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Dairy farming is one the most important sectors of United Kingdom (UK) agriculture. It faces major challenges due to climate change, which will have direct impacts on dairy cows as a result of heat stress. In the absence of adaptations, this could potentially lead to considerable milk loss. Using an 11-member climate projection ensemble, as well as an ensemble of 18 milk loss estimation methods, temporal changes in milk production of UK dairy cows were estimated for the 21st century at a 25 km resolution in a spatially-explicit way. While increases in UK temperatures are projected to lead to relatively low average annual milk losses, even for southern UK regions (<180 kg/cow), the ‘hottest’ 25×25 km grid cell in the hottest year in the 2090s, showed an annual milk loss exceeding 1300 kg/cow. This figure represents approximately 17% of the potential milk production of today’s average cow. Despite the potential considerable inter-annual variability of annual milk loss, as well as the large differences between the climate projections, the variety of calculation methods is likely to introduce even greater uncertainty into milk loss estimations. To address this issue, a novel, more biologically-appropriate mechanism of estimating milk loss is proposed that provides more realistic future projections. We conclude that South West England is the region most vulnerable to climate change economically, because it is characterised by a high dairy herd density and therefore potentially high heat stress-related milk loss. In the absence of mitigation measures, estimated heat stress-related annual income loss for this region by the end of this century may reach £13.4M in average years and £33.8M in extreme years.
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RESEARCH ARTICLE
Spatially explicit estimation of heat stress-
related impacts of climate change on the milk
production of dairy cows in the United
Kingdom
Na
´ndor Fodor
1,2
*, Andreas Foskolos
3
, Cairistiona F. E. Topp
4
, Jon M. Moorby
3
,
La
´szlo
´Pa
´sztor
5
, Christine H. Foyer
1
1Centre for Plant Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom,
2Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences, Martonva
´sa
´r,
Hungary, 3Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth,
United Kingdom, 4Crop and Soil Systems, Scotland’s Rural College, Edinburgh, United Kingdom, 5Institute
for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of
Sciences, Budapest, Hungary
*fodor.nandor@agrar.mta.hu
Abstract
Dairy farming is one the most important sectors of United Kingdom (UK) agriculture. It faces
major challenges due to climate change, which will have direct impacts on dairy cows as a
result of heat stress. In the absence of adaptations, this could potentially lead to consider-
able milk loss. Using an 11-member climate projection ensemble, as well as an ensemble of
18 milk loss estimation methods, temporal changes in milk production of UK dairy cows
were estimated for the 21st century at a 25 km resolution in a spatially-explicit way. While
increases in UK temperatures are projected to lead to relatively low average annual milk
losses, even for southern UK regions (<180 kg/cow), the ‘hottest’ 25×25 km grid cell in the
hottest year in the 2090s, showed an annual milk loss exceeding 1300 kg/cow. This figure
represents approximately 17% of the potential milk production of today’s average cow.
Despite the potential considerable inter-annual variability of annual milk loss, as well as the
large differences between the climate projections, the variety of calculation methods is likely
to introduce even greater uncertainty into milk loss estimations. To address this issue, a
novel, more biologically-appropriate mechanism of estimating milk loss is proposed that pro-
vides more realistic future projections. We conclude that South West England is the region
most vulnerable to climate change economically, because it is characterised by a high dairy
herd density and therefore potentially high heat stress-related milk loss. In the absence of
mitigation measures, estimated heat stress-related annual income loss for this region by the
end of this century may reach £13.4M in average years and £33.8M in extreme years.
PLOS ONE | https://doi.org/10.1371/journal.pone.0197076 May 8, 2018 1 / 18
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OPEN ACCESS
Citation: Fodor N, Foskolos A, Topp CFE, Moorby
JM, Pa
´sztor L, Foyer CH (2018) Spatially explicit
estimation of heat stress-related impacts of climate
change on the milk production of dairy cows in the
United Kingdom. PLoS ONE 13(5): e0197076.
https://doi.org/10.1371/journal.pone.0197076
Editor: Juan J. Loor, University of Illinois, UNITED
STATES
Received: January 23, 2018
Accepted: April 25, 2018
Published: May 8, 2018
Copyright: ©2018 Fodor et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: We have solely used
open databases for the calculations, specifically: 1)
BADC, UKCP09 spatially coherent climate
projections (SCPs; 2018 [cited 2018 Apr 9],
Database: figshare [Internet], Available from:http://
dx.doi.org/10.6084/m9.figshare.6115274; 2) AHDB
Dairy, Farm-gate milk price data in the UK; 2018
[cited 2018 Apr 9], Database: figshare [Internet],
Available from: http://dx.doi.org/10.6084/m9.
figshare.6115379; 3) AHDB Dairy, Data on calving
and milking patterns in the UK, 2018 [cited 2018
Apr 9], Database: figshare [Internet], Available
Introduction
Global consumption of milk is increasing in most parts of the world, driven by population and
income growth, urbanization and changes in diets [1]. The UK has approximately 1.6 million
dairy cows, producing about 14.6 billion litres of milk per year, making it the 10th largest milk
producing country in the world. The value of UK milk production is around £4.6 billion per
year, approximately 18% of gross agricultural economic output [2]. The average yield per dairy
cow is over 7500 litres per annum [2].Like other agricultural sectors, milk production is influ-
enced by the weather and climate. These factors determine what feed crops can be grown, and
the availability of grass for grazing. A large proportion of UK dairy farming is based on cows
grazing pastures for approximately six months of the year [3]. During the grazing period, dairy
cows are more exposed to environmental factors and are thus likely to be more vulnerable to
climate change than cows that are housed, especially if we consider that cooling devices can be
used as a relief for cattle.
Projected changes in climate will directly impact the dairy cow, mainly as a result of heat
stress, but also through the indirect effects climate change will have on pasture yield and qual-
ity, and the length of the growing and grazing season [4,5]. Farm animals have specific thermo-
neutral zones, which are the ranges of ambient temperatures in which body heat production is
in equilibrium with body heat loss, when there is no need for additional warming (e.g. shiver-
ing) or cooling (e.g. sweating and panting) mechanisms or behaviours (e.g. seeking shelter).
Abiotic factors that affect these are relative humidity (RH), wind speed and the intensity of
solar radiation. Ambient temperatures (T) higher than the upper critical T of the thermoneu-
tral zone will result in heat stress [6], leading to a net decrease in milk production in cows [7]
and thus milk loss from dairy farms [8,9]. The need to predict both heat stress and correspond-
ing milk losses led to the development of the temperature humidity index (THI), which com-
bines effects of T and RH associated with the level of thermal stress. An animal is considered
to be heat stressed with THI above specific thresholds (THI
thr
). Several THI calculation meth-
ods and THI
thr
have been proposed in the literature [712] but there is no single standard pro-
cedure for calculating THI from T and RH data. Once a THI method is defined, empirical
equations can be used to quantify the impact of heat stress on milk yield reductions.
As heat stress is likely to be a direct effect of climate change on dairy cows, the overall aim
of the present study was to apply a modular approach to investigate the potential outcomes. In
the first analysis, we considered only the impact of heat stress on milk losses from dairy cows,
assuming no mitigation measures are taken. We recognise that other factors, such as cow fertil-
ity, disease and mortality rate [13] may also be affected by future heat episodes, in addition to
the impact of these on general animal comfort and welfare [14], but these will be assessed in
later studies. The specific objectives of this study were: 1) to predict future changes of heat
stress-related milk loss of dairy cows in the UK in a spatially-explicit manner, 2) to estimate
the uncertainty associated with calculated milk losses, 3) to project the possible economic con-
sequences of milk loss due to heat stress, and 4) to assess differences between milk loss calcula-
tion methods.
Materials and methods
Climatic data
In the framework of the UKCP09 project [15], an 11-member data ensemble was created using
11 variants of a regional climate model (RCM, called HadRM3), based on the medium emis-
sion scenario (A1B) with data produced on a daily time scale at a 25 km spatial resolution [16].
The spatially coherent projections (SCPs) were generated by applying scaling factors to the
Impact of heat stress on UK milk production
PLOS ONE | https://doi.org/10.1371/journal.pone.0197076 May 8, 2018 2 / 18
from: http://dx.doi.org/10.6084/m9.figshare.
6120881; 4) AHDB Dairy, Average herd sizes in the
UK at NUTS-1 level; 2018 [cited 2018 Apr 9],
Database: figshare [Internet], Available from: http://
dx.doi.org/10.6084/m9.figshare.6120911. All data
needed to replicate all of the figures, graphs, tables,
statistics, and other values are provided within the
submission, its Supporting Information files, and
the data found in the links provided in the Data
Access Statement.
Funding: The results were obtained within an
international research project named “FACCE
MACSUR – Modelling European Agriculture with
Climate Change for Food Security, a FACCE JPI
knowledge hub” and acknowledge the respective
national or regional funding organizations. NF and
CF thank BBSRC for financial support (BB/
N004914/1). Financial support was provided by the
Scottish Government RESAS Strategic Research
Programme, and the Welsh Government and
Higher Education Funding Council for Wales
through the Sêr Cymru National Research Network
for Low Carbon, Energy and Environment. Project:
Consequential Life Cycle Assessment of
Environmental and Economic Effects of Dairy and
Beef Consolidation and Intensification Pathways –
‘Cleaner Cows’. The research was supported by the
Sze
´chenyi 2020 programme, the European
Regional Development Fund—"Investing in your
future" and the Hungarian Government (GINOP-
2.3.2-15-2016-00028).
