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Cluster and Principle Component Analysis of Soybean Grown at Various Row Spacings, Planting Dates and Plant Populations

Charanjit Singh Kahlon
  • Department of Experiment Statistics, AgCenter, Louisiana State University, Baton Rouge, NC-70803, USA
  • Other articles by this author:
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/ Bin Li
  • Department of Experiment Statistics, AgCenter, Louisiana State University, Baton Rouge, NC-70803, USA
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/ James Board
  • School of Plant, Environmental and Soil Sciences, Ag- Center, Louisiana State University, Baton Rouge, LA-70803, USA
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/ Mahendra Dia
  • Department of Horticultural Science, North Carolina State University, Raleigh, NC 27695-7609, USA
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/ Parmodh Sharma
  • Economics, Applied Statistics & International Business Department, New Mexico State University, Las Cruces, NM 88003, USA
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/ Prahlad Jat
  • Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC, USA
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Published Online: 2018-05-17 | DOI: https://doi.org/10.1515/opag-2018-0011

Abstract

Increased light interception (LI), along with concomitant increases in crop growth rate (CGR), is the main factor explaining how cultural factors such as row spacing, plant population, and planting date affect soybean yield. Leaf area index (LAI), LI, and CGR are interrelated in a “virtuous spiral” where increased LAI leads to greater LI resulting in a higher CGR and more total dry matter per area (TDM). This increases LAI, thus accelerating the entire physiological process to a higher level. A greater understanding of this complex growth dynamic process could be achieved through use of cluster analysis and principle components analysis (PCA). Cluster analysis involves grouping of similar objects in such way that objects in same cluster are similar to each other and dissimilar to objects in other cluster. PCA is a technique used to reduce a large set of variables to a few meaningful ones. Seasonal relative leaf area index (RLAI), relative light interception (RLI), and relative total dry matter (RTDM) response curves were determined from the data by a stepwise regression analysis in which these parameters were regressed against relative days after emergence (RDAE). Greatest levels of RLAI, RLI and RTDM were observed in soybean planted early on narrow row spacings and recorded greater plant population. In contrast, lower levels of these parameters occurred on plants with wide row spacings at late planting dates. For farmers, these results are useful in terms of adopting certain cultural practices which can help in the management of stress in soybean.

Keywords : Cluster analysis; principle components; leaf are index; light interception; total dry matter; planting dates and row spacing

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About the article

Received: 2017-12-25

Accepted: 2018-03-21

Published Online: 2018-05-17


Citation Information: Open Agriculture, Volume 3, Issue 1, Pages 110–121, ISSN (Online) 2391-9531, DOI: https://doi.org/10.1515/opag-2018-0011.

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© 2018. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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