The utility of unmanned aerial vehicles (UAV) imagery in retrieving phenotypic data to support plant breeding research has been a topic of increasing interest in recent years. The advantages of image-based phenotyping are related to the high spatial and temporal resolution of the retrieved data and the non-destructive and rapid method of data acquisition. This study trains parametric and nonparametric regression models to retrieve leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fractional vegetation cover (fCover), leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and grain yield (GY) of winter durum wheat breeding experiment from four-bands UAV images. A ground dataset, collected during two field campaigns and complemented with data from a previous study, is used for model development. The dataset is split at random into two parts, one for training and one for testing the models. The tested parametric models use the vegetation index formula and parametric functions. The tested nonparametric models are partial least square regression (PLSR), random forest regression (RFR), support vector regression (SVR), kernel ridge regression (KRR), and Gaussian processes regression (GPR). The retrieved biophysical variables along with traditional phenotypic traits (plant height, yield, and tillering) are analysed for detection of genetic diversity, proximity, and similarity in the studied genotypes. Analysis of variance (ANOVA), Duncan’s multiple range test, correlation analysis, and principal component analysis (PCA) are performed with the phenotypic traits. The parametric and nonparametric models show close results for GY retrieval, with parametric models indicating slightly higher accuracy (R² = 0.49; RMSE = 0.58 kg/plot; rRMSE = 6.1%). However, the nonparametric model, GPR, computes per pixel uncertainty estimation, making it more appealing for operational use. Furthermore, our results demonstrate that grain filling was better than flowering phenological stage to predict GY. The nonparametric models show better results for biophysical variables retrieval, with GPR presenting the highest prediction performance. Nonetheless, robust models are found only for LAI (R² = 0.48; RMSE = 0.64; rRMSE = 13.5%) and LCC (R² = 0.49; RMSE = 31.57 mg m⁻²; rRMSE = 6.4%) and therefore these are the only remotely sensed phenotypic traits included in the statistical analysis for preliminary assessment of wheat productivity. The results from ANOVA and PCA illustrate that the retrieved remotely sensed phenotypic traits are a valuable addition to the traditional phenotypic traits for plant breeding studies. We believe that these preliminary results could speed up crop improvement programs; however, stronger interdisciplinary research is still needed, as well as uncertainty estimation of the remotely sensed phenotypic traits.

Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial resolution and frequent revisit times, could serve as an alternative data source for phenotyping. In this study, we separately evaluated pan-sharpened Pléiades satellite imagery (50 cm) and UAV imagery (2.5 cm) to phenotype durum wheat in small-plot (12 m × 1.10 m) breeding trials. The Gaussian process regression (GPR) algorithm, which provides predictions with uncertainty estimates, was trained with spectral bands and а selected set of vegetation indexes (VIs) as independent variables. Grain protein content (GPC) was better predicted with Pléiades data at the growth stage of 20% of inflorescence emerged but with only moderate accuracy (validation R²: 0.58). The grain yield (GY) and protein yield (PY) were better predicted using UAV data at the late milk and watery ripe growth stages, respectively (validation: R² 0.67 and 0.62, respectively). The cumulative VIs (the sum of VIs over the available images within the growing season) did not increase the accuracy of the models for either sensor. When mapping the estimated parameters, the spatial resolution of Pléiades revealed certain limitations. Nevertheless, our findings regarding GPC suggested that the usefulness of pan-sharpened Pléiades images for phenotyping should not be dismissed and warrants further exploration, particularly for breeding experiments with larger plot sizes.

Durum wheat is mainly used for products for human consumption, the quality of which depends on the content of protein and yellow pigments in the semolina. The challenges faced by modern breeding, related to population growth and climate change, imply improvement of both grain yields and quality in durum wheat germplasm well adapted to specific agro-climatic conditions. To address those challenges, a better understanding of the genetic architecture of agronomic and quality traits is needed. In the current study we used the Genome-Wide Association Study (GWAS) approach in a panel of Bulgarian and foreign genotypes to define loci controlling agronomic and quality traits in durum wheat. We mapped 26 marker traits associations (MTAs) for four of the six studied traits—grain yield, grain protein content, seed yellow colour (CIELAB b*), and plant height. The greatest number of MTAs was detected for grain yield. Seven MTAs were detected for each grain protein content and seed colour, and one MTA for plant height. Most of the reported associations had confidence intervals overlapping with already reported quantitative trait loci (QTLs). Two loci controlling grain yield were not reported previously. The MTAs reported here may be a valuable tool in future breeding for improvement of both grain yield and quality in durum wheat.

