Non-foliar photosynthesis and nitrogen assimilation influence grain yield in durum wheat regardless of water conditions.

There is a need to generate improved crop varieties adapted to the ongoing changes in the climate. We studied durum wheat canopy and central metabolism of six different photosynthetic organs in two yield-contrasting varieties. The aim was to understand the mechanisms associated with the water stress response and yield performance. Water stress strongly reduced grain yield, plant biomass, and leaf photosynthesis, and down-regulated C/N-metabolism genes and key protein levels, which occurred mainly in leaf blades. By contrast, higher yield was associated with high ear dry weight and lower biomass and ears per area, highlighting the advantage of reduced tillering and the consequent improvement in sink strength, which promoted C/N metabolism at the whole plant level. An improved C metabolism in blades and ear bracts and N assimilation in all photosynthetic organs facilitated C/N remobilization to the grain and promoted yield. Therefore, we propose that further yield gains in Mediterranean conditions could be achieved by considering the source-sink dynamics and the contribution of non-foliar organs, and particularly N assimilation and remobilization during the late growth stages. We highlight the power of linking phenotyping with plant metabolism to identify novel traits at the whole plant level to support breeding programmes.

Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging.

Durum wheat is an important cereal that is widely grown in the Mediterranean basin. In addition to high yield, grain quality traits are of high importance for farmers. The strong influence of climatic conditions makes the improvement of grain quality traits, like protein content, vitreousness, and test weight, a challenging task. Evaluation of quality traits post-harvest is time- and labor-intensive and requires expensive equipment, such as near-infrared spectroscopes or hyperspectral imagers. Predicting not only yield but also important quality traits in the field before harvest is of high value for breeders aiming to optimize resource allocation. Implementation of efficient approaches for trait prediction, such as the use of high-resolution spectral data acquired by a multispectral camera mounted on unmanned aerial vehicles (UAVs), needs to be explored. In this study, we have acquired multispectral image data with an 11-band multispectral camera mounted on a UAV and analyzed the data with machine learning (ML) models to predict grain yield and important quality traits in breeding micro-plots. Combining 11-band multispectral data for 34 cultivars and 16 environments allowed to develop ML models with good prediction capability. Applying the trained models to test sets explained a considerable degree of phenotypic variance with good accuracy showing r squared values of 0.84, 0.69, 0.64, and 0.61 and normalized root mean squared errors of 0.17, 0.07, 0.14, and 0.03 for grain yield, protein content, vitreousness, and test weight, respectively.

Analysis of durum wheat photosynthetic organs during grain filling reveals the ear as a water stress-tolerant organ and the peduncle as the largest pool of primary metabolites.

MAIN CONCLUSION: The pool of carbon- and nitrogen-rich metabolites is quantitatively relevant in non-foliar photosynthetic organs during grain filling, which have a better response to water limitation than flag leaves. The response of durum wheat to contrasting water regimes has been extensively studied at leaf and agronomic level in previous studies, but the water stress effects on source-sink dynamics, particularly non-foliar photosynthetic organs, is more limited. Our study aims to investigate the response of different photosynthetic organs to water stress and to quantify the pool of carbon and nitrogen metabolites available for grain filling. Five durum wheat varieties were grown in field trials in the Spanish region of Castile and León under irrigated and rainfed conditions. Water stress led to a significant decrease in yield, biomass, and carbon and nitrogen assimilation, improved water use efficiency, and modified grain quality traits in the five varieties. The pool of carbon (glucose, glucose-6-phosphate, fructose, sucrose, starch, and malate) and nitrogen (glutamate, amino acids, proteins and chlorophylls) metabolites in leaf blades and sheaths, peduncles, awns, glumes and lemmas were also analysed. The results showed that the metabolism of the blades and peduncles was the most susceptible to water stress, while ear metabolism showed higher stability, particularly at mid-grain filling. Interestingly, the total metabolite content per organ highlighted that a large source of nutrients, which may be directly involved in grain filling, are found outside the blades, with the peduncles being quantitatively the most relevant. We conclude that yield improvements in our Mediterranean agro-ecosystem are highly linked to the success of shoots in producing ears and a higher number of grains, while grain filling is highly dependent on the capacity of non-foliar organs to fix CO2 and N. The ear organs show higher stress resilience than other organs, which deserves our attention in future breeding programmes.

