Phenotypic variation in leaf photosynthetic traits, leaf area index, and carbon discrimination of field-grown wheat genotypes and their relationship with yield performance in Mediterranean environments.

MAIN CONCLUSION: Leaf water potential, gas exchange, and chlorophyll fluorescence exhibited significant differences among genotypes, high environmental effects, but low heritability. The highest-yielding and drought-tolerant genotypes presented superior harvest index and grain weight, compared to drought-susceptible ones. Physiological phenotyping can help identify useful traits related to crop performance under water-limited conditions. A set of fourteen bread wheat genotypes with contrasting grain yield (GY) was studied in eight Mediterranean environments in Chile, resulting from the combination of two sites (Cauquenes and Santa Rosa), two water conditions (rainfed-WL and irrigated-WW), and four growing seasons (2015-2018). The objectives were to (i) evaluate the phenotypic variation of leaf photosynthetic traits after heading (anthesis and grain filling) in different environments; (ii) analyze the relationship between GY and leaf photosynthetic traits and carbon isotope discrimination (Δ13C); and (iii) identify those traits that could have a greater impact in the determination of tolerant genotypes under field conditions. Agronomic traits exhibited significant genotypic differences and genotype × environment (GxE) interaction. The average GY under the WW condition at Santa Rosa was 9.2 Mg ha-1 (range 8.2-9.9 Mg ha-1) and under the WL condition at Cauquenes was 6.2 Mg ha-1 (range 3.7-8.3 Mg ha-1). The GY was closely related to the harvest index (HI) in 14 out of 16 environments, a trait exhibiting a relatively high heritability. In general terms, the leaf photosynthetic traits presented low GxE interaction, but high environmental effects and low heritability, except for the chlorophyll content. The relationships between GY and leaf photosynthetic traits were weaker when performed across genotypes in each environment, indicating low genotypic effects, and stronger when performed across environments for each genotype. The leaf area index and Δ13C also presented high environmental effects and low heritability, and their correlations with GY were influenced by environmental effects. The highest-yielding and drought-tolerant genotypes presented superior HI and grain weight, but no clear differences in leaf photosynthetic traits or Δ13C, compared to drought-susceptible ones. It seems that the phenotypic plasticity of agronomic and leaf photosynthetic traits is very important for crop adaptation to Mediterranean environments.

Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting Environments.

The global concern about the gap between food production and consumption has intensified the research on the genetics, ecophysiology, and breeding of cereal crops. In this sense, several genetic studies have been conducted to assess the effectiveness and sustainability of collections of germplasm accessions of major crops. In this study, a spectral-based classification approach for the assignment of wheat cultivars to genetically differentiated subpopulations (genetic structure) was carried out using a panel of 316 spring bread cultivars grown in two environments with different water regimes (rainfed and fully irrigated). For that, different machine-learning models were trained with foliar spectral and genetic information to assign the wheat cultivars to subpopulations. The results revealed that, in general, the hyperparameters ReLU (as the activation function), adam (as the optimizer), and a size batch of 10 give neural network models better accuracy. Genetically differentiated groups showed smaller differences in mean wavelengths under rainfed than under full irrigation, which coincided with a reduction in clustering accuracy in neural network models. The comparison of models indicated that the Convolutional Neural Network (CNN) was significantly more accurate in classifying individuals into their respective subpopulations, with 92 and 93% of correct individual assignments in water-limited and fully irrigated environments, respectively, whereas 92% (full irrigation) and 78% (rainfed) of cultivars were correctly assigned to their respective classes by the multilayer perceptron method and partial least squares discriminant analysis, respectively. Notably, CNN did not show significant differences between both environments, which indicates stability in the prediction independent of the different water regimes. It is concluded that foliar spectral variation can be used to accurately infer the belonging of a cultivar to its respective genetically differentiated group, even considering radically different environments, which is highly desirable in the context of crop genetic resources management.

Improving bitter pit prediction by the use of X-ray fluorescence (XRF): A new approach by multivariate classification.

