“the livelihood crisis of farmers – a regional perspective from australia and the southwest...
TRANSCRIPT
“THE LIVELIHOOD CRISIS OF FARMERS – A REGIONAL PERSPECTIVE FROM AUSTRALIA AND THE SOUTHWEST PACIFIC, WITH PARTICULAR REFERENCE TO WEATHER
AND CLIMATE RISKS AND UNCERTAINTIES”
Roger Stone, University of Southern Queensland, Australia.
WMO International Workshop on “Addressing the Livelihood Crisis of Farmers: Weather and Climate Services”, Belo Horizonte, Brazil, 12-14 July 2010
Acknowledgement : Australian Managing Climate Variability Program (MCVP)
Outline•Both a detailed analyses - eg: climate and farm cash income + some case study reports and an overview of climate systems affecting this region – (strong ENSO impact).
• Focus on aspects associated with extremes of climate variability and how these impact on farmer well-being and livelihood – links to agricultural yield – also vulnerability associated with stocking rates.
•Climate variability – yield - farm profit – linking to decisions at a wide range of temporal scales.
• Forecasting farm cash income - using key climate indicators – links to seasonal climate management issues –
•The sensitivity of farm-cash income to climate variability.
•Whole value-chain approaches (farm/mill/transport) valuable in understanding farmer livelihood.
•Value in taking an ‘optimality approach’ according to the type of climate decade..
In this region, Australia, in particular, has very high year-to-year rainfall variability
Variability of Annual rainfall
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Australia S. Africa Germany France NZ India UK Canada China USA Russia
Country
Coe
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ient
(%)
(100 years of data for Australia and generally also for the other countries)
(Love, 2005)
And parts of Australia have also long-term shifts in rainfall (mm/decade)
10-year running mean rainfall Rockhampton
The relevance of the ‘intersection’ of climate variability and longer-term climate shifts
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1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002
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Variability in Australian wheat yield - annual variation in Australian rainfed wheat yield and the SOI (Nicholls).
Variation in shire wheat yield (Moree Plains Shire) - stronger relationship in eastern regions - new insurance, derivative products?
Motivation
by Birchip Cropping Group, Victoria, Australia (courtesy D Rodriguez)
Cereal yield, climate (growing season rainfall) and farm profit - in southern regions (note the shift to major loss of profits, due to the
continued run-down in resources, capital, ‘farmer energy’, income).
Decision type (eg. only) Frequency (year)
Logistics (eg. scheduling of planting / harvest operations)
Intraseasonal (>0.2)
Tactical crop management (eg. fertiliser/pesticide use)
Intraseasonal (0.2-0.5)
Crop type (eg. wheat or chickpeas) Seasonal (0.5-1.0)
Crop sequence (eg. long or short fallows) Interannual (0.5-2.0)
Crop rotation (eg. winter or summer crop) Annual/biennial (1-2)
Crop industry (eg. grain or cotton, phase farming)
Decadal (~10)
Agricultural industry (eg. crop or pasture) Interdecadal (10-20)
Landuse (eg. Agriculture or natural system) Multidecadal (20+)
Landuse and adaptation of current systems Climate change
The key issue of agricultural management decisions on many scales - and connecting to climate systems on many scales (Meinke and
Stone, 2005)
Climate change
IPO
Climate change
IPO
BoM
Logistics (intra-seasonal, >0.2 years) Sowing / harvesting Application of agrochemicals
Tactical (intra-seasonal, 0.2-0.5 years) Crop nitrogen management Crop agrochemicals
Tactical (seasonal, 0.5-1 years) Crop type (e.g. wheat or chickpea, maize or sorghum) Marketing (e.g. sell, buy or store) (Rodriguez, 2010)
The costs of inputs – inputs required to make operational and tactical
farm management decisions.....
AU$50-200K pa
AU$200 – 500K
AU$100-200K (based
on farmer interviews)
Climate change
IPO
Climate change
IPO
Crop sequences (inter-annual, 1-4 years) Long or short fallows (CI, summer/winter cropping) To irrigate cotton or grains
Crop rotations (multi-annual to decadal, 2-10 years) Grains or cotton
Cropping or livestock / fodder
Transformational (inter-decadal, 10-20 years) Irrigated or dryland cropping Cropping or livestock / fodder Farm investment (storages, land, machinery, etc) Succession planning (Rodriguez, 2010)
Medium to longer-term farm business planning
and climate systems
AU$300-500K pa
AU$500K – 1M
AU$1m-$20m
BoM
Nelson and Kokic,
2004
It possible to forecast farm cash incomes? (in
order to develop improved planning
strategies or identify those years when
government aid may be more in demand) –
method is to forecast farm cash income using
climate indicators –
“probability that simulated farm cash income will exceed the long-term median value”
(1900-2003)
Based on ABARE’s AgFIRM model for forecasting the
impact of climate variability on farm incomes
– utilises hindcasting forecasts of crop yields and pasture
growth combined with prices available at the end of June to forecast farm incomes for
the coming year.
