Risk definitions |
There is little coherence in the literature about the quantitative
assessment of risk, including climatic risk, in agriculture. According
to White (1994), agronomists and engineers (for instance Nash and Nash,
1995) tend to define risk as a loss, while economists tend to use the
word as a synomym of "probability of occurrence of a damaging event".
This note adopts the first definition, which loosely follows the approach
adopted by Reilly (1996) in a climate change impact context. It provides
a convenient and consistent way of defining vulnerability and several
related concepts which are often dealt with in a very "impressionistic"
We start with the simple definition below. For a given factor (or stress), we have:
which we can rewrite as
where the loss can be expressed using different units (for instance
loss of agricultural production in metric tons, loss of human life,
loss of income, etc.). If losses can be due to several different
factors, the unit acts as a common denominator, which is a convenient
way of expressing a combined loss.
Most geophysical factors can be expressed on a scale of intensities,
in which case the definition above applies for each intensity (discrete
case) and becomes
According to the definitions adopted here, total risk and impact are
Figure 1 below illustrates a hypothetical case where the frequency
displays an asymmetrical distribution as a function of intensity.
This is very often the case with climatic variables, where J-shaped
distributions are typical of rainfall in semi-arid areas, U-shaped
curves apply to cloud cover, S-shapes to relative moisture...
Positively skewed bell-shapes apply to vapor pressure, wind speed,
rainfall in the more humid climates, etc. Of course, the shape of
the distribution also depends on the time interval considered (see,
for instance, Arléry et al, 1962, or any text of statistical climatology).
Figure 1: Variation of
vulnerability and frequency as a function of the intensity of an
The risk, as a function of the intensity of the environmental stress
is shown in Figure 2. The total risk, for the factor under consideration,
is the sum of the risks associated with each intensity. Again, the
asymmetrical curve is typical. Most real world examples would be
considerably more skewed than shown in figure 2, resulting in the
largest portion of risk (losses) being due to relatively low-intensity
factors (chronic risk), while extreme factors, i.e. by definitions
those with a low probability of occurrence (major risks), have a
relatively minor impact in absolute terms (see note 1).
Risk as a function of the intensity of an environmental factor
In many real-world situations, the response of a system as a function
of an environmental factor goes through positive values and negative
ones. It is only the negative ones that actually correspond to a stress.
An example is given in figure 3 for food production in Niger (after
Gommes, 1998). A National rainfall Index is used as a indicator of the
quality of he agricultural season
for more details about the national Rainfall Index). For values above
500 mm, the response is positive. Below the threshold, we enter vulnerable
conditions corresponding to an average food production and consumption deficit
Per capita surplus and deficit cereal production in Niger as
a function of a national Rainfall Index RI. The figure assumes
that consumption requirements amount to 240 Kg/year, equivalent
to a production of 300 Kg with a post harvest loss of 20%. The
cereal Yield/RI relation was calibrated using 1932-93 statistics
Discussion of concepts
This section examines to what extent the concepts above are relevant in
an agricultural and a climate/climate change context. To start with,
the system being impacted must be relatively well defined if "risk"
and "vulnerability" are to be meaningful. As in most agricultural
modeling, things tend to be theoretically satisfactory at the very
local scale only (plant and field). When moving to regions or countries,
many concepts become rather fuzzy, for instance "soil moisture" or "crop
yield response to rainfall", etc., which is one of the reasons why the
classical crop models are mostly of little help in global studies.
The scale problem obviously also applies to the "intensity of environmental
factor" used in Figure 1 and Figure 2. If we consider that climate
behaves as a set of coherent and correlated variables, we can obviously
replace the "intensity" with a indicator which will incorporate most
of the variations of climate as far as they are relevant for regional
agriculture. This approach was used in a study of African food security
where the "intensity" was replaced by a "national rainfall index" biased
towards agriculture (Gommes and Petrassi, 1994; Gommes, 1998).
The question to be answered is thus: what indicators can be used in
the current context? Their desirable features include that they must
be synthetic, generic and normative (refer to Bakkes et al., 1994,
for a good overview of indicators).
Synthetic: the indicator must incorporate most of the factors which
are known to be of relevance for agricultural production, for instance
crop water consumption, a key factor in production, and dependent on
water supply, radiation, CO2 concentrations... The word "synthetic"
insists on the fact that the indicator reduces the dimension of the
problem, i.e. the number of variables actually to be dealt with.
Generic: this feature points at the fact that the indicator should be
robust enough to be applicable outside its original context. If it
performs correctly in different areas and years, it may remain meaningful
in the future as well.
Normative: an indicator is said to be normative if it can be compared
to some reference value, which implies that the sensitivity of vulnerability
to the indicator should be roughly linear or, at least, that the variation of
vulnerability as a function of the indicator should not behave unpredictably.
In Figure 1, vulnerability is approximately linear up to an intensity of 12,
after which the response levels off; the reference value (possibly the average)
does have a physical and statistical significance.
The IPCC definition of vulnerability as the extent to which climate may damage
or harm a system. It depends not only on a system's sensitivity but also on
its ability to adapt to new climatic conditions (IPCC, 1995) is consistent
with the approach above, if we take "extent" quantitatively.
Many difficulties are, however, associated with the notion of vulnerability.
They include the following:
vulnerability can be determined empirically through impact assessments.
Since extreme conditions tend to be rare, the statistical base for such
assessments is bound to remain weak. In addition, the intensity of the
stress factor is not always known;
vulnerability depends on the history of the impacted system. An
excellent example is provided by the Sahelian droughts in the seventies,
the impact of which was far more dramatic than in 1984, when the drought
vulnerability changes over time, under the influence of numerous
factors, for instance population pressure and the resulting short
fallow and horizontal expansion of agriculture into marginal areas.
In Asia, for instance, FAO studies (FAO, 1989) indicate that some
countries have reached the limit of available agricultural land,
completely modifying the vulnerability profiles;
the main difficulties are again linked with the scale, in the
case of stresses that originate (spatially) outside the area covered
by the impacted system (pest and disease outbreaks, for instance).
vulnerability can be reduced through adaptation to stresses (as they
occur; see note 2) or through mitigation (measures aiming at reducing
future vulnerability), including structural and non-structural measures.
Note 1. "Structural risk", as in "structural food deficit" is equivalent
to "chronic risk". The risk associated with low probabilities and high
intensities would be "conjunctural risk".
Note 2. Adaptability refers to the degree to which adjustments are
possible in practices, processes or structures of systems to projected
or actual changes of climate. Adaptation can be spontaneous or planned,
and can be carried out in response to or in anticipation of changes in
conditions. (IPCC, 1995)
Arléry, R., H. Grissolet and B. Guilmet, 1973. Climatologie, méthodes
et pratiques. Gauthier-Villards, Paris. 434 pp.
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and J.D.E. Parker, 1994. An overview of environmental indicators:
state of the Art and perspectives. UNEP/EATR.94-01, RIVM/402001001, RIVM, Bilthoven.
Gommes, R., and F. Petrassi. 1994. Rainfall variability and drought in
sub-Saharan Africa since 1960. FAO Agrometeorology Series Working Papers
N. 9 : 100 pp. Click here for a abridged version.
Gommes, R. 1998. Some Aspects of Climate variability and food security
in sub-Saharan Africa. Proc. Bull. Soc. Royale Sc. Outremer, Brussels. In press.
IPCC, 1995. Contributions of working group II to the IPCC second assessment
report, IPCC-XI/Doc. 4. IPCC Geneva.
Nash, J.C. and M.M. Nash, 1995
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