Archivos Latinoamericanos de Producción Animal. 2023. 31 (1)

A dynamic simulation model to assess farmlevel effects of pasture

intensification strategies on beef herd outputs and carbon footprints

in acid soil savannas of Eastern Colombia

Received: November 25, 2021. Accepted: January 18, 2023

1Corresponding author: rvi.2005@gmail.com

5 Formerly International Center for Tropical Agriculture (CIAT), Km 17 CaliPalmira CP 763537, Apartado Aéreo 6713, Cali, Colombia.

21

Carlos A. RamírezRestrepo3,5

Raúl R. VeraInfanzón1,5

Abstract. The neotropical savannas of Eastern Colombia (Llanos) are subjected to changes in land use associated

with the intensification of beef production, and there is limited knowledge on the longterm impacts of these change

processes. Furthermore, the effects of spatial and temporal intensification at the farm level via the introduction of

sown pastures on beef herd outputs, their greenhouse gas (GHG) emissions and the resulting carbon (C) footprints

in contrasting savanna landscapes of the Llanos are unknown. This study aimed to develop and use a dynamic

simulation model to assess beef herd outputs and C footprints at a whole farm level for 30 years in the Serrania and

Plains savanna regions of the Colombian Orinoco Basin. A dynamic model was developed to simulate the gradual

introduction of two types of tropical pastures in a region with dissected and steep slopes and limited tillable areas

where cattle had access to Serrania savanna and sown pastures, versus one located in the Plains that were fully

transformed over time with the exclusion of native rangelands. Marked changes in herd demography, animal

outputs, and GHG emissions were found over time. The C footprint of all systems varied over time depending upon

the length of time that pastures contributed to soil organic C accumulation and the balance between savanna and

sown pasture areas at a whole farm level. In conclusion, the dynamics of the systems subject to intensification were

marked and dependent on the temporal and spatial deployment of sown forage resources. Therefore, generalizing

the trends for the region as a whole reults in uncertainty. Nevertheless, examination of simulated prototypes may

shed light on the expected trends and provides guidance for decisionmaking.

Keywords: environmental impacts, reproduction, land use change, cowcalf systems, Orinoco Basin.

Modelo dinámico para evaluar los efectos de estrategias de la intensificación

gradual con pasturas sobre el desempeño animal y la huella de carbono de hatos

de cría en las sabanas ácidas del Oriente de Colombia

Resumen. Las sabanas neotropicales del oriente de Colombia (Llanos) experimentan cambios en el uso de los

recursos de tierra asociados a la intensificación de la producción de carne bovina, pero hay poco conocimiento de

los impactos de estos cambios. Los efectos espaciales y temporales de la intensificación con pasturas sembradas

sobre la producción de carne, la emisión de gases de efecto invernadero (GEI) y la huella de carbono (HC) en

sistemas de cría asentados en la Altillanura Plana bien drenada y en la Serranía son desconocidos. El objetivo de este

estudio fue desarrollar y usar un modelo dinámico de simulación para estimar la producción de carne y huella de C

a nivel de finca durante 30 años en las regiones de Serrania y Altillanura Plana de la Cuenca del Orinoco de

Colombia. Se desarrolló un modelo dinámico para simular la introducción de dos tipos de pasturas en las áreas

cultivables de la Serranía, versus el reemplazo total de la sabana en la Altillanura Plana. A lo largo del tiempo

ocurrieron cambios marcados en la demografía de los hatos, la producción de carne y las emisiones de GEI y HC. La

huella de C varió dependiendo del tiempo en que las pasturas contribuyeron a la acumulación de C orgánico del

suelo y el balance sabana versus pasturas introducidas. En conclusión, los cambios espaciales y temporales fueron

influidos por la incorporación temporal de nuevos recursos forrajeros. En consecuencia, es difícil generalizar los

www.doi.org/10.53588/alpa.310102

1Consultant, 2 Norte 443, Viña del Mar, Chile.

2International Center for Tropical Agriculture (CIAT), Km 17 CaliPalmira CP 763537, Apartado Aéreo 6713, Cali, Colombia.

3 CR Ecoefficient Agriculture Consultancy (CREAC©), Research and Education, 46 Bilbao Place, Bushland Beach, QLD 4818, Australia.

4Consultant, Carrera 45 N° 49B22, Villavicencio, Meta, Colombia.

Idupulapati M. Rao2

Fhanor HoyosGarcés4,5

22

Beef production has been a controversial issue over

the last two decades, particularly under extensive

tropical grazing conditions due to assumed high

greenhouse gas (GHG) emissions, production

inefficiency, loss of biodiversity, natural resources

degradation, and multidimensional socioeconomic and

cultural implications (Viglizzo et al., 2019).

Nevertheless, extensive tropical systems characterized

by lowquality forages are considered essential to

provide variable income opportunities over time

(Garrity et al., 2012; Martin, 2015) to farmers and

indigenous populations (Blench, 2001; Anthony, 2004).

Extensive pastoral systems, when subjected to the

intensification of production, pose significant

challenges to farmers and enterprises (Bentley et al.,

2008), including (i) mid to high investment; (ii) changes

in herd structure, animal numbers, and evolving sales

strategies; (iii) variable feed resources associated with

demanding management requirements; and (iv) the

environmental criticism. However, these issues can be

addressed using a combination of a dynamic

simulation backed up by a substantial database of

legacy experimental and farmlevel data.

The acid soil savannas of the Eastern Plains (Llanos)

of Colombia, located in the Orinoco River Basin, have

been used for extensive cattle breeding since the

Spanish colonization (Rausch, 2013). In the late XX and

early XXI centuries, the tillable flat savanna areas,

Plains (slopes < 3 %, Plains) were viewed as a potential

land resource to cultivate annual crops and perennial

plantations (RomeroRuiz et al., 2012), as opposed to

the dissected Serrania savannas (slopes up to 40 %, Serr)

that have numerous edaphic and physiographic

constraints. Nevertheless, sown pastures are present in

most of the tillable land. Despite the availability of

higher quality commercial grass cultivars of potentially

high forage and animal production, landscapes are still

dominated by lower quality, highly persistent and

resilient forage species that require low external inputs.

These include Brachiaria (syn. Genus Urochloa)

humidicola (Rendle) Morrone & Zuloaga cultivar Tully

(accession CIAT 679) and B. dictyoneura cultivar

Llanero (Rendle) Schweick (syn. B. humidicola 

accession CIAT 6133, Triviño et al., 2017), with smaller

proportions of other grass species, that replace

remnants of savanna and cropping areas in large parts

of the Plains (RodríguezBorray et al., 2019).

