295
ing for traits of interest is taking place, until four generations. As a result, the value
for important reproductive traits is being determined, including age at first mating,
age at first kidding, adult productive life, and generation interval among other traits.
The simulation assumed that all goats come from the same breeding population and
have the same conception and survival rates after weaning. Furthermore, the authors
determined productive potential in their model, including the estimated 210 kg of
milk in 210 days (Nziku et al. 2017b, c). Finally, considerable accuracy in selection
intensity is gained when many bucks are tested for traits such as daily milk yield,
than when only few of them are tested. However, the authors noted the importance
of revising the breeding programs from time to time, due to the fact that one breeding may not fit in all production systems. This confirms the ideas in Falconer and
Mackay (1996) that the best animal selected under temperate environment may not
be the best under tropical conditions. In Mgeta’s situation, testing 30 young bucks
per year may be the best current option. However, the proposed breeding program
may not be perfect in the future due to constant shifts in the available knowledge
and practical options. It is therefore necessary to revise breeding programs from
time to time.
3.3.2 Use of Records to Design a Breeding System for Dairy Goats
Recently, Nziku et al. (2017c) used data obtained from 62 stallholder dairy goat
farms in Mgeta (Mvomero district) to suggest a breeding program for this community. The authors based their program on the little data available from the farmers
regarding traits in dairy goats, including milk yield per doe per day, and weights at
different stages of growth (growth performance). In the analyses, the authors also
took into account other information that they considered important in designing
breeding program in dairy goats. This included the individual identities (ID), parents of individual goats (buck and doe IDs), birth date, sex, herd dynamics (kids
born, animals sold, animals slaughtered, number of deaths), inseminations (date of
insemination and semen ID), and health of animals (e.g., type of diseases and treatment effected). Based on the analyses, it was concluded that a simplified breeding
plan using buck rotation and occasional AI as the one initially planned at Mulbadaw
nucleus flock was beneficial to farmers (Nziku et al. 2017a, b, c).
4 Discussion
In the above data presentation, we have highlighted the practices of farmers and
other stakeholders, including researchers in dairy goat breeding. Although there
have been successful results from various programs, we note the chain stops at some
points; and we also note that farmers are responsive to instructions of researchers
when projects are ongoing. However, goats have short generation interval, and it is
imperative to strictly supervise breeding. Norway is one of the countries in the
The Need for Farmer Support and Record Keeping to Enhance Sustainable Dairy Goat…
ing for traits of interest is taking place, until four generations. As a result, the value
for important reproductive traits is being determined, including age at first mating,
age at first kidding, adult productive life, and generation interval among other traits.
The simulation assumed that all goats come from the same breeding population and
have the same conception and survival rates after weaning. Furthermore, the authors
determined productive potential in their model, including the estimated 210 kg of
milk in 210 days (Nziku et al. 2017b, c). Finally, considerable accuracy in selection
intensity is gained when many bucks are tested for traits such as daily milk yield,
than when only few of them are tested. However, the authors noted the importance
of revising the breeding programs from time to time, due to the fact that one breeding may not fit in all production systems. This confirms the ideas in Falconer and
Mackay (1996) that the best animal selected under temperate environment may not
be the best under tropical conditions. In Mgeta’s situation, testing 30 young bucks
per year may be the best current option. However, the proposed breeding program
may not be perfect in the future due to constant shifts in the available knowledge
and practical options. It is therefore necessary to revise breeding programs from
time to time.
3.3.2 Use of Records to Design a Breeding System for Dairy Goats
Recently, Nziku et al. (2017c) used data obtained from 62 stallholder dairy goat
farms in Mgeta (Mvomero district) to suggest a breeding program for this community. The authors based their program on the little data available from the farmers
regarding traits in dairy goats, including milk yield per doe per day, and weights at
different stages of growth (growth performance). In the analyses, the authors also
took into account other information that they considered important in designing
breeding program in dairy goats. This included the individual identities (ID), parents of individual goats (buck and doe IDs), birth date, sex, herd dynamics (kids
born, animals sold, animals slaughtered, number of deaths), inseminations (date of
insemination and semen ID), and health of animals (e.g., type of diseases and treatment effected). Based on the analyses, it was concluded that a simplified breeding
plan using buck rotation and occasional AI as the one initially planned at Mulbadaw
nucleus flock was beneficial to farmers (Nziku et al. 2017a, b, c).
4 Discussion
In the above data presentation, we have highlighted the practices of farmers and
other stakeholders, including researchers in dairy goat breeding. Although there
have been successful results from various programs, we note the chain stops at some
points; and we also note that farmers are responsive to instructions of researchers
when projects are ongoing. However, goats have short generation interval, and it is
imperative to strictly supervise breeding. Norway is one of the countries in the
The Need for Farmer Support and Record Keeping to Enhance Sustainable Dairy Goat…