Transport properties of heterogeneous materials derived from Gaussian random fields: Bounds and Simulation

We investigate the effective conductivity ($\sigma_e$) of a class of amorphous media defined by the level-cut of a Gaussian random field. The three point solid-solid correlation function is derived and utilised in the evaluation of the Beran-Milton bounds. Simulations are used to calculate $\sigma_e$ for a variety of fields and volume fractions at several different conductivity contrasts. Relatively large differences in $\sigma_e$ are observed between the Gaussian media and the identical overlapping sphere model used previously as a `model' amorphous medium. In contrast $\sigma_e$ shows little variability between different Gaussian media.Comment: 15 pages, 14 figure

Pore size regulates operating stomatal conductance, while stomatal densities drive the partitioning of conductance between leaf sides

Leaf gas exchange is influenced by stomatal size, density, distribution between the leaf adaxial and abaxial sides, as well as by pore dimensions. This study aims to quantify which of these traits mainly underlie genetic differences in operating stomatal conductance (gs) and addresses possible links between anatomical traits and regulation of pore width. Stomatal responsiveness to desiccation, gs-related anatomical traits of each leaf side and estimated gs (based on these traits) were determined for 54 introgression lines (ILs) generated by introgressing segments of Solanum pennelli into the S. lycopersicum ‘M82’. A quantitative trait locus (QTL) analysis for stomatal traits was also performed. A wide genetic variation in stomatal responsiveness to desiccation was observed, a large part of which was explained by stomatal length. Operating gs ranged over a factor of five between ILs. The pore area per stomatal area varied 8-fold among ILs (2–16 %), and was the main determinant of differences in operating gs between ILs. Operating gs was primarily positioned on the abaxial surface (60–83 %), due to higher abaxial stomatal density and, secondarily, to larger abaxial pore area. An analysis revealed 64 QTLs for stomatal traits in the ILs, most of which were in the direction of S. pennellii. The data indicate that operating and maximum gs of non-stressed leaves maintained under stable conditions deviate considerably (by 45–91 %), because stomatal size inadequately reflects operating pore area (R2 = 0·46). Furthermore, it was found that variation between ILs in both stomatal sensitivity to desiccation and operating gs is associated with features of individual stoma. In contrast, genotypic variation in gs partitioning depends on the distribution of stomata between the leaf adaxial and abaxial epidermis

Preface

One of the current challenges in plant biology is the development of quantitative phenotyping approaches to link the genotype and the environment to plant structural, functional, and yield characteristics in order to meet the growing demands for sustainable food, feed, and fuel. The genotype of a plant consists of all of the hereditary information within the individual, whilst the phenotype, which represents the morphological, physiological, anatomical, and developmental characteristics, is the result of the interaction between the genotype and the environment. Understanding this interaction is one of the major challenges in plant sciences. In plant breeding, the ultimate goal is the improvement of traits of agricultural importance related to disease resistance, high yields, and the plant’s ability to grow in unfavourable environmental conditions. Currently, breeding approaches produce an annual yield increase of approximately 1% for major crops, which is below the over 2% increase needed to meet the global demands for food by 2050 (Ray et al., 2013).Rapid developments in plant molecular biology and in molecular-based breeding techniques have resulted in an increasing number of species being sequenced and large collections of mutants, accessions, and recombinant lines allowing detailed analysis of gene functions. High-definition genotyping can now be carried out on thousands of plants in an automated way at continuously decreasing costs, thereby facilitating association genetics and the determination of multi-parental quantitative trait loci (QTLs) (Poland and Rife, 2012). For transcriptomic, proteomic, and metabolomic analyses large, often robotized, platforms are available allowing detailed characterization of the biochemical status of plants at a reasonable cost (Ehrhardt and Frommer, 2012). By contrast, an understanding of the link between genotype and phenotype has progressed more slowly and is the major limiting step i

From genes to policy: mission-oriented governance of plant-breeding research and technologies

Mission-oriented governance of research focuses on inspirational, yet attainable goals and targets the sustainable development goals through innovation pathways. We disentangle its implications for plant breeding research and thus impacting the sustainability transformation of agricultural systems, as it requires improved crop varieties and management practices. Speedy success in plant breeding is vital to lower the use of chemical fertilizers and pesticides, increase crop resilience to climate stresses and reduce postharvest losses. A key question is how this success may come about? So far plant breeding research has ignored wider social systems feedbacks, but governance also failed to deliver a set of systemic breeding goals providing directionality and organization to research policy of the same. To address these challenges, we propose a heuristic illustrating the core elements needed for governing plant breeding research: Genetics, Environment, Management and Social system (GxExMxS) are the core elements for defining directions for future breeding. We illustrate this based on historic cases in context of current developments in plant phenotyping technologies and derive implications for governing research infrastructures and breeding programs. As part of mission-oriented governance we deem long-term investments into human resources and experimental set-ups for agricultural systems necessary to ensure a symbiotic relationship for private and public breeding actors and recommend fostering collaboration between social and natural sciences for working towards transdisciplinary collaboration

Measuring the dynamic photosynthome

Background: Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes inenvironmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The ‘dynamic’ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope: It is known that photosynthesis does not respond in a timely manner, and even a small temporal “mismatch” between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions: There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of ‘phenomics’ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the ‘photosynthome’. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved