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Edited by
Slawomir Wycislak
Jagiellonian University, Poland
Reviewed by
Bikash Koli Dey
SRM Institute of Science and Technology, India
Enrica Vesce
University of Turin, Italy
Table of contents

 * Abstract
 * 1 Introduction
 * 2 Background
 * 3 Problem formulation
 * 4 Results and discussion
 * 5 Conclusion
 * Data availability statement
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Front. Sustain., 10 July 2024
Sec. Sustainable Supply Chain Management
Volume 5 - 2024 | https://doi.org/10.3389/frsus.2024.1416964
This article is part of the Research Topic Supply Chain Transformation for
Pursuing Carbon-neutrality View all 9 articles


IMPLEMENTING CONCEPTS FROM GREEN LOGISTICS IN THE TURKEY PRODUCTION SUPPLY CHAIN

Griffin Wilson*Bazyl HorseyRichard Stone
 * Department of Industrial and Manufacturing Systems Engineering, Iowa State
   University, Ames, IA, United States

Introduction: The global turkey market represents a sector of increasing growth
in the previous decade, and projections for the next decade reflect the probable
continuation of this growth. Industry trends also indicate the globalization of
turkey meat production, as the loci of production has continually shifted from
one dominated by the United States to one with an increasing number of
production units globally. In contrast with other popular meat products,
comparatively fewer resources have been devoted to academic research concerning
the growth, production, distribution, and sale of turkey and turkey products.
This lack of research is particularly notable in the area of supply chain
management and environmental sustainability. Given the increasing volume of
turkey production and lower volume of academic interest, it stands to reason
that there remain many opportunities for improvement across the value chain in
this industry.

Methods: In this paper, we take a “green logistics” approach and use data
provided by one of the largest turkey producers in the United States to
formulate a mixed-integer program aimed at minimizing the environmental impact
of turkey products in a segment of the product supply chain.

Results: Implementation of the resulting brooder-finisher farm assignments
developed by the model would yield an average 50% decrease (184 metric ton) in
greenhouse gas emissions at the operation under investigation while also
addressing other areas of significant vulnerability for the industry (production
costs, biosecurity risk, and animal wellbeing).

Discussion: As consolidated turkey meat production systems continue to expand
globally, we argue that a similar approach could readily be deployed by these
growing and emerging production systems.




1 INTRODUCTION

In the last two decades, a significant volume of research has arisen with the
objective of reducing the greenhouse gas (GHG) emissions across the global value
chain. While many models and frameworks have been developed, more work is needed
in detailing practical changes that firms might readily deploy in their
continual effort toward minimizing emissions (Bratt et al., 2021). Concurrent
with these research endeavors surrounding green logistics, which may be defined
as “the systematic measurement, analysis, and ultimately, mitigation of the
environmental impact of logistics activities,” a gradual transformation has
occurred in the global turkey market (Blanco and Sheffi, 2017). While global
turkey production volume has increased gradually over the last decade, the loci
of production and consumption has continually shifted from a market dominated by
the United States (US) toward a market that has an increasing number of global
producers and consumers (Kálmán and Szollosi, 2023). This transition presents a
unique opportunity to implement “green” supply chain solutions as additional
production units continue to emerge.

An outline of the paper may be summarized as follows: firstly, after providing
additional context surrounding the global turkey market (Section 2.1) and the
turkey market's challenges (Section 2.2), we aim to describe the supply chain
associated with the production of turkey products (Section 2.3). Secondly, after
providing some additional context related to green logistics (Section 2.4), we
formulate a mixed-integer programming (MIP) model with the goal of minimizing
the GHG emissions associated with a segment of the turkey production supply
chain (Section 3). Thirdly, using data provided by one of the largest turkey
producers in the United States (US), which we refer to as “the Company,” we
apply the MIP model and compare the resulting green logistics model with
historical data (Section 4.1). Finally, we discuss the limitations of this green
logistics model and the significance of the findings for other large turkey
manufacturers (Sections 4.2 and 5).

The output generated by the MIP model described herein develops optimal
brooder-finisher assignments1 in a turkey growth and production network. We
argue that implementation of this model at any large turkey manufacturer would
result in an organization of their supply chain in such a way that would respond
to the preeminent challenges faced by the turkey industry, including a reduced
Global Warming Potential (GWP, the kg of CO2-equivalents generated per kg of
goods manufactured) of turkey products, increased biosecurity, and increased
animal wellbeing. Previous research in the turkey industry directed at improving
these parameters has focused heavily on optimizing feed efficiency,2 modifying
feed composition or farm worker behavior, and manipulating a variety of
environmental factors (stocking density, temperature, light/chemical exposure,
etc.).3 However, there remains a notable gap in the foregoing academic research
in the area of supply chain solutions for the turkey industry. Moreover, even a
description of the supply chain processes analyzed in this paper has previously
been confined to popular press articles and industry manuals, and not as an
object of academic investigation. Thus, this paper finds its primary
contribution in the successful application of a well-studied solution
methodology (the Vehicle Routing Problem) to a “new” industry and, in doing so,
provides a framework that other turkey manufacturers might readily adopt.


2 BACKGROUND

The recorded history of the turkey spans some 500 years, with the bird having
served as an important staple to the Native American diet for thousands of years
prior. Archaeological evidence suggests that several Native American groups had
domesticated the bird before European arrival in the Americas, and records
indicate that the bird was then domesticated in Europe shortly thereafter
(Brant, 1998; Peres and Ledford, 2016). In the most recent century, turkey has
remained a commodity that enjoys significant seasonality due to its association
with Thanksgiving and Christmas holidays. Additionally, owing to a favorable
nutritional profile, adaptability to various climactic conditions, lack of
religious constraints, and increased globalization, turkey products have become
increasingly popular throughout the year and around the world (Henrikson et al.,
2018; Famous et al., 2019; Khatko and Shirokova, 2022).


