Deep learning-based segmentation for disease identification

Plant diseases have recently increased and exacerbated due to several factors such as climate change, chemicals’ misuse and pollution. They represent a severe threat for both economy and global food security. Recently, several researches have been proposed for plant disease identification through modern image-based recognition systems based on deep learning. However, several challenges still require further investigation. One is related to the high variety of leaf diseases/ species along with constraints related to the collection and annotation of real-world datasets. Other challenges are related to the study of leaf disease in uncontrolled environment. Compared to major existing researches, we propose in this article a new perspective to handle the problem with two main differences: First, while most approach aims to identify simultaneously a pair of species-disease, we propose to identify diseases independently of leaf species. This helps to recognize new species holding diseases that were previously learnt. Moreover, instead of using the global leaf image, we directly predict disease on the basis of the local disease symptom features. We believe that this may decrease the bias related to common context and/or background and enables to build a more generalised model for disease classification. In particular, we propose an hybrid system that combines strengths of deep learning-based semantic segmentation with classification capabilities to respectively extract infected regions and determine their identity. For that, an extensive experimentation including a comparison of different semantic segmentation and classification CNNs has been conducted on PlantVillage dataset (leaves within homogeneous background) in order to study the extent of use of local disease symptoms features to identify diseases. Specifically, a particular enhancement of disease identification accuracy has been demonstrated in IPM and BING datasets (leaves within uncontrolled background).     

Oviposition site selection on oats: the effect of plant architecture,plant and leaf age,tissue toughness,and hardness on cereal leaf beetle,Oulema melanopus

In sequentially planted oat stands, the cereal leaf beetle (CLB), Oulema melanopus (L.) (Coleoptera: Chrysomelidae: Lemini), is found in greater numbers, and lays more eggs, on later planted (younger) oats (Avena sativa L.) (Poaceae). Plant characteristics that could explain this ovipositional preference were examined in a series of experiments. Cage and open field whole plant preference tests confirmed the attraction of ovipositing females to younger oats. A cage effect illustrated the role of plant architecture (plant height) in CLB host selection. Two multiple‐choice and one no‐choice excised leaf experiments determined that characteristics of individual leaves associated with leaf insertion level (leaf number from base to apex) and age influence ovipositional site selection. Leaves of higher insertion level have higher nitrogen content, but fewer eggs are laid on those leaves. Two experiments examining the interaction between total leaf nitrogen and leaf insertion level showed that only leaf insertion level affected oviposition choice. Published literature suggests variation in secondary plant compounds cannot explain O. melanopus ovipositional preference among leaves. Grass leaves of higher insertion level have more extensively developed cells associated with tissue toughness and hardness. The data and supporting literature suggest tissue toughness and hardness are deterring oviposition on oat leaves of higher insertion level. However, newly eclosed larvae are able to feed on leaves usually avoided as oviposition sites. The explanation for this result may be a lack of correlation between host suitability and ovipositional preference.     

Hormaphis hamamelidis and gall size: a test of the plant vigor hypothesis

We tested the Plant Vigor Hypothesis by determining the distribution of galls formed on leaves of witch hazel, Hamamelis virginiana , by the aphid Hormaphis hamamelidis , and by determining various factors that affect the fecundity of the gall-forming fundatrices. We also studied the role of the fundatrix in host plant manipulation. While the mean number of galls per leaf was low, galls had an aggregated distribution among leaves. Among trees, the average number of galls per leaf was not related to the mean leaf size, contrary to the preference prediction of the Plant Vigor Hypothesis. While fundatrices preferred the distal leaves of buds, which grew more than the proximal leaves, being on distal leaves conferred no increase in fecundity for fundatrices, contrary to predictions of the Plant Vigor Hypothesis. Gall size was the factor that explained the largest proportion of variation in fundatrix fecundity; fundatrix size explained somewhat less of the variation. Also, gall position on the leaf, number of aphid galls on the leaf, and on which leaf of the bud the gall was located all played small, statistically significant roles in explaining fundatrix fecundity, but their effects were variable between experiments. Removal of fundatrices shortly after galls had enclosed them limited the growth of galls, indicating the role of the fundatrices in gall growth. We compare and contrast this system versus other gall-forming insects, as well as discuss the adaptive significance of the aphid manipulation of the host plant. Much of the data contradict predictions of the Plant Vigor Hypothesis, and we discuss how gall size, as a measure of plant growth caused by insect manipulation, explains the observed patterns.     

