In ‘Proceedings of the 32nd California alfalfa and forage symposium, Reno, Nevada’. (2002) Harvesting maximum value from small grain cereal forages. (2002 ) Evolution of the GRAZPLAN decision support tools and adoption by grazing industry in temperate Australia. Proceedings of a workshop held at ILCA, Addis Ababa, Ethiopia, 7–10 December 1987’. In ‘Plant breeding and the nutritive value of crop residues. (1988) Genetic variation in the feeding value of barley and wheat straw.
Journal of the Science of Food and Agriculture 68, 111–116. (1995 ) Composition and in-vitro digestibility of carbohydrates of wheat plants harvested at bloom and soft-dough stages. (1998 ) Performance of the APSIM-wheat model in Western Australia. | Crossref | GoogleScholarGoogle Scholar | Animal Feed Science and Technology 53, 29–43. (1995 ) Effect of free-air CO2 enrichment (FACE) on forage quality of wheat. Overall, the findings of this study suggest that making tactical use of a wheat crop for forage in situations with low grain yield prospects is a major opportunity to increase profitability and help respond to climate variability in mixed farms in many areas of the Western Australian wheatbelt.ĪBARE (2007) ‘Australian Commodity Statistics 2007.’ (Australian Bureau of Agricultural and Resource Economics: Canberra) Available at This approach, combining crop simulation with partial budgets, was useful for developing simple management rules for a complex system. In higher rainfall environments and on better soil types grazing was rarely a better option unless livestock prices were high relative to grain. This critical grain yield ranged from 1.3 to 1.7 t/ha on shallow gravel soil and 1.9 to 2.2 t/ha on a deep sand. In these situations, by tactically grazing when grain yield is below a critical level economic returns could be increased by more than A$50/ha in 30–40% of years and over the long term average revenues could be increased by A$30/ha.year. Partial budgets showed that under average commodity prices, grazing a wheat crop could be more profitable 40–75% of the time on poorer soil types in lower rainfall environments. Grazing earlier necessitated lighter stocking rates but surprisingly had little benefit for animal performance. Dynamic simulations of grazing showed that livestock production increased as grazing was delayed stocking rate had little impact at this time. We then simulated wheat quality and livestock production on spring wheat grazed at different stages of crop development and at a range of stocking rates. This was simulated for a factorial of soil types and locations varying in mean annual rainfall. First, we developed a simple partial budget calculation to estimate and compare revenue from grain or grazing alternatives using data for grain yield and standing biomass at flowering. This simulation study used two APSIM (Agricultural Production Systems Simulator)-based approaches to investigate the circumstances under which more revenue might be obtained by sacrificing a wheat crop for grazing rather than harvesting it for grain in Western Australia’s grainbelt. However, specific simulation studies to predict biomass, yield, drainage and nitrate leaching are now possible for wheat crops on the tested soil types and rainfall zones in Western Australia.Failing grain crops are sometimes sacrificed for grazing by mixed farmers, a decision involving a complex range of factors. In particular, grain protein tended to be overpredicted at high protein levels and underpredicted at low levels. Simulation of grain protein, and depth to the perched water table showed limited accuracy when compared with field measurements. Yields tended to be underestimated during terminal droughts due to insufficient pre-anthesis stored carbohydrates being remobilised to the grain. Grain yields were well predicted with a coefficient of determination r 2(1:1)=0.77, despite some underestimation during severe terminal droughts.
The overall APSIM model predictions of shoot growth, root depth, water and N uptake, soil water, soil N, drainage and nitrate leaching were found to be acceptable. The field experiments covered 10 seasons, with variations in sowing date, plant density, N fertiliser, deep ripping and irrigation. Model outputs were compared with detailed field experiments from four rainfall zones, three soil types, and five wheat genotypes. The model was used to simulate above- and belowground growth, grain yield, water and N uptake, and soil water and soil N in wheat crops in Western Australia. APSIM-wheat is a crop system simulation model, consisting of modules that incorporate aspects of soil water, nitrogen (N), residues, and crop development.