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An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differen

This study explores the potential of Spanish germplasm for the development of hybrids adapted to southern Europe, a new region for hybrid barley (Hordeum vulgare). A set of 140 locally adapted, advanced breeding lines, developed in a Spanish public breeding program, were evaluated for their potential to widen the germplasm available for hybrid barley development. A subset of 24 lines was introduced into three-way hybrid combinations and tested in a field trial network of four locations and 2 yr. The hybrid performance of the rest of the lines was estimated based on genomic prediction models. No three-way hybrid exceeded the best check. We succeeded in identifying lines with high general combining ability, which could produce promising two-way hybrids. Some were among those tested in the field, whereas others resulted from genomic predictions, validating the strategy followed of mining locally adapted breeding lines. Rachis brittleness detected in field trials revealed a low but detrimental effect of the presence of alternative brittle mutations in the hybrid combination. The success of hybrid breeding in southern Europe requires further investigation of the underlying heterotic patterns, and appropriate management of brittle genes through prebreeding, to widen the germplasm accessible for hybrid barley breeding.

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

Wheat grain quality characteristics have experienced increasing attention as a central factor affecting wheat end-use products quality and human health. Nonetheless, in the last decades a reduction in grain quality has been observed. Therefore, it is central to develop efficient quality-related phenotyping tools. In this sense, one of the most relevant wheat features related to grain quality traits is grain nitrogen content, which is directly linked to grain protein content and monitorable with remote sensing approaches. Moreover, the relation between nitrogen fertilization and grain nitrogen content (protein) plays a central role in the sustainability of agriculture. Both aiming to develop efficient phenotyping tools using remote sensing instruments and to advance towards a field-level efficient and sustainable monitoring of grain nitrogen status, this paper studies the efficacy of various sensors, multispectral and visible red–greenblue (RGB), at different scales, ground and unmanned aerial vehicle (UAV), and phenological stages (anthesis and grain filling) to estimate grain nitrogen content. Linear models were calculated using vegetation indices at each sensing level, sensor type and phenological stage. Furthermore, this study explores the up-scalability of the best performing model to satellite level Sentinel-2 equivalent data. We found that models built at the phenological stage of anthesis with UAV-level multispectral cameras using red-edge bands outperformed grain nitrogen content estimation (R2 = 0.42, RMSE = 0.18%) in comparison with those models built with RGB imagery at ground and aerial level, as well as with those built with widely used ground-level multispectral sensors. We also demonstrated the possibility to use UAV-built multispectral linear models at the satellite scale to determine grain nitrogen content effectively (R2 = 0.40, RMSE = 0.29%) at actual wheat fields.

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.

Knowledge of the agronomic and physiological traits associated with genetic gains in yield is essential to improve understanding of yield-limiting factors and to inform future breeding strategies. The aim of this paper is to dissect the agronomic and physiological traits related to genetic gain and to propose an ideotype with high yield that is best adapted to Spanish Mediterranean environments. Six semi-dwarf (i.e. modern) durum wheat genotypes were grown in a wide range of growing conditions in Spain during two successive years. Diverse agronomic, physiological and leaf morphological traits were evaluated. Kernels spike-1 was the yield component most affected by the genetic gain. While no interaction between genotype and growing conditions existed for grain yield, the more productive genotypes were characterized by a plant height of around 85 cm, small erect flag leaves, more open stomata, a better balance between N sources and N sinks and a higher capacity to re-fix CO2 respired by the grain. Moreover, in general the non-laminar parts of the plants play a key role in providing assimilates during grain filling. The high heritability of most of the studied parameters allows their consideration as traits for phenotyping durum wheat better adapted to a wide range of Mediterranean conditions.