Plant Vascular Architecture Determines the Pattern of Herbivore-Induced Systemic Responses in Arabidopsis thaliana

The induction of systemic responses in plants is associated with the connectivity between damaged and undamaged leaves, as determined by vascular architecture. Despite the widespread appreciation for studying variation in induced plant defense, few studies have characterized spatial variability of induction in the model species, Arabidopsis thaliana. Here we show that plant architecture generates fine scale spatial variation in the systemic induction of invertase and phenolic compounds. We examined whether the arrangement of leaves along the stem (phyllotaxy) produces predictable spatial patterns of cell-wall bound and soluble invertase activities, and downstream phenolic accumulation following feeding by the dietary specialist herbivore, Pieris rapae and the generalist, Spodoptera exigua. Responses were measured in leaves within and outside of the damaged orthostichy (leaves sharing direct vascular connections), and compared to those from plants where source-sink transport was disrupted by source leaf removal and by an insertional mutation in a sucrose transporter gene (suc2-1). Following herbivore damage to a single, middle-aged leaf, induction of cell-wall and soluble invertase was most pronounced in young and old leaves within the damaged orthostichy. The pattern of accumulation of phenolics was also predicted by these vascular connections and was, in part, dependent on the presence of source leaves and intact sucrose transporter function. Induction also occurred in leaves outside of the damaged orthostichy, suggesting that mechanisms may exist to overcome vascular constraints in this system. Our results demonstrate that systemic responses vary widely according to orthostichy, are often herbivore-specific, and partially rely on transport between source and sink leaves. We also provide evidence that patterns of induction are more integrated in A. thaliana than previously described. This work highlights the importance of plant vascular architecture in determining patterns of systemic induction, which is likely to be ecologically important to insect herbivores and plant pathogens.     

Variation in Nickel Content in the Nickel-Hyperaccumulating Shrub Psychotria douarrei (Rubiaceae) from New Caledonia1

Plants that hyperaccumulate Ni contain > 1000 ppm (dry wt.) in their tissues. Variation of Ni content within hyperaccumulating plant species is poorly explored. Using the Ni-hyperaccumulating shrub Psychotria douarrei, we documented variation of leaf Ni levels within individual shrubs, and variation with respect to plant size and leaf age. Plant size did not correlate significantly with leaf Ni content, and leaf Ni content did not correlate significantly with soil Ni content. Older leaves contained twice as much Ni as younger leaves. Older leaves also contained greater concentrations of Ca, Fe, and Cr but less K, P, and Cu. Five elements (Zn, Pb, Co, Mn, Mg) showed no significant variation due to leaf age. We also examined the effect of leaf age on epiphyll cover, finding increased epiphyll cover on the upper surface of older leaves. The dominant leafy liverwort epiphyll had a relatively high Ni content (400 ppm), suggesting that epiphylls of Ni hyperaccumulators obtain some Ni from host leaves. Individual shrubs differed in mean leaf Ni content almost two-fold (14,900-27,700 ppm). Variation among branches within individuals also ranged widely; however, this intraplant variability was not strongly correlated with the mean leaf Ni content of an individual shrub. We concluded that Ni contents in leaves of P. douarrei vary considerably due to leaf age, among individual shrubs, and among branches within a shrub.     

The leaf size – twig size spectrum and its relationship to other important spectra of variation among species

There is a spectrum from species with narrow, frequently branched twigs carrying small leaves and other appendages, to species with thick twigs carrying large leaves and appendages. Here we investigate the allometry of this spectrum and its relationship to two other important spectra of ecological variation between species, the seed mass-seed output spectrum and the specific leaf area-leaf lifespan spectrum. Our main dataset covered 33 woody dicotyledonous species in sclerophyll fire-prone vegetation on low nutrient soil at 1,200 mm annual rainfall near Sydney, Australia. These were phylogenetically selected to contribute 32 evolutionary divergences. Two smaller datasets, from 390 mm annual rainfall, were also examined to assess generality of cross-species patterns. There was two to three orders of magnitude variation in twig cross-sectional area, individual leaf size and total leaf area supported on a twig across the study species. As expected, species with thicker twigs had larger leaves and branched less often than species with thin twigs. Total leaf area supported on a twig was mainly driven by leaf size rather than by the number of leaves. Total leaf area was strongly correlated with twig cross-section area, both across present-day species and across evolutionary divergences. The common log-log slope of 1.45 was significantly steeper than 1. Thus on average, species with tenfold larger leaves supported about threefold more leaf area per twig cross-section, which must have considerable implications for other aspects of water relations. Species at the low rainfall site on loamy sand supported about half as much leaf area, at a given twig cross-section, as species at the low rainfall site on light clay, or at the high rainfall site. Within sites, leaf and twig size were positively correlated with seed mass, and negatively correlated with specific leaf area. Identifying and understanding leading spectra of ecological variation among species is an important challenge for plant ecology. The seed mass-seed output and specific leaf area-leaf lifespan spectra are each underpinned by a single, comprehensible trade-off and their consequences are fairly well understood. The leaf-size-twig-size spectrum has obvious consequences for the texture of canopies, but we are only just beginning to understand the costs and benefits of large versus small leaf and twig size.     

Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.     

Leaf size of woody dicots predicts ecosystem primary productivity

A key challenge in ecology is to understand the relationships between organismal traits and ecosystem processes. Here, with a novel dataset of leaf length and width for 10 480 woody dicots in China and 2374 in North America, we show that the variation in community mean leaf size is highly correlated with the variation in climate and ecosystem primary productivity, independent of plant life form. These relationships likely reflect how natural selection modifies leaf size across varying climates in conjunction with how climate influences canopy total leaf area. We find that the leaf size?primary productivity functions based on the Chinese dataset can predict productivity in North America and vice‐versa. In addition to advancing understanding of the relationship between a climate‐driven trait and ecosystem functioning, our findings suggest that leaf size can also be a promising tool in palaeoecology for scaling from fossil leaves to palaeo‐primary productivity of woody ecosystems.     

烟粉虱成虫密度自动计数系统

为改善烟粉虱Bemisia tabaci(Gennadius)种群密度调查的手段,根据图像识别原理,形成了一套田间烟粉虱成虫密度自动计数系统,该系统通过成像装置采集烟粉虱成虫的图像,测量成像装置与成像对象之间的距离,对图像中的烟粉虱图像进行识别并计数,计算寄主植物叶面积,最后得出烟粉虱成虫密度。应用该系统对烟粉虱成虫密度进行调查,准确率在90%以上。     

Architecture of coastal and desert Encelia farinosa (Asteraceae): consequences of plastic and heritable variation in leaf characters

The shrub Encelia farinosa (Asteraceae) exhibits geographic variation in aboveground architecture and leaf traits in parallel with environmental variation in temperature and moisture. Measurements of plants occurring across a natural gradient demonstrated that plants in desert populations produce smaller, more pubescent leaves and are more compact and branched than plants in more mesic coastal environments. This phenotypic variation is interpreted in part as adaptive genetic differentiation; small size and pubescence reduce leaf temperature and thus increase water-use efficiency but at the cost of lower photosynthetic rate, which results in slower growth and more compact growth form. We explored the basis of phenotypic variation by planting seed offspring from coastal and desert populations in common gardens in both environments. Phenotypic differences among populations persisted in both common gardens, suggesting a genetic basis for trait variation. Desert offspring outperformed coastal offspring in the desert garden, suggesting superior adaptation to hot, dry conditions. Herbivore damage was greater for all offspring in the coastal garden. Phenotypic characters also showed plastic responses; all offspring had smaller, more pubescent leaves and more compact growth form in the desert garden. Our results confirm that leaf size and pubescence are heritable characters associated with pronounced variation in plant architecture.     

Nondestructive, Stereological Estimation of Canopy Surface Area

Summary .  We describe a stereological procedure to estimate the total leaf surface area of a plant canopy in vivo, and address the problem of how to predict the variance of the corresponding estimator. The procedure involves three nested systematic uniform random sampling stages: (i) selection of plants from a canopy using the  smooth fractionator , (ii) sampling of leaves from the selected plants using the  fractionator , and (iii) area estimation of the sampled leaves using  point counting . We apply this procedure to estimate the total area of a chrysanthemum ( Chrysanthemum morifolium L. ) canopy and evaluate both the time required and the precision of the estimator. Furthermore, we compare the precision of point counting for three different grid intensities with that of several standard leaf area measurement techniques. Results showed that the precision of the plant leaf area estimator based on point counting is high. Using a grid intensity of 1.76 cm²/point we estimated plant and canopy surface areas with accuracies similar to or better than those obtained using image analysis and a commercial leaf area meter. For canopy surface areas of approximately 1 m² (10 plants), the fractionator leaf approach with sampling fraction equal to 1/9 followed by point counting using a 4.3 cm²/point grid produced a coefficient of error of less than 7%. The  smooth fractionator  can be used to ensure that the additional contribution to the estimator variance due to between-plant variability is small.     

