Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agrifood and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.

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In wheat and other grain species, variations in kernel size are related to variations in ovary size. It is not known whether environmental factors that are known to affect kernel size may also affect ovary size, thus maintaining the kernel size‐ovary size correlation. Additionally, it is not known whether variations in ovary size in wheat depend on cell size, cell number or both. In this study, ovary size was measured with a stereomicroscope, and ovary wall cell size and number were measured in equatorial cross‐sectional areas of the ovary wall in two common wheat cultivars with different kernel size: Bora (large kernels) and Bologna (small kernels). Plants were grown at low and high N fertilization (0 and 240 kg ha−1) with normal density (450 seeds m−2), and at low and high plant density (200 and 650 plants m−2) with normal fertilization (160 kg N ha−1). The variation in ovary wall size across all data pooled (i.e., including two genotypes, two N and two density treatments, and including within treatment variations), was related to cell number and not to the cell size, which did not vary significantly with cultivar and field practices. With increasing ovule size, ovary wall size increased less than proportionally, thus the ovule/ovary ratio increased. Cell size in the ovary wall showed an upper limit across all treatments, which decreased with increasing cell number. These findings may be useful for breeding programs and field practices aimed at increasing kernel size and yield.

Grasses (Poaceae) are very common plants, which are widespread in all environments and urban areas. Despite their economical importance, they can represent a problem to humans due to their abundant production of allergenic pollen. Detailed information about the pollen season for these species is needed in order to plan adequate therapies and to warn allergic people about the risks they take in certain areas at certain moments. Moreover, precise identification of the causative species and their allergens is necessary when the patient is treated with allergen‐specific immunotherapy. The intrafamily morphological similarity of grass pollen grains makes it impossible to distinguish which particular species is present in the atmosphere at a given moment. This study aimed at developing new biomolecular tools to analyze aerobiological samples and identifying major allergenic Poaceae taxa at subfamily or species level, exploiting fast real‐time PCR. Protocols were tested for DNA extraction from pollen sampled with volumetric and gravimetric methods. A fragment of the matK plastidial gene was amplified and sequenced in Poaceae species known to have high allergological impact. Species‐ and subfamily‐specific primer–probe systems were designed and tested in fast real‐time PCRs to evaluate the presence of these taxa in aerobiological pollen samples. Species‐specific systems were obtained for four of five studied species. A primer–probe set was also proposed for the detection of Pooideae (a grass subfamily that includes also major cereal grains) in aerobiological samples, as this subfamily includes species carrying both grass allergens from groups 1 and 5. These, among the 11 groups in which grass pollen allergens are classified, are considered responsible for the most frequent and severe symptoms.