2.1 THE GLOBAL TURKEY MARKET

Concurrent with advancements in genetics, feed science, and animal husbandry,
the global turkey market has become increasingly productive, saturated, and
competitive (Herendy et al., 2003). In 1962, turkey production in the US
comprised nearly 61.1% of global output; in 2022, US production consisted of
only 40.8% of global output (FAO, 2024). In the last 15 years, US production has
remained relatively constant at 2.7 (+/−0.2) million metric tons while global
production has increased from 5.5 million tons in 2007 to 6.2 million tons in
2021 (IndexBox, 2024; USDA, 2024b). Additional countries which hold significant
share in the global turkey market and have seen a decline in production and
market share in recent years include France (38% decrease in production between
2011 and 2021, 6.8% global market share in 2021), Germany (12.3% decrease, 6.3%
share), Brazil (48% decrease, 2.8% share), and the United Kingdom (29% decrease,
2.3% share; FAO, 2024).

As the US, Brazil, and many western European countries have seen either stable
or declining production, a variety of other players have emerged on or
strengthened their position in the global market. Between 2011 and 2021, Russia
increased production the most with an increase of 350,000 tons (Kálmán and
Szollosi, 2023). This represents a 615% increase from 2011 and a 6.5% share of
global production in 2021. Other nations increasing their production over the
same time period include Poland (55% increase, 6.7% share), Spain (64% increase,
5.0% share), Morocco (52% increase, 4.1% share), and Tunisia (78% increase, 2.4%
share; FAO, 2024). Nations which represent a notable (1–5%) share of the market
and have demonstrated consistent production in the last 10 years include
Argentina, Australia, Canada, Hungary, Italy, and Israel (FAO, 2024).

Top importing countries include Mexico, Germany, and Benin while top exporting
countries include the US, Poland, and Germany (Kálmán and Szollosi, 2023).
Global per capita consumption has remained relatively constant at ~0.75
kg/capita-year for the last decade (~5% of global poultry consumption); Israel
held the greatest per capita consumption at 9.56 kg/capita-year in 2021, with
Qatar, the US, Germany, and the Bahamas having the next greatest per capita
consumption rates (Kálmán and Szollosi, 2023).

Continuous increases in turkey production and consumption are expected in the
next 5–10 years. Roiter et al. (2021) project a 10–12% increase in finished
turkey product consumption in Russia by 2030, as increasing turkey production
represents an important aspect of Russia's long-term food security strategy
(Zimnyakov and Dmitrieva, 2018; Askerov et al., 2021). In comparison, EU
production is expected to continue to decline due to increasing domestic and
environmental costs while US production is projected to remain relatively stable
as in the previous decade (OECD/FAO, 2022; IBIS, 2024). Global turkey
consumption of 6.7 million tons is projected for 2025, an 8% increase from 2021
(Hristakieva, 2021). More broadly, global meat consumption is projected to reach
377 tons by 2031, a 48% increase from a 255 ton 2019–2021 baseline; the greatest
share of this growth (42.7%) is expected to come from increases in poultry
consumption, particularly in developing countries (OECD/FAO, 2022). This
livestock expansion is expected to be fueled by an increased consolidation of
production units, indicating a continuous shift from small, local farms toward
those resembling integrated growth and manufacturing systems as described in
Section 2.3 of this paper (OECD/FAO, 2022).

We conclude this section by emphasizing the following points: (1) global turkey
meat production has increased steadily over the last decade and is expected to
continue to increase over the next decade, (2) this increased production has
been and is expected to continue to be fueled by a disproportionately large
increase in new production units in developing nations which offset the decline
in production units seen in many developed nations, and (3) these new production
units will likely resemble the integrated systems such as the one shown in
Figure 1. As a result, the turkey supply chain will become increasingly
homogenized, representing an opportunity for global manufacturers to more
readily implement “greener” supply chain solutions such as the one described in
Section 3 of this paper.


Figure 1

Figure 1. Depiction of the turkey product supply chain. The segment of the
supply chain analyzed in this paper is highlighted in red.





2.2 CHALLENGES FACING THE TURKEY MARKET

Despite the historical and expected continual growth of the turkey market, there
remain many challenges manufacturers face when beginning, maintaining, and
expanding production. Aside from the economic challenges associated with meeting
increased global demand, manufacturers must contend with social challenges
including pressure from consumer concerns over animal wellbeing, challenges
related to biosecurity, and challenges related to environmental sustainability.

2.2.1 SOCIAL

As the technology and practices utilized in large-scale meat production have
changed in the last several decades, a significant body of literature has arisen
characterizing consumer attitudes, preferences, and understanding relating to
animal welfare. Notwithstanding limited knowledge surrounding the animal
husbandry systems utilized by large producers, consumers consistently rate
animal welfare as important to them (Verbeke and Viaene, 2000; Frewer et al.,
2005; Fleming et al., 2020). Tonsor et al. (2009) demonstrated that media
coverage in the US related to animal husbandry and welfare increased between
1982 to 2008, and that there was a statistically significant relationship
between negative coverage and decreased demand. The majority of consumers in
developed countries receive substantial information about food products from
television, the popular press, and social media, thus, large producers suffering
from negative media coverage related to poor animal welfare practices face a
significant risk of lost revenue (Kalaitzandonakes et al., 2004; Coleman et al.,
2022). Few articles discuss public perception of turkey welfare and production
processes, however, Bir et al. (2019) found that, for turkey, poor nutrition and
illness rank as the top concerns amongst US consumers.

