Broomrape is a root parasitic plant causing yield losses in sunflower production. Since sunflower is an important oil crop, the development of broomrape-resistant hybrids is the prime breeding objective. Using conventional plant breeding methods, breeders have identified resistant genes and developed a number of hybrids resistant to broomrape, adapted to different growing regions worldwide. However, the spread of broomrape into new countries and the development of new and more virulent races have been noted intensively. Recent advances in sunflower genomics provide additional tools for plant breeders to improve resistance and find durable solutions for broomrape spread and virulence. This review describes the structure and distribution of new, virulent physiological broomrape races, sources of resistance for introduction into susceptible cultivated sunflower, qualitative and quantitative resistance genes along with gene pyramiding and marker assisted selection (MAS) strategies applied in the process of increasing sunflower resistance. In addition, it presents an overview of underutilized biotechnological tools, such as phenotyping, -omics, and genome editing techniques, which need to be introduced in the study of sunflower resistance to broomrape in order to achieve durable resistance.

Domestication and the first steps of sunflower breeding date back more than 4000 years. As an interesting crop to humans, sunflower underwent significant changes in the past to finally find its place as one of the most significant oil crops today. Substantial progress has already been made in understanding how sunflower was domesticated. Recent advances in molecular techniques with improved experimental designs contributed to further understanding of the genetic and molecular basis underlying the architectural and phenotypic changes that occurred during domestication and improvements in sunflower breeding. Understanding the domestication process and assessing the current situation concerning available genotypic variations are essential in order for breeders to face future challenges. A review of the tools that are used for exploring the genetic and genome changes associated with sunflower domestication is given in the paper, along with a discussion of their possible implications on classical sunflower breeding techniques and goals.

Winter oilseed rape (WOSR) production is dependent on weather conditions, but is also characterized by low nitrogen (N) use efficiency. The objectives of this study were to: (i) evaluate sources of variability for the seed yield and oil content of four rapeseed cultivars under the influence of three sowing dates (SD trial) and five nitrogen dosages (N trial) during four growing seasons; (ii) understand year-related interactions and the effect of climatic variables in different growth stages; and (iii) assess the presence of interactions cultivar by year (C × Y) and treatment by year (T × Y). Six climatic factors were observed, during germination, overwintering, budding, flowering and ripening. The mixed effect split-plot analysis of variance was used, as well as factorial regression models. The C × Y interaction was the most important for the oil content in both trials. The precipitation at budding stage (75.8%), relative air humidity at overwintering (63.3%) and flowering stage (53.0%) accounted for the highest proportion of T × Y interaction for the seed yield, as well as precipitation at flowering (92.0%) and ripening (85.0%) for the oil content. Water availability was the main determinant of the seed yield and/or oil content accompanied with cooler temperatures during the seed development. The study successfully dissected the effect of year-related climatic variables on the agronomical traits in winter rapeseed. Based on this, appropriate agronomic practices can be applied at specific growing stages to ensure a high seed and oil yield.

Hybrid breeding in sunflowers based on CMS PET1 requires development of restorer lines carrying, in most cases, the restorer gene Rf1. Markers for marker-assisted selection have been developed, but there is still need for closer, more versatile, and co-dominant markers linked to Rf1. Homology searches against the reference sunflower genome using sequences of cloned markers, as well as Bacterial Artificial Chromosome (BAC)-end sequences of clones hybridizing to them, allowed the identification of two genomic regions of 30 and 3.9 Mb, respectively, as possible physical locations of the restorer gene Rf1 on linkage group 13. Nine potential candidate genes, encoding six pentatricopeptide repeat proteins, one tetratricopeptide-like helical domain, a probable aldehyde dehydrogenase 22A1, and a probable poly(A) polymerase 3 (PAPS3), were identified in these two genomic regions. Amplicon targeted next generation sequencing of these nine candidate genes for Rf1 was performed in an association panel consisting of 27 maintainer and 32 restorer lines and revealed the presence of 210 Single Nucleotide Polymorphisms (SNPs) and 67 Insertions/Deletions (INDELs). Association studies showed significant associations of 10 SNPs with fertility restoration (p-value < 10⁻⁴), narrowing Rf1 down to three candidate genes. Three new markers, one co-dominant marker 67N04_P and two dominant markers, PPR621.5R for restorer, and PPR621.5M for maintainer lines were developed and verified in the association panel of 59 sunflower lines. The versatility of the three newly developed markers, as well as of three existing markers for the restorer gene Rf1 (HRG01 and HRG02, Cleaved Amplified Polymorphic Sequence (CAPS)-marker H13), was analyzed in a large association panel consisting of 557 accessions.

Single nucleotide polymorphisms (SNPs) were significantly associated with fertility restoration of cytoplasmic male sterility (CMS) PET1 by the restorer gene Rf1. For these SNPs, four Kompetitive allele-specific PCR (KASP) markers were successfully designed. The KASP markers cover the fertility restorer locus Rf1, spanning about 3 Mb, and clearly differentiate restorer and maintainer lines. For genetic purity testing in sunflower hybrid production, the efficiency for detecting contaminations in samples was simulated using mixtures of hypocotyls or leaves. Contaminations of restorer lines with 1%, 3%, 5%, 10%, and 50% of maintainer lines were screened with all four KASP markers. Contaminations of 10% could be clearly detected in pools of 100 plants. Contaminations below this level require detection on a single plant level. For single plant detections, ethyl methanesulfonate-treated sunflower F1 hybrids, which had been phenotypically evaluated for male sterility (potential mutation in the Rf1 gene) were screened. Nine identified either partially male-sterile or male-sterile plants were analyzed with all four KASP markers and only one proved to be a hybrid with a mutation, seven were male-sterile contaminants in the F1 seeds used (1.6%) and one a recombinant plant. The four KASP markers should be valuable tools for marker-assisted selection (MAS) in sunflower breeding regarding the restorer locus Rf1.

Due to the increased demand for sunflower production, its breeding assignment is the intensification of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunflower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artificial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunflower oil yield but it is highly dependable on weather conditions that affect the oil content and seed yield. Up to our knowledge, this is the first study in which ML was used for sunflower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most effective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.