Physiological and biochemical evaluations and the use of machine learning to elucidate thermoregulatory resilience in Holstein x Nigerian White Fulani crossbred cows
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 28, 2025
Abstract
Climate
change-induced
heat
stress
poses
a
global
threat
to
livestock
productivity,
particularly
in
tropical
agroecologies
where
smallholder
dairy
systems
dominate.
This
study
investigates
the
thermoregulatory,
metabolic,
and
productive
responses
of
Nigerian
White
Fulani
×
Holstein
Friesian
crossbred
cows
(n
=
45)
under
natural
farm
conditions.
The
used
Temperature-Humidity
Index
(THI),
physiological
parameters
(respiration
rate,
pulse
rectal
temperature),
milk
yield,
biochemical
markers
(ammonia,
pyruvate,
tyrosine)
alongside
machine
learning
modelling
elucidate
effect
on
performance
cows.
Under
severe
(THI
≥
80),
indicators
significantly
increased
(p
<
0.001),
while
yield
declined
by
23%
0.01).
There
were
observations
disruptions,
including
elevated
ammonia
(+
35%,
p
0.01)
tyrosine
45%,
0.01),
which
highlighted
metabolic
strain.
tool
(random
forest
model)
integrating
THI,
feed
intake,
pyruvate
achieved
robust
prediction
(R²
0.82),
outperforming
traditional
regression
approaches.
presents
key
link
thermotolerance
production
resilience
conditions
demonstrating
learning’s
utility
prediction.
findings
emphasise
potentials
strategic
crossbreeding
precision
management
sustain
productivity
warm
climates,
offering
actionable
insights
for
genomic
selection
programmes
targeting
resilience.
Language: Английский
Toward Climate-Smart Ruminant Production: Best Practices and Future Directions
Springer eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 35
Published: Jan. 1, 2025
Language: Английский