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models.

MAIN CONCLUSION: By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.

Crop phenotyping in a context of global change: What to measure and how to do it.

High-throughput crop phenotyping, particularly under field conditions, is nowadays perceived as a key factor limiting crop genetic advance. Phenotyping not only facilitates conventional breeding, but it is necessary to fully exploit the capabilities of molecular breeding, and it can be exploited to predict breeding targets for the years ahead at the regional level through more advanced simulation models and decision support systems. In terms of phenotyping, it is necessary to determined which selection traits are relevant in each situation, and which phenotyping tools/methods are available to assess such traits. Remote sensing methodologies are currently the most popular approaches, even when lab-based analyses are still relevant in many circumstances. On top of that, data processing and automation, together with machine learning/deep learning are contributing to the wide range of applications for phenotyping. This review addresses spectral and red-green-blue sensing as the most popular remote sensing approaches, alongside stable isotope composition as an example of a lab-based tool, and root phenotyping, which represents one of the frontiers for field phenotyping. Further, we consider the two most promising forms of aerial platforms (unmanned aerial vehicle and satellites) and some of the emerging data-processing techniques. The review includes three Boxes that examine specific case studies.

Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data.

Hyperspectral techniques are currently used to retrieve information concerning plant biophysical traits, predominantly targeting pigments, water, and nitrogen-protein contents, structural elements, and the leaf area index. Even so, hyperspectral data could be more extensively exploited to overcome the breeding challenges being faced under global climate change by advancing high-throughput field phenotyping. In this study, we explore the potential of field spectroscopy to predict the metabolite profiles in flag leaves and ear bracts in durum wheat. The full-range reflectance spectra (visible (VIS)-near-infrared (NIR)-short wave infrared (SWIR)) of flag leaves, ears and canopies were recorded in a collection of contrasting genotypes grown in four environments under different water regimes. GC-MS metabolite profiles were analyzed in the flag leaves, ear bracts, glumes, and lemmas. The results from regression models exceeded 50% of the explained variation (adj-R2 in the validation sets) for at least 15 metabolites in each plant organ, whereas their errors were considerably low. The best regressions were obtained for malate (82%), glycerate and serine (63%) in leaves; myo-inositol (81%) in lemmas; glycolate (80%) in glumes; sucrose in leaves and glumes (68%); γ-aminobutyric acid (GABA) in leaves and glumes (61% and 71%, respectively); proline and glucose in lemmas (74% and 71%, respectively) and glumes (72% and 69%, respectively). The selection of wavebands in the models and the performance of the models based on canopy and VIS organ spectra and yield prediction are discussed. We feel that this technique will likely to be of interest due to its broad applicability in ecophysiology research, plant breeding programmes, and the agri-food industry.

Bridging the genotype-phenotype gap for a Mediterranean pine by semi-automatic crown identification and multispectral imagery.

Progress in high-throughput phenotyping and genomics provides the potential to understand the genetic basis of plant functional differentiation. We developed a semi-automatic methodology based on unmanned aerial vehicle (UAV) imagery for deriving tree-level phenotypes followed by genome-wide association study (GWAS). An RGB-based point cloud was used for tree crown identification in a common garden of Pinus halepensis in Spain. Crowns were combined with multispectral and thermal orthomosaics to retrieve growth traits, vegetation indices and canopy temperature. Thereafter, GWAS was performed to analyse the association between phenotypes and genomic variation at 235 single nucleotide polymorphisms (SNPs). Growth traits were associated with 12 SNPs involved in cellulose and carbohydrate metabolism. Indices related to transpiration and leaf water content were associated with six SNPs involved in stomata dynamics. Indices related to leaf pigments and leaf area were associated with 11 SNPs involved in signalling and peroxisome metabolism. About 16-20% of trait variance was explained by combinations of several SNPs, indicating polygenic control of morpho-physiological traits. Despite a limited availability of markers and individuals, this study is provides a successful proof-of-concept for the combination of high-throughput UAV-based phenotyping with cost-effective genotyping to disentangle the genetic architecture of phenotypic variation in a widespread conifer.

