Spatial, functional and genetic characteristics of field-planted and naturally-regenerated populations of white spruce (Picea glauca (Moench) Voss)

The spatial structure of white spruce populations was studied in 52 stands. White spruce tree density increased with age in the 200-year chronosequence after fire. Tree height and DBH peaked at about 120 years after fire. Sapling density along the chronosequence after clearcutting exhibited similar pattern to that after fire, but peaked earlier. White spruce seedlings were present in various densities and heights along the chronosequence after fire, producing uneven-aged stands. Seedling regeneration was mostly on the LFH layer (72%) in younger plots and on logs (97%) in old plots. Seedlings in both regeneration types were evenly spaced at a young age. This pattern changed to random and clumped in older stands. Artificially planted clearcuts formed more even-aged stands. Physiological, morphological and growth responses to sun and shade treatments in the greenhouse were examined in white spruce seedlings collected from three naturally-regenerated (N1, N2 and N3) and three field-planted (P1, P2, and P3) stands. Dark respiration and light compensation points declined by 70 and 81% respectively, in shade- compared to sun-acclimated seedlings. Quantum yield, total chlorophyll content, specific leaf area and absolute water content increased by 45, 33, 32 and 50% respectively, in response to shade treatment. Height was not affected by light regime. Fewer and longer secondary branches were noticed in the shade compared to full sun. At light saturation, populations P1 and N3 showed similar photosynthetic responses under both light regimes (around 6 [mu]mol m-2 s-1). Populations P2, P3 and N2 performed more poorly in the sun than in the shade (8.2, 8.7 and 9.1 in shade, versus 5.1, 4.1 and 5.5 in full sun, respectively). Photosynthetic rate in N1 was greater in full sun than in shade (14.7 and 11.1 [mu]mol m -2 s-1, respectively). Differences in physiological responses to light among populations suggest the presence of more than one ecotype. The variation in physiological and morphological parameters within field-planted and naturally-regenerated populations was large, and did not show any obvious differences among populations. RAPD analysis showed abundant polymorphism in all populations. The naturally-regenerated arid the field-planted populations demonstrated similar within and among regeneration-type variation. Of the total genetic variation 82.9% was due to intra-population variation, while inter-population variation and regeneration type accounted for 16.7 and 0.4% of the total variation, respectively. It appears that selection pressure during reforestation was not great enough to cause a significant decline in the genetic diversity of field-planted compared to naturally-regenerated white spruce

Hiring leaders: Inference and disagreement about the best person for the job

Hiring is a critical determinant of organizational performance and has received considerable attention in economics where the focus is on identifying who is the best person for the job (an adverse selection problem) and ensuring that the person hired has incentives to behave in a desirable manner (a moral hazard problem). The implicit assumption in this literature is that everyone agrees on what constitutes the “best candidate.” In this paper we show that the economics literature fails to recognize that people will generally disagree over “what is best?” Answering this question requires people to make inferences about the environment the organization expects to experience in the future and to match this environment with leader characteristics. Given the idiosyncratic nature of inference, there will be disagreement on the “best person for the job,” even when everyone shares the same goals. The purpose of this paper is to outline why conflict regarding the most desirable person for the job emerges in rapidly changing environments and how this conflict is different from conflict that arises from self-interest and the presence of decision-making biases. The paper shows that conflict from inference, if properly dealt with, can actually improve decision-making, and what can be done to create the right conditions for this to occur. The paper also shows why hiring always involves an element of luck

Leveraging Image Analysis for High-Throughput Plant Phenotyping

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant’s phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field

Leveraging Image Analysis for High-Throughput Plant Phenotyping

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field

Ecophysiolgoical Responses of \u3ci\u3eSchizachyrium scoparium\u3c/i\u3e to Water and Nitrogen Manipulations

