TANZANIA GRASS STRUCTURE GRAZED BY GOATS MANAGED WITH DIFFERENT RESIDUAL LEAF AREA INDEX UNDER INTERMITTENT STOCKING

The objective of this study was to evaluate the structure of Tanzania grassland grazed by goats managed with different residue leaf area index (RLAI) under intermittent stocking. The experiment was carried out from February to August, 2008. The treatments consisted of three different targets RLAI (0.8, 1.6 and 2.4) and 95% light interception (LI) criterion determined the rest period. Forage samples were collected at average height sampling points and weighed. Subsequently, a smaller sample was removed to separate the morphological components (leaf, stem and dead material) and to determine the structural and productive features. The canopy architecture was evaluated by the method of inclined point quadrat. The pre-grazing height in the paddocks were significantly different among treatments. RLAI influenced dry matter contents of green forage, leaf, stem and total, with the exception of dry matter of dead material, where the lowest values were observed for 0.8 RLAI. Thus, RLAI modifies canopy structure and is sensitive to canopy height changes throughout the year. Pasture regrowth is not compromised by residual leaf area indexes between 0.8 and 2.4, when climatic factors are not limiting.

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The length of the dry season may be associated with leaf scleromorphism in cerrado plants

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Environmental characteristics, and growth traits and leaf chemistry of tundra plants in a warming experiment at Alexandra Fiord

Understanding plant trait responses to elevated temperatures in the Arctic is critical in light of recent and continuing climate change, especially because these traits act as key mechanisms in climate-vegetation feedbacks. Since 1992, we have artificially warmed three plant communities at Alexandra Fiord, Nunavut, Canada (79°N). In each of the communities, we used open-top chambers (OTCs) to passively warm vegetation by 1-2 °C. In the summer of 2008, we investigated the intraspecific trait responses of five key species to 16 years of continuous warming. We examined eight traits that quantify different aspects of plant performance: leaf size, specific leaf area (SLA), leaf dry matter content (LDMC), plant height, leaf carbon concentration, leaf nitrogen concentration, leaf carbon isotope discrimination (LCID), and leaf d15N. Long-term artificial warming affected five traits, including at least one trait in every species studied. The evergreen shrub Cassiope tetragona responded most frequently (increased leaf size and plant height/decreased SLA, leaf carbon concentration, and LCID), followed by the deciduous shrub Salix arctica (increased leaf size and plant height/decreased SLA) and the evergreen shrub Dryas integrifolia (increased leaf size and plant height/decreased LCID), the forb Oxyria digyna (increased leaf size and plant height), and the sedge Eriophorum angustifolium spp. triste (decreased leaf carbon concentration). Warming did not affect d15N, leaf nitrogen concentration, or LDMC. Overall, growth traits were more sensitive to warming than leaf chemistry traits. Notably, we found that responses to warming were sustained, even after many years of treatment. Our work suggests that tundra plants in the High Arctic will show a multifaceted response to warming, often including taller shoots with larger leaves.

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management.

Ground cover and leaf area index relationship in a grass, legume and crucifer crop

Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leaf area index (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photosynthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relationship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI mayor que 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley.

Towards developing reliable models of leaf area on grapevines (Vitis vinifera L.)

A field study in three vineyards in southern Queensland (Australia) was carried out to develop predictive models for individual leaf area and shoot leaf area of two cultivars (Cabernet Sauvignon and Shiraz) of grapevines (Vitis Vinifera L.). Digital image analysis was used to measure leaf vein length and leaf area. Stepwise regressions of untransformed and transformed models consisting of up to six predictor variables for leaf area and three predictor variables for shoot leaf area were carried out to obtain the most efficient models. High correlation coefficients were found for log10 transformed individual leaf and shoot leaf area models. The significance of predictor variables in the models varied across vineyards and cultivars, demonstrating the discontinuous and heterogeneous nature of vineyards. The application of this work in a grapevine modeling environment and in a dynamic vineyard management context are discussed. Sample sizes for quantification of individual leaf areas and areas of leaves on shoots are proposed based on target margins of errors of sampled data.

Leaf Functional Traits and Forest Structure of Tropical Dry Forest Species Along a Rainfall Gradient in Florida and Puerto Rico

This study examined how different rainfall regimes affect a set of leaf functional traits related to plant stress and forest structure in tropical dry forest (TDF) species on limestone substrate. One hundred fifty eight individuals of four tree species were sampled in six ecological sites in south Florida and Puerto Rico, ranging in mean annual rainfall from 858 to 1933 mm yr-1. Leaf nitrogen content, specific leaf area (SLA), and N:P ratio of evergreen species, but not deciduous species, responded positively to increasing rainfall. Phosphorus content was unaffected in both groups. Canopy height and basal area reached maxima of 10.3 m and 31.4 m2 ha-1, respectively, at 1168 mm annual rainfall. Leaf traits reflected soil properties only to a small extent. This led us to the conclusion that water is a major limiting factor in TDF and some species that comprise TDF ecosystems are limited by nitrogen in limestone sites with less than ~1012 mm rainfall, but organismal, biological and/or abiotic forces other than rainfall control forest structure in moister sites.

Time-series of snow spectral albedo and superficial snow specific surface area at Dome C in Antarctica, 2012-2015

Spectral albedo has been measured at Dome C since December 2012 in the visible and near infrared (400 - 1050 nm) at sub-hourly resolution using a home-made spectral radiometer. Superficial specific surface area (SSA) has been estimated by fitting the observed albedo spectra to the analytical Asymptotic Approximation Radiative Transfer theory (AART). The dataset includes fully-calibrated albedo and SSA that pass several quality checks as described in the companion article. Only data for solar zenith angles less than 75° have been included, which theoretically spans the period October-March. In addition, to correct for residual errors still affecting data after the calibration, especially at the solar zenith angles higher than 60°, we produced a higher quality albedo time-series as follows: In the SSA estimation process described in the companion paper, a scaling coefficient A between the observed albedo and the theoretical model predictions was introduced to cope with these errors. This coefficient thus provides a first order estimate of the residual error. By dividing the albedo by this coefficient, we produced the "scaled fully-calibrated albedo". We strongly recommend to use the latter for most applications because it generally remains in the physical range 0-1. The former albedo is provided for reference to the companion paper and because it does not depend on the SSA estimation process and its underlying assumptions.