The Hydrogen Isotope Composition δ2H Reflects Plant Performance.

The stable carbon (δ13C) and oxygen (δ18O) isotope compositions in plant matter reflect photosynthetic and transpirative conditions in plants, respectively. However, the nature of hydrogen isotope composition (δ2H) and what it reflects of plant performance is poorly understood. Using durum wheat (Triticum turgidum var durum), this study evaluated the effect of different water and nitrogen growing field conditions on transpiration and how this effect influenced the performance of δ2H in autotrophic (flag leaf), mixotrophic (ears), and heterotrophic (grains and roots) organs. Moreover, δ2H was compared with the δ13C and δ18O in the same organs. Isotope compositions were analyzed in dry matter, the water-soluble fraction, and in water from different tissues of a set of genotypes. Similar to δ13C, the δ2H correlated negatively with stomatal conductance, whereas no correlation was observed for δ18O. Moreover, δ2H was not only affected by changes in transpiration but also by photosynthetic reactions, probably as a consequence of NADPH formation in autotrophic organs. Compared with the δ2H of stem water, plant δ2H was strongly diminished in photosynthetic organs such as the flag leaves, whereas it strongly increased in heterotrophic organs such as grains and roots. In heterotrophic organs, δ2H was associated with postphotosynthetic effects because there are several processes that lead to 2H-enrichment of carbohydrates. In summary, δ2H exhibited specific features that inform about the water conditions of the wheat crop, together with the photosynthetic characteristics of the plant part considered. Moreover, correlations of δ2H with grain yield illustrate that this isotope can be used to assess plant performance under different growing conditions.

Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques.

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red-green-blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP's potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.

Is vegetative area, photosynthesis, or grape C uploading involved in the climate change-related grape sugar/anthocyanin decoupling in Tempranillo?

Foreseen climate change is expected to impact on grape composition, both sugar and pigment content. We tested the hypothesis that interactions between main factors associated with climate change (elevated CO2, elevated temperature, and water deficit) decouple sugars and anthocyanins, and explored the possible involvement of vegetative area, photosynthesis, and grape C uploading on the decoupling. Tempranillo grapevine fruit-bearing cuttings were exposed to CO2 (700 vs. 400 ppm), temperature (ambient vs. + 4 °C), and irrigation levels (partial vs. full) in temperature-gradient greenhouses. In a search for mechanistic insights into the underlying processes, experiments 1 and 2 were designed to maximize photosynthesis and enlarge leaf area range among treatments, whereas plant growth was manipulated in order to deliberately down-regulate photosynthesis and control vegetative area in experiments 3 and 4. Towards this aim, treatments were applied either from fruit set to maturity with free vegetation and fully irrigated or at 5-8% of pot capacity (experiments 1 and 2), or from veraison to maturity with controlled vegetation and fully irrigated or at 40% of pot capacity (experiments 3 and 4). Modification of air 13C isotopic composition under elevated CO2 enabled the further characterization of whole C fixation period and C partitioning to grapes. Increases of the grape sugars-to-anthocyanins ratio were highly and positively correlated with photosynthesis and grape 13C labeling, but not with vegetative area. Evidence is presented for photosynthesis, from fruit set to veraison, and grape C uploading, from veraison to maturity, as key processes involved in the establishment and development, respectively, of the grape sugars to anthocyanins decoupling.

Leaf dorsoventrality as a paramount factor determining spectral performance in field-grown wheat under contrasting water regimes.

