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A new empirical equation to describe the vertical leaf distribution profile of maize
- P. P. Fan, Y. Y. Li, J. B. Evers, B. Ming, C. X. Wang, S. K. Li, R. Z. Xie
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- Journal:
- The Journal of Agricultural Science / Volume 158 / Issue 8-9 / November 2020
- Published online by Cambridge University Press:
- 10 February 2021, pp. 676-686
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The characteristic traits of maize (Zea mays L.) leaves affect light interception and photosynthesis. Measurement or estimation of individual leaf area has been described using discontinuous equations or bell-shaped functions. However, new maize hybrids show different canopy architecture, such as leaf angle in modern maize which is more upright and ear leaf and adjacent leaves which are longer than older hybrids. The original equations and their parameters, which have been used for older maize hybrids and grown at low plant densities, will not accurately represent modern hybrids. Therefore, the aim of this paper was to develop a new empirical equation that captures vertical leaf distribution. To characterize the vertical leaf profile, we conducted a field experiment in Jilin province, Northeast China from 2015 to 2018. Our new equation for the vertical distribution of leaf profile describes leaf length, width or leaf area as a function of leaf rank, using parameters for the maximum value for leaf length, width or area, the leaf rank at which the maximum value is obtained, and the width of the curve. It thus involves one parameter less than the previously used equations. By analysing the characteristics of this new equation, we identified four key leaf ranks (4, 8, 14 and 20) for which leaf parameter values need to be quantified in order to have a good estimation of leaf length, width and area. Together, the method of leaf area estimation proposed here adds versatility for use in modern maize hybrids and simplifies the field measurements by using the four key leaf ranks to estimate vertical leaf distribution in maize canopy instead of all leaf ranks.
SPICA—A Large Cryogenic Infrared Space Telescope: Unveiling the Obscured Universe
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- P. R. Roelfsema, H. Shibai, L. Armus, D. Arrazola, M. Audard, M. D. Audley, C.M. Bradford, I. Charles, P. Dieleman, Y. Doi, L. Duband, M. Eggens, J. Evers, I. Funaki, J. R. Gao, M. Giard, A. di Giorgio, L. M. González Fernández, M. Griffin, F. P. Helmich, R. Hijmering, R. Huisman, D. Ishihara, N. Isobe, B. Jackson, H. Jacobs, W. Jellema, I. Kamp, H. Kaneda, M. Kawada, F. Kemper, F. Kerschbaum, P. Khosropanah, K. Kohno, P. P. Kooijman, O. Krause, J. van der Kuur, J. Kwon, W. M. Laauwen, G. de Lange, B. Larsson, D. van Loon, S. C. Madden, H. Matsuhara, F. Najarro, T. Nakagawa, D. Naylor, H. Ogawa, T. Onaka, S. Oyabu, A. Poglitsch, V. Reveret, L. Rodriguez, L. Spinoglio, I. Sakon, Y. Sato, K. Shinozaki, R. Shipman, H. Sugita, T. Suzuki, F. F. S. van der Tak, J. Torres Redondo, T. Wada, S. Y. Wang, C. K. Wafelbakker, H. van Weers, S. Withington, B. Vandenbussche, T. Yamada, I. Yamamura
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- Journal:
- Publications of the Astronomical Society of Australia / Volume 35 / 2018
- Published online by Cambridge University Press:
- 28 August 2018, e030
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Measurements in the infrared wavelength domain allow direct assessment of the physical state and energy balance of cool matter in space, enabling the detailed study of the processes that govern the formation and evolution of stars and planetary systems in galaxies over cosmic time. Previous infrared missions revealed a great deal about the obscured Universe, but were hampered by limited sensitivity.
SPICA takes the next step in infrared observational capability by combining a large 2.5-meter diameter telescope, cooled to below 8 K, with instruments employing ultra-sensitive detectors. A combination of passive cooling and mechanical coolers will be used to cool both the telescope and the instruments. With mechanical coolers the mission lifetime is not limited by the supply of cryogen. With the combination of low telescope background and instruments with state-of-the-art detectors SPICA provides a huge advance on the capabilities of previous missions.
SPICA instruments offer spectral resolving power ranging from R ~50 through 11 000 in the 17–230 μm domain and R ~28.000 spectroscopy between 12 and 18 μm. SPICA will provide efficient 30–37 μm broad band mapping, and small field spectroscopic and polarimetric imaging at 100, 200 and 350 μm. SPICA will provide infrared spectroscopy with an unprecedented sensitivity of ~5 × 10−20 W m−2 (5σ/1 h)—over two orders of magnitude improvement over what earlier missions. This exceptional performance leap, will open entirely new domains in infrared astronomy; galaxy evolution and metal production over cosmic time, dust formation and evolution from very early epochs onwards, the formation history of planetary systems.
