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REVIEW

Row configuration as a tool for managing rain-fed cotton systems: review and simulation analysis

M. P. Bange A B F , P. S. Carberry A C , J. Marshall A D and S. P. Milroy A E
+ Author Affiliations
- Author Affiliations

A Australian Cotton Cooperative Research Centre, Narrabri, NSW 2390, Australia.

B CSIRO Plant Industry, Locked Bag 59, Narrabri, NSW 2390, Australia.

C CSIRO Sustainable Ecosystems, Agricultural Production Systems Research Unit, PO Box 102, Toowoomba, Qld 4350, Australia.

D Cotton Seed Distributors, PO Box 756, Dalby, Qld 4405, Australia.

E CSIRO Division of Plant Industry, Private Bag 5, Wembley, WA 6913, Australia.

F Corresponding author. Email: Michael.Bange@csiro.au

Australian Journal of Experimental Agriculture 45(1) 65-77 https://doi.org/10.1071/EA03254
Submitted: 22 November 2003  Accepted: 14 March 2004   Published: 21 February 2005

Abstract

Rain-fed cotton production can be a significant proportion (average 17%) of the Australian Cotton Industry. One of the management techniques that rain-fed cotton growers have is to modify row configuration. Configurations that have entire rows missing from the sowing configuration are often referred to as ‘skip row’. Skip configurations are used to: increase the amount of soil water available for the crop, which can influence the potential lint yield; reduce the level of variability or risk associated with production; enhance fibre quality; and reduce input costs. Choosing the correct row configuration for a particular environment involves many, often complex, considerations. This paper presents an examination of how rain-fed cotton production in Australia is influenced by row configuration with different management and environmental factors. Data collated from field experiments and the cotton crop simulation model OZCOT, were used to explore the impact of agronomic decisions on potential lint yield and fibre quality and consequent economic benefit. Some key findings were: (i) soil water available at sowing did not increase the advantage of skip row relative to solid configurations; (ii) reduced row spacing (75 cm) did not alter lint yield significantly in skip row crops; (iii) skip row, rain-fed crops show reasonable plasticity in terms of optimum plant spacing within the row (simular to irrigated cotton); (iv) sowing time of rain-fed crops would appear to differ between solid and skip row arrangements; (v) skip row configurations markedly reduce the risk of price discounts due to short fibre or low micronaire and this should be carefully considered in the choice of row configuration; and (vi) skip configurations can also provide some savings in variable costs. In situations where rain-fed cotton sown in solid row configurations is subject to water stress that may affect lint yield or fibre quality, skip row configurations would be a preferential alternative to reduce risk of financial loss.

Additional keywords: management, model, OZCOT.


Acknowledgments

Thanks to Bruce Pyke, Brian Hearn and Phil Goyne for access to original field data used in this study. The Cotton Research and Development Corporation provided partial financial support for crop simulation activities.


References


Bange MP, Carberry PS (1998) Application of soil monitoring, benchmarking and crop simulation in commercial dryland cotton management. In ‘Proceedings of the 2nd world cotton conference’. (Ed. FM Gilliam) pp. 360–367. (P. Petridis: Athens, Greece)

Boyce Chartered Accountants (2002) Australian cotton comparative analysis 2002 crop. Report for the Cotton Research and Development Corporation, Narrabri, NSW.

Buxton DR, Briggs RE, Patterson LL, Watkins SD (1977) Canopy characteristics of narrow-row cotton as influenced by plant density. Agronomy Journal 69, 929–933. (verified 18 January 2005).

Scott F (2002) Dryland cotton budgets. In ‘Australian dryland cotton production guide’. 3rd edn. pp. 20–31. (Cotton Research and Development Corporation: Narrabri, NSW)

Stone RC, Hammer GL, Marcussen T (1996) Prediction of global rainfall probabilities using phases of the southern oscillation index. Nature 384, 52–55.
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