Predicted climate change impacts on northern farming systems

Author: | Date: 05 Mar 2019

Take home messages

An increasing body of scientific evidence regarding the impact of human activity on the earth’s climate has shifted the debate from “Is climate change real?” to “What can we do about it?”  Adapting current management activities must include considerations of both climate variability and change.  Advisers have a vital role in helping to develop information-rich farming systems that will improve responses to current climate variability and that can enhance adaptation to climate changes.

Historical changes in climate?

Globally averaged air temperature has warmed by over 1oC since records began in 1850, and each of the last four decades has been warmer than the previous one (IPCC 2018).  This warming is driven by increasing concentrations of all the major long-lived greenhouse gases in the atmosphere, with carbon dioxide (CO2) concentrations rising above 400ppm and the CO2 equivalent (CO2-e) of all gases reaching 500ppm for the first time in at least 800,000 years (Foster et al., 2017).

In Australia, the pattern of warming (average temperature) has been largely similar to that experienced globally, with warming of just over 1oC since 1910 (BoM & CSIRO 2018).  Examining the shift in the distributions of monthly day and night-time temperature shows that very high monthly maximum temperatures that occurred around 2% of the time in the past (1951–1980) now occur around 12% of the time (2003–2017) (BoM & CSIRO 2018). Very warm monthly minimum, or night-time, temperatures have shown a similar change from 2% of the time in the past (1951–1980) to 12% more recently. This shift in the distributions towards hotter temperatures and more extreme high temperature conditions has occurred across all seasons, with the largest change being in spring (BoM & CSIRO 2018).

In the Goondiwindi region over the period 1950 to 2018 (length of the temperature record), warming has occurred in both minimum and maximum temperatures with mean temperatures now approximately 1.1oC warmer than in 1950. For the period 1950 to 1985 a maximum daily temperature of 29oC occurred, on average, 14% of the year. More recently (1986 to 2018) this temperature now occurs on average 35% of the year.  Similarly mean minimum temperatures have warmed with the frequency of a minimum temperature of 21oC increasing from 48 to 102 times each year (Figure 1). Despite warming in both minimum and maximum temperatures the number of frost events (i.e. defined here as the temperatures below 0oC) has more than tripled during June to August, with an average of 9 events occurring most recently.

This is a set of two graphs showing the probability distributions of mean daily maximum temperature (left) and mean daily minimum temperatures (right) for Goondiwindi for two periods. The Goondiwindi rainfall record exhibits a declining trend, with declines during the June to August and September to November periods most pronounced.

Figure 1. Probability distributions of mean daily maximum temperature (left) and mean daily minimum temperatures (right) for Goondiwindi for two periods,
namely 1960 to 1985 and 1986 to 2018

The Goondiwindi rainfall record exhibits a declining trend, with declines during the June to August and September to November periods most pronounced.  Mean dry spell lengths have also increased, with the average time between rainfall events now three days longer during June to August (i.e. an average dry spell length of 12 days for the period 1986 to 2018) (Figure 2). Similarly, the number of heavy rainfall events (i.e. greater than the 90th percentile) across the whole year has declined, again most notably during the June to August and September to November periods. The maximum number of consecutive dry days has increased across the whole year with March to May, June to August and September to November periods increasing by 3, 4 and 5 days respectively (i.e. now 33, 28 and 22, days respectively).

This is a set of two graphs showing the mean annual dry spell length (left) and seasonal dry spell length for December to January (DJF), March to May (MAM), June to August (JJA) and September to November (SON). Dry spell lengths are expressed in days. Mean dry spell lengths have also increased, with the average time between rainfall events now three days longer during June to August (i.e. an average dry spell length of 12 days for the period 1986 to 2018).

Figure 2. Mean annual dry spell length (left) and seasonal dry spell length for December to January (DJF), March to May (MAM), June to August (JJA) and September to November (SON). Dry spell lengths are expressed in days.

The current acceleration of global warming is expected to continue based on future greenhouse gas (GHG) emissions trajectories. Previous studies have examined how the rates of record-breaking have changed in the US (Anderson et al., 2011), the UK (Kendon, 2014), and Australia (Lewis & King, 2015). These studies have found increased rates of hot temperature records and decreased record setting for cold temperatures in recent decades (King et al., 2015; King, 2017). Lewis and King (2015) found that from 2000 to 2014 there were 12 times as many hot record‐breaking temperatures as cold records in Australia and attributed this to anthropogenic climate change. Across the world, there were about five times more record‐breaking monthly temperatures than would be expected without a long‐term warming trend (Coumou et al., 2013) over the early 21st century.