The AusBeef model for beef production: II. sensitivity analysis
- H. C. DOUGHERTY, E. KEBREAB, M. EVERED, B. A. LITTLE, A. B. INGHAM, J. V. NOLAN, R. S. HEGARTY, D. PACHECO, M. J. MCPHEE
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- Journal:
- The Journal of Agricultural Science / Volume 155 / Issue 9 / November 2017
- Published online by Cambridge University Press:
- 03 August 2017, pp. 1459-1474
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The present study evaluated the behaviour of the AusBeef model for beef production as part of a 2 × 2 study simulating performance on forage-based and concentrate-based diets from Oceania and North America for four methane (CH4)-relevant outputs of interest. Three sensitivity analysis methods, one local and two global, were conducted. Different patterns of sensitivity were observed between forage-based and concentrate-based diets, but patterns were consistent within diet types. For the local analysis, 36, 196, 47 and 8 out of 305 model parameters had normalized sensitivities of 0, >0, >0·01 and >0·1 across all diets and outputs, respectively. No parameters had a normalized local sensitivity >1 across all diets and outputs. However, daily CH4 production had the greatest number of parameters with normalized local sensitivities >1 for each individual diet. Parameters that were highly sensitive for global and local analyses across the range of diets and outputs examined included terms involved in microbial growth, volatile fatty acid (VFA) yields, maximum absorption rates and their inhibition due to pH effects and particle exit rates. Global sensitivity analysis I showed the high sensitivity of forage-based diets to lipid entering the rumen, which may be a result of the use of a feedlot-optimized model to represent high-forage diets and warrants further investigation. Global sensitivity analysis II showed that when all parameter values were simultaneously varied within ±10% of initial value, >96% of output values were within ±20% of the baseline, which decreased to >50% when parameter value boundaries were expanded to ±25% of their original values, giving a range for robustness of model outputs with regards to potential different ‘true’ parameter values. There were output-specific differences in sensitivity, where outputs that had greater maximum local sensitivities displayed greater degrees of non-linear interaction in global sensitivity analysis I and less variance in output values for global sensitivity analysis II. For outputs with less interaction, such as the acetate : propionate ratio and microbial protein production, the single most sensitive term in global sensitivity analysis I contributed more to the overall total-order sensitivity than for outputs with more interaction, with an average of 49, 33, 15 and 14% of total-order sensitivity for microbial protein production, acetate : propionate ratio, CH4 production and energy from absorbed VFAs, respectively. Future studies should include data collection for highly sensitive parameters reported in the present study to improve overall model accuracy.
The AusBeef model for beef production: I. Description and evaluation
- H. C. DOUGHERTY, E. KEBREAB, M. EVERED, B. A. LITTLE, A. B. INGHAM, R. S. HEGARTY, D. PACHECO, M. J. MCPHEE
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- Journal:
- The Journal of Agricultural Science / Volume 155 / Issue 9 / November 2017
- Published online by Cambridge University Press:
- 03 August 2017, pp. 1442-1458
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As demand for animal products, such as meat and milk, increases, and concern over environmental impact grows, mechanistic models can be useful tools to better represent and understand ruminant systems and evaluate mitigation options to reduce greenhouse gas emissions without compromising productivity. The objectives of the present study were to describe the representation of processes for growth and enteric methane (CH4) production in AusBeef, a whole-animal, dynamic, mechanistic model for beef production; evaluate AusBeef for its ability to predict daily methane production (DMP, g/day), gross energy intake (GEI, MJ/day) and methane yield (MJ CH4/MJ GEI) using an independent data set; and to compare AusBeef estimates to those from the empirical equations featured in the current National Academies of Sciences, Engineering and Medicine (NASEM, 2016) beef cattle requirements for growth and the Ruminant Nutrition System (RNS), a dynamic, mechanistic model of Tedeschi & Fox, 2016. AusBeef incorporates a unique fermentation stoichiometry that represents four microbial groups: protozoa, amylolytic bacteria, cellulolytic bacteria and lactate-utilizing bacteria. AusBeef also accounts for the effects of ruminal pH on microbial degradation of feed particles. Methane emissions are calculated from net ruminal hydrogen balance, which is defined as the difference between inputs from fermentation and outputs due to microbial use and biohydrogenation. AusBeef performed similarly to the NASEM empirical model in terms of prediction accuracy and error decomposition, and with less root mean square predicted error (RMSPE) than the RNS mechanistic model when expressed as a percentage of the observed mean (RMSPE, %), and the majority of error was non-systematic. For DMP, RMSPE for AusBeef, NASEM and RNS were 24·0, 19·8 and 50·0 g/day for the full data set (n = 35); 25·6, 18·2 and 56·2 g/day for forage diets (n = 19); and 21·8, 21·5 and 41·5 g/day for mixed diets (n = 16), respectively. Concordance correlation coefficients (CCC) were highest for GEI, with all models having CCC > 0·66, and higher CCC for forage diets than mixed, while CCC were lowest for MY, particularly forage diets. Systematic error increased for all models on forage diets, largely due to an increase in error due to mean bias, and while all models performed well for mixed diets, further refinements are required to improve the prediction of CH4 on forage diets.