A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images

Plant diseases cause significant food loss and hence economic loss around the globe. Therefore, automatic plant disease identification is a primary task to take proper medications for controlling the spread of the diseases. Large variety of plants species and their dissimilar phytopathological symptoms call for the implementation of supervised machine learning techniques for efficient and reliable disease identification and classification. With the development of deep learning strategies, convolutional neural network (CNN) has paved its way for classification of multiple plant diseases by extracting rich features. However, several characteristics of the input images especially captured in real world environment, viz. complex or indistinguishable background, presence of multiple leaves with the diseased leaf, small lesion area, solemnly affect the robustness and accuracy of the CNN modules. Available strategies usually applied standard CNN architectures on the images captured in the laboratory environment and very few have considered practical in-field leaf images for their studies. However, those studies are limited with very limited number of plant species. Therefore, there is need of a robust CNN module which can successfully recognize and classify the dissimilar leaf health conditions of non-identical plants from the in-field RGB images. To achieve the above goal, an attention dense learning (ADL) mechanism is proposed in this article by merging mixed sigmoid attention learning with the basic dense learning process of deep CNN. The basic dense learning process derives new features at higher layer considering all lower layer features and that provides fast and efficient training process. Further, the attention learning process amplifies the learning ability of the dense block by discriminating the meaningful lesion portions of the images from the background areas. Other than adding an extra layer for attention learning, in the proposed ADL block the output features from higher layer dense learning are used as an attention mask to the lower layers. For an effective and fast classification process, five ADL blocks are stacked to build a new CNN architecture named DADCNN-5 for obtaining classification robustness and higher testing accuracy. Initially, the proposed DADCNN-5 module is applied on publicly available extended PlantVillage dataset to classify 38 different health conditions of 14 plant species from 54,305 images. Classification accuracy of 99.93% proves that the proposed CNN module can be used for successful leaf disease identification. Further, the efficacy of the DADCNN-5 model is checked after performing stringent experiments on a new real world plant leaf database, created by the authors. The new leaf database contains 10,851 real-world RGB leaf images of 17 plant species for classifying their 44 distinguished health conditions. Experimental outcomes reveal that the proposed DADCNN-5 outperforms the existing machine learning and standard CNN architectures, and achieved 97.33% accuracy. The obtained sensitivity, specificity and false positive rate values are 96.57%, 99.94% and 0.063% respectively. The module takes approximately 3235 min for training process and achieves 99.86% of training accuracy. Visualization of Class activation mapping (CAM) depicts that DADCNN-5 is able to learn distinguishable features from semantically important regions (i.e. lesion regions) on the leaves. Further, the robustness of the DADCNN-5 is established after experimenting with augmented and noise contaminated images of the practical database.     

Vascular architecture and patchy nutrient availability generate within-plant heterogeneity in plant traits important to herbivores

Within-plant heterogeneity in growth, morphology, and chemistry is ubiquitous, and is commonly attributed to differences in tissue age, light availability, or previous damage by herbivores. Although these factors are important, we argue that plant vascular architecture is an underappreciated determinant of heterogeneity. Vascular architecture can restrict the transport of resources (nutrients, photosynthate, hormones, etc.) to within specific sectors of the plant: this is referred to as sectoriality. Although studies have documented sectoriality in the transport of isotopes and dyes from roots to shoots, the ecological consequences of this sectoriality remain poorly understood. We tested the hypothesis that spatial variation in belowground nutrient availability combined with sectorial transport results in localized "fertilization" of aboveground plant parts and generates heterogeneity in traits important to herbivores. Our split-root experiments with tomato (Lycopersicon esculentum Mill) clearly demonstrate that fertilization to isolated lateral roots generates heterogeneity in leaf morphology, phenolic chemistry, and side-shoot growth. Specifically, leaflets with direct connections to these lateral roots were larger and had lower levels of rutin and chlorogenic acid than did leaflets in other sectors lacking direct vascular connections. Moreover, side-shoot production was greater in the connected sectors. We discuss the implications of this heterogeneity for plant-herbivore interactions.     