Climate change has been found to not only increase the likelihood of breaking high temperature records (e.g. Lewis and Karoly, 2013), but record‐breaking hot summers and years over previous decades are also attributable to anthropogenic climate change (King et al., 2016). More recent research by Mann et al. (2018) has shown that the synoptic features (large scale weather systems) responsible for prolonged heatwaves are on average 50% more prevalent under a business-as-usual GHG emissions trajectory.

In addition to record breaking temperatures, changes in rainfall patterns, sea levels, rates of glacial retreat and biological responses have also been detected consistent with expected climate change projections.  This mounting evidence has led to scientific consensus that:

Emissions of greenhouse gases and aerosols due to human activities continue to alter the atmosphere in ways that affect the climate system and these changes and resultant trends will continue for the foreseeable future; and

There is at least 95% confidence that humans are the main cause of global warming since 1950, and most likely responsible for 100% of that temperature rise (IPCC, 2013) with a less than 1 in 100 000 chance that human activities are not responsible for the observed increase in global temperatures (Kokic et al. 2014).

These changes are already likely to have negatively impacted on Australian agriculture, acting as a major drag on yield growth (Huong et al., 2018) with similar impacts on yield growth globally for the major crops (Porter et al., 2014).

A major issue in understanding historical and future climate change is how much are the various human-induced climate forcings (greenhouse gas emissions, stratospheric ozone depletion, Asian aerosols, and landcover change) interact with components of natural variability (Watkins 2005, McKeon 2006). Thus, it is important for successful climate adaptation that agricultural decision-makers keep informed of the evolving climate science and updated climate change scenarios.  As scientific understanding improves and there is more confidence in emission scenarios, current and future uncertainties can be rapidly assessed in terms of decision making.

What is expected to happen in the future?

In response to the continued growth in atmospheric GHG concentrations, scientists estimate that global average temperatures could increase by up to 4.8oC by the end of the present century, dependent on global population growth, technological advancement and economic growth (IPCC, 2013). To put this in context, the difference between our historical temperatures and those of the last ice age was only about 5oC. So even though 4.8oC does not sound like much, it signals a huge change in how the climate-ocean-land systems of the earth function and hence how agriculture will operate.

In Australia, national projections suggest up to 1.3oC of additional warming could be experienced by 2030 and up to 5.1oC of warming could be experienced by 2090, with the greatest warming being in inland Australia and the lesser warming along the southern coast and Tasmania (CSIRO, 2015). Global studies indicate that a rule of thumb is that global potential crop production drops by 6% per degree warming (Porter et al., 2014).

Whilst changes in rainfall are more uncertain, projections suggest drier conditions in the southern half of Australia, particularly in the south-west and during the cool season months of May to October, with as much as 20% less by 2030 and up to 50% less rainfall by 2090 (CSIRO, 2015).

At a regional scale projected change in climate for the eastern Downs region (Goondiwindi represents a southern town in this study region) are summarised in Table 1.  In addition to warmer temperatures and declines in mean annual rainfall, evaporation rates are likely to increase.  The annual potential evaporation (1986-2005) for the region is 1539 mm. By 2050 the median value of annual potential evaporation is projected to increase by 6 % under a high emissions scenario.

Table 1. Projected changes in temperature and rainfall for eastern Downs region (Goondiwindi is on found on the southern part of this region).  Present average temperatures and rainfall are calculated for the period 1986 to 2005.  The data contained in this table represents information compiled from the Queensland Department of Environment and Science, SILO database.

