Archivos Latinoamericanos de Producción Animal. 2023. 31 (1)
Carbon cycle of native pasturebased beef cattle production systems
in the Pantanal area of the Paraguayan Chaco
Recibido: November 11, 2022. Accepted: March 10, 2023
1 Correspondent author: docamposolmedo@gmail.com
103
Diego Avilio Ocampos Olmedo1
Abstract. The objective of the present study was to evaluate the carbon cycle in Paraguayan Pantanal area native
grassland ecosystems by measuring pasture growth and simulating animal grazing. For this purpose, soil analyzes,
annual productivity and forage quality were carried out in 4 agro ecological sites of a 20,000 ha located in Alto
Paraguay Department (21º 1' 29.85”S and 58º 17' 38.55” W), from Dec 21, 2018 to Feb 12, 2020. In the selected
location was installed an 8 m x 8 m exclusion cage with three treatments (4 m2 subplots), corresponding to cutting
intervals (35, 70 and 105 CI days, respectively). The seasonal dry matter (DM) production and the pasture growth
rate, adjusted stocking rate at three production levels (50, 70 and 75 % breeding rate, respectively), carbon
contained in organic matter (OM), root system and aerial biomass accumulated in ground were evaluated. The data
were compared using Tukey test with a 5 % probability. The emission of greenhouse gases (GHG) per head and per
ha was simulated and adjusted to breeding livestock to a total area of 6,600 ha and 5,000 grassland ha. The 35 days
CI presented 32 % higher productivity than 105 days CI in carbon sequestration. The best capture/emission balance
per ha was observed in 35 days CI with the 50 % breeding rate, producing 1,481 kg of CO2e ha1, intensifying and
increasing the breeding rate to 75 %, the balance decreases to 1,294 kg of CO2e ha1. In all cases, livestock in
grassland has presented a positive balance when accounting for GHG per unit area.
Key words: grazing management, free grazing; grass fed; greenhouse gases; production system
Ciclo del carbono de los sistemas de producción de bovino de carne
sobre pastizales nativos en zonas del Pantanal del Chaco Paraguayo
Resumen. El presente trabajo se realizó con el objetivo de evaluar el ciclo del carbono en los ecosistemas de
pastizales nativos del Pantanal Paraguayo mediante la medición del crecimiento de los pastos y la simulación del
pastoreo de animales. Para ello, se realizaron análisis de suelo, productividad anual y calidad forrajera en 4 sitios
agroecológicos de una propiedad de 20.000 ha ubicados en el departamento de Alto Paraguay, entre diciembre de
2018 a febrero de 2020. En el sitio seleccionado se instaló una jaula de exclusión de 8 m x 8 m con tres tratamientos
(subparcelas de 4 m2) correspondientes a intervalos de corte (35, 70 y 105 días de IC, respectivamente). Se evaluó la
producción estacional de materia seca (MS) y la tasa de crecimiento del pasto, la carga animal ajustada en tres
niveles de producción (50, 70 y 75 % de tasa de producción, respectivamente), el carbono contenido en materia
orgánica (MO), el sistema radicular y la biomasa aérea acumulados de ellos en el suelo. Los datos se compararon
mediante la prueba de Tukey con un 5% de probabilidad. Se simuló la emisión de gases de efecto invernadero (GEI)
por cabeza y por hectárea, ajustada a una ganadería de cría sobre una superficie total de 6.600 ha y 5.000 ha de
pastizal. El IC de 35 días presentó una productividad 32 % mayor que el IC de 105 días en la captura de carbono. El
mejor balance captura/emisión por hectárea se observa en 35 días IC con el 50 % de crianza produciendo 1.481 kg
de CO2e ha1, intensificando y aumentando la crianza al 75 %, el balance disminuye a 1.294 kg de CO2e ha1. En
todos los casos, la ganadería en pastizal ha presentado un saldo positivo al contabilizar los GEI emitidos por
unidad de superficie.
Palabras claves: manejo del pastoreo, pastoreo libre, alimentado con pasto, gases de efecto invernadero, sistemas
de producción.
https://doi.org/10.53588/alpa.310107
Facultad de Ciencias Agrarias, Universidad Nacional de Asunción, San Lorenzo, Paraguay.
Pedro Luis Paniagua
Luis Alonzo Griffith Guido Arnaldo Portillo
104
The greatest challenges faced by the livestock
industry is to meet the growing demand of
environmentally safe food by consumers, and
minimize the environmental and social impacts
generated by this activity (AsemHiablie et al., 2018).
The livestock industry is considered one of the main
agricultural activities responsible for anthropogenic
emissions of greenhouse gases (GHG), with an
apparent annual emission of 7 gigatons (ToroMujica,
2021), representing between 14.5 and 18 % of
agricultural activity globally (Opio et al., 2013; Gerber
et al., 2013). These GHG emissions are in the form of
carbon dioxide equivalent (CO2e) from: i) methane
(CH4) from enteric fermentation (28 %) and manure
management (13 %); ii) crop and soil management in
the form of nitrous oxide (N2O) from the use of
nitrogenous fertilizer (58 %) and; iii) carbon dioxide
(1%) (AguirreVillegas and Larson, 2017; Dangal et al.,
2019; Oliveira et al., 2020).
Several studies indicate that in the confinement
bovine production system there is less GHG emission
per kg of carcass produced, while in pastoral systems
the opposite occurs. It occurs because the animals
consume a diet with more fibrous content, and
therefore, more CH4 is released and the finishing
periods are longer, resulting in lower carcass weights
(Capper, 2012; Desjardins et al. 2012; Stackhouse
Lawson et al. 2012; Lupo et al. 2013; Swain et al. 2018).
Although these emissions are highly dependent on the
type of grazing management system (Brilli et al. 2017).
However, extensive pasturebased animal production
systems have a high GHG mitigation potential through
soil organic carbon (SOC) sequestration, since pastures
regrow rapidly after grazing, allowing a continuous
CO2 capture and sequestration in plant components
ingested by animals and incorporated as organic
matter into the soil due to its deposition, becoming a
virtuous cycle of GHG sink (Meier et al. 2020).
The GHG cycle depends partially of the effects of
environmental factors, including soil management
activities and the interaction with the physiological
processes of plants and microorganisms present in the
soil (Dignac et al. 2017).
Most of the studies on carbon sequestration and
fixation have been carried out in temperatehumid
environments, and there are few investigations in areas
considered to be of a marginal type, such as hot
climates with abrupt variations from rainy to dry
throughout the year (Hontoria et al., 2004).The
objective of this study was to evaluate the GHG
balance and the carbon cycle in grassland ecosystems,
to estimate its capacity as a carbon sink and associate it
to the animal production system developed in the
typical grassland of the Pantanal region of the
Paraguayan Chaco.
Introduction
Ciclo do carbono de sistemas de produção de bovinos de corte baseados em
pastagens nativas na área do Pantanal do Chaco Paraguaio
Resumo. O presente trabalho foi realizado com o objetivo de avaliar o ciclo do carbono nos ecossistemas de
pastagens nativas do Pantanal Paraguaio por meio da medição do crescimento das plantas e da simulação do
pastejo de animais. Para isso, a análise de solo, a produtividade anual e a qualidade de forragem foram realizadas
em 4 locais agroecológicos de uma propriedade de 20.000 ha localizados no departamento de Alto Paraguai, entre
dezembro de 2018 a fevereiro de 2020. No local selecionado, uma jaula de exclusão de 8 m x 8 m foi instalada com
três tratamentos (subparcelas de 4 m2) correspondentes a intervalos de corte (35, 70 e 105 dias de IC,
respectivamente). As variáveis resposta foram produção sazonal de matéria seca (MS), taxa de crescimento do
pasto, carga animal ajustada em três níveis de produção (50, 70 e 75 % da tarefa de produção, respectivamente),
carbono contido na matéria orgânica (MO), sistema radicular e biomassa aérea acumulada no solo. Os dados foram
comparados por meio do teste de Tukey com um 5 % de probabilidade. A emissão de gases de efeito estufa (GEE)
por cabeça por hectare foi simulada e ajustada a um rebanho de cria sobre uma superfície total de 6.600 ha e 5.000
ha de pasto. O IC de 35 dias apresentou uma produtividade 32 % maior que o IC de 105 dias na captura de carbono.
O melhor balanço captura/emissão por hectare observouse aos 35 dias IC com 50 % de taxa de criação, produzindo
1.481 kg de CO2e ha1, intensificando e aumentando a cria para 75 %, o saldo diminuiu para 1.294 kg de CO2e ha1.
Em todos os casos, o rebanho em sistema de pastoreio apresentou um saldo positivo para contabilizar os GEE
emitidos por unidade de superfície.
Palavraschave: manejo de pastejo, pastejo livre, alimentado com pasto, gases de efeito estufa, sistemas de
produção.
Ocampos et al
ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 103 114
Material and Methods
105
Carbon cycle of beef cattle production
Location, edaphoclimatic characteristics and
vegetation of the experimental area
The research was carried out in the Western Region
(Paraguayan Chaco), in Alto Paraguay Department, at
“La Marianna” farm property of Riacho Periquito
Agricultural company (21º 1’ 29,85” S latitude and 58º
17’ 38,55” W longitude). The sample collecting period
was from December 21, 2018 to February 12, 2020, and
data were adjusted to complete 365 evaluation days.
The climate of the region presents an average annual
air temperature of 21 ºC with a maximum of 39 ºC in
summer and a minimum of 2 ºC in winter.
Precipitation usually varies between 1,000 and 1,200
mm, with an annual average of relative humidity
around 79 %. Figure 1 shows average data on
precipitation (1,606 mm) and average temperature
(30.31 ºC) from the beginning (December 2018) until
the end (March 2020) of the evaluation period.
Vegetation at the study site corresponds to palm
savannah (badly drained forest areas), whose botanical
composition is a mixture of species of low forage and
nutritional value, erect or creeping and bushy species
(Rhynchospora corymbosa, Eleocharis elegans, E. nodulosa,
E. contracta, Canna glauca, Ludwidgia hexapetala,
Polygonum punctatum, etc). According to the geological
formation, the soil of the study area is classified as
subrecent siltsandy Holocene (Solonetz) associated
with flat floodable lands (low drainage), made up of
unconsolidated material of saline origin. The
topography was generally found to be fairly level at
the time of the assessment.
Experimental design
The experimental design was completely randomi
zed, with a factorial arrangement of 4 x 3 x 4 with three
repetitions, where factor A refers to the location of the
exclusion cage (Table 3, four sites or visual
differentiation points of native pastureland), factor B
refers to the cut intervals (CI at 35, 70, and 105 days
CI), and factor C is associated with seasons of the year
(spring, summer, fall, and winter).
Evaluated Variables
Green matter: four agroecological sites were
randomly selected where the exclusion cages were
placed, which had dimensions of 8 m x 8 m.
Subsequently, we proceeded to collect forage from
each site using a 1 m2 square, making the cut at 10 cm
from the ground level, to determine the yield in green
matter (GM). This operation was carried out for each
season of the year, as well as for each cutting frequency
established in the treatments. The weight was
determined using an electronic balance of 10 g
precision.
Dry matter content (DM): samples of approximately
300 g of the material harvested in each season were
collected, which were dried in a forced air oven at a
temperature of 65 ºC for 72 h or until constant final
weight. All obtained data in both GM and DM were
extrapolated to 1 ha.
Figure 1. Temperature and precipitation variation throughout evaluation period.
ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 103 114
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Area Description Coordinates
Latitude Longitude
1 NP1 20° 55’ 39,76932’’ S 58º 14’ 35,96712’’ W
2 NP2 20° 53’ 51,56844’’ S 58° 16’ 26,14764’’ W
3 NP3 20° 54’ 02,25684’’ S 58° 16’ 24,13524’’ W
4 NP4 20° 54’ 24,82776’’ S 58° 14’ 44,87424’’ W
NP: Native pastureland
Stocking rate (SR): stocking rate was calculated with
the following equation:
SR= (FP × UF)/(EI × AU × GT) (1)
Where:
FP: Forage Production (in KgDM ha1)
UF: Utilization Factor (50 %)
EI: Estimated Intake (2 to 2.3 % of live weight)
AU : Animal Unit (400 kg)
GT: grazing time (T)
Carbon content in biomass area of forage: to es
timate carbon stock in the pastureland, various cutting
frequencies for each agroecological native pastureland
in the study site were made and the below equation
was used (Deng et al., 2014):
VCS= BV × BC (2)
Where:
VCS: vegetation carbon stock (kg ha1),
VB: vegetation biomass (kg ha1),
BC: plant biomass carbon stock coefficient (0.454;
Yerena et al. (2011)).
