Canopy Traits Research Articles

Competitiveness of Three Warm‐Season Turfgrasses during Spring Transition of Overseeded Turfs

Prior research on the topic of southern winter overseeding systems has been limited to the testing of management practices, seeding rates, and cool‐season grass genotypes. A desirable spring transition may also be dependent on warm‐season turfgrass species. A field study was conducted in College Station, TX, on overseeded (Lolium perenne L.) and nonoverseeded zoysiagrass (Zoysia spp. Willd. ‘Cavalier’), seashore paspalum (Paspalum vaginatum Swarz. ‘Sea Isle 1’), and hybrid bermudagrass [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy ‘Tifway’] fairway turfs. The objectives of the experiment were to measure eight canopy attributes of the warm‐season species and to relate differences to competitiveness during the spring transition. Samples of canopies were collected in May, July, and September from each warm‐season turfgrass. Leaf area, leaf and shoot weight, and leaf and shoot counts were measured and used to calculate common plant canopy metrics such as leaf area index, leaf area ratio, specific leaf area, and leaf weight ratio. Grasses capable of extending vertical stems as an overtopping mechanism, as conveyed by lower leaf weight ratios, appear more competitive for light in a spring overseeded turf (e.g., hybrid bermudagrass) than grasses that invest significant resources towards leaf production (e.g., zoysiagrass). High shoot plasticity (change in canopy traits due to overseeding treatment) and efficient leaf area production (i.e., high specific leaf area) of seashore paspalum also contributed a competitive advantage during the spring transition period. This research demonstrates that canopy morphology can be a contributing factor to competitiveness of warm‐season turfgrasses in an overseeded turfgrass system.

Heat wave hinders green wave: The impact of climate extreme on the phenology of a mountain grassland

Climate extremes can have tremendous impacts on the terrestrial biosphere and their frequency is very likely going to increase in the coming years. In this study we examine the impact of the 2015 summer heat wave on a mountain grassland in the Western European Alps by jointly analyzing phenocam greenness (GCC) trajectories, proximal sensing, CO2 flux data and structural canopy traits. Phenocam effectively tracked the impact of the heat wave, showing 39% of reduction in maximum canopy greenness and a senescence advance of 32 days compared to mean values. The same patterns (i.e. reduction of maximum values and senescence advance) were observed for all considered canopy traits and photosynthetic ecosystem functional properties, in particular the maximum light-saturated rate of CO2 uptake (Amax), LAI and PRI. Pixel-level analysis of phenocam images allowed us to further highlight that forbs were more heavily impacted than grasses. Moreover the effect of the extreme event on greenness seasonal course was evaluated testing new formulations of the Growing Season Index (GSI) model. Results demonstrate that a combination of water and high temperature stress was responsible for the observed reduction of canopy greenness during the heat wave.

Investigation of the Influence of Leaf Thickness on Canopy Reflectance and Physiological Traits in Upland and Pima Cotton Populations.

Many systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology's nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland (Gossypium hirsutum L.) recombinant inbred line (RIL) population of 95 lines and a Pima (G. barbadense L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered (WW) conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum r = −0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination ( = −0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.

Maize Grain Yield and Kernel Component Relationships to Morphophysiological Traits in Commercial Hybrids Separated by Four Decades

Hybrid era and management practices like nitrogen (N) rate and plant density influence canopy traits and their correlations with grain yield, kernel number, and kernel weight in maize (Zea mays L.). A four site‐year study was conducted employing two N rates (55 and 220 kg N ha−1) with three plant densities between 54,000 and 104,000 plants ha−1 for two newer hybrids (2005) and two older hybrids (1967 and 1975). Hybrids varied in anthesis–silking interval, specific leaf nitrogen (SLN), specific leaf area, and leaf greenness (soil plant analysis development [SPAD] readings), but not in leaf area index (LAI) at silking. Consistently higher kernel weight in newer hybrids (15% in 2012, 23% in 2013) across management treatments was related to their higher SLN at silking and green leaf number retention during grain filling. The threshold LAI at silking for maximum kernel number for 2005 hybrids (averaging 3917 kernels m−2) occurred at 4.0 m2 m−2, compared with 3.28 m2 m−2 for the 1975 hybrid (3893 kernels m−2), and kernel number declined when LAI exceeded 3.43 m2 m−2 for the 1967 hybrid. Higher leaf biomass, leaf N content, and therefore higher SLN at silking in newer hybrids contributed to greater green leaf retention during grain filling. Applying more N weakened correlations between grain yield and canopy attributes, including leaf retention in both years and yield correlations with leaf biomass, leaf N content, LAI, and SLN in 2013 (more favorable weather). Grain yields for all hybrids in 2013 had higher correlations with LAI at silking and during grain fill when grown at higher densities.

