Journalartikel

Jahr der Veröffentlichung: 2023

Seiten: 1481-1489

Zeitschrift: Agroforestry Systems

Bandnummer: 97

Heftnummer: 8

ISSN: 0167-4366

eISSN: 1572-9680

Open Access Status: Hybrid

DOI Link: https://doi.org/10.1007/s10457-023-00871-x

Verlag: Springer

Abstract: 
Agroforestry is a land-use system that combines arable and/or livestock management with tree cultivation, which has been shown to provide a wide range of socio-economic and ecological benefits. It is considered a promising strategy for enhancing resilience of agricultural systems that must remain productive despite increasing environmental and societal pressures. However, agroforestry systems pose a number of challenges for experimental research and scientific hypothesis testing because of their inherent spatiotemporal complexity. We reviewed current approaches to data analysis and sampling strategies of bio-physico-chemical indicators, including crop yield, in European temperate agroforestry systems to examine the existing statistical methods used in agroforestry experiments. We found multilevel models, which are commonly employed in ecology, to be underused and under-described in agroforestry system analysis. This Short Communication together with a companion R script are designed to act as an introduction to multilevel models and to promote their use in agroforestry research.

Harvard-Zitierstil: Golicz, K., Piepho, H., Minarsch, E., Niether, W., Große-Stoltenberg, A., Oldeland, J., et al. (2023) Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems, Agroforestry Systems, 97(8), pp. 1481-1489. https://doi.org/10.1007/s10457-023-00871-x

APA-Zitierstil: Golicz, K., Piepho, H., Minarsch, E., Niether, W., Große-Stoltenberg, A., Oldeland, J., Breuer, L., Gattinger, A., & Jacobs, S. (2023). Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems. Agroforestry Systems. 97(8), 1481-1489. https://doi.org/10.1007/s10457-023-00871-x