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Presentation from the WCCA 2011 conference in Brisbane, Australia.
Citation preview
Robustness of livestock farmers to climate variability: a case study from
Uruguay
Valentín PicassoUniversity of the Republic,
Montevideo, Uruguay
rationale• Adaptation to increased climatic
variability• Goal of designing “climate robust
systems”• Need for practical indicators to measure
robustness at farm level• How do technological and structural
features of farms relate to robustness?• Case study: Livestock farms in Uruguay
objectives
1. Propose set of operational indicators to measure climate robustness dimensions at the farm level.
2. Calculate these indicators using data from a network of livestock farms.
3. Test empirically the hypotheses that structural and technological features of farm impact climate robustness.
the problem
Meat Productivity
Time (years)
stability-type concepts1. Variability: changes over time
1. Standard Deviation 2. Variance Coefficient (STD/MEAN *100)3. Variability Coefficient (90% - 10%)/50%4. Probability to fall below a threshold 5. RMSE of regression
2. Response to perturbation (e.g. drought)1. Robustness – amount of perturbation a system
can tolerate 2. Resilience – speed of recovery3. Resistance – ability to remain unchanged under
perturbation
indicators for variability Meat Productivity
Years
Meat productivity
YearsStandard DeviationRange
Variance CoefficientVariability Coefficient
indicators for variability Meat productivity
Years
Meat productivity
Years
T
Probability to fall below a threshold
RMSE of regression
Indicators for response to perturbation (e.g. drought)
Robustness
Meat Productivity
Robustness was measured by the ratio of the minimum in drought year over the value predicted by the regression of the five previous years.
10
Uruguay livestock farming systems:- Mixed grazing cattle and sheep- Beef cattle Hereford breed- Cow calf or full cycle (finishing)- Natural grasslands mainly- <20% improved pastures
data sources• FUCREA – livestock farmers network
– 350 livestock farmers• Group “Queguay Chico-Soto”
– Years 1973 – 2008– N = 7 farmers– Variables:
• Meat productivity (kg/ha)• soil productivity index, area under grazing, % area in improved
pastures, livestock stocking rate (livestock units/ha), and sheep to cattle ratio.
• Drought year 1988
correlations among variability
r RANGE90 VARCOEF VARIAB RMSE
STD 0,96 0,70 0,70 0,55
RANGE90 0,67 0,74 0,64
VARCOEF 0,95 0,23
VARIAB 0,35
There was no association between any variability measure and average meat production
1983 1985 1987 1989 1991 19930
50
100
150
Drought 1988 - Less robust farmers
QCS-02QCS-03QCS-05QCS-10
Year
Mea
t (
kg/h
a)
1983 1985 1987 1989 1991 19930
50
100
150
Drought 1988 - More robust farmers
QCS-01QCS-07QCS-08
Year
Mea
t (k
g/h
a)
robustness vs structural features
structural variable correlation (r)
beef production 0.60
stocking rate 0.60
area in improved pastures 0.52
soil productivity index 0.49
sheep to cattle ratio -0.32
grazing area -0.31
variability and robustness
50 75 1000
10
20
30
-
.20000
.40000
.60000
.80000
1.00000
1.20000
Average Meat Productivity
Vari
ance
Coe
ffici
ent
Robu
stne
ss
final message
• Variability and robustness can be quatitatively meassured and related to structural features of farms.
• Need for improved measures and larger datasets.
• Need for interaction with farmers networks.
coauthors• Laura Astigarraga and Rafael Terra,
Interdisciplinary Centre in Response to Climatic Variability and Change, University of the Republic, Uruguay
• Ignacio Buffa, Diego Sotelo and Gustavo Américo, FUCREA Farmers network, Uruguay
• Pepijn van Oort and Holger Meinke,Centre for Crop Systems Analysis, Wageningen University, The Netherlands