Variable

Season

Historical Mean (1986 to 2005)

2030

2050

2070

Mean Temperature Change

(oC Change)

Annual

19.4oC

1.1

(0.5 to 1.6)

1.9

(1.1 to 2.6)

2.9

(2.0 to 3.8)

Summer

25.4oC

1.1

(0.4 to 1.8)

2.0

(1.0 to 2.9)

3.0

(2.0 to 4.3)

Autumn

19.8oC

1.0

(0.1 to 1.6)

1.8

(0.9 to 2.6)

2.9

(1.8 to 3.6)

Winter

12.4oC

1.0

(0.1 to 1.7)

1.9

(1.2 to 2.5)

3.0

(2.1 to 3.8)

Spring

20.0oC

1.1

(0.5 to 1.8)

1.9

(1.0 to 3.2)

3.0

(2.0 to 4.2)

Mean Rainfall Change

(% Change)

Annual

614mm

-5

(-20 to +7)

-6

(-23 to +14)

-9

(-23 to +13)

Summer

246mm

0

(-15 to +21)

0

(-23 to +27)

-2

(-21 to +29)

Autumn

132mm

-3

(-28 to +27)

-4

(-33 to +36)

-8

(-42 to +41)

Winter

86mm

-1

(-25 to +13)

-14

(-39 to +13)

0

(-49 to +14)

Spring

151mm

-6

(-22 to +20)

-8

(-34 to +12)

0

(-42 to +21)

The impacts of climate change on wheat production for the Goondiwindi region have been simulated using the Agricultural Production Simulator (APSIM).  The simulations are based on a continuous wheat rotation with a Hartog wheat variety, grown on a black vertosol soil.  The simulations where run using daily climate data for the period 1990 to 2018, with future scenarios for 2030, 2050 and 2070 produced by scaling daily temperature and rainfall from the historical baseline period by the mean annual values found in Table 1.

If the 1990 to 2018 climate were to change, with a mean increase in temperature of 1.1oC and a 5% decline in annual rainfall (i.e. the mean 2030 projection) small improvements (approximately 80 kg per hectare) might be possible for 5th and 25th percentile yields (Figure 3).  The 75th percentile yields could also improve by as much as 200 kg per hectare, however the 95th percentile yields could decline by as much as 500 kg per hectare (Figure 3).

If temperatures were to increase by 1.9oC and annual rainfall where to decline by 6% from the 1990 to 2018 base period, significant reductions in large yields (i.e. 75th percentile and above) are possible. In this simulation the 95th percentile yields decline by almost 100 kg per hectare (Figure 3). Median to lower percentile yields remain similar to the baseline yields.

If the 1990 to 2018 climate were to change, with a temperature increase of 2.9oC and a 9% decline in annual rainfall, simulated yields decline by between 200 kg per hectare (i.e. 5th percentile yields) and 1000 kg per hectare (i.e. 95th percentile yields).

This simple example highlights the sensitivity of wheat production at Goondiwindi to temperature increases and modest changes in annual rainfall, but does not take into consideration the compounding effects such as changes in runoff (Figure 4). This simulation exercise does begin to make a case for adaptation at a range of spatial scales including farm-level and regional scales as well as changes to strategic planning and polices at the state and national level.

This is a boxplot graph showing  wheat yield for Goondiwindi for the period 1990 to 2018 (baseline), for a 28 year period centred on 2030, 2050 and 2070. If the 1990 to 2018 climate were to change, with a mean increase in temperature of 1.1oC and a 5% decline in annual rainfall (i.e. the mean 2030 projection) small improvements (approximately 80 kg per hectare) might be possible for 5th and 25th percentile yields (Figure 3).  The 75th percentile yields could also improve by as much as 200 kg per hectare, however the 95th percentile yields could decline by as much as 500 kg per hectare.

Figure 3. Boxplots of wheat yield for Goondiwindi for the period 1990 to 2018 (baseline), for a 28 year period centred on 2030, 2050 and 2070. Simulations were undertaken using APSIM based on a continuous wheat rotation with the Hartog wheat variety on a black vertosol soil. Yields are expressed in kilograms per hectare. The horizontal line indicates the average yield, the top and bottom of the ‘box’ indicates the 25th and 75th percentiles (i.e. the yields exceeded in 3/4 and 1/4 of years) and the tops and bottoms of the ‘whiskers’ indicate the 95th percentile and bottom 5th percentile values).  Climate scenarios for the 2030, 2050 and 2070 simulations are based on the mean annual projections of change in temperature and rainfall found in Table 1.

This is a map of Australia showing the mid-range assessment of changes in average runoff per degree global temperature increase (IPCC 2014). This simple example highlights the sensitivity of wheat production at Goondiwindi to temperature increases and modest changes in annual rainfall, but does not take into consideration the compounding effects such as changes in runoff.

Figure 4. Mid-range assessment of changes in average runoff per degree global temperature increase (IPCC 2014). Run-off integrates the effects of changes in temperature, rainfall and evaporation. For example, where the map shows a 25% reduction in runoff per degree and global temperatures rose by 2oC then runoff is likely to halve (i.e. 2 times 25%) with major implications for water resource management including for irrigated agriculture.