Given the link between public perception and demand, manufacturers clearly have
an incentive to maintain a high level of wellbeing for their turkey flocks.
Supplementing this incentive is the fact that less diseased, less stressed, more
energetic, and better-fed flocks result in fewer mortalities, greater feed
efficiency, and greater yield for the manufacturer (Erasmus, 2018). As such,
optimizing flock performance via improved animal welfare is an area of
significant interest for academia and industry alike. The effects of hot and
cold exposure, chemical exposure, stocking density, lighting, antibiotic use,
feed composition, and various transportation strategies are all topics of high
and prolonged interest (Sherwin et al., 1999; Erasmus, 2017; Wein et al., 2017;
Cândido et al., 2018). The relationship between travel distance and mortality
rates (for the Company) is explained further in Section 4.1.2 of this paper.
Transport represents one of the most stressful events in poultry husbandry and,
as such, any supply chain solution or modification must consider the potential
impact on animal wellbeing (Marchewka et al., 2013).

2.2.2 BIOSECURITY

A significant threat to meeting the large and increasing demand on the global
turkey meat supply is presented by communicable disease outbreaks, particularly
Exotic Newcastle Disease (END, also called Paramoxyvirus) and Highly Pathogenic
Avian Influenza (HPAI; Frame, 2010; WOAH, 2024). Due to a natural susceptibility
to respiratory infections, both END and HPAI pose significantly greater risks to
turkey populations than other forms of poultry (Russell et al., 1989). In one
study, Aldous et al. (2010) found turkey to be over 200 times more susceptible
than chicken to two recent strains of HPAI. Given the ease of transmission, high
prevalence, high mortality rate, and the often-low efficacy of inoculation for
these pathogens, the implications of an uncontrolled and widespread outbreak are
severe. Consequently, the US Department of Agriculture, EU, and Russian Ministry
of Agriculture (as a few case examples) have developed sizeable regulatory
frameworks, research programs, monitoring networks, and emergency response
procedures aimed at curtailing the risk (Code of Federal Regulations, 2006;
Cardona et al., 2018; European Food Safety Authority, 2021; Vorotnikov, 2024).

Preventative measures taken to reduce the risks of infectious disease include
proper facility siting (away from water, other livestock), pest control,
limiting access to farms, personal protective equipment requirements for farm
workers, sanitization procedures (for farm equipment and personnel, replacing
litter between flocks), allowing “down time” between flocks, farm climate
control, removal of dead livestock, and frequent flock surveillance (USDA, 2013;
van Staaveren et al., 2020; Islam et al., 2024). In their assessment evaluating
the risk of HPAI infection throughout the turkey-growing process, Cardona et al.
(2018) identify transportation and load-out of birds as the segment of the
process which poses the greatest risk to spreading infection. Thus, any
satisfactory supply chain change or solution must contend with potential impacts
on biosecurity.

2.2.3 ENVIRONMENTAL SUSTAINABILITY

Agricultural production accounts for 19–29% of global anthropogenic GHG
emissions, a significant proportion of which (up to 80%) may be attributed to
meat production (Fiala, 2008; Vermeulen et al., 2012; Barthelmie, 2022). As
noted in Section 2.1 of this paper, global meat consumption is expected to
increase 48% (122 tons) by 2031, with the largest share of this change coming
from increased poultry consumption. Accordingly, reducing the GWP of poultry
products represents a critical component of improving the sustainability of the
world's food supply. Numerous Life Cycle Assessments (LCAs) have been conducted
estimating the environmental sustainability of chicken, and authors have
calculated GWP values ranging from 1.06 kg CO2-e to 9.98 kg CO2-e with a mean of
4.12 kg CO2-e (de Vries and de Boer, 2010; Clune et al., 2017; Costantini et
al., 2021). Comparatively fewer LCAs for turkey are available for analysis,
however, Leinonen et al. (2016) and Kheiralipour et al. (2017) estimated the GWP
for turkey at 3.63 kg CO2-e and 4.57 kg CO2-e, respectively. In a 2023 LCA
commissioned by the Turkey Farmers of Canada, the agri-food analysis firm Groupe
AGÉCO calculated the GWP for turkey at 3.50 kg CO2-e, with 77% of these
emissions coming from the growing of the bird. The largest contributing factors
to emissions, both in turkey growing and in the overall product life cycle, are
attributable to feed (~35% total emissions), energy for farm upkeep (~17% total
emissions), and transportation (~12% total emissions; MacKimmie, 2023).

Many groups advocate for sweeping dietary changes as an effective method of
curbing climate change (Yue et al., 2017; United Nations, 2024). In comparison
with beef (average GWP: 28.73 kg CO2-e) and other ruminant meat, turkey presents
a promising alternative (Clune et al., 2017). Hallström et al. (2015) calculated
that replacing ruminant meat with monogastric meat (chicken, pork, turkey, etc.)
would result in a 20–35% decline in GHG emissions from dietary sources.
Nonetheless, turkey holds a significantly higher GWP than most plant-based
sources (with GWPs ranging from 0.20–1.50 kg CO2-e; Clune et al., 2017). Given
the increasing rates of turkey consumption, turkey's high relative GWP when
compared to plant-based sources, and the sizeable contribution of transportation
to turkey's GWP, implementing sustainable supply chain solutions remains an
imperative for the turkey industry.


2.3 THE TURKEY SUPPLY CHAIN

As further context for the MIP model and ensuing discussion, this section aims
at characterizing the turkey supply chain, with a special emphasis on steps two
to three of the process described below. A brief overview of the supply chain in
its entirety may be described as follows (also depicted in Figure 1)4:

1. Incubation of eggs at a hatchery.

2. Delivery of 1-day-old chicks to farms termed “brooders” in the industry.
These farms are specially outfitted to care for birds in their first 4–6 weeks
of life.

3. Upon sufficient maturation of the juvenile turkeys (also called “poults”),
the livestock is transported from the brooder to a farm referred to as a
“finisher” farm. At the finishers, birds are raised to full maturity, which
could be expected to take a further 15 weeks.