Carbon content in soil and roots systems: 8
agricultural pits with an area of 1 m2 and a depth of 1
m were dug in two periods of the year (dry season and
dryrainy transition) near each exclusion cage. The
depths for the samplings were 05; 510; 1115; 1620;
2140 and 4180 cm, completing 5 samples per station
and it was carried out using an aluminum ring for bulk
density (BD) (60 samples per well in total).
Determination of organic carbon in soil was performed
through the Walkley and Black (1934) methodology,
and the content of accumulated carbon in the soil was
estimated through the adaptation established by
Burrman et al. (2004).
C=Conc × BD × T × 103 × 104 (3)
Where :
C= soil C2 accumulated content (Mg ha1)
Conc= accumulation (in kg) of soil samplings
BD= Bulk density (kg m3)
T= sampling depth (m)
104= factor to express in kg ha1
This relation was used for the calculation of radicu
lar biomass carbon and to aerial biomass as well
Table 1. Coordinates of the permanent parcels where exclusion cages were installed. These spots were used for reference at the
time of soil sampling for carbon content.
Table 2. Physical and Reproductive Efficiency Indicators for three productive levels with 50, 70 and 75 % weaning and full
delimitated cycle in a total area of approximately of 6,600 has of total Property (5,000 has of native pastureland, 1,600 of reserve
areas).
General Results
Marking
Efficiency Indicators 50% 70% 75%
Total area Ha 5.000 5.000 5.000
Rangeland stocking rate Head ha1 0,92 1,11 1,16
Rangeland stocking rate AU ha1 0,29 0,38 0,41
Total area animal head stock Head ha1 0,53 0,64 0,63
Total area animal unit stock AU ha1 0,17 0,22 0.24
Male slaughter weight kg 450 450 450
Average weight steer gains 210 260 300
Average weight heifer gains gr/d 310 382 420
Weight at 1st Calving kg 324 324 324
Slaughter age Months 50 50 50
Births Head year1 1.093 1.430 1.514
Productivity ecarcass kg head year1 34,1 37,7 38,0
Ocampos et al
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(Etchevers et al., 2005). The potential for carbon
sequesters was estimated through the relationship of
carbon stored per hectare of the system between
desertion time, for the carbon dioxide equivalent (CO2e)
content conversion to hectare. The carbon content was
determined according to IPCC (2006). Roots carbon
content for each cutting interval was carried out three
times from an area of 1 m2 isolated in the surroundings
of each exclusion cage (30 m radius to the border point
of location of the cages), using a sampler made of
corrugated aluminum prepared for undisturbed
samples up to 1.2 m. Collected roots were washed and
separated from the ground through a sieves mesh of 1
mm. They were then dried in an oven at 65 ºC to
constant weight to determine DM. Thick and fine roots
distinguishable to the naked eye were separated
manually, finer roots up to 1mm were separated by
sieves (Etchevers et al., 2005), and the roots of >1 mm
were excluded because it was undifferentiable from
leaves in the OM of the soil.
Animal carbon emission simulation (ACES) and
capture based on data collected at the field: a
simulation was done over the base of a unit with 5,000
hectares of usable pastureland, further livestock
related assumptions are described below. The
evaluation included a combination of various
nutritional models such as NRC software (2000) and
Simple mix (nutritional optimization algorithm). To
establish the degree of adequacy to reality of the
proposed simulations from the nutritional point of
view, an Excel sheet was used with macro algorithms
that allow evaluating the forage balance and adjusting
the load to account for the emissions of a complete
livestock cycle (breeding, rearing and finishing) for one
year. All the formulas related to emission were
adjusted to standards established by IPCC for the
calculation of livestock emissions. Model outputs were
employed as a whole and combined for modeling of
cattle raising system to recreate a realistic, probable,
and relevant scenario. The complete cycle of livestock
on grassland allows a holistic analysis of the
production process, creating multiple combinations
and scenarios within the studied establishment, and
could be translated to similar ranch. This is how we can
find at least eight events in a chain that can be initially
analyzed by about six probably combinations each.
This provides a scenario of productive combinations
that allow for 279,926 combinations. The plasticity of
cattle raising complicates the evaluation of the scenario
in terms of selective modeling; because we cannot
evaluate all scenarios, a capture and emissions scenario
was adjusted as a function of the probably animal
charge without implementing external supplies or
inputs with growing productivity levels but likely to be
obtained by incorporating process technologies (at a
low cost).
To summarize and to ensure a clear line of
understanding, pastureland livestock should not be
environmentally evaluated in terms of productive
efficacy by the level of GHG kg1 emissions of
produced beef, but in terms of emissions and capture
of GHG ha1 of utilized pastureland.
1) Characteristics of a livestock farm:
a. 25 % forest
b. 17 % windbreaker curtain and fossil rivers
c. 0.5 % routes
d. 0.06 % urban areas
e. 57.44 % pastureland, equal to 5,000 hectares
2) Cattle raising model proposed follows the below
productive and reproductive indicators:
a. % cows over total heads year1 65 % cows over
total headcount
b.% extraction of calves 15 % per year
c.% Replacement 15 % per year
d. Pregnancy rate 65 %, 75 %, and 90%
Medium low, Medium High, and High respectively.
The last level is obtained only through the application
of technology of processes
e. % Decrease in pointed palpation 5 % index in
Pantanal
f. % Decrease in pointed weaning 10 %
g.% Total decrease 15 %
h. Yearling male calf weight 150kg
i. Yearling female calf weight 135kg
j. % General mortality 5 % of the herd
k. % Weaning 50 %, 60 %, and 75 %
l. % Extraction 15 % annual, this is the
total of animals sale in a year of cattle raising
m. Age of the first service 3 years
3) In the rebreeding and fattening scenarios :
a. Animals begin rebreeding with an average
weight of 150 kg for male and 135 kg for females of
eight monthsold.
b. Male stay at the establishment for approximately
28 months until reaching a weight of sale of between
430 and 450 kg.
c. Considering the weight at the times of buy and
sale, animals gain about 280 kg during their 30 months
stay, which represents a yearly benefit of about 113 kg
of beef (0.311 kg.day1). For the case of females the
crossing is estimated with an average weight of 320 kg
which results in a yearly benefit of 75 kg of beef (0.205
kg.day1)
d. This is based on a system of cattle rising for beef
production over native pastureland with adjustment of
Carbon cycle of beef cattle production
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Table 3. Production of average dry matter (DM) accumulated
by season and year (kgDM ha1) of native pastureland for
three evaluated cutting intervals (35, 70, and 105 days).
d. This is based on a system of cattle rising for beef
production over native pastureland with adjustment of
charge. The premise of adjustment of charge is key to
guarantee the sustainability of the productive system,
including the premises of atmospheric carbon capture.