Bitter pit (BP) is one of the most relevant post-harvest disorders for apple industry worldwide, which is often related to calcium (Ca) deficiency at the calyx end of the fruit. Its occurrence takes place along with an imbalance with other minerals, such as potassium (K). Although the K/Ca ratio is considered a valuable indicator of BP, a high variability in the levels of these elements occurs within the fruit, between fruits of the same plant, and between plants and orchards. Prediction systems based on the content of elements in fruit have a high variability because they are determined in samples composed of various fruits. With X-ray fluorescence (XRF) spectrometry, it is possible to characterize non-destructively the signal intensity for several mineral elements at a given position in individual fruit and thus, the complete signal of the mineral composition can be used to perform a predictive model to determine the incidence of bitter pit. Therefore, it was hypothesized that using a multivariate modeling approach, other elements beyond the K and Ca could be found that could improve the current clutter prediction capability. Two studies were carried out: on the first one an experiment was conducted to determine the K/Ca and the whole spectrum using XRF of a balanced sample of affected and non-affected 'Granny Smith' apples. On the second study apples of three cultivars ('Granny Smith', 'Brookfield' and 'Fuji'), were harvested from two commercial orchards to evaluate the use of XRF to predict BP. With data from the first study a multivariate classification system was trained (balanced database of healthy and BP fruit, consisting in 176 from each group) and then the model was applied on the second study to fruit from two orchards with a history of BP. Results show that when dimensionality reduction was performed on the XRF spectra (1.5 - 8 KeV) of 'Granny Smith' apples, comparing fruit with and without BP, along with K and Ca, four other elements (i.e., Cl, Si, P, and S) were found to be deterministic. However, the PCA revealed that the classification between samples (BP vs. non-BP fruit) was not possible by univariate analysis (individual elements or the K/Ca ratio).Therefore, a multivariate classification approach was applied, and the classification measures (sensitivity, specificity, and balanced precision) of the PLS-DA models for all cultivars evaluated ('Granny Smith', 'Fuji' and 'Brookfield') on the full training samples and with both validation procedures (Venetian and Monte Carlo), ranged from 0.76 to 0.92. The results of this work indicate that using this technology at the individual fruit level is essential to understand the factors that determine this disorder and can improve BP prediction of intact fruit.

Challenges for a Massive Implementation of Phenomics in Plant Breeding Programs.

Due to climate change and expected food shortage in the coming decades, not only will it be necessary to develop cultivars with greater tolerance to environmental stress, but it is also imperative to reduce breeding cycle time. In addition to yield evaluation, plant breeders resort to many sensory assessments and some others of intermediate complexity. However, to develop cultivars better adapted to current/future constraints, it is necessary to incorporate a new set of traits, such as morphophysiological and physicochemical attributes, information relevant to the successful selection of genotypes or parents. Unfortunately, because of the large number of genotypes to be screened, measurements with conventional equipment are unfeasible, especially under field conditions. High-throughput plant phenotyping (HTPP) facilitates collecting a significant amount of data quickly; however, it is necessary to transform all this information (e.g., plant reflectance) into helpful descriptors to the breeder. To the extent that a holistic characterization of the plant (phenomics) is performed in challenging environments, it will be possible to select the best genotypes (forward phenomics) objectively but also understand why the said individual differs from the rest (reverse phenomics). Unfortunately, several elements had prevented phenomics from developing as desired. Consequently, a new set of prediction/validation methodologies, seasonal ambient information, and the fusion of data matrices (e.g., genotypic and phenotypic information) need to be incorporated into the modeling. In this sense, for the massive implementation of phenomics in plant breeding, it will be essential to count an interdisciplinary team that responds to the urgent need to release material with greater capacity to tolerate environmental stress. Therefore, breeding programs should (i) be more efficient (e.g., early discarding of unsuitable material), (ii) have shorter breeding cycles (fewer crosses to achieve the desired cultivar), and (iii) be more productive, increasing the probability of success at the end of the breeding process (percentage of cultivars released to the number of initial crosses).