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

BackgroundPhenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of plant stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict plant stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field.ResultsWe investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful ‘population canopy graph’, connecting the automatically extracted canopy trait features with plant stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy.ConclusionWe expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated stress trait detection and quantification for plant research, breeding and stress scouting applications.

Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation.

Functional biogeography may bridge a gap between field-based biodiversity information and satellite-based Earth system studies, thereby supporting conservation plans to protect more species and their contributions to ecosystem functioning. We used airborne laser-guided imaging spectroscopy with environmental modeling to derive large-scale, multivariate forest canopy functional trait maps of the Peruvian Andes-to-Amazon biodiversity hotspot. Seven mapped canopy traits revealed functional variation in a geospatial pattern explained by geology, topography, hydrology, and climate. Clustering of canopy traits yielded a map of forest beta functional diversity for land-use analysis. Up to 53% of each mapped, functionally distinct forest presents an opportunity for new conservation action. Mapping functional diversity advances our understanding of the biosphere to conserve more biodiversity in the face of land use and climate change.

Female age as a factor determining the patterns of tree canopy utilisation by the European cherry fruit fly, Rhagoletis cerasi

ABSTRACTThe European cherry fruit fly, Rhagoletis cerasi is a univoltine, oligophagous pest of cultivated sweet cherries. The pest is closely associated with the canopy of its host tree, and its on-farm behaviour and mobility are determined by the crop structure and spatial arrangement of the farm. However, in spite of its relevance to integrated pest management, little is known about the within-canopy behaviour of R. cerasi, or the role of host and non-host canopy traits in local pest translocations. This paper reports the results of our exploratory studies, which reveal the modulating role of female age on various aspects of R. cerasi on-farm behaviour, such as diurnal and lifetime patterns of canopy utilisation and within-canopy activity, the propensity to undertake local explorative errands and the response to volatiles emanating from host and non-host canopies. The potential implications of our findings on the development of site-optimised IPM and/or pest-resilient agro-landscapes were also discussed.

Yield, growth, canopy traits and photosynthesis in high-yielding, synthetic hexaploid-derived wheats cultivars compared with non-synthetic wheats

Continuous improvement of potential yield is one of the most important goals of wheat breeding. The introduction of synthetic hexaploid wheat (SHW) germplasm has broken the bottleneck in potential yield, taking wheat breeding in China’s Sichuan Basin to a new level. However, systematic research on the physiological basis of high-yielding, SHW-derived cultivars has lagged behind. In the present study, three SHW-derived, high-yielding cultivars and three typical, non-synthetic cultivars widely used in wheat production were chosen for a 5-year study. Post-anthesis canopy structure, rates of canopy apparent photosynthesis (CAP), attenuation during grain filling, dry matter partitioning and other physiological parameters were studied. The average yield of the SHW-derived cultivars was 9154 kg ha–1, which was 13.5% higher than that of the non-synthetic cultivars. The increased yield was due to increased biomass and/or increased harvest index (HI). SHW-derived cultivars had shorter but wider flag leaves, with length : width ratio <10. The basal angle and open angle were small at the beginning of anthesis, which gradually increased as grain-filling progressed; the SPAD readings of the flag leaf and penultimate leaf of the SHW-derived cultivars was significantly higher than that of the non-synthetic cultivars from anthesis to mid–late grainfill. The CAP values at anthesis and 20 days post-anthesis were significantly higher in the SHW-derived cultivars than in non-synthetic cultivars, in which the difference was most significant between 10 : 00 and 12 : 00. The dry matter partitioning at anthesis varied significantly among cultivars, and the stem and sheath proportion of the SHW-derived cultivars was larger than that of the non-synthetic cultivars. At maturation, the spike rachis and leaves of the SHW-derived cultivars accounted for significantly smaller proportions of the total aboveground dry weight. Accordingly, the grain proportion was increased by 1–4 percentage points. Yield components were closely related to measured physiological parameters; e.g. grain yield correlated positively with SPAD values (r = 0.960**) and negatively with the proportion of spike rachis at maturation (r = –0.946**). This indicated that a semi-compact plant morphology, with high SPAD readings and high CAP and greater HI, was the physiological basis of high yield in SHW-derived cultivars.