WA
NT
SA
NSW
VIC
TAS
Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data
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#
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#WA
NT
SA
NSW
VIC
TAS
Roma
Dalby
Emerald
Goondiwindi
Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data
(a) (b)
Strong relationship to the forecast of agricultural commodities: Probabilities of exceeding long-term median wheat yields for every wheat producing shire – July 2001 and July 2002. – combining crop simulation modelling with climate modelling (Potgieter et al)
July 2001 July 2002
Use of AgFIRM to simulate mean farm cash income - Nelson and Kokic, 2004 (Kokic et al. (2004) developed and tested a hybrid modelilng system, the Agricultural Farm Income Risk Model (AgFIRM), that brought together the best available biophysical models of Australian crop and pasture yield, with ABARE’s econometric model of farm incomes. The model provides a capacity to simulate the regional impact of climate variability on farm incomes. It can also provide conditional forecasts of the likely impact of climate variability on farm incomes one year into the future, using well established methods of seasonal climate forecasting.)
Large variability in farm cash income by ENSO impact
ENSO/SOI phase probability distributions/boxplots and simulated farm cash income (Nelson and Kokic, 2004)
Nelson and Kokic, 2004
A regional perspective – central NSW – large variability in farm cash
income depending on ENSO state
Probability distribution (boxplots) of simulated mean farm cash income by ‘SOI phase’ (Nelson
and Kokic, 2004)
Nelson and Kokic, 2004
Sensitivity of farm incomes to climate
variability - “The relationship between forecast seasonal
conditions and the sensitivity of Australian crop farm
incomes to climate variability.
“The sensitivity of farm cash income to climate variability is generally
lower in years when the SOI phase is ‘consistently positive’ or ‘rapidly rising’
at the end of May and June”.
“Conversely, in years when the SOI phase is
‘consistently negative’ or ‘rapid fall; incomes are much more sensitive to
climate variability”. This is true even for regions with low income variability
such as SW Western Australia and the Eastern Darling
Downs”.
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Dry decade
Dry decade
Also take an optimality approach according to the type of decade–Example of present farm business position (red dot) and the range of possible outcomes in terms of profit, economic risk and soil loss, for a 2000ha farm-during a ‘dry decade’ (the value of decadal forecasting?) (Cox et al, 2009).
Up to 50%95Late sorghum
Up to 70%85Sorghum
Up to 46%60Early sorghum
Up to 70%60Spring sorghum
Up to 80%90Wheat
Up to 26%95Maize
Up to 90%95Chickpea
Exposure (%farm land)
Risk (mm PAW)
Enterprise
Up to 50%95Late sorghum
Up to 70%85Sorghum
Up to 46%60Early sorghum
Up to 70%60Spring sorghum
Up to 80%90Wheat
Up to 26%95Maize
Up to 90%95Chickpea
Exposure (%farm land)
Risk (mm PAW)
Enterprise
Improved strategyEnterprise
Be more risk averse Wheat
Be more risk averse particularly with late plantingsSorghum
Be more risk averse & reduce exposure by reducing areas or do not sowMaize
Increase areas as the opportunity arisesChickpea
Improved strategyEnterprise
Be more risk averse Wheat
Be more risk averse particularly with late plantingsSorghum
Be more risk averse & reduce exposure by reducing areas or do not sowMaize
Increase areas as the opportunity arisesChickpea
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100000
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0102030405060
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Op
era
ting
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turn
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/fa
rm.y
ear) Wet decade
Wet decade
Optimality Approach – Example of present farm business position (red dot) and the ‘landscape’ of possible outcomes in terms of profit, economic risk and soil loss, for a 2000ha farm -during a wet decade-the value of decadal forecasting? (Cox et al, 2009).