Heritable Variation of Foliar Spectral Reflectance Enhances Genomic Prediction of Hydrogen Cyanide in a Genetically Structured Population of Eucalyptus.

Plants produce a wide diversity of specialized metabolites, which fulfill a wide range of biological functions, helping plants to interact with biotic and abiotic factors. In this study, an integrated approach based on high-throughput plant phenotyping, genome-wide haplotypes, and pedigree information was performed to examine the extent of heritable variation of foliar spectral reflectance and to predict the leaf hydrogen cyanide content in a genetically structured population of a cyanogenic eucalyptus (Eucalyptus cladocalyx F. Muell). In addition, the heritable variation (based on pedigree and genomic data) of more of 100 common spectral reflectance indices was examined. The first profile of heritable variation along the spectral reflectance curve indicated the highest estimate of genomic heritability ( h g 2 =0.41) within the visible region of the spectrum, suggesting that several physiological and biological responses of trees to environmental stimuli (ex., light) are under moderate genetic control. The spectral reflectance index with the highest genomic-based heritability was leaf rust disease severity index 1 ( h g 2 =0.58), followed by the anthocyanin reflectance index and the Browning reflectance index ( h g 2 =0.54). Among the Bayesian prediction models based on spectral reflectance data, Bayes B had a better goodness of fit than the Bayes-C and Bayesian ridge regression models (in terms of the deviance information criterion). All models that included spectral reflectance data outperformed conventional genomic prediction models in their predictive ability and goodness-of-fit measures. Finally, we confirmed the proposed hypothesis that high-throughput phenotyping indirectly capture endophenotypic variants related to specialized metabolites (defense chemistry), and therefore, generally more accurate predictions can be made integrating phenomics and genomics.

Exploring plant responses to abiotic stress by contrasting spectral signature changes.

In this study, daily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes (Triticum aestivum L.) susceptible to adverse conditions. Four genotypes were grown in pots experiments under semi-controlled conditions in Chile and Spain. Three treatments were applied: i) control (C), ii) water stress (WS), and iii) combined water and heat shock (WS+T). Spectral reflectance, gas exchange and chlorophyll fluorescence measurements were performed on flag leaves for three consecutive days at anthesis. High canopy temperature ( H CT ) genotypes showed less variability in their mean spectral reflectance signature and chlorophyll fluorescence, which was related to weaker responses to environmental fluctuations. While low canopy temperature ( L CT ) genotypes showed greater variability. The genotypes spectral signature changes, in accordance with environmental fluctuation, were associated with variations in their stomatal conductance under both stress conditions (WS and WS+T); L CT genotypes showed an anisohydric response compared that of H CT , which was isohydric. This approach could be used in breeding programs for screening a large number of genotypes through proximal or remote sensing tools and be a novel but simple way to identify groups of genotypes with contrasting performances.

Genome-wide association study of cyanogenic glycosides, proline, sugars, and pigments in Eucalyptus cladocalyx after 18 consecutive dry summers.

Natural variation of cyanogenic glycosides, soluble sugars, proline, and nondestructive optical sensing of pigments (chlorophyll, flavonols, and anthocyanins) was examined in ex situ natural populations of Eucalyptus cladocalyx F. Muell. grown under dry environmental conditions in the southern Atacama Desert, Chile. After 18 consecutive dry seasons, considerable plant-to-plant phenotypic variation for all the traits was observed in the field. For example, leaf hydrogen cyanide (HCN) concentrations varied from 0 (two acyanogenic individuals) to 1.54 mg cyanide g-1 DW. Subsequent genome-wide association study revealed associations with several genes with a known function in plants. HCN content was associated robustly with genes encoding Cytochrome P450 proteins, and with genes involved in the detoxification mechanism of HCN in cells (ß-cyanoalanine synthase and cyanoalanine nitrilase). Another important finding was that sugars, proline, and pigment content were linked to genes involved in transport, biosynthesis, and/or catabolism. Estimates of genomic heritability (based on haplotypes) ranged between 0.46 and 0.84 (HCN and proline content, respectively). Proline and soluble sugars had the highest predictive ability of genomic prediction models (PA = 0.65 and PA = 0.71, respectively). PA values for HCN content and flavonols were relatively moderate, with estimates ranging from 0.44 to 0.50. These findings provide new understanding on the genetic architecture of cyanogenic capacity, and other key complex traits in cyanogenic E. cladocalyx.

