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Luo Q, Bellotti WD, Williams M, Bryan B (2005a) Potential impact of climate change on wheat yield in South Australia.
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Luo Q, Williams M, Bellotti WD, Bryan B (2003) Quantitative and visual assessment of climate change impacts on South Australian wheat production. Luo Q (2003) Assessment of the potential impacts of climate change on South Australian wheat production. Ludwig F, Senthold A (2006) Climate change impacts on wheat production in a Mediterranean environment in Western Australia. Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ (2003) An overview of APSIM, a model designed for farming systems simulation. Report to the Australian Greenhouse, Office. Howden SM, Reyenga PJ, Meinke H (1999) Global change impacts on Australian wheat cropping. Helton JC, Davis FJ (2003) Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. ASA Special Publication, Number 59:147–161.Ĭleveland WS (1993) The elements of graphing data. Rosenzweig C, Ritchie JT, Jones JW (eds) Climate change and agriculture: analysis of potential international impacts. Available at īrklacich M, Stewart RB (1995) Impacts of climate change on wheat yield in the Canadian prairies.
Apsim model phyllochron rate sensitivity manual#
Mac Learn J 45:5–32īreiman L (2002) Manual on setting up, using, and understanding random forests v3.1. Chapman and Hall, New Yorkīreiman L (2001) Random forest. J Agric Meteorol 48(5):811–814īreiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. These findings provided a sound basis for preliminary scoping and prioritising adaptation options.Īustralian Bureau of Statistics (ABS), Value of agricultural commodities produced, 2009–2010, Catalogue no.7503.0Īggarwal PK, Sinha SK (1993) Effect of probable increase in carbon dioxide and temperature on wheat yields in India. This study also showed that rainfall was more important under sandy soil conditions than under kandosol soil conditions. This study showed that environmental factors have significant effects on wheat grain yield, with soil as the most important factor, followed by site (reflecting both soil and climate), changes in atmospheric CO 2, rainfall and temperature. It was also found that the rate of decrease in median grain yield was more for higher temperatures in contrast to lower temperatures, and the rate of increase in median grain yield was less for higher rainfall and pCO 2 compared with the lower levels of these two variables. It was found that there was a negative relationship between median grain yield and temperature while there were positive correlations of median grain yield with atmospheric pCO 2 and rainfall across all locations and soils considered. Seven change levels (from 0 to 6☌ at an interval of 1☌) in temperature, five change levels (from −20 to 20 % at an interval of 10 %) in rainfall and three change levels (0, 171 and 316 ppm) in atmospheric pCO 2 were taken into account. The Agricultural Production System Simulator-Wheat model was used to test the sensitivity of wheat cropping system in NSW to a range of changes in temperature, rainfall and atmospheric carbon dioxide concentration based on wheat cultivars Sunvale and Janz under two extreme soil types (kandosol and sand) at six locations.