浙江天童太白山不同群落植物构型比较

植物构型是植株构件在空间上的分配方式,反映了植物对环境的响应策略。通过对浙江天童太白山海拔差异很小的栲树(Castanopsis fargesii)、小叶青冈(Cyclobalanopsis gracilis)和云山青冈(Cyclobalanopsis sessilifolia)群落类型中所有植株的树高、树冠厚度、树冠面积、叶片聚集度、枝下高和距地45 cm基径等植物构型性状,以及树冠曝光指数、土壤含水率、空气温湿度、土壤p H值和风速6个环境因子的测定,分别分析乔灌木层植物构型性状及性状关系在3个群落间的变化规律。结果表明:(1)从栲树到小叶青冈至云山青冈群落,灌木层的树高、树冠厚度、枝下高和距地45 cm基径增加,叶片聚集度减小;乔木层的树高、树冠厚度、树冠面积、枝下高和距地45 cm基径均减小,叶片聚集度增大;(2)3个群落灌木层构型性状间显著相关(P0.001),而乔木层只在中低海拔群落存在相关性;(3)从栲树到小叶青冈至云山青冈群落,乔灌木层的冠层曝光指数显著增加(P0.05);(4)多元逐步回归表明,树冠曝光指数对灌木层构型性状变异的贡献最大,而风速、土壤含水率和p H值对乔木构型性状的变异起主导作用。综上得知,天童太白山乔灌木植物在不同群落间存在构型分异,植物对光资源的竞争是引起灌木构型在不同群落间变化的主要驱动因子,而对乔木植物,其构型变化更多受到风速和土壤含水量的影响。     

Do Clonal and Bud Bank Traits Vary in Correspondence with Soil Properties and Resource Acquisition Strategies? Patterns in Alpine Communities in the Scandian Mountains

Plant traits associated with resource acquisition strategies (specific leaf area (SLA), leaf dry matter content (LDMC), leaf size and plant height) change along gradients of soil properties, being the most conservative in a resource-poor environment and the most dynamic in a resource-rich environment. Clonal attributes also vary along soil and other environmental conditions. We hypothesized that in alpine communities in the Scandian Mts. (1) the average composition of traits in a plant assemblage in terms of i) the predominance of different clonal growth organ types, ii) the number of buds in the bud bank, iii) the distribution of the bud-bank (above- and below ground), iv) the distance of lateral spread and v) the longevity of plant – offspring connections would change along a gradient of soil properties and (2) that this variation would be in correspondence with that of traits associated with resource acquisition strategies (SLA, LDMC, leaf size and plant height). Analysis of clonal and bud bank traits for species of alpine communities supported our first hypothesis: with decreasing soil quality the most common clonal growth organs were rhizomes, and there was a predominance of perennial bud banks located at the soil surface or below-ground, low rates of lateral spread and long persistence of plant – offspring connections. Our second hypothesis was partly supported. As predicted, at the level of the plant assemblage, these clonal and bud bank traits were positively associated with LDMC, and negatively with leaf size and plant height. These observations reinforce the hypotheses about trade-offs between acquisition and retention strategies in plants. The only result that was in contradiction with our expectations was the lack of correspondence between clonal and bud bank traits and SLA that could be attributed to errors associated to the measurement of the area of narrow and small leaves or to the dependence of the SLA index on species-specific morphological attributes.     

Seeing the forest with the leaves – clues to canopy placement from leaf fossil size and venation characteristics

Although a variety of leaf characteristics appear to be induced by light environment during development, analysis of ontogenetic changes in living broad leaved trees has suggested that a number of other traits also lumped into the classic 'sun' versus 'shade' morphological distinctions, including leaf size, shape, and vein density, are instead controlled largely by local hydraulic environment within the tree canopy. The regularity in how these traits vary with canopy placement suggests a method for addressing a classic paleobotanical quandary: the stature of the source plant – from herb or shrub to canopy tree – is typically unknown for leaf fossils. The study of Ginkgo here complements previous work on Quercus that indicated that leaves throughout the crown are identical in size and venation at the time of bud break and that morphological adaptation to the local microenvironment takes place largely during the expansion phase after the determination of the vascular architecture is complete. Hence, variation in vein density does not reflect differential vein production so much as the distortion of similar vein networks over different final surface areas driven by variation in local hydraulic supply during expansion. Unlike the diffusely growing leaves of the angiosperm, Quercus , the marginally growing leaves of Ginkgo do show some potential for differential vein production, but expansion effects still dominate. The approach suggested here may prove useful for assessing the likelihood that two distinct fossil morphospecies actually represent leaves of the same plant and to gather information concerning canopy structure from disarticulated leaves.     