4. Upon reaching full maturity, grown turkeys are then sent from the finisher to
the processing facility where they are harvested.

5. Following the harvesting of the animal, the assortment of products gathered
from the bird are then sent to other facilities for further processing and
packaging, as differing products (thigh meat, breast meat, offal, etc.) may be
allocated to different commodities (sausage, hot dogs, sandwich meat, pet food,
etc.). As an alternative to this step, some larger manufacturers have combined
processing and finished product facilities.

6. Finished product is sent to distribution warehouses and customers.

In comparison with industries representing a greater share of global meat
consumption such as chicken, pork, and cattle, less information relating the
turkey production process and supply chain is publicly available. Much of this
information is derived from the author's personal experience working in the
industry, from historical data provided by the Company, and from consulting with
Company farm managers and production planners, however, additional and
confirmatory details may be found in USDA (2013) and Cardona et al. (2018).

As stated above, the MIP model formulated in this paper aims to reduce
transportation distance and subsequent GHG emissions from the movement of poults
between brooders and finishers (steps two and three). The following points are
important in clarifying to the reader the details of this process and in
generating assumptions and constraints for the model described in Section 3.3 of
this paper.

The Importance of Brooders, Former Utilization of “Brood to Finish” Farms: the
brooder to finisher step in the turkey supply chain is one element which
distinguishes this commodity from other types of poultry, such as chicken or
duck. In comparison to the ~20-week life cycle and 20 kg harvest weight for
turkey at the Company, commercial broiler chicken may take only 7 weeks to grow
and weigh a comparative 3 kg at harvest (USDA, 2024a). Thus, manufacturers find
it convenient to have chicken spend their entire life cycle in one building.
Such a production model was formerly favored in the turkey industry via the use
of so-called “brood to finish” farms, however, this model has fallen out of
favor, mainly due to the increased biosecurity risk, higher cost, and poor
utilization of space associated with “brood to finish” growing schemes.

Farm Capacity: as implied by Figure 1 and explained in Section 3 of this paper,
the number of brooder farms is generally significantly less than the number of
finisher farms. Brooders and finishers also have varying capacities of flock
sizes they accommodate. It remains important to note that the demand is fixed at
the brooder to finisher level, as contracts with the turkey hatcheries supplying
the brooders are negotiated years in advance. Historical data (3 years) and
production forecasts (2 years) provided by the Company indicate that demand at
the hatchery to brooder level is also consistent, further validating this
fixed-demand assumption.

Timing: some elements related to this constraint have been discussed in previous
sections. The amount of time required to raise the turkey would depend on the
breed, the sex, the feeding schedule, and a variety of other factors. The
turkeys utilized by the Company in this problem spent an average of 5 weeks in
the brooder prior to transfer to a finisher, where the livestock was grown for a
further 15 weeks. This schedule may be extended or condensed to some extent
dependent upon the performance of the flock and the processing facility,
however, every effort is made to harvest the birds at a weight of 20 kg, which,
in the case of the Company, is the design specification of the turkey harvesting
equipment at the processing facility; too great of a deviation from this weight
results in decreased yield. Due to the concern for biosecurity, there is also a
need to sanitize farm equipment and replace litter between each flock. The time
allotted for this would depend slightly on the production schedule and flock
performance, but, for the Company's farms, this historically took 3–4 weeks for
brooder farms and 2–3 weeks for finisher farms. The time to completely
turnaround a brooder farm for a new flock shall be taken as 60 days (35 days
growing, 25 days sanitization/preparation) and the time taken to completely
turnaround a finisher farm shall be taken as 120 days (105 days growing, 15 days
sanitization/preparation). Thus, the model will formulate an “A/B” system
wherein each brooder farm will be assigned to two groups of finisher farms; this
could be contrasted against the Company's current “first available” system in
which poults from a brooder are sent to whichever finishers are currently
available, without consideration for distance traveled.

Biosecurity: the importance of this constraint was discussed at length in
Section 2.2.2. As it relates to its application in this problem, the primary
biosecurity concern taken into account in the model formulation is that
different brooder flocks may not be combined upon transfer to the finisher
(finisher farms are comprised of multiple barn buildings). While it may be
efficient from a logistical point of view to allow brooders to “share” finisher
assignments, this would result in an unacceptable biosecurity risk. Thus,
finishers may be assigned to only one brooder.

Further Remarks on Transportation Between Brooders and Finishers: upon the
transfer of birds from brooders to finishers, poults are loaded into trailers at
the brooder farm and then unloaded at one of a few finishers. Trailers used for
transfer hold up to 3,000 poults. Thus, a brooder with a capacity of 120,000
would require 40 trailer trips to empty to a set of finishers, a finisher with a
total capacity of 33,000 would require 11 trips to fill, and so on.


2.4 PREVIOUS RESEARCH IN GREEN LOGISTICS AND APPLICATION IN THE PRESENT STUDY

Prior to the 1960's, relatively little concern in academic literature was given
to the environmental degradation caused by freight transport, and a common
assumption was held that the environment's ability to effectively absorb wastes
and replace resources was effectively infinite (Murphy et al., 1995). However,
as the environmental externalities associated with freight transport continued
to mount throughout the latter half of the twentieth century this erroneous
assumption was increasingly cast aside, and an increasing number of logistics
publications and corporations began to investigate methods of decreasing the
negative environmental impacts of their supply chains (Aronsson and Huge-Brodin,
2006; Mckinnon, 2015). In a more recent review by Ma and Kim (2023), the
researchers reveal that “green logistics” has become a vibrant area of scholarly
inquiry. They demonstrate the rapid growth of the field from only a few 100
publications annually in the mid-2000's to almost 3,500 in 2021, and identify
“optimization analysis of low-carbon vehicle routing and time” (the subject of
this paper) as one of the most active research topics in the last few years.

