Agriculture Reference
In-Depth Information
2. METHODS
In this study, two methods for estimating future yields of biomass crops are
used. The first method extrapolates statistical trends observed in food and
fodder crops to biomass crops. In food and fodder crops, long-term records
of average yields are available. By identifying the factors that have
determined or affected trends, general relationships are inferred, that may
also be applicable to biomass crops.
Analysis by several authors (
e.g.
Elmore 1980) of the mechanisms
behind the large increase in productivity achieved in agricultural crops
revealed no connection with the efficiency of the photosynthetic apparatus.
The large gain in yield was largely obtained by shifting the pattern of dry
matter distribution within the plant to those plant parts that are harvested
(
e.g.
Gifford and Evans 1981). In biomass crops, though, a large part of the
standing dry matter is used. This may limit the possibilities for yield
increase by shifting dry matter distribution from non-harvestable to
harvestable plant parts. A proper method seems to be comparison with food
and fodder crops whose above-ground dry matter is harvested totally, or for
the larger part. In this study, a comparison is made with cereals and silage
maize. For cereals, statistical data are available on both grain and straw
yields, so that the total above-ground dry matter can be calculated. For
silage maize, production figures relate to total above-ground dry matter.
Maize shares an interesting trait with
Miscanthus
in that both are C
4
grasses that have recently been introduced in north-western Europe.
Statistical data on crop yields for the Netherlands are supplied by the
Agricultural Economics Research Centre (LEI-DLO) (Anonymous 1954-
1997).
The second method for estimating future yields of biomass crops
assesses the effects of changes in physiological crop parameters on
biomass production through crop growth simulation. In mechanistic
models that simulate growth of biomass crops, physiological knowledge on
growth and production of the crops is integrated. Crop traits that have a
strong influence on biomass production and that may be crucial in plant
breeding are identified. The degree to which these traits may be changed
by breeding is quantified, and by changing crop parameters in growth
models, the effect on growth and yield is estimated.
In the analysis of crop physiology in this study, advancing the start
of the growing season and shifting the allocation pattern are the options
that are explored by crop growth simulation. In the crop growth model that
was used in the study (see Vleeshouwers 2001), simulated yield is
determined by (1) the efficiency with which radiation is intercepted by the
crop canopy, (2) the efficiency with which the intercepted radiation is
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