Genotypic variations in leaf and whole-plant water use efficiencies are closely related in bread wheat genotypes under well-watered and water-limited conditions during grain filling.

Wheat plants growing under Mediterranean rain-fed conditions are exposed to water deficit, particularly during the grain filling period, and this can lead to a strong reduction in grain yield (GY). This study examines the effects of water deficit after during the grain filling period on photosynthetic and water-use efficiencies at the leaf and whole-plant level for 14 bread wheat genotypes grown in pots under glasshouse conditions. Two glasshouse experiments were conducted, one in a conventional glasshouse at the Universidad de Talca, Chile (Experiment 1), and another at the National Plant Phenomics Centre (NPPC), Aberystwyth, UK (Experiment 2), in 2015. Plants were grown under well-watered (WW) and water-limited (WL) conditions during grain filling. The reductions in leaf water potential (Ψ), net CO2 assimilation (An) and stomatal conductance (gs) due to water deficit were 79, 35 and 55%, respectively, during grain filling but no significant differences were found among genotypes. However, chlorophyll fluorescence parameters (as determined on dark-adapted and illuminated leaves) and chlorophyll content (Chl) were significantly different among genotypes, but not between water conditions. Under both water conditions, An presented a positive and linear relationship with the effective photochemical quantum yield of Photosystem II (Y(II)) and the maximum rate of electron transport (ETRmax), and negative with the quantum yield of non-photochemical energy conversion in Photosystem II (Y(NPQ)). The relationship between An and Chl was positive and linear for both water conditions, but under WL conditions An tended to be lower at any Chl value. Both, instantaneous (An/E) and intrinsic (An/gs) water-use efficiencies at the leaf level exhibited a positive and linear relationship with plant water-use efficiency (WUEp = plant dry weight/water use). Carbon discrimination (Δ13C) in kernels presented a negative relationship with WUEp, at both WW and WL conditions, and a positive relationship with GY. Our results indicate that during grain filling wheat plants face limitations to the assimilation process due to natural senesce and water stress. The reduction in An and gs after anthesis in both water conditions was mainly due a decline in the chlorophyll content (non-stomatal limitation), whereas the observed differences between water conditions were mainly due to a stomatal limitation.

Thermal Imaging Reliability for Estimating Grain Yield and Carbon Isotope Discrimination in Wheat Genotypes: Importance of the Environmental Conditions.

Canopy temperature (Tc) by thermal imaging is a useful tool to study plant water status and estimate other crop traits. This work seeks to estimate grain yield (GY) and carbon discrimination (Δ13C) from stress degree day (SDD = Tc - air temperature, Ta), considering the effect of a number of environmental variables such as the averages of the maximum vapor pressure deficit (VPDmax) and the ambient temperature (Tmax), and the soil water content (SWC). For this, a set of 384 and a subset of 16 genotypes of spring bread wheat were evaluated in two Mediterranean-climate sites under water stress (WS) and full irrigation (FI) conditions, in 2011 and 2012, and 2014 and 2015, respectively. The relationship between the GY of the 384 wheat genotypes and SDD was negative and highly significant in 2011 (r2 = 0.52 to 0.68), but not significant in 2012 (r2 = 0.03 to 0.12). Under WS, the average GY, Δ13C, and SDD of wheat genotypes growing in ten environments were more associated with changes in VPDmax and Tmax than with the SWC. Therefore, the amount of water available to the plant is not enough information to assume that a particular genotype is experiencing a stress condition.

Exploring Agronomic and Physiological Traits Associated With the Differences in Productivity Between Triticale and Bread Wheat in Mediterranean Environments.