The effects of leaf dorsoventrality and its interaction with environmentally induced changes in the leaf spectral response are still poorly understood, particularly for isobilateral leaves. We investigated the spectral performance of 24 genotypes of field-grown durum wheat at two locations under both rainfed and irrigated conditions. Flag leaf reflectance spectra in the VIS-NIR-SWIR (visible-near-infrared-short-wave infrared) regions were recorded in the adaxial and abaxial leaf sides and at the canopy level, while traits providing information on water status and grain yield were evaluated. Moreover, leaf anatomical parameters were measured in a subset of five genotypes. The spectral traits studied were more affected by the leaf side than by the water regime. Leaf dorsoventral differences suggested higher accessory pigment content in the abaxial leaf side, while water regime differences were related to increased chlorophyll, nitrogen, and water contents in the leaves in the irrigated treatment. These variations were associated with anatomical changes. Additionally, leaf dorsoventral differences were less in the rainfed treatment, suggesting the existence of leaf-side-specific responses at the anatomical and biochemical level. Finally, the accuracy in yield prediction was enhanced when abaxial leaf spectra were employed. We concluded that the importance of dorsoventrality in spectral traits is paramount, even in isobilateral leaves.

Measuring the dynamic photosynthome.

Background: Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes in environmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The 'dynamic' changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope: It is known that photosynthesis does not respond in a timely manner, and even a small temporal 'mismatch' between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions: There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of 'phenomics' which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the 'photosynthome'. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved.

Interactive effect of water and nitrogen regimes on plant growth, root traits and water status of old and modern durum wheat genotypes.

MAIN CONCLUSIONS: The selection of the ideal root drought adaptive traits should take into account the production and maintenance of root tissues alongside the capacity to capture soil resources. Ten old and modern Spanish durum wheat (Triticum turgidum L. var durum) genotypes were grown in lysimeters under two contrasting water and nitrogen regimes to study the effect of such growth conditions on: (1) the aerial biomass, (2) the growth and structure of the roots and (3) the relationships of the root structure with aerial biomass, photosynthetic and transpirative characteristics and water use efficiency. Both high water and nitrogen regimes significantly increased aerial biomass. Root dry biomass and root length increased and decreased in response to improved water supply and nitrogen regimes, respectively. No significant correlations were detected between aerial biomass and any root trait under well-watered conditions. Under water stress aerial biomass was negatively correlated with root dry biomass, root length and root weight density and positively correlated with the specific root length, particularly for the subset of old genotypes. The high nitrogen regime significantly enriched the carbon isotope composition of the flag leaf (δ (13)CFL) and hindered the effect of the high water regime on decreasing δ (13)CFL enrichment. Thus, positive correlations of aerial biomass with δ (13)CFL were detected regardless of the water regime. The study revealed: (1) the importance of root traits for higher aerial biomass under the low water regime; (2) that the interaction between nitrogen and the water regime may affect the predictive nature of the δ (13)C in drought breeding programs; and (3) the selection of the ideal root system structure should take into account the metabolic costs of the production and maintenance of root tissues alongside the capacity of capturing resources.

Factors preventing the performance of oxygen isotope ratios as indicators of grain yield in maize.

MAIN CONCLUSION: This paper provides new insights into source-sink relationships and transpiration processes which will eventually help to interpret δ (18) O as a genotype selection and ecophysiological tool for maize adaptation to drought. Oxygen isotope composition (δ(18)O) has been proposed as a phenotyping tool to integrate leaf transpiration in C4 crops, such as maize. Within this context we hypothesize that δ(18)O in leaves may reflect primarily environmental and genetic variability in evaporative processes, but that this signal may become dampened in transit from source to sink tissues. The aim of this study was to assess the relative importance of transpirative or translocation-related factors affecting δ(18)O in plant tissues of maize. We performed two water regime experiments, one with two varieties under semi-controlled conditions, and another in the field with 100 genotypes during two consecutive years. The δ(18)O in organic matter at the leaf base was strongly correlated with the δ(18)O in stem water, indicating that it could be a good proxy for source water in extensive samplings. Compared to leaves, we observed an (18)O depletion in silks and grains, but not in stem-soluble organic matter. We interpret this as evidence of exchange with unenriched water from source to sink, but mainly occurring within sink tissues. Although grain yield (GY) and physiological variables did not show clear intra-trial patterns against δ(18)O, the only tissues that correlated with GY in the linear regression approach were that of silks, giving an insight of evapotranspirative demand during female flowering and thus of potential maize lines that are better adapted to drought. This finding will eventually help to interpret δ(18)O as a genotype selection and ecophysiological tool for the adaption of maize and other crops to drought, offering insight into source-sink relationships and transpiration processes.