Distribution of imported 14C in developing leaves of eastern cottonwood according to phyllotaxy

Summary Individual leaves of eastern cottonwood (Populus deltoides Bartr.), representing an ontogenetic series from leaf plastochron index (LPI) 3.0 to 8.0, were fed ¹⁴CO₂ and harvested after 2–24 h. Importing leaves from LPI-1.0 through 8.0 on each plant were sectioned into 9 parts, and each part was quantitatively assayed for ¹⁴C activity. The highest level of ¹⁴C import was by leaves from LPI 1.0 to 3.0, irrespective of source-leaf age. ¹⁴C was translocated preferentially to either the right or left lamina-half depending on the position of the importing leaf in the phyllotactic sequence and its stage of development. For example, import was high when the importing leaf and the source leaf had two vascular bundles in common, moderately high with one bundle in common, and low with no bundles in common. The distribution of ¹⁴C within young importing leaves was highest in the lamina tip and decreased toward the base. With increasing leaf age, incorporation declined in the lamina tip and increased in the base.It may be concluded that each cottonwood leaf progresses through a continuum of importing and exporting stages as its lamina expands. The photosynthate imported by a given leaf is compartmentalized, with different exporting leaves supplying photosynthate to rather restricted regions of the lamina. Such localization within the importing leaf depends on its vascular connections with each of the exporting leaves, and these are predictable from a knowledge of the phyllotaxy.Plant Physiologists.     

植物叶片功能性状及其环境适应研究

植物叶片是连接植物与外界环境的重要桥梁,其功能性状变化受外界环境和系统发育的共同影响。充分了解不同环境下叶片功能性状的变化对探讨植物对环境的适应性具有重要意义。本文阐述了叶片功能性状的类型及其功能意义,综述了影响叶片功能性状2个主要因素（环境因子和系统发育历史）的相关研究,探讨了叶片功能性状对植物适应环境的意义。最后,对叶片功能性状研究的现状及未来趋势进行了展望。     

Plant phenology,leaf traits and leaf litterfall of contrasting life forms in the arid Patagonian Monte,Argentina

Question: Do coexisting plant life forms differ in overall phenology, leaf traits and patterns of leaf litterfall? Location: Patagonian Monte, Chubut Province, Argentina. Methods: We assessed phenology, traits of green and senesced leaves and the pattern of leaf litterfall in 12 species of coexisting life forms (perennial grasses, deciduous shrubs, evergreen shrubs). Results: We did not identify differences in phenology, leaf traits and patterns of leaf litterfall among life forms but these attributes contrasted among species. Independent of the life form, the maintenance of green leaves or vegetative growth during the dry season was mostly associated with leaves with high leaf mass per area (LMA) and high concentration of secondary compounds. Low LMA species produced low litterfall mass with low concentration of secondary compounds, and high N concentration. High LMA species produced the largest mass of leaf litterfall. Accordingly, species were distributed along two main dimensions of ecological variation, the dimension secondary compounds in leaves ‐ length and timing of the vegetative growth period (SC ‐ VGP) and the dimension leaf mass per area ‐ leaf litterfall mass (LMA ‐ LLM). Conclusions: Phenology, leaf traits and leaf litterfall varied among species and overlapped among life forms. The two dimensions of ecological variation among species (SC ‐ VGP, LMA ‐ LLM) represent distinct combinations of plant traits or strategies related to resource acquisition and drought tolerance which are reflected in the patterns of leaf litterfall.     

Harnessing Genetic Variation in Leaf Angle to Increase Productivity of Sorghum bicolor

The efficiency with which a plant intercepts solar radiation is determined primarily by its architecture. Understanding the genetic regulation of plant architecture and how changes in architecture affect performance can be used to improve plant productivity. Leaf inclination angle, the angle at which a leaf emerges with respect to the stem, is a feature of plant architecture that influences how a plant canopy intercepts solar radiation. Here we identify extensive genetic variation for leaf inclination angle in the crop plant Sorghum bicolor, a C4 grass species used for the production of grain, forage, and bioenergy. Multiple genetic loci that regulate leaf inclination angle were identified in recombinant inbred line populations of grain and bioenergy sorghum. Alleles of sorghum dwarf-3, a gene encoding a P-glycoprotein involved in polar auxin transport, are shown to change leaf inclination angle by up to 34° (0.59 rad). The impact of heritable variation in leaf inclination angle on light interception in sorghum canopies was assessed using functional-structural plant models and field experiments. Smaller leaf inclination angles caused solar radiation to penetrate deeper into the canopy, and the resulting redistribution of light is predicted to increase the biomass yield potential of bioenergy sorghum by at least 3%. These results show that sorghum leaf angle is a heritable trait regulated by multiple loci and that genetic variation in leaf angle can be used to modify plant architecture to improve sorghum crop performance.