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Sunflower (Helianthus annuus L.) crop is often labelled as environmental-friendly for many objective reasons: limited amounts of N fertiliser, no irrigation, and limited use of pesticides. In addition, sunflower has a potential for providing multiple ecosystem services in diverse cropping systems (e.g. pollinators feeding). However agroecological innovations have been less developed or disseminated than for cereals or oilseed rape. Based on results from the sunflower research consortium in Toulouse (Mestries and Debaeke. 2016. Journées d’échanges Tournesol, 28 et 29 juin 2016, Toulouse (France)), we illustrate some innovating and promising approaches for more agroecological practices in sunflower cropping. Our results suggested that: integrated crop management could be proposed to limit the use of pesticides and mitigate crop damages; cover crops could be used as biofumigants to control soilborne diseases in sunflower; intercropping sunflower with soybean could be a valuable option for maximizing resource-use efficiency in low-input environments; sunflower yield could be maintained at good level in very low input cropping systems. Previous examples point out how agroecological principles could be applied to sunflower crop to improve its production in low-input conditions, and enhance the ecosystem services deliverable by this oilseed crop.

In order to make more efficient plant breeding and gain in competitiveness, the sector of oil-protein crops decided to intensify agronomic research on sunflower crop. The “Sunflower” Joint Technological Unit (Unité Mixte Technologique (UMT) “Tournesol”, in French) was launched in the Toulouse area in 2006, associating closely INRA and Terres Inovia. First focused on improving oil production through an agronomic approach, the UMT was renewed in 2011 with a broader partnership and a more assertive orientation towards the development of decision-making tools. The objective of this paper is to highlight the relevance and productivity of this user-oriented research facility. The main results relate to (i) the co-construction of a simulation model (SUNFLO) that can be parameterized and manipulated by Terres Inovia engineers, (ii) the joint exploration of supra-field scales and new methods for agronomic diagnosis and yield forecasting based on remote sensing, (iii) the tuning and dissemination of operational decision rules, (iv) the production of essential knowledge on emergent and/or damaging fungal diseases, as well as on complex interactions between genotype, environment and crop management. After a concluding symposium in 2016, new requests for sunflower research were formulated by the participants. They also advocated for a diversification of crops to consider in order to better meet the needs of the whole oil-protein sector.

Author(s): Hlne Badouin [1]; Jrme Gouzy [1]; Christopher J. Grassa [1, 2]; Florent Murat [3]; S. Evan Staton [2]; Ludovic Cottret [1]; Christine Lelandais-Brire [4, 5]; Gregory L. Owens [2]; [...]

Key message: This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Abstract: Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids. [ABSTRACT FROM AUTHOR]

Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.