Up to 95%110Late sorghum
Up to 65%60Sorghum
Up to 45%60Early sorghum
Up to 30%60Spring sorghum
Up to 100%95Wheat
Up to 60%100Maize
Up to 45%80Chickpea
Exposure (%farm land)
Risk (mm PAW)
Enterprise
Up to 95%110Late sorghum
Up to 65%60Sorghum
Up to 45%60Early sorghum
Up to 30%60Spring sorghum
Up to 100%95Wheat
Up to 60%100Maize
Up to 45%80Chickpea
Exposure (%farm land)
Risk (mm PAW)
Enterprise
Improved strategyEnterprise
Be prepared to increase area even if prices are close to averageWheat
Be less risk averseSorghum
Be less risk averse & prepare to increase areas in the good seasonsMaize
Be less risk averse & increase areasChickpea
Improved strategyEnterprise
Be prepared to increase area even if prices are close to averageWheat
Be less risk averseSorghum
Be less risk averse & prepare to increase areas in the good seasonsMaize
Be less risk averse & increase areasChickpea
-500000
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ar-9
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$/f
arm
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Present farm management
SOI driven area planted
APSFARM simulation
And…The value of a whole-farm systems approach in linking climate forecasting to farmer’s decisions and operating returns..
Rodriguez et al., 2006
Assessing likely yields according to decision options -the key value in the linking role of simulation modelling
• Provides historical yield of crops and pastures
• Assimilates key soil processes (water, N, carbon)
• Assimilated surface residue dynamics & erosion
• Provides a range of management options
• crop rotations + fallowing• short or long term effects
Agricultural Production Systems Simulator (APSIM) simulatesAgricultural Production Systems Simulator (APSIM) simulates
APSIM: precise daily time step
model that mathematically reproduces the
physical processes taking place in a
cropping system
“The value of climate forecast information for farming decisions impacts on likely yields – use of Whopper Cropper which utilises pre-run APSIM simulations – examples: When to sow my sorghum crop to achieve the highest potential yield? Effect of sowing date on sorghum yield range at Miles, Southern QLD with a ‘consistently negative’ SOI phase for September/October (Other parameters - 150mm PAWC, 2/3 full at sowing, 6pl/m2, medium maturity. Source; WhopperCropper
Sow date & SOI Phase
15-SepNegative
15-OctNegative
15-NovNegative
15-DecNegative
15-JanNegative
Yie
ld (
kg/h
a)6000
5500
5000
4500
4000
3500
3000
2500
2000
1500
• A general over-expectation of safe carrying capacity of stock by farm managers, investors and governments.
•Stock numbers, other herbivores (eg rabbits and kangaroos) and, in some cases, woody weed seedlings, increase in response to periods of above-average rainfall that precede drought episodes and thus lead to subsequent high levels of land degradation.
Cattle industry - insights into management practices leading to producer livelihood vulnerability related to climate variability:
•Intermittent dry seasons within an otherwise wetter period (decade) result in heavy grazing pressure, damage to the ‘desirable’ perennial species and ultimately the grazing land resource. This leads to the rapid collapse of the land to carry animals at the onset of major drought.
•Extreme utilisation in the first years of drought by retaining stock causes further loss of perennial species, exacerbating the drought impacts in subsequent years.
•A rapid decline in, or generally low, commodity prices results in some managers retaining stock in the hope of better prices or the fear of a high cost of restocking.•Continued retention of stock through a long drought period compounds damage to the resource and delayed recovery from the drought.
•A sequence of drought years results in a rapid decline of surface cover, which reveals the extent of previous resource damage and further accelerates the degradation process, thereby further increasing the vulnerability to subsequent drought (Stone et al., 2003; McKeon et al., 2004).
Salinger, 2009.
New Zealand issues – case studies
• NZ Case Study One - ‘lack of reliable rainfall in both summer and winter is the biggest climatic challenge’.....’this has become more of a factor in recent years’ ...
• El Niño and La Niña events both cause problems....
• the main challenge with unreliable rainfall is that the hills dry out quickly and so farmers try to get rid of their lambs early....
• A downstream consequence of dry summers is poorer lambing percentages....
• Also because of the land committed to pine-trees, most of which was in gorse, and effectively under pressure to reafforest, these farmers do not have any land to fall back on when conditions do get dry...” (Ian and Barbara Stuart - Adapting to climate change in eastern New Zealand”)...
Case Study Two - “Carry over effects - key points for the Bay of Plenty region, New Zealand
•The carryover effects of the 2007/8 drought - combined with variable weather, including a very dry autumn in 2009, production only recovered partially (up 7 percent compared with 2007/08) to 97 500 kilograms of milksolids.
• Reduced payout, down to $5.20 per kilogram of milksolids, net cash income dropped 28 percent to $519 500, compared with 2007/08.
