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Previous greenhouse gas (GHG) inventories did not include game as an emissions source. Recently game farming has become a recognized commercial enterprise in the agricultural sector in South Africa, contributing approximately R10 billion to the sectorial gross domestic product. The objective of this study was to estimate methane(CH4) and nitrous oxide(N2O) emissions from privately owned game animals based on international recognized methodologies.The emissions were calculated on the basis of a large stock unit(LSU) selecting different quality diets. Daily enteric methane emissions were estimated as 0.28, 0.22 and 0.18 kg CH4/LSU/day consuming diets of 55%, 65% and 75% digestibility, respectively.The game industry contributed an estimated 131.9 Giga grams(Gg) of methane annually to agricultural emissions with the provinces of Limpopo, Eastern Cape and Nothern Cape being the three largest contributors with 43.4, 37.3 and 21 Gg methane, respectively. The total privately owned game population was estimated at 2991370 animals, utilizing 20.5 million hectares.
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South African Journal of Animal Science 2013, 43 (No. 3)
URL: http://www.sasas.co.za
ISSN 0375-1589 (print), ISSN 2221-4062 (online)
Publisher: South African Society for Animal Science http://dx.doi.org/10.4314/sajas.v43i3.10
Direct greenhouse gas emissions of the game industry in South Africa
C.J.L. du Toit1,3#, H.H Meissner2 & W.A. van Niekerk3
1Department of Animal Science, Tshwane University of Technology, Private Bag X680, Pretoria,0001, South Africa
2189 van Riebeeck Avenue, Lyttelton Manor, Centurion, 0157, South Africa
3Department of Animal and Wildlife Sciences, University of Pretoria, 0002, South Africa
Copyright resides with the authors in terms of the Creative Commons Attribution 2.5 South African Licence.
See: http://creativecommons.org/licenses/by/2.5/za
Condition of use: The user may copy, distribute, transmit and adapt the work, but must recognise the authors and the South African Journal of Animal
Science.
________________________________________________________________________________
Abstract
Previous greenhouse gas (GHG) inventories did not include game as an emissions source. Recently
game farming has become a recognized commercial enterprise in the agricultural sector in South Africa,
contributing approximately R10 billion to the sectorial gross domestic product. The objective of this study
was to estimate methane (CH4) and nitrous oxide (N2O) emissions from privately owned game animals based
on international recognized methodologies. The emissions were calculated on the basis of a large stock unit
(LSU) selecting different quality diets. Daily enteric methane emissions were estimated as 0.28, 0.22, and
0.18 kg CH4/LSU/day consuming diets of 55%, 65% and 75% digestibility, respectively. The game industry
contributed an estimated 131.9 Giga grams (Gg) of methane annually to agricultural emissions with the
provinces of Limpopo, Eastern Cape and Northern Cape being the three largest contributors with 43.4, 37.3
and 21 Gg methane, respectively. The total privately owned game population was estimated at 299 1370
animals, utilizing 20.5 million hectares.
________________________________________________________________________________
Keywords: Methane, nitrous oxide, wildlife, emission factors
#Corresponding author: dutoitcjl@tut.ac.za
Introduction
Game or wild ungulates have always inhabited southern Africa, although the population size has
fluctuated greatly over the past 100 years. The establishment and growth of the private game industry is
largely responsible for an increase in the number of game in recent years (Eloff, 2002; Bothma & Van
Rooyen, 2005). Similarly, the industry has shown a steady growth in the number of game farms from
2 280 in 1980 to 9 000 in 1992 (Nell, 2003) and approximately 10 000 currently (G. Dry, 2013, Pers.
Comm., Wildlife Ranching South Africa, P.O. Box 23073, Gezina, 0031, South Africa). The private game
ranching industry occupies 16.8% (20 500 000 ha) of South Africa’s total land area. This figure equates to
24% of South Africa’s 84 million hectares of grazing land (Dry, 2011). This is more than double the area of
officially declared conservation areas and approximately fivefold the area of the national parks (Carruthers,
2004).
Game farming or ranching has become an organized and recognized enterprise in the agricultural
industry (Eloff, 1996; Van Der Waal & Dekker, 2000). According to a recent article by Van Rooyen (2013)
the wildlife industry ranked fifth largest in the agricultural sector, contributing R10 billion to the country’s
gross domestic product (GDP). Game farming is defined as an agricultural system in which wild animals are
maintained in order to harvest by-products such as meat and skins in a domesticated or semi-domesticated
manner by being enclosed in relatively small areas and provided with regular supplementary feeding and
water (Carruthers, 2004; Du Toit, 2007). Part of the success of the industry is the ability of game to produce
higher returns, compared to conventional livestock farming, under particular circumstances that may enhance
the utilization of land with low agricultural potential (ABSA, 2003).
Herbivorous game, with the exception of elephant, rhinoceros, hippopotamus, zebra, warthogs and
bushpigs, are ruminants. Ruminants contribute to greenhouse gas (GHG) emissions through methane
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
377
emissions directly from digestive processes and methane (CH4) and nitrous oxide (N2O) emissions
originating from manure. The quantity of CH4 produced by ruminants is influenced by the level of intake,
composition of the diet, and level of production of the animal. Game species select for diet quality in
accordance with their feeding habits, and were classified by Hofmann (1973) as bulk and roughage eaters
(grazers), selectors of concentrated herbage (browsers) and intermediate feeders (grazing and browsing).
These three groups typically select diets with an approximate digestibility of 55%, 75% and 65%,
respectively (Meissner et al., 1983). These differences in diet quality influence energy intake as well as the
amount of gross energy intake, which is lost as methane and thus methane emissions.
Game is considered a source of anthropogenic emissions. Previous GHG inventories for the livestock
sector in South Africa did not include privately owned game as an emission source. The game industry has
developed into a commercial farming sector, and emissions from all such sectors in the livestock industry
need to be included in order to provide a complete and representative emissions inventory of the livestock
sector. The aim of this study was to calculate methane emissions originating from privately owned game.
Methodology
Various sources have reported on the privately owned game population, which have varied from as
low as 1.7 million (Van der Merwe & Saayman, 2003), to 2.5 million (G. Dry, 2013, Pers. Comm., Wildlife
Ranching South Africa, P.O. Box 23073, Gezina, 0031, South Africa), to 9 million (NAMC, 2006), to 16
million (Van Rooyen, 2013), to as high as 18.6 million (ABSA, 2008). The majority of sources agreed on the
surface area under private game nationally of 20.5 million hectares (NAMC, 2006; ABSA, 2008; Cousins
et al., 2008, Dry, 2011). Owing to the large variations in literature quotes of the number of privately owned
game in South Africa, game emissions were calculated according to the grazing capacity of an area on a
provincial basis in terms of large stock units (LSU) and were not based on individual population figures.
The calculations followed the principles of the IPCC (2006) guidelines. Grazing capacity is defined as
the area of land required to maintain a single LSU over an extended number of years without deterioration of
the vegetation or soil. It was assumed that wildlife farmers stock their farms according to the ecological
carrying capacity of the farm. Table 1 indicates the number of exempted game farms in South Africa, based
on data from 2000, according to Eloff (2002) and Van der Merwe & Saayman (2003).
