A wide range of species can be sown as cover crops during fallow periods to provide various ecosystem services. Plant establishment is a key stage, especially when sowing occurs in summer with high soil temperatures and low water availability. The aim of this study was to determine the response of germination to temperature and water potential for diverse cover crop species. Based on these characteristics, we developed contrasting functional groups that group species with the same germination ability, which may be useful to adapt species choice to climatic sowing conditions. Germination of 36 different species from six botanical families was measured in the laboratory at eight temperatures ranging from 4.5–43°C and at four water potentials. Final germination percentages, germination rate, cardinal temperatures, base temperature and base water potential were calculated for each species. Optimal temperatures varied from 21.3–37.2°C, maximum temperatures at which the species could germinate varied from 27.7–43.0°C and base water potentials varied from -0.1 to -2.6 MPa. Most cover crops were adapted to summer sowing with a relatively high mean optimal temperature for germination, but some Fabaceae species were more sensitive to high temperatures. Species mainly from Poaceae and Brassicaceae were the most resistant to water deficit and germinated under a low base water potential. Species were classified, independent of family, according to their ability to germinate under a range of temperatures and according to their base water potential in order to group species by functional germination groups. These groups may help in choosing the most adapted cover crop species to sow based on climatic conditions in order to favor plant establishment and the services provided by cover crops during fallow periods. Our data can also be useful as germination parameters in crop models to simulate the emergence of cover crops under different pedoclimatic conditions and crop management practices.

Enhancing the knowledge on the genetic basis of germination and heterotrophic growth at extreme temperatures is of major importance for improving crop establishment. A quantitative trait loci (QTL) analysis was carried out at sub- and supra-optimal temperatures at these early stages in the model Legume Medicago truncatula. On the basis of an ecophysiological model framework, two populations of recombinant inbred lines were chosen for the contrasting behaviours of parental lines: LR5 at sub-optimal temperatures (5 or 10°C) and LR4 at a supra-optimal temperature (20°C). Seed masses were measured in all lines. For LR5, germination rates and hypocotyl growth were measured by hand, whereas for LR4, imbibition and germination rates as well as early embryonic axis growth were measured using an automated image capture and analysis device. QTLs were found for all traits. The phenotyping framework we defined for measuring variables, distinguished stages and enabled identification of distinct QTLs for seed mass (chromosomes 1, 5, 7 and 8), imbibition (chromosome 4), germination (chromosomes 3, 5, 7 and 8) and heterotrophic growth (chromosomes 1, 2, 3 and 8). The three QTL identified for hypocotyl length at sub-optimal temperature explained the largest part of the phenotypic variation (60% together). One digenic interaction was found for hypocotyl width at sub-optimal temperature and the loci involved were linked to additive QTLs for hypocotyl elongation at low temperature. Together with working on a model plant, this approach facilitated the identification of genes specific to each stage that could provide reliable markers for assisting selection and improving crop establishment. With this aim in view, an initial set of putative candidate genes was identified in the light of the role of abscissic acid/gibberellin balance in regulating germination at high temperatures (e.g. ABI4, ABI5), the molecular cascade in response to cold stress (e.g. CBF1, ICE1) and hypotheses on changes in cell elongation (e.g. GASA1, AtEXPA11) with changes in temperatures based on studies at the whole plant scale.

BackgroundThe traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor intensive and requiring experienced seed analysts. Thus, the objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea (Vigna unguiculata) seeds. An automated computer-vision germination system was utilized for uninterrupted monitoring of seeds during imbibition and germination to identify different categories of all individual seeds. By using spectral signatures of single cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discriminant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination.ResultsThe results revealed that the LDA models had good accuracy in distinguishing ‘Aged’ and ‘Non-aged’ seeds with an overall correct classification (OCC) of 97.51, 96.76 and 97%, ‘Germinated’ and ‘Non-germinated’ seeds with OCC of 81.80, 79.05 and 81.0%, ‘Early germinated’, ‘Medium germinated’ and ‘Dead’ seeds with OCC of 77.21, 74.93 and 68.00% and among seeds that give ‘Normal’ and ‘Abnormal’ seedlings with OCC of 68.08, 64.34 and 62.00% in training, cross-validation and independent validation data sets, respectively. Image processing routines were also developed to exploit the full power of the multispectral imaging system in visualizing the difference among seed categories by applying the discriminant model in a pixel-wise manner.ConclusionThe results demonstrated the capability of the multispectral imaging system in the ultraviolet, visible and shortwave near infrared range to provide the required information necessary for the discrimination of individual cowpea seeds to different classes. Considering the short time of image acquisition and limited sample preparation, this stat-of-the art multispectral imaging method and chemometric analysis in classifying seeds could be a valuable tool for on-line classification protocols in cost-effective real-time sorting and grading processes as it provides not only morphological and physical features but also chemical information for the seeds being examined. Implementing image processing algorithms specific for seed quality assessment along with the declining cost and increasing power of computer hardware is very efficient to make the development of such computer-integrated systems more attractive in automatic inspection of seed quality.

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