Broadening International Perspectives on the Legal Environment for Personnel Selection
- Brett Myors, Filip Lievens, Eveline Schollaert, Greet Van Hoye, Steven F. Cronshaw, Antonio Mladinic, Viviana Rodríguez, Herman Aguinis, Dirk D. Steiner, Florence Rolland, Heinz Schuler, Andreas Frintrup, Ioannis Nikolaou, Maria Tomprou, S. Subramony, Shabu B. Raj, Shay Tzafrir, Peter Bamberger, Marilena Bertolino, Marco Mariani, Franco Fraccaroli, Tomoki Sekiguchi, Betty Onyura, Hyuckseung Yang, Neil Anderson, Arne Evers, Oleksandr Chernyshenko, Paul Englert, Hennie J. Kriek, Tina Joubert, Jesús f. Salgado, Cornelius J. König, Larissa A. Thommen, Aichia Chuang, Handan Kepir Sinangil, Mahmut Bayazit, Mark Cook, Winny Shen, Paul R. Sackett
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- Journal:
- Industrial and Organizational Psychology / Volume 1 / Issue 2 / June 2008
- Published online by Cambridge University Press:
- 07 January 2015, pp. 266-270
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International Perspectives on the Legal Environment for Selection
- Brett Myors, Filip Lievens, Eveline Schollaert, Greet Van Hoye, Steven F. Cronshaw, Antonio Mladinic, Viviana Rodríguez, Herman Aguinis, Dirk D. Steiner, Florence Rolland, Heinz Schuler, Andreas Frintrup, Ioannis Nikolaou, Maria Tomprou, S. Subramony, Shabu B. Raj, Shay Tzafrir, Peter Bamberger, Marilena Bertolino, Marco Mariani, Franco Fraccaroli, Tomoki Sekiguchi, Betty Onyura, Hyuckseung Yang, Neil Anderson, Arne Evers, Oleksandr Chernyshenko, Paul Englert, Hennie J. Kriek, Tina Joubert, Jesús F. Salgado, Cornelius J. König, Larissa A. Thommen, Aichia Chuang, Handan Kepir Sinangil, Mahmut Bayazit, Mark Cook, Winny Shen, Paul R. Sackett
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- Journal:
- Industrial and Organizational Psychology / Volume 1 / Issue 2 / June 2008
- Published online by Cambridge University Press:
- 07 January 2015, pp. 206-246
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Perspectives from 22 countries on aspects of the legal environment for selection are presented in this article. Issues addressed include (a) whether there are racial/ethnic/religious subgroups viewed as “disadvantaged,” (b) whether research documents mean differences between groups on individual difference measures relevant to job performance, (c) whether there are laws prohibiting discrimination against specific groups, (d) the evidence required to make and refute a claim of discrimination, (e) the consequences of violation of the laws, (f) whether particular selection methods are limited or banned, (g) whether preferential treatment of members of disadvantaged groups is permitted, and (h) whether the practice of industrial and organizational psychology has been affected by the legal environment.
