Presentation from the WCCA 2011 conference in Brisbane, Australia.

1. Robustness of
livestock farmers to
climate variability:
a case study from
Uruguay
Valentín Picasso
University of the
Republic, Montevideo, Uruguay

2. 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

3. 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.

4. the problem
Meat Productivity
Time (years)

5. stability-type concepts
1. 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 recovery
3. Resistance – ability to remain unchanged under
perturbation

6. indicators for variability
Meat Productivity Meat productivity
Years Years
Standard Deviation Variance Coefficient
Range Variability Coefficient

7. indicators for variability
Meat productivity Meat productivity
T
Years Years
Probability to fall below
RMSE of regression
a threshold

8. Indicators for response to
perturbation (e.g. drought)
Meat Productivity
Robustness
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
10

11. 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

12. 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

13. Drought 1988 - Less robust farmers
150
QCS-02
QCS-03
QCS-05
Meat (kg/ha)
QCS-10
100
50
0
1983 1985 1987 1989 1991 1993
Year

14. Drought 1988 - More robust farmers
150
QCS-01
QCS-07
QCS-08
100
Meat (kg/ha)
50
0
1983 1985 1987 1989 1991 1993
Year

15. 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

16. variability and robustness
30 1.2000
Variance Coefficient 1.000
20 .8000
Robustness
.6000
10 .4000
.2000
0 -
50 75 100
Average Meat Productivity

17. 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.

18. 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