Although many definitions of varying scope have been proposed for the term
“green logistics,” for the purposes of this paper we utilize the definition
provided by Blanco and Sheffi (2017): “the systematic measurement, analysis, and
ultimately, mitigation of the environmental impact of logistics activities.”
This might encompass supply chain activities involved in purchasing,
warehousing, production, transportation, delivery, or reverse logistics. One
might reasonably assume that any type of cost-reduction effort realized by a
more “efficient” supply chain could be considered “green,” however, this is not
the case. As many studies have shown, some supply chain “efficiencies” including
centralization of inventory, wider sourcing of materials, and just-in-time
inventory systems come with a greater environmental cost (Whitelegg, 1994;
Garnett, 2003; Matthews and Henrickson, 2003). Nevertheless, implementing green
logistics solutions are frequently associated with decreased cost and improved
financial performance (Rao and Holt, 2005; Wang and Sarkis, 2013; Ahmad et al.,
2022). In PwC/APIC's 2013 survey of 162 supply chain professionals representing
large US companies, “cost reduction” was cited as the top benefit derived from
sustainable supply chain initiatives (PwC and APIC, 2014).

In this study, we develop a MIP model with the objective of reducing the
distance traveled (and, subsequently, GHG emissions) when transporting turkey
poults from brooders to finishers. This transportation problem may be classified
as a Vehicle Routing Problem (VRP), a class of problems first described in 1959
by Dantzig and Ramser which seeks to determine the least-cost delivery route
from a facility to a set of geographically disbursed customers; this class of
problem has seen numerous successful applications (Dantzig and Ramser, 1959;
Laporte, 2009). The Pollution Routing Problem (PRP), is a variation of VRP in
which variables such as speed of travel, terrain, equipment characteristics,
load weight, loading time, and congestion are utilized in the construction of
the optimal network design (Bektaş and Laporte, 2011). While in this model we do
consider equipment characteristics in the overall GHG emissions calculation,
factors such as speed of travel, terrain, and congestion are assumed to be
negligible due to homogeneity of the vehicles used in transport and the flatness
of terrain and lack of congestion in the area surrounding the farms located in
this study. As such, the way we formulate this problem more accurately resembles
a traditional VRP.

Several authors have applied a variety of mathematical modeling approaches
toward improving the efficiency and/or sustainability of chicken processing and
distribution operations, although none have ventured to apply similar methods to
turkey growing. A significant proportion of these studies apply linear
programming approaches to either nutrition delivery (Chagwiza et al., 2016;
Alqaisi et al., 2017) or manure management (Ma et al., 2018; Deng et al., 2022).
Islam et al. (2016) applied a MIP approach to the poultry industry in
Bangladesh, effectively assigning retailers to manufacturers with the objective
of maximizing profits to retailers. Boudahri et al. (2011) approached a chicken
processing facility and chicken farm citing problem wherein processing
facilities were allocated to areas around customer clusters and farms allocated
around processing facilities; this was done in such a way to minimize the
transportation costs in the network. While both of these studies approach
similar problems as those addressed in this study (assignment and transportation
cost minimization), neither adequately approximates the circumstances.

Expanding the scope of this literature review beyond just the poultry industry,
one may—in some respects—find a greater similarity between the pre-processing
animal transport supply chain of cattle and turkey than between chicken and
turkey. In the cattle industry, calves are raised with their mothers for ~6
months prior to being weaned (for 2 months on average) and then transported to
feedlots; additional transport nodes and logistics stopovers are possible at
auction markets, classification centers, or health checkpoints (Miranda-de la
Lama et al., 2014; Machado and Michael, 2022). This may be contrasted against
the transport nodes found in the pre-processing turkey supply chain (hatcheries,
brooders, finishers, and processing facility). In an attempt to optimize this
aspect of the cattle supply chain, a variety of authors taken approaches similar
to the one we present in this paper. Frisk et al. (2018) deployed a MIP via the
RuttOpt route optimization system for the purposes of solving a pick-up and
delivery problem with the objectives of minimizing transport time and distance
driven. Morel-Journel et al. (2021) effectively assigned weaned calves to
sorting centers via an algorithm that could be classified as an MIP with Time
Windows (TW) with the objective minimizing transport distance. As a final case
example, Gebresenbet et al. (2011) utilized the commercially available Route
LogiX software to simulate and arrange transportation assignments between
feedlots and an existing and prospective new processing facility. A summary of
the objectives, methodology used, and results obtained by some of the foregoing
research relevant to our paper is reviewed below in Table 1.


Table 1

Table 1. Summary of related research.




From this review of green logistics and some recent applications, we conclude
that (1) implementing supply chain solutions directed at reducing GHG emissions
is a topic of increasing importance and interest, (2) amongst a variety of
approaches, the use of VRP MIPs for the purposes of solving assignment problems
has been successful in similar industries, and (3) no similar approach to
optimizing the turkey growing supply chain has yet been proposed. Thus, a
significant contribution of this study lies in its novel application of a
well-proven and applied method to the turkey industry.


3 PROBLEM FORMULATION

Now that the reader has been provided with a sufficient level of background
information necessary to understand the challenges faced by the turkey industry,
the energy-intensive nature of transporting poults between brooders and
finishers, and the potential for application of green logistics in this process,
we propose the following MIP model formulated as a VRP.


3.1 SYMBOL DESCRIPTIONS

Let i denote the index of brooders and j denote the index of finishers. The
capacity of brooder i shall be given as ai and the capacity for finisher j shall
be given as bj. The number of trailer trips required to fill finisher j would be
equal to the capacity of finisher j divided by the number of poults delivered to
the finisher by each trailer trip (3,000). Thus, a finisher with a capacity of
33,000 would take 33,000/3,000 = 11 trailer trips to fill. The number of trailer
trips times the distance between brooder i and finisher j would equal the total
travel distance required to fill finisher j from poults provided by brooder i, a
value which shall be described as dij. The binary decision variable xij shall be
equal to 1 if finisher j is assigned to receive poults from brooder i and 0
otherwise. The notation for this VRP is summarized in Table 2.