The Serr area (115,000 km2, 2/3 of the total area)

has gone through substantially fewer land use changes

since tillable areas represent at most 2040 % of that

landscape (Cochrane et al., 1985). To a variable extent,

tillable areas within Serr have also been planted with

Introduction

Modelo dinâmico para avaliar os efeitos de estratégias de intensificação gradual

com pastagens sobre o desempenho animal e a pegada de carbono de rebanhos

reprodutores nas savanas ácidas do leste da Colômbia

Resumo. As savanas neotropicais do leste da Colômbia (Llanos) experimentam mudanças no uso dos recursos da

terra associadas à intensificação da produção de carne bovina, mas há pouco conhecimento sobre os impactos

dessas mudanças. Os efeitos espaciais e temporais da intensificação com pastagens semeadas para a produção de

carne bovina, nas emissões de gases de efeito estufa (GEE) e na pegada de carbono (PC) em sistemas de cultivo

implantados na bem drenada Altillanura Plana e na Serrania são desconhecidos. O objetivo deste estudo foi elaborar

un modelo de simulação matemática para avaliar a produção de carne e a pegada de C durante 30 anos na Serrania e

Altillanura Plana da Bacia do Orinoco na Colombia.. Um modelo dinâmico foi desenvolvido para simular a

introdução de dois tipos de pastagens nas áreas aráveis da Serranía, versus a substituição total da savana na

Altillanura Plana. Ao longo do tempo, mudanças marcantes ocorreram na demografia do rebanho, na produção de

carne bovina e nas emissões de GEE e HC. A pegada de C variou conforme o tempo em que as pastagens

contribuíram para o acúmulo de C orgânico no solo e o balanço de savana versus pastagem introduzida. Em

conclusão, as mudanças espaciais e temporais foram influenciadas pela incorporação temporal de novos recursos

forrageiros. Consequentemente, é difícil generalizar os efeitos para toda a região, mas a análise de diferentes

protótipos pode esclarecer tendências temporais e orientar a tomada de decisões.

Palavraschave: impacto ambiental, bovinocultura de corte; simulação, uso da terra, bacia do Orinoco

VeraInfazón et al.

efectos para toda la región, pero el análisis de diferentes prototipos puede esclarecer las tendencias temporales y

puede guiar la toma de decisiones.

Palabras clave: impacto ambiental, ganado de carne, simulación, uso de la tierra, cuenca del Orinoco

ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 21  41

Material and Methods

Location and prototypes

Mean annual ambient temperature during the field

studies was 26.5 °C ranging from 25.2 °C in July to 28.1

°C in March, while the average annual precipitation

was 2.682 mm with ~93 % of the total rainfall recor

ded between April and November (Vera and Ramírez

Restrepo, 2017).

Simulated scenarios for 30 years were developed

for two highly contrasting subecosystems, differing in

physiography and feasible property development

paths. The first set of simulations referred to the most

extreme physiography of the undulating Serr

(Serrania, Cochrane and Sanchez, 1985), a situation

that was chosen as the most limiting case scenario in

terms of beef production and farm development. A

property of 1400 ha located in the Serr, characterized

in detail over four years by Kleinheisterkamp and

Habich (1985) that included 20 % of tillable land was

chosen as an initial representative prototype of a cow

calf ranch that depended exclusively on native

savannas at the outset. The recorded mean data on

herd demography and LWs were used as starting

conditions. The simulated control system depending

exclusively on Serr native savannas (SerrSav) was also

run for 30 years.

Two intensification paths for the system were

contrasted based on the gradual introduction of sown

pasture options in the tillable areas. The first path

simulated the introduction of B. humidicola (SerrBh)

pastures over time, whereas representative data for

Andropogon gayanus (G)  Stylosanthes capitata (L) were

used to mimic a notional second path of the grass

legume mixture (SerrGL) pastures. The temporal

pattern of pasture establishment was the same in both

paths and covered 20 % of the area by the 20th year.

The areas established over time are representative of

realistic commercial size operations. The SerrGL path

represents a more managementdemanding situation,

in contrast to the low management required by the

former, grassonly path of pasture development, and it

exemplifies a conceptual system of possible future

developments.

Simulations included pasture establishment (offset

disking, followed by light disking and initial

fertilization (kg/ha) with 24, 25, 85, and 18 of P, K, Ca,

and Mg, respectively (Rincón et al., 2010) while

maintenance fertilization (1/3 of the establishment

rates) was applied every 3 years. Mechanical

renovation occurred every 10 years following the

frequency recorded in a farm survey of commercial

ranches (Vera et al., 1998). Notwithstanding, it is noted

that onfarm Bh pastures can persist without reseeding

for more than 20 years if reasonably well managed

(Rincón, 2018; Hyman et al., 2022). Machinery fuel use

values and fertilizer inputs were computed and

transformed to carbon dioxide equivalent (CO2eq)

emission values.

The second set of simulations represented a 432ha

property located in the welldrained Plains (3.4 million

ha), endowed with deep and tillable soils of low

fertility, and subjected to some subdivision, occasional

use of rotational grazing in sown pastures, and native

savannas with modestly higher carrying capacity than

in Serr (Hoyos et al., 1992; qualified informers, and

personal observations). As before, the simulated farm

evolved from a native savannaonly system to one that

is fully dependent on the same sown pastures (100 %)

23

Modeling pasture intensification in the Colombian Llanos

the same pasture species as mentioned above. These

forages lend themselves well to Bos indicusbased

breeding systems, largely Brahman and its crosses,

where the main outputs are cull cows, weaners, and

occasionally backgrounded 13 yearsold animals

destined for fattening elsewhere (RamírezRestrepo

and RamírezRestrepo and Vera, 2019). The beef

production efficiency of native savanna, even when

wellmanaged, does not exceed 1020 kg of liveweight

gain (LWG)/ha/year, onequarter of the value that

was observed even for a degraded B. decumbens Stapf

(syn. U. decumbens) pasture (Vera and Hoyos, 2019),

and much less than wellmanaged sown pastures

(Rincón Castillo et al., 2010; RamírezRestrepo et al.,

2023). Therefore, there is an economic incentive to

replace native savanna vegetation whenever feasible.

Simulated farms were subjected to the transforma

tion of land use from native savanna to the gradual

implementation of pasture alternatives over time. We

tested the hypothesis that gradual intensification of

grazing management with the introduction of

improved tropical pasture options at a whole farm

level in both Serrania and Plains locations would

change the herd structure, increase farm outputs, and

reduce C footprints over time until soil organic carbon

(SOC) accumulation reaches to an equilibrium. The aim

of this study was to develop and use a dynamic

simulation model to assess beef herd outputs and C

footprints at a whole farm level for 30 years in the

Serrania and Plains savanna regions of the Colombian

Orinoco Basin.

ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 21  41

24

Additionally, the model includes four modules to

keep track of LWs in each category, which in turn are

used as inputs to the enteric methane (CH4)

calculations that are included in the third module,

following the approach of RamírezRestrepo et al.

(2019, 2020, 2023) with prediction equations that yield

outputs similar to those estimated in the reviews of

KuVera et al. (2018) and Patra (2017) for a variety of

tropical systems with grassfed cattle. Lastly, a simple

management module ensures that the model does not

exceed the evolving whole farm’s carrying capacity

that is determined by the proportion of savanna and

sown pasture areas. All animal sales and computation

of outputs take place at the end of each year.