Metabolite Profiles of Maize Leaves in Drought, Heat, and Combined Stress Field Trials Reveal the Relationship between Metabolism and Grain Yield.

The development of abiotic stress-resistant cultivars is of premium importance for the agriculture of developing countries. Further progress in maize (Zea mays) performance under stresses is expected by combining marker-assisted breeding with metabolite markers. In order to dissect metabolic responses and to identify promising metabolite marker candidates, metabolite profiles of maize leaves were analyzed and compared with grain yield in field trials. Plants were grown under well-watered conditions (control) or exposed to drought, heat, and both stresses simultaneously. Trials were conducted in 2010 and 2011 using 10 tropical hybrids selected to exhibit diverse abiotic stress tolerance. Drought stress evoked the accumulation of many amino acids, including isoleucine, valine, threonine, and 4-aminobutanoate, which has been commonly reported in both field and greenhouse experiments in many plant species. Two photorespiratory amino acids, glycine and serine, and myoinositol also accumulated under drought. The combination of drought and heat evoked relatively few specific responses, and most of the metabolic changes were predictable from the sum of the responses to individual stresses. Statistical analysis revealed significant correlation between levels of glycine and myoinositol and grain yield under drought. Levels of myoinositol in control conditions were also related to grain yield under drought. Furthermore, multiple linear regression models very well explained the variation of grain yield via the combination of several metabolites. These results indicate the importance of photorespiration and raffinose family oligosaccharide metabolism in grain yield under drought and suggest single or multiple metabolites as potential metabolic markers for the breeding of abiotic stress-tolerant maize.

Photosynthetic contribution of the ear to grain filling in wheat: a comparison of different methodologies for evaluation.

The culm (particularly the flag leaf) and the ear are believed to play a major role in providing assimilates for grain filling in wheat. However, the results obtained in the past varied depending on the methodology applied. Three different methodologies were compared that aimed to assess the relative contribution of the culm (photosynthetic organs below the ear) and the ear to grain filling. The first two consisted of applications of photosynthesis inhibition treatments, including the use of the herbicide DCMU and organ shading. The third was a non-intrusive method that compared the carbon isotope composition (δ(13)C) of mature kernels with the δ(13)C of the water-soluble fraction of the peduncle, awns and glumes. Several advanced CIMMYT lines were tested under good agronomic conditions. The δ(13)C approach assigned a higher photosynthetic contribution to the ear than to the culm. However, some methodological considerations should be taken into account when applying the δ(13)C approach, particularly the sampling method used, in order to prevent post-harvest respiration. The shading approach assigned a similar contribution to the ear as to the culm. The DCMU approach assigned a greater role to the culm but herbicide application to the culm affected the ear, thus biasing the final grain weight. Moreover DCMU and shading approaches may cause compensatory effects which overestimated the contribution of unaffected organs. This study may help to develop precise phenotyping tools to identify physiological traits such as ear photosynthesis that could contribute towards increasing grain yield.

Gene expression and physiological responses to salinity and water stress of contrasting durum wheat genotypes.

Elucidating the relationships between gene expression and the physiological mechanisms remains a bottleneck in breeding for resistance to salinity and drought. This study related the expression of key target genes with the physiological performance of durum wheat under different combinations of salinity and irrigation. The candidate genes assayed included two encoding for the DREB (dehydration responsive element binding) transcription factors TaDREB1A and TaDREB2B, another two for the cytosolic and plastidic glutamine synthetase (TaGS1 and TaGS2), and one for the specific Na(+) /H(+) vacuolar antiporter (TaNHX1). Expression of these genes was related to growth and different trait indicators of nitrogen metabolism (nitrogen content, stable nitrogen isotope composition, and glutamine synthetase and nitrate reductase activities), photosynthetic carbon metabolism (stable carbon isotope composition and different gas exchange traits) and ion accumulation. Significant interaction between genotype and growing conditions occurred for growth, nitrogen content, and the expression of most genes. In general terms, higher expression of TaGS1, TaGS2, TaDREB2B, and to a lesser extent of TaNHX1 were associated with a better genotypic performance in growth, nitrogen, and carbon photosynthetic metabolism under salinity and water stress. However, TaDREB1A was increased in expression under stress compared with control conditions, with tolerant genotypes exhibiting lower expression than susceptible ones.

