incorporates more physiology in specific processes of crop models can increase their predictive power under variable environments.

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.

The bZIP transcription factor SPA Heterodimerizing Protein represses glutenin synthesis in Triticum aestivum

The quality of wheat grain is mainly determined by the quantity and composition of its grain storage proteins (GSPs). Grain storage proteins consist of low- and high-molecular-weight glutenins (LMW-GS and HMW-GS, respectively) and gliadins. The synthesis of these proteins is essentially regulated at the transcriptional level and by the availability of nitrogen and sulfur. The regulation network has been extensively studied in barley where BLZ1 and BLZ2, members of the basic leucine zipper (bZIP) family, activate the synthesis of hordeins. To date, in wheat, only the ortholog of BLZ2, Storage Protein Activator (SPA), has been identified as playing a major role in the regulation of GSP synthesis. Here, the ortholog of BLZ1, named SPA Heterodimerizing Protein (SHP), was identified and its involvement in the transcriptional regulation of the genes coding for GSPs was analyzed. In gel mobility shift assays, SHP binds cis-motifs known to bind to bZIP family transcription factors in HMW-GS and LMW-GS promoters. Moreover, we showed by transient expression assays in wheat endosperm that SHP acts as a repressor of the activity of these gene promoters. This result was confirmed in transgenic lines overexpressing SHP, which were grown with low and high nitrogen supply. The phenotype of SHP-overexpressing lines showed a lower quantity of both LMW-GS and HMW-GS, while the quantity of gliadin was unchanged, whatever the nitrogen availability. Thus, the gliadin/glutenin ratio was increased, which suggests that gliadin and glutenin genes may be differently regulated.

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.

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.

Diverging importance of drought stress for maize and winter wheat in Europe

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO₂ offering no yield benefit in these years.

Drivers of crop yield variability require quantification, and historical records can help in improving understanding. Here, Webber et al. report that drought stress will remain a key driver of yield losses in wheat and maize across Europe, and benefits from CO₂ will be limited in low-yielding years.

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

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

A Model of Leaf Coordination to Scale-Up Leaf Expansion from the Organ to the Canopy

Process-based crop growth models are popular tools with which to analyze and understand the impact of crop management, genotype-by-environment interactions, or climate change. The ability to predict leaf area development is critical to predict crop growth, particularly under conditions of limited resources. Here, we aimed at deciphering growth coordination rules between wheat (Triticum aestivum) plant organs (i.e. between leaves within a stem, between laminae and sheaths, and between the mainstem and axillary tillers) to model the dynamics of canopy development. We found a unique relationship between laminae area and leaf rank for the mainstem and its tillers, which was robust across a range of sowing dates and plant densities. Robust relationships between laminae and sheath areas also were found, highlighting the tight control of organ growth within and between phytomers. These relationships identified at the phytomer scale were used to develop a simulation model of leaf area dynamics at the canopy level that was integrated in the wheat model SiriusQuality. The model was then evaluated using several independent experiments. The model accurately predicts leaf area dynamics under different scenarios of nitrogen and water limitations. It accounted for 85%, 64%, and 73% of the variability of the surface area of leaf cohorts, total leaf area index, and total green area index, respectively. The process-based model of the dynamics of leaf area described here is a key element to quantify the value of candidate traits for use in plant breeding and to project the impact of climate change on wheat growth.

An AgMIP framework for improved agricultural representation in IAMs

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Grain subproteome responses to nitrogen and sulfur supply in diploid wheat Triticum monococcum ssp. monococcum

Wheat grain storage proteins (GSPs) make up most of the protein content of grain and determine flour end-use value. The synthesis and accumulation of GSPs depend highly on nitrogen (N) and sulfur (S) availability and it is important to understand the underlying control mechanisms. Here we studied how the einkorn (Triticum monococcum ssp. monococcum) grain proteome responds to different amounts of N and S supply during grain development. GSP composition at grain maturity was clearly impacted by nutrition treatments, due to early changes in the rate of GSP accumulation during grain filling. Large-scale analysis of the nuclear and albumin-globulin subproteomes during this key developmental phase revealed that the abundance of 203 proteins was significantly modified by the nutrition treatments. Our results showed that the grain proteome was highly affected by perturbation in the N:S balance. S supply strongly increased the rate of accumulation of S-rich α/β-gliadin and γ-gliadin, and the abundance of several other proteins involved in glutathione metabolism. Post-anthesis N supply resulted in the activation of amino acid metabolism at the expense of carbohydrate metabolism and the activation of transport processes including nucleocytoplasmic transit. Protein accumulation networks were analyzed. Several central actors in the response were identified whose variation in abundance was related to variation in the amounts of many other proteins and are thus potentially important for GSP accumulation. This detailed analysis of grain subproteomes provides information on how wheat GSP composition can possibly be controlled in low-level fertilization condition.

Temperature increase reduces global yields of major crops in four independent estimates

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions

This corrects the article DOI: 10.1038/nplants.2017.102.

The uncertainty of crop yield projections is reduced by improved temperature response functions

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.

Contribution of Crop Models to Adaptation in Wheat

With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, crop models are useful tools to simulate crop yields. Wheat (Triticum spp.) crop models have been evolving since the 1960s to translate processes related to crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat crop models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture.

Ear Rachis Xylem Occlusion and Associated Loss in Hydraulic Conductance Coincide with the End of Grain Filling for Wheat

Seed dehydration is the normal terminal event in the development of orthodox seeds and is physiologically related to the cessation of grain dry mass accumulation and crop grain yield. For a better understanding of grain dehydration, we evaluated the hypothesis that hydraulic conductance of the ear decreases during the latter stages of development and that this decrease results from disruption or occlusion of xylem conduits. Whole ear, rachis, and stem nodes hydraulic conductance and percentage loss of xylem conductivity were measured from flowering to harvest-ripeness on bread wheat (Triticum aestivum L.) cv. Récital grown under controlled environments. Flag leaf transpiration, stomatal conductance, chlorophyll content and grain and ear water potentials were also measured during grain development. We show that grain dehydration was not related with whole plant physiology and leaf senescence, but closely correlated with the hydraulic properties of the xylem conduits irrigating the grains. Indeed, there was a substantial decrease in rachis hydraulic conductance at the onset of the grain dehydration phase. This hydraulic impairment was not caused by the presence of air embolism in xylem conduits of the stem internodes or rachis but by the occlusion of the xylem lumens by polysaccharides (pectins and callose). Our results demonstrate that xylem hydraulics plays a key role during grain maturation.

Changes in the nuclear proteome of developing wheat (Triticum aestivum L.) grain

Wheat grain end-use value is determined by complex molecular interactions that occur during grain development, including those in the cell nucleus. However, our knowledge of how the nuclear proteome changes during grain development is limited. Here, we analyzed nuclear proteins of developing wheat grains collected during the cellularization, effective grain-filling, and maturation phases of development, respectively. Nuclear proteins were extracted and separated by two-dimensional gel electrophoresis. Image analysis revealed 371 and 299 reproducible spots in gels with first dimension separation along pH 4-7 and pH 6-11 isoelectric gradients, respectively. The relative abundance of 464 (67%) protein spots changed during grain development. Abundance profiles of these proteins clustered in six groups associated with the major phases and phase transitions of grain development. Using nano liquid chromatography-tandem mass spectrometry to analyse 387 variant and non-variant protein spots, 114 different proteins were identified that were classified into 16 functional classes. We noted that some proteins involved in the regulation of transcription, like HMG1/2-like protein and histone deacetylase HDAC2, were most abundant before the phase transition from cellularization to grain-filling, suggesting that major transcriptional changes occur during this key developmental phase. The maturation period was characterized by high relative abundance of proteins involved in ribosome biogenesis. Data are available via ProteomeXchange with identifier PXD002999.

