Robustness of livestock farmers to climate variability: a case study from Uruguay. Valentin Picasso

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