Table 2

Table 2. Notation for the brooder-finisher VRP.





3.2 DATA COLLECTION

The dataset used in the application of this model may be found in the
Supplementary material. All data related to capacity and distance is based on
the operational and geographical data provided by one of the largest turkey
manufacturers in the US (referred to as “the Company”). The Company operates 10
brooder farms with a capacity of 110,000 to 187,000 birds/farm and 66 finisher
farms with a capacity of 22,000, 33,000, 44,000, or 55,000 birds/farm. The
distances between brooder and finisher farms ranges from 1.5 to 135 km. On an
annual basis, this collection of farms would be expected to grow ~9 million
turkeys/year.


3.3 MODEL FORMULATION AND SOLUTION METHODOLOGY

The objective function applied to optimize brooder-finisher routing is described
in Equation (1):

Minimize Z=n∑i=1m∑j=1dijxij    (1)Minimize Z=∑i=1n∑j=1mdijxij    (1)


The model is subject to the following constraints:

∑mj=1x1j+x2j+x3j+x4j+x5j+x6j+x7j+x8j+x9j+x10j=1    (2)∑j=1mx1j+x2j+x3j+x4j+x5j+x6j+x7j+x8j+x9j+x10j=1    (2)

∑ni=12ai≤∑mj=1bjx1j    (3)∑i=1n2ai≤∑j=1mbjx1j    (3)

xij∈{0,1}     (4)xij∈{0,1}     (4)


Equation (1) seeks to minimize the total travel distance between brooders and
their assigned finishers. The constraints represented by Equations (2) and (4)
ensure that each finisher may be assigned to only one brooder while the
constraint represented by Equation (3) ensures the collective capacity in those
finishers assigned to brooder i is sufficient to accept two flocks of brooder
poults. This also ensures that assignments intrinsically allow any output to
observe the timing constraints, since the turnaround time for finishers is twice
that of brooders. Additionally, as argued in the following section, the use of
Equation (3) results in a system organization that better promotes biosecurity
than a “first available” production model.

The solution methodology we employ for solving this MIP is exemplified by the
combination of root relaxation and the branch-and-bound algorithm. Initially,
the root node is determined via root relaxation, wherein the integer constraints
are relaxed, allowing the variables to assume continuous values. As the
branch-and-bound algorithm progresses, cutting planes are employed to further
tighten the bounds and eliminate fractional solutions, thereby enhancing the
efficiency of the search process.


4 RESULTS AND DISCUSSION

The objective function and constraints described in the previous section were
built into a program utilizing the Gurobi Optimizer software (output and
parameters may be found in the data availability statement). This model was then
solved utilizing the capacity and distance data provided by the Company as well
as the solution methodology just described.


4.1 MODEL RESULTS

Solving the foregoing model with the given constraints yields the optimal
brooder-finisher assignments (Data and code may be found at this repository:
https://github.com/bazylhorsey/livestock-logistic-optimizer). This output
details the exact assignments of brooders to each finisher given the objective
and constraints detailed above. For example, brooder 2 in this case is assigned
to send poults to finishers 8, 16, 22, 23, 24, 33, 40, 46, and 64. The capacity
of the identified finishers would enable the associated brooder to continuously
supply these finishers indefinitely given a constant production demand, a
reasonably justifiable assumption given 3 years of historical production records
and the 2-year production forecast (see Section 2.3).

With this derived set of brooder-finisher assignments, the expected annual
travel distance may easily be calculated and compared against historical
records. The value of the solution obtained by the model is 27,856.1 km. Note
that this is the travel distance for trailers going to the finisher from the
assigned brooder once. Thus, the expected value of the annual travel distance
may be calculated by multiplying the solution value by the number of times per
year each finisher could be expected to receive birds
(120 day finisher turnaround time365daysyear=3.04deliveriesyear)(120 day finisher turnaround time365daysyear=3.04deliveriesyear),
and again by two to account for return trips. Executing this calculation yields
the following:

27,856.1kmdelivery⋅3.04deliveriesyear⋅2=169,458kmyear 27,856.1kmdelivery·3.04deliveriesyear·2=169,458kmyear 


Given that the brooder-finisher production supply chain arrangement is shared by
most large turkey manufacturers, this model could be readily re-applied by
another manufacturer, given considerations to some of the parameters that may
not be shared in common between manufacturers (breed, growing time, farm
capacity, etc.—further discussed in Section 4.2). Turkey manufacturers
implementing the framework provided by this model could expect operational
improvements including a reduction in GHG emissions/costs from transportation
and improved animal wellbeing and biosecurity, as demonstrated in the two
sub-sections below.

4.1.1 REDUCTION IN GHG EMISSIONS AND TRANSPORT COSTS

Based on records provided by the Company, a total of 371,825 and 309,978 km were
traveled (delivery and return trips) in their 2020 and 2021 fiscal years,
respectively. This could be contrasted against the 169,458 km of expected travel
distance determined by the optimal assignment model constructed by the MIP. A
performance comparison between the optimal assignment model and previous years
is demonstrated in Table 3.


Table 3

Table 3. Distance/GHG emissions performance under optimal assignment model
compared to previous years.