Parameters and model equations are listed in

Supplementary Tables 1 and 2, respectively. Contrary

to frequent practice, model parameters are not

constant regardless of the systems’ dynamics, given

that introduction of technical innovations is usually

associated with temporal and spatial changes in the

values of parameters, as discussed at length by Luo

and Schuur (2019). Initially, the basal savannaonly

(one each for Serr and Plains), model was run for 100

years with constant, initial values, to verify that it

would quickly reach steadystate conditions and that it

would be stable over the long term. It was also

subjected to extensive sensitivity analysis with the

same purpose. Thereafter, all simulations lasted up to

as above and established using the same temporal

pattern as in Serr, including a small area (8 % of the

total) of remnant native savanna, service areas, and

gallery forests. The simulated farm represents a

smaller version of one monitored in a farm study by

Vera and Seré (1989). As in the previous scenario,

intensification was carried out via the introduction of

either planted Bh pastures (PlBh) or the higher

nutritional quality GL association (PlGL) pastures.

Over the last decade, pastures used are mainly based

on Bh and B. dictyoneura (ENA, 2019; Rodríguez

Borray et al., 2019).

The sown pasture area with improved pasture

options in both subecosystems increased stepwise in

years 5, 10, 16, and 21 of the simulation runs, with

paddocks covering 40, 100, 200, and 280 ha total for

Serr, and 40, 200, 300, 400 ha for Pl, respectively,

regardless of the species sown (Figure 1 in

Supplementary Material). The temporal evolution of

systems’ outputs and derived variables were analyzed

graphically. Also, overall C footprints (see below) and

other important aggregated variables were computed

for the years 8 and 25 of the simulation runs, once the

minimum and maximum sown areas, respectively had

been used for 3 consecutive years to provide a

comparison of the overall picture of changes over time.

In all simulated scenarios, male calves were sold as

yearlings, and cull cows (RamírezRestrepo and Vera

Infanzón, 2019) had first access to sown pastures

during the 46 months of the rainy season. These are

commonly observed strategies that allow partial

recovery of the costs of the establishment of new

pastures. Lastly, sales of excess replacement heifers,

when available, constituted an additional source of

income. We also assumed that bulls are the only

animals regularly bought, and that the herd would

selfreplenish from their heifers, as frequently reported

in extensive commercial (Escribano, 2016) or

experimental (Rivera, 1988) beef production systems.

A dynamic simulation model

The dynamic simulation model was developed in

STELLA®. The section representing the herd is an

aging chain (Sterman, 2000) with a time step of one

year and dt of 0.25, whereby animal categories age at

yearly intervals, as shown in the flow module of

Figure 1.

Figure 1. Structure of the model section depicting the flow of animals as they age.

VeraInfazón et al.

ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 21  41

25

30 years and began with four years of savanna land

use, followed by the gradual intensification through

the introduction of sown pastures within the farm.

Animal performance

Initial LW values for each animal category were

those recorded onfarm by Kleinheisterkamp and

Habich (1985), supplemented with the authors’ records

from the International Center for Tropical Agriculture

(CIAT) at Carimagua Research Centre (CRC;

4°36'44.6"N, 74°08'42.2"W) and numerous ranches in

the Llanos (Vera and Hoyos, 2019). Values used for

LWGs both on savanna and sown pastures were based

on longterm monitoring of farms (Vera and Hoyos,

2019; and unpublished data), and these were

compared with numerous experimental results to

validate their reliability (Lascano and Euclides, 1996;

RincónCastillo et al., 2010). Without exception, highly

conservative parameters of carrying capacity and

LWGs were used, consistent with sustainable, long

term use of grassland resources. In all cases,

experimental and other LWGs observed in sown

pastures of less than 3 years of age were discarded,

and conservative values were used for the remaining

time. Given the assumptions of good grazing

management and periodic pasture maintenance,

pasture degradation was not considered for

simulation.

Onstation, longterm experimental outputs provi

ded data on reproductive performance and lifetime

LWGs on B. humidicola pastures (Vera and Hoyos,

2019; VeraInfanzón and RamírezRestrepo, 2020)

while onfarm A. gayanusbased grasslands

(unpublished data) were also used to derive

parameters for the simulation model.

Kleinheisterkamp and Habich (1985) found cyclic

reproductive performance on closely monitored farms,

resulting in significant negative correlations of calving

and weaning rates over consecutive years. This finding

was corroborated in other studies both onstation

(VeraInfanzón and RamírezRestrepo, 2020) and on

farm (Vera and Seré, 1989). Thus, calving rates in the

model depended upon the area of sown pastures, and

were simulated using a mean calving rate subjected to

a sine wave function of period 2 to represent variation

between consecutive years.

Carbon footprints

Model outputs of animal numbers and LWGs were

transferred to an EXCEL® spreadsheet for further

calculations of derived variables. The approach

followed to estimate GHG emissions in CO2eq values

from animals [CH4 enteric emissions and emissions of

CH4 and nitrous oxide (N2O) from feces and urine],

soil (CH4 and N2O), and from fertilizer inputs and

tillage, was similar to that reported by Ramírez

Restrepo et al. (2020). CO2eq values of global warming

potential used for CH4 and N2O were 34 and 298,

respectively. The vegetation and soil parameters used

in estimating the C footprint of grazing systems are

listed in Table 1.

Dry matter (DM) digestibility and fecal N were

derived from several local grazing experiments on Bh

grass pasture [Glover et al., 1957; CIAT Tropical

Pasture Program (TPP), 198791], and were compared

with Whitehead (1995), Dong et al. (2014), and Zhu et

al. (2018) findings. Urinary N excretion was computed

as in Marcondes et al. (2011) and Waldrip et al. (2013).

Fecal N2O and CH4 emissions factors, and urinary N2O

emissions factors were derived from Lessa et al. (2014)

and were compared for consistency with those of Braz

et al. (2013), Pelster et al. (2016), and Zhu et al. (2018),

plus locally derived emissions from Chirinda et al.

(2019). In several randomly selected cases, the

calculated N balances were compared with data from

Marcondes et al. (2011) to determine the compatibility

of computed values with actual LWG. Similarly, DM

intake (DMI) and resulting energy retention values

were checked against the values reported by Valadares

Filho et al. (2016) for 3yearold empty, nonlactating

females of 280 kg LW. Lastly, the estimated export of

carcass N (34 kg/year) in savanna animals was

slightly less than the amount of N received from

rainfall and biological N2 fixation reported by López

Hernández and HernándezValencia (2009) for native

savanna, thus validating the estimated outputs by the

model.

We used values for GHG emissions from Serr and

Plains savanna (PlSav) controls from the review article

on soils by Castaldi et al. (2006). The values assumed

for the rate of SOC accumulation in SerrSav and PlSav

are the same in the absence of published information

on SerrSav. We used the value of 150 kg C ha/year or

550 kg CO2eq/ha/year for savanna as reported by

RamírezRestrepo et al. (2019). Rates of soil GHG

emissions and SOC accumulation of Bh and GL

pastures were adapted from RamírezRestrepo et al.

