The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities

Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field

Breeding for resilience to climate change requires considering adaptive traits such as plant architecture, stomatal conductance and growth, beyond the current selection for yield. Robotized indoor phenotyping allows measuring such traits at high throughput for speed breeding, but is often considered as non-relevant for field conditions. Here, we show that maize adaptive traits can be inferred in different fields, based on genotypic values obtained indoor and on environmental conditions in each considered field. The modelling of environmental effects allows translation from indoor to fields, but also from one field to another field. Furthermore, genotypic values of considered traits match between indoor and field conditions. Genomic prediction results in adequate ranking of genotypes for the tested traits, although with lesser precision for elite varieties presenting reduced phenotypic variability. Hence, it distinguishes genotypes with high or low values for adaptive traits, conferring either spender or conservative strategies for water use under future climates.

More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop

Using in silico experiment in crop model, we identified different physiological regulations of yield and yield stability, as well as quantify the genotype and environment numbers required for analysing yield stability convincingly. Identifying target traits for breeding stable and high-yielded cultivars simultaneously is difficult due to limited knowledge of physiological mechanisms behind yield stability. Besides, there is no consensus about the adequacy of a stability index (SI) and the minimal number of environments and genotypes required for evaluating yield stability. We studied this question using the crop model APSIM-Wheat to simulate 9100 virtual genotypes grown under 9000 environments. By analysing the simulated data, we showed that the shape of phenotype distributions affected the correlation between SI and mean yield and the genotypic superiority measure (P_i) was least affected among 11 SI. P_i was used as index to demonstrate that more than 150 environments were required to estimate yield stability of a genotype convincingly and more than 1000 genotypes were necessary to evaluate the contribution of a physiological parameter to yield stability. Network analyses suggested that a physiological parameter contributed preferentially to yield or P_i. For example, soil water absorption efficiency and potential grain filling rate explained better the variations in yield than in P_i; while light extinction coefficient and radiation use efficiency were more correlated with P_i than with yield. The high number of genotypes and environments required for studying P_i highlight the necessity and potential of in silico experiments to better understand the mechanisms behind yield stability.

Linking quantitative genetics with community-level performance: Are there operational models for plant breeding?

Plant breeding is focused on the genotype and population levels while targeting effects at higher levels of biodiversity, from crop covers to agroecosystems. Making predictions across nested levels of biodiversity is therefore a major challenge for the development of intercropping practices. New prediction tools and concepts are required to design breeding strategies with desirable outcomes at the crop community level. We reviewed theoretical advances in the field of evolutionary ecology to identify potentially operational ways of predicting the effects of artificial selection on community-level performances. We identified three main types of approaches differing in the way they model interspecific indirect genetic effects (IIGEs) at the community level: (1) The community heritability approach estimates the variance for IIGE induced by a focal species at the community level; (2) the joint phenotype approach quantifies genetic constraints between direct genetic effects and IIGE for a set of interacting species; (3) the community-trait genetic gradient approach decomposes the IIGE for a focal species across a multivariate set of its functional traits. We discuss the potential operational capacities of these approaches and stress that each is a special case of a general multitrait and multispecies selection index. Choosing one therefore involves assumptions and goals regarding the breeding target and strategy. Obtaining reliable quantitative, community-level predictions at the genetic level is constrained by the size and complexity of the experimental designs usually required. Breeding strategies should instead be compared using theoretically informed qualitative predictions. The need to estimate genetic covariances between traits measured both within and among species (for IIGE) is another obstacle, as the two are not determined by the exact same biological processes. We suggest future research directions and strategies to overcome these limits. Our synthesis offers an integrative theoretical framework for breeders interested in the genetic improvement of crop communities but also for scientists interested in the genetic bases of plant community functioning.

Long-Amplicon Single-Molecule Sequencing Reveals Novel, Trait-Associated Variants of VERNALIZATION1 Homoeologs in Hexaploid Wheat