Adapting to projected climate changes

Climate change is likely to pose a significant challenge for Australian agriculture. Of greatest concern are likely to be changes in water availability, and the change in frequency of climatic extremes (e.g. heatwaves, drought and floods).

Many of the actions required for adapting to climate change are extensions of those currently used for managing climate variability.  For this reason, efforts to improve current levels of adaptation to climate variability will have positive benefits in addressing likely climate change impacts.

Examples of likely farm level adaptation options include:

  • Enhancing the current implementation of zero tillage and other minimum disturbance techniques, retaining crop residues, extending fallows, changing row spacing, changing planting density, staggering planting times, traffic and erosion controls
  • Alter planting decisions to be more opportunistic – more effectively considering environmental condition (e.g. soil moisture), climate (e.g. seasonal climate forecasting) and market conditions
  • Expand routine record keeping of weather, production, degradation, pest and diseases, weed invasion
  • Incorporating seasonal climate forecasts and climate change into farm enterprise plans
  • Improve efficiency of water distribution systems (to reduce leakage and evaporation), irrigation practices and moisture monitoring
  • Learning from farmers in currently more marginal areas
  • Selection of varieties with appropriate thermal time and vernalisation requirements, heat shock resistance, drought tolerance, high protein levels, resistance to new pests and diseases and perhaps that set flowers in hot/windy conditions
  • Enhance current consideration of decision support tools/training to access/interpret climate data and analyse alternative management options (e.g. APSIM, EverCrop).

There are also longer-term decisions at a family farm level - to sell up, to buy more land, where to invest. These are especially pertinent for farmers in low rainfall regions and it will increasingly be more difficult to find no-regret decisions if climate change progresses as anticipated (Hayman 2005). These decisions, along with industry infrastructure (silos etc.) and industry support (drought policy) are hard decisions requiring full understanding of the likely future risks (Hayman 2005).

The value of adaptation

There is a growing international body of research examining the benefits of adaptation to climate variability and change, showing a number of adaptation options are available to reduce the possible impacts of climate change.

In Australia a number of studies have examined the economic benefits of adaptation in the wheat industry at both national and regional scales under a range of likely future climate conditions.  Hochman et al. (2017) highlighted that the adoption of new technology and management systems has held actual yields fairly steady: without these advances, water-limited yield would have dropped by 27%. It was estimated that rainfall declines should have accounted for about three-quarters of the fall in simulated yield potential, whilst observed warming should have accounted for about a quarter of fall in yield potential.

Continued adaptation to climate change has been estimated to add an additional AU$500M per annum to Australia’s annual income from wheat exports (Howden and Crimp 2011) via the introduction of improved water-use efficiency options and may mitigate potential yield losses by up to 18% through broader scale adaptation (Ghaharamni et al. 2015).

The results suggest a number of adaptation options exist to manage increased future downside risk, however the effectiveness of adaptation is driven by the extent of future change. Under conditions of large climate change, tactical adaptation will only have limited effectiveness and more extensive adaptation options, often defined as transformation adaptation, may be required.

Advisers have a key role to play in changing the nature of the climate change dialogue. In the space of about five years many grain growers and their advisers have moved from asking "What is climate change?" or "Is it real?" to "How do we manage for climate change?" and "What will the impact be on the grains industry?"

Advisers have a vital role to play in this dialogue, not only in assisting grain growers in reducing greenhouse gas emissions from on-farm activities, but also in developing information systems that growers can tap into in order to build farming systems that can cope with current climate variability and can adjust to ongoing climate changes.

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Acknowledgements

The authors would also like to acknowledge that this research was made possible via financial support from the New South Wales Department of Environment and Heritage. We would like to acknowledge the Australian Bureau of Meteorology (BoM) for provision of its Australian Climate Observations Reference Network – Surface Air Temperature (ACORN-SAT) data and the Science Division of the Queensland Department of Environment and Science (DES) for provision of it SILO climate projections for analysis.

Contact details

Dr. Steve Crimp
Climate Change Institute
Australian National University
Fenner School of Environment and Society
Building 141, Linnaeus Way, Action, ACT 2601
Ph: (02) 6125 7265
Email: Steven.Crimp@anu.edu.au