Variability and Losses
Causes of variability in
Figure 1 provides a theoretical illustration of some of the factors
which affect the variability of agricultural yields (heavy read line).
They can be roughly grouped in three categories according to their
variations over time:
growing smoothly, such as more or less regular technology and management
trends (i.e. mechanization, varieties, irrigation, and the farmers know-how),
but growing more abruptly for several years in succession in the case of
innovations. A typical innovation would be the new introduction of irrigation;
discontinuous, like extreme factors of various origins and policy decisions
which affect management (for instance, farmers may decide to use less
fertilizer if it is no longer subsidized) or infrastructure, such as the
construction of a road which provides access to new markets;
- pseudo-cyclic, like weather
The result may be a complex curve where it is often rather difficult
to separate weather from other factors, particularly if weather itself
is affected by a trend... In addition, the figure assumes that the
function linking yield and the factors is known (a simple addition in this case).
Some of the factors affecting agricultural yields (represented by
a heavy red line: technology and management trends, innovation, policy,
extreme factors and weather)
Although it is generally agreed that, under normal circumstances (see note 1),
weather variability remains one of the main factors behind the inter-annual
variability of agricultural production, it is very difficult to estimate
how much production is lost due to variability.
This is due to several factors, including the following
the fact that agricultural statistics are reported by administrative
areas, which are rarely homogeneous from an agricultural and agro-ecological
(including climatic) point of view. The area-wide average therefore cancels
weather can impact agricultural production systems at many levels
(production, harvest, storage, transportation...) both directly and
indirectly through diseases, pests, damage to infrastructure, etc. This
subject has received a lot of attention and it is not necessary to
repeat it here.
not only are agricultural statistics spatially aggregated values, but
some practices of national services, like for instance the reporting of
harvested rather than planted areas also tend to depress the effect of
We simply underline that climate variability impacts agricultural output
(production) through its effects on yields and areas planted. Yields are
affected, as indicated, by weather as the main "random" factor, but also
by mostly continuous technological trends (including new varieties and
management), innovations (including management innovations), agricultural
policies (mostly national policies) and extreme factors of various origins.
Areas depend more on economic factors; planted areas vary according to
labour availability, level of mechanization and expected return (prices).
Areas harvested are often strongly linked to environmental conditions,
including poor weather during the cycle, damage to infrastructure due to
extreme conditions, or simply very low yields for which it is not economical
to harvest at all.
Quantification of loss due to
variability, and how much is due to climate?
An attempt can be made to estimate how much production is lost currently
because of the variability of climate. The following methodology was followed:
- take a national production time series
for each year Y, take the maximum production value Pm in the 7-year
interval from Y-3 to Y+3
compute the difference between the production P of year Y and Pm,
and express it as a percentage "loss": (Pm-P)/Pm*100%.
The approach assumes that no marked technological progress took place in
the seven year period. An example for Thailand is shown in Figure 2: the
"loss" varies between 0 and about 25% and shows a slight downward trend
probably due to stagnating productions since 1980.
Total cereal production and production loss due to inter-annual
variability in Thailand between 1961 and 1994 (based on FAO statistics).
The 7-year moving interval used for defining the "maximum yield" becomes
an asymmetrical interval at the end of the series, i.e. 1991 (1988-94)
is the last complete interval, followed by 1989-94 for 1992 etc.
The same approach can be applied to areas harvested and to yields, leading
to Figure 3. Logically, production undergoes the largest fluctuations.
The comparison of areas and yields is interesting, as the two curves keep
crossing, suggesting that, according to the years, areas planted (i.e.
mainly socio-economic factors) or yields (see note 2) (i.e. mainly
environmental factors) have played the main part. It can be assumed that
a large fraction of the "losses" thus put into evidence are directly
ascribable to weather.
Percent loss of cereal production, yield and areas in Thailand
between 1961 and 1994 (based on FAO statistics)
Figure 4 indicates that, in the case of Niger, the crude above-mentioned
National Rainfall index accounts for about 25% of the loss. This is
clearly an underestimate, as rainfall was affected by a negative trend
during much of the period under consideration. The second example below
(wheat in Italy, Figure 5) shows a marked dependence of yield on radiation
(solar energy) which accounts for about half the non-technology variability.
