Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-02T19:11:21.087Z Has data issue: false hasContentIssue false
  • English
  • Français

Integrating diverse forage sources reduces feed gaps on mixed crop-livestock farms

Published online by Cambridge University Press:  04 December 2017

L. W. Bell ,
A. D. Moore  and
D. T. Thomas
L. W. Bell*
Affiliation:
CSIRO Agriculture and Food, PO Box 102, Toowoomba, Qld 4350, Australia
A. D. Moore
Affiliation:
CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT 2601, Australia
D. T. Thomas
Affiliation:
CSIRO Agriculture and Food, Private Bag 5, Wembley, WA 6913, Australia
*
†E-mail: [email protected]
Get access

Abstract

Highly variable climates induce large variability in the supply of forage for livestock and so farmers must manage their livestock systems to reduce the risk of feed gaps (i.e. periods when livestock feed demand exceeds forage supply). However, mixed crop-livestock farmers can utilise a range of feed sources on their farms to help mitigate these risks. This paper reports on the development and application of a simple whole-farm feed-energy balance calculator which is used to evaluate the frequency and magnitude of feed gaps. The calculator matches long-term simulations of variation in forage and metabolisable energy supply from diverse sources against energy demand for different livestock enterprises. Scenarios of increasing the diversity of forage sources in livestock systems is investigated for six locations selected to span Australia’s crop-livestock zone. We found that systems relying on only one feed source were prone to higher risk of feed gaps, and hence, would often have to reduce stocking rates to mitigate these risks or use supplementary feed. At all sites, by adding more feed sources to the farm feedbase the continuity of supply of both fresh and carry-over forage was improved, reducing the frequency and magnitude of feed deficits. However, there were diminishing returns from making the feedbase more complex, with combinations of two to three feed sources typically achieving the maximum benefits in terms of reducing the risk of feed gaps. Higher stocking rates could be maintained while limiting risk when combinations of other feed sources were introduced into the feedbase. For the same level of risk, a feedbase relying on a diversity of forage sources could support stocking rates 1.4 to 3 times higher than if they were using a single pasture source. This suggests that there is significant capacity to mitigate both risk of feed gaps at the same time as increasing ‘safe’ stocking rates through better integration of feed sources on mixed crop-livestock farms across diverse regions and climates.

