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60 SCF Folio
...The notion that probabilistic forecasts cannot be used in
decisionmaking is not true. Notwithstanding difficulties in
communication, a forecast should be presented as a
probability because it is the honest way of doing it...The
challenge is how to communicate and use in decisionmaking
skillful but uncertain forecasts that are best represented as
shifts in climatological probability distributions.
excessive rains rather than to a more qualified
statement of increased chances of the events occurring.
Implied in these inclinations is the notion that
probabilistic forecasts cannot be used in
decisionmaking. This notion, however, needs to be
corrected because it is not true. Notwithstanding
difficulties in communication, a forecast should be
presented as a probability because it is the honest way
of doing it. As an expert puts it, the atmosphere is a
complex chaotic fluid, and although patterns of ocean
temperatures ‘nudge’ this chaos in certain directions,
there will always be a significant proportion of
unexplained variation. Hence, the challenge is how to
communicate and use in decisionmaking skillful but
uncertain forecasts that are best represented as shifts
in climatological probability distributions.
Probabilistic forecasts also ensure that risk
management is not hindered. A farmer who
misunderstands SCF as a categorical forecast may be
led to devise poorer risk management strategies
compared to a situation where he did not hear of the
Figure 1. Decision tree analysis showing gross margins for different fertilizer rates and season
types, and probability weighted value for each of the three fertilizer rates
forecast at all. A crop grower may plan for a wide range
of outcomes in the absence of a forecast. But if only
one outcome is in his mind, then the planning exercise
will definitely be narrower.
An imposing challenge therefore is how to use
uncertain information for decisionmaking. The use of
decision analysis was mentioned as an approach that
provides a logical framework for a decisionmaker to
formulate preferences, assess uncertainty, and make
judgments. There has been a tradition in agricultural
science to talk the language of choice-consequence.
For example, if you put on x units of nitrogen, you will
get a yield of y. A more forward-looking language is that
of choice-chance-consequences. This means that if
you put on x units of nitrogen, depending on the season
type, you will get a yield of either y 1
, y 2
, or y 3
.
A good example (Figure 1) is the use of decision
tree analysis in determining the level of fertilizer inputs
given uncertain forecasts. Decision tree analysis is a
technique to aid decisionmakers in identifying the
outcomes for each decision alternative. It involves
assessing the probabilities associated with each
outcome, assigning payoffs, and keeping the sequence
of outcomes and decisions in the proper chronological
order. Because the decision tree reflects choices,
probabilities, and consequences, it thereupon
effectively illustrates how uncertain forecasts might be
used to change fertilizer decisions.
The figure shows how forecasts can influence the
decision of N fertilizer rates application. For instance,
during the season with a
poor, average, and good
outcome, about 20, 60, and
100 units, respectively, of N
fertilizer will be applied.
Given this information and
knowing the expected
season from the seasonal
climate forecast, the farmer
can therefore decide on the
level of fertilizer application.
Results from the figure
show that if the forecast is
neutral, the farmer is better
off when he will apply 100
units than 20 units and even
60 units of N fertilizer.
However, if he will apply 100