Data interpretation and processing
All data obtained was analyzed with 95 % reliability
for each parameter evaluated. The results that showed
statistical differences were compared using the Tukey
test with a 5 % probability of error.
Results and Discussion
Table 3 shows the seasonal accumulation of forage
production for each cutting interval (CI), this DM
accumulation was closely related to carbon capture in
the cycle. That’s the importance to associate high DM
production with high carbon capture. The shortest
cutting interval (35 days) had a forage production 32 %
higher than the highest CI (105 days) and 25 % higher
than the 70day CI (Table 3). The CI of 70 and 105 days
did not present statistical differences between them (P
> 0.05). Although these cutting intervals can be
considered high, Dehesa is an extensive production
system. Therefore, large paddocks were used and
rotations were low or null.
Cutting Production accumulated (kgDM ha1)
Interval Fall Winter Spring Summer Total
35 days 1500Aa 666Ab 1504Aa 1172Aa 4842A
70 days 1399Aa 643Ab 684Bb 1141Aa 3867BC
105 days 1555Aa 696Ac 506Bc 916Bb 3673C
SD 301 123.3 133.7 205.3
(A, B, C) The different letters capital in the columns, differ from each other by
Tukey's test (P < 0.05)
(a, b) The different letters lowercase in the rows, differ from each other by
Tukey's test (P < 0.05)
SD: standard deviation
Frequent mowing and high grazing intensity can
decrease the persistence of grasslands or at least of the
most susceptible species. Likewise, lowfrequency
grazing such as those with longer intervals can cause
a greater loss of forage due to senescence because of
greater shade added to the decrease in tiller formation
(Gomide et al. 2007). CutrinJunior et al. (2011)
established that the optimal cutting interval is when it
reaches an active photosynthetic radiation index
(APRI) of 97 % and a residual leaf area index (RLAI)
of 1.0, which is obtained with a rest period of 38 days
or less. This highlights that better and higher growth
rates in the grassland of the erectbush type would be
below the 38day cutting interval. In the same sense,
Barbosa (2020) argues that grasslands with lower
cutting severity (low to medium UF) are grazed more
frequently, presenting shorter grazing intervals, with
a higher level of postgrazing residues, which allowed
a strong regrowth in the growth period in the
evaluation of the two years of the experiment. This is
caused by the higher level of light interception,
considered during pre and post grazing, resulting in a
rapid rate of forage recuperation. In addition to this, it
is possible to highlight the higher proportion of inert
material associated with the management of
pastureland with higher severity of regrowth (high
UF), likely due to the longer time to recover the point
of grazing and the higher proportion of dead leaves
found in this study.
According to the data provided by the
aforementioned work, we can argue that grazing
management types relatively shorter (closer to 30
days rest) and a medium to low UF could favor the
forage as well as the faster recuperation post grazing;
leaving a higher post grazing residual, on one hand
and on the other, to allow a higher selectivity for the
animal and guarantee higher bite rates by increasing
availability. The strategy of reducing grazing intervals
could be applied to Pantanal grassland regions
gradually, so smaller paddocks are suggested to
achieve this, in addition to a correct distribution of
water and feeding spots to guarantee proper
management. Under current conditions, the most
reasonable thing to do is still to prioritize the offering
of grazing through low charge and low to medium
UF, although losses will exist due to lixiviation or
nutrients erosion, will be low and the carbon cycle, in
the sense of capture and emission, will be favored.
Table 4 shows the potential load of the grassland
assuming the restriction imposed by the
implementation of a use factor (UF) of 50 %, which
also causes a remnant of grazing in the field of 50 % of
the total forage production. As expected, the winter
station had lower carrying capacity (0.37 AU ha1)
than the other stations. In this station, it has not been
observed effects of ICs on the charging rate. This is
due to the agroclimatic conditions prevailing in this
period, which negatively affects the biomass
production of the grasslands, and consequently the
carrying capacity (Meier et al., 2017; Florentino et al.,
2022).
Ocampos et al
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Table 4. Seasonal charge capacity per hectare in animal
units (AU), in native pastureland, assuming a usage factor
On the other hand, it was unusually observed that
the autumn season was the one with the highest
carrying capacity (Figure 1). Normally, would be
expected for this behavior to occur during spring and
summer, when traditionally higher accumulated
productions were found. In March 2019, high rainfall
was recorded that was maintained even until April, in
addition to a relatively warm climate, which made it
possible to sustain a higher stocking rate in this period
of the year by allowing higher forage production.
In relation to the stocking capacities recorded in
spring, the CI of 35 days presented a higher value (0.83
AU ha1); decreasing said capacity with the increase of
the CI. This is perhaps due to the higher proportion of
senescent leafs that grasses normally present as they
mature, which reduces the amount of consumable
forage and, consequently, the stocking capacity (Van
Soest, 1994; Ferreira et al. 2018).
In the Table 5, shows the average of root system
carbon content (RSCC) and aerial biomass carbon
content (ABCC) values in kg ha1 for each period of the
year and shows the global summaries of total
productivity per hectare for each cutting interval
analyzed. There were significantly higher levels of total
production for the CI of 35 days, with 1,557 kg ha1
year1 (RSCC) and 2,083 kg ha1 year1 (ABCC).
Regarding the seasonal content, during the autumn a
higher carbon content was recorded for both the root
system and the aerial part. This demonstrates that
frequent cuts are an effective way to improve carbon
sequestration by maintaining good relation between
capture and emission total turnover of emissions and
capture, with lower emissions per kilo of animal
produced by higherquality forage. By calculating the
capture, by forage biomass on the ground and
associated root development, it is possible to establish
the bases of the dynamics of the organic carbon cycle
inextricably associated with the grazing animal. In
addition, a frequent cut and a certain intensity allow
sunlight to reach the base of the plants, which allows
intense tillering, this, in turn, will provide the plant
with greater growth capacity (Edvan et al., 2011), and
therefore higher captures of atmospheric carbon for the
photosynthesis process (Taiz and Zeiger, 2006).
According to the data presented in Table 6, it seemed
that the carbon content varies according to the depth of
the soil, although statistically no effects were detected
(P ≥ 0.05). This may be due to the large variations between
Interval Charge capacity (AU ha1) Stock Rate
Autumn Winter Spring Summer AU ha1 Prom.
35 days 0.82 0.37 0.83 0.64 0.67
70 days 0.77 0.35 0.38 0.63 0.53
105 days 0.85 0.38 0.28 0.50 0.50
Mean 0.81 0.37 0.50 0.60
Source: own elaboration based on productivity data.
Table 5. Root system (RSCC) and aerial biomass carbon
content (ABCC) for each cut interval studied per season and
accumulated yearly.
Cutting Biomass carbon content (kg RCC ha1)
Interval
Autumn Winter Spring Summer Total
Root system
35 days 461Aa 239Ac 498Aa 360Ab 1,557A
70 days 429Aa 230Ac 226Bc 350Ab 1,236B
105 days 464Aa 248Abc 177Bc 283Bb 1,171B
Mean 451,33 239 300,33 331
Aerial biomass
35 days 646Aa 287Ac 647Aa 504Ab 2.083A
70 days 601Aba 276Ac 294Bc 490Ab 1.661B
105 days 572Ba 300Abc 218Bb 394Bb 1.484B
Mean 606,33 287,67 386,33 462,67
(A, B) The different letters capital in the columns, differ from each other by
Tukey's test (P < 0.05)
(a, b, c) The different letters lowercase in the rows, differ from each other by
Tukey's test (P < 0.05)
On a global scale, soil is considered the biggest
carbon reservoir which holds approximately over four
times the carbon present in the biomass of plants and
atmosphere community, acting as a source or
atmospheric deposition of CO2 (Assefa et al. 2017).
Although the spatial and depth scale of the sampling
contributes significantly to the fluctuations in the
quantification of soil organic carbon (Maillard et al.
2017). Pastureland plays a fundamental role in the
movement, the cycle, and the content of carbon in the
soil. According to Braga (2006), around 20 % of the
carbon circulation in the planet and 12 % of the carbon
stored in the soil belong to or are associated with
pastureland. This author explains this is due to the
expressive content of MOS and its slow decomposition
in this ecosystem. Several factors contribute to the
increase or decrease in existences and dynamics of
carbon in the soil, among them, the change in use of the
soil.
Table 6. Average Carbon content (ACC) for each ground depth
in total ton. ha1
Depth Soil Total Carbon Proportion (%)
(cm) (ton ha1)
05 17.25 25.72
510 12.38 18.47
1015 7.96 11.88
1520 5.94 8.86
2040 12.42 18.53
4080 11.09 16.54
CV(%) 25.7
(A,B,C,D) The different letters capital in the columns differ
from each other by Tukey's test (P < 0.05).
Carbon cycle of beef cattle production
ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 103 114
110
the values obtained for each depth (CV: 25.70 %).
However, higher concentrations are observed at
depths between 0 and 10 cm (44.19 %); at depths
between 1020 cm (20.74 %), they are drastically
reduced. While, at depths between 20 and 80 cm, an
increase in carbon content was recorded again with a
content of 35.07 %.
The carbon stock recorded in a pastoral system by
Castellano et al. (2022) in the soil layer of 020 cm (51
%) was lower compared to the sum obtained from this
study for that depth, although in the layer of 2040 cm
(30.1 %) they reported higher value, as also for the
depth of 4060 cm (66.2 %). The results obtained in this
experiment are lower than those reported by Marques
and Cely (2013) who performed studies in non
intervened areas and recuperation areas of the Paramo
de la Contadera, Colombia, where they found a mean
of 81.36 Ton SOC.ha1 and for intervened areas, a
mean of 15.18 Ton SOC.ha1, a lower result to the
general mean obtained in this study which was of 67
Ton SOC.ha1 until 80 cm of evaluated depth. These
divergences could be the result of predominant
vegetation and the applied methodology. Similarly,
Taymer et al. (2007) working on silvopastoral and
native pastureland systems, reported levels of 24 Ton
SOC.ha1 respectively and both are below results
found in this research work.
Table 7: GHG emission based on the total number of Heads of the Cattle Breeding Herd, calculated based on the weaning
percentage
General Results
Weaning (%)
Emission Characterization Unit 50 70 75
Total Emission of CO2e kg year1 6,817,439 7,374,228 7,513,426
Productivity kg ecarcass ha1 year1 31.4 41.9 44.6
Total Emission of CH4 kg year1 203,132 218,372 222,183
Total Emission of N2O kg year1 8,231 8,995 9,186
Total Emission of CO2e kg head1 year1 1,481 1,328 1,294
Emission of CH4 kg head1 year1 44.13 39.23 38.26
Emission of N2O kg head1 year1 1.8 1.6 1.5
Emission of CO2e kg hectare1year1 1,363.5 1,474.8 1,502.7
Source: own elaboration basing on data from IPCC and simulation data
Table 7 presents the emissions calculated for CH4
and N2O per head like the CO2e head1 and CO2e ha
1 considering the premise by which pastureland cattle
raising should be according to the calculation of
emissions and captures per hectare. Per capita
emissions are reduced as much as productive
efficiency is increased when starting from a
reproductive efficiency of 50 and rising to 75 %, and
this creates an overall reduction of 13 % in GHG
emissions. Total emissions increase to the extent that
zootechnical indicators improve as a result of
productive intensification. In the first scenario we
have global emissions with a 50 % tagging rate of 1,364
kg ha1 and CO2 and with a 75 % tagging rate the
emission increases to 1,503 kg ha1 of CO2.
If the logic of intensification imposed by a vision
excessively focused on increasing productivity per
hectare is followed, reductions in the rate of emissions
per kg of meat produced can be found, but it must be
remembered that what is being reviewed here is the
sustainability of production. Grazing system, its
characterization as a net producer of emissions.
Considering that the evaluation must be analyzed
from the perspective of emission or capture per
hectare of pasture used in extensive or even intensive
livestock but with a high percentage of pasture use in
the production cycle.