In Mediterranean climates soil water deficit occurs mainly during the spring and summer, having a great impact on cereal productivity. While previous studies have indicated that the grain yield (GY) of triticale is usually higher than bread wheat (Triticum aestivum L.), comparatively little is known about the performance of these crops under water-limited conditions or the physiological traits involved in the different yields of both crops. For this purpose, two sets of experiments were conducted in order to compare a high yielding triticale (cv. Aguacero) and spring wheat (cvs. Pandora and Domo). The first experiment, aiming to analyze the agronomic performance, was carried out in 10 sites located across a wide range of Mediterranean and temperate environments, distributed between 33°34' and 38°41' S. The second experiment, aiming to identify potential physiological traits linked to the different yields of the two crops, was conducted in two Mediterranean sites (Cauquenes and Santa Rosa) in which crops were grown under well-watered (WW) and water-limited (WL) conditions. The relationship between GY and the environmental index revealed that triticale exhibited a higher regression coefficient (Finlay and Wilkinson slope), indicating a more stable response to the environment, accompanied by higher yields than bread wheat. Harvest index was not significantly different between the two cereals, but triticale had higher kernels per spike (35%) and 1000 kernel weight (16%) than wheat, despite a lower number of spikes per square meter. The higher yield of triticale was linked to higher values of chlorophyll content, leaf net photosynthesis (An), the maximum rate of electron transport (ETRmax), the photochemical quantum yield of PSII [Y(II)] and leaf water-use efficiency. GY was positively correlated with Ci at anthesis and Δ13C in both species, as well as with gs at anthesis in triticale, but negatively correlated with non-photochemical fluorescence quenching and quantum yield of non-photochemical energy conversion at grain filling in wheat. These results revealed that triticale presented higher photosynthetic rates that contributed to increase plant growth and yield in the different environments, whereas wheat showed higher photoprotection system in detriment of assimilate production.

Firmness at Harvest Impacts Postharvest Fruit Softening and Internal Browning Development in Mechanically Damaged and Non-damaged Highbush Blueberries (Vaccinium corymbosum L.).

Fresh blueberries are very susceptible to mechanical damage, which limits postharvest life and firmness. Softening and susceptibility of cultivars "Duke" and "Brigitta" to developing internal browning (IB) after mechanical impact and subsequent storage was evaluated during a 2-year study (2011/2012, 2012/2013). On each season fruit were carefully hand-picked, segregated into soft (<1.60 N), medium (1.61-1.80 N), and firm (1.81-2.00 N) categories, and then either were dropped (32 cm) onto a hard plastic surface or remained non-dropped. All fruit were kept under refrigerated storage (0°C and 85-88% relative humidity) to assess firmness loss and IB after 7, 14, 21, 28, and 35 days. In general, regardless of cultivar or season, high variability in fruit firmness was observed within each commercial harvest, and significant differences in IB and softening rates were found. "Duke" exhibited high softening rates, as well as high and significant r2 between firmness and IB, but little differences for dropped vs. non-dropped fruit. "Brigitta," having lesser firmness rates, exhibited almost no relationships between firmness and IB (especially for non-dropped fruit), but marked differences between dropping treatments. Firmness loss and IB development were related to firmness at harvest, soft and firm fruit being the most and least damaged, respectively. Soft fruit were characterized by greater IB development during storage along with high soluble solids/acid ratio, which could be used together with firmness to estimate harvest date and storage potential of fruit. Results of this work suggest that the differences in fruit quality traits at harvest could be related to the time that fruit stay on the plant after turning blue, soft fruit being more advanced in maturity. Finally, the observed differences between segregated categories reinforce the importance of analyzing fruit condition for each sorted group separately.

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.

Latin America: A Development Pole for Phenomics.

