POLLINATORS POLLINATION AND FOOD PRODUCTION

THE ASSESSMENT REPORT ON POLLINATORS, POLLINATION AND FOOD PRODUCTION
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4. ECONOMIC VALUATION OF POLLINATOR GAINS
AND LOSSES
Methodology: These studies use dependence ratios,
theoretical metrics of the proportion of crop yield lost in the
absence of pollination, to estimate the current contribution of
pollination to crop production within a region. This proportion
of crop production is multiplied by the producer price per
tonne (or other unit of production) to estimate the total
benefits of pollination services. The expected proportion of
yield lost can also be multiplied by yield dependent producer
costs (such as labour costs) to estimate producer benefits.
Unlike yield analyses, which utilize primary data collected
from the field, dependence ratios are based on secondary
data such as personal communications with agronomists
(e.g., Morse and Calderone, 2000) or from literature on
agronomic experiments comparing yields with and without
pollination services (e.g., Allsopp et al., 2008), often using the
same methods as employed in yield analyses.
Strengths: By estimating the proportion of yield lost,
dependence ratio studies theoretically capture the link
between pollination services and yield, without the need for
further primary data collection (Melathopoulos et al., 2015).
Because of the large body of literature available (e.g., Klein
et al., 2007), dependence ratio studies are relatively simple
to undertake and can be readily applied across a range of
crops at any regional, national or international scale (e.g.,
Lautenbach et al., 2012).
Weaknesses: As with yield analyses (above) dependence
ratio studies neglect the impacts of other inputs on crop
production potentially biasing estimates upwards. Most
dependence ratio studies are based on subjective personal
communications which lack an empirical backing (e.g.,
Morse and Calderone, 2000) or from reviews, particularly
Klein et al. (2007) and Gallai et al. (2009a) which, although
a synthesis of available knowledge, bases many of its
estimates on a small number of often older studies (see
Section 4.5.2.2). Consequently, the metrics are generalized
for a whole crop, regardless of variations in benefits between
cultivars or the effects that variations in environmental
factors or inputs have on the level of benefits (Section 4.5).
When applied over large areas where multiple cultivars
and environmental conditions are present, this can result
in substantial inaccuracies (Melathopoulos et al., 2015). As
the dependence ratio metrics typically represent a complete
loss of pollination services, they inherently assume either
that pollination services within the region are presently
at maximum and that the studies they are drawn from
compare no pollination to maximum levels, neither of which
may be accurate (e.g., Garratt et al., 2014). In most cases,
no assessment is made of the marginal benefits of different
pollinator populations or consumers and producer’s capacity
to switch between crops (Hein, 2009).
Data required: Crop yield per hectare, crop market price
per unit, measure of insect pollinator dependence ratio (e.g.,
Klein et al., 2007).
Examples: Leonhardt et al. (2013); Lautenbach et al.
(2012); Brading et al. (2009).
Suitable to use: As the dependence ratios used are often
rough approximation of pollinator dependence, this method
is mostly suited to illustrate the benefits of pollination
services to crops larger scales. Due to their inability to
distinguish differences in benefits between locations,
cultivars and management and their implicit assumption that
services are at a maximum level the method is less suitable
for making more informed management decisions but can
act as an initial estimate.
2.2.3 Production function models
What it Measures: The market price of additional
crop production resulting from marginal changes in
pollination services in relation to other factors influencing
crop production.
Methodology: Production functions measure the role
of pollination as part of a broader suite of inputs (e.g.,
fertilizers, pesticides and labour) and environmental factors
(e.g., water) allowing for an estimation benefits relative to
other factors (Bateman et al., 2011; Hanley et al., 2015).
Production functions can take a number of forms depending
on the relationships between the variables involved:
e.g., additive functions assume that inputs can perfectly
substitute for one another, Cobb-Douglas function assumes
that inputs cannot be substituted at all. All of these forms
assume that inputs have diminishing marginal returns – i.e.,
after a certain point and all things being the same, the
benefits of additional units of input gets progressively smaller
and may eventually become negative. By incorporating the
costs of inputs (e.g., the costs of hiring managed pollinators
or the opportunity costs of sustaining wild pollinators), it is
possible to determine economically optimal combinations of
inputs that maximize output relative to cost.
By incorporating the costs of each input, these crop
production functions can accurately relate pollinator
gains and losses to benefits under different management
strategies. The resultant effects on output can be
incorporated into partial or general equilibrium models
(see Section 2.4) of surplus loss. Separate pollination
production functions can also be developed to estimate
the levels of pollination services provided by a pollinator
community, depending on the efficiency of the species
within the community and any additive, multiplicative or
negative effects arising from their activities (e.g., Brittain et
al., 2013) and interactions (Greenleaf and Kremen, 2006).
The sum of these relationships and the crop and variety
specific thresholds of pollen grains required will determine
the overall service delivery of the community (Winfree et
al., 2011). By focusing on functional groups of pollinators,