Keywords

energymodelsimulatedemandsupply
Type
Research Article
Information
animal , Volume 12 , Issue 9 , September 2018 , pp. 1967 - 1980
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bell, LW, Dove, H, McDonald, SE and Kirkegaard, JA 2015. Integrating canola and wheat into high-rainfall livestock systems in south-east Australia. 3. An extrapolation to whole-farm grazing potential and productivity. Crop and Pasture Science 66, 390–398.Google Scholar
Bell, LW, Robertson, MJ, Revell, DK, Lilley, JM and Moore, AD 2008. Approaches for assessing some attributes of feedbase systems in mixed farming enterprises. Australian Journal of Experimental Agriculture 48, 789–798.Google Scholar
Byrne, F, Robertson, MJ, Bathgate, A and Hoque, Z 2010. Factors influencing potential scale of adoption of a perennial pasture in a mixed crop-livestock farming system. Agricultural Systems 103, 453–462.Google Scholar
Chapman, DF, Kenny, SN, Beca, D and Johnson, IR 2008. Pasture and forage crop systems for non-irrigated dairy farms in southern Australia. 1. Physical production and economic performance. Agricultural Systems 97, 108–125.Google Scholar
Commonwealth Scientific and Industrial Research Organisation (CSIRO) 2007. Nutrient requirements of domesticated ruminants. CSIRO Publishing, Collingwood, Melbourne, Australia.Google Scholar
Freer, M, Moore, AD and Donnelly, JR 1997. GRAZPLAN: decision support systems for Australian grazing enterprises. II The animal biology model for feed intake, production and reproduction and the GrazFeed DSS. Agricultural Systems 54, 77–126.Google Scholar
Gouttenoire, L, Cournut, S and Ingrand, S 2011. Modelling as a tool to redesign livestock farming systems: a literature review. Animal 5, 1957–1971.Google Scholar
Hall, D, Knight, T, Coble, K, Baquet, A and Patrick, G 2003. Analysis of beef producers’ risk management perceptions and desire for further risk management education. Review of Agricultural Economics 25, 430–448.Google Scholar
Herrero, M, Thornton, PK, Notenbaert, AM, Wood, S, Msangi, S, Freeman, HA, Bossio, D, Dixon, J, Peters, M, van de Steeg, J, Lynam, J, Parthasarathy Rao, P, Macmillan, S, Gerard, B, McDermott, J, Seré, C and Rosegrant, M 2010. Smart investments in sustainable food production: revisiting mixed crop-livestock systems. Science 327, 822–825.Google Scholar
Holzworth, DP, Huth, NI, deVoil, PG, Zurcher, E, Herrmann, N, McLean, G, Chenu, K, van Oosterom, E, Snow, V, Murphy, C, Moore, A, Brown, H, Whish, JPM, Verrall, S, Fainges, J, Bell, LW, Peake, AS, Poulton, PL, Hochman, Z, Thorburn, PJ, Gaydon, DS, Dalgliesh, NP, Rodriguez, D, Cox, H, Chapman, S, Doherty, A, Teixeira, E, Sharp, J, Cichota, R, Vogeler, I, Li, FY, Wang, E, Hammer, GL, Robertson, MJ, Dimes, J, Carberry, PS, Hargreaves, JNG, MacLeod, N, McDonald, C, Harsdorf, J, Wedgwood, S and Keating, BA 2014. APSIM – evolution towards a new generation of agricultural systems simulation. Environmental Modelling and Software 62, 327–350.Google Scholar
Hutchinson, MF, McIntyre, S, Hobbs, RJ, Stein, JL, Garnett, S and Kinloch, J 2005. Integrating a global agro-climatic classification with bioregional boundaries in Australia. Global Ecology and Biogeography 14, 197–212.Google Scholar
Kirkegaard, JA, Conyers, MK, Hunt, JR, Kirkby, CA, Watt, M and Rebetzke, GJ 2014. Sense and nonsense in conservation agriculture: principles, pragmatism and productivity in Australian mixed farming systems. Agriculture, Ecosystems & Environment 187, 133–147.Google Scholar
Martin, F and Magne, MA 2015. Agricultural diversity to increase adaptive capacity and reduce vulnerability of livestock systems against weather variability – a farm-scale simulation study. Agriculture, Ecosystems & Environment 199, 301–311.Google Scholar
Martin, G, Felten, B and Duru, M 2011. Forage rummy: a game to support the participatory design of adapted livestock systems. Environmental Modelling & Software 26, 1442–1453.Google Scholar
McKeon, G, Day, K, Howden, S, Mott, J, Orr, D, Scattini, W and Weston, E 1990. Northern Australian savannas: management for pastoral production. Journal of Biogeography 17, 355–372.Google Scholar
Monjardino, M, Revell, D and Pannell, DJ 2010. The potential contribution of forage shrubs to economic returns and environmental management in Australian dryland agricultural systems. Agricultural Systems 103, 187–197.Google Scholar
Moore, AD 2014. The case for and against perennial forages in the Australian sheep–wheat zone: modelling livestock production, business risk and environmental interactions. Animal Production Science 54, 2029–2041.Google Scholar
Moore, AD, Bell, LW and Revell, DK 2009. Feed gaps in mixed-farming systems: insights from the Grain & Graze program. Animal Production Science 49, 736–748.Google Scholar
Moore, AD, Donnelly, JR and Freer, M 1997. GRAZPLAN: decision support systems for Australian grazing enterprises. III. Pasture growth and soil moisture submodels, and the GrassGro DSS. Agricultural Systems 55, 535–582.Google Scholar
Pannell, DJ 1999. Social and economic challenges in the development of complex farming systems. Agroforestry Systems 45, 393–409.Google Scholar
Poppi, DP and McLennan, SR 1995. Protein and energy utilisation by ruminants at pasture. Journal of Animal Science 73, 278–290.Google Scholar
Steinfeld, H, Gerber, P, Wassenaar, TD, Castel, V, Rosales, M and de Haan, C 2006. Livestock’s long shadow: environmental issues and options. Food and Agriculture Organization of the United Nations, Rome, Italy. <www.fao.org/docrep/010/a0701e/a0701e00.htm>>Google Scholar
Thomas, DT, Finlayson, J, Moore, AD and Robertson, MJ 2010. Profitability of grazing crop stubbles may be overestimated by using the metabolisable energy intake from the stubble. Animal Production Science 50, 699–704.Google Scholar
Van der Linden, A, Oosting, SJ, van de Ven, GWJ, de Boer, IJM and van Ittersum, MK 2015. A framework for quantitative analysis of livestock systems using theoretical concepts of production ecology. Agricultural Systems 139, 100–109.Google Scholar
Wylie, P 2007. Economics of pastures versus grain or forage crops. Tropical Grasslands 41, 229–233.Google Scholar
Supplementary material: File

Bell et al supplementary material S1

Bell supplementary material

Download Bell et al supplementary material S1(File)
File 163.6 KB