- Economic Impact Of Climate Change On Agriculture
- Global Food Security Under Climate Change
Economic Impact Of Climate Change On Agriculture – In recent decades, Australia has experienced a shift towards warmer temperatures and lower winter rainfall, with significant consequences for many farmers. Despite these trends, much uncertainty remains regarding the long-term effects of climate change on agricultural enterprises. This article presents the latest ABARES modelling, examining the effects of recent and possible future climate change on Australian farm profitability. Productivity trends are also presented, showing how farm adaptations have helped mitigate the effects of warmer and drier conditions to date.
As detailed in the State of the Climate 2020 report (BOM and CSIRO 2020), Australia’s climate has warmed by an average of 1.4°C since 1910, with most of this occurring since 1950. There has also been a decline winter season (April to October) rainfall in south-west Australia (20% since 1970) and south-east Australia (12% since 2000, BOM and CSIRO 2020).
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Previous ABARES research (Hughes, Galeano and Hatfield-Dodds 2019) assessed the impacts of these changes in conditions on Australian bushland farms, using the farmpredict model (Hughes et al. 2019) to simulate the effects of rainfall and temperature changes on all other factors (including commodity prices, technology and farm size) constant.
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The latest ABARES modeling (Hughes, Lu et al. 2021, shown in Figure 1 and Table 2) estimates that changes in seasonal conditions in the period 2001 to 2020 (relative to 1950 to 2000) reduced annual average farm profit by 23%, or about $29,200 per farm. These impacts were most pronounced in southwestern and southeastern Australia (Figure 1), while northern Australia and coastal areas with higher rainfall were less affected.
Post-2000 conditions also contributed to increased risk in terms of more variable cash flows and profits. In the 20 years since 2000, the risk of very low agricultural returns (due to climate variability) has essentially doubled (compared to the period from 1950 to 2000), increasing from an incidence of 1 in 10 to more than 1 in 5 (Hughes , Galeano and Hatfield-Dodds 2019).
Notes: Simulated broad spectrum farm profit with current (2015-16 to 2018-2019) farm and commodity prices and recent climate (2000-1 to 2019-2020). The map represents the interpolated percentage changes at the farm level (relative to the climate from 1949–1950 to 1999–2000), calculated using the symmetric mean absolute percentage error (SMAPE) metric.
Climate model projections provide some insight into the possible climate future that farmers could face in the long term. For this study, precipitation and temperature projections to 2050 were obtained from Climate Change Australia (CSIRO and BOM 2015). These projections cover two representative concentration trajectories (RCPs): RCP4.5 and RCP8.5 and a range of global circulation models (GCMs).
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The RCP8.5 scenario assumes a limited decrease in global emissions, so that CO2 concentrations reach about 540 ppm by 2050. According to RCP4.5, global emissions peak by 2040 and CO2 concentrations reach about 485 ppm by 2050 (for more details, see the section on methods).
For each emission scenario, a wide range of possible outcomes are predicted for each of the different GCMs, particularly for precipitation. For example, under RCP4.5 average seasonal (April to October) rainfall declines for Australian farmers are projected for 2050 to be between 2.7% and 20.6%, compared to an observed decline since 2001 of 16.2 %. According to RCP8.5, larger reductions of 6.1% to 30.1% on average are predicted (Table 1).
These climate projections were combined with the ABARES farmpredict model to simulate the potential impacts of future climate change on the profits of Australian farmers (see methods section for more details). Importantly, this analysis does not take into account the positive effects of farm adaptation or technological improvement (or any changes in global commodity prices). As such, the results are not projections of likely outcomes in 2050, but estimates of ‘adaptation pressures’: identifying which regions, sectors and farm types are likely to be under greater pressure to adapt to climate change.
The results are presented in Table 2 (and Figures 2 and 4) for the seven main agricultural regions and industry groupings (defined in Figure 3). The results show a wide range of outcomes, with simulated changes in average farm profits in the future scenario (RCP4.5 2050) ranging from -31.9% to -2.0%, and for the RCP8.5 scenario ranging from -49.9 % to – 10.7% (Table 2).
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Although much uncertainty remains, the results suggest that climate change could make conditions more difficult for Australian farmers and will require significant adaptation responses. However, projected results vary considerably across agricultural regions and industries.