(2020). The rate of SOC accumulation in Bh and GL

pastures was assumed to increase gradually over time

to reach a conservatively estimated value of 1000 kg C/

ha/year or 3667 kg CO2/ha/year and maintenance of

this value for 6 years (assumption A), 10 years

(assumption B), or 20 years (assumption C) before this

value starts to decrease gradually to eventually

reaching to an equilibrium value over 30 years

(Franzluebbers et al., 2012). These three assumptions

Modeling pasture intensification in the Colombian Llanos

ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 21  41

26

are based on the published reports on wellmanaged

tropical pastures in the Colombian Llanos (Fisher et al.,

1994; RamírezRestrepo et al., 2020; Hyman et al., 2022)

and the Brazilian Cerrados (Baptistella et al., 2020; de

Campos Bernardi et al., 2021). Notwithstanding, we are

also aware of published reports (Qian and Follett,

2002; Follett and Schuman, 2005; Viglizzo et al., 2019;

Durrer et al., 2021; Stoner et al., 2021; Hyman et al.,

2022), where the time period to reach an equilibrium

in SOC accumulation in introduced tropical pastures

could take a much longer period.

Table 1. Vegetation and soil parameters (and reported ranges) used in estimating the carbon footprint of grazing systems.

Parameter Valuerange References

Plains/Serrania/pastures

Soil organic matter to 1 m depth in savanna; t/ha 120150 Fisher et al. (1994); Rao (1998); Rao et al. (2001); Trujillo et al. (2006)

Standing aboveground DM biomass; kg/ha 20006000 Fisher et al. (1998); Rao (1998); Rao et al. (2001); Grace et al. (2006)

Standing root DM biomass; kg/ha 15003000 Rao (1998); Rao et al. (2001); Trujillo et al. (2006)

Total C stock in shoot and root biomass; kg/ha 35009000 Fisher et al. (1994); Rao (1998); Rao et al. (2001); Trujillo et al. (2006)

CO2eq CH4 emission from savanna soil; kg/ha/year,

mean and (reported range) 25.7 (59 to 28) Castaldi et al. (2006)

CO2eq CH4 emission from pasture soil; kg/ha/year

7

to 95 Castaldi et al. (2006)

CO2eq N2O emission from savanna soil; kg/ha/year 518 Castaldi et al. (2006)

(reported range) 349522

CO2eq N2O emission from pasture soil; kg/ha/year 225414

Soil organic C (SOC) accumulation; kg/ha/year 150 RamírezRestrepo et al. (2019)

CO2eq SOC accumulation; kg/ha/year 550 RamírezRestrepo et al. (2019)

Brachiaria decumbens pasture

CO2eq CH4 emission from soil; kg/ha/year 6.75 RamírezRestrepo et al. (2020)

CO2eq N2O emission from soil; kg/ha/year 225.5 RamírezRestrepo et al. (2020)

CO2eq emission from fertilizer and tillage; kg/ha/year 24.64 RamírezRestrepo et al. (2020)

SOC accumulation kg/ha/year (reported range) 10003000 RamírezRestrepo et al. (2020)

CO2eq SOC accumulation; kg/ha/year (reported range) 366711000 RamírezRestrepo et al. (2020)

B. humidicola pasture

Emission factor for fecal CH4; kg CH4/kg fecal C 0.0112 Rainy days inhibit CH4 emissions, Lessa et al. (2014)

Emission factor for fecal N2O; kg N2O/kg fecal N 0.0014 Lessa et al. (2014)

Emission factor for urinary N; kg N2O/kg urinary N 0.016 Weighted mean rainy + dry days (Lessa et al., 2014)

N emissions probably overestimated given BNI Subbarao et al. (2009); Byrnes et al. (2017)

CO2eq mineral supplement; kg/ha/year 1.27 RamírezRestrepo et al. (2020)

CO2eq savanna conversion to pasture prorated over

15 years; kg/ha/year 30.85 RamírezRestrepo et al. (2020)

CO2eq CH4 emission from soil; kg/ha/year 3.2

(reported range) 0.4 to 4.3 Fernandes et al. (2011)

CO2eq N2O emission from soil; kg/ha/year

(reported range) 225518 RamírezRestrepo et al. (2019)

SOC to 1m depth; t/ha 130160 Fisher et al. (1994); Rondón et al. (2006)

Standing aboveground DM biomass; t/ha 1.23.5 Fisher et al. (1998); Rao (1998); Rao et al. (2001); Fisher et al. (2007)

Standing root DM biomass; t/ha 2.83.5 Fisher et al. (1998); Rao (1998); Rao et al. (2001); Fisher et al. (2007)

SOC accumulation rate; kg/ha/year (reported range) 1501000 Fisher et al. (1994); Rao (1998); Rao et al. (2001); Trujillo et al. (2006)

CO2eq SOC accumulation; kg/ha/year (reported range) 5503667 Fisher et al. (1994); Rao (1998); Rao et al. (2001); Trujillo et al. (2006)

Animal parameters and factors

CP digestibility; % Authors equation Glover et al. (1957)

Urinary N; kg/head/day Authors equation Waldrip et al. (2013; Eq. 11)

BNI: Biological nitrification inhibition; SOC= Soil organic carbon; CP= Crude protein

Following a conservative approach, we used

relatively high soil GHG emission values for planted

pastures of Bh and GL. It is important to note that the

total area of introduced pastures within the farm

contains pastures of different ages and therefore,

weighted yearly averages were used for the whole

system (savanna + pastures of varying ages) over time.

Values of GHG emissions related to machinery use

(fossil fuels) and use of fertilizers for pasture

establishment and maintenance were calculated based

on published data for Colombia (CarrascoLeal, 2015).

We did not include the C costs of seeds and fences for

planted pastures.

Model results were available yearly, but the detai

led CO2eq partitioning of emissions and capture is

shown only for the 8th and 25th years of the simulation

runs as noted above, corresponding to the early effects

of pasture intensification (fourth year) and once the

intended maximum intensification was achieved at the

farm level (21st year). Nevertheless, 30yearlong

trends in overall emissions and SOC accumulation

under three different assumptions (A, B and C as

described above) are presented graphically.

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Serrania Plains

SerrSav SerrBh SerrGL PlSav PlBh PlGL

Number of AU (450 kg) per ranch † 180 (59) 304 (100) 303 (100) 71 (37) 191 (100)

207 (108)

SR (AU/ha) 0.13 0.22 0.22 0.18 0.30 0.52

SR (animals/ha) ** 0.23 (62)

0.34 (100) 0.29 (85) 0.32 (94) 0.71 (209)

0.76 (224)

LW sold (kg/year/ha) ** 9.8 (28) 34.6 (100) 31.1 (90) 14 (40) 65.3 (189) 73.3 (212)

t CH4/t LW sold ** 1.00 (238)

0.42 (100)

0.45 (107) 0.81 (193) 0.54 (129)

0.47 (112)

t CH4/ha ** 9.8 (73) 13.4 (100) 13.2 (99) 11.3 (84) 33.6 (251) 29.5 (220)

Results

Overall systems’ performance

Averaged over the 30year period of simulation,

large absolute and relative differences in stocking rates

(SRs), sales, and enteric CH4 emissions were found

(Table 2), and the increase in CH4 emissions per ha

contrasted with decreasing emissions per kg LW sold.

