Luisa Miguel Trindade

The construction industry is a major contributor to climate change, due to the extensive use of non-renewable materials, such as concrete and steel. Bio-based materials manufactured from diverse plant biomass sources – mainly wood, lignocellulosic biomass, and plant fibres – offer sustainable alternatives, potentially transforming buildings into net carbon sinks. However, the establishment of effective value chains for the provision and deployment of biomass in “largely bio-based” houses (i.e. houses with main elements made up of bio-based materials) is still far from being reached. This depends largely on the level of optimisation of bio-based vs conventional construction materials. In this context, this opinion paper explores the feasibility of building “largely bio-based” houses by discussing both the availability and the diverse functional roles that different biomass types from diverse plant species can have in construction applications. Moreover, the article highlights current research challenges in the supply of high-quality biomass for “bio-based houses”. Finally, it discusses how the effective integration of plant science, material engineering, as well as environmental and economic research in trans-disciplinary research efforts is key to set up operational and self-standing bio-based construction value chains.

Miscanthus breeding programs have focused on developing intraspecific (M. sinensis × M. sinensis) and interspecific (M. sinensis × M. sacchariflorus) seed‐based hybrids with distinct cell wall characteristics for different biomass value chains. Here, we evaluated the performance of 13 novel hybrids (including seed‐based intraspecific, seed‐based interspecific, and one clonally propagated interspecific hybrid) relative to Miscanthus × giganteus (M × g). We compared the cell wall composition, saccharification efficiency, and yield after spring harvests in 2021 and 2022 across six European locations. Cell wall content and composition varied significantly among hybrids and were influenced by environmental conditions, yet differences due to parental background were largely consistent across locations. On average, seed‐based interspecific hybrids (80.6%–84.0% neutral detergent fiber) had a lower total cell wall content than the other hybrids evaluated in this study (88.3%–90.8%). In contrast, cellulose was ~5.5% higher in hybrids with an M. sinensis × M. sacchariflorus background relative to the intraspecific hybrids, while hemicellulose averaged above 34% for intraspecific hybrids, 29.4% to 31.8% in the interspecific hybrids, and below 27% for M × g. Lignin content was highest in M × g (~13.8%), intermediate in the interspecific hybrids (11.0%–12.2%), and lowest in the intraspecific hybrids (~10%). These compositional traits translated into saccharification efficiencies that were 32.9% higher for the intraspecific hybrids and 9.8%–13.1% higher for the interspecific hybrids (seed‐based and clonally propagated) compared to M × g. Accounting for biomass yield, either several seed‐based hybrids or the novel clonally propagated hybrid exceeded the theoretical ethanol potential of M × g at all trial locations, indicating strong potential for their use in lignocellulosic biofuel production.

BACKGROUND: Most components from the roots of sugar beet ( Beta vulgaris ssp. vulgaris ) are valorized by industry. However, the leaves are currently left on the field, even though they contain large amounts of protein. To support leaf protein valorization, high‐throughput methods and phenotyping tools were developed to facilitate the selection of beet varieties with superior protein production. This study presents high‐throughput methods to measure total protein content in leaves and to determine the amount of soluble protein extracted through pressing, enabling the calculation of leaf protein extractability. RESULTS: The influence of harvested leaf sample size and within‐plant leaf selection on protein measurements is demonstrated. Representative samples of a plot entailed collection of the middle leaves from a minimum of 25 plants. To determine total leaf protein, a near‐infrared‐based model was developed, exhibiting excellent predictive performance. Protein measurements on leaf protein extracts, based on total nitrogen, were found to correlate strongly with RuBisCO quantification obtained through size‐exclusion chromatography. Non‐protein nitrogenous compounds were measured to assess their impact on protein estimates derived from total nitrogen measurements. A strong correlation between total nitrogen and proteinogenic nitrogen was observed, confirming total nitrogen as a reliable indicator of true protein content in sugar beet leaves. CONCLUSION: This study provided high‐throughput methods for assessing leaf protein content and extractability in sugar beet. The strong correlation between total nitrogen and true protein confirms their reliability for protein quantification. These findings aid efficient screening of sugar beet germplasm for improved leaf protein yield, contributing to sustainable leaf valorization. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Establishing faba bean (Vicia faba L.) as a major protein crop in Europe requires developing high-yielding varieties. However, the genetic regulation of yield-related traits is currently under-explored. These traits can be improved by exploiting the extensive but largely uncharacterized faba bean germplasm. Our study aimed to identify associations between 38,014 single nucleotide polymorphisms (SNPs) and flowering time (FT), plant height (PH), pod length (PL), seeds per pod (SP), and single seed weight (SSW) in 245 faba bean accessions (CGN panel) via a Genome-Wide Association Study (GWAS). The accessions were grown in 2021 and 2022 in the Netherlands. Additionally, we developed genomic selection (GS) models to predict the genetic merit within large germplasm collections for the mentioned traits, as well as yield (YLD). The CGN panel was an optimal panel for performing high-resolution GWAS, showing large phenotypic variation, high narrow-sense heritability for all traits, and minimal genetic relatedness among accessions. Population structure analysis revealed the presence of four genetic groups. GWAS uncovered 33 SNP-trait associations in 2021 and 17 in 2022. We identified one stable QTL for FT and four for SSW over the two years, representing key molecular markers for testing in breeding applications. Short linkage disequilibrium decay (~ 268 Kbp) facilitated the identification of several important candidate genes with interesting homologs in other crops. Ten SNPs in 2021 and five in 2022 were predicted to be intra-genic missense variants, potentially altering protein function. Moreover, modeling the SNP effect simultaneously via Bayesian GS showed promising predictive ability (PA) and prediction accuracy (ACC), supporting their potential application in germplasm-improvement programs. Predictive ability ranged from 0.58 to 0.81 in 2021, and 0.47 to 0.85 in 2022 for different traits. Additionally, across-year predictions showed stable PA. GWAS revealed promising QTLs for use in molecular breeding and highlighted new candidate genes. Interestingly, the prediction of intra-genic SNPs categorized 15 SNPs as putatively affecting protein function. Moreover, we demonstrated for the first time in faba bean that GS has the potential to unlock untapped diversity in genebank collections and accelerate trait integration into faba bean breeding programs.

Faba bean (Vicia faba L.) is a valuable ingredient in plant-based foods such as meat and dairy analogues. However, its typical taste and aroma are considered off-flavours in these food applications, representing a bottleneck during processing. Breeding is needed to develop varieties with minimal off-flavours and high protein content. The genetic regulation of these traits is underexplored. To dissect their genetic architecture, we performed a genome-wide association study (GWAS). A total of 245 faba bean accessions (the CGN population) were genotyped using the 90K-SPET targeted assay. These accessions were phenotyped in 2021 and 2022 in the Netherlands for protein, oil, fatty acids, lipid-derived products, phenolic acids, flavonoids, and tannins. The CGN population showed large phenotypic variation and moderate-to-high narrow-sense heritability for most traits. The growing environment significantly affected all traits, with trait-specific genotype-by-year (GxY) interactions. Condensed tannins and fatty acids were the most stable across the two years and had the highest heritability estimates (h² > 0.6). GWAS identified a total of 148 single nucleotide polymorphisms (SNPs) loci in 2021 and 167 in 2022. Key candidate regulators included genes involved in lipid biosynthesis (ATS2, KAS, LPP), amino acid transport (CAT4) for protein storage, zero tannins locus-1 (zt-1), and regulators of the phenylpropanoid pathway, such as a shikimate kinase gene and transcription factors bHLH137-like and MYB. These results pave the way for validation studies and biotechnological applications to improve the quality of faba bean-based foods.