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RESEARCH ARTICLE

Using morphological traits to identify persistent lucernes for dryland agriculture in NSW, Australia

S. P. Boschma A C and R. W. Williams B
+ Author Affiliations
- Author Affiliations

A NSW Department of Primary Industries, Tamworth Agricultural Institute, 4 Marsden Park Road, Calala, NSW 2340, Australia.

B Queensland Department of Primary Industries and Fisheries, PO Box 102, Toowoomba, Qld 4350, Australia.

C Corresponding author. Email: suzanne.boschma@dpi.nsw.gov.au

Australian Journal of Agricultural Research 59(1) 69-79 https://doi.org/10.1071/AR06206
Submitted: 20 June 2006  Accepted: 17 September 2007   Published: 14 January 2008

Abstract

This paper reports on several studies conducted to better understand the variability between lucerne cultivars and lines, and use this to predict persistence in dryland grazing pastures in eastern Australia. Morphological traits of 20 cultivars/lines were measured in irrigated and dryland spaced plant experiments. Studies were also conducted to describe variation among lucernes in their utilisation of starch and responses to water deficit, pests and diseases. Multiple regression analyses were used to develop simple models where the measured traits could be used to predict persistence of lucerne lines in dryland evaluation experiments.

Although there was significant variation among cultivars/lines in most measured traits, no single trait reliably predicted persistence of cultivars/lines in dryland evaluation experiments. However, variation in persistence at both sites could be explained by models developed by multiple regression using differences in the mean lengths of the longest stems at 10% flower in summer and winter. Persistent lucernes were those that had relatively long stems in summer and short stems in winter. Water use efficiencies, starch utilisation patterns and resistances to pests and diseases of different lucernes provided some improvement to this simple model, but these improvements were not consistent.

Additional keywords: morphological traits, multivariate analysis, NIR, persistence, starch, WUE.


Acknowledgments

We thank Allan Milvain and Walter Jackson for their technical support, Tim O’Brien for assistance with the pest and disease screening, Dr Neil Coombes for statistical advice, Dr Iain Hume for determining PWP of the soil used in the water deficit experiment, and Andrew Watson and Joshua Gordon for providing weather data. We would also thank the anonymous referees who provided constructive comments on this paper. Funding was gratefully received from Australian wool growers (currently Australian Wool Innovation Limited).


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