Intensified systems with the selected pasture

technologies halved emissions per unit LW sold

relative to values of the respective savanna controls.

Table 2. Simulated performance of systems, averaged over 30 years, of farm intensification, using flat and dissected Serrania

savannas (SerrSav) and plains savannas (PlSav) of Eastern Colombia through the introduction of Brachiaria humidicola (Bh) grass

alone or a prototype grasslegume (GL) association (Andropogon gayanus and Stylosanthes capitata) pastures, compared to savanna

(Sav).

Serrania: 1400 ha with maximum 240 ha sown pastures. Plains: 432 ha with maximum 400 ha sown pastures.

† Percentages relative to Bh within each physiography. ** Percentages relative to SerrBh across all systems.

Data shown between parentheses are relative values in relation to pasture used and physiography.

AU: Animal unit. CH4: Methane. LW: Liveweight. SR: Stocking rate.

Animal performance and demography

Animal LWs carried per hectare increased with time

,

with a 12year delay relative to the expansion of the

sown areas (Figure 2), a process particularly noticeable

in Plains where values for PlGL were considerably

larger than PlBh, with the difference increasing over

time. On the contrary, in Serr, the two pasture curves

were similar, and for clarity, only one of them is

shown in Figure 2.

Figure 2. Yearly liveweight (LW, kg/ha) in each of the simulated systems.

Arrows indicate the time of expansion of the area sown to introduced grasses.

The number of animals per farm increased as the

area of sown pastures rose over time. In the initial 10

years, as pasture intensification proceeded, there were

marked adjustments in the composition of the herd

(Figure 3), as reproductive performance slowly and

modestly increased, with a trend to initially overshoot

the number of breeding cows required to provide

replacements, a proportion that eventually stabilized

in all systems around values of 4550 % of the herd.

The changes were more noticeable in the number of

cows due to the time taken for the expression of

increasing calving rates as well as the time demanded

by the growth of the replacement females. These

effects were more marked in the smaller Pl farm where

the number of adult cows increased more slowly than

in Serr farm. There was a large difference in herd

composition between the Sav control and the

improved pasture systems, whereby the proportion of

cull cows increased rapidly and very significantly in

the intensified pasture systems in line with their

increasing availability and their prioritized access to

the sown pastures. Smaller differences between Serr

and Plains were related to the proportion of other

animal categories (Figure 3), but the trends were

similar across the four transformed grazing systems.

Differences between grazing systems at the farm level

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regarding the contribution of calves, heifers and cull

cows to the herd were noticeable, whereas the final

percentage of breeding cows was similar across systems,

notwithstanding temporal differences between systems.

Figure 3. The proportion of major animal categories, as a

fraction of the total animals in (a) SerrBh, (b) SerrGL, (c) PlBh,

and (d) PlGL production systems across three decades of

simulation.

Figure 4. Yearly liveweight sold in relation to six contrasting

production system.

As noted in Table 1, SRs increased in response to

pasture intensification as was the case for LW sold/ha

(Figure 4) with a oneyear delay. Large differences

between grazing systems in yearly LW sales (Figures 4

and 5) were found, with PlGL gradually and

consistently exceeding PlBh system, whereas the reverse

was true for Serr control, although with a smaller

relative difference.

Sales in Serr behaved more irregularly than in Pl, as

a consequence of the very large difference in relative

contributions of savanna and sown pastures to the

grazing systems’ carrying capacity, with periodic and

sudden increases following the rise of the sown areas.

This phenomenon led to the recurring retention of

females to satisfy the carrying capacity of the grazing

system, followed by a surge in sales, as shown in

Figure 5.

Enteric emissions

Animal CH4 emissions per ha increased over time

(Figure 6), and this was associated with the increased

systems’ carrying capacity and evolving LWs. The

effect of increased mean LW on carrying capacity was

particularly noticeable in the Pl scenarios, while at the

same time, the amount of CH4 emission per ton of LW

sold decreased exponentially (Figure 7), where the

most marked difference was between the SerrBh and

the rest of the improved systems. Lastly, all variables

reached a steady state towards the end of the period

examined, with slight variation between years.

Carbon footprint

The detailed C footprint in CO2eq value at a whole

farm level was estimated for the 8th and 25th years of

the simulation, corresponding to the early phase of

pasture introduction, and the final phase of pasture

intensification at the end of the simulation period,

respectively. These values are shown in Table 3, where

positive C footprints represent net CO2 emissions from

the system, and negative values of C footprints imply

net CO2 capture by the systems’ soils, assuming SOC

accumulation over 10 years (assumption B).

Depending upon the stage of pasture intensification at

the farm level, some of the systems transitioned from

early net CO2 emitting systems to net CO2 capturing

systems later on.

Total emissions increased with time in parallel with

the increasing area of sown pastures and carrying

capacity, while the C footprint per unit area at a farm

level decreased linearly with increasing time. Results

presented in Figure 8 shows the evolution of total

emissions and the three assumed SOC accumulation

estimates for the 6, 10, and 20years’ C capture

scenarios. These data demonstrate marked oscillations

in SOC accumulation as different aged pastures begin

to contribute at a farm level in different systems and

later cease to accumulate SOC, reaching steadystate

values.

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Figure 5. Comparative sales of three categories of animals as a fraction of the total sales in three contrasting production systems.

Figure 7. Yearly enteric methane (CH4) emissions in relation to kg LW sold, from Serrania and Plains production systems.

Figure 6. Yearly enteric methane (CH4) emissions from three contrasting and intensified production systems.

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Table 3. Estimated greenhouse gas (GHG) emissions and C footprint in carbon dioxide equivalent (kg CO2eq/ha/year) values at

the 8th and 25th year of grazing simulation for six contrasting systems: Serrania savanna (SerrSav), SerrSav + Brachiaria humidicola

(SerrBh); Plains savanna (PlSav); PlSav + B. humidicola (PlBh), and PlSav + grasslegume association (Andropogon gayanus +

Stylosanthes capitata, PlGL). Soil organic carbon (SOC) accumulation under grass and grasslegume pastures is assumed to continue

over the 10 years period.

