Simulating the effects of climate change on the profitability of Australian farms
ABARES Working Paper
Published 29 July 2021
Authors: Neal Hughes, Michael Lu, Wei Ying Soh and Kenton Lawson
Recent shifts in the Australian climate including both higher temperatures and lower winter rainfall, have had significant effects on the agriculture sector. Despite these recent trends, there remains uncertainty over the future climate and its potential impacts on Australian farm businesses. In this study, a statistical model of Australian cropping and livestock farms is applied to simulate the potential effects of climate change on farm profits. This farm model is combined with a range of downscaled projections for temperature and rainfall by 2050. The results provide an indication of adaptation pressure: showing which regions, sectors and farm types may be under greater pressure to adapt or adjust to climate change. The future climate scenarios produce a wide range of outcomes, with simulated changes in average farm profits (without adaptation) ranging from -2% to -32% under an ‘intermediate’ global emissions scenario (RCP4.5) and -11% to -50% under a ‘high’ global emissions scenario (RCP8.5), relative to the reference period climate of 1950 to 2000. Generally, larger negative effects are simulated in the more in-land parts of the agricultural zone. While the future capacity of the sector to adapt is uncertain, Australian farmers have adapted effectively to recent shifts in climate, both through improvements in technology and migration of cropping activity. In future, farm-scale modelling could help support adaptation by providing farm businesses with personalised risk analysis, measuring the potential effects of climate change on specific farm businesses given their location, size and other key characteristics.
Australian farms are highly exposed to climate variability and to the potential impacts of long-term climate change. Recent droughts across eastern Australia in 2018-19 and 2019-20 had dramatic effects on farm businesses (Martin and Topp 2019; Hughes, Galeano, Hatfield-Dodds 2019) adding to longstanding concerns around the emerging effects of climate change on Australian agriculture (Davis and Doyle 2019; Hambrett 2019).
In addition to higher temperatures, Australia has experienced significant changes in rainfall over the last 20 to 30 years. Average winter rainfall has declined in southern Australia, while summer rainfall has increased in north-western Australia (BOM and CSIRO 2020). These trends are at least partly related to global warming atmospheric changes (see Cai et al. 2014; Cai and Cowan 2013; Cai, Cowan and Thatcher 2012).
These changes have already had large effects on Australian agriculture. Hochman, Gobbett and Horan (2017) estimate that changes in climate have reduced Australian wheat yields by around 27% per cent since 1990. Hughes, Lawson and Valle (2017) found that changes in climate have negatively affected the productivity of Australian cropping farms since 2000 (Kingwell et al. 2014, Islam, Xayavong and Kingwell 2014 found similar effects for farms in south-western Australia). These studies also found evidence of adaptation, including changes in farm practices and migration of cropping activity helping to offset climate effects (Hughes et al. 2017, Kingwell et al. 2014).
Given the difficulty in separating long-term climate change from natural variability, there remains uncertainty over what these trends mean for Australian agriculture over the long-term. Simulating the future effect of climate change on agriculture, therefore remains a large and active area of research (Pearson et al. 2011, Hertel 2018 and Blanc and Reilly 2017).
Much of this research has focused on crop yields, using either statistical models or ‘process-based’ bio-physical simulation models. Recent studies of Australian wheat yields include Ghahramani et al. (2015) and Wang et al. (2019) (who both applied the APSIM model, see Keating et al. 2003). Statistical and process-based models have some obvious relative strengths and weaknesses (see for example Blanc and Reilly 2017). However, recent reviews (Lobell and Burke 2010; Lobell and Asseng 2017; Moore, Baldos and Hertel 2017) show both methods generate similar responses to climate change, at least after accounting for CO2 fertilisation effects (which are generally excluded from statistical models).
Less research has been focused on whole-of-farm outcomes, particularly farm profits. This is important in the context of Australian broadacre farms, which undertake a range of interrelated crop and livestock activities. Most Australian studies on farm-scale outcomes (Ghahramani, Kingwell and Maraseni 2020; Ghahramani and Bowran 2018; Thamo et al. 2017; Ghahramani and Moore 2016; Rodriguez et al. 2014) have applied process-based models to case-study farms (often using the AusFarm framework, drawing on the APSIM model). These studies generally find negative effects of climate change on Australian farm profits on average (Ghahramani and Bowran 2018; Ghahramani et al. 2020) with a wide range of simulated outcomes, due mostly to uncertainty over rainfall projections.
An alternative way to simulate farm outcomes is to use statistical models which link panel data on farm economic outcomes with observed weather data (in keeping with the growing climate-economy literature, see Dell et al. 2014). Key examples in the United States include Fisher et al. (2012); Deschênes and Greenstone (2012); Segerson and Dixon (1999), while Nelson et al. (2010) applied a similar approach for Australian broadacre farms1. Such models capture the responses of farms under real world conditions, dependent on both bio-physical and socio-economic factors (i.e., the behaviour of farm managers). They also tend to provide broader spatial coverage, supporting the simulation of national and industry-wide outcomes of relevance to policy makers.
In this study, a new statistical model of Australian farms farmpredict (Hughes et al. 2019) is applied to simulate the potential effects of climate change on the profits of Australian farms. farmpredict is a data-driven model of Australian broadacre (extensive cropping / livestock) farms, which simulates the effects of weather conditions and prices on the production and financial outcomes of individual farm businesses. The model offers detailed estimates of output and revenue; input use and costs; changes in farm inventories and in-turn various measures of farm profit. farmpredict is based on Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) farm survey data and provides national coverage of the Australian broadacre sector.
Downscaled climate change projections for rainfall and temperature (produced by the CSIRO and BoM 2015) are applied to the farmpredict model. Farm outcomes are simulated under projected 2050 climate (for a range of greenhouse gas pathways and general circulation models) and compared to the historical reference period 1949-50 to 1998-1999. For contrast, results are also presented for the recently observed climate (1999-2000 to 2019-20).
Given the statistical approach, the results of this study do not account for the positive effects of long-run adaptation, technological advances and carbon dioxide fertilisation. Further, the scenarios also do not account for potential long-run changes in global supply and demand of agricultural commodities (and any related effects on world commodity prices) or the effects on Australian farms of domestic or international climate change mitigation policy.
In effect, the model results simulate how current day farmers, facing current technology and prices would perform under a sudden shift to 2050 climate conditions. Rather than projecting likely outcomes in 2050, the results provide an indication of ‘adaptation pressure’: identifying which regions, sectors and farm types may be under greater pressure to adapt or adjust to climate change effects.
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