RGB imagery has been widely used for crop management practices and phenotyping applications in recent years. Although RGB wavelengths (400-700 nm) are not able to capture all essential plant data (such as with full ultraviolet, near and long infrared wavelength coverage), RGB cameras are the most common types of cameras and are among the versatile imaging devices for proximal remote sensing applications. Deep learning strategies have improved a wide range of processes and deep learning concepts can be included in many applications. This work uses the Very Deep Super-Resolution (VDSP) technique to improve low-resolution RGB images in order to study grain yield assessment in wheat using vegetation indexes. The results show no significant differences between indexes calculated from low-resolution images and low-resolution images processed using VDSP with grain yield.

Modern plant breeding practices have narrowed the genetic base of wheat throughout the world, increasing crop vulnerability. Therefore, there is clearly a need for introducing new germplasm in breeding programs to search for variability related to traits of agronomic interest for wheat improvement. The existence of subsets of accessions (core collections) that represent the diversity conserved in germplasm collections is a favored approach for breeders to explore novel variation and enhance the use of germplasm. In this study, a core collection of Spanish landraces of bread wheat has been created using high-throughput genotyping technologies (DArTseq), which yielded more than 50 K molecular markers. This marker system not only provides a robust estimate of the diversity, but also information about its distribution in the genome. Two core collections of 94 entries were created by using two common sampling strategies: the maximization strategy and the population structure-based method. Both core collections showed high geographic, phenotypic and genetic representativeness, but the collection obtained with the maximization strategy captured better the diversity displayed by the initial collection. This core collection, which includes a broad range of adapted genotypes, can be efficiently utilized for mining new alleles for useful traits in wheat breeding.

Although the relevance of spike bracts in stress acclimation and contribution to wheat yield was recently revealed, the metabolome of this organ and its response to water stress is still unknown. The metabolite profiles of flag leaves, glumes and lemmas were characterized under contrasting field water regimes in five durum wheat cultivars. Water conditions during growth were characterized through spectral vegetation indices, canopy temperature and isotope composition. Spike bracts exhibited better coordination of carbon and nitrogen metabolisms than the flag leaves in terms of photorespiration, nitrogen assimilation and respiration paths. This coordination facilitated an accumulation of organic and amino acids in spike bracts, especially under water stress. The metabolomic response to water stress also involved an accumulation of antioxidant and drought tolerance related sugars, particularly in the spikes. Furthermore, certain cell wall, respiratory and protective metabolites were associated with genotypic outperformance and yield stability. In addition, grain yield was strongly predicted by leaf and spike bracts metabolomes independently. This study supports the role of the spike as a key organ during wheat grain filling, particularly under stress conditions and provides relevant information to explore new ways to improve wheat productivity including potential biomarkers for yield prediction.

The integration of high-throughput phenotyping and metabolic approaches is a suitable strategy to study the genotype-by-environment interaction and identify novel traits for crop improvement from canopy to an organ level. Our aims were to study the phenotypic and metabolic traits that are related to grain yield and quality at canopy and organ levels, with a special focus on source-sink coordination under contrasting N supplies. Four modern durum wheat varieties with contrasting grain yield were grown in field conditions under two N fertilization levels in north-eastern Spain. We evaluated canopy vegetation indices taken throughout the growing season, physiological and metabolic traits in different photosynthetic organs (flag leaf blade, sheath, peduncle, awn, glume, and lemma) at anthesis and mid-grain filling stages, and agronomic and grain quality traits at harvest. Low N supply triggered an imbalance of C and N coordination at the whole plant level, leading to a reduction of grain yield and nutrient composition. The activities of key enzymes in C and N metabolism as well as the levels of photoassimilates showed that each organ plays an important role during grain filling, some with a higher photosynthetic capacity, others for nutrient storage for later stages of grain filling, or N assimilation and recycling. Interestingly, the enzyme activities and sucrose content of the ear organs were positively associated with grain yield and quality, suggesting, together with the regression models using isotope signatures, the potential contribution of these organs during grain filling. This study highlights the use of holistic approaches to the identification of novel targets to improve grain yield and quality in C3 cereals and the key role of non-foliar organs at late-growth stages.