There exist a variety of popular methods for calculating GHG emissions from
freight transport. These include fuel-based, distance-based, and weight-distance
based methods. Fuel-based approaches require knowledge of total fuel
consumption, which is not a metric tracked by the Company, and thus not a viable
approach for calculations in this study. Weight-distance methods are generally
applied when using shared modes of transportation or when only a minimal amount
of information (related to the exact vehicle used and route) is known about a
shipment (Blanco and Sheffi, 2017). Thus, the most appropriate calculation for
GHG emissions in this study would be the distance-based approach using the
appropriate emissions factor (EF, as defined by the GHG Protocol) of
1.07kg CO2−ekm1.07kg CO2-ekm for diesel-powered articulated heavy goods vehicles
(IPCC, 2017). Thus, calculating GHG emissions in this problem is executed as
follows:

Emissons=EF⋅∑(distance traveled)    (5)Emissons=EF·∑(distance traveled)    (5)


As shown by Table 3, implementation of the brooder-finisher assignments
according to the model results would yield an ~50% reduction in travel distance
and GHGs emitted. Other benefits which could be reasonably associated with
adoption of the results of this model would include reduction in driver labor
costs (fewer drivers required, less time driving), reduction in costs associated
with vehicle repair and maintenance, and reduction in the comparatively high
administrative production planning overhead affiliated with a “first available”
system.

4.1.2 IMPROVED WELLBEING AND BIOSECURITY

As emphasized in Section 2.2.1, transportation of turkey poults from brooders to
finishers represents a very stressful event for the livestock. Upon transport,
poults are removed from their environmentally-regulated pens by laborers and
loaded into trucks. During this process, the poults are subjected to the
psychological and physical stresses of being handled by laborers as well as the
stresses associated with exposure to the outside environment. As the
transportation distance between brooders and finishers increases, the time
poults spend exposed to these psychological and climactic conditions increases.
Table 4 exhibits this association. As demonstrated by the historical data
collected by the Company (columns 2–4 of Table 4), poult mortality rate is
heavily associated with transport distance. Columns 5 and 6 of Table 4
demonstrate the expected number of trips taken in each distance category and the
subsequent number of moralities due to transport (given similar trip mortality
rates as previous years) over 1 year. The estimate of total mortalities after
adoption of the brooder-finisher MIP model results indicates that an annual
mortality reduction of almost 40% (~7,000 fewer mortalities per year) could
likely be achieved. This reduction in mortalities would indicate an overall
improvement in animal wellbeing because the poults would find themselves
subjected to the stresses associated with transport for significantly less time.


Table 4

Table 4. Comparison of historical poult mortality rates during transport and
expected mortality rates after adoption of MIP model results.




A significant additional benefit of the “A/B” assignment model formulated by the
MIP over the “first available” system currently used at the Company could be
attributed to reduced biosecurity risk. Figure 2 shows an modified depiction of
the turkey supply chain given adoption of the MIP model assignments. A visual
comparison of Figures 1, 2 indicates this potential biosecurity improvement.
Under a “first available” system, poults are sent to a finisher which formerly
received birds from a different brooder, whereas in the supply chain arrangement
organized by the MIP each brooder-finisher group is segregated. Despite
sanitization efforts between finisher flocks, variants of the HPAI virus possess
a demonstrated ability to persist in a variety of media for over a month
(Cardona et al., 2018). Thus, in the event of an infectious disease outbreak, a
“first available” system leaves poults from an incoming flock at risk of
contracting a disease that may have formerly been confined to only the flocks
associated with a different brooder farm. Adoption of segregated
brooder-finisher groups generated by the MIP model would thus represent a more
effective way of confining disease outbreaks.


Figure 2

Figure 2. Modified depiction of turkey supply chain given adoption of MIP model
results.





4.2 STUDY LIMITATIONS

The primary limitations of this study could be categorized into three main
groups. Firstly, assumptions related to brooder-finisher turnaround time and a
variety of other related factors (bird breed/sex, farm capacity, network design,
etc.) are derived from only one turkey manufacturer. The second broad category
of limitations could be ascribed to the difficulty of implementation
pre-existing turkey manufacturers may face when attempting to transition from a
“first available” brooder-finisher turkey growing system to one resembling
optimal brooder-finisher assignments formulated by a MIP. Finally, more accurate
and comprehensive methods of measuring GHG emissions may yield improved results.

In addressing the first limitation described in the preceding paragraph, a
turkey manufacturer must consider the breed and sex of the turkeys which they
grow, the feed efficiency which they are able to realize, and the potential
flexibility of the sanitization times and processing facility schedule. These
factors all contribute to the overall turnaround time of a brooder or finisher,
and the performance of this or any similar model would be sensitive to a
modification in these parameters. Production delays at the processing facility
or an underperforming finisher flock might require a delay in the processing of
a given finisher flock or group of finisher flocks, which would in turn delay
the arrival of a new finisher flock from the finisher's respective brooder and
thus require a more condensed sanitization schedule for both finisher and
brooder farms. Despite the near-inevitability of such delays in any
manufacturing operation, the Company analyzed in this study demonstrated
significant flexibility when faced with such challenges. For example, historical
data reveal that the sanitization time for brooders and finishers could be
condensed to as little as 1 week or extended to as much as 4 or 6 weeks,
respectively; this could be contrasted against the greater-than 3-week and
2-week brooder/finisher sanitization time assumptions adopted in the foregoing
model. The growing time of poults at a brooder had also historically been
extended for up to an additional week. Furthermore, operating the processing
facility for an additional shift (a common practice at the Company) would
constitute another strategy for addressing a surplus of fully-grown finisher
flocks. A stochastic MIP model was considered in an attempt to capture this
variability, however, on account of the fixed hatchery → brooder → finisher →
processing facility growing/production volume (see Section 2.3) and the
operational flexibility just described, a stochastic model was determined to
have little additional benefit. Sensitivity analyses examining the effects of
turnaround time modification (again, generally a function of a combination of
bird breed and sex, feed composition, feed efficiency, husbandry practices, and
labor availability) or siting of additional farms may demonstrate improvements
in the model, or require modifications to the model constraints and input
variables. In summary, any change in the turnaround time or capacity assumptions
described in Section 2.3 would necessitate a change in or additions to Equations
(2, 3) of the foregoing model. However, the overall framework of the model could
nonetheless see adoption by any turkey manufacturer, given that the manufacturer
operates under the industry standard practice of having standalone brooder and
finisher farms.