A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

Collecting plant phenotypic data with sufficient resolution (in both space and time) and accuracy represents a long standing challenge in plant science research, and has been a major limiting factor for the effective use of genomic data for crop improvement. This is particularly true in plant breeding where collecting large-scale field-based plant phenotypes can be very labor intensive and costly. In this paper we reported a multi-sensor system for high throughput phenotyping in plant breeding. The system comprised five sensor modules (ultrasonic distance sensors, thermal infrared radiometers, NDVI sensors, portable spectrometers, and RGB web cameras) to measure crop canopy traits from field plots. A GPS was used to geo-reference the sensor measurements. Two environmental sensors (a solar radiation sensor and air temperature/relative humidity sensor) were also integrated into the system to collect simultaneous environmental data. A LabVIEW program was developed to control and synchronize measurements from all sensor modules and stored sensor readings in the host computer. Canopy reflectance spectra (by portable spectrometers) were post processed to extract NDVI and red-edge NDVI spectral indices; and RGB images were post processed to extract canopy green pixel fraction (as a proxy for biomass). The sensor system was tested in a soybean and wheat field trial. The results showed strong correlations among the sensor-based plant traits at both early and late growing season. Significant correlations were also found between the sensor-based traits and final grain yield at the early season (Pearson’s correlation coefficient r ranged from 0.41 to 0.55) and late season (r from 0.55 to 0.70), suggesting the potential use of the sensor system to assist in phenotypic selection for plant breeding. The sensor system performed satisfactorily and robustly in the field tests. It was concluded that the sensor system could be a powerful tool for plant breeders to collect field-based, high throughput plant phenotyping data.

Scale dependence of canopy trait distributions along a tropical forest elevation gradient

Average responses of forest foliar traits to elevation are well understood, but far less is known about trait distributional responses to elevation at multiple ecological scales. This limits our understanding of the ecological scales at which trait variation occurs in response to environmental drivers and change. We analyzed and compared multiple canopy foliar trait distributions using field sampling and airborne imaging spectroscopy along an Andes-to-Amazon elevation gradient. Field-estimated traits were generated from three community-weighting methods, and remotely sensed estimates of traits were made at three scales defined by sampling grain size and ecological extent. Field and remote sensing approaches revealed increases in average leaf mass per unit area (LMA), water, nonstructural carbohydrates (NSCs) and polyphenols with increasing elevation. Foliar nutrients and photosynthetic pigments displayed little to no elevation trend. Sample weighting approaches had little impact on field-estimated trait responses to elevation. Plot representativeness of trait distributions at landscape scales decreased with increasing elevation. Remote sensing indicated elevation-dependent increases in trait variance and distributional skew. Multiscale invariance of LMA, leaf water and NSC mark these traits as candidates for tracking forest responses to changing climate. Trait-based ecological studies can be greatly enhanced with multiscale studies made possible by imaging spectroscopy.

Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton.

The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Toward this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across 2010–2012. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate-to-high broad-sense heritabilities, as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy traits and agronomic traits, such as lint yield, displayed a time-dependent relationship. We also found that the genomic position of some QTL controlling HTPP canopy traits were shared with those of QTL identified for agronomic and physiological traits. This work demonstrates the novel use of a field-based HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars.

The INDETERMINATE DOMAIN Protein BROAD LEAF1 Limits Barley Leaf Width by Restricting Lateral Proliferation

Variation in the size, shape, and positioning of leaves as the major photosynthetic organs strongly impacts crop yield, and optimizing these aspects is a central aim of cereal breeding [1, 2]. Leaf growth in grasses is driven by cell proliferation and cell expansion in a basal growth zone [3]. Although several factors influencing final leaf size and shape have been identified from rice and maize [4-14], what limits grass leaf growth in the longitudinal or transverse directions during leaf development remains poorly understood. To identify factors involved in this process, we characterized the barley mutant broad leaf1 (blf1). Mutants form wider but slightly shorter leaves due to changes in the numbers of longitudinal cell files and of cells along the leaf length. These differences arise during primordia outgrowth because of more cell divisions in the width direction increasing the number of cell files. Positional cloning, analysis of independent alleles, and transgenic complementation confirm that BLF1 encodes a presumed transcriptional regulator of the INDETERMINATE DOMAIN family. In contrast to loss-of-function mutants, moderate overexpression of BLF1 decreases leaf width below wild-type levels. A functional BLF1-vYFP fusion protein expressed from the endogenous promoter shows a dynamic expression pattern in the shoot apical meristem and young leaf primordia. Thus, we propose that the BLF1 gene regulates barley leaf size by restricting cell proliferation in the leaf-width direction. Given the agronomic importance of canopy traits in cereals, identifying functionally different BLF1 alleles promises to allow for the generation of optimized cereal ideotypes.

