Agriculture Reference
In-Depth Information
Optimization
intake curves are used (NRC, 2012). Despite
the challenges, there are models that pre-
dict feed intake with varying degrees of
success (Black et al ., 1986; Ferguson et al .,
1994; Knap, 1999; Wellock et al ., 2003b).
Watson ® predicts feed intake by consider-
ing both what the animal would need to eat
to satisfy the requirement for the most
limiting nutrient under non-limiting condi-
tions (i.e. desired feed intake), and what
it is constrained to eat by gut capacity, the
diet, social stressors and environmental
factors (i.e. constrained feed intake). With
this approach it is possible to predict the
voluntary feed intake with a reasonably
high degree of accuracy (<5% deviation
from actual) (Wellock et al ., 2003b; Ferguson,
2006). This approach to predicting feed in-
take allows for changes in animal defin-
ition, nutrient profile and supply, social
and physical environment and health sta-
tus to be reflected in the amount of feed
consumed and in the subsequent growth
of the animal. It is also important to note
that these responses will differ between
individuals within a population (Ferguson
et  al ., 1997; Knap, 2000; Wellock et al .,
2004; Brossard et al ., 2009; Hauschild
et al ., 2010).
Given the unprecedented challenges pork
producers are facing, not to mention the
volatility in the ingredient commodity
market, only financially optimal nutrition
and management solutions are sustainable.
Running a single simulation will not pro-
vide an optimal solution; rather it is necessary
to run multiple simulations simultaneously
to achieve the optimum solution for a given
objective (e.g. maximum margin over feed
cost (MOFC) or minimum feed:gain). Op-
timizing nutritional strategies based on
economic returns or animal performance
rather than least cost formulation for a de-
fined set of nutrient requirements is the
most appropriate method for improving
performance and profitability at the farm
level. Gous and Berhe (2006) defined the
criteria required for optimization as: (i) feed
costs at defined nutrient levels; (ii) animal
responses to changing nutrient profiles;
(iii) fixed and variable costs associated with
the production system; and (iv) definition of
revenue generating processes. Figure 11.1
illustrates the conceptual relationship be-
tween animal biology, optimization and
animal variation.
Nutrient specifications
Optimizer
Stochastic
animal model
Least cost
formulation
• Combinations
• Evolutionary logic
OPTIMUM
SOLUTION
Economics
Fig. 11.1. The main components of the optimization process.
 
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