The gene VERNALIZATION1 (VRN1) is a key controller of vernalization requirement in wheat. The genome of hexaploid wheat (Triticum aestivum) harbors three homoeologous VRN1 loci on chromosomes 5A, 5B, and 5D. Structural sequence variants including small and large deletions and insertions and single nucleotide polymorphisms (SNPs) in the three homoeologous VRN1 genes not only play an important role in the control of vernalization requirement, but also have been reported to be associated with other yield related traits of wheat. Here we used single-molecule sequencing of barcoded long-amplicons to assay the full-length sequences (∼13 kbp plus 700 bp from the promoter sequence) of the three homoeologous VRN1 genes in a panel of 192 predominantly European winter wheat cultivars. Long read sequences revealed previously undetected duplications, insertions and single-nucleotide polymorphisms in the three homoeologous VRN1 genes. All the polymorphisms were confirmed by Sanger sequencing. Sequence analysis showed the predominance of the winter alleles vrn-A1, vrn-B1, and vrn-D1 across the investigated cultivars. Associations of SNPs and structural variations within the three VRN1 genes with 20 economically relevant traits including yield, nodal root-angle index and quality related traits were evaluated at the levels of alleles, haplotypes, and copy number variants. Cultivars carrying structural variants within VRN1 genes showed lower grain yield, protein yield and biomass compared to those with intact genes. Cultivars carrying a single vrn-A1 copy and a unique haplotype with a high number of SNPs were found to have elevated grain yield, kernels per spike and kernels per m2 along with lower grain sedimentation values. In addition, we detected a novel SNP polymorphism within the G-quadruplex region of the promoter of vrn-A1 that was associated with deeper roots in winter wheat. Our findings show that multiplex, single-molecule long-amplicon sequencing is a useful tool for detecting variants in target genes within large plant populations, and can be used to simultaneously assay sequence variants among target multiple gene homoeologs in polyploid crops. Numerous novel VRN1 haplotypes and alleles were identified that showed significantly associations to economically important traits. These polymorphisms were converted into PCR or KASP assays for use in marker-assisted breeding.

Effect of extracellular hyperosmolality during normothermia and hyperthermia on the autophagic response in peripheral blood mononuclear cells from young men

Heat-stress induced dehydration is associated with extracellular hyperosmolality. To counteract the associated stress, cells employ cytoprotective mechanisms, including autophagy, however, the autophagic response to hyperosmotic stress has yet to be evaluated in humans. Thus, we investigated autophagy and associated cellular stress pathways (the heat shock response [HSR], apoptosis, and the acute inflammatory response) to isosmotic and hyperosmotic conditions with and without hyperthermia in twelve young men (mean [SD]; 25 [5] years). Participants received a 90-min intravenous infusion of either isosmotic (ISO; 0.9% NaCl; serum osmolality of 293 [4] mOsm/kg) or hyperosmotic (HYP; 3.0% NaCl; 300 [6] mOsm/kg) saline, followed by passive whole-body heating using a water perfused suit to increase esophageal temperature by ~0.8⁰C. Peripheral blood mononuclear cells were harvested at baseline (pre-infusion), post-infusion, and after heating, and changes in protein content were analyzed via Western blotting. Post-infusion, the LC3-II/I ratio was higher in HYP compared to ISO infusion (p<0.001), although no other protein changes were observed (all p>0.050). Following passive heating, autophagy increased in HYP, as demonstrated by an increase in LC3-II from baseline (p=0.004) and an elevated LC3-II/I ratio compared to ISO (p=0.035), and a decrease in p62 when compared to the ISO condition (p=0.019). This was accompanied by an elevation in cleaved caspase-3 following heating in the HYP condition (p<0.010), however, the HSR and acute inflammatory response did not change under any condition (all p>0.050). Taken together, our findings indicate that serum hyperosmolality induces autophagy and apoptotic signaling during mild hyperthermia with minimal autophagic activation during normothermia.

Genome-wide identification of loci modifying spike-branching in tetraploid wheat

Genetic modification of spike architecture is essential for improving wheat yield. Newly identified loci for the 'Miracle wheat' phenotype on chromosomes 1AS and 2BS have significant effects on spike traits. The wheat (Triticum ssp.) inflorescence, also known as a spike, forms an unbranched inflorescence in which the inflorescence meristem generates axillary spikelet meristems (SMs) destined to become sessile spikelets. Previously, we identified the putatively causative mutation in the branched head^t (bh^t) gene (TtBH-A1) of tetraploid wheat (T. turgidum convar. compositum (L.f.) Filat.) responsible for the loss of SM identity, converting the non-branching spike to a branched wheat spike. In the current study, we performed whole-genome quantitative trait loci (QTL) analysis using 146 recombinant inbred lines (RILs) derived from a cross between spike-branching wheat ('Miracle wheat') and an elite durum wheat cultivar showing broad phenotypic variation for spike architecture. Besides the previously found gene at the bh^t-A1 locus on the short arm of chromosome 2A, we also mapped two new modifier QTL for spike-branching on the short arm of chromosome 1A, termed bh^t-A2, and 2BS. Using biparental mapping population and GWAS in 302 diverse accessions, the 2BS locus was highly associated with coding sequence variation found at the homoeo-allele of TtBH-B1 (bh^t-B1). Thus, RILs that combined both bh^t-A1 and bh^t-B1 alleles showed additive genetic effects leading to increased penetrance and expressivity of the supernumerary spikelet and/or mini-spike formation.

Sentinel-2 Data for Precision Agriculture?-A UAV-Based Assessment

An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R² = 0.82, mean absolute error = 0.52 m²/m²). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture.

Applications of UAS in Crop Biomass Monitoring: A Review

Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.