Table 1 Proportion of exempted game farms in South Africa (Eloff, 2002; Van der Merwe & Saayman,
2003)
Province (year 2000) % of game farms
% of game farms according to
hectares
Free State
3.56
1.43
Limpopo
49.0
32.1
North West
6.72
3.51
Mpumalanga
4.05
2.66
Gauteng
1.42
0.79
KwaZulu-Natal
1.78
1.63
Eastern Cape
12.3
8.51
Northern Cape
19.5
46.8
Western Cape
1.62
2.56
Total
100
100
Similar ratios on the percentage of game farms per province have been reported by ABSA (2008) and
Dry (2011), although the total surface area of the game farms has increased from 10.4 million hectares in
2000 (Eloff, 2002) to 20.5 million hectares currently (Dry, 2011). The estimation of the surface area of
private game farms per province was based on the ratio reported in Table 1 and the national total of 20.5
378
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
million hectares. The emissions calculations in this study were based on surface area under game farms
incorporating carrying capacity of regions, owing to the uncertainty in game population numbers.
Provinces in South Africa were divided into five ecological regions, namely Grassland, Lowveld,
Bushveld, Kalahari and Karoo, according to Bredenkamp et al. (1996). Grassland is defined as the higher
inner plateau with an annual rainfall of between 500 mm and 800 mm, dominated by various grass types with
limited trees and shrubs. The Lowveld, Bushveld and Kalahari regions are grouped as savannah areas. The
Lowveld region covers low-lying areas east of the Northern Drakensberg escarpment with an annual rainfall
of between 400 mm and 600 mm. The Bushveld region refers to the northern parts of South Africa, west of
the Drakensberg escarpment, including the Limpopo valley, with an annual rainfall of between 300 mm and
600 mm. The Kalahari region is classified as arid savannah, with an annual rainfall of between 200 mm and
400 mm per annum. The western part of the Karoo region is classified as semi-desert with an annual rainfall
of less than 200 mm (Bredenkamp et al., 1996; ABSA, 2003). The ecological carrying capacity (ha/LSU) of
these regions was reported by ABSA (2003) as 4, 12, 15, 30, and 55 for Grassland, Lowveld, Bushveld,
Kalahari and Karoo regions, respectively. The average farm size was estimated according to data reported by
Van der Merwe & Saayman (2003). The area per ecological region per province is reported in Table 2.
Table 2 Average game farm size and surface area of ecological regions per province in South Africa
Province Total
area (ha)
(‘000)
Ave
farm size
(ha)
Surface area/ ecological region/ province
Grassland
(ha)
Lowveld
(ha)
Bushveld
(ha)
Kalahari
(ha)
Karoo
(ha)
Free State
287
206 066
18 942
61 992
Limpopo
6 581
210 576
921 270
5 461 815
6 581
North West
718
208 075
157 850
351 575
Mpumalanga
554
354 240
132 840
66 420
Gauteng
164
127 104
36 900
KZN
328
118 080
101 680
108 240
Eastern Cape
1 743
702 809
476 284
563 409
Northern Cape
9 594
32 620
2 830 230
6 732 110
Western Cape
533
5 330
26 650
501 020
KZN: KwaZulu-Natal.
It was assumed that approximately 30% of the farms per province are larger than the average farm size
according to research by Van der Waal & Dekker (2000). The habitat and size of the farm influence the
minimum herd size and relative species distribution of a game farm (Appendices 1A & 1B). The total LSUs
according to the ecological carrying capacity on a provincial basis are given in Table 3. A LSU is defined as
a steer of 450 kg, which gains 500 g/day on a pasture with a mean digestibility (DE) of 55% (Meissner et al.,
1983). The proportion of grazers, browsers and mixed feeders as a percentage of total large stock units per
ecological region is reported in Table 4. The relative distribution of animal species on private game farms is
different from that of national parks in South Africa (ABSA, 2003) and varies according to the size of the
farm. The relative distributions of animal species and herd size per ecological region for small and large
farms are reported in Appendices 1A and 1B.
Enteric methane emissions originating from game were calculated based on dry matter intake (I), (kg
DM/head/day). The daily intake of animal types was calculated based on metabolizable energy requirements
(MJ/day) of large stock units according to Meissner et al. (1983). The daily metabolizable energy (ME)
requirements (MJ/day) of animals selecting diets with various levels of digestible energy concentrations were
based on the net energy requirements of an LSU and the efficiency coefficients of ME utilization at a certain
level of production, according to Meissner et al. (1983). Daily intake per animal type was calculated by
dividing the ME requirement (MJ/day) by the ME concentration (MJ/ kg) of the selected diet.
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
379
Table 3 Distribution of large stock units per province according to ecological carrying capacity
Province Large stock units
Large farm Small farm Total
Free State
15 982
36 946
52 928
Gauteng
10 271
23 965
34 236
Limpopo
148 127
345 631
493 758
Mpumalanga
31 217
72 841
104 058
KwaZulu-Natal
13 563
32 396
45 959
Western Cape
3 666
8 554
12 220
Northern Cape
67 470
157 429
224 899
North West
22 279
51 982
74 261
Eastern Cape
64 803
334 382
399 185
Table 4 Animal types per ecological region as a percentage of large stock units (ABSA, 2003)
Animal type Ecological region
Grassland Lowveld Bushveld Kalahari Karoo
Low selective grazers
20
25
20
10
2
High selective grazers
50
30
30
65
60
Mixed feeders
28
25
30
20
35
Browsers 2 20 20 5 3
Daily enteric methane (M), (kg/head/day) production was calculated according to Kurihara et al.
(1999) based on emissions from cattle fed tropical grass species as:
M = (34.9 x I – 30.8)/1000
Methane emissions from manure (M), (kg/head/day) of all game were calculated according to ANIR
(2009) as:
M = I x (1 DMD) x MEF
Where: I = dry matter intake (kg DM/head/day)
MEF = emissions factor (kg CH4/ kg DM manure). The factor of 1.4 x 10-5
based on the work of Gonzalez-Avalos & Ruiz-Suarez (2001) was used.
DMD = diet digestibility (55% for grazers, 65% for browsers and 75% for
concentrate selectors).
Game production systems are mainly extensive and manure is deposited directly on veld or rangeland.
According to the IPCC (2006), N2O emissions from manure deposited on rangeland or veld are reported
under the managed soils section in the national inventory report format and not under livestock emissions.
Nitrous oxide emissions originating from faeces and urine deposited on rangeland was calculated according
to the ANIR (2009).
380
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
Results and Discussion
Game farming has become a recognized agricultural enterprise (Bothma, 1995; Eloff, 1996; Van der
Waal & Dekker, 2000) but previous agricultural GHG inventories did not include game farming as an
emission source (Blignaut et al., 2005; Otter, 2010). The daily intake, estimated CH4 emissions originating
from enteric fermentation and manure, and estimated N2O emissions from faecal matter deposited on soils
from large stock units selecting various diets are presented in Table 5.
Table 5 Estimated daily intake, methane and nitrous oxide emissions of large stock units selecting different
diet qualities
Animal class
Diet
digestibility
(%)
Intake (kg
DM/day) Enteric CH4
(kg/head/day) Manure CH4
(kg/head/day) Soil N2O
(kg/head/day)
Grazer
55
8.81
0.277
5.6 x 10-5
5.4 x 10-4
Intermediate
feeders
65 7.08 0.216 3.5 x 10-5 7.4 x 10-4
Browsers
75
5.89
0.175
2.1 x 10-5
1.07 x 10-3
Every farm differs and has its own unique carrying capacity and game composition potential. The
number of animals kept on a land unit is determined by the size of the habitat area, the carrying capacity of
the unit, the social and spatial needs of the animals, as well as the interaction and composition of the animal
species (Furstenburg, 2011). Domestic livestock have lost their natural social structure and territorial
behaviour over the years, and carrying capacity is based on fodder production, consumption and veld type
(Furstenburg, 2011). The carrying capacity on game farms incorporates animal social needs and habitat
requirements. The use of grazing capacity as a base for the calculations is a source of uncertainty, as there is
a difference between the grazing capacity of the veld and the stocking rate. Grazing capacity refers to the
true number of animals the vegetation can sustain, and the stocking rate to the number of animals the farm
manager perceives it can sustain (Smit, 2012). Smit (2012) stated that the use of LSU values for herbivorous
game species does not allow for ecological separation, and overlooks the potential for using the specialized
and complementary resource-use habits of wildlife to maximize veld utilization. The approach, however, is
based on sound scientific principles and the error associated with an approach based on individual animal
numbers will be larger owing to the large variation in reported game population numbers in South Africa.
The methane emissions of wildlife on private game farms per province are presented in Table 6. The
game industry contributes an estimated 132 Gg in methane emissions per annum. These figures were
calculated based on the average carrying capacity of game farms in each province. Limpopo was the largest
contributor in terms of methane emissions from farmed wildlife followed by Eastern Cape and Northern
Cape, with 43.4 Gg (32.9%), 37.3 Gg (28.3%) and 21 Gg (15.9%) respectively of the total emissions. The
emission calculations were based on LSUs as defined by Meissner et al. (1983). This may lead to a possible
over-estimation of game emissions, as not all game animals are ruminants. Northern Cape has the largest
surface area under private game farming (46.8%), followed by Limpopo (32.1%) and Eastern Cape (8.5%).
The difference between provincial ranking according to surface area and methane emissions is because of the
average carrying capacity of the provinces. Northern Cape has the largest surface area under private game
farming, but it ranks only third in terms of methane emissions originating from private game. This is owing
to the relatively low carrying capacity of the Karoo (55 ha/LSU), which covers approximately 70% of
Northern Cape, compared to the carrying capacity of the Bushveld (15 ha/LSU) and Grassland (4 ha/LSU)
which cover approximately 86% and 68% of Limpopo and Eastern Cape, respectively.
The methane emissions per individual animal were calculated based on the energy requirements as
described above. The calculated dry matter intake as a percentage of liveweight is lower than that reported
by Smit (2012) for game species. Meissner (1982) indicated that the feed intake of wild ungulates in
subtropical regions is less than that of domestic livestock of comparable size. Curtzen et al. (1986) reported
annual methane emissions of 34 kg, 50 kg, 5 kg, 26 kg, and 5 kg for buffalo, giraffe, impala, elephant and
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
381
zebra, respectively. These estimates are considerably lower than those calculated in this study and reported in
Table 7. The emission estimates reported by Curtzen et al. (1986) were based on animals with lower
liveweights and gross energy intakes than when compared with those reported in Table 7. The CH4 emissions
for elephant and zebra were based on emission values of horses, which have similar digestive systems, as
3.5% of digestible energy intake (Curtzen et al., 1986). The emissions from black wildebeest, tsessebe,
blesbok, impala and springbok were based on the equation developed by Howden & Reyenga (1987) based
on respiration chamber experiments on sheep in Australia. Warthog emissions were estimated according to
the IPCC (2006) based on pigs in developing countries. All other methane emission estimates for game
(giraffe, eland, buffalo, kudu, waterbuck and blue wildebeest) reported in Table 7 were based on an equation
developed by Kurihara et al. (1999) based on cattle fed tropical pastures.
Table 6 Estimated methane emissions (Gg/year) and number of large stock units per animal class and
province in South Africa
Province Animal class Large stock
units Enteric CH4
(Gg/year) Total CH4
(Gg/year)
% contribution
to total
emissions
Free State
Grazers
37 019
3.74
4.98 3.78
Mixed feeders
14 824
1.17
Browsers
1 085
0.07
Gauteng
Grazers
23 473
2.37
3.21 2.43
Mixed feeders
9 635
0.76
Browsers
1 128
0.07
Limpopo
Grazers
261 302
26.4
43.4 32.9
Mixed feeders
143 214
11.3
Browsers
89 243
5.7
Mpumalanga
Grazers
70 295
7.11
9.70 7.35
Mixed feeders
28 893
2.28
Browsers
4 871
0.31
KwaZulu-Natal
Grazers
29 457
2.98
4.22 3.20
Mixed feeders
12 758
1.01
Browsers
3 743
0.24
Western Cape
Grazers
7 470
0.76
1.12 0.85
Mixed feeders
4 095
0.32
Browsers
655
0.04
Northern Cape
Grazers
152 354
15.4
21 15.9 Mixed feeders 63 993 5.05
Browsers
8 552
0.55
North West
Grazers
50 464
5.10
6.92 5.25
Mixed feeders
20 066
1.58
Browsers
13 738
0.24
Eastern Cape
Grazers
272 337
27.5
37.3 28.3
Mixed feeders
113 110
8.92
Browsers
126 746
0.88
Total
1 441 504
131.9
131.9
100
382
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
Giraffe and eland had comparable daily CH4 emission factors (g CH4/kg LW/day) to commercial beef
bulls and cows with similar liveweights (LW), according to Du Toit et al. (2013a), with 0.46 g CH4/kg
LW/day compared to 0.42 g CH4/kg LW/day for giraffe and commercial bulls and 0.51 g CH4/kg LW/day
compared to 0.53 g CH4/kg LW/day for eland and commercial beef cows, respectively. Buffalo had higher
calculated daily CH4 emission factors (0.67 g CH4/kg LW/day) compared to commercial beef cows (0.53 g
CH4/kg LW/day) with similar liveweights (Du Toit et al., 2013a). The daily CH4 emission factors of smaller
antelope reported in Table 7 were compared to commercial small stock emission factors with similar
liveweights according to Du Toit et al. (2013b). Black wildebeest and tsessebe had estimated daily CH4
emission factors (g CH4/kg LW/day) that are similar to those of commercial dual purpose breeding rams, but
lower emission factors than those of commercial breeding goat bucks with 0.39, 0.38, 0.37 and 0.43 for black
wildebeest, tsessebe, commercial dual purpose breeding rams and breeding goat bucks, respectively. Impala
and springbok had numerically higher estimated daily CH4 emissions factors (g CH4/kg LW/day) than
commercially farmed goats with similar liveweights as reported by Du Toit et al. (2013b) with 0.50 and 0.48
compared to 0.40 and 0.44 for impala, springbok, young does and kids, respectively.
Table 7 Approximate liveweight (LW), large stock unit (LSU) substitution, diet digestibility, intake (% of
live weight) and methane emissions of selected game species
Species
Weight
(kg)
#
LSU
Diet DE*
(%)
Intake
(%/LW)
CH
4
(kg/head/year)
CH
4
(g/kg
LW/day)
Elephant 2 436 3.83 55 1.4 81.0 0.10
Giraffe 826 1.51 65 1.4 136 0.46
Eland 528 1.08 65 1.6 93.7 0.51
Buffalo 466 1.08 55 2.1 113 0.67
Zebra 266 0.66 55 2.2 13.9 0.15
Kudu 155 0.44 65 2.2 31.3 0.56
Waterbuck 150 0.41 55 2.5 35.9 0.67
Blue
wildebeest
153 0.43 75 1.8 24.8 0.44
Black
wildebeest
106 0.30 75 1.9 14.3 0.39
Tsessebe 105 0.03 65 1.8 13.8 0.38
Blesbok 62 0.19 75 2.0 9.08 0.43
Warthog 59 0.21 75 2.4 2.22 0.18
Impala 42 0.15 75 2.4 7.40 0.50
Springbok 28 0.09 75 2.2 4.72 0.48
# Animal live weight and daily energy requirements used in intake calculations were sourced from Meissner et al.
(1983). * DE: feed digestibility.
Tables 8a and 8b reports on the estimated South African privately owned game population according
to province, based on the norms presented by ABSA (2003) in Appendices 1A and 1B. The total game
population is estimated at 2 991 370 animals. This is in line with the figure reported by Dry (2011) of 2.5
million animals, but smaller than other figures reported in the literature (NAMC, 2006; ABSA, 2008: Van
Rooyen, 2013).
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
383
Annual enteric methane emissions for individual game species reported in Appendix 2 were calculated
based on daily intake using the equations of Howden & Reyenga (1987), Kurihara et al. (1999), and the
IPCC (2006), as discussed earlier. For hippopotamus and rhinoceros, the methane emissions were based on
the daily methane emissions of elephant of 0.1 g CH4/kg LW/day. The liveweights of game animals were
sourced from Meissner et al. (1983) and Smit (2012). By basing the emission estimates on individual animal
populations of approximately 3 million, the total methane emissions for the commercial game industry come
to 59.9 Gg per year. This is considerably lower that the emission estimate based on LSUs and stocking rates
of 132 Gg reported in Table 6. The variation in emission estimates is very large when game populations are
Table 8a Estimated game numbers per province based on norms reported by ABSA (2003)
Animal Species Provinces
Gt Mpum NC NW EC Lim FS KZN WC Total
Low selective grazers
LSU/animal
1.07 Buffalo 288 2075 862 625 1732 12475 439 1299 26 19821
2.24 Hippo 7 49 0 28 84 1232 0 48 5 1453
2.75
White
Rhino
112 335 181 224 674 1574 170 143 9 3422
0.66
Zebra
(Burchell)
9418 27447 2249 17151 111990 124411 14206 11704 841 319418
0.66
Zebra
(Cape
mountain)
0 0 16073 1536 308 29 106 0 276 18328
High selective grazers
LSU/animal
0.22 Blesbok 14444 40255 3707 23645 26836 23929 26836 13759 606 174017
0.56 Gemsbok 20 36 72194 4165 3515 3003 470 58 2942 86403
0.37
Red
hartebeest
4 464 12273 37525 9814 11786 32248 8216 4588 1780 122694
0.25 Reedbuck 3325 9786 816 5833 7788 31718 5904 3968 240 69378
0.64 Roan 17 109 0 74 222 3100 0 110 12 3644
0.64 Sable 17 109 0 74 222 3100 0 110 12 3644
0.15 Springbok 21184 59040 274963 49914 51540 35382 41116 20180 11821 565140
0.38 Tsessebe 233 1468 0 998 2975 41769 0 1486 168 49097
0.5 Waterbuck 251 1581 0 1073 3203 44972 0 1600 181 52861
0.46
Wildebeest
(black)
15543 43317 3989 25444 28878 25750 28878 14806 653 187258
0.5
Wildebeest
(blue)
782 5593 80858 7917 13640 144896 527 5498 3844 263555
Gt: Gauteng; Mpum: Mpumalanga; NC: Northern Cape; NW: North West; EC: Eastern Cape; Lim: Limpopo;
FS: Free State; KZN: KwaZulu-Natal; WC: Western Cape.
384
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
used, 50.05 Gg from 2.5 million animals to 336.34 Gg from 18.6 million animals. The type of diet selected
by game, the amount of methane produced per unit of feed intake, and variation in daily feed intake are
further causes of uncertainty when emission estimates are based on animal populations.
Table 8b Estimated game numbers per province based on norms reported by ABSA (2003)
Animal Species Provinces Total
Gt Mpum NC NW EC Lim FS KZN WC
Mixed feeders
LSU/animal
0.09 Duiker 2223 6660 19610 4888 7999 44604 3797 3146 1323 94250
1.08 Eland 7922 22061 46592 14226 18789 27678
14828
7795 3113 163004
5 Elephant 15 94 0 66 198 2754 0 96 11 3236
0.2 Impala 1292 9382 25000 8630 16486 240164 167 9190 933 311244
0.23 Nyala 0 1023 0 0 0 7093 0 783 0 8899
0.38 Ostrich 97 772 9857 940 1707 18525 64 742 490 33194
0.25
Reedbuck
(mountain)
1156 3467 3595 2539 3114 17188 2006 1531 109 34705
0.25 Warthog 148 1594 9057 1756 1884 31080 61 1450 107 47137
Browse
LSU/animal
0.13 Bushbuck 114 954 0 486 1449 22001 0 907 82 25993
1.58 Giraffe 156 1072 269 699 1987 28537 2 1063 112 33897
0.07 Klipspringer 1160 3701 8108 2503 2954 15944 1736 1662 428 38196
0.54 Kudu 556 2386 8154 2407 4516 48619 933 1911 346 69828
0.13
Rhebuck
(grey)
1800 5654 977 3314 3947 24798 2501 2527 153 45671
1.65
Rhino
(Black)
9 56 129 54 114 1604 1 57 6 2030
0.06 Steenbuck 3371 10258 46959 6998 10842 49285 4680 4318 3245 139953
Total (a + b) 90124 272607 671724 198021 341379 1109462 157644 116533 33880 2991370
Gt: Gauteng; Mpum: Mpumalanga; NC: Northern Cape; NW: North West; EC: Eastern Cape; Lim: Limpopo;
FS: Free State; KZN: KwaZulu-Natal; WC: Western Cape.
The CH4 emissions estimates per species are reported in Appendix 2. As CH4 emissions originating
from manure of game are very low, it is not reported in the table in Appendix 2. Although the N2O emitted
from soil through the metabolism of manure and urine is not reported under livestock emissions according to
the IPCC (2006) good practice guidelines, it is mentioned to provide a more complete scenario of emissions
associated with game on privately owned land. Nitrogen in faecal matter is primarily in an organic form and
must first be mineralized before it becomes a source of N2O. The mineralization process occurs at significant
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
385
rates in higher rainfall regions. However, the decay of faeces in drier areas is much slower, with faeces
remaining largely intact for months to years (ANIR, 2009). The N2O emissions from faeces and urine voided
in rangeland were estimated at 0.39 Gg N2O/year on a national scale using emission factors of 0.005 and
0.004 Gg N2O-N/Gg N for faeces and urine, respectively, according to the ANIR (2009). Penttilä et al.
(2013) reported that dung beetles could potentially increase GHG emissions from faeces voided on rangeland
or veld, mainly due to increased N2O emissions. The possible effect of dung beetles is noted but not included
in the present inventory due to insufficient data under South African conditions. The Limpopo province had
the largest emissions originating from game followed by Northern Cape and Eastern Cape provinces.
Conclusion
Game was not included in previous inventories, but was identified as a key CH4 emissions source in
the present inventory, contributing 132 Gg of CH4. Nitrous oxide emissions from rangeland soils originating
from faecal matter were estimated at 0.39 Gg N2O/year. There is a great deal of uncertainty in the estimation
of GHG emissions from game on game farms. To base the CH4 emission estimation on the ecological
carrying capacity of commercial game farms remains the soundest approach, as the variations in game
population numbers and intake estimations are extremely large. Multiple sources agreed on the figure for the
surface area under private game in South Africa of 20.5 million hectares and this appears the only justifiable
basis for the emissions estimation.
Acknowledgement
This work is based on the research supported wholly by the National Research Foundation of South
Africa and the RMRD SA.
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Appendix
Appendix 1A Minimum herd size and relative distribution of animal species per ecological region for larger farms
(ABSA, 2003)
Relative distribution of animal species as a % of LSU*
Animal species
Min. social
herd size
Grassland Lowveld Bushveld Kalahari Karoo
Low selective grazers 20% 25% 20% 10% 2%
Buffalo 15 15 50 15 30
Hippo 15 10 10
White Rhino 5 15 10 15 15
Zebra (Burchell) 5 70 30 60
Zebra (Cape mountain) 10 55 100
High selective grazers 50% 30% 30% 65% 60%
Blesbok 12 20
Gemsbok 12 5 30 30
Red hartebeest 12 10 5 10 10
Reedbuck 8 5 5 5
Roan 12 5 5
Sable 12 5 5
Springbok 15 20 30 30
Tsessebe 12 5 5
Waterbuck 12 10 10
Wildebeest (black) 12 45
Wildebeest (blue) 12 70 60 30 30
Mixed feeders 28% 25% 30% 20% 35%
Duiker 6 2 3 3 3 3
Eland 12 95 10 14 54 92
Elephant 12 40 35
Impala 15 30 35 30
Nyala 12 5
Ostrich 6 4 5 5 5
Reedbuck (mountain) 8 3 3 3 3
Warthog 12 5 5 5
Browsers 2% 20% 20% 5% 3%
Bushbuck 8 3 3
Giraffe 8 60 50 30
Klipspringer 4 5 1 1 5 5
Kudu 12 80 20 30 40 90
Rhebuck (grey) 8 10 3 3 5
Rhino (black) 5 10 10 15
Steenbok 5 5 3 3 5 5
*LSU: large stock unit.
388
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
Appendix 1B Minimum herd size and relative distribution of animal species per ecological region for smaller farms
(ABSA, 2003)
Relative distribution of animal species as a % of LSU*
Animal species
Min. social
herd size
Grassland Lowveld Bushveld Kalahari Karoo
Low selective grazers 20% 25% 20% 10% 2%
Buffalo 15 50
Zebra (Burchell) 5 100 50 100
Zebra (Cape mountain) 10 100 100
High selective grazers 50% 30% 30% 65% 60%
Blesbok 12 20
Gemsbok 12 30 30
Red hartebeest 12 10 10 10 10
Reedbuck 8 5 5 5
Springbok 15 20 30 30
Tsessebe 12 15 15
Waterbuck 12 20 20
Wildebeest (black) 12 45
Wildebeest (blue) 12 60 50 30 30
Mixed feeders 28% 25% 30% 20% 35%
Duiker 6 2 2 2 2 3
Eland 12 95 43 92
Impala 15 60 70 25
Nyala 12 10
Ostrich 6 10 10 10 5
Reedbuck (mountain) 8 3 3 3 5
Warthog 12 15 15 15
Browsers 2% 20% 20% 5% 3%
Bushbuck 8 5 5
Giraffe 8 55 50
Klipspringer 4 15 2 2 5 10
Kudu 12 30 35 85
Rhebuck (grey) 8 45 5 5
Steenbok 5 40 3 3 10 90
*LSU: large stock unit.
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
389
Appendix 2A Breakdown of animal species, energy requirements, diet characteristics, intake and annual enteric
methane emissions
Animal Species
Animal
characteristics
LSU
Diet
characteristics
Intake (kg
DM/day) Intake
(%/LW) CH4
kg/h/year
Weight
(kg)
ME
requirements
(MJ/day)
Diet
DE% ME
MJ/kg
Elephant
Calf
(5 years)
850 84.8 1.13 55 8.3 10.2 1.2 23.9
Cow, dry
(15 years)
1850 285 3.80 55 8.3 34.3 1.9 80.4
Cow, dry
(50 years)
3300 291 3.88 55 8.3 35.1 1.1 82.1
Co w with c alf
(15 years)
1850 362 4.83 55 8.3 43.6 2.4 102.1
Co w with c alf
(50 years)
3300 375 5.00 55 8.3 45.2 1.4 105.8
Bull
(15 years)
2200 303 4.04 55 8.3 36.5 1.7 85.5
Bull
(50 years)
3700 310 4.13 55 8.3 37.3 1.0 87.5
Average 2435.7 287.3 3.83 55 8.3 34.6 1.4 81.0
Giraffe
Calf
(9 months)
390 57.8 0.77 65 9.81 5.9 1.5 63.8
Cow, dry
(5 years)
770 111.0 1.48 65 9.81 11.3 1.5 132.9
Cow, dry
(10 years)
850 101.0 1.35 65 9.81 10.3 1.2 119.9
Co w with c alf
(5 years)
770 139.0 1.85 65 9.81 14.2 1.8 169.3
Co w with c alf
(10 years)
850 130.0 1.73 65 9.81 13.3 1.6 157.6
Bull (5 years) 960 126.0 1.68 65 9.81 12.8 1.3 152.4
Bull (6 years) 1190 127.0 1.69 65 9.81 12.9 1.1 153.7
Average 825.7 113.1 1.51 65 9.81 11.5 1.4 135.6
Eland
Calf
(8 months)
200 38.9 0.52 65 9.81 4.0 2.0 39.3
Cow dry
(3 years)
460 75.5 1.01 65 9.81 7.7 1.7 86.8
Cow dry
(6 years)
500 72.1 0.96 65 9.81 7.3 1.5 82.4
Co w with c alf
(3 years)
460 96.6 1.29 65 9.81 9.8 2.1 114.2
Co w with c alf
(6 years)
500 87.1 1.16 65 9.81 8.9 1.8 101.9
Bull (3 years) 760 99.5 1.33 65 9.81 10.1 1.3 118.0
Bull (6 years) 815 96.0 1.28 65 9.81 9.8 1.2 113.4
Average 528 80.8 1.1 65 9.81 8.2 1.6 93.7
LSU: large stock unit; ME: metabolizable energy; DE: digestibility; DM: dry matter; LW: liveweight;
CH4: methane; kg/h/year = kg/head/year.
390
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
Appendix 2B Breakdown of animal species, energy requirements, diet characteristics, intake and annual enteric
methane emissions
Animal species
Animal
characteristics
LSU
Diet
characteristics
Intake(kg
DM/day) Intake
(%/ LW) CH4
(kg/h/year)
Weight
(kg)
ME
requirements
(MJ/day)
Diet
DE% ME
(MJ/kg)
Buffalo
Calf
(8 months)
145 31.8 0.42 55 8.3 3.8 2.6 37.6
Cow dry
(4 years)
460 79.1 1.05 55 8.3 9.5 2.1 110.2
Cow dry
(10 years)
530 76.4 1.02 55 8.3 9.2 1.7 106.0
Co w with c alf
(4 years)
460 101.0 1.35 55 8.3 12.2 2.6 143.2
Co w with c alf
(10 years)
530 99.3 1.32 55 8.3 12.0 2.3 141.2
Bull (4 years) 500 89.6 1.19 55 8.3 10.8 2.2 126.3
Bull (10 years) 640 87.7 1.17 55 8.3 10.6 1.7 123.4
Average 466.4 80.7 1.08 55 8.3 9.7 2.1 112.6
Zebra
Foal
(5 months)
95 24.6 0.33 55 8.3 3.0 3.1 6.9
Mare dry
(4 years)
270 48.9 0.65 55 8.3 5.9 2.2 13.8
Mare dry
(7 years)
290 45.0 0.60 55 8.3 5.4 1.9 12.7
Mare with foal
(4 years)
270 61.0 0.81 55 8.3 7.3 2.7 17.2
Mare with foal
(7 years)
290 58.9 0.79 55 8.3 7.1 2.4 16.6
Stallion
(4 years)
310 54.0 0.72 55 8.3 6.5 2.1 15.2
Stallion
(7 years)
335 52.1 0.69 55 8.3 6.3 1.9 14.7
Average 265.7 49.2 0.66 55 8.3 5.9 2.2 13.9
Kudu
Calf
(6 months)
55 15.8 0.21 65 9.81 1.6 2.9 9.3
Cow dry
(3 years)
125 27.9 0.37 65 9.81 2.8 2.3 25.0
Cow dry
(5 years)
160 29.8 0.40 65 9.81 3.0 1.9 27.5
Co w with c alf
(3 years)
125 34.9 0.47 65 9.81 3.6 2.8 34.1
Co w with c alf
(5 years)
160 38.7 0.52 65 9.81 3.9 2.5 39.0
Bull (3 years) 220 42.1 0.56 65 9.81 4.3 2.0 43.4
Bull (5 years) 240 39.9 0.53 65 9.81 4.1 1.7 40.6
Average 155 32.7 0.44 65 9.81 3.3 2.2 31.3
LSU: large stock unit; ME: metabolizable energy; DE: digestibility; DM: dry matter; LW: liveweight;
CH4: methane; kg/h/year = kg/head/year.
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
391
Appendix 2C Breakdown of animal species, energy requirements, diet characteristics, intake and annual enteric
methane emissions
Animal species
Animal
characteristics
LSU
Diet
characteristics
Intake(kg
DM/day) Intake
(%/LW) CH4
(kg/h/day)
Weight
(kg)
ME
requirements
(MJ/day)
Diet
DE% ME
MJ/kg
Waterbuck
Lamb
(5 months)
47 15.0 0.20 55 8.3 1.8 3.8 11.8
Ewe dry
(3 years)
130 27.6 0.37 55 8.3 3.3 2.6 31.1
Ewe dry
(5 years)
160 28.1 0.37 55 8.3 3.4 2.1 31.9
Ewe with lamb
(3 years)
130 34.6 0.46 55 8.3 4.2 3.2 41.9
Ewe with lamb
(5 years)
160 36.6 0.49 55 8.3 4.4 2.8 44.9
Ram (3 years) 195 37.3 0.50 55 8.3 4.5 2.3 46.0
Ram (5 years) 225 35.6 0.47 55 8.3 4.3 1.9 43.4
Average 149.6 30.7 0.41 55 8.3 3.7 2.5 35.9
Blue wildebeest
Calf
(4 months)
51 15.6 0.21 75 11.32 1.4 2.7 6.3
Cow dry
(3 years)
145 29.8 0.40 75 11.32 2.6 1.8 22.3
Cow dry
(5 years)
160 29.4 0.39 75 11.32 2.6 1.6 21.8
Co w with c alf
(3 years)
145 37.3 0.50 75 11.32 3.3 2.3 30.7
Co w with c alf
(5 years)
160 38.3 0.51 75 11.32 3.4 2.1 31.9
Bull (3 years) 195 37.2 0.50 75 11.32 3.3 1.7 30.6
Bull (5 years) 215 36.3 0.48 75 11.32 3.2 1.5 29.6
Average 153 32.0 0.43 75 11.32 2.8 1.8 24.8
Black wildebeest
Calf
(4 months)
40 12.5 0.17 75 11.32 1.1 2.8 8.2
Cow dry
(3 years)
105 20.3 0.27 75 11.32 1.8 1.7 12.9
Cow dry
(5 years)
115 21.6 0.29 75 11.32 1.9 1.7 13.7
Co w with c alf
(3 years)
105 25.4 0.34 75 11.32 2.2 2.1 15.7
Co w with c alf
(5 years)
115 28.2 0.38 75 11.32 2.5 2.2 17.7
Bull (3 years) 125 25.1 0.33 75 11.32 2.2 1.8 15.8
Bull (5 years) 135 25.3 0.34 75 11.32 2.2 1.7 15.9
Average 105.7 22.6 0.30 75 11.32 2.0 1.9 14.3
LSU: large stock unit; ME: metabolizable energy; DE: digestibility; DM: dry matter; LW: liveweight;
CH4:methane; kg/h/year = kg/head/year.
392
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Appendix 2D Breakdown of animal species, energy requirements, diet characteristics, intake and annual enteric
methane emissions
Animal species
Animal
characteristics
LSU
Diet
characteristics
Intake (kg
DM/day) Intake
(%/LW) CH4
(kg/h/day)
Weight
(kg)
ME
requirements
(MJ/day)
Diet
DE% ME
MJ/kg
Tsessebe
Lamb
(5 months)
38 12.2 0.16 65 11.32 1.1 2.8 8.0
Ewe dry
(3 years)
104 19.6 0.26 65 11.32 1.7 1.7 12.5
Ewe dry
(5 years)
113 20.9 0.28 65 11.32 1.8 1.6 13.3
Ewe with lamb
(3 years)
104 24.6 0.33 65 11.32 2.2 2.1 15.5
Ewe with lamb
(5 years)
113 27.2 0.36 65 11.32 2.4 2.1 17.1
Ram (3 years) 126 24.2 0.32 65 11.32 2.1 1.7 15.3
Ram (5 years) 138 24.2 0.32 65 11.32 2.1 1.5 15.3
Average 105.1 21.8 0.29 65 11.32 1.9 1.8 13.8
Blesbok
Lamb
(5 months)
23 7.6 0.10 75 11.32 0.7 2.9 5.2
Ewe dry
(3 years)
60 12.3 0.16 75 11.32 1.1 1.8 8.0
Ewe dry
(5 years)
67 14.7 0.20 75 11.32 1.3 1.9 9.5
Ewe with lamb
(3 years)
60 15.4 0.21 75 11.32 1.4 2.3 9.9
Ewe with lamb
(5 years)
67 19.1 0.25 75 11.32 1.7 2.5 12.2
Ram (3 years) 73 14.3 0.19 75 11.32 1.3 1.7 9.3
Ram (5 years) 81 14.8 0.20 75 11.32 1.3 1.6 9.6
Average 61.6 14.0 0.19 75 11.32 1.2 2.0 9.1
Warthog
Piglet
(3 months)
13 6.2 0.08 75 11.32 0.5 4.2 3.6
Sow dry
(2 years)
59 15.0 0.20 75 11.32 1.3 2.2 1.9
Sow dry
(3 years)
65 13.9 0.19 75 11.32 1.2 1.9 1.6
Sow with litter
(2 years)
59 21.1 0.28 75 11.32 1.9 3.2 2.7
Sow with litter
(3 years)
65 20.1 0.27 75 11.32 1.8 2.7 2.3
Boar (2 years) 74 18.4 0.25 75 11.32 1.6 2.2 1.9
Boar (3 years) 80 16.2 0.22 75 11.32 1.4 1.8 1.5
Average 59.3 15.8 0.21 75 11.32 1.4 2.4 2.2
LSU: large stock unit; ME: metabolizable energy; DE: digestibility; DM: dry matter; LW: liveweight;
CH4: methane; kg/h/year = kg/head/year.