• Higher costs and lower payout resulted in a significant cash loss of $53 800 in 2008/09 –
• As a result of the combination of losses in 2008/09 and the lower than expected $4.55 per kilogram of milksolids payout in 2009/10, “farmer morale is subdued”.
Carry-over impacts on farm profit in New Zealand - Bay of Plenty Region (Courtesy: Ministry of Agriculture and Forestry, 2009)
Fiji (Fiji Meteorological Service Headquarters in Namaka, Nadi
Airport)..
Aspects related to the sugar industry in the
overall region -
Farmer decisions and viability relate to issues across the whole value chain in sugar industry
Understanding issues across the whole value chain
The CanePlant
Sugarcane Production
Harvest & Transport
Raw Sugar Milling
Marketing & Shipping
• Best use of scarce/costlywater resources
• Better decisions onfarm operations
• Improved planningfor wet weatherdisruption
• Best cane supplyarrangements
- crush start andfinish times
• Better schedulingof mill operations- crop estimates- early season
cane supply
• Better marketing decisions basedon likely sugar quality
• More effective forward sellingbased on likely crop size
• Improved efficiency of sugarshipments based on supplypattern during harvest season
Everingham et al, 2002
Climate information application for sustained profitability in the sugar industry – Fiji and Australia (Everingham, et al 2002).
Farm Harvest, Transport, Mill Catchment Marketing PolicyFarm Harvest, Transport, Mill Catchment Marketing Policy
Industry Scale Axis
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C l i m a t e forecast information
•Irrigation•Fertilisation•fallow practice• land prep • planting• weed manag.• pest manag.
• Improved Planning for wet weather disruption – season start and finish•Crop size forecast•CCS, fibre levels•Civil works schedule
• Land & Water Resource Management
•Environmental Management
• Water allocation•Planning and policy associated with exceptional Events
Industry
Business and Resource ManagersBusiness and Resource Managers
GovernmentGovernment
• Crop size Crop size ForecastForecast•Early Season Early Season SupplySupply•Supply PatternsSupply Patterns-ShippingShipping-Global SupplyGlobal Supply
Fiji Sugar farmer and miller: case study decisions related to climate in order to improve viability:
Grower decisions:•Land preparation – ‘after the wet spell the land needs to be dry for tractors to move in the field’ – important April onwards...•Seed cane planting – rain necessary for good establishment of cane – important April/May.•Herbicide and fertiliser efficiency is improved if advanced knowledge of weather/climate is known.
Miller decisions:• Mill opening and closing – high value in advanced knowledge-Eg: high rainfall in May/June and low rainfall in December there is high value in late opening and late closing.-: low rainfall in May/June and high rainfall in December there is high value in early opening and early closing.
Shipping decisions:• Good knowledge of possible weather conditions can assist in predicting sugar production and thus arrange for shipments in advance (Gawander, 2009).
Ways to prevent a crisis? Case study example - better sugar farm management decisions – Farmer Darren Reinaudo, 22 April 2002.
• ‘Climate pattern in transitional stage so I keep a watchful eye on the climate updates
• I take special interest in the sea surface temperatures (SST) particularly in the Nino 3 region.
• There is currently some indications of warming in the Niño 3 region which hints at a possible El Niño pattern developing
• Replant would be kept to a minimum
• Harvest drier areas earlier, even if CCS maybe effected.
• We don’t run the farm based solely on climate information and forecasts, it’s just another tool to consider when making decisions”.
ExposureVulnerabilityConsiders only:
natural and physical capital
Considers all sources of capital:
natural, physical, financial, and social
10% (most extreme) 10 to 25% (extreme)10 to 25% (extreme) < 25% (least extreme)
ExposureVulnerabilityConsiders only:
natural and physical capital
Considers all sources of capital:
natural, physical, financial, and social
10% (most extreme) 10 to 25% (extreme)10 to 25% (extreme) < 25% (least extreme)
Additional factors contributing to livelihood? A wide range of other aspects besides climate - vulnerability exposure variation in Australia for rainfed pasture producing regions, depending on whether a suite of measures are included (left-hand image) (and including such aspects as significant land degradation) or only natural and physical capital is included (right-hand image) (Nelson et al., 2005).
Also - the potential for more extreme climate and weather events – further crises for
farmers….
P. J. Webster et al., Science 309, 1844 -1846 (2005)
Intensity of hurricanes according to the Saffir-Simpson scale(categories 1 to 5):
100% increase in Category 5 and Category 4 systems since 1970.
