David M. Deery

High-throughput phenotyping technologies allow accurate selection of stay-green

Improved genotypic performance in water-limited environments relies on traits, like ‘stay-green’, that are robust and repeatable, correlate well across a broader range of target environments and are genetically more tractable than assessment of yield per se. Christopher et al. (see pages 5159–5172) used multi-temporal, Normalised Difference Vegetative Index (NDVI) measurements with crop simulation modelling to demonstrate the value of various stay-green phenotype parameters for improving grain yield across different environment types.

Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography

Lower canopy temperature (CT), resulting from increased stomatal conductance, has been associated with increased yield in wheat. Historically, CT has been measured with hand-held infrared thermometers. Using the hand-held CT method on large field trials is problematic, mostly because measurements are confounded by temporal weather changes during the time required to measure all plots. The hand-held CT method is laborious and yet the resulting heritability low, thereby reducing confidence in selection in large scale breeding endeavors. We have developed a reliable and scalable crop phenotyping method for assessing CT in large field experiments. The method involves airborne thermography from a manned helicopter using a radiometrically-calibrated thermal camera. Thermal image data is acquired from large experiments in the order of seconds, thereby enabling simultaneous measurement of CT on potentially 1000s of plots. Effects of temporal weather variation when phenotyping large experiments using hand-held infrared thermometers are therefore reduced. The method is designed for cost-effective and large-scale use by the non-technical user and includes custom-developed software for data processing to obtain CT data on a single-plot basis for analysis. Broad-sense heritability was routinely >0.50, and as high as 0.79, for airborne thermography CT measured near anthesis on a wheat experiment comprising 768 plots of size 2 × 6 m. Image analysis based on the frequency distribution of temperature pixels to remove the possible influence of background soil did not improve broad-sense heritability. Total image acquisition and processing time was ca. 25 min and required only one person (excluding the helicopter pilot). The results indicate the potential to phenotype CT on large populations in genetics studies or for selection within a plant breeding program.

SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data.

BackgroundTo our knowledge, there is no software or database solution that supports large volumes of biological time series sensor data efficiently and enables data visualization and analysis in real time. Existing solutions for managing data typically use unstructured file systems or relational databases. These systems are not designed to provide instantaneous response to user queries. Furthermore, they do not support rapid data analysis and visualization to enable interactive experiments. In large scale experiments, this behaviour slows research discovery, discourages the widespread sharing and reuse of data that could otherwise inform critical decisions in a timely manner and encourage effective collaboration between groups.ResultsIn this paper we present SensorDB, a web based virtual laboratory that can manage large volumes of biological time series sensor data while supporting rapid data queries and real-time user interaction. SensorDB is sensor agnostic and uses web-based, state-of-the-art cloud and storage technologies to efficiently gather, analyse and visualize data.Conclusions Collaboration and data sharing between different agencies and groups is thereby facilitated. SensorDB is available online at http://sensordb.csiro.au.

Uptake of water from a Kandosol subsoil. II. Control of water uptake by roots

AimTo test for the presence of an impediment to water flow at the soil-root interface.MethodsWheat plants were grown in repacked and undisturbed field soil. Their transpiration rate, E, was varied in several steps from low to high and then back to low again, while the hydrostatic pressure in the leaf xylem, ψxylem, was measured non-destructively and continuously. These measurements were compared to a mathematical model that calculated ψxylem by assuming that the hydraulic resistance across the plant was constant and that the radial flow of water to unit length of a typical plant root generated gradients in pressure in the soil water.ResultsFor the repacked soil, the radial flow model could not match the experiment during the falling phase of E, unless it was assumed that either an additional, constant, interfacial resistance between the soil and the roots had developed when E was large and ψxylem was rapidly falling, or that the resistance within the plant had changed. For the undisturbed field soil, the radial flow model did not agree with the experiment. Plausible agreement was achieved when plant water uptake was accounted for using a distributed sink model in HYDRUS-1D, with E integrated across the rootzone. This approach was based on the measured large variation in the vertical distribution of roots.ConclusionsThere was no strong evidence of large drawdowns of soil water in the rhizosphere, even when ψxylem was falling rapidly when E was large and the soil was moderately dry. Thus, there seems to have been an additional impediment to water flow from soil to plant, either within the plant, or at the interface between the two.

High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

Crop improvement efforts are targeting increased above-ground biomass and radiation-use efficiency as drivers for greater yield. Early ground cover and canopy height contribute to biomass production, but manual measurements of these traits, and in particular above-ground biomass, are slow and labor-intensive, more so when made at multiple developmental stages. These constraints limit the ability to capture these data in a temporal fashion, hampering insights that could be gained from multi-dimensional data. Here we demonstrate the capacity of Light Detection and Ranging (LiDAR), mounted on a lightweight, mobile, ground-based platform, for rapid multi-temporal and non-destructive estimation of canopy height, ground cover and above-ground biomass. Field validation of LiDAR measurements is presented. For canopy height, strong relationships with LiDAR (r2 of 0.99 and root mean square error of 0.017 m) were obtained. Ground cover was estimated from LiDAR using two methodologies: red reflectance image and canopy height. In contrast to NDVI, LiDAR was not affected by saturation at high ground cover, and the comparison of both LiDAR methodologies showed strong association (r2 = 0.92 and slope = 1.02) at ground cover above 0.8. For above-ground biomass, a dedicated field experiment was performed with destructive biomass sampled eight times across different developmental stages. Two methodologies are presented for the estimation of biomass from LiDAR: 3D voxel index (3DVI) and 3D profile index (3DPI). The parameters involved in the calculation of 3DVI and 3DPI were optimized for each sample event from tillering to maturity, as well as generalized for any developmental stage. Individual sample point predictions were strong while predictions across all eight sample events, provided the strongest association with biomass (r2 = 0.93 and r2 = 0.92) for 3DPI and 3DVI, respectively. Given these results, we believe that application of this system will provide new opportunities to deliver improved genotypes and agronomic interventions via more efficient and reliable phenotyping of these important traits in large experiments.