Competing interests: The authors have declared
that no competing interests exist.
RCM data, in a way that the changes in the SCP ensemble were linearly related to changes in
global temperature. A time scaling method [17] was used to incorporate the uncertainty in
global temperature from emission scenario, carbon cycle, sulphur cycle and ocean physics, into
the RCM data, making the spread of the scaled RCM data consistent with the overall spread in
the probabilistic (General Circulation Model based) projections [15]. The SCP ensemble was
designed to be used for trend analysis as the RCM provides continuous projections for the
1950–2100 period. The SCPs used in this study explore an even wider range of climate change
than the General Circulation Model driven RCM projections of the UKCP09 but still include 11
equally plausible projections of future climate conditions [16]. The grid of climate projections
covers the inland territory of the UK with 440 25×25 km cells. For each grid cell, 11 different
series of daily maximum and minimum temperature (T
max
and T
min
) as well as average relative
humidity (RH
mean
) data for the 2000–2100 period were used according to the 11-member SCPs.
Heat waves (frequency and length) were the focus of particular attention in these climate
projections because this information is required for milk loss methods (model M5 and M6 in
Table 1) that were firstly introduced in this study. A heat wave is defined as a period when the
daily maximum temperature exceeds the 90th percentile of a reference distribution (years
between 1980 and 2009) for at least 3 consecutive days [18].
Milk loss estimation methods
The daily milk loss values (kg/cow) were calculated for each grid cell by using six methods (2
sub-daily step, 2 daily step, and 2 mixed) described in Table 1 and in the supplemental mate-
rial. Daily step methods use only daily values of meteorological parameters (e.g. mean relative
humidity) while sub-daily step methods take the diurnal changes of meteorological parameters
into account. Sub-daily climatic data were produced from the daily values by postulating an
idealised sinusoidal diurnal course of the climatic variables [8]. All the investigated models
incorporate a combination of THI and milk loss equations. However, for M5 and M6
(Table 1) a mixed formula was used to account for the capacity of dairy cows to avoid heat
stress in shorter periods of heat stresses risk representing a more biologically appropriate way
of heat stress related milk loss estimation [19,20]. These two models include a sub-daily step
milk loss equation for days of heat waves and a daily step milk loss method on other days.
In European studies, the THI threshold (THI
thr
) used to calculate the risk of heat stress var-
ies with the production system, with values ranging from 60 [11,7] to 70 [23]. For high yielding
dairy cows, Zimbelman et al (2009) proposed a THI
thr
of 68 [24]. In the USA, typically the
threshold is set at 72 [810]. Most of these studies used T
max
and RH
min
[11,7], but in others
the average daily THI of an hourly calculated THI [11] or the THI load [8] were used. There-
fore, models M1-6 were combined with THI
thr
values of 68, 70 and 72 resulting in 18 different
investigated models.
These models were used to estimate milk loss in each grid cell without taking into account
the type of dairy farming system (at pasture vs indoors). It was assumed that temperature and
relative humidity were the same for all systems, and that no mitigation practices were imple-
mented. We also assumed that cattle were not significantly different from the current UK
breed types, even though breeding for heat stress tolerance is one of the proposed measures to
mitigate effects of climate change on dairy farms [25].
Assessment of the impact of climate change and the uncertainty of milk
loss projection
The annual milk loss per cow (AML, kg/cow/y) value was used to assess the projected impact
of climate change on milk production and was calculated using each model as the summation
Impact of heat stress on UK milk production
PLOS ONE | https://doi.org/10.1371/journal.pone.0197076 May 8, 2018 3 / 18
of predicted daily losses for each year. The 11 climate projections and the 18 calculation meth-
ods resulted in 1980 AML values for every grid cell for every decade from the 2010s to the
2090s. The uncertainty of the calculated AML values was characterised with the coefficient of
variation (CV, standard deviation (SD) divided by the mean) for each grid cell. The uncer-
tainty of the AML figures originates from three major sources: 1) Year effect: caused by the
interannual variability of temperature and humidity patterns within a decade; 2) Climate Pro-
jection effect: caused by the differences in the climate model projections; 3) Method effect:
caused by the differences in the milk loss calculation methods. The contribution of these three
factors to the overall uncertainty of AML was quantified as follows: 1) Year effect: for every
year the average of AMLs obtained for each climate projection and method combination was
calculated (average of 198 values). Then, the coefficient of variation was calculated across the
years (CV of 10 values). 2) Climate Projection effect: for every climate projection the average
of AMLs obtained for each year and method combination was calculated (average of 180 val-
ues). Then, the coefficient of variation was calculated across the climate projections (CV of 11
values). 3) Method effect: for every method the average of AMLs obtained for each year and
climate projection combination was calculated (average of 110 values). Then, the coefficient of
variation was calculated across the methods (CV of 18 values).
In order to obtain comparable CV values that are calculated from samples having consider-
ably different sizes (10 or 11 versus 18) the jackknife resampling method [26] was applied. In
case of calculating the CV indicating the Method effect 10 member sub-sets were selected from
the 18 member base set in all possible ways resulting in C
18,10
= 18!/10!/8! = 43,758 different
sub-sets. The CV values of each of the sub-sets were calculated and the mean of the 43,758
member distribution was used as an indicator for the Method effect.
A detailed assessment of the sub-daily (M1-2) and daily (M3-4) methods was performed to
reveal the most important cause of the differences between the results of the two method types.
This analysis was carried out using all three THI thresholds (68, 70 and 72) but only the results
obtained with THI
thr
= 70 were presented for a selected grid cell. The number of days affected
by heat stress (THI
d
>THI
thr
) as well as the number of days characterised by THI
max
>THI
thr
and THI
d
<THI
thr
was determined for each grid cell and for every year of the 2010–2100
period. The latter indicates the days when the daily step methods predict no heat-stress and no
milk loss while sub-daily step methods predict a considerable milk loss. In general, conditions
when THId >THIthr represent greater severe heat stress potential than at other times.
The characteristics of trends in AML and number of heat stress days from years 2010–2100
were investigated by regression analysis in STATISTICA 12.0 [27]. An example grid cell in
Table 1. Summary of the THI-based milk loss estimation models.
# THI calculation Milk Loss (ML) equation Time step Reference for THI method Reference for ML method
M1 THI = T + 0.36×T
dew
+ 41.2 ML = 0.0695×(THI
max
THI
thr
)
2
×D sub-daily [21] [8]
M2 THI = 1.8×T+32–(0.55–0.0055×RH)×(1.8×T– 26) ML = 0.0695×(THI
max
THI
thr
)
2
×D sub-daily [22] [8]
M3 THI = T + 0.36×T
dew
+ 41.2 ML = max(THITHI
thr
, 0)×0.37 daily [21] [9]
M4 THI = 1.8×T+32–(0.55–0.0055×RH)×(1.8×T– 26) ML = max(THITHI
thr
, 0)×0.39 daily [22] [9]
M5 M1 on heat wave days
M3 on non heat wave days
M1 on heat wave days
M3 on non heat wave days
mixed [21] [8,9]
M6 M2 on heat wave days
M4 on non heat wave days
M2 on heat wave days
M4 on non heat wave days
mixed [22] [8,9]
T, T
dew
, RH, THI
max
and THI
thr
denote temperature [˚C], dew point temperature [˚C], daily maximum of THI [] and the threshold THI [], respectively. D denotes the
THI load, the duration of time the cows are experiencing heat stress in a day. T, T
dew
, and RH denote daily and hourly averages in case of daily and sub-daily models,
respectively.
https://doi.org/10.1371/journal.pone.0197076.t001
Impact of heat stress on UK milk production
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South-East England (centroid: 51.0˚N, 0.7˚W) was selected to represent an area that climate
change is projected to cause considerable changes compared to the baseline, and detailed tem-
poral changes of AML and number of heat stress days were calculated for this cell. Linear and
exponential trends of these data were considered, with the best regression model fits being
determined using the coefficient of determination (R
2
) and the normalised root-mean-square
error (NRMSE = 100×RMSE/MEAN, where RMSE and MEAN were the root-mean-square
error and the average of the calculated values, respectively) of the fitted curves. The curve type
providing the better statistical indicators was used to characterise the trend in question. The
significance of the difference between the milk loss projections was tested using Mann–Whit-
ney U test in STATISTICA.