Du Toit et al., 2013. S. Afr. J. Anim. Sci. vol. 43
393
Appendix 2E Breakdown of animal species, energy requirements, diet characteristics, intake and annual enteric
methane emissions
Animal species
Animal
characteristics
LSU
Diet
characteristics
Intake (kg
DM/day) Intake
(%/LW) CH4
(kg/h/day)
Weight
(kg)
ME
requirements
(MJ/day)
Diet
DE% ME
MJ/day
Impala
Lamb
(4 months)
19 5.8 0.08 75 11.32 0.5 2.7 4.1
Ewe dry
(2 years)
37 10.8 0.14 75 11.32 1.0 2.6 7.1
Ewe dry
(4 years)
45 10.2 0.14 75 11.32 0.9 2.0 6.8
Ewe with lamb
(2 years)
37 14.0 0.19 75 11.32 1.2 3.3 9.1
Ewe with lamb
(4 years)
45 13.9 0.19 75 11.32 1.2 2.7 9.0
Ram (2 years) 51 11.9 0.16 75 11.32 1.1 2.1 7.8
Ram (4 years) 60 12.2 0.16 75 11.32 1.1 1.8 8.0
Average 42 11.3 0.15 75 11.32 1.0 2.4 7.4
Springbok
Lamb
(2.5 months)
12 3.2 0.04 75 11.32 0.3 2.3 2.5
Ewe dry
(18 months)
27 6.3 0.08 75 11.32 0.6 2.1 4.4
Ewe dry
(3 years)
31 7.0 0.09 75 11.32 0.6 2.0 4.8
Ewe with lamb
(18 months)
27 7.9 0.10 75 11.32 0.7 2.6 5.3
Ewe with lamb
(3 years)
31 9.1 0.12 75 11.32 0.8 2.6 6.1
Ram
(18 months)
30 7.1 0.09 75 11.32 0.6 2.1 4.9
Ram (3 years) 36 7.4 0.10 75 11.32 0.7 1.8 5.0
Average 27.7 6.8 0.09 75 11.32 0.6 2.2 4.7
LSU: large stock unit; ME: metabolizable energy; DE: digestibility; DM: dry matter; LW: liveweight;
CH4: methane; kg/h/year = kg/head/year.
... Wildlife ranching is capable of producing higher economic returns in regions that are not suited for livestock or crop production (Dry, 2015). The number of wildlife ranches grew from 2,280 in 1980 to more than 10,000 in 2013 (du Toit, Meissner, & van Niekerk, 2013). Although, the exact wildlife numbers in South Africa are unknown (Dry, 2011(Dry, , 2015du Toit et al., 2013), a growth in the number of animals sold at game auctions is evident. ...
... The number of wildlife ranches grew from 2,280 in 1980 to more than 10,000 in 2013 (du Toit, Meissner, & van Niekerk, 2013). Although, the exact wildlife numbers in South Africa are unknown (Dry, 2011(Dry, , 2015du Toit et al., 2013), a growth in the number of animals sold at game auctions is evident. Since 2009, the number of animals sold at game auctions has grown by 16.7%, and the annual turnover at auctions increased with 35.8%, resulting in wildlife ranching becoming the sixth largest agricultural commodity in South Africa (ABSA, 2015). ...
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Predation is a well‐known problem in South Africa with large losses in the small and large livestock sectors. Predation in the wildlife ranching industry has also become more of a concern, as the financial losses due to predation on valuable antelope species are large. Predation data for small, large, and scarce/colour‐variant antelope species were collected using a structured questionnaire from wildlife ranchers in the Limpopo province, South Africa. We explore the factors that influence predation on these species by determining whether the perceptions of predation and consequent managerial decisions affect predation. The use of nonlethal control methods can be successfully employed to reduce the probability of predation occurrences, however, a combination of lethal and nonlethal control methods were used to reduce the level of predation. The type of antelope species will determine the type of predation control method to be employed. Therefore, the antelope species should be taken into account when making predation management decisions. Résumé fr La prédation est un problème bien connu en Afrique australe ; il entraîne des pertes importantes aussi bien dans le secteur du petit bétail que dans celui du gros. La prédation dans l'industrie de l’élevage d'animaux sauvages devient aussi plus inquiétante car les pertes financières touchant des espèces intéressantes d'antilopes deviennent plus importantes. Nous avons collecté des données sur la prédation d'espèces d'antilopes petites ou grandes, discrètes ou bien visibles, au moyen d'un questionnaire structuré rempli par des gardes de la faune de la Province du Limpopo, en Afrique du Sud. Dans ce but, nous explorons les facteurs qui influencent la prédation de ces espèces en déterminant si les mêmes perceptions de la prédation et les mêmes décisions de gestion affectent l'occurrence et le niveau de la prédation. Le recours à des méthodes de contrôle non létales peut réussir à réduire la probabilité des occurrences de prédation tandis qu'une combinaison de méthodes de contrôle létales et non létales a été utilisée pour réduire le taux de prédation. Cependant, les méthodes de contrôle de la prédation sont spécifiques à l'espèce d'antilope, et il faut donc tenir compte de l'espèce en question pour prendre des décisions en matière de gestion
... Currently, there are approximately 180 game farms in eastern Free State, with an average farm size of 821 ha (Du Toit et al 2013), which focus on game capturing, selling, and hunting (Shroyer and Blignaut 2003). Typical species farmed are eland (Taurotragus oryx), blesbuck (Damaliscus pygargus phillipsi), springbuck (Antidorcas marsupialis), grey rhebok (Pelea capreolus), mountain reedbuck (Redunca fulvorufula), Burchell's zebra (Equus quagga burchellii), bushbuck (Tragelaphus sylvaticus), black (Connochaetes gnou) and blue wildebeest (Connochaetes taurinus), and red hartebeest (Alcelaphus buselaphus caama). ...
... Currently, there are approximately 180 game farms in eastern Free State, with an average farm size of 821 ha (Du Toit et al 2013), which focus on game capturing, selling, and hunting (Shroyer and Blignaut 2003). Typical species farmed are eland (Taurotragus oryx), blesbuck (Damaliscus pygargus phillipsi), springbuck (Antidorcas marsupialis), grey rhebok (Pelea capreolus), mountain reedbuck (Redunca fulvorufula), Burchell's zebra (Equus quagga burchellii), bushbuck (Tragelaphus sylvaticus), black (Connochaetes gnou) and blue wildebeest (Connochaetes taurinus), and red hartebeest (Alcelaphus buselaphus caama). ...
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Achieving sustainable food security is a critical goal for smallholder farmers in mountainous regions around the world. In the 40,000 km2 Maloti–Drakensberg mountains (South Africa and Lesotho), one of the important mountain ranges of southern Africa, farmers are directly dependent on natural resources. Natural resource management is currently unsustainable, driving landscape degradation and entrenching poverty cycles. Through a comprehensive literature review, we explore the current status of knowledge, opportunities, and agriculture-dependent natural resource sustainability in the Maloti–Drakensberg, and outline the priorities for future research in mountain agriculture in southern Africa. The Maloti–Drakensberg has diverse land tenure systems and climatic heterogeneity that together determine farming practices. Agropastoralism is the predominant agricultural practice, occupying 79% of the land, because of the natural grass-dominated vegetation. Despite decades of concern, the sustainable management of communal rangeland remains elusive. Arable cropping is practiced on 12% of the land at subsistence levels, while game farming contributes a small amount to local revenues. A multipronged research approach is needed to understand the complex social–ecological issues around soil degradation and sustainable utilization of the limited agricultural natural resources base. Innovative and adaptive strategies that take into account local and indigenous knowledge, mitigate soil degradation, and enhance water and rangeland conservation are needed to promote sustainable food production in the Maloti–Drakensberg.
... However, the traits could be assigned weights that could be linked to carbon footprints or credits (sequestration) and not only economic weights. It would be possible to link the annual carbon footprint with an LSU, as done by Du Toit et al., 2013). ...