Latin America and the Caribbean (LAC) has long been associated with the production and export of a diverse range of agricultural commodities. Due to its strategic geographic location, which encompasses a wide range of climates, it is possible to produce almost any crop. The climate diversity in LAC is a major factor in its agricultural potential but this also means climate change represents a real threat to the region. Therefore, LAC farming must prepare and quickly adapt to an environment that is likely to feature long periods of drought, excessive rainfall and extreme temperatures. With the aim of moving toward a more resilient agriculture, LAC scientists have created the Latin American Plant Phenomics Network (LatPPN) which focuses on LAC's economically important crops. LatPPN's key strategies to achieve its main goal are: (1) training of LAC members on plant phenomics and phenotyping, (2) establish international and multidisciplinary collaborations, (3) develop standards for data exchange and research protocols, (4) share equipment and infrastructure, (5) disseminate data and research results, (6) identify funding opportunities and (7) develop strategies to guarantee LatPPN's relevance and sustainability across time. Despite the challenges ahead, LatPPN represents a big step forward toward the consolidation of a common mind-set in the field of plant phenotyping and phenomics in LAC.

Spectral Knowledge (SK-UTALCA): Software for Exploratory Analysis of High-Resolution Spectral Reflectance Data on Plant Breeding.

This article describes public, free software that provides efficient exploratory analysis of high-resolution spectral reflectance data. Spectral reflectance data can suffer from problems such as poor signal to noise ratios in various wavebands or invalid measurements due to changes in incoming solar radiation or operator fatigue leading to poor orientation of sensors. Thus, exploratory data analysis is essential to identify appropriate data for further analyses. This software overcomes the problem that analysis tools such as Excel are cumbersome to use for the high number of wavelengths and samples typically acquired in these studies. The software, Spectral Knowledge (SK-UTALCA), was initially developed for plant breeding, but it is also suitable for other studies such as precision agriculture, crop protection, ecophysiology plant nutrition, and soil fertility. Various spectral reflectance indices (SRIs) are often used to relate crop characteristics to spectral data and the software is loaded with 255 SRIs which can be applied quickly to the data. This article describes the architecture and functions of SK-UTALCA and the features of the data that led to the development of each of its modules.

Breeding blueberries for a changing global environment: a review.

Today, blueberries are recognized worldwide as one of the foremost health foods, becoming one of the crops with the highest productive and commercial projections. Over the last 100 years, the geographical area where highbush blueberries are grown has extended dramatically into hotter and drier environments. The expansion of highbush blueberry growing into warmer regions will be challenged in the future by increases in average global temperature and extreme fluctuations in temperature and rainfall patterns. Considerable genetic variability exists within the blueberry gene pool that breeders can use to meet these challenges, but traditional selection techniques can be slow and inefficient and the precise adaptations of genotypes often remain hidden. Marker assisted breeding (MAB) and phenomics could aid greatly in identifying those individuals carrying adventitious traits, increasing selection efficiency and shortening the rate of cultivar release. While phenomics have begun to be used in the breeding of grain crops in the last 10 years, their use in fruit breeding programs it is almost non-existent.

Chlorophyll, anthocyanin, and gas exchange changes assessed by spectroradiometry in Fragaria chiloensis under salt stress.

Chlorophyll and anthocyanin contents provide a valuable indicator of the status of a plant's physiology, but to be more widely utilized it needs to be assessed easily and non-destructively. This is particularly evident in terms of assessing and exploiting germplasm for plant-breeding programs. We report, for the first time, experiments with Fragaria chiloensis (L.) Duch. and the estimation of the effects of response to salinity stress (0, 30, and 60 mmol NaCl/L) in terms of these pigments content and gas exchange. It is shown that both pigments (which interestingly, themselves show a high correlation) give a good indication of stress response. Both pigments can be accurately predicted using spectral reflectance indices (SRI); however, the accuracy of the predictions was slightly improved using multilinear regression analysis models and genetic algorithm analysis. Specifically for chlorophyll content, unlike other species, the use of published SRI gave better indications of stress response than Normalized Difference Vegetation Index. The effect of salt on gas exchange is only evident at the highest concentration and some SRI gave better prediction performance than the known Photochemical Reflectance Index. This information will therefore be useful for identifying tolerant genotypes to salt stress for incorporation in breeding programs.

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.