Convergent elevation trends in canopy chemical traits of tropical forests.

The functional biogeography of tropical forests is expressed in foliar chemicals that are key physiologically based predictors of plant adaptation to changing environmental conditions including climate. However, understanding the degree to which environmental filters sort the canopy chemical characteristics of forest canopies remains a challenge. Here, we report on the elevation and soil-type dependence of forest canopy chemistry among 75 compositionally and environmentally distinct forests in nine regions, with a total of 7819 individual trees representing 3246 species collected, identified and assayed for foliar traits. We assessed whether there are consistent relationships between canopy chemical traits and both elevation and soil type, and evaluated the general role of phylogeny in mediating patterns of canopy traits within and across communities. Chemical trait variation and partitioning suggested a general model based on four interconnected findings. First, geographic variation at the soil-Order level, expressing broad changes in fertility, underpins major shifts in foliar phosphorus (P) and calcium (Ca). Second, elevation-dependent shifts in average community leaf dry mass per area (LMA), chlorophyll, and carbon allocation (including nonstructural carbohydrates) are most strongly correlated with changes in foliar Ca. Third, chemical diversity within communities is driven by differences between species rather than by plasticity within species. Finally, elevation- and soil-dependent changes in N, LMA and leaf carbon allocation are mediated by canopy compositional turnover, whereas foliar P and Ca are driven more by changes in site conditions than by phylogeny. Our findings have broad implications for understanding the global ecology of humid tropical forests, and their functional responses to changing climate.

Are trees of intermediate density more facilitative? Canopy effects of four East African legume trees

AbstractQuestionsDo East African legume trees differ in their canopy effects? Have species with intermediate canopy density more pronounced facilitative effects on understorey primary production, community composition and diversity? How are canopy effects related to species' encroachment status, and modulated by a local aridity gradient?LocationUpland and lowland environments of the Borana rangelands, an arid thornbush savanna in southern Ethiopia.MethodsWe harnessed pastoralists' local ecological knowledge to rank the encroachment status of six legume tree species previously known as Acacia, and correlated this rank to a dendrometric proxy of canopy density. Vegetation relevés (1 m²) were placed in sub‐canopy and adjacent inter‐canopy habitats of four legume tree species that differed in canopy density and encroachment status (Vachellia bussei, V. drepanolobium, V. seyal, V. tortilis). Using mixed‐effects ANOVA, we evaluated effects of tree species and habitat (sub‐canopy or inter‐canopy) on total, forb and grass biomass, and on species diversity, comparing results for lowland and upland sites. Effects on floristic composition were assessed via PERMANOVA and NMDS.ResultsSpecies' encroachment status and canopy density were negatively correlated. Most pronounced facilitative effects (more frequent and larger differences between sub‐canopy and inter‐canopy habitats) were found for a species with intermediate canopy density (V. bussei). Diversity in its understorey vegetation increased up to 32% (total biomass: 29%, forb biomass: 40%). In contrast, the species with the most open canopy (V. drepanolobium; also the most encroaching species) never exerted significant facilitative effects. In contrast to expectations, canopy effects were more frequent in (climatically less arid) upland environments, probably due to a redistribution of water within the landscape. Large differences in facilitative effects across tree individuals indicate that a high intraspecific variability of canopy traits and/or small‐scale differences in abiotic site conditions have partly overridden species‐specific differences in canopy architecture.ConclusionOur study provides new insights on why encroaching legume tree species may decrease herbaceous production: they tend to have more open canopies, with small facilitative effects on sub‐canopy herbaceous production. We thus recommend promoting an open savanna with the tree layer dominated by species with a high facilitative potential to maintain forage provision and species diversity.