The durum wheat breeding program in Field crops institute FCI - Chirpan started in 1928. In the last ten years, a number of varieties have been created to meet modern cultural requirements. Genetic distance is of great importance for the success of the combining breeding. In the study are included 13 varieties created over the last 10 years and 8 of the Institute's best advanced durum wheat breeding lines. The field experiment was conducted in experimental field of FCI-Chirpan during 2015-2017 year. A randomized block design in four replicated was used with a trial plot size of 15 m2. A standard farming technology for durum wheat in the country was applied. Tre traits: grain yield, plant height, heading date, test weight, 1000 kernel weight, protein content, wet gluten and vitreousness were observed. The means of three-year trials for all studied traits were calculated and genetic differences between genotypes were found. The coefficients of variation (CV%) obtained for the individual traits show that the heading date and grain yields are the most variable. The correlation analysis determines significant coeficients for 9 out of 28 possible combinations of traits. The multivariate methods for determining the genetic distance between the involved genotypes in the study were used. According to the dendrogram of the cluster analysis, the distances between the four main groups formed are determined. The applied graphical PC analysis shows the interrelations between the traits and the grouping of genotypes in terms of the genetic distance between them. These methods can help for faster breeding progress using the established genetic distance as the basis for deploying the breeding program.

The purpose of the study is to determine the variability and stability of traits related to the amount of yellow pigments in the grain, semolina and the finished pasta product. In addition the polymorphism in one of the phytoene synthase gene on chromosome 7A related to carotenoids synthesis was detected. In the investigation were included 10 durum wheat genotypes - cultivars and breeding lines of different origin - Bulgaria - Field Crops Institute - Chirpan and Dobroudja Agricultural Institute - General Toshevo, Europe, CYMIT-Mexico. All genotypes were grown in field conditions in competitive variety trials in three replications in harvesting years 2014/2016. The results obtained were processed statistically via basic statistics (TIBCO statistica 13.3.0 software package). Great variability was found between genotypes on the study traits. The breeding lines: M-431 (10.71 ppm in grain, 10.42 ppm in semolina and 8.96 ppm in pasta) and TD-97 (9.83 ppm in grain, 9.58 ppm in semolina and 8.52 ppm in pasta) and the Bulgarian standard cultivar Predel (9.25 ppm in grain, 8.87 ppm in semolina and 7.27 ppm in pasta) are characterized by the highest yellow pigment content in grain, semolina and pasta. Both lines are also distinguished by the lowest total pigment losses (2.6%) in milling grain to semolina and in processing to pasta product (11 - 11.4 %), while line TD-97 has the lowest total pigment losses (13.6%). Furthermore the genotypes characterized by the highest yellow pigment content (M-431 and TD-97) have an acceptable stability and can be used in the hybridization program to improve the color potential of Bulgarian durum wheat varieties. The Psy-A1 codominant marker, based on a 37 bp insertion at the 5' end of intron 2 of the PsyA1 gene amplifies two fragments. The Psy-A1a allele with a 194 bp fragment in length was observed in all 7 studied cultivars and lines of durum wheat. The 231-bp Psy-A1b allele is found in 3 genotypes. The obtained results could not be associated with phenotypic differences in the yellow pigment content in the durum wheat genotypes studied.

Генетичното разнообразие, характерно за дивите видове от сем. Triticeae може да бъде източник на генетични вариации, свързани с адаптивността на културата към абиотичен и биотичен стрес, към компонентите на добива и показатели, свързани с качеството на зърното. В изследването са използвани шест напреднали линии твърда пшеница от селекционната програма на Института по полски култури - гр. Чирпан, получени в резултат на междувидова и междуродова хибридизация (три кръстоски F10-12, една кръстоска F11-13, една кръстоска BC2 F3-5 и BC2 F2-4 генерации). За сравнителна оценка на материалите е използван стандартния сорт Предел. Всички генотипове са отгледани през три реколтни години – 2020/2022 в конкурсен сортов опит в четири повторения. Целта на изследването е да се направи оценка на напредналите линиите по добив и признаци, свързани с качеството на зърното. За статистическа обработка на данните е използван анализ на варианса. Чрез анализа на варианса е установено доказано влияние на генотипа, годината на отглеждане и взаимодействието между двата фактора върху варирането на почти всички изследвани признаци. Влиянието на генотипа е значително във варирането на признаците: SDS- седиментационна стойност (97.34%), стъкловидност (85.55%), жълт индекс b* (77.85%), съдържание на протеин в зърното (50.08%), добив зърно (44.24%) и маса на 1000 зърна (36.50%). Значително е влиянието на годината във варирането на признака височина на растението (59.74%). Най висок добив (672.6 kg/da) средно за трите години на изследването е установен при линия ДВ-8360. Силен глутен по признака SDS- седиментационна стойност е установен за стандартния сорт Предел (43.0 cm3) и за линия Д-232 (35.3 cm3). Идентифицирана е глиадинова фракция γ-45 и НМГ-2 тип за линия Д-232, свързани със силен глутен.