Martina Huber

PlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use‐case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEAS: PlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. PlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. The software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science.

Societal Impact Statement: Agricultural production systems in the global North combine monocultures of specialised varieties and breeds with external interventions and inputs. Increasing the diversity of varieties, breeds and species may increase the system's resilience to external pressures through beneficial interactions. However, more diverse agricultural systems actually show large variation in their response to stresses. We propose to use functional‐structural modelling, alternating with field experiments, to optimise the design of biodiverse systems and validate predicted interactions under stresses. This information can then be used to design biodiverse production systems that are more resilient and guide crop variety and livestock breeding for them. Summary: Many arable, horticultural, livestock and forestry production systems are optimised for productivity in monocultures, with environmental factors managed primarily through external interventions and inputs (fertilizers, herbicides, pesticides, antibiotics, irrigation). Enhancing system resilience may be achieved by (re)introducing diversity within and between species and breeds of crops, livestock and trees. While the ecological concepts underlying the effect of biodiversity on resilience are known, in practice the response of more biodiverse agricultural production systems to external pressures shows large variation. Hence, resilient production systems do not emerge automatically from greater diversity but must be designed. Studying the specific combining advantages of different species/varieties/breeds at the production system level may provide the required information, but the number of possible combinations and interactions is too large to screen with controlled field experiments only. We argue that modelling provides a solution to this combinatorial problem by allowing for an in silico exhaustive search of possible interactions. We describe various crop models and discuss the use of functional‐structural models for (re‐)designing production systems, specifying the functional traits to be selected for in crop variety and livestock breeding. While process‐based functional‐structural models can predict how genotypes will respond to environmental stressors, experimental trials measure these responses in the field. An iterative process– where models inform experiments and experiments, in turn, refine models– may lead to a nuanced understanding of resilience mechanisms and a robust set of tools for designing diversified production systems. We discuss opportunities and pitfalls of this combined approach.

Leaf photosynthesis models are used extensively in photosynthesis research and are embedded in many larger scale models. Typical photosynthesis models simplify light intensity as the integrated intensity over the 400–700 nm waveband (photosynthetic active radiation, PAR). However, far‐red light (700–750 nm, FR) also drives photosynthesis when supplied in addition to light within the PAR spectrum. Currently, it is unknown how much far‐red light contributes to carbon assimilation under various spectral light conditions. We developed a combined experimental and computational method to quantify FR stimulation. Gas‐exchange parameters and incident light spectra were measured simultaneously and analysed with wavelength‐dependent modelling of light harvesting. Hereto, separate excitation of Photosystem I and Photosystem II was calculated from incident light spectra. The effect of FR supplementation on photosynthesis was subsequently modelled and expressed as a single parameter ρ. We tested our method on Solanum dulcamara, Lactuca sativa and Phaseolus vulgaris under various light conditions. Results show consistent ρ‐values across a range of FR levels. Our method provides an approach to consistently quantify the effect of FR stimulation on photosynthesis and harmonise the interpretation of photosynthesis measurements under different light regimes, for example in (experimental) setups with artificial FR supplementation or in canopies.

Societal Impact Statement: Rice farming is transitioning from transplanting rice seedlings towards the less labour‐intensive and less water‐demanding method of directly seeding rice. This, however, is accompanied by increased weed proliferation. To tackle this issue, this study seeks to identify how the crop itself can better suppress weeds, with a focus on light competition via shading. Using a rice diversity panel, traits were identified that contribute to enhanced shading capacity, and these traits were encapsulated into a single shading capacity metric. This was followed by the identification of the genetic loci underpinning variation in the core traits. The identified haplotypes can be used in breeding programmes to improve weed suppression by rice, thus contributing to sustainable agriculture.Summary: In modern rice farming, one of the major constraints is weed proliferation and the entailed ecological impact of herbicide application. This requires increased weed competitiveness in current rice varieties, achieved via enhanced shade casting to limit the growth of shade‐sensitive weeds. To identify traits that increase rice shading capacity, we exhaustively phenotyped a rice diversity panel of 344 varieties at an early vegetative stage. A genome‐wide association study (GWAS) revealed genetic loci underlying variation in canopy architecture traits linked with shading capacity. The screen shows considerable natural variation in shoot architecture for 13 examined traits, of which shading potential is mostly determined by projected shoot area, number of leaves, culm height and canopy solidity. The shading rank, a metric based on these core traits, identifies varieties with the highest shading potential. Five genetic loci were found to be associated with canopy architecture, shading potential and early vigour. Identification of traits contributing to shading capacity and underlying allelic variation will serve future genomic‐assisted breeding programmes. Implementing the presented genetic resources for increased shading and weed competitiveness in rice breeding will make its farming less dependent on herbicides and contribute towards more environmentally sustainable agriculture.