Transcriptional and metabolic alternations rebalance wheat grain storage protein accumulation under variable nitrogen and sulfur supply

Wheat (Triticum aestivum L.) grain storage proteins (GSPs) are major determinants of flour end-use value. Biological and molecular mechanisms underlying the developmental and nutritional determination of GSP accumulation in cereals are as yet poorly understood. Here we timed the accumulation of GSPs during wheat grain maturation relative to changes in metabolite and transcript pools in different conditions of nitrogen (N) and sulfur (S) availability. We found that the N/S supply ratio modulated the duration of accumulation of S-rich GSPs and the rate of accumulation of S-poor GSPs. These changes are likely to be the result of distinct relationships between N and S allocation, depending on the S content of the GSP. Most developmental and nutritional modifications in GSP synthesis correlated with the abundance of structural gene transcripts. Changes in the expression of transport and metabolism genes altered the concentrations of several free amino acids under variable conditions of N and S supply, and these amino acids seem to be essential in determining GSP expression. The comprehensive data set generated and analyzed here provides insights that will be useful in adapting fertilizer use to variable N and S supply, or for breeding new cultivars with balanced and robust GSP composition.

In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management

Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work.

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.

Conserved cis-regulatory modules in promoters of genes encoding wheat high-molecular-weight glutenin subunits

The concentration and composition of the gliadin and glutenin seed storage proteins (SSPs) in wheat flour are the most important determinants of its end-use value. In cereals, the synthesis of SSPs is predominantly regulated at the transcriptional level by a complex network involving at least five cis-elements in gene promoters. The high-molecular-weight glutenin subunits (HMW-GS) are encoded by two tightly linked genes located on the long arms of group 1 chromosomes. Here, we sequenced and annotated the HMW-GS gene promoters of 22 electrophoretic wheat alleles to identify putative cis-regulatory motifs. We focused on 24 motifs known to be involved in SSP gene regulation. Most of them were identified in at least one HMW-GS gene promoter sequence. A common regulatory framework was observed in all the HMW-GS gene promoters, as they shared conserved cis-regulatory modules (CCRMs) including all the five motifs known to regulate the transcription of SSP genes. This common regulatory framework comprises a composite box made of the GATA motifs and GCN4-like Motifs (GLMs) and was shown to be functional as the GLMs are able to bind a bZIP transcriptional factor SPA (Storage Protein Activator). In addition to this regulatory framework, each HMW-GS gene promoter had additional motifs organized differently. The promoters of most highly expressed x-type HMW-GS genes contain an additional box predicted to bind R2R3-MYB transcriptional factors. However, the differences in annotation between promoter alleles could not be related to their level of expression. In summary, we identified a common modular organization of HMW-GS gene promoters but the lack of correlation between the cis-motifs of each HMW-GS gene promoter and their level of expression suggests that other cis-elements or other mechanisms regulate HMW-GS gene expression.

Association study of wheat grain protein composition reveals that gliadin and glutenin composition are trans-regulated by different chromosome regions

Wheat grain storage protein (GSP) content and composition are the main determinants of the end-use value of bread wheat (Triticum aestivum L.) grain. The accumulation of glutenins and gliadins, the two main classes of GSP in wheat, is believed to be mainly controlled at the transcriptional level through a network of transcription factors. This regulation network could lead to stable cross-environment allometric scaling relationships between the quantity of GSP classes/subunits and the total quantity of nitrogen per grain. This work conducted a genetic mapping study of GSP content and composition and allometric scaling parameters of grain N allocation using a bread wheat worldwide core collection grown in three environments. The core collection was genotyped with 873 markers for genome-wide association and 167 single nucleotide polymorphism markers in 51 candidate genes for candidate association. The candidate genes included 35 transcription factors (TFs) expressed in grain. This work identified 74 loci associated with 38 variables, of which 19 were candidate genes or were tightly linked with candidate genes. Besides structural GSP genes, several loci putatively trans-regulating GSP accumulation were identified. Seven candidate TFs, including four wheat orthologues of barley TFs that control hordein gene expression, were associated or in strong linkage disequilibrium with markers associated with the composition or quantity of glutenin or gliadin, or allometric grain N allocation parameters, confirming the importance of the transcriptional control of GSP accumulation. Genome-wide association results suggest that the genes regulating glutenin and gliadin compositions are mostly distinct from each other and operate differently.