Dry matter partitioning and canopy traits in wheat and barley under varying N supply

Abstract The prediction of the allocation of carbon and nitrogen into the different organs, e.g., leaves, stems, roots of a growing plant is a pivotal part of mechanistic growth models. Based on 2 year (winter wheat) and 3 year (winter barley) field trials with different N treatments (0–240 kg N ha −1 ), the objectives of this study were to verify currently used parameters for (i) specific leaf area, and (ii) allometric relationships between leaf and stem dry matter (DM). Green area index (GAI) differed between years and increased with N supply rate. The course of the specific leaf area (SLA) within the growth period after winter showed a large variation between the years as well as between the N treatments. During stem elongation, SLA positively correlated with GAI in both crops, indicating that leaves became thinner when the canopy was denser. The allometric relationships between stem and leaf dry DM clearly varied with the growth stages. Stem DM of wheat showed a higher proportion of the total above-ground biomass compared to barley. The effect of N fertilization on allometry remained small during early growth stages in barley, whereas in wheat, an increased N supply significantly decreased the proportion of stem DM. Our results suggest similar GAI and SLA patterns for wheat and barley, albeit depending on the N supply. Especially, using a constant SLA in plant growth models seems to be not suitable.

Detection of gradients of forest composition in an urban area using imaging spectroscopy

Forests play an important role in urban environments by providing a range of ecosystem services. However, forest composition and function in urban ecosystems are ecologically complex because of high heterogeneity of the landscape and long legacies of intensive human management. Accurate quantification and mapping of composition of tree taxa in urban forests using remote sensing require high spatial and spectral resolution imagery. As such, mapping of individual species over large areas is impractical, so instead we map gradients of composition based on canopy traits derived from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) and lidar. This trait-based approach depicts emergent functional overlaps due to pixel-scale mixing of disparate species or genera found in the region. This provides a basis for understanding forest functioning in urban ecosystems where multiple taxa are functionally and spectrally similar. We used non-metric multidimensional scaling (NMDS) to ordinate multi-dimensional forest composition data, where NMDS scores (NMDS1 and NMDS2) represent compositional gradients. Predictive models of NMDS1 and NMDS2 were built using AVIRIS-derived foliar traits such as nitrogen concentration and lidar structural variables. Gradients of composition were strongly related to AVIRIS-derived traits (R2=0.67 for NMDS1 and R2=0.47 for NMDS2). Lidar-derived structural variables provided no improvement to prediction of the first NMDS axis estimates, and a minimal improvement (3% more variance explained) on NMDS2. We applied predictive models of NMDS1 and NMDS2 to maps of predictor traits to map NMDS1 and NMDS2. From the NMDS1 and NMDS2 maps, we mapped potential composition patterns and associated uncertainties by genus from the ordination of the field data. Our maps of composition gradients show the potential for data from imaging spectrometers such as HyspIRI to comprehensively characterize the diversity of ecosystems in heterogeneous landscapes. Moderate resolution satellite imagery may not facilitate detection of individual species, but imaging spectroscopy provides a basis to estimate the potential distribution of particular taxa based on assemblages associated with foliar traits that can be derived from imaging spectroscopy. Comprehensive satellite-based mapping will provide basic understanding and spatially explicit information on forest functioning suitable for monitoring and management of urban ecosystems.

LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget.

In this paper, we describe the thought process and initial data behind the development of an imaging platform (LeasyScan) combined with lysimetric capacity, to assess canopy traits affecting water use (leaf area, leaf area index, transpiration). LeasyScan is based on a novel 3D scanning technique to capture leaf area development continuously, a scanner-to-plant concept to increase imaging throughput and analytical scales to combine gravimetric transpiration measurements. The paper presents how the technology functions, how data are visualised via a web-based interface and how data extraction and analysis is interfaced through 'R' libraries. Close agreement between scanned and observed leaf area data of individual plants in different crops was found (R(2) between 0.86 and 0.94). Similar agreement was found when comparing scanned and observed area of plants cultivated at densities reflecting field conditions (R(2) between 0.80 and 0.96). An example in monitoring plant transpiration by the analytical scales is presented. The last section illustrates some of the early ongoing applications of the platform to target key phenotypes: (i) the comparison of the leaf area development pattern of fine mapping recombinants of pearl millet; (ii) the leaf area development pattern of pearl millet breeding material targeted to different agro-ecological zones; (iii) the assessment of the transpiration response to high VPD in sorghum and pearl millet. This new platform has the potential to phenotype for traits controlling plant water use at a high rate and precision, of critical importance for drought adaptation, and creates an opportunity to harness their genetics for the breeding of improved varieties.