Economic consequence of milk loss due to heat stress
The financial aspect of heat stress related milk loss was estimated for each of the NUTS-1
regions of the UK. The Nomenclature of Territorial Units for Statistics (NUTS) system is a
geocode standard for referencing the subdivisions of EU member countries for statistical pur-
poses [28]. The regional annual milk loss (RAML) values (kg/cow) were calculated by aggre-
gating the AML values of the grid cells belonging to the particular NUTS1 region. According
to a DairyCo report [29], approximately 81% of a herd is potentially affected by heat stress as
its lactating period overlaps with the summer months. The average herd sizes (AHS) of the
NUTS1 regions were retrieved from the AHDB database [30]. Numbers of cows per dairy
farm have steadily increased during the past 20 years with a rate of 3.5 cow/y across the whole
of the UK, and thus the income loss (IL, £/y) of a typical dairy farm (having an AHS, at pas-
ture) was calculated for each NUTS-1 region according to two scenarios. Scenario_1 postu-
lated no more centralisation of herds, thus stagnating AHS (constant AHS) and Scenario_2
postulated a continuous growth of AHS with a rate that was observed in the past two decades
(increasing AHS). As the farm-gate milk price fluctuates around £0.3 per litre and does not
show any specific long-term trend [2], the income loss of a typical UK dairy farm can be esti-
mated with the following equation:
IL ¼RAML AHS 0:81 0:3ð1Þ
Income losses were calculated for average years (when RAML is the average of AMLs) as
well as for extreme years (when RAML equates to the 90th percentile of AMLs).
Results
Spatial and temporal changes of heat stress and milk loss
Fig 1. shows the trends of temperature changes in the UK for the summer period (April-Sep-
tember) defined by the SCPs. As a result of these, the AML per cow values varied between
regions across the UK. The average current AML was calculated to be around 1 kg/cow in the
north of the UK while in the south it may reach 40 kg/cow. This difference is expected to
increase under future climate scenarios (Fig 2). By the end of the century, dairy cattle in large
portions of Scotland and Northern Ireland will experience the same level of heat stress as cattle
in southern-England today. In South East England the average AML was projected to exceed
170 kg/cow based on the average of the 18 investigated methods. The projected AML values
were highly dependent on the selected threshold THI. Changing THI
thr
from 72 to 68 resulted
in an increase of projected AML from 80 to 320 kg/cow for the most affected regions in the
South (Fig 2). The average AML predicted for the 2090s was relatively low (2.4% of the annual
milk yield) even for the South East England region. On the other hand, an unlikely extreme
Impact of heat stress on UK milk production
PLOS ONE | https://doi.org/10.1371/journal.pone.0197076 May 8, 2018 5 / 18
event (once in every 10 years) would mean the maximum AML may be close to 600 kg/cow
(8.0% of the annual milk yield) (Table 2). At the most extreme, in the ‘hottest’ 25 km grid cells
(around the Greater London area) in the hottest years, AML may exceed 1300 kg/cow which is
17% of the potential current mean milk production. The uncertainty (CV) of the AML was
lower in the South than in the North due to the fact that the AML values were considerably
lower in the North. The uncertainty is expected to decline slowly during the century but the
North-South difference will remain constant according to the projections. The decrease in the
coefficient of variation was the result of the average of AMLs increasing more rapidly than
their standard deviation.
Uncertainty of milk loss projection
Except for the 2030s, the CV associated with the milk loss calculation method effect was con-
sistently higher than that associated with methods of calculating climate projection and inter-
annual variability for the investigated future time slots (Table 3). Despite the considerable
inter-annual variability of the AML, as well as the large differences between the climate projec-
tions (Fig 1), the arbitrary selection of a milk loss calculation method may introduce similar,
or even greater, uncertainty in milk loss projections. Toward the end of this century, the effect
of all three calculation factors gradually decreased, which reflects the fact that the average of
Fig 1. Changes in mean daily temperature according to the 11-member spatially-coherent RCM projection ensemble for the summer period (April-September) in
the UK. The vertical bars denote the range between the minimum and the maximum values predicted by the 11 climate projections. Baseline period: 2010s.
https://doi.org/10.1371/journal.pone.0197076.g001
Impact of heat stress on UK milk production
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AMLs increases exponentially. The UK average of the AMLs in the 2090s is projected to be 4.6
times greater than the baseline, as a result of more frequent and more pronounced periods of
heat stress. However, the SD of the AML values was calculated to increase linearly, so that the
UK average of the SDs in the 2090s was 3.0 times greater than that of the baseline.
Assessing the milk loss estimation methods
Both the daily and the sub-daily step methods showed an exponential increase in AMLs. The
sub-daily step methods (M1-2), however, project a much more substantial rise in AML. Fig 3
Fig 2. Maps of annual milk loss for each THI
thr
and the CV for the 2010s, 2030s, 2050s, 2070s and 2090s in the
UK. CV of 1980 values per each cell.
https://doi.org/10.1371/journal.pone.0197076.g002
Impact of heat stress on UK milk production
PLOS ONE | https://doi.org/10.1371/journal.pone.0197076 May 8, 2018 7 / 18
Table 2. Statistical description of heat stress days and milk loss values in the 11 UK NUTS-1 regions.
NUTS-1 region Heat stress days Milk loss (kg/cow/y)
Day1 Day2 Max Mean StDev Median 90th percentile
Scotland 2010s 0.6 6.4 191.0 1.8 6.4 0.0 4.7
2050s 2.0 13.9 240.4 5.0 11.9 0.8 13.9
2090s 6.6 21.3 650.2 15.6 33.9 3.6 41.5
Northern Ireland 2010s 1.0 10.0 164.4 2.7 7.7 0.2 6.9
2050s 3.5 20.8 212.3 7.8 16.3 2.4 21.4
2090s 11.6 32.5 515.7 25.8 43.0 10.6 67.7
North West England 2010s 2.5 14.8 310.6 7.1 16.9 1.4 19.3
2050s 7.3 28.0 428.6 19.4 34.1 6.9 53.5
2090s 17.2 36.7 900.3 48.1 74.1 20.4 126.5
North East England 2010s 2.0 15.4 153.6 5.5 12.0 1.0 14.9
2050s 7.0 29.5 275.2 16.0 25.8 6.0 43.7
2090s 16.9 38.4 546.6 42.1 59.0 19.7 109.0
Yorkshire and The Humber 2010s 3.4 21.5 217.5 9.2 17.9 2.4 27.0
2050s 10.4 37.2 387.9 26.2 40.9 11.5 73.2
2090s 22.6 45.2 757.6 61.2 80.4 31.0 159.0
West Midlands 2010s 7.4 32.6 391.9 21.7 34.5 7.6 59.3
2050s 19.7 47.8 605.5 56.5 75.1 29.4 154.4
2090s 35.8 52.4 1129.5 112.9 137.1 64.9 297.7
East Midlands 2010s 7.6 34.2 363.8 22.2 35.4 8.2 60.0
2050s 19.9 48.6 628.3 57.6 75.8 29.5 153.7
2090s 35.8 52.7 1116.8 111.3 132.1 65.4 292.1
Wales 2010s 4.4 18.7 424.5 11.4 24.2 2.9 31.3
2050s 12.0 33.5 613.6 32.6 51.6 13.0 83.0
2090s 24.8 40.6 1222.7 70.5 105.4 33.3 187.6
East of England 2010s 10.7 40.2 379.7 29.8 43.4 13.3 79.2
2050s 26.3 51.4 727.0 75.7 88.5 40.4 194.6
2090s 43.8 52.7 1257.8 136.4 149.4 81.8 348.1
South West England 2010s 8.6 32.0 459.0 22.9 39.0 7.8 62.6
2050s 23.2 46.0 656.1 63.4 83.5 31.7 160.7
2090s 41.3 49.5 1270.2 130.6 153.3 71.1 329.5
South East England 2010s 13.6 42.5 469.9 37.9 50.8 17.3 101.3
2050s 31.8 51.7 741.8 92.8 108.6 52.6 235.4
2090s 51.0 52.0 1310.3 171.9 178.1 105.7 432.9
Day1: number of days when THI
d
>70; Day2: number of days when THI
d
<70 but THI
max
>70. Greater London was merged with the SE England when statistics were
calculated. Both the minimum and the 10
th
percentile are practically zero for all the NUTS-1 regions in the UK.
https://doi.org/10.1371/journal.pone.0197076.t002
Table 3. Uncertainty (measured by CV, %) of milk loss projections originating from different sources.
Decades
2010s 2030s 2050s 2070s 2090s
Year effect 51.4 77.3 42.0 31.0 26.8
CP effect 54.6 68.3 38.6 38.8 24.7
Method effect 54.3 63.2 45.9 41.0 35.9
Sources of uncertainty: inter-annual variability (Year effect); differences between the climate projections (CP effect); different milk loss estimation methods (Method
effect).
https://doi.org/10.1371/journal.pone.0197076.t003
Impact of heat stress on UK milk production
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presents an example of the changes of AML throughout the investigated period for the selected
grid cell in South-East England.
The exponential increase in milk loss (Fig 3) was due to the fact that the number of days
affected by heat stress (THI
d
>THI
thr
) was projected to increase exponentially in the future
irrespective of which method was selected to calculate THI (Fig 4). However, there is a linear
increase in the number of days with heat stress that was only predicted using the sub-daily step
methods (Fig 4). On days with THI
max
>THI
thr
and THI
d
<THI
thr
, sub-daily methods suggest
that milk loss could be as high as 2.9 kg/cow/d whereas the daily step methods would predict
no milk loss. Since the number of these partially heat-stress affected days is projected to
increase linearly (Fig 4), the difference between the sub-daily step and daily step methods is
also projected to increase in the future. Currently (in the 2010s), the daily step methods would
indicate that the number of heat stressed days is approximately 20% of those calculated using
the sub-daily methods (Fig 4). Due to the projected increase in temperature, the difference
between the daily and sub-daily methods will decline, but daily methods will still only capture
around 50% of the days predicted by the sub-daily methods as heat stressed. However, the fre-
quency and length of heat waves is predicted to increase in the UK throughout the century
(Fig 5), with a subsequent potential effect on AML.