Contributors
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- By Mohamed Aboulghar, Ahmed Abou-Setta, Mary E. Abusief, G. David Adamson, R. J. Aitken, Hesham Al-Inany, Baris Ata, Hamdy Azab, Adam Balen, David H. Barad, Pedro N. Barri, C. Blockeel, Giuseppe Botta, Mark Bowman, Chris Brewer, Dominique M. Butawan, Sandra A. Carson, Hai Ying Chen, Anne Clark, Buenaventura Coroleu, S. Das, C. Dechanet, H. Déchaud, Cora de Klerk, Sheryl de Lacey, S. Deutsch-Bringer, P. Devroey, Didier Dewailly, Hakan E. Duran, Walid El Sherbiny, Tarek El-Toukhy, Johannes L. H. Evers, Cynthia Farquhar, Rodney D. Franklin, Juan A. Garcia-Velasco, David K. Gardner, Norbert Gleicher, Gedis Grudzinskas, Roger Hart, B Hédon, Colin M. Howles, Jack Yu Jen Huang, N. P. Johnson, Hey-Joo Kang, Gab Kovacs, Ben Kroon, Anver Kuliev, William H. Kutteh, Nick Macklon, Ragaa Mansour, Lamiya Mohiyiddeen, Lisa J. Moran, David Mortimer, Sharon T. Mortimer, Luciano G. Nardo, Robert J. Norman, Willem Ombelet, Luk Rombauts, Zev Rosenwaks, Francisco J. Ruiz Flores, Anthony J. Rutherford, Gavin Sacks, Denny Sakkas, M. W. Seif, Ayse Seyhan, Caroline Smith, Kate Stern, Elizabeth A. Sullivan, Sesh Kamal Sunkara, Seang Lin Tan, Mohamed Taranissi, Kelton P. Tremellen, Wendy S. Vitek, V. Vloeberghs, Bradley J. Van Voorhis, S. F. van Voorst, Amr Wahba, Yueping A. Wang, Klaus E. Wiemer
- Edited by Gab Kovacs, Monash University, Victoria
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- Book:
- How to Improve your ART Success Rates
- Published online:
- 05 July 2011
- Print publication:
- 30 June 2011, pp viii-xii
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Contributors
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- By Waiel Almoustadi, Brian J. Anderson, David B. Auyong, Michael Avidan, Michael J. Avram, Roland J. Bainton, Jeffrey R. Balser, Juliana Barr, W. Scott Beattie, Manfred Blobner, T. Andrew Bowdle, Walter A. Boyle, Eugene B. Campbell, Laura F. Cavallone, Mario Cibelli, C. Michael Crowder, Ola Dale, M. Frances Davies, Mark Dershwitz, George Despotis, Clifford S. Deutschman, Brian S. Donahue, Marcel E. Durieux, Thomas J. Ebert, Talmage D. Egan, Helge Eilers, E. Wesley Ely, Charles W. Emala, Alex S. Evers, Heidrun Fink, Pierre Foëx, Stuart A. Forman, Helen F. Galley, Josephine M. Garcia-Ferrer, Robert W. Gereau, Tony Gin, David Glick, B. Joseph Guglielmo, Dhanesh K. Gupta, Howard B. Gutstein, Robert G. Hahn, Greg B. Hammer, Brian P. Head, Helen Higham, Laureen Hill, Kirk Hogan, Charles W. Hogue, Christopher G. Hughes, Eric Jacobsohn, Roger A. Johns, Dean R. Jones, Max Kelz, Evan D. Kharasch, Ellen W. King, W. Andrew Kofke, Tom C. Krejcie, Richard M. Langford, H. T. Lee, Isobel Lever, Jerrold H. Levy, J. Lance Lichtor, Larry Lindenbaum, Hung Pin Liu, Geoff Lockwood, Alex Macario, Conan MacDougall, M. B. MacIver, Aman Mahajan, Nándor Marczin, J. A. Jeevendra Martyn, George A. Mashour, Mervyn Maze, Thomas McDowell, Stuart McGrane, Berend Mets, Patrick Meybohm, Charles F. Minto, Jonathan Moss, Mohamed Naguib, Istvan Nagy, Nick Oliver, Paul S. Pagel, Pratik P. Pandharipande, Piyush Patel, Andrew J. Patterson, Robert A. Pearce, Ronald G. Pearl, Misha Perouansky, Kristof Racz, Chinniampalayam Rajamohan, Nilesh Randive, Imre Redai, Stephen Robinson, Richard W. Rosenquist, Carl E. Rosow, Uwe Rudolph, Francis V. Salinas, Robert D. Sanders, Sunita Sastry, Michael Schäfer, Jens Scholz, Thomas W. Schnider, Mark A. Schumacher, John W. Sear, Frédérique S. Servin, Jeffrey H. Silverstein, Tom De Smet, Martin Smith, Joe Henry Steinbach, Markus Steinfath, David F. Stowe, Gary R. Strichartz, Michel M. R. F. Struys, Isao Tsuneyoshi, Robert A. Veselis, Arthur Wallace, Robert P. Walt, David C. Warltier, Nigel R. Webster, Jeanine Wiener-Kronish, Troy Wildes, Paul Wischmeyer, Ling-Gang Wu, Stephen Yang
- Edited by Alex S. Evers, Washington University School of Medicine, St Louis, Mervyn Maze, University of California, San Francisco, Evan D. Kharasch, Washington University School of Medicine, St Louis
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- Book:
- Anesthetic Pharmacology
- Published online:
- 11 April 2011
- Print publication:
- 10 March 2011, pp viii-xiv
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