Tree traits and meteorological factors influencing the initiation and rate of stemflow from isolated deciduous trees

AbstractTree canopy processes affect the volume and biogeochemistry of inputs to the hydrological cycle in cities. From June 2012 to November 2013, we studied stemflow production from 37 isolated deciduous park trees in a semi‐arid climate dominated by small precipitation events. To clarify the effects of canopy traits on stemflow metrics, we analysed branch angles, bark relief (one component of roughness), tree size, canopy and wood cover fraction, median leaf size, and branch and leader counts. High branch angles contributed to stemflow production in both single‐leader and multi‐leader trees. While bark relief was negatively correlated with stemflow rates in multi‐leader trees, it was positively correlated with rates for single‐leader trees, possibly reflecting the conducive role of linear furrows once bark of single‐leader trees is saturated. The association between numerous leaders, low stemflow initiation thresholds, and high rates deserves further study. Among meteorological variables, rain depth was strongly correlated with stemflow yields; rainfall inclination angle and wind speed were positively correlated with yields, while total intra‐storm break duration and vapour pressure deficit were inversely related. For rain depths <3 mm, greater stemflow was generally associated with leafless canopies. In support of integrated stormwater management, our results can inform climate‐sensitive selection and siting of urban trees with traits that tend to either promote or minimize stemflow, depending on infiltration potential. Copyright © 2015 John Wiley & Sons, Ltd.

Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy

Tropical forest functional diversity, which is a measure of the diversity of organismal interactions with the environment, is poorly understood despite its importance for linking evolutionary biology to ecosystem biogeochemistry. Functional diversity is reflected in functional traits such as the concentrations of different compounds in leaves or the density of leaf mass, which are related to plant activities such as plant defence, nutrient cycling, or growth. In the Amazonian lowlands, river movement and microtopography control nutrient mobility, which may influence functional trait distributions. Here we use airborne laser-guided imaging spectroscopy to develop maps of 16 forest canopy traits, throughout four large landscapes that harbour three common forest community types on the Madre de Dios and Tambopata rivers in southwestern Amazonia. Our maps, which are based on quantitative chemometric analysis of forest canopies with visible-to-near infrared (400–2,500 nm) spectroscopy, reveal substantial variation in canopy traits and their distributions within and among forested landscapes. Forest canopy trait distributions are arranged in a nested pattern, with location along rivers controlling trait variation between different landscapes, and microtopography controlling trait variation within landscapes. We suggest that processes of nutrient deposition and depletion drive increasing phosphorus limitation, and a corresponding increase in plant defence, in an eastward direction from the base of the Andes into the Amazon Basin. The controls on plant functional diversity are unclear. Analysis of spectral data from the tree canopy in the Amazonian lowlands implies that plant functional traits are influenced by nutrient supply, which in turn varies with topography.

Common bean canopy characteristics and N assimilation as affected by weed pressure and nitrogen rate

SUMMARYCrop canopy characteristics and management factors that affect the canopy structure can influence crop competitive ability against weeds. The goal of the current study was to understand the interaction between nitrogen (N) fertilizer rate and redroot pigweed (Amaranthus retroflexus) density on common bean (Phaseolus vulgarisL.) canopy characteristics and N assimilation. Experiments were conducted to determine the growth responses of two bean genotypes with contrasting growth habits to different N fertilizer rates under no, low and high redroot pigweed pressure. Nitrogen was applied at rates of 0, 50, 100 and 200 kg/ha for a semi-erect variety and 0, 35, 70 and 140 kg/ha for an erect bean variety. Leaf area index (LAI), leaf area duration (LAD), leaf chlorophyll, N assimilation and N assimilation rate for both bean growth habits increased with N application rate at no and low weed density. The negative effect of redroot pigweed competition on LAI, LAD, N assimilation and N assimilation rate increased with redroot pigweed density, and with N application rate for the high weed density. Stepwise multiple regression analysis showed that N assimilation and chlorophyll concentration accounted for 0·73–0·87, overall, of the variation in yield loss due to weed competition. It is concluded that greater N assimilation in the semi-erect growth habit compared with the erect growth habit is the cause of its higher competitive ability against the weed, as it improves the effective canopy traits that confer potential for resource pre-emption.