Fig 3. Changes of average annual milk loss values calculated with six different milk loss methods (THI
thr
= 70) for a 25×25 km grid cell in South-East England
(centroid: 51.0˚N, 0.7˚W). Form of the fitted exponential curve: AML = a×e
b×(y-2010)
; M1-2: a = 27.86, b = 0.0234, SE
a
= 1.68, SE
b
= 0.00074, R
2
= 0.989,
NRMSE = 7.4%; M3-4: a = 6.13, b = 0.0251, SE
a
= 0.456, SE
b
= 0.0009, R
2
= 0.986, NRMSE = 6.6%; M5-6: a = 17.19, b = 0.0229, SE
a
= 1.11, SE
b
= 0.00079, R
2
= 0.986;
NRMSE = 6.8%. U test showed significant difference (P <0.001) between the milk loss calculation methods: M1-2 different from M3-4 and both pairs different from
M5-6.
https://doi.org/10.1371/journal.pone.0197076.g003
Impact of heat stress on UK milk production
PLOS ONE | https://doi.org/10.1371/journal.pone.0197076 May 8, 2018 9 / 18
Economic consequence of milk loss due to heat stress
Compared to current the UK average annual dairy farm business income (£80,000) the heat
stress-related income loss was projected to be less than 7% even in the most affected southern
UK regions towards the end of the century (Table 4). In extreme years, however, the income
loss may reach as high as 18% in South East England, though the dairy cow density in this
region is relatively low. South West England is the most vulnerable to climate change as this is
the region which is characterised by a high dairy herd density and therefore high potential
heat stress-related milk loss. Estimated heat stress-related annual income loss of the region
may reach £13.4M in average years, and £33.8M in extreme years (at current values), by the
end of the century if no action is taken to mitigate it.
Discussion
Animal responses to heat stress
A relatively low rate of occurrence of heat stress in UK dairy cows in the current climate
(2010s) was estimated by all the methods used in the present study. Similarly, Dunn et al
Fig 4. Changes in the number of days affected by heat stress (HS) calculated with the St-Pierre method (see M2 method description; THI
thr
= 70) for a 25×25 km
grid cell in South-East England (centroid: 51.0˚N, 0.7˚W). Triangles (and the fitted dotted linear) denote the days with heat stress that are detected only by the sub-
daily methods (THI
d
<THI
thr
and THI
max
>THI
thr
). Circles (and the fitted exponential curve) denote the days with heat stress that are detected by both the sub-daily and
daily methods (THI
d
>THI
thr
). Circles: number of heat stress days = a×e
b×(y-2010)
, a = 9.69, b = 0.0199, SE
a
= 0.523, SE
b
= 0.00077, R
2
= 0.91, NRMSE = 16.2%; Triangles:
number of heat stress days = a×(y-2010)+b, a = 0.23, b = 38.28, SE
a
= 0.0184, SE
b
= 0.95, R
2
= 0.639; NRMSE = 15.7%.
https://doi.org/10.1371/journal.pone.0197076.g004
Impact of heat stress on UK milk production
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(2014) and Hill and Wall (2015) reported an average of one day of heat stress conditions per
year [23,31]. However this can increase to five days when years with heat waves are considered
[23]. However, these studies failed to detect any significant milk yield reductions due to heat
stress in 2003 and 2006, when strong heat waves were recorded [23,31]. Our analysis suggests
that the average AML in regions with high heat stresses (e.g. South East England) is 40 kg/cow.
However, this reduction was calculated from total days of heat stress conditions without taking
into account the fact that these days were not consecutive. Cattle initially respond to mild heat
stress by sweating, panting, drinking more, and seeking shade when possible. At higher tem-
peratures cows reduce their feed intake, which leads to a fall in milk production. When heat
stress is temporary, lasting only one or two days, it is possible that cows will not reduce their
feed intake or their milk production [32]. Therefore, it is not surprising that under current UK
climatic conditions there are no evident milk yield penalties even when model simulations pre-
dict small decreases. By the end of the century, the average UK daily temperature was projected
to be 4˚C higher than the current temperature (Fig 1). This will result in a projected increase
in the number of heat stress periods across all the regions of the UK. The corresponding AML
was estimated to be 105 kg/cow if no steps are taken to adapt to changing climate conditions
and UK dairy cattle remain the same in terms of genetic merit and heat tolerance. Even though
this cannot be categorised as severe heat stress conditions, a noteworthy AML was estimated.
Fig 5. Frequency and length of heat waves (HW) in the UK. The presented values are the average of 11 UKCP09 SCP climate projections.
https://doi.org/10.1371/journal.pone.0197076.g005
Impact of heat stress on UK milk production
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This estimation assumes no difference in cows kept indoors and outdoors because it is not pos-
sible to differentiate between farm types in each grid cell. However, it is well established that
temperatures in dairy barns are 3 to 6˚C higher than those measured outdoors [33,34], and the
projected temperature and relative humidity values our calculations used were for outdoors.
Thus, unless some form of indoor temperature management is adopted, actual AML may be
even higher than calculated here for some farm types.
British dairy farming is heavily reliant on pasture use [35]. A number of relatively low cost
adaptation measures could help minimize adverse consequences of heat stress in dairy cows.
The provision of natural or artificial shade is the most efficient and inexpensive way to reduce
Table 4. Income loss of average size dairy farms in different UK NUTS-1 regions due to heat stress (£/y) assuming no mitigation actions are taken.
NUTS-1 region Period Average Year Extreme Year
scenario_1 scenario_2 scenario_1 scenario_2
Scotland 2010s 86 86 217 217
2050s 232 401 649 1122
2090s 727 1787 1936 4760
Northern Ireland 2010s 72 72 187 187
2050s 210 475 578 1308
2090s 696 2452 1826 6431
North West England 2010s 235 235 638 638
2050s 641 1301 1768 3588
2090s 1591 4867 4181 12788
North East England 2010s 183 183 494 494
2050s 530 1075 1444 2930
2090s 1390 4252 3602 11019
Yorkshire and the Humber 2010s 305 305 893 893
2050s 867 1760 2421 4913
2090s 2021 6182 5254 16070
West Midlands 2010s 717 717 1958 1958
2050s 1866 3787 5103 10356
2090s 3732 11415 9839 30096
East Midlands 2010s 733 733 1985 1985
2050s 1905 3866 5079 10306
2090s 3680 11255 9652 29525
Wales 2010s 350 350 966 966
2050s 1007 2116 2560 5382
2090s 2175 6970 5790 18555
East of England 2010s 984 984 2619 2619
2050s 2503 5079 6430 13048
2090s 4509 13792 11505 35192
South West England 2010s 758 758 2070 2070
2050s 2097 4255 5310 10776
2090s 4317 13205 10889 33308
South East England 2010s 1253 1253 3346 3346
2050s 3068 6226 7779 15787
2090s 5682 17382 14307 43763
Average year (milk loss = average of AMLs). Extreme year (milk loss = 90th percentile of AMLs). Scenario_1: no more centralisation of herds (constant AHS);
Scenario_2: continuous growth of AHS with a rate that was observed in the past two decades (increasing AHS).
https://doi.org/10.1371/journal.pone.0197076.t004
Impact of heat stress on UK milk production
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heat accumulation from solar radiation, leading to reduced signs of heat load, rectal tempera-
tures and hyperchloraemia [36,37]. Shade provision can lead to increased milk yield of pas-
ture-based cows [38]. Recently, a field study was conducted to investigate the effects of the
amount of shade on 8 Holstein-Friesian pasture-based herds in New Zealand for 2 consecutive
summers. It was reported that providing more shade increased the proportion of animals
within the herd that used this resource and reduced respiratory signs of heat load [39]. Simi-
larly, when shade was provided to Holstein cows on pasture using young trees to support
shade cloths, it tempered the effects of increased THI by reducing rectal temperature, hyper-
chloraemia and the regulation of liver metabolism [40]. Heat-stressed dairy cattle kept indoor
increase their water intake by 22–27% [41,42]. Although these figures maybe lower for out-
door cattle due to the availability of fresh forage with a high water content the distance between
available water and the grazing area should allow at least twice daily visits by the cattle [43].
Nutritional management might also provide a cost-effective mechanism to support heat-
stressed cattle. This might include supplementation of rumen-protected proteins and fats, elec-
trolytes, and specific feed additives [44].
