4.7 Article

Specific interactions leading to transgressive overyielding in cover crop mixtures

期刊

AGRICULTURE ECOSYSTEMS & ENVIRONMENT
卷 241, 期 -, 页码 88-99

出版社

ELSEVIER
DOI: 10.1016/j.agee.2017.03.003

关键词

Intraspecific and interspecific competition; Response surface design; Resource partitioning; Facilitation; Relative dominance; Land equivalency ratio (LER)

资金

  1. Swiss National Science Foundation [406840-143063]
  2. Swiss National Science Foundation (SNF) [406840_143063] Funding Source: Swiss National Science Foundation (SNF)

向作者/读者索取更多资源

Growing mixtures of species instead of sole crops is expected to increase the ecosystem services provided by cover crops. This study aimed at understanding the interactions between species and investigating how they affect the performance of the mixture. Four species were combined in six bispecific mixtures in a field experiment. The performance of each species when grown in a mixture was compared to its performance as a sole crop at different sowing densities, to characterise the influence of intra- and interspecific competition for each species. Intra- and interspecific competition coefficients were quantified using a response surface design and the hyperbolic yield-density equation. Interactions between the four species ranged from facilitation to competition. Most of the mixtures exhibited transgressive overyielding. Without nitrogen (N) fertilisation, high complementarity between species allowed to achieve the highest biomass. With N fertilisation, high dominance of one mixture component should be avoided to achieve good performance. A revised approach in the use of the land equivalent ratio for the evaluation of cover crop mixtures is also proposed in this study. It allows to better identify transgressive overyielding in mixtures and to better characterise the effect of one species on the other within the mixture. (C) 2017 The Authors. Published by Elsevier B.V.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据