Table 8 shows the levels of carbon capture where
Carbon Aerial Part (CAP) is considered without
counting for the total sum of what was captured by
the Radicular Carbon (RC) and the soil stock, there are
considered but not counted in the calculation of
CO2.ha1.
Levels of capture depend on the levels of
reproductive efficiency of the cattle herd but are
related to the interval cuts used, which are
incrementally associated with the reduction of the
interval cut implemented (higher frequency rotations),
in this situation we can observe that carbon capture
with a 35 days interval cut was of 5,780 kg ha1 year1
and when interval got increased to 105 days capture
capacity is reduced to 4,118 kg ha1 year1. Pastureland
assumes a fundamental role in the movement and
content of carbon in the soil, according to Braga (2006)
Table 8. Capture of CO2 for each interval cutting (35, 50, and
105 days).
General Results
Capture of CO2e Unit Amount
35 days IC Kg ha1 year1 5.780
70 days IC Kg ha1 year1 4.609
105 days IC Kg ha1 year1 4.118
Source: own elaboration based on productivity data gathered
throughout the year of evaluation.
Ocampos et al
ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 103 114
111
of the 20 % of circulating carbon in the planet around
12 % is kept captured in pastureland soil due to its
slow decomposition in this ecosystem.12 % is kept
captured in pastureland soil due to its slow
decomposition in this ecosystem.
The SOC levels trapped by roots during normal
cycle growth were not counted in the cycle since a
better adjustment is required to estimate appropriately
the carbon decomposition rate in Soil Organic Matter
(SOM) when pastureland undergoes grazing;
according to results obtained by Maluf et al., (2015)
showed that decomposition of the pastureland
residual were significantly influenced by the
concentrations of N2, just like the liberation rates of
macronutrients were influenced by the number of
grazing residuals, K+ being the most rapidly expelled
nutrient and exposed to decomposition since it is not a
constituent part of vegetal structures. When these
capture numbers are considered to the animal and are
transformed to the equivalent CO2 to establish the
balance, the date of emission per hectare is obtained,
and thus the capture per hectare is necessary to
calculate the information displayed in Table 9.
Table 9. Total emissions balance per hectare in relation to the interval cut and the reproductive efficiency (50, 70, and 75 % of
marking) for the 5,000 hectares of pastureland evaluated in a 6,600 hectares property.
General Results
Weaning (%)
Balance (captureemission) CO2e Unit 50 70 75
35 days CI Kg ha1 year1 4,416 4,305 4,227
70 days CI Kg ha1 year1 3,245 3,134 3,106
105 days CI Kg ha1 year1 2,754 2,643 2,615
Source: Own elaboration based in field capture data and emissions simulation model (IPCC,
2006)
By improving grazing practices through planned
rotations in the paddocks we could estimate an
improvement in the total capture verifiable through
the capture/emission balance, obtained for each
grazing interval. By reducing the interval from 105 to
35 days CI we can find a productivity increase of 32 %
which results in a higher grazing support capacity and
a higher possibility for GHG capture. When
improving indexes of productivity but without
betterments in grazing practices or using grazing
intervals too long that prioritize the accumulation of
forage but associated to lower quality and a decrease
in the rate of carbon capture, we have a simulated
scenario of marking levels of 75 %, but with low
productivity of pastureland due to low or null
efficiency in management, equivalent to 105 days
interval, we obtained a balance of 2,615 kg CO2e.ha1
capture against the 4,227 kg e CO2e ha1 susceptible of
being capture maintaining the same production levels
but incorporating better grazing management
resulting in shorter interval cuts, which means cuts of
35 days in this research work. This represents an
increase of 62 % in the capture, with grazing intervals
of 35 days CI to the interval cut of 105 days CI.
Considering the above, there are indicators more
than acceptable to estimate a very favorable balance
for cattle raising in pastureland with continuous or
rotative charges associated with grazing of medium to
low intensity in the Pantanal zone. The ultra
conservationist links cattle raising with pastureland
degradation and analyze only superficially the capture
capacity of pastureland and pretend to keep them
without animals, avoiding the effects of degradation
attributed to grazing practices, and thus, improving
the ecosystemic services. This simplistic analysis
would result in a mediumterm (once said pastureland
reaches its climax which is the point of equilibrium
between growth and senescence) to an exponential
growth in the probability of uncontrolled fires that
eventually affect not only the balance of a GHG
favorable to pastureland, but also increment the risk of
mount burning with the known loss of biodiversity,
which in the view of the authors, is much more
important the higher or lower increase of CO2
atmospheric as a result of pastureland management.
Conclusions
Utilizing the exposed relations of carbon capture
through evaluation of forage productivity, the
radicular development that follows the productive
forage performance (in association with the capacity
for charge and the reserve of forage residual unused
by the animal included in this study in order to
guarantee the ecosystemic functionality of the
pastureland); the relations between emission and
capture of GHG of cattle raising for a scenario of 5,000
hectares of native pastureland in the Pantanal zone,
displaying a favorable balance in all simulations
including those with higher productive intensity
Carbon cycle of beef cattle production
ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 103 114
AguirreVillegas, H. A., R. A. Larson. 2017. Evaluating
greenhouse gas emissions from dairy manure
management practices using survey data and
lifecycle tools. Journal of Cleaner Production,
143:169179.
https://doi.org/10.1016/j.jclepro.2016.12.133
112
Implications and recommendation
within evaluated ranges (50 to 75 % weaning) and
with higher interval cuts (105 days between
eachgrazing), which means with the lower forage
growth rates.
Based on the above, we conclude that grazing cattle
raising with low charge in the Pantanal zone could not
be considered a net producer of GHG, but an efficient
capturer of these gasses emitted by other components
in the beef chain, such as processing, transport, etc.
The best capture/emission balance per hectare was
observed in the most frequent grazing intervals with
high or low breeding rate. In all cases, livestock in
grassland has presented a positive balance when
accounting for GHG per unit area.
Grazing cattle raising should be in all cases eva
luated according to the balance obtained by unit of
surface utilized and not by efficiency in the gaining of
weight when evaluated by the amount of beef
produced per GHG emission; like it has been
evaluated until now under the protection of the
paradigm of productive intensification without
considering the ecosystemic implications at each
zone. Nevertheless, further and more detailed studies
are required to improve the established stoichiometric
rates in key balance spots of animal emission during
grazing, all of which are subject to modifications
whether because of higher of lower quality of ingested
forage. For practical purposes, established
assumptions were used for the IPCC but all of them
are considered unfavorable to grazing cattle raising,
which is why they should be adjusted in the future to
new productive conditions through more and better
research works in order to consolidate the
considerations utilized in this study.
Conflicts of interest: The authors declare that there has been no interest(s) that might raise the question of bias in
the reported research or the manuscript's inferences, opinions, or conclusions.
Author contributions: CARR: Investigation, Data curation, Conceptualization, Methodology, Model development,
Writing, Review, and Editing. RRVI: Investigation, Data curation, Conceptualization, Methodology, Model
development, Writing, Review, and Editing. IMR: Methodology, Writing, Review, and Editing.
Funding: The original pasture research conducted was financially supported by the German Federal Ministry for
the Environment, Nature Conservation and Nuclear Safety through the International Climate Initiative (IKI) and
project "Land Use Change in Savannas and Grasslands: Approaches by Policy Engagement, Land Use Planning and Best
Management Practices". implemented by WWF Py
Acknowledgments: We would like to thanks the technical assistance given by WWFPy staff at the researchers
along the pasture evaluation in Pantanal grassland zone.
Edited by Emilio Ungerfeld.
Literature Cited
Assefa, D., Rewald, B., Sandén, H., Rosinger, C., Abiyu,
A., Yitaferu, B., and Godbold, D. L. (2017).
Deforestation and land use strongly effect soil
organic carbon and nitrogen stock in Northwest
Ethiopia. Catena, 153, 8999.
Barbosa R.A.; Nascimento J.D.;Euclides,V.P.; Da
Silva,S.;Zimmer,A.H.;Torres, J.R.A. 2007 Capim
Tanzania submetido a combinacoes entre
intensidade e frequencia de pastejo. Pesq. Agropec.
Bras. Brasilia, v.42 n. 3.p. 329340.
Braga, G. 2006. Contribución de los pastos al secuestro
de carbono. Simposio Sobre Gestión de Pasto, 23:
271296.
Brilli, L., L. Bechini, M. Bindi, M. Carozzi, D. Cavalli, R.
Conant, C. D. Dorich, L. Doro, F. Ehrhardt, R.
Farina, R. Ferrise, N. Fitton, R. Francaviglia, P.
Grace, I. Iocola, K. Klumpp, J. Léonard, R. Martin, R.
S. Massad, S. Recous, G. Seddaiu, J. Sharp, P. Smith,
W. N. Smith, J. F. Soussana and G. Bellocchi, G.
2017. Review and analysis of strengths and
weaknesses of agroecosystem models for
simulating C and N fluxes. Science of The Total
Environment. 598: 445–470.
http://dx.doi.org/10.1016/j.scitotenv.2017.03.208
Buurman, P., M. Ibrahim, and M. Amezquita. 2004.
Mitigation of greenhouse gas emisions by
silvopastoral systens: Optimism and facts. In Proc.
2nd Intl. Congress in Agroforestry Systems, Mérida,
México
Capper, J. L. 2012. Is the grass always greener?
Comparing the environmental impact of
conventional, natural and grassfed beef production
systems. Animals, 2:127–143.
https://doi.org/10.3390/ani2020127
Ocampos et al
ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 103 114
113
Florentino, L. da S., Silva, MJ dos S., dos Santos, RM,
Shockness, L. dos SF, de Vasconcelos, FMT, de
Oliveira, KP, & Júnior, JMB (2022). Evaluación de la
producción de biomasa de forraje de capim Panicum
maximum cv. Mombaça submetido a adubação
mineral e orgânica. Revista Brasileña de Desarrollo ,
8 (1), 1131–1144.
https://doi.org/10.34117/bjdv8n1072
Castellano, GR, LA Santos and AA Menegário. 2022.
Carbon Soil Storage and Technologies to Increase
Soil Carbon Stocks in the South American Savanna.
Sustentability, 14(5571): 1–22. 4.
https://doi.org/10.3390/su14095571
CutrinJunior, J. A. A., M. J. D. Candido, B.S.M.
Valente, Carneiro and H. A. V. Carneiro. 2011.
Structural characteristc of tanzania grass canopy
submitted to three frequencies of defoliation and
two postgrazing residues. Revista Brasileira de
Zootecnia, 40(3):489497.
https://doi.org/10.1590/S151635982011000300005
Dangal, S. R. S., H. Tian, R. Xu, J. Chang, J. G. Canadell,
P. Ciais, S. Pan, J. Yang, B. Zhang. 2019. Global
nitrous oxide emissions from pasturelands and
rangelands: magnitude, spatiotemporal patterns,
and attribution. Global Biogeochemical Cycles
33:200–222. https://doi.org/10.1029/2018GB006091
Deng, L., G. B. and Z. P. Shangguan. 2014. Landuse
conversion and changing soil carbon stocks in
china’s ‘GrainforGreen’ program: A synthesis.
Global Change Biology, 20(11): 3544–3556.
https://doi.org/10.1111/gcb.12508
Desjardins, R., D. Worth, X. Vergé, D. Maxime, J. Dyer,
D. Cerkowniak. 2012. Carbon footprint of beef
cattle. Sustainability. 4(12):3279–3301.
https://doi.org/10.3390/su4123279
Dignac, M. F., D. Derrien, P. Barré, S. Barot, L.
Cécillon, C. Chenu, T. Chevallier, G. T. Freschet, P.
Garnier, B. Guenet, M. Hedde, K. Klumpp, G.
Lashermes, P.A. Maron, N. Nunan, C. Roumet and
I. BasileDoelsch. 2017. Increasing soil carbon
storage: mechanisms, effects of agricultural practices
and proxies. A review. Agronomy for Sustainable
Development. 37, 14.
https://doi.org/10.1007/s1359301704212
Edvan, R. L., E. M. Santos, D. S. Silva da, A. P. Andrade
de, R. G. Costa and W. A. Vasconcelos. 2011.
Characteristics of production of buffel grass as
function of cutting intensity and frequency.
Archivos de Zootecnia, 60 (232), 12811289.
https://dx.doi.org/10.4321/S0004
05922011000400043
Etchevers, J. D., C. M. Monreal, C. Hidalgo, M. Acosta,
J. Padilla and RM López. 2005. Manual para la
determinación de carbono en la parte aérea y
subterránea de sistemas de producción en laderas.
