Novelty traits to improve cow-calf efficiency in climate smart beef production systems
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Date
2015-01
Authors
Mokolobate, Motshabi Catherine
Journal Title
Journal ISSN
Volume Title
Publisher
University of the Free State
Abstract
The objective of this study was to identify novelty traits as possible selection criteria to
improve cow-calf efficiency and to describe cow efficiency in extensive systems that
will support climate smart beef production. The traits investigated were calf weaning
weight as trait of the dam and kilogram calf weaned per Large Stock Unit (KgC/LSU);
the latter trait being a measure (value) that expresses performance (calf weaning
weight) per constant unit, viz. per LSU. This may be a useful breeding objective/goal
to increase production efficiency, which may reduce the carbon footprint of extensive
cow-calf production systems. No reference could be found in the literature where
KgC/LSU or weaning weight as trait of the dam were considered as breeding
objectives. Therefore it was decided to investigate the novelty traits proposed above
as measures of cow- calf efficiency.
The investigation using breed averages of 30 beef and dual purpose breeds found that
KgC/LSU was independent of cow weight and the next step was to do a genetic analyses on breed level to estimate the genetic parameters for this trait and its genetic correlations with other traits of relevance.
For the purpose of the studies reported later, breed frame size specific equations were
developed to estimate LSU units, using published information. The differences in LSU
units between animals of the same body weight, but with different frame sizes is based
on the principle that there are differences in the voluntary feed intake between such
animals, although they have the same body weight.
A Bonsmara (most numerous breed in South Africa) dataset, comprising of 34 884
complete cow-calf records for the first three parities was used to investigate KgC/LSU
and calf weaning weight (1<205), both as traits of the dam, as breeding objectives to
improve efficiency in extensive cow-calf production systems. A number of models were
evaluated and the simplest models with improved (smaller) log likelihood values was
used. Heritability estimates of KgC/LSU, K205 and dam weight (OW) were 0.26±0.02,
0.11 ±0.01 and 0.45±0.02 respectively. Genetic correlations of 0.39±0.03 between
KgC/LSU and 1<205, 0.17±0.02 between OW and 1<205 and -0.83±0.01 between OW
and KgC/LSU were found. The very high negative genetic correlation (-0.83) between
KgC/LSU and OW suggests that direct selection for KgC/LSU will decrease dam
weight. On the contrary selection for K205 will result in a slight increase in OW, since
the correlation between the two is only +0.17. Of interest is the moderate positive
correlation between K205 and KgC/LSU of +0.38, indicating that selection for weaning
weight as trait of the dam may increase cow efficiency, albeit that cow weight will
possibly show a small increase as well. It therefore seems that selection or KgC/LSU
will have the same defects as other ratio traits. A more effective alternative will be a
"cow efficiency index" which include OW and K205 but with a restriction on OW. A
restricted selection index will therefore restrict increases in OW (and implicitly LSU).
The relationship between the novel traits and conventional pre-weaning traits were
also investigated, where the conventional traits were the weaning weight of the dam
(OWW) and the average weaning weight of her three calves (ACWW). The Estimated
Breeding Values (EBV's) for the different traits were used to run a Pearson correlation
analysis and this correlation was used as approximation of the genetic correlation.
Heritability estimates for ACWW and OWW were 0.81±0.02 and 0.26±0.03
respectively. The correlations between K205 and KgC/LSU; ACWW and OWW are 0.42, 0.52 and 0.42 respectively; that between KgC/LSU and ACWW and DWW are
0.24 and 0.08 respectively; whereas that between ACWW and DWW is 0.54. The low
correlation of 0.08 between DWW and KgC/LSU indicates that KgC/LSU is
independent of the weaning weight of the dam. This result suggests that an alternative
approach to selection or selection practices may be needed to increase the efficiency
of beef cattle in climate smart agriculture.
The investigation on the novel traits were extended to three diverse breeds namely
the Afrikaner (indigenous Bos taurus africanis), Angus (British Bos taurus) and
Charolais (European Bos taurus), with 6 104, 7 581 and 2 291 complete cow-calf
records respectively, using the same approach as with the Bonsmara. The
heritabilities for KgC/LSU were 0.52, 0.24 and 0.21 for the Afrikaner, Angus and
Charolais respectively and that for K205 0.40, 0.17 and 0.13 respectively. In many
cases the genetic correlations could not be estimated. There were major differences
in the nature of the relationship between KgC/LSU and K205 between the Angus and
the Charolais, the latter indicated a strong negative correlation (-0.75) and the Angus
a strong positive correlation (+0.84). These results support the findings on the
Bonsmara, namely that a "cow efficiency index" may be a more effective alternative,
with minimal to no defects.
The cow efficiency in crossbreeding systems as defined by KgC/LSU was also
investigated, using the results of an extensive crossbreeding experiment. The results
was obtained by crossing the Brahman (B), Charolais (C), Hereford (H) and
Simmentaler (S) as sire line breeds on the Afrikaner (A) and F1 the genotypes as dam
lines. Crossbreeding with the A as dam line increased the KgC/LSU on average by
12.8 kg (11 .6 %), with the CA calf producing on average the most KgC/LSU (an
increase of 15.5%). In the case of F1 cows, cow productivity of as high as 46% above
that of the pure A was achieved. From this study it is clear that cow productivity can
be increased without additional herd costs to the farmer through properly designed
crossbreeding systems, thereby promoting climate smart beef production systems and
reducing the carbon footprint of beef production. The fact that there are large
differences in the KgC/LSU between certain genotypes, points to genetic differences
and holds the potential for improvement through selection and the use of
complementarity between breeds.
An effective way to reduce the carbon footprint from beef production and to support
climate smart production, is to reduce the cattle numbers and increase the production
per animal. This study attempted to identify novelty traits as possible selection criteria
to improve cow-calf efficiency in extensive systems, as well as the quantification of
crossbreeding results to demonstrate the effect of appropriate crossbreeding on cow
efficiency.
The first recommendation is to investigate possible selection criteria to increase the
weaning weight of calves in relation to a cow LSU unit in extensive beef production
systems. The combination of calf weight as a trait of the dam and dam weight in a
selection index might be a feasible option. Another alternative could be to use the
relationship between weight of calf produced and the estimated feed inputs required
to sustain the cow and allowing her to provide for the calf. Normally, the traits in a
selection index are weighed with their economic value. However, in this case the traits
can even be assigned weights that can be linked to carbon footprints or credits
(sequestration) and not only economic weights.
This study demonstrated that the correct use of crossbreeding can improve cow
efficiency substantially. However, the commercial beef producers in South Africa face
the problem of choosing a breeding bull from bulls of different breeds, without having
a tool to compare the breeding potential of these bulls directly. Within breed EBV's are
available, but cannot be used by commercial breeders to compare bulls across breeds.
The second recommendation is therefore to consider the development of breed
conversion factors that can be used to convert EBV's between breeds to the same
scale.
Description
Keywords
Cow-calf efficiency, Smart beef production, Climate, Breeding, Dissertation (M.Sc.Agric. (Animal, Wildlife and Grassland Sciences))--University of the Free State, 2015