Percent "loss" of total cereal production in Niger as a function
of National Rainfall Index (mm). R=0.63 for the figured regression
line (heat capacity model)
De-trended national wheat yield (see note 3) in Italy (1951-86) as a
function of estimated September-March global radiation, together with
Although this is still only an assumption, it seems thus that at least
50% of the variability of agricultural production is due to weather,
in developing and developed countries alike.
Table 1 and Table 2 illustrate the "average loss" (as defined above) over
the period from 1964 to 1991 in several countries and groups of countries.
Average loss of production, area and yield of total cereals between
1964 and 1991 in several countries. Three values are given
for yields: (a) the average ;(b) the 90% percentile (the value
exceeded on average 1 year out of ten) and (c) the average of the
3 highest losses during the period. Based on FAO statistics.
|Yield (a: average)||7||17||16||9||11||11|
|Yield (b: p90)||14||29||31||15||20||23|
|Yield (c: 3 highest)||16||34||64||18||23||28|
As noted above in the case of Thailand, production shows the highest
average losses, while the relative importance of area and yield varies,
with area being usually highest. It is also worth observing that some
very high values occasionally occur in developing countries. At the
global scale, values tend to be lower due to the averaging effects.
Average loss of production, area and yield of total cereals, total
roots and total pulses between 1964 and 1991 in Africa and
the world as a whole. Refer to Table 1 or the definition of the
different yield loss statistics. Based on FAO statistics.
|Yield (a: average)||8||7||4||5|
|Yield (b: p90)||16||10||8||11|
|Yield (c: 3 highest)||19||12||10||11|
Regarding extreme factors, their impact can be shown to have marked
effects at the national level only in some particularly disaster-prone
countries, like Bangladesh (Figure 5) where the losses due to the extreme
conditions can reach 2.5 million tons (in 1988, according to Hofer and
Messerli (1997)), or about 15% of the total rice production (see note 4).
In comparison, factors like hail, frost or fires are only of marginal significance.
Production of three rice typologies in Bangladesh. Boro is a dry
season (winter) irrigated crop; aus is pre-monsoon crop (Apr.-Aug.)
and Aman is a late-monsoon crop (June-Dec.). Extreme conditions are
indicated as F for floods, D for drought, C for tropical cyclones and
W for war. After Gommes, 1992.
In summary, it appears to be rather difficult, to quantify the impact
of climate variability on agricultural production. In agreement with
Palm and Dagnelie (1993) and Palm (1997), it may be concluded that,
at regional scale, the largest fraction of inter-annual variability
of crop yields and production can be ascribed technology and management
in developed countries (see note 5). In Europe and for annual crops,
non-weather factors account for more than 50% of the variance in 75%
of the cases studied, and for more than 75% in 40% of the cases. Roughly 20%
of the variability is due to other factors, of which at least half is
According to Oerke et al. (1994), production losses losses due to pests,
diseases and weeds amount to 15%, 14% and 13 % on average for the main
cereals and potatoes, in the absence of control measures. This refers to
actual conditions. When compared with potential yields, the loss reduction
is roughly 70 % about equally distributed between pests, diseases and weeds.
In developing countries, particularly in semi-arid areas, the fraction of
variability due to weather is at least of the same order of magnitude, but
because technology trends tend to be much less marked, the actual role of
climate appears to be much larger, up to 100% in extreme cases of virtual
complete crop loss due to generalized drought.
Note 1. Thus excluding war, major epidemics, etc.
Note 2. If reported areas correspond to harvested ones rather than to
planted ones, the effect of weather variability on yields is artificially depressed.
Note 3. The detrended yield is the departure of yield values from the
time-trend, assumed to take into account the technology and management
component of yield. Like in many developed countries, radiation, not
rainfall, is the main limiting factor to agricultural production. The
time-trend accounts for about 80% of the total variation of yields,
leaving about 20% to be accounted for by other factors.
Note 4. Hofer and Messerli see the erosion of river banks (loss of land)
as one of the main problems in Bangladesh.
Note 5. For perennial crops, the trends account for between 25 and 75% of
the variance in about half the countries studied by Palm and Dagnelie.
36 % show no trend at all.
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