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12 Pág. Dirección Técnica de Evaluación de Variedades y Laboratorios (DTEVL), 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., This study was supported in part by the Spanish project PID2019-106650RB-C21 from the Ministerio de Ciencia e Innovación. TV is recipient of a Juan de la Cierva-Formación post-doctoral contract, from the Ministerio de Ciencia e Innovación, Spain. SK is supported by the Ramon y Cajal RYC-2019-027818-I research fellowship from the Ministerio de Ciencia e Innovación, Spain. JLA also acknowledges the support of the Catalan Institution for Research and Advanced Studies (ICREA, Generalitat de Catalunya, Spain), through the ICREA Academia Program.

14 Pág., 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., This study was supported by the Spanish project AGL2016-76527-R from MINECO, Spain. Fadia Chairi is the recipient of an FPI doctoral fellowship from the same institution. Jose Luis Araus acknowledges the support from the ICREA Academia of the Catalan Government, Spain.