Although average UK temperature increases are estimated to have relatively minor impact
in many regions, our analysis predicted that heat waves could lead to severe heat stress in dairy
cows with projected AMLs greater than 1,200 kg/cow by the end of the century in high-risk
areas. These areas are Wales, South West, South East England, and East of England, although
potential total milk losses in Wales and the South West are likely to be higher than in the
South East and East of England because of the higher concentrations of dairy cattle in the west
of the UK [45]. This finding is in accordance with other studies that reported increased para-
site risk in specific UK regions due to climate change [46]. The increased occurrence of heat
waves worldwide [47] and in the UK (Fig 5) is expected to cause additional heat stress in dairy
cows, as reflected to our analysis where extreme years show a correlation between a high fre-
quency of heat waves and the maximum AML. The current frequency of 1–2 heat waves per
decade may increase to 3–5 by the end of the century, which is much lower than currently
observed, for example, in Italy (5 heat waves per year [48]). However, the length of a heat wave
is projected to reach 8 to 15 days similar to that currently reported for Italy [48]. In such condi-
tions, heat stress is correlated not only with increased milk loss, but also with increased cattle
mortality [13,48] and culling due to reduced fertility [49]. In these cases, simple adaptation
measures, such as the provision of shade, may not be sufficient to mitigate negative heat stress
effects on milk production. However, current technologies used in other, hotter, parts of the
world (e.g. fans and water misting) could be applied to British dairy farming, which is already
changing towards intensive indoor systems [35], to care for dairy cows during these heat
waves [50]. Moreover, breeding for increased heat tolerance is a potential strategy to help miti-
gate negative effects of increased frequency of heat waves [25]. This can be beneficial for main-
taining pasture-based systems [51,52]. Even though the main strategy to date has been
crossing Holstein cows with local breeds [53], genomic predictions for heat tolerance of Hol-
stein cows have been identified suggesting that genomic selection may accelerate breeding for
heat tolerance [54,55]. In addition, changing the location of farming operations is a current
practice used to address economic challenges worldwide [56,57]. Even though there is little
indication that movement of dairy farming operations is a feasible strategy to decrease the
risks of environmental challenges in the UK [58], the increased use of regions with little or no
prediction of conditions leading to heat stress (e.g. Scotland) may provide an additional adap-
tation measure for UK dairy farming depending on the availability of pasture. At the end of
this century, heat stress-related annual income losses of average size dairy farms in the most
affected regions may vary between £2000-£6000 and £6000-£14000 in average and extreme
years respectively. Armed with these figures, farmers can easily create preliminary financial
Impact of heat stress on UK milk production
PLOS ONE | https://doi.org/10.1371/journal.pone.0197076 May 8, 2018 13 / 18
plans to assess the pay-offs of possible mitigation options such as planting trees or installing
shades. It is likely that the hotter UK areas will see a reduction in cattle numbers, perhaps with
increases in other areas, e.g. further north or at higher altitudes, if cropping and grazing
options change to become more favourable for cows.
Uncertainty and assessment of model simulations
The variability of the AML projections was disaggregated into three major components and
the uncertainty originating from, 1) the different climate projections, 2) the inter-annual vari-
ability of the weather, and 3) the different milk loss calculation methods. These were estimated
by using the coefficient of variation of the AML values. Despite the considerable inter-annual
variability of the AML as well as the large differences between the climate projections, the vari-
ety of the calculation methods may introduce even larger uncertainty in the milk loss projec-
tions for the future. This finding is in line with previous climate change impact assessments.
Inter-annual variability was predicted to increase slightly at higher temperatures (toward the
end of the century) but this effect was generally less than inter-model variability [59]. Model
differences introduced more uncertainty in the climate change impact projections than the dif-
ferences caused by the climate projections [60]. This finding emphasizes the importance of
using multi-model ensembles in order to provide robust projections [61]. Though they investi-
gated global maize production, Bassu et al (2014) reported that only an ensemble of at least
8–10 models was able to simulate absolute yields accurately [59]. Here we demonstrate that for
the South-East of England (Fig 4) this uncertainty was introduced by the selection of the daily
or sub-daily AML calculation methods, where the sub-daily methods over-predicted heat stress
days. Indeed, for current South-East of England conditions, the sub-daily methods predict 39
days of heat stress, while the daily step methods estimate 9 days (Fig 4). The latter is perhaps
closer to current British conditions, where heat stress is not generally considered a major issue.
In general, THI
max
is used for the sub-daily methods whereas THI
d
is used for the daily step
methods. This methodological difference explains why there was a greater difference in the
projected number of days of heat stress between the daily and sub-daily methods of calculation
for the 2010s compared to the 2090s. The increased T towards the end of this century increased
the number of days when THI
d
>THI
thr
, equalizing the number of days when THI
max
>
THI
thr
(Fig 4). However, even in this situation the severity of heat stress, which is taken into
account only in the case of the sub-daily estimation methods, increases AML (Fig 3).
The sub-daily method [8] is sensitive because it can detect days with relatively small heat
stress loads, when the overall THI is below the threshold. This approach can quantify the load
of heat stress rather than the average heat stress in estimating the extent and cumulative sever-
ity of heat stress within days. Thus, it is important in regions with temperate climates to quan-
tify animal responses at acute stressful events, such as summer heat waves, because these
responses will depend on the magnitude and duration of the heat wave [62]. Therefore, the
combination of daily and sub-daily methods was used to represent a more biologically appro-
priate way of estimating milk loss from dairy cows due to heat stress. Indeed, the mixed “heat
wave” methods (M5 and M6) are closer to current estimation of milk loses during heat waves
[23] and probably provided a more realistic outcome for the future. The adequacy of the heat
wave-based mixed method is also supported by the fact that its results match with that of the
ensemble mean of all investigated methods.
In conclusion, we have developed a modelling framework to estimate potential effects of cli-
mate change on milk production of pasture-based dairy cattle using the UK as an example. We
estimated relatively low AML that can be mitigated by implementing current practices for heat
stress relief of cows on pasture. However, we detected specific regions of current dairy farming
Impact of heat stress on UK milk production
PLOS ONE | https://doi.org/10.1371/journal.pone.0197076 May 8, 2018 14 / 18
importance, where AML were projected to reach 17% of current annual milk yield in extreme
years due to an increased frequency, duration and severity of heat waves. For these regions, the
application of sophisticated technologies should be implemented to reduce projected losses.
The choice of different THI threshold values made a large difference to projected milk loss.
This observation alone emphasises the need for more intensive research seeking to determine
the most biologically relevant THI
thr
values of milk loss estimation methods and exploring the
factors that influence this parameter. While this remains a challenging and complex issue [23],
the approaches used in the present study provide a plausible solution that can be used in future
climate change impact studies on pasture based dairy systems.
Supporting information
S1 Text. Milk loss estimation methods used in the study.
(DOCX)
Author Contributions
Conceptualization: Na
´ndor Fodor, Andreas Foskolos, Cairistiona F. E. Topp.
Data curation: Na
´ndor Fodor, Cairistiona F. E. Topp.
Methodology: Na
´ndor Fodor, Andreas Foskolos, Cairistiona F. E. Topp.
Software: Na
´ndor Fodor.
Supervision: Christine H. Foyer.
Visualization: Na
´ndor Fodor, La
´szlo
´Pa
´sztor.
Writing – original draft: Na
´ndor Fodor, Andreas Foskolos, Cairistiona F. E. Topp, Jon M.
Moorby.
Writing – review & editing: Na
´ndor Fodor, Andreas Foskolos, Cairistiona F. E. Topp, Jon M.
Moorby, La
´szlo
´Pa
´sztor, Christine H. Foyer.
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Supplementary resource (1)

... In the United Kingdom, the robust heatwaves were reported in 2003 and 2006; however, the studies of Dunn et al. (2014) and Hill and Wall (2015) did not reveal a significant milk yield decrease as a result of heat stress, and they stated an average of 1 d of heat stress each year. This is not surprising because Fodor et al. (2018) also reported a relatively low heat stress occurrence rate in UK dairy cows in the 2010s, with the predicted average annual milk yield loss of 1 kg/cow in the north of the United Kingdom, indicating no evidence of milk yield penalty with the predicted average of 0.6 d with the THI above 70. Moreover, the study of Fodor et al. (2018) indicated that the average annual milk yield loss in the 2090s in the United Kingdom is predicted to be relatively low (2.4%), including the South East England region. ...
... This is not surprising because Fodor et al. (2018) also reported a relatively low heat stress occurrence rate in UK dairy cows in the 2010s, with the predicted average annual milk yield loss of 1 kg/cow in the north of the United Kingdom, indicating no evidence of milk yield penalty with the predicted average of 0.6 d with the THI above 70. Moreover, the study of Fodor et al. (2018) indicated that the average annual milk yield loss in the 2090s in the United Kingdom is predicted to be relatively low (2.4%), including the South East England region. This is the region that is predicted to have an average of 51 d with THI exceeding 70. ...
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We investigated the effects of environmental factors on average daily milk yield and day-to-day variation in milk yield of barn-housed Scottish dairy cows milked with an automated milking system. An incomplete Wood gamma function was fitted to derive parameters describing the milk yield curve including initial milk yield, inclining slope, declining slope, peak milk yield, time of peak, persistency (time in which the cow maintains high yield beyond the peak), and predicted total lactation milk yield (PTLMY). Lactation curves were fitted using generalized linear mixed models incorporating the above parameters (initial milk yield, inclining and declining slopes) and both the indoor and outdoor weather variables (temperature, humidity, and temperature-humidity index) as fixed effects. There was a higher initial milk yield and PTLMY in multiparous cows, but the incline slope parameter and persistency were greatest in primiparous cows. Primiparous cows took 54 d longer to attain a peak yield (mean ± standard error) of 34.25 ± 0.58 kg than multiparous (47.3 ± 0.45 kg); however, multiparous cows yielded 2,209 kg more PTLMY. The best models incorporated 2-d lagged minimum temperature. However, effect of temperature was minimal (primiparous decreased milk yield by 0.006 kg/d and multiparous by 0.001 kg/d for each degree increase in temperature). Both primiparous and multiparous cows significantly decreased in day-to-day variation in milk yield as temperature increased (primiparous cows decreased 0.05 kg/d for every degree increase in 2-d lagged minimum temperature indoors, which was greater than the effect in multiparous cows of 0.008 kg/d). Though the model estimates for both indoor and outdoor were different, a similar pattern of the average daily milk yield and day-to-day variation in milk yield and milk yield's dependence on environmental factors was observed for both primiparous and multiparous cows. In Scotland, primiparous cows were more greatly affected by the 2-d lagged minimum temperature compared with multiparous cows. After peak lactation had been reached, primiparous and multiparous cows decreased milk yield as indoor and outdoor minimum temperature increased.
... Indeed, the sector is likely to be increasingly affected by climatic change, including higher temperatures and more frequent extreme events such as heat waves, droughts, storms and heavy rainfalls (Ahmad et al., 2009;IPCC, 2014IPCC, , 2019, which are expected to lead to both direct and indirect impacts. With increasing summer temperatures, heat stress is likely to affect dairy cow productivity more frequently, even in temperate climate regions (Armstrong, 1994;Fodor et al., 2018). Heat stress negatively affects dairy cattle welfare and productivity in multiple ways, including reduced feed intake, and increased body temperature and respiratory rate, as cows cope with high environmental temperatures and humidity (Armstrong, 1994;Bernabucci et al., 2014;Ammer et al., 2018). ...
... The literature on dairy systems typically analyses the effects of climate on yields or revenues in very specific contexts or countries, and impacts are often measured only for a limited time period (Bernabucci et al., 2014;Fodor et al., 2018). In addition, very few of those studies focus specifically on the impacts of environmental conditions on efficiency. ...
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This paper assesses the influence of heat and drought stress on the economic performance of the European dairy sector. Climatic data from the Gridded Agro-Meteorological data in Europe were combined with dairy enterprise data from the Farm Accountancy Data Network, resulting in a data set of 4412 farms in 22 European Union (EU) countries over the period 2007–2013. Since the performance of dairy farms is influenced by the context in which they operate, farms were grouped into areas representing similar climatic conditions through the use of a latent class analysis. Technical efficiency (TE) and economic downside risk were used as performance indicators against which the effect of climatic stress factors was evaluated. TE was estimated using a ‘true-fixed’ effect stochastic frontier model. Economic downside risk was based on gross margin deviations. Regression analysis suggests a significant negative effect of drought and heat stress on both TE and the downside gross margin difference in most climatic classes, with few exceptions. Results imply that both drought and heat stress-related issues need to be considered when designing adaptation strategies to address threats to the economic performance of the EU dairy sector.
... The replacement of commonly used unsustainably grown soybean and palm with readily available, alternative feed resources will improve the efficiency and sustainability of meat production (Kumar et al. 2023). The negative effect on crops and traditional forage due to climate change and global warming has brought the issue of novel and alternative non-conventional feed sources at the forefront (Fodor et al. 2018). ...
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Treating livestock as senseless production machines has led to rampant depletion of natural resources, enhanced greenhouse gas emissions, gross animal welfare violations, and other ethical issues. it has essentially instigated constant scrutiny of conventional meat production by various experts and scientists. Sustainably in the meat sector is a big challenge which requires a multifaced and holistic approach. Novel tools like digitalization of the farming system and livestock market, precision livestock farming, application of remote sensing and artificial intelligence to manage production and environmental impact/GHG emission, can help in attaining sustainability in this sector. Further, improving nutrient use efficiency and recycling in feed and animal production through integration with agroecology and industrial ecology, improving individual animal and herd health by ensuring proper biosecurity measures and selective breeding, and welfare by mitigating animal stress during production are also key elements in achieving sustainability in meat production. in addition, sustainability bears a direct relationship with various social dimensions of meat production efficiency such as non-market attributes, balance between demand and consumption, market and policy failures. The present review critically examines the various aspects that significantly impact the efficiency and sustainability of meat production.
... Together, this is becoming a big issue, especially when producers seek to increase production and even more while dealing with the consequences of heat stress at the same time. Conservative estimates show that heat stress alone is currently causing substantial annual economic losses to the dairy industry of up to USD 897 million in the United States (St-Pierre et al., 2003); up to AUD 300 [~USD 215 million based on February 2022 exchange rate] million in Australia (DairyBio, 2018; https:// dairybio.com.au/); and up to £33 million [~USD 45 million based on February 2022 exchange rate] in the South-West region of the United Kingdom (Fodor et al., 2018). ...
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Heat tolerance is the ability of an animal to maintain production and reproduction levels under hot and humid conditions and is now a trait of economic relevance in dairy systems worldwide because of an escalating warming climate. The Australian dairy population is one of the excellent study models for enhancing our understanding of the biology of heat tolerance because they are predominantly kept outdoors on pastures where they experience direct effects of weather elements (e.g., solar radiation). In this article, we focus on evidence from recent studies in Australia that leveraged large a dataset [∼40,000 animals with phenotypes and 15 million whole-genome sequence variants] to elucidate the genetic basis of thermal stress as a critical part of the strategy to breed cattle adapted to warmer environments. Genotype-by-environment interaction (i.e., G × E) due to temperature and humidity variation is increasing, meaning animals are becoming less adapted (i.e., more sensitive) to changing environments. There are opportunities to reverse this trend and accelerate adaptation to warming climate by 1) selecting robust or heat-resilient animals and 2) including resilience indicators in breeding goals. Candidate causal variants related to the nervous system and metabolic functions are relevant for heat tolerance and, therefore, key for improving this trait. This could include adding these variants in the custom SNP panels used for routine genomic evaluations or as the basis to design specific agonist or antagonist compounds for lowering core body temperature under heat stress conditions. Indeed, it was encouraging to see that adding prioritized functionally relevant variants into the 50k SNP panel (i.e., the industry panel used for genomic evaluation in Australia) increased the prediction accuracy of heat tolerance by up to 10% units. This gain in accuracy is critical because genetic improvement has a linear relationship with prediction accuracy. Overall, while this article used data mainly from Australia, this could benefit other countries that aim to develop breeding values for heat tolerance, considering that the warming climate is becoming a topical issue worldwide.
... Other factors outside the direct control of farmers, including farm location and climatic factors, have also been found to influence dairy productivity. There is a general conclusion in the literature that adverse weather has a negative impact on productivity [7,32,33], explaining that farms, particularly those with high herd stocking density, are susceptible to climate shocks, such as extreme heat and increased temperatures, resulting in lower milk yield. Evidence from Kimura and Sauer [1] and Romagnoli, Giaccio, Mastronardi and Forleo [16] shows that farms located in environmentally disadvantaged areas in Estonia, England, Wales, and Italy are less productive, suggesting that natural conditions could limit productivity improvement. ...
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This study examines the farm-level factors that influence differences in total factor productivity (TFP) on dairy farms. To this end, a fixed-effects regression approach is applied to panel data for dairy farms obtained from the Farm Accountancy Data Network for Northern Ireland over the period of 2005 to 2016. The findings are largely consistent with existing empirical evidence, showing that herd size, milk yield, stocking density, and share of hired labour have a positive and statistically significant impact on TFP, while labour input per cow, purchased feed input per cow, and share of direct payments in total farm output have a negative and statistically significant impact. The more complex relationships, namely age, education, and investment, have been unpacked using interaction terms and nonlinear approximation. The impact of age is negative, and the drag on productivity grows as age increases. Capital investment and education both have a positive impact on farm-level TFP, as well as on their interaction. Policy recommendations on strategies and best practices to help dairy farms tackle productivity constraints are suggested.
... However, cattle initially respond to mild heat stress by sweating, panting, drinking more, and seeking shade when possible. When THI increases and reaches the upper critical value and imbalance between heat production and dissipation capacity is induced, the risk of milk production losses increases (Bernabucci et al., 2014), dry matter intake decreases (Fodor et al., 2018), and infectious and metabolic diseases and mortality risks increase (Olde Riekerink et al., 2007;Vitali et al., 2015). Gaughan et al. (2008) reported that there are upper and lower thresholds, at which cattle accumulate or dissipate heat, respectively. ...