Notes: Change in simulated weighted average farm profit for the broad farm, assuming current farm and commodity prices (2015-16 to 2018-19), relative to historical climate conditions (1949-50 to 1999-2000). Farm profit is defined as ‘full capital farm profit’, including imputed unpaid family labor and depreciation charges, changes in the value of stock (including stock and livestock), but excluding rent, interest and other financing costs.
Notes: Change in simulated average farm profit for a wide range of farms, assuming current farm and commodity prices (2015-16 to 2018-19), relative to historical climate conditions (1949-50 to 1999-2000). The bars show the minimum, maximum, and average across the GCMs for each scenario.
Notes: Agricultural farms include Australian and New Zealand Standard Industrial Classification (ANZSIC) classes Other grain growing and grain sheep and cattle. Cattle farms include ANZSIC cattle breeding. Sheep – lamb farms include those in ANZSIC sheep farming, with a base lamb production of more than 200 head, sheep farms – mixed farms include all remaining ANZSIC sheep farms.
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Agricultural farms in Western Australia are more affected than other regions in most climate scenarios, mainly due to the more significant predicted decline in winter rainfall and the consequent effects on crop yields. For these farms, the declines in profits under the 2050 projection scenarios (average of 6 GCMs) are greater than those observed in the recent period (for both RCP4.5 and RCP8.5).
In the beef and sheep sectors, the projected impacts under RCP4.5 remain relatively modest, so that for most scenarios the changes in profits are less than those observed in the recent climate period. However, impacts in the livestock sector become much more significant under RCP8.5 due to larger projected temperature increases.
In general, more severe impacts on farms are seen in the ‘fringe’ (lower rainfall/inland) parts of Australia’s agricultural zone (Figures 4 and 5). The greatest impacts are estimated for the northern edge of the crop zone of Western Australia, parts of western NSW and central Queensland. Relatively mild impacts are simulated in Tasmania and many coastal locations with high rainfall on land.
FIGURE 4 Percentage change in farm profits relative to historical climate (1950 to 2000), RCP4.5 average of 6 GCM
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Notes: Simulated broad-spectrum farm profit with current (2015-2016 to 2018-2019) farm and commodity prices and future RCP4.5 2050 climate. Map represents interpolated farm-level percent changes (relative to 1949 climate). – 1950 to 1999 – 2000), calculated using the symmetric mean absolute percentage error (SMAPE) metric.
FIGURE 5 Percentage change in farm profits relative to historical (1950 to 2000) climate, RCP8.5 average of 6 GCM
Notes: Simulated broad-spectrum farm profit with current (2015-2016 to 2018-2019) farm and commodity prices and future RCP8.5 2050 climate. Map represents interpolated farm-level percent changes (relative to 1949 climate). – 1950 to 1999 – 2000), calculated using the symmetric mean absolute percentage error (SMAPE) metric.
Modeled future impacts on agricultural economies in eastern Australia (southern and northern growing regions) are relatively modest compared to the effects of the recent climate period. However, there remains significant uncertainty about future precipitation projections in eastern Australia, with limited agreement among GCMs (CSIRO and BOM 2015) and a disconnect emerging in some regions between projections and observations, with recent trends following extreme dry end of the intended range. (see BOM and CSIRO 2020, page 23). This uncertainty about the future climate remains a potential constraint to adaptation in the agricultural sector, as discussed in the next section.
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The simulation results also predict increases in average Australian prices for major grains (wheat, barley and sorghum) ranging from 3% to 24% (Hughes, Lu et al. 2021). This represents climate change increasing the frequency and severity of drought-induced grain shortages and associated price spikes, similar to those observed in Australia during the 2018–19 and 2019–20 droughts (Hughes, Soh et al. 2020). These higher grain prices help limit the impacts of climate change on agricultural economies, but pass on additional costs to grain consumers, including sectors not modeled here, such as intensive livestock operations (e.g. livestock feedlots, poultry and pig production) and food production .
There is already evidence of strong agricultural adaptation responses to recent climate change with improvements in technology and management practices helping to increase farm productivity (see Hochman et al. 2017; Hughes et al. 2017).
Figures 6 and 7 present estimates of total farm factor productivity (TFP), which measures industry performance over time, including gains from the adoption of new technologies.
Estimates of farm TFP are strongly influenced by climate variability (see Figures 6 and 7) and these climate effects can obscure underlying trends in farm performance. This is a problem when measuring productivity growth over the most recent period (1989 to 2020) given the strong trends in average precipitation and temperature.
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Recently, ABARES developed a methodology for producing ‘climate-adjusted’ assessments