Influence of management and environment on Australian wheat: information for sustainable intensification and closing yield gaps

Abstract

In the future, agriculture will need to produce more, from less land, more sustainably. But currently, in many places, actual crop yields are below those attainable. We quantified the ability for agricultural management to increase wheat yields across 179 Mha of potentially arable land in Australia. Using the Agricultural Production Systems Simulator (APSIM), we simulated the impact on wheat yield of 225 fertilization and residue management scenarios at a high spatial, temporal, and agronomic resolution from 1900 to 2010. The influence of management and environmental variables on wheat yield was then assessed using Spearman's non-parametric correlation test with bootstrapping. While residue management showed little correlation, fertilization strongly increased wheat yield up to around 100 kg N ha −1 yr −1 . However, this effect was highly dependent on the key environment variables of rainfall, temperature, and soil water holding capacity. The influence of fertilization on yield was stronger in cooler, wetter climates, and in soils with greater water holding capacity. We conclude that the effectiveness of management intensification to increase wheat yield is highly dependent upon local climate and soil conditions. We provide context-specific information on the yield benefits of fertilization to support adaptive agronomic decision-making and contribute to the closure of yield gaps. We also suggest that future assessments consider the economic and environmental sustainability of management intensification for closing yield gaps.

Farmer risk-aversion limits closure of yield and profit gaps: A study of nitrogen management in the southern Australian wheatbelt

“…This analysis is consistent with our previous work on the economic benefits of adopting higher N rates and/or tactical N by soil zone in farms located in Karoonda in the Mallee region of southern Australia (Monjardino et al, 2013), as well as with a range of other studies demonstrating the economic benefits of tactical N management (Angus, 2001;Broun, 2007;Kingwell et al, 1993;Lobell, 2007;McDonald, 1989;Moeller et al, 2009;Nordblom et al, 1985;Oliver and Robertson, 2009), agricultural intensification of marginal cropping land (Asseng et al, 2001;Babcock, 1992;Bryan et al, 2014;Good, 2004;Sadras, 2002;Sadras and Rodriguez, 2010;Sadras and Roget, 2004;Spiertz, 2010). Importantly, our results demonstrate the value of applying a range of research tools, such as crop growth simulation models in combination with economic-risk measures and risk-aversion theory.…”

The value of adapting to climate change in Australian wheat farm systems: farm to cross-regional scale

“…Thus, initial years prior to 1980 were removed to equilibrate soil water. Models were setup for continuous sowing (Bryan et al, 2014) while soil nitrogen was reset at the end of each year.…”

Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling

“…Change in land use and land cover is regarded as the single most important element of global change affecting ecosystems (Foley et al, 2005;Vitousek, 1994). Land use maps are fundamental data for mapping and modelling ecosystem services and their value (Crossman et al, 2013a(Crossman et al, , 2013bDe Groot et al, 2010;Liu et al, 2010) at all scales including local (Butler et al, 2013;Raudsepp-Hearne et al, 2010), regional (Ausseil et al, 2013;Bryan and Crossman, 2013;Bryan et al, 2009b), national (Bateman et al, 2013;Bryan et al, 2014a;Lawler et al, 2014), and global (Costanza et al, 1997;Verburg et al, 2011Verburg et al, , 2013. Global and continental scale land use maps are commonly generated by the classification of remotely-sensed imagery (Gong et al, 2013;Hansen et al, 2000;Schneider et al, 2010).…”