‘Based on a range of models, it is likely that future tropical cyclones will become more intense, with larger peak wind speeds and more heavy
precipitation associated with ongoing increases in tropical SSTs’
Finally - fisher livelihood?
Aspects related to climate
‘drivers’ may also be
important.
Relationships between
Spanner Crab Catch off eastern
Queensland and the Nino3 SST (Stone, Meinke, and
Williams, 2005).
Summary• Overall, high relationship between climate variability (ENSO), yield and farm profit/farm cash income with potential links to crisis periods for producers and farmers– value in linking to decisions at a wide range of temporal scales – and value in forecasting farm cash as well as yield to assist preparedness (by government or farmers).
•Climate variability and agricultural yield vary enormously in the region –strong relationship to ENSO and decadal patterns – over expectation of good seasons and thus excessively high stocking rates (cattle) and subsequent poor returns and high land degradation in subsequent drought .
•The likely sensitivity of farm cash income to climate variability - generally lower than normal income sensitivity in years when indicators suggest break of El Niño or La Niña pattern at the end of May and June - higher income sensitivity under developing El Niño.
•Key aspects related to whole-farm and also whole-industry and value chain – example of sugar industry (eg Fiji).. •Flow on impacts in years beyond major droughts (eg New Zealand)..key aspects of more extreme events impacting on farm viability.
Thanks to the following colleagues:
Dr Rohan Nelson (Federal Department of Climate Change), Canberra.
Dr Daniel Rodriguez, Agricultural Production Systems Research Unit (APSRU/DEEDI), (Queensland Government, Toowoomba).
Howard Cox, Agricultural Production Systems Research Unit (APSRU/DEEDI), (Queensland Government, Toowoomba).
Sid Plant, Farmer, Kulpi, Queensland.
Anthony Clark, NIWA, New Zealand.
Thank youThank youThank youThank you
Best practice in the delivery of seasonal forecasting systems to farmers - the important aspect of co-learning with farmers if we are serious about improving the livelihood of farmers- ‘Participative R&D in action’. Tully Consultative Group sugar/climate project (Russell Muchow; Yvette Everingham, Roger Stone; CSIRO/JCU/DPI&F/USQ)
Assessing the potential for ‘exceptional drought assistance’ for farmers- use of simulation models to determine the relevance of recent
agricultural droughts in an historical context.
5-year running mean - Wentworth, 1950 to 1998
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Simulated Wheat Yield 1950+
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Simulated Wheat Yield 1890+5-year running mean - Wentworth, 1884 to 1998
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om th
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ean
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New Caledonia
•The term “drought” ‘not well defined’ in New Caledonia – its definition depends on its use...
•For agricultural purposes New Caledonia uses a weekly estimation of the Penman Monteith potential evapotranspiration for various sites which gives a proxy of the stress imposed by meteorological conditions for plants ..
•Drought also monitored indirectly with the bushfire weather forecasting systems based on the MacArthur index.
(Source: Alexandre Peltier, Meteo-France, Nouvelle Caledonie).
Conclusions:
Degradation increasing overall vulnerability in rainfed pasture producing regions
Average profit versus the risk of making a loss for a range of urea rates applied as fixed amounts each year at sowing (full line and filled diamonds), applied each year at top dressing (broken line and empty diamonds), and top-dressed adjusting the rate each year depending on the June-July SOI phase (squares). The numbers in italic associated with
the continuous and broken lines indicate the total amount of urea applied at sowing or applied at topdressing (sowing + topdressing) irrespective of the SOI. The numbers in the framed legend associated with each square indicate the
total amount of urea (sowing + topdressing) applied when the June-July SOI was consistently negative (CN), consistently positive (CP), rapidly falling (RF), rapidly rising (RR), and near zero (NZ), respectively. The different
figures represent different initial conditions reset every year on the 1st of January in the simulations. This is 10 or 50% of plant available water (PAW) and 10 or 60 kg N / ha in the soil profile.
Input parameters Number of factors
Crop type the common summer and winter crops
Soil water-holding capacity up to five levels
Soil water at planting 1/3, 2/3 and full
Planting date up to five dates
Maturity length three categories
Plant population typically three levels
Row configuration wide rows in sorghum and cotton
Effect of soil nitrogen content typically three levels
Nitrogen fertiliser rate (planting and in-crop)
typically six nitrogen rates
Southern Oscillation Index phase system five-level phase system
Main input factors and numbers of factor included in WhopperCropper