Wheat ear carbon assimilation and nitrogen remobilization contribute significantly to grain yield.

The role of wheat ears as a source of nitrogen (N) and carbon (C) in the grain filling process has barely been studied. To resolve this question, five wheat genotypes were labeled with 15 N-enriched nutrient solution. N remobilization and absorption were estimated via the nitrogen isotope composition of total organic matter and Rubisco. Gas exchange analyses showed that ear photosynthesis contributed substantially to grain filling in spite of the great loss of C due to respiration. Of the total kernel N, 64.7% was derived from the N acquired between sowing and anthesis, while the remaining 35.3% was derived from the N acquired between anthesis and maturity. In addition, 1.87 times more N was remobilized to the developing kernel from the ear than from the flag leaf. The higher yielding genotypes showed an increased N remobilization to the kernel compared to the lower yielding genotypes. In addition, the higher yielding genotypes remobilized more N from the ears to the kernel than the lower yielding genotypes, while the lower yielding genotypes remobilized more N from the flag leaf to the kernel. Therefore, the ears contribute significantly toward fulfilling C and N demands during grain filling.

Relative contribution of shoot and ear photosynthesis to grain filling in wheat under good agronomical conditions assessed by differential organ δ13C.

During grain filling in C3 cereals, the shoot (particularly the flag leaf) and the ear are believed to play major roles as sources of assimilates. However, both the cost and the intrusive nature of most of the methodologies available to investigate this have prevented conclusive results being obtained. This study compared the carbon isotope composition (δ(13)C) in its natural abundance in mature kernels with the δ(13)C of the water-soluble fraction of the peduncle, glumes, and awns to assess the relative contribution of the shoot (understood as the whole set of photosynthetic organs below the peduncle) and ear to grain filling in a set of highly productive wheat lines from the International Maize and Wheat Improvement Center, Mexico, under good agronomic conditions. In overall terms, the contribution of the ear was greater in comparison with that of the shoot. The specific contribution of the flag leaf blade to grain filling was also assessed by comparing the δ(13)C of grains with the δ(13)C of the water-soluble fraction of the flag leaf and the awns. The contribution of the flag leaf was minor, ranging between 3 and 18%. Complementary analyses performed such as gas-exchange rates and the accumulated water-soluble carbohydrates in both organs and light intercepted by the canopy at different strata suggested that the ear has a photosynthetic capacity at least comparable to that of the flag leaf. In this sense, selection for a higher contribution of ear photosynthesis to grain yield in breeding programmes could be addressed with the use of stable isotopes.

Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance.

A collection of 368 advanced lines and cultivars of spring wheat (Triticum aestivum L.) from Chile, Uruguay, and CIMMYT (Centro Internacional de Mejoramiento de Maíz y Trigo), with good agronomic characteristics were evaluated under the Mediterranean conditions of central Chile. Three different water regimes were assayed: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation around booting), and full irrigation (FI; four irrigations: at tillering, flag leaf appearance, heading, and middle grain filling). Traits evaluated were grain yield (GY), agronomical yield components, days from sowing to heading, carbon isotope discrimination (Δ(13) C) in kernels, and canopy spectral reflectance. Correlation analyses were performed for 70 spectral reflectance indices (SRI) and the other traits evaluated in the three trials. GY and Δ(13) C were the traits best correlated with SRI, particularly when these indices were measured during grain filling. However, only GY could be predicted using a single regression, with Normalized Difference Moisture Index (NDMI2: 2,200; 1,100) having the best fit to the data for the three trials. For Δ(13) C, only individual regressions could be forecast under FI (r(2): 0.25-0.37) and MWS (r(2): 0.45-0.59) but not under SWS (r(2): 0.03-0.09). NIR-based SRI proved to be better predictors than those that combine visible and NIR wavelengths.