The second, and perhaps foremost, limitation could briefly be encapsulated as
“difficulty of implementation for existing manufacturers.” The model formulated
above generates the optimal brooder-finisher assignments, but does not detail
how a manufacturer might transition from a “first available” or other system to
this more sustainable system. When attempting to implement the results of this
more sustainable production and transportation system, and existing manufacturer
might: (1) cease growing and production for a period of several months before
re-starting under the brooder-finisher assignments resulting from model
execution or (2) engage in a carefully-planned slow transition (likely spanning
at least 2–3 years) from the manufacturer's current practices to the
arrangements identified by the MIP model output. Barring anomalous
circumstances, the first option would likely present an untenable solution to
most large manufacturers. Thus, in implementing the recommended solution from
the model designed in this paper an existing manufacturer would likely need to
select the second option, which represents the approach currently being taken by
the Company in this study. Further research in this area, describing how a
turkey manufacturer might quickly transition from their current state to a more
sustainable state, would constitute an auspicious area of inquiry. Ideally, a
manufacturer would consider the sustainability of a potential new turkey growing
and processing operation prior to construction of the network; the review in
Section 2.4 of this paper as well as reviews conducted by other authors indicate
that manufacturers are increasingly considering sustainability in their network
design (Joshi, 2022). In such a case, the prospective manufacturer would be able
to more easily execute and implement a similar MIP model as the one described
herein.

In this study, the distance-based method was utilized in calculating GHG
emissions. This represents an inferior approach to the fuel-based method (not
used due to a lack of data), but a superior approach to the weight-distance
based method (in this case). Factors related to congestion and landscape were
considered as negligible to the overall GHG emissions. Given the geographical
setting of the Company, this was an appropriate assumption, however, this
assumption may not be valid in a re-application of this approach. Additionally,
factors such as vehicle idling time, vehicle speed, and road characteristics
were not considered. Consequently, the GHG emissions reduction calculations
presented in this paper likely present an underestimate of the actual reductions
realizable upon implementation. With additional data related to road
characteristics, vehicle characteristics, and idling time, the objective
function might be reformulated as a PRP (with factors affecting GHG emissions in
the objective function) rather than a VRP. PRPs constitute a new and developing
area of academic inquiry, and reformulation of the objective function to account
for the assortment of variables affecting GHG emissions in freight transport
presents a promising direction for future research (Marrekchi et al., 2019).


5 CONCLUSION

In this paper, we develop a MIP model aimed at reducing the travel distance and
subsequent GHG emissions in a network of turkey brooders and finishers. This
model is then applied to a network owned by one of the largest turkey
manufacturers in the US. Implementation of the ensuing model results could be
expected to reduce GHG emissions in the network by ~50% (a 184 metric ton CO2-e
reduction) while also favorably addressing the other preeminent challenges
currently facing the turkey sector (cost of production, biosecurity, and animal
wellbeing). The foremost limitation of this model may be identified as the
difficulty an existing manufacturer might face when implementing the model
results. However, it must be pointed out that trends in the global turkey market
indicate the sustained increase in new production units in developing countries.
As such, this timely publication may enable these new and expanding turkey
operations an avenue by which to decrease the environmental impact of their
products. This opportunity is underscored by the model's simplicity and
subsequent ease of execution when supplied with a new data set. Furthermore,
this study provides a broader contribution to the existing literature by
describing and analyzing the supply chain of turkey products, a commodity whose
supply chain had previously only been sparsely described in the popular press
and industry manuals.


DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The
names of the repository/repositories and accession number(s) can be found in the
article/supplementary material.


AUTHOR CONTRIBUTIONS

GW: Conceptualization, Data curation, Formal analysis, Investigation,
Methodology, Project administration, Validation, Visualization, Writing –
original draft, Writing – review & editing. BH: Formal analysis, Methodology,
Software, Visualization, Writing – review & editing. RS: Resources, Supervision,
Writing – review & editing.


FUNDING

The author(s) declare that no financial support was received for the research,
authorship, and/or publication of this article.


CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.


PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not
necessarily represent those of their affiliated organizations, or those of the
publisher, the editors and the reviewers. Any product that may be evaluated in
this article, or claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.


FOOTNOTES

1. ^See Section 2.3 for a detailed description of the turkey production process,
including definitions of the terms “brooder” and “finisher.”

2. ^Feed efficiency is a measure of how much saleable product is produced per
unit of feed consumed.

3. ^See Section 2.4 for a more thorough treatment of this topic.

4. ^Statements related to timing are specific to the data provided by the
Company. However, the time required to raise a turkey would generally depend on
the sex, the breed, the feed efficiency, and the health of each flock.


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Keywords: optimization, mixed-integer programming, green logistics, agriculture,
poultry, turkey

Citation: Wilson G, Horsey B and Stone R (2024) Implementing concepts from green
logistics in the turkey production supply chain. Front. Sustain. 5:1416964. doi:
10.3389/frsus.2024.1416964

Received: 13 April 2024; Accepted: 21 June 2024;
Published: 10 July 2024.

Edited by:

Slawomir Wycislak, Jagiellonian University, Poland

Reviewed by:

Enrica Vesce, University of Turin, Italy
Bikash Koli Dey, Hongik University, Republic of Korea

Copyright © 2024 Wilson, Horsey and Stone. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other forums is permitted, provided the
original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not
comply with these terms.

*Correspondence: Griffin Wilson, gmwilson@iastate.edu



Disclaimer: All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations, or
those of the publisher, the editors and the reviewers. Any product that may be
evaluated in this article or claim that may be made by its manufacturer is not
guaranteed or endorsed by the publisher.




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