Colegio de Postgraduados. Distrito Federal, México.
29 p. ISBN: 9688394688.
Ferreira, E. A., J. G. Abreu de; J. C. Martinez, T. G. Braz;
D. P. Ferreira. 2018. Cutting ages of elephant grass
for chopped hay production. Pesquisa Agropecuaria
Tropical, Goiânia, 48(3):245253.
https://doi.org/10.1590/198340632018v4851569
Gerber, P. J., H. Steinfeld, B. Henderson, A. Mottet, C.
Opio, J. Dijkman, A. Falcucci, G. Tempio, 2013.
Tackling climate change through livestock: a global
assessment of emissions and mitigation
opportunities. Food and Agriculture Organization
of the United Nations, Rome, 115 págs. ISBN:
9789251079201
Gomide, C.A.M.; Gomide, J.A.; Alexandrino, E. 2007
Características estruturais e produção de forragem
em pastos de capimmombaça submetidos a
períodos de descanso. Pesquisa Agropecuária
Brasileira, v.42, n.10, p.14871494, 2007.
Hontoria, C., J. Rodríguez and A. Saa. 2004. Contenido
de carbono orgánico en el suelo y factores de control
en la España Peninsular. Edafología, 11(2):149156.
ISSN 11356863
Intergovernmental Panel on Climate Change (IPCC).
2006. IPCC Guidelines for Greenhouse Gas
Inventories. Instituto de Estrategias Ambientales
Globales (IGES). ISBN 4887880324.
Lupo C. D., D. E. Clay, J. L. Benning, J. J. Stone. 2013.
Lifecycle assessment of the beef cattle production
system for the northern great plains, USA. Journal
of Environmental Quality, 42:1386–1394.
https://doi.org/10.2134/jeq2013.03.0101
Maillard E., McConkey B.G., Angers D.A. 2017.
Increased uncertainty in soil carbon stock
measurement with spatial scale and sampling
profile depth in world grasslands: A systematic
analysis. Agriculture, Ecosystems & Environment, v
236: 268276.
https://doi.org/10.1016/j.agee.2016.11.024.
Maluf, H. J. G. M., E. M. B. Soares, I. R. Silva, J. C. L.
Neves and L. O. G. Silva. 2015. Crop residue
decomposition and nutrient mineralization in soil
with different textures Revista Brasileira de Soil
Science, 39:16811689.
https://doi.org/10.1590/01000683rbcs20140657
Márquez, L., y Cely, G. 2013 El páramo y su potencial
de captura de carbono; experiencia paramo la
Cortadera – Boyacá. Congreso de Investigación y
Pedagogía. Universidad Pedagógica y Tecnológica
de Colombia. 33 p.
http://repositorio.uptc.edu.co/handle/001/8334
Carbon cycle of beef cattle production
ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 103 114
114
Meier C., Meira D., Queiróz de Souza V., Schmidt D..
2017. Produção de matéria seca em diferentes
combinações com forrageiras de inverno Production of
dry matters in different combinati. Revista Brasileira de
Iniciação Científica, Itapetininga, v. 4, n. 5: 144151.
Yerena, Y., J. Jiménez, O. A. Aguirre and E. J. Treviño.
2011. Concentración de carbono en la biomasa aérea del
matorral espinoso tamaulipeco. Revista Chapingo Serie
Ciencias Forestales y del Ambiente. 17(2):283291.
http://dx.doi.org/10.5154/r.rchscfa.2010.02.004
Meier, E. A., P. J. Thorburn, L. W. Bell, M.T. Harrison
and J. S. Biggs. 2020. Greenhouse Gas Emissions From
Cropping and Grazed Pastures Are Similar: A
Simulation Analysis in Australia. Frontiers in
Sustainable Food Systems, 3(121):118.
https://doi.org/10.3389/fsufs.2019.00121
Oliveira, P. P. A., A. Berndt, A. F. Pedroso, T. C. Alves,
J. R. M. Pezzopane, L. S. Sakamoto, F. L. Henrique and
P. H. M. Rodrigues. 2020. Greenhouse gas balance and
carbon footprint of pasturebased beef cattle production
systems in the tropical region (Atlantic Forest biome).
Animal, 14:427437.
https://doi.org/10.1017/S1751731120001822
Opio, C., P. Gerber, A. Mottet, A. Falcucci, G. Tempio,
M. MacLeod, T. Vellinga, B. Henderson and H.
Steinfeld. 2013. Greenhouse gas emissions from
ruminant supply chains – A global life cycle
assessment. Food and Agriculture Organization of the
United Nations (FAO), Rome. 191 pp. EISBN 978925
1079454.
StackhouseLawson, K. R., C. A. Rotz, J. W. Oltjen, F.
M. Mitloehner. 2012. Carbon footprint and ammonia
emissions of California beef production systems.
Journal of Animal Science, 90:4641–4655.
http://dx.doi.org/10.2527/jas.20114653
Swain, M., L. Blomqvist, J. McNamara, W. J. Ripple.
2018. Reducing the environmental impact of global
diets. Journal of Environmental and Public Health.
2012:17.
http://dx.doi.org/10.1016/j.scitotenv.2017.08.125
Taiz, L. and E. Zeiger. 2006. Plant phisiology. 3. ed.
Castello de la Plana, Universidad de Jaume, D.L. 1338
pp. ISBN 97884^80216012.
Taymer, M., Machado, R., Machado, H. y Duquesne, P.
(2007) Carbono secuestrado en ecosistemas
agropecuarios cubanos y su valoración económica.
Estudio de caso. Pastos y Forrajes. 55 p
ToroMujica, P. 2021. Evaluation of Feed Strategies and
Changes of Stocking Rate to Decrease the Carbon
Footprint in a Traditional CowCalf System: A
Simulation Model. Frontier of Veterinary Science,
8:587168. https://doi.org/10.3389/fvets.2021.587168
Van Soest, P. J. 1994. Nutritional ecology of the
ruminant. 2º ed, Cornell University Press, Ithaca, NY.
436 págs. eISBN: 9781501732355.
Walkley, A., I. A. Black. 1934. An examination of
Degtjareff method for determining soil organic matter,
and proposed modification of the chromic acid tritation
method. Soil Science 37:2938.
http://dx.doi.org/10.1097/0001069419340100000003
Ocampos et al
ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 103 114