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The aim is of the study was to identify novelty traits that could be used as breeding objectives to improve cow-calf efficiency and describe cow efficiency in extensive systems in support of climate-smart production in beef cattle. The traits that were investigated were 'weaning weight of the calf as a trait of the dam' (K205) and 'kilogram calf weaned per large stock unit' (KgC/LSU. The latter trait is a value that expresses performance (calf weaning weight) per constant unit, namely per LSU. This may be a useful breeding objective or goal to increase production efficiency, which may reduce the carbon footprint of extensive beef cow-calf production systems. The investigation of the novel traits was conducted on three diverse breeds, namely Afrikaner, Angus and Charolais, with 6104, 7581 and 2291 complete cow-calf records, respectively. Only cows with all three first parities recorded were used to investigate KgC/LSU and K205, as breeding objectives to improve cow-calf efficiency. The heritabilities for KgC/LSU were 0.52, 0.24 and 0.21 for the Afrikaner, Angus and Charolais, respectively, and for K205 were 0.40, 0.17 and 0.13 respectively. The genetic relationship between KgC/LSU and K205 for Angus and Charolais varied substantially. In Charolais cows a strong negative correlation (-0.75) was found, while a strong positive correlation (+0.84) was estimated in Angus cows. These results indicate that a 'cow efficiency index' in which several traits (production, fertility and efficiency) are included may be a more effective alternative breeding strategy. Breeding strategies and production systems to improve the production efficiency of beef cattle could play a significant role in reducing the carbon footprint and would enhance climate-smart beef production.
... Grasslands are also sources of greenhouse gases as ruminant livestock produce methane (CH 4 ), although so do many wild ungulates (Du Toit et al. 2014). The water table in grasslands also affects GHG fluxes; wetter grasslands often produce methane, while drier grasslands do not (Acreman et al. 2011). ...
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Extensively managed grasslands are recognized globally for their high biodiversity and their social and cultural values. However, their capacity to deliver multiple ecosystem services (ES) as parts of agricultural systems is surprisingly understudied compared to other production systems. We undertook a comprehensive overview of ES provided by natural and semi‐natural grasslands, using southern Africa (SA) and northwest Europe as case studies, respectively. We show that these grasslands can supply additional non‐agricultural services, such as water supply and flow regulation, carbon storage, erosion control, climate mitigation, pollination, and cultural ES. While demand for ecosystems services seems to balance supply in natural grasslands of SA, the smaller areas of semi‐natural grasslands in Europe appear to not meet the demand for many services. We identified three bundles of related ES from grasslands: water ES including fodder production, cultural ES connected to livestock production, and population‐based regulating services (e.g., pollination and biological control), which also linked to biodiversity. Greenhouse gas emission mitigation seemed unrelated to the three bundles. The similarities among the bundles in SA and northwestern Europe suggest that there are generalities in ES relations among natural and semi‐natural grassland areas. We assessed trade‐offs and synergies among services in relation to management practices and found that although some trade‐offs are inevitable, appropriate management may create synergies and avoid trade‐offs among many services. We argue that ecosystem service and food security research and policy should give higher priority to how grasslands can be managed for fodder and meat production alongside other ES. By integrating grasslands into agricultural production systems and land‐use decisions locally and regionally, their potential to contribute to functional landscapes and to food security and sustainable livelihoods can be greatly enhanced.
... The livestock sector is a significant source of greenhouse gas emissions in South Africa, contributing 60% of total agricultural CO 2equivalent emissions (Meissner et al. 2013). Beef cattle, sheep and privately owned game enterprises rely mainly on extensive forage-based production systems and account for 85% of total livestock methane (CH 4 ) emissions in South Africa (Du Toit et al. 2013a, 2013b, 2013c. ...
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The aim of the study was to evaluate the effect of nitrogen (N) fertilisation on certain quality parameters and in vitro total gas and methane production of improved grass species commonly used as fodder species in South Africa. Treatments included seven grass species representing two photosynthetic pathways (C3 and C4) with three levels of N fertilisation (0, 50 and 100 kg N ha⁻¹). Plants were grown in a greenhouse and N was applied in a single application after a simulated defoliation. Sample material was harvested by hand after an 8-week regrowth period. Grass species and rate of N fertiliser both had effects (P < 0.05) on the nutritive value and in vitro organic matter digestibility of the selected species. Crude protein concentration increased (P < 0.05) and neutral detergent fibre concentration tended to decrease as the level of N fertilisation increased for both C3 and C4 species. Generally, no effect was found of N fertilisation on in vitro total gas or methane production however, increasing the level of N fertiliser increased (P < 0.05) the methanogenic potential (in vitro methane/in vitro total gas production) of D. glomerata, F. arundinacea and C. ciliaris after a 24-h incubation period but no significant effects were reported after a 48-h incubation period.
... Direct methane (CH 4 ) emissions by livestock, including privately owned game, was estimated at 1330 Gg CH 4 /year and accounts for 95% of total livestock and 60% of total agricultural CO 2 equivalent emissions in South Africa (Meissner et al. 2013). Beef cattle, small stock and privately owned game rely mainly on extensive forage-based production systems accounting for 85% of total livestock CH 4 emissions in South Africa (Du Toit et al. 2013a, 2013b, 2013c. ...
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The development of greenhouse gas mitigation strategies has become an important issue globally. Enteric methane (CH4) emissions from livestock do not only contribute substantially to the environmental footprint of livestock production but it also represents a loss of energy that could be channelled towards animal growth and production. In this study 14 sub-tropical grass species typical of transitional rangeland regions of South Africa were characterised in terms of ecological status, chemical composition, in vitro total gas and CH4 production. The aim of the study was 2-fold: to identify grass species that could be selected for low enteric CH4 production; evaluate the influence of rangeland ecological status on the methanogenic potential of a rangeland. Grass samples were collected by hand, air-dried, milled and analysed for nutrient composition, in vitro organic matter digestibility (IVOMD) and in vitro gas and CH4 production. Cenchrus ciliaris and Urelytrum agropyriodes produced the highest 48-h in vitro CH4 of 17.49 and 14.05 mL/g DM digested respectively. The lowest 48-h in vitro CH4 was produced by Andropogan gayanus and Bothriochloa bladhii with 5.98 and 6.08 mL/g DM digested respectively. The evaluated grass species were overall of poor quality with low CP concentrations ranging from 2.4% for Trachypogon spicatus to 6.7% for Digitaria eriantha andIVOMDranging from 22.5% for Andropogon gayanus to 42.2% for Urelytrum agropyriodes. Decreaser grass species presented with higher in vitro CH4 production compared with Increaser I and Increaser II grass species in the present study. The results of the study emphasise the importance of including the nutritional potential of grass species for improved livestock production when evaluating grass species for possible greenhouse gas mitigation strategies.
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Human activities have increased the atmospheric concentration of methane by about 140% since pre- industrial times. The accumulation of methane and other ëgreenhouseí gases is anticipated to cause significant climate changes in the future. Ruminant livestock are the largest producers of methane in Australia and this source constitutes about 12% of the national net emissions. Australia is a signatory to the Kyoto Protocol, which, if it comes into force, requires limiting annual emissions during the period 2008ñ2012 to 8% over the 1990 value. Australian livestock emissions are projected to increase by 7% by 2010 with total Australian emissions expected to increase by 28ñ43%. Emissions per unit GDP are higher for the livestock sector than for most other sectors and this may neg- atively affect the sector if free market emission trading is implemented and no new technologies to reduce emis- sions cost-effectively are introduced. Using information from the National Greenhouse Gas Inventory, we demonstrate that reductions in emissions per unit product are already occurring in at least one Australian livestock industry and discuss ways to ensure that similar future changes will be recorded. Cautionary notes are made regard- ing options of grain feeding and more intensive production, which appear to be attractive but may lead to increas- ing emissions when viewed on a broader basis. The potential for increased animal production with new technologies developed to reduce methane emissions suggests that there may be significant opportunities for the Australian live- stock industries arising from the issue of greenhouse gas reductions. Opportunities to establish carbon sinks are also discussed. We suggest that addressing reduction of emissions per hectare rather than per head or per kilo of product results in a strong alignment with the development of more sustainable livestock industries.