Grazing systems in Serrania Grazing systems in Plains

Parameters SerrSav SerrBh PlSav PlBh PlGL

Simulation year 029 8 25 029 8 25 8 25

Savanna area + sown area (ha) 1400 1360 + 40 1120 + 280 400 360 + 40 0 + 400 360 + 40 0 + 400

Enteric CO2eq CH4 ** 334.3 (89.1) 354.8 (87.3) 671.6 (89.1) 384.8 (87.1) 655.4 (89.0) 2116 (90.4) 562.6 (86.8) 1862 (88.6)

Feces CO2eq CH4 ** 37.87 (9.4) 44.68 (11.0) 68.85 (9.1) 53.67 (11.1) 70.09 (9.5) 189.0 (8.1) 70.31 (10.9) 182.4 (8.7)

Feces CO2eq N2O ** 1.24 (0.3) 1.47 (0.4)

2.78 (0.4) 1.74 (0.4) 2.42 (0.3) 7.68 (0.3) 2.95 (0.5) 8.34 (0.4)

Urine CO2eq N2O ** 4.83 (1.2) 5.38 (1.3) 10.65 (1.4) 6.87 (1.4) 8.72 (1.2) 29.01 (1.2) 12.62 (1.9) 48.08 (2.3)

Totalanimal CO2eq GHG emissions 378.2 406.3 753.9 447.1 736.6

2 342 648.5 2101

CO2eq emissions from inputs and tillage 0.0 73.0 26.0 0.0 73.0 26.0 73.0 26.0

CO2eq emissions from salt supplement 0.53 0.68 1.22 0.79 1.10 1.46 1.13 2.46

Soil CO2eq CH4 † 26.0 25.06 19.45 26.0 22.72 6.75 22.72 6.75

Soil CO2eq N2O † 518.0 509.6 459.4 518.0 488.7 225.0 488.7 225.0

CO2eq SOC accumulation † 550 665 1173 550 953 3667 953 3667

Estimated CO2eq C footprint 372.7

349.7 86.97 441.9 369.1 1079 281.0 1319

** Values in parenthesis indicate the contribution of each GHG source as a percentage of the total animal GHG emissions.

†Soil GHG emissions and SOC accumulation of SerrSav, PlSav, PlBh, and PlGL. Positive values for the C footprint indicate net

emissions; negative values indicate that the system behaves as a C sink.

Figure 8. Calculated total greenhouse gas (GHG) emissions and soil organic carbon (SOC) accumulation in carbon dioxide

equivalent values (CO2eq t/ha) over a 30year period for two contrasting systems for Serrania + Brachiaria humidicola (SerrBh)

and Plains + B. humidicola (PlBh) with continuous SOC accumulation of 1 t/ha/year assumed to last 6, 10, or 20 years. Note the

difference in scale in the Yaxis between both panels.

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Discussion

31

Values of the thirtyyears mean ratios of CO2eq SOC

accumulation to GHG emissions from three

contrasting systems as a function of total number of

years of continuous contribution of SOC accumulation

from sown grass alone or grasslegume pastures are

presented in Table 4. The values that are higher than

1.0 indicate the pasture systems that contribute to net

SOC accumulation over time.

Modelling farm resources, herd dynamics, and

animal footprints

The model outputs, as well as additional variables

derived from them, provided numerous insights into

the effects of spatial and temporal changes in the

structure and behavior of the simulated systems that

would not be generally obtained from shortterm field

experiments. As indicated by Jaurena and Cantet

(2016), there is a need to consider different spatial and

temporal scales when dealing with relatively complex

models of production, and that is certainly the case

with breeding herds that include numerous animal

categories operating on different time scales (Dick and

da Silva, 2015).

The structure of our dynamic simulation model

allowed us to assess the farmlevel effects of the

gradual introduction of sown pastures in varying

proportions in two contrasting savanna sub

ecosystems. It also provided insights into the behavior

of the systems’ outputs at the farm level that resulted

from numerous multidimensional interactions.

Similarly, Alcock and Hegarty (2006) simulated the

impact of replacing native rangelands with up to 100

% sown pastures in sheep enterprises and, as in the

present case, SRs and animal outputs increased

significantly. Also, enteric CH4 emissions per unit of

output decreased, and the authors estimated that

profits from sales were larger than the assumed cost of

the GHG emissions. In the case of cattle systems,

Turner et al. (2013) model showed that increasing sales

of cows and yearlings resulted in higher financial

incomes, an outcome that would most likely be similar

in the present simulations. Other studies on extensive

systems also reported increasing economic

performance due to the fattening of cull cows in

Colombia (Romero et al., 2018), Uruguay

(Lagomarsino et al., 2015; Montossi, 2017), Brazil

(Missio et al., 2015), and Australia (Harrison et al.,

2016). The latter model suggested that optimization of

the herd structure, reduction of the number of

breeding cows, and improvement of feeding strategies

would lead to increased emissions efficiency if all

management interventions are complementary and

work in unison (Beukes et al., 2010). Indeed,

synchronization of management strategies is a major

challenge for extensive, lowintensity management

systems, and it is an important conjecture in the

present analyses, which is predicated on the

assumption of wellmanaged, sustainable pastures,

with appropriate and timely decisionmaking.

Nevertheless, farmermanaged Bh pastures have been

documented to last more than 20 years under very

variable management (Rincón et al., 2018; Vera and

Hoyos, 2019; Hyman et al., 2022), a trait that facilitates

management resolutions that are not necessarily

highly sensitive to opportune stocking and other

decisions.

Livestock systems are considered complex and

subject to considerable variation, cyclic behavior, and a

finite supply of animals (Nielsen and Kristensen, 2015;

Jaurena and Cantet, 2016). This heterogeneity is also

depicted by our simulation model in terms of

uniformity (or lack of), cyclicity, and animal

performance (BenAri et al., 1983). Although the model

deals with a limited number of possible alternatives, it

illustrates the most noticeable tradeoffs (de Souza

Filho et al., 2019), trends, and expectations despite

avoiding a larger number of spatial and temporal

dimensions. Caballero et al. (2009) and Nielsen and

Kristensen (2015) pointed out that sophisticated

models require very large amounts of data and

Table 4. Thirtyyear mean ratio of CO2eq soil organic carbon (SOC) accumulation to greenhouse gas (GHG) emissions from

three contrasting systems as a function of the total number of years of continuous SOC accumulation from sown pastures. Ratios

are based on the respective CO2eq values for both SOC accumulation and GHG emissions. Serrania savanna + Brachiaria

humidicola (SerrBh); Plains savanna + B. humidicola (PlBh), and Plains savanna + grasslegume association (PlGL; Andropogon

gayanus + Stylosanthes capitata).

Total number of years of continuous

SOC accumulation by sown pastures SerrBh PlBh PlGl

6 1.15 0.82 0.89

10 1.24 0.99 1.09

20 1.37 1.22 1.35

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32

demand the estimation of numerous parameters. This

further suggests that relatively parsimonious models

such as the present one may generally be easier to use,

adapt and understand when applied to relatively

constrained environmental situations. Crosson et al.

(2011) and Rawnsley et al. (2018) reviewed research on

whole farm models, and posited that they constitute

an essential tool to examine ranchlevel strategies to

estimate GHG emissions and mitigation strategies.

This view applies in the present case that allowed

examination of a set of highly divergent paths of

property development through pasture intensification,

and their consequences in terms of herd structure, and

animal and GHG outputs, as well as various estimates

of technical efficiency. Nevertheless, the model lends

itself to imagine numerous other alternative strategies

for improving farmlevel outcomes.

Model outputs demonstrated important short and

longterm changes in herd composition relative to

savannaonly systems, brought about by the

introduction of sown pastures. These changes were

particularly large in the case of cull cows, a major

source of income for extensive systems, as mentioned

previously, that were most noticeable in the early

years of intensification, despite making a modest

contribution to the farms’ animal population.

Simulated LWGs and carrying capacities used

conservative values and are probably underestimated

compared to field data from wellmanaged pastures.

Nevertheless, as expected, all sown pastures boosted

animal outputs relative to the native savanna (Rincón

et al., 2010; Pérez et al., 2017). The amounts of LW sold

per year as the systems evolved with time, and the

high proportion of LW sold relative to that carried by

the system was unexpected, and to the authors’

knowledge, it has not been previously quantified in

the study region. These relatively high LW outputs

reflect the improved efficiency of beef production

induced by sown pastures, while at the same time,

unavoidably increasing CH4 emissions per ha, but

with decreasing emissions per ton of LW sold. The

system level GHG emissions increased gradually over

time in response to the increasing areas of sown

pastures and carrying capacities. Sown pastures,

while increasing the system level emissions, decreased

them per unit of output sold. The behavior of SOC

accumulation over time was variable due to the

varying contributions of sown pastures to SOC and the

assumed length of time of their contribution

(assumptions of A, B, and C), an effect that was

particularly noticeable in the more intensive system of

PlBh. Due to decreased enteric CH4 emissions,

associated with a higher quality pasture, the PlGL (not

shown) demonstrated a slightly higher, and longer

lasting, ratio of SOC accumulation to GHG emissions.

Present average estimates of emissions and C

footprints are reasonably comparable to those

reported by Cerri et al. (2016) for large ranches, using a

larger quantity of inputs, in Matto Grosso, Brazil,

amounting to 4.85.2 kg CO2eq kg/LW. Also in Brazil,

de Figueiredo et al. (2017) used the Intergovernmental

Panel on Climate Change (IPPC, 2006) parameters and

estimated that degraded and wellmanaged sown

pastures had C footprints around 8.0 and 84.5 Mg

CO2eq per ha, and 18.5 and 9.4 kg CO2eq per kg LW

over 10 years, respectively. Cardoso et al. (2016) used

IPCC Tier 2 methods to estimate GHG emissions for

beef production in the Brazilian Cerrados and

reported a 50 % decrease in CO2eq kg/carcass weight

following systems intensification of a larger

magnitude than in the present case. Similarly, they

found a 7fold reduction in the area required to

produce a unit of beef carcass, a decrease that is

slightly larger than that found presently for the Pl

scenarios (5 to 6fold). To the authors’ knowledge, no

comparable data are available for the Colombian and

Venezuelan savannas. The changes in herd

composition, though anticipated, were large and

variable over time, particularly in the Pl scenarios, and

were associated with the strategy of relying on the

herd to selfreplenish with breeders. It is possible that

for those particular scenarios, acquisition of

replacement heifers and young cows would have

accelerated reaching the plateaus of LW carried per

farm and corresponding sales. As before, we are not

aware of equivalent analyses for the neotropical

savannas.

Lastly, Beauchemin et al. (2010) reported that cows

in Canadian cowcalf operations accounted for 82 % of

the CH4 produced by the breeding herd, and

suggested that they therefore constitute the main

targets for mitigation of CH4 emissions. In our case,

primiparous and mature breeding cows grazing

savanna emitted 67 % of the herd’s total CH4

emissions, and this value was reduced to 50 % when

the planned sown pastures were fully implemented at

a whole farm level.

Modelling overall C footprints of beef cattle systems

at the farm level

Dick et al. (2015) compared 17 static scenarios for

breeding herds in Southern Brazil that differed in the

type and number of technological interventions and

concluded that changes in the quantity, distribution,

and quality of forage production, together with

improved reproductive performance, and early

weaning reduced emissions by 4.8 to 8.9 and 16.3 to

24.2fold, respectively. Intensification of pasture use

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33

reduced emissions from 22 kg in the base scenario to

0.8 CO2eq/kg LWG, thus demonstrating mitigation of

GHG emissions.

There is abundant evidence that reasonably well

managed tropical pastures can increase SOC

accumulation relative to other land uses, in amounts

that may offset C emissions under some circumstances

(Conant et al., 2017; Lorenz and Lal, 2018), but there is

no general, agreedupon methods to estimate their C

balance (FAO, 2019). Estimation of the systems level

overall C footprint requires consideration of the

dynamics of soil C stocks, soil emissions, and the

supply of N by rainfall. Rondón et al. (2006) reviewed

the size of soil C stocks and the potential for SOC

accumulation by introducing improved grasses in the

savannas of Colombia and Venezuela. They showed

that C stocks under native savannas are resilient under

various management practices, and can be

significantly increased when sown to deeprooted

grass species such as Bh in medium texture soils, a

species that under good management can accumulate

34 times as much root biomass as native savannas, a

majority of which is concentrated in the upper soil

layers, thus contributing large amounts of C (Rao et al.,

2001). San José et al. (2014) quantified the C fluxes in

the neotropical savannas of Colombia and Venezuela

and concluded that under most circumstances, the

native savannas are a weak C sink (360 kg/ha/year).

In this study, we assumed a C sink of 150 kg/ha/year

for Pl and Serr savanna subecosystems. Regarding N

input to facilitate SOC accumulation, Copeland et al.

(2012) reported net inputs of N by rainfall to Brachiaria

pastures (1238 kg N/ha/year) in the Brazilian Cerrados,

a nutrient essential for a sustainable rate of SOC

accumulation (Ayarza et al., 2022).

Nitrogen leaching from urine patches is a concern

in

wellfertilized dairy temperate pastures (Chichota et

al., 2018). In the present case, this is an unlikely

concern given the low N content of species such as Bh

and Ag, unless associated with a substantial legume

content, plus the fact that these grasses have a very

deep and abundant root system, but with a greater

proportion of roots in the upper layers up to 40 cm

depth, where much of the labile N concentrates.

Furthermore, species such as Bh are known to inhibit

nitrification in soils, thus potentially reducing the

leaching of N (Byrnes et al., 2017).

Under the assumption of conservatively managed

pastures, the present model shows that the integration

of productive sown pastures may offset animal

emissions via SOC capture under some, but not all

conditions, critically depending upon the balance of

savanna areas with sown areas. The limits of that

approach are clearly shown in the estimated values of

the ratio of SOC accumulation/GHG emissions (Table

4) that depended upon the temporal deployment of

pastures relative to the native savanna. The values of

SOC accumulation to GHG emissions ratio that are

higher than 1.0 in all 3 pasture systems from both sub

ecosystems with the assumption of continuous SOC

accumulation over 20 years period indicate that the

gradual intensification of pasture systems at the farm

level can lead to net SOC accumulation over time.