Article
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Climate change is worldwide impacting efficiency and welfare standards in livestock production systems. Considering the sensibility to heat stress reported for different milk production patterns in Italian Brown Swiss, this study aims to evaluate the effect of heat waves of different length on some milk production traits (fat-corrected milk, energy corrected milk, protein and fat yield, protein percentage, cheese production at 24h and cheese yield). A 10-year data set (2009–2018), containing 202,776 test-day records from 23,296 Brown Swiss cows was used. The dataset was merged both with the daily maximum THI recorded by weather stations and with the daily maximum THI threshold for each trait in Italian Brown Swiss cows. The study considered 4 different heat waves according to their length: two, three, four and five consecutive days before the test-day over the weighted THI threshold. Milk production traits were determined as the difference in losses compared to those after only one day before the test day over the weighted maximum THI. All traits showed to be affected by heat waves. Particularly, protein percentage losses increased from -0.047% to -0.070% after 2 consecutive days over the daily THI threshold, reaching -0.10% to -0.14% after 5 days (P < 0.01), showing a worsening trend with the increasing length of heat waves. First parity cows showed to be more sensitive to heat waves than other parity classes, recording greater losses after shorter heatwaves, compared to multiparous cows, for protein yield and, consequently, for cheese production at 24h. This suggests less efficient metabolic response to heat stress and exposure time in primiparous, compared to multiparous cows, probably due to their incomplete growth process that overlap milk production, making more difficult for them to dissipate heat. Although actions to mitigate heat stress are always needed in livestock, this study points out that often time exposure to warm periods worsen milk production traits in Brown Swiss cows.
... Gridded data have also been used to analyze historical heat stress trends and predict likely future changes in heat stress incidence and severity for a dairy region in Australia (Nidumolu et al., 2014). In the United Kingdom, grid cells of 25 km 2 spatial resolution have been applied in estimation and projection of milk yield loss and the economic consequence due to heat stress in dairy cows (Fodor et al., 2018). In a case of big dairy data analysis, Duruz et al. (2020) applied data interpolated from weather stations to test the influence of among others, environmental factors on dairy cattle productivity during transhumance in the Swiss Alps. ...
Article
Genetic selection for heat tolerance is a sustainable strategy to support mitigation of the adverse effects of heat stress that may be achieved through housing and feeding modification in livestock. To identify and select heat tolerant animals, it is necessary to have high temporal resolution animal performance records and high spatio-temporal resolution weather information. The temporal resolution being the amount of time detail in terms of sampling frequency and the spatial resolution the amount of space area i.e. area covered and how detailed an observation is. This review highlights some approaches to improve the resolution of data necessary for evaluating heat stress in dairy cattle. Because heat stress occurs only periodically, mainly during summer, information from monthly test-day milk records only is limited in phenotypic data. The use of only test-day records over time captures a small fraction of the response due to heat stress. It is expected that daily milk production records would allow for better estimation of heat stress effects. In regions where data from weather stations are scarce or lacking, grid interpolated weather data may improve accuracy of prediction. This is because grid interpolation enables creation of reliable virtual weather stations that can provide complete and long-term weather data. However, studies are lacking to compare the agreement of measured weather data and grid interpolated data to assess the effects of heat stress in livestock. To evaluate the performance of animals under various climatic conditions, environmental parameters are applied either individually or augmented into an index and merged with production records. Although there are many indices defined in literature, most have not been applied in genetic evaluation studies and grid interpolated indices have not been developed for use in livestock studies. This review highlights appr oaches to contribute to improved temporal resolution of performance records as well as spatial and temporal resolution of weather data necessary for improvement of heat stress evaluation in dairy cattle.
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Changes in the global climate are among the factors threatening the stability of the global food supply. Precipitation and temperature are important components of the agricultural sector directly affecting the production. However, factors such as temperature increases, drought and sudden weather events due to natural disasters increase the concerns in the agricultural sector. Climate is one of the most determining factors in agricultural production and productivity. Preventing the negative effects of climate change on agricultural production, increasing the adaptation of agricultural production to climate change and ensuring sustainable food security should be among the priorities of the governments. In this study, it is aimed to reveal the effects of global climate change on the agricultural sector in Ardahan province for the time period of 1990-2020. For this purpose, the relationship between milk and honey production, important in Ardahan economy, with average temperature and total precipitation was examined by linear regression analysis. According to the findings, while annual total precipitation decreased milk and honey production, average temperatures did not have a significant effect on milk and honey production.
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The present study assesses the sensitivity of dairy animals to thermal stress, and projects the economic losses due to heat stress in the Trans and Upper Gangetic plains region of India with Representative Concentration Pathway (RCP) 4.5 climate scenario for the time slice 2010–2039 and two subperiods, 2020–2029 and 2030–2039. The projections were carried out for two different scenarios of population and productivity growth of dairy animals, Business-as-Usual (BAU) and Alternate, whereby land, feed and fodder constraints were applied. The potential annual loss in milk production due to heat stress in the region was esti�mated to be around 361 and 377 thousand tons for the time slice 2010–2039 under BAU and Alternate scenario, respectively. In economic terms these losses, at current prices, would be equivalent to INR 11.93 billion and INR 12.44 billion, respectively. This gives an indication of the level of financial investment that can be made in adaptation measures to arrest the loss due to climate change.
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Background Globally, climate change is a challenge for the dairy sector and its effects are expected to have important consequences on the environmental performance of the dairy products value chains. At the same time, this sector significantly contributes to global warming and other environmental impacts. Scope and approach This paper addresses this twin challenge from a life cycle perspective, i.e. covering from dairy farms, dairy factory, distribution and retail, to consumption. To do so, literature reviews were done on the contribution of the sector to climate change and on the biophysical impacts of climate change on the dairy sector in the near term in Europe. Both reviews were linked to qualitatively analyse the interaction and connect in a matrix the biophysical impacts caused by the effects of climate change on the environmental performance of the sector. Key findings and conclusions Not surprisingly, dairy farms were identified as the major contributor to the total greenhouse gas emissions across the dairy value chains but also as the most vulnerable stage to climate change. Depending on the region, the dairy sector will face opportunities but also threats such as significant cows' heat stress, crop cultivation variability, on-farm water availability, cows' diseases, crop pests' pressure and product safety risk, which is associated with product losses and waste. Measures will be needed to mitigate them but with an environmental cost. The clear definition of the dairy sector-climate change interaction is the starting point to begin preparing this sector for a near-future under climate change conditions.
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Milk and beef production cause 9% of global greenhouse gas (GHG) emissions. Previous life cycle assessment (LCA) studies have shown that dairy intensification reduces the carbon footprint of milk by increasing animal productivity and feed conversion efficiency. None of these studies simultaneously evaluated indirect GHG effects incurred via teleconnections with expansion of feed crop production and replacement suckler-beef production. We applied consequential LCA to incorporate these effects into GHG mitigation calculations for intensification scenarios among grazing-based dairy farms in an industrialized country (UK), in which milk production shifts from average to intensive farm typologies, involving higher milk yields per cow and more maize and concentrate feed in cattle diets. Attributional LCA indicated a reduction of up to 0.10 kg CO2e kg−1 milk following intensification, reflecting improved feed conversion efficiency. However, consequential LCA indicated that land use change associated with increased demand for maize and concentrate feed, plus additional suckler-beef production to replace reduced dairy-beef output, significantly increased GHG emissions following intensification. International displacement of replacement suckler-beef production to the “global beef frontier” in Brazil resulted in small GHG savings for the UK GHG inventory, but contributed to a net increase in international GHG emissions equivalent to 0.63 kg CO2e kg−1 milk. Use of spared dairy grassland for intensive beef production can lead to net GHG mitigation by replacing extensive beef production, enabling afforestation on larger areas of lower quality grassland, or by avoiding expansion of international (Brazilian) beef production. We recommend that LCA boundaries are expanded when evaluating livestock intensification pathways, to avoid potentially misleading conclusions being drawn from “snapshot” carbon footprints. We conclude that dairy intensification in industrialized countries can lead to significant international carbon leakage, and only achieves GHG mitigation when spared dairy grassland is used to intensify beef production, freeing up larger areas for afforestation.
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Dairy products are a key source of valuable proteins and fats for many millions of people worldwide. Dairy cattle are highly susceptible to heat-stress induced decline in milk production, and as the frequency and duration of heat-stress events increases, the long term security of nutrition from dairy products is threatened. Identification of dairy cattle more tolerant of heat stress conditions would be an important progression towards breeding better adapted dairy herds to future climates. Breeding for heat tolerance could be accelerated with genomic selection, using genome wide DNA markers that predict tolerance to heat stress. Here we demonstrate the value of genomic predictions for heat tolerance in cohorts of Holstein cows predicted to be heat tolerant and heat susceptible using controlled-climate chambers simulating a moderate heatwave event. Not only was the heat challenge stimulated decline in milk production less in cows genomically predicted to be heat-tolerant, physiological indicators such as rectal and intra-vaginal temperatures had reduced increases over the 4 day heat challenge. This demonstrates that genomic selection for heat tolerance in dairy cattle is a step towards securing a valuable source of nutrition and improving animal welfare facing a future with predicted increases in heat stress events.
