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Effects of nutrient level and planting density on population relationship in soybean and wheat intercropping populations

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  • Jialing Huang
  • Yihang Li
  • Yu Shi
  • Lihong Wang
  • Qing Zhou
  • Xiaohua Huang

Abstract

A positive interaction between plant populations is a type of population relationship formed during long-term evolution. This interaction can alleviate population competition, improve resource utilization in populations, and promote population harmony and community stability. However, cultivated plant populations may have insufficient time to establish a positive interaction, thereby hindering the formation of the positive interaction. As current studies have not fully addressed these issues, our study established soybean/wheat intercropping populations beneficial for growth and explored the effects of nutrient level and planting density on the positive interaction between the two crops. Changes across population modules in both sole cropping and intercropping populations of soybean and wheat were analyzed. Results using nutrient levels of ½- or ¼-strength Hoagland solution indicated that soybean/wheat intercropping population modules significantly increased at low planting densities (D20 and D26) and significantly decreased at high planting densities (D32 and D60). Therefore, as planting density increased, the modules of both intercropping populations initially increased before decreasing. Similarly, positive interaction initially strengthened before weakening. Moreover, at an intermediate planting density, the population modules reached their maxima, and the positive interaction was the strongest. Under the same planting density, ¼-strength Hoagland solution recorded better growth for the soybean/wheat intercropping population modules compared to results using the ½-strength Hoagland solution. These findings indicated that low nutrient level can increase the positive interaction of intercropping populations at a given planting density, and that environmental nutrient level and population planting densities constrain the positive interaction between soybean and wheat populations in the intercropping system. This study highlights issues that need to be addressed when constructing intercropping populations.

Suggested Citation

  • Jialing Huang & Yihang Li & Yu Shi & Lihong Wang & Qing Zhou & Xiaohua Huang, 2019. "Effects of nutrient level and planting density on population relationship in soybean and wheat intercropping populations," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-12, December.
    • Handle: RePEc:plo:pone00:0225810
    DOI: 10.1371/journal.pone.0225810
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    1. Ragan M. Callaway & R. W. Brooker & Philippe Choler & Zaal Kikvidze & Christopher J. Lortie & Richard Michalet & Leonardo Paolini & Francisco I. Pugnaire & Beth Newingham & Erik T. Aschehoug & Cristin, 2002. "Positive interactions among alpine plants increase with stress," Nature, Nature, vol. 417(6891), pages 844-848, June.
    2. Bair, Eric & Hastie, Trevor & Paul, Debashis & Tibshirani, Robert, 2006. "Prediction by Supervised Principal Components," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 119-137, March.
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