These results are similar to several published reports

that showed an increase in SOC stocks that can reduce

the overall C footprints from wellmanaged, longterm

tropical pastures under grazing (Fisher et al., 1994;

Qian and Follett, 2002; Follett and Schuman, 2005;

Viglizzo et al., 2019; RamírezRestrepo et al., 2020;

Baptistella et al., 2020; Durrer et al., 2021; Stoner et al.,

2021; de Campos Bernardi et al., 2021; Costa et al.,

2022).

Furthermore, the model provided insights into the

dynamic interplay of native and introduced forage

resources as the system evolves with time. These

findings contrast with the control, savannaonly,

system lacking in sown pastures, where emissions per

ha and at the system level were low, but so were beef

outputs. An unsolved issue is the possible effect of the

systems’ increased carrying capacity on the remaining

savanna vegetation, despite careful adjustment of the

SRs based on the proportions of native and introduced

pastures.

The magnitude of CO2eq CH4 emission/kg of LW

sold is roughly comparable to that reported by Clerc et

al. (2012) for cowcalf systems in Brazil. Lecomte et al.

(2014) reported values of 80 kg CO2eq/kg of the

carcass in savannabased systems, and 18 kg CO2eq/

kg of the carcass for systems intensified with Brachiaria

pastures in the Democratic Republic of Congo.

Similarly, Cardoso et al. (2016) reported that

intensification decreased emissions per unit of animal

output with a reduction of 50 % in CO2eq/kg carcass.

These and other authors (Vigne et al., 2016) infer that

intensification via the introduction of sustainable sown

pastures is the best way to mitigate the emissions of

enteric CH4 per unit of animal output, but present

results show the limits of the process in terms of C

footprints. In effect, simulated results show that

systems that are subjected to intensification over time,

may shift from positive to negative C footprints

depending upon the relative proportions of the use of

improved forage resources and their respective animal

carrying capacities, and the temporal pattern of

pasture intensification. Thus, the C footprint in a given

environment is not inherent to a given production

system but varies according to decisions made on the

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system but varies according to decisions made on the

temporal and spatial deployment and use of the feed

resources and their management, a phenomenon that

does not appear to have been previously documented

at a whole farm level. This of course poses a major

challenge when aggregated regional analyses are

made (GonzálezQuintero et al., 2021), since they do

not account for evolving changes in systems dynamics,

but rather consider them at a fixed point in time (a

snap shot approach).

Lastly, regular pasture renewal as simulated in our

model may influence the dynamics and amount of

SOC stocks, as documented for temperate pastures

(Liang et al., 2020; Giltrap et al., 2021). Thus, the

dynamics of SOC accumulation per unit area within

the farm based on three assumptions (A, B, and C)

with extended periods of continuous accumulation of

SOC for up to 20 years may result in systems that

offset GHG emissions and contribute to sustainable

intensification of beef cattle systems in two contrasting

acid soil savanna subecosystems of Colombia.

Implications of modelling extensive beef systems for

scenario analyses

Extensive grazing systems in marginal areas are

expected to intensify if climate, soil resources, and

economic conditions allow but, at the same time, they

must be protected for their value in landscape

diversity, the provision of environmental services, and

the preservation of rural livelihoods among others

(Caballero, 2009). But at the same time, as noted by

Alcock and Hegarty (2006), newly sown pastures

inevitably lead to increases in systems SRs and

attending consequences. In the Llanos, it is uncertain

how ranchers and the general public weigh these

sometimesconflicting views, although as in the case

investigated by MorganDavies et al. (2014), there is

some anecdotal evidence that different views coexist in

the region (Van Ausdal, 2009, 2020).

The analysis of simulated scenarios is a growing

trend that can take advantage of large, frequently

underused, but expensive to collect databases,

particularly those derived from longterm and costly

field experiments (Tedeschi, 2019; Nizar et al., 2021),

and it may assist in strategic decision and policy

making in varied circumstances (Greiner et al., 2014;

Ash et al., 2015). As pointed out above, the evaluation

of these systems needs to consider many other

possible qualitative and quantitative environmental

and sociocultural dimensions (Navas Ríos, 1999;

Painter et al., 2020). These include the preservation of

traditional rural customs, the use of grazing resources

that currently have no alternative outlets, and the

provision of environmental services if the system is

sustainably managed (Caballero, 2009; Beauchemin et

al., 2010; Hoogsteijn and Hoogesteijn, 2010). Another

little quantified dimension is the possible increase in

animal losses to carnivorous predators (Hoogsteijn

and Hoogesteijn, 2008; Boron et al., 2016) as the density

of cattle increases, particularly in smaller paddocks.

Lastly, the consequences of climate change are

uncertain, which constitutes an important challenge

(McKeon et al., 2009; Whish et al., 2014; Escribano,

2016) for future work, and it is posited that simulation

models will play an important role in that context

(ToroMujica et al., 2017). Nevertheless, it is also

hypothesized that flexibility (Chia et al., 2006) in

animal sales, culling strategies, retention of animals or

otherwise, and changes in the degree of pasture

intensification may reduce the risk associated with

climate change and evolving socioeconomic

conditions. Under these circumstances, property

development may need periodic adjustments and

rework that would lead to a more variable process

than otherwise (Mourik et al., 2021).

Lastly, it is challenging to generalize the overall C

footprints of extensive grazing systems that are subject

to multidimensional and varying degrees of

intensification over time and space, particularly if they

depend upon forage resources with vastly different

nutritive values, carrying capacities, and management

demands. But we learned from this study that a

relatively small set of prototypes may inform

appropriate policy and decisionmaking, as opposed

to the region or countrywide generalizations.

Conclusions

Simulation modeling using a relatively simple

approach, supported by longterm field legacy data,

proved to be a useful tool to examine the temporal

development of beef cattle systems that are subjected

to intensification at a whole farm level in contrasting

environmental circumstances. When comparing two

subecosystems that differ widely in their potential to

intensify via pasture introduction, modeling allowed

numerous insights concerning to herd demography

and animal CH4 emissions. The model also showed

that the fraction of tillable lands as a proportion of the

farm size is of major importance in determining the

technical and environmental performance of the

system over time. Inferences derived from the

dynamic simulation model suggested several

management challenges posed to primary producers

VeraInfazón et al.

ISSNL 10221301. Archivos Latinoamericanos de Producción Animal. 2023. 31 (1): 21  41

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Conflicts of interest: The authors declare no competing interests.

Ethics statement: No ethical conflicts are recognized by the authors.

Author contributions: RRVI developed the initial structure and wrote the code of the mathematical simulation

model. Model parameters and equations were jointly discussed and developed by all the authors. FHG collected the

views and opinions of researchers and farmers. RRVI wrote the first draft of the manuscript, that was revised and

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