Background and Aims Predicting light partitioning in crop mixtures is a critical step in improving the productivity of such complex systems, and light interception has been shown to be closely linked to plant architecture. The aim of the present work was to analyse the relationships between plant architecture and light partitioning within wheat–pea (Triticum aestivum–Pisum sativum) mixtures. An existing model for wheat was utilized and a new model for pea morphogenesis was developed. Both models were then used to assess the effects of architectural variations in light partitioning. Methods First, a deterministic model (L-Pea) was developed in order to obtain dynamic reconstructions of pea architecture. The L-Pea model is based on L-systems formalism and consists of modules for ‘vegetative development’ and ‘organ extension’. A tripartite simulator was then built up from pea and wheat models interfaced with a radiative transfer model. Architectural parameters from both plant models, selected on the basis of their contribution to leaf area index (LAI), height and leaf geometry, were then modified in order to generate contrasting architectures of wheat and pea. Key results By scaling down the analysis to the organ level, it could be shown that the number of branches/tillers and length of internodes significantly determined the partitioning of light within mixtures. Temporal relationships between light partitioning and the LAI and height of the different species showed that light capture was mainly related to the architectural traits involved in plant LAI during the early stages of development, and in plant height during the onset of interspecific competition. Conclusions In silico experiments enabled the study of the intrinsic effects of architectural parameters on the partitioning of light in crop mixtures of wheat and pea. The findings show that plant architecture is an important criterion for the identification/breeding of plant ideotypes, particularly with respect to light partitioning.

Genetic studies and breeding of agricultural crops frequently involve phenotypic characterization of large collections of genotypes grown in field conditions. These evaluations are typically based on visual observations and manual (destructive) measurements. Robust image capture and analysis procedures that allow phenotyping large collections of genotypes in time series during developmental phases represent a clear advantage as they allow non-destructive monitoring of plant growth and performance. A L. perenne germplasm panel including wild accessions, breeding material and commercial varieties has been used to develop a low-cost, high-throughput phenotyping tool for determining plant growth based on images of individual plants during two consecutive growing seasons. Further we have determined the correlation between image analysis-based estimates of the plant’s base area and the capacity to regrow after cutting, with manual counts of tiller number and measurements of leaf growth 2 weeks after cutting, respectively. When working with field-grown plants, image acquisition and image segmentation are particularly challenging as outdoor light conditions vary throughout the day and the season, and variable soil colours hamper the delineation of the object of interest in the image. Therefore we have used several segmentation methods including colour-, texture- and edge-based approaches, and factors derived after a fast Fourier transformation. The performance of the procedure developed has been analysed in terms of effectiveness across different environmental conditions and time points in the season. The procedure developed was able to analyse correctly 77.2 % of the 24,048 top view images processed. High correlations were found between plant’s base area (image analysis-based) and tiller number (manual measurement) and between regrowth after cutting (image analysis-based) and leaf growth 2 weeks after cutting (manual measurement), with r values up to 0.792 and 0.824, respectively. Nevertheless, these relations depend on the origin of the plant material (forage breeding lines, current forage varieties, current turf varieties, and wild accessions) and the period in the season. The image-derived parameters presented here deliver reliable, objective data, complementary to the breeders’ scores, and are useful for genetic studies. Furthermore, large variation was shown among genotypes for the parameters investigated.

Light is one of the most important components to be included in functional–structural plant models that simulate the biophysical processes, such as photosynthesis, evapotranspiration and photomorphogenesis, involved in plant growth and development. In general, in these models, light is treated using a turbid medium approach in which radiation attenuation is described by the Beer–Lambert law. In the present study, we assessed the hypothesis of leaf random dispersion in the Beer–Lambert law at the whole-canopy, horizontal-layer and local scales. We compared two calculation methods of radiation attenuation: a 3D turbid medium model using the Beer–Lambert law and the other based on a projective method. The two models were compared by applying the calculations to two walnut trees and two sorghum canopies, which have contrasting structural characteristics. The structures of these canopies were measured in 3D to take into account the arrangement and orientation features of the plant elements. The assumptions made by the Beer–Lambert law allowed adequate simulation of light interception in a structure with little overlapping at the horizontal-layer and whole-canopy scales. At the local scale, discrepancies between the turbid medium model and the model based on a virtual plant were reduced with an adequate choice of structural parameters, such as the leaf inclination distribution function.

The productivity and stability of grazed grassland rely on dynamic interactions between the sward and the animal. The descriptions of the sward canopies by standard 2-D representations in studies of animal–sward interactions at the bite scale need to be improved to account for the effect of local canopy heterogeneity on bite size and regrowth ability. The aim of this study was to assess a methodology of 3-D digitized canopies in order to understand the balance between bite mass and light interception by the residual sward. 3-D canopy structures of four white clover swards were recorded using a POLHEMUS electromagnetic digitizer and adapted software (POL95). Plant components were removed after digitizing to determine aerial dry matter. Virtual canopies were synthesized and then used to derive canopy geometrical parameters, to compute directional interception and to calculate bite mass. The bit masses of cattle and sheep were simulated according to their form, depth and placement on the patch, taking account of explicit sward architecture. The resulting light interception efficiency (LIE) of each organ was then calculated using a projective method applied to the virtual residual sward. This process enabled an evaluation of light interception based on Beer's law at the bite scale. The patterns of the vertical profiles of LAI appeared as bimodal, triangular or skewed parabolic functions. For a single bite of similar area and depth, the lowest mass was observed with half-spherical form and the highest for the cylindrical form, whatever the initial sward structure. The differences between the actual LIE and that calculated by Beer's law were marked for residual swards shorter than 8 cm. Bite mass and LIE values after grazing were more strongly affected by the initial structure of the sward than by bite form and placement. 3-D digitizing techniques enabled a definition of the geometry of each component in sward canopies and an accurate description of their vertical and horizontal heterogeneities. The discrepancy between Beer's law results and actual light interception was reduced when the sward regrew rapidly and if the rest period was long. Studies on the biting process would greatly benefit from this method as a framework to formulate and test hypotheses in a quantitative manner.