Chenopodium quinoa Willd (quinoa) has acquired an increased agronomical and nutritional relevance due to the capacity of adaptation to different environments and the exceptional nutritional properties of their seeds. These include high mineral and protein contents, a balanced amino acid composition, an elevated antioxidant capacity related to the high phenol content, and the absence of gluten. Although it is known that these properties can be determined by the environment, limited efforts have been made to determine the exact changes occurring at a nutritional level under changing environmental conditions in this crop. To shed light on this, this study aimed at characterizing variations in nutritional-related parameters associated with the year of cultivation and different genotypes. Various nutritional and physiological traits were analyzed in seeds of different quinoa cultivars grown in the field during three consecutive years. We found differences among cultivars for most of the nutritional parameters analyzed. It was observed that the year of cultivation was a determinant factor in every parameter studied, being 2018 the year with lower yields, germination rates, and antioxidant capacity, but higher seed weights and seed protein contents. Overall, this work will greatly contribute to increase our knowledge of the impact of the environment and genotype on the nutritional properties of quinoa seeds, especially in areas that share climatic conditions to Southern Europe.

Chenopodium quinoa Willd (quinoa) has acquired an increased agronomical and nutritional relevance due to the capacity of adaptation to different environments and the exceptional nutritional properties of their seeds. These include high mineral and protein contents, a balanced amino acid composition, an elevated antioxidant capacity related to the high phenol content, and the absence of gluten. Although it is known that these properties can be determined by the environment, limited efforts have been made to determine the exact changes occurring at a nutritional level under changing environmental conditions in this crop. To shed light on this, this study aimed at characterizing variations in nutritional-related parameters associated with the year of cultivation and different genotypes. Various nutritional and physiological traits were analyzed in seeds of different quinoa cultivars grown in the field during three consecutive years. We found differences among cultivars for most of the nutritional parameters analyzed. It was observed that the year of cultivation was a determinant factor in every parameter studied, being 2018 the year with lower yields, germination rates, and antioxidant capacity, but higher seed weights and seed protein contents. Overall, this work will greatly contribute to increase our knowledge of the impact of the environment and genotype on the nutritional properties of quinoa seeds, especially in areas that share climatic conditions to Southern Europe.

[Background], The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image. [Results], The results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears. [Conclusions], Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is hig

This paper addresses the question of whether there has been any genetic gain in yield for durum wheat released in Spain after the Green Revolution and assesses the agronomical and physiological traits associated with evolution of the crop during this time. Field experiments were carried out with a wide range of durum wheat cultivars (released in Spain from 1980 to 2009) and were conducted in different sites embracing a wide range of growing temperatures and water regimes at Aranjuez and Zamadueñas during three consecutive growing seasons (2013/14, 2014/15, 2015/16) under rainfed and supplemental irrigation and at Coria for two consecutive seasons (2014/15 and 2015/16) under rainfed conditions alone. Grain yield increased with the year of release of cultivars at a rate of 24 kg ha−1 y−1 (0.44% y−1) from 1980 to 2003, with no clear additional improvements thereafter. The moderate grain yield improvement from 1980 and 2003 was associated with kernels m−2 and kernels spike−1, with an increase of 117 kernels m−2 y−1 and 0.24 kernels spike−1 y−1, respectively. Moreover, aerial biomass at harvest and grain nitrogen yield increased with the year of release of cultivars for the entire period. However, no differences were found for thousand kernel weight, number of spikes m−2, days to heading, plant height, harvest index, canopy temperature depression, carbon isotope discrimination or grain nitrogen concentration. Overall, these results indicated that the rate of genetic progress in the yield of durum wheat in Spain after the Green Revolution has been low and has even stopped during the last decade, while no clear trend in some grain quality traits (TKW and grain N concentration) was recorded. However, the absolute and relative genetic gains estimated for yield were positively associated with the average mean and maximum daily temperatures from sowing to harvest of the testing site, which suggest that breeding has been performed under high-temperature environments.

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.