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Temperature and humidity levels above a certain threshold decrease milk production in dairy cattle, and genetic variation is associated with the amount of lost production. To enable selection for improved heat tolerance, the aim of this study was to develop genomic estimated breeding values (GEBV) for heat tolerance in dairy cattle. Heat tolerance was defined as the rate of decline in production under heat stress. We combined herd test-day recording data from 366,835 Holstein and 76,852 Jersey cows with daily temperature and humidity measurements from weather stations closest to the tested herds for test days between 2003 and 2013. We used daily mean values of temperature-humidity index averaged for the day of test and the 4 previous days as the measure of heat stress. Tolerance to heat stress was estimated for each cow using a random regression model with a common threshold of temperature-humidity index = 60 for all cows. The slope solutions for cows from this model were used to define the daughter trait deviations of their sires. Genomic best linear unbiased prediction was used to calculate GEBV for heat tolerance for milk, fat, and protein yield. Two reference populations were used, the first consisted of genotyped sires only (2,300 Holstein and 575 Jersey sires), and the other included genotyped sires and cows (2,189 Holstein and 1,188 Jersey cows). The remainder of the genotyped sires were used as a validation set. All animals had genotypes for 632,003 single nucleotide polymorphisms. When using only genotyped sires in the reference set and only the first parity data, the accuracy of GEBV for heat tolerance in relation to changes in milk, fat, and protein yield were 0.48, 0.50, and 0.49 in the Holstein validation sires and 0.44, 0.61, and 0.53 in the Jersey validation sires, respectively. Some slight improvement in the accuracy of prediction was achieved when cows were included in the reference population for Holsteins. No clear improvements in the accuracy of genomic prediction were observed when data from the second and third parities were included. Correlations of GEBV for heat tolerance with Australian Breeding Values for other traits suggested heat tolerance had a favorable genetic correlation with fertility (0.29–0.39 in Holsteins and 0.15–0.27 in Jerseys), but unfavorable correlations for some production traits. Options to improve heat tolerance with genomic selection in Australian dairy cattle are discussed.
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Background: Heat stress is a physiological response to extreme environmental heat such as heat waves. Heat stress can result in mortality in dairy cows when extreme heat is both rapidly changing and has a long duration. As a result of climate change, heat waves, which are defined as 3 days of temperatures of 32 °C or above, are an increasingly frequent extreme weather phenomenon in Southern Ontario. Heat waves are increasing the risk for on-farm dairy cow mortality in Southern Ontario. Heat stress indices (HSIs) are generally based on temperature and humidity and provide a relative measure of discomfort which can be used to predict increased risk of on-farm dairy cow mortality. In what follows, the heat stress distribution was described over space and presented with maps. Similarly, on-farm mortality was described and mapped. The goal of this study was to demonstrate that heat waves and related HSI increases during 2010-2012 were associated with increased on-farm dairy cow mortality in Southern Ontario. Mortality records and farm locations for all farms registered in the CanWest Dairy Herd Improvement Program in Southern Ontario were retrieved for 3 heat waves and 6 three-day control periods from 2010 to 2012. A random sample of controls (2:1) was taken from the data set to create a risk-based hybrid design. On-farm heat stress was estimated using data from 37 weather stations and subsequently interpolated across Southern Ontario by geostatistical kriging. A Poisson regression model was applied to assess the on-farm mortality in relation to varying levels of the HSI. Results: For every one unit increase in HSI the on-farm mortality rate across Southern Ontario increases by 1.03 times (CI95% (IRR) = (1.025,1.035); p = ≤ 0.001). With a typical 8.6 unit increase in HSI from a control period to a heat wave, mortality rates are predicted to increase by 1.27 times. Conclusions: Southern Ontario was affected by heat waves, as demonstrated by high levels of heat stress and increased on-farm mortality. Farmers should be aware of these risks, and informed of appropriate methods to mitigate such risks.
Article
Genomic selection has led to opportunities for developing new breeding values that rely on phenotypes in dedicated reference populations of genotyped cows. In Australia, it has been applied to 2 novel traits: feed efficiency, which was released in 2015 as feed saved breeding values, and heat tolerance genomic breeding values, released for the first time in 2017. Feed saved is already included in the national breeding objective, which is focused on profitability and designed to be in line with farmer preferences. Our future focus is on traits associated with animal health, either directly or in combination with predictor traits, such as mid-infrared spectral data and, into the future, automated data capture. Although it is common for many evaluated traits to have genomic reliabilities ranging between 60 and 75%, many new, genomic information-only traits are likely to have reliabilities of less than 50%. Pooling of phenotype data internationally and investing in maintenance of reference populations is one option to increase the reliability of these traits; the other is to apply improved genomic prediction methods. For example, advances in the use of sequence data, in addition to gene expression studies, can lead to improved persistence of genomic breeding values across breeds and generations and potentially lead to greater reliabilities. Lower genomic reliabilities of novel traits could reduce the overall index reliability. However, provided these traits contribute to the overall breeding objective (e.g., profit), they are worth including. Bull selection tools and personalized genetic trends are already available, but increased access to economic and automatic capture farm data may see even better use of data to improve farm management and selection decisions.
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The effects of high ambient temperatures on production animals, once thought to be limited to tropical areas, has extended into northern latitudes in response to the increasing global temperature. The number of days where the temperature-humidity index (THI) exceeds the comfort threshold (>72) is increasing in the northern United States, Canada, and Europe. Compounded by the increasing number of dairy animals and the intensification of production, heat stress has become one of the most important challenges facing the dairy industry today. The objectives of this review were to present an overview of the effects of heat stress on dairy cattle welfare and highlight important research gaps in the literature. We will also briefly discuss current heat abatement strategies, as well as the sustainability of future heat stress management. Heat stress has negative effects on the health and biological functioning of dairy cows through depressed milk production and reduced reproductive performance. Heat stress can also compromise the affective state of dairy cows by inducing feelings of hunger and thirst, and we have highlighted the need for research efforts to examine the potential relationship between heat stress, frustration, aggression, and pain. Little work has examined how heat stress affects an animal's natural coping behaviors, as well as how the animal's evolutionary adaptations for thermoregulation are managed in modern dairy systems. More research is needed to identify improved comprehensive cow-side measurements that can indicate real-time responses to elevated ambient temperatures and that could be incorporated into heat abatement management decisions.
Article
Excessive ambient temperature and humidity can impair milk production and fertility of dairy cows. Selection for heat-tolerant animals is one possible option to mitigate the effects of heat stress. To enable selection for this trait, we describe the development of a heat tolerance breeding value for Australian dairy cattle. We estimated the direct genomic values of decline in milk, fat, and protein yield per unit increase of temperature-humidity index (THI) using 46,726 single nucleotide polymorphisms and a reference population of 2,236 sires and 11,853 cows for Holsteins and 506 sires and 4,268 cows for Jerseys. This new direct genomic value is the Australian genomic breeding value for heat tolerance (HT ABVg). The components of the HT ABVg are the decline in milk, fat, and protein per unit increase in THI when THI increases above the threshold of 60. These components are weighted by their respective economic values, assumed to be equivalent to the weights applied to milk, fat, and protein yield in the Australian selection indices. Within each breed, the HT ABVg is then standardized to have a mean of 100 and standard deviation (SD) of 5, which is consistent with the presentation of breeding values for many other traits in Australia. The HT ABVg ranged from −4 to +3 SD in Holsteins and −3 to +4 SD in Jerseys. The mean reliabilities of HT ABVg among validation sires, calculated from the prediction error variance and additive genetic variance, were 38% in both breeds. The range in ABVg and their reliability suggests that HT can be improved using genomic selection. There has been a deterioration in the genetic trend of HT, and to moderate the decline it is suggested that the HT ABVg should be included in a multitrait economic index with other traits that contribute to farm profit.
Article
Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensification of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a priority. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are applied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. Developing the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems related to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks.
Article
Heat stress results from the animal's inability to dissipate sufficient heat to maintain homeothermy. Environmental factors, including ambient temperature, radiant energy, relative humidity, and metabolic heat associated with maintenance and productive processes, contribute to heat stress. The focus of this article is to identify environmental and metabolic factors that contribute to excessive heat load, describe how disruption of homeothermy alters physiologic systems of the cow, and discuss nutritional modifications that help to maintain homeostasis or prevent nutrient deficiencies that result from heat stress. Changes in diet are needed during hot weather to maintain nutrient intake, increase dietary nutrient density, or to reestablish homeostasis. Formulation for adequate nutrient intake is challenging because of the competition between nutrient density and other needs for the cow, including energy density and adequate dietary fiber. Lower DMI during hot weather reduces nutrients available for absorption, and absorbed nutrients are used less efficiently. An excess of degradable dietary protein is undesirable because of energy costs to metabolize and excrete excess N as urea. Optimizing ruminally undegraded protein improves milk yield in hot climates. Mineral losses via sweating (primarily K) and changes in blood acid-base chemistry resulting from hyperventilation reduce blood bicarbonate and blood buffering capacity and increase urinary excretion of electrolytes. Theoretical heat production favors feed ingredients with a lower heat increment, such as concentrates and fats, whereas forages have a greater heat increment. Improved dietary energy density and the lower heat increment associated with the inclusion of dietary fat must be coupled with limitations to fat feeding to avoid ruminal and metabolic disorders. Numerous nutritional modifications are used for hot weather feeding; however, many need further investigation to achieve specific recommendations.