Plant responses to abiotic stresses are complex and dynamic, and involve changes in different traits, either as the direct consequence of the stress, or as an active acclimatory response. Abiotic stresses frequently occur simultaneously or in succession, rather than in isolation. Despite this, most studies have focused on a single stress and single or few plant traits. To address this gap, our study comprehensively and categorically quantified the individual and combined effects of three major abiotic stresses associated with climate change (flooding, progressive drought and high temperature) on 12 phenotypic traits related to morphology, development, growth and fitness, at different developmental stages in four Arabidopsis thaliana accessions. Combined sub-lethal stresses were applied either simultaneously (high temperature and drought) or sequentially (flooding followed by drought). In total, we analyzed the phenotypic responses of 1782 individuals across these stresses and different developmental stages. Overall, abiotic stresses and their combinations resulted in distinct patterns of effects across the traits analyzed, with both quantitative and qualitative differences across accessions. Stress combinations had additive effects on some traits, whereas clear positive and negative interactions were observed for other traits: 9 out of 12 traits for high temperature and drought, 6 out of 12 traits for post-submergence and drought showed significant interactions. In many cases where the stresses interacted, the strength of interactions varied across accessions. Hence, our results indicated a general pattern of response in most phenotypic traits to the different stresses and stress combinations, but it also indicated a natural genetic variation in the strength of these responses. Overall, our study provides a rich characterization of trait responses of Arabidopsis plants to sub-lethal abiotic stresses at the phenotypic level and can serve as starting point for further in-depth physiological research and plant modelling efforts.

Rice feeds more than half of the world’s human population. In modern rice farming, a major constraint for productivity is weed proliferation and the ecological impact of herbicide application. Increased weed competitiveness of commercial rice varieties requires enhanced shade casting to limit growth of shade-sensitive weeds and the need for herbicide. We aimed to identify traits that enhance rice shading capacity based on the canopy architecture and the underlying genetic components. We performed a phenotypic screen of a rice diversity panel comprised of 344 varieties, examining 13 canopy architecture traits linked with shading capacity in 4-week-old plants. The analysis revealed a vast range of phenotypic variation across the diversity panel. We used trait correlation and clustering to identify core traits that define shading capacity to be shoot area, number of leaves, culm and solidity (the compactness of the shoot). To simplify the complex canopy architecture, these traits were combined into a Shading Rank metric that is indicative of a plant’s ability to cast shade. Genome wide association study (GWAS) revealed genetic loci underlying canopy architecture traits, out of which five loci were substantially contributing to shading potential. Subsequent haplotype analysis further explored allelic variation and identified seven haplotypes associated with increased shading. Identification of traits contributing to shading capacity and underlying allelic variation presented in this study will serve future genomic assisted breeding programmes. The investigated diversity panel, including widely grown varieties, shows that there is big potential and genetic resources for improvement of elite breeding lines. Implementing increased shading in rice breeding will make its farming less dependent on herbicides and contribute towards more environmentally sustainable agriculture. One sentence summary Through screening a rice diversity panel for variation in shoot architecture, we identified traits corresponding to plant shading potential and their genetic constituents.

Plants growing at high densities interact via a multitude of pathways. Here, we provide an overview of mechanisms and functional consequences of plant architectural responses initiated by light cues that occur in dense vegetation. We will review the current state of knowledge about shade avoidance, as well as its possible applications. On an individual level, plants perceive neighbour‐associated changes in light quality and quantity mainly with phytochromes for red and far‐red light and cryptochromes and phototropins for blue light. Downstream of these photoreceptors, elaborate signalling and integration takes place with the PHYTOCHROME INTERACTING FACTORS, several hormones and other regulators. This signalling leads to the shade avoidance responses, consisting of hyponasty, stem and petiole elongation, apical dominance and life cycle adjustments. Architectural changes of the individual plant have consequences for the plant community, affecting canopy structure, species composition and population fitness. In this context, we highlight the ecological, evolutionary and agricultural importance of shade avoidance.

"The effectiveness of OSCE Missions" published on 01 Jan 2003 by Brill | Nijhoff.