Nitrogen is increasing in terrestrial ecosystems as a result of agricultural practices and the burning of fossil fuels. This increase is expected to be accompanied by changes in water availability due to global warming. We examined the effects of nitrogen and water manipulations on Schizachyrium scoparium, one of the dominant grasses in the Great Plains. Schizachyrium scoparium responded positively to watering, with an increase in photosynthesis, stomatal conductance, water and nitrogen use efficiencies, and water potential. Under watered conditions, fertilization had no significant effect on measured parameters, except for nitrogen-use efficiency. Significant differences appeared between fertilized and nonfertilized plants under moderate drought, with fertilized plants maintaining higher photosynthesis and water-use efficiency than nonfertilized plants. Water potential declined with water stress but did not differ between fertilization treatments, while nitrogen-use efficiency was significantly higher under non fertilized than fertilized treatment. Differences among fertilization treatments disappeared under severe drought. We conclude that S. scoparium will likely respond positively to fertilization under moderate drought in the Great Plains. However, under severe drought, fertilization will not provide any physiological advantages to S. scoparium

Holistic and component plant phenotyping using temporal image sequence

Background: Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored. Results: A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska–Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1. Conclusion: The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science

Ecophysiology of seedlings of three Mediterranean pine species in contrasting light regimes

Seasonal dynamics of net photosynthesis (Anet) in 2-year-old seedlings of Pinus brutia Ten., Pinus pinea L. and Pinus pinaster Ait. were investigated. Seedlings were grown in the field in two light regimes: sun (ambient light) and shade (25% of photosynthetically active radiation (PAR)). Repeated measures analyses over a 12-month period showed that Anet varied significantly among species and from season to season. Maximum Anet in sun-acclimated seedlings was low in winter (yet remained positive) and peaked during summer. Maximum Anet was observed in June in P. pinea (12 μmol m–2 s–1), July in P. pinaster (23 μmol m–2 s–1) and August in P. brutia (20 μmol m–2 s–1). Photosynthetic light response curves saturated at a PAR of 200–300 μmol m–2 s–1 in winter and in shade-acclimated seedlings in summer. Net photosynthesis in sun-acclimated seedlings did not saturate at PAR up to 1900 μmol m–2 s–1 in P. brutia and P. pinaster. Minimum air temperature of the preceding night was apparently one of the main factors controlling Anet during the day. In shade-acclimated seedlings, photosynthetic rates were reduced by 50% in P. brutia and P. pinaster and by 20% in P. pinea compared with those in sun-acclimated seedlings. Stomatal conductance was generally lower in shaded seedlings than in seedlings grown in the sun, except on days with a high vapor pressure deficit. Total chlorophyll concentration per unit leaf area, specific leaf area (SLA) and height significantly increased in P. pinea in response to shade, but not in P. pinaster or P. brutia. In response to shade, P. brutia showed a significant increase in total chlorophyll concentration but not SLA. Photosynthetic and growth data indicate that P. pinaster and P. brutia are more light-demanding than P. pinea

Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis

Image-based plant phenotyping analysis refers to the monitoring and quantification of phenotyping traits by analyzing images of the plants captured by different types of cameras at regular intervals in a controlled environment. Extracting meaningful phenotypes for temporal phenotyping analysis by considering individual parts of a plant, e.g., leaves and stem, using computer-vision based techniques remains a critical bottleneck due to constantly in- creasing complexity in plant architecture with variations in self-occlusions and phyllotaxy. The paper introduces an algorithm to compute the stem angle, a potential measure for plants’ susceptibility to lodging, i.e., the bending of stem of the plant. Annual yield losses due to stem lodging in the U.S. range between 5 and 25%. In addition to outright yield losses, grain quality may also decline as a result of stem lodging. The algorithm to compute stem angle involves the identification of leaf-tips and leaf-junctions based on a graph theoretic approach. The efficacy of the proposed method is demonstrated based on experimental analysis on a publicly available dataset called Panicoid Phenomap-1. A time-series clustering analysis is also performed on the values of stem angles for a significant time interval during vegetative stage life cycle of the maize plants. This analysis effectively summarizes the temporal patterns of the stem angles into three main groups, which provides further insight into genotype specific behavior of the plants. A comparison of genotypic purity using time series analysis establishes that the temporal variation of the stem angles is likely to be regulated by genetic variation under similar environmental conditions

Leveraging Image Analysis to Compute 3D Plant Phenotypes Based on Voxel-Grid Plant Reconstruction

High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions