Introduction
There has been an increased consumer interest in the availability of local beef products in the past few decades (Hayek and Garrett, Reference Hayek and Garrett2018). Among those, grass-finished beef represents a potentially valuable market as consumer demand grows and market channels expand due to the perception that this meat product is more environmentally sustainable and healthier (McCluskey et al., Reference Mccluskey, Wahl, Li and Wandschneider2005; Cheung and McMahon, Reference Cheung and McMahon2017). Consumer increasing demand drives the growth in the sustainable regional food industry. In turn, there is growing interest in beef production from cattle fed in pasture-based systems rather than grain-finishing systems in the USA (Hayek and Garrett, Reference Hayek and Garrett2018).
Grass-finished beef operations are relatively small in the northeastern USA. However, strategic opportunities may exist for producing foods based on perennial forage crops because pasture land in the region is underused (USDA/NRCS, 2018; Wolfe et al., Reference Wolfe, DeGaetano, Peck, Carey, Ziska, Lea-Cox, Kemanian, Hoffmann and Hollinger2018). According to recent survey results from the USDA/NRCS (2018), only 43% of the northeast's pasture-covered area is actually used for grazing. This is far lower than most other parts of the USA. (There are several barriers to increasing the use of grasslands for cattle production. One of these barriers is farmers' limited access to these underutilized grasslands as well as capital for agricultural development. In certain sections of the northeast, land is too expensive. Farm credit can help producers purchase more land and make the necessary investments to expand and consolidate their cattle raisings. However, credit may not be provided to people interested in grazing livestock because it is believed that such livestock farming cannot be profitable. New grass-based agriculture also faces unique knowledge and production issues that must be addressed through research, teaching, and extension.) In addition to having unutilized agricultural land, forage crops and grazing have far larger yield gaps than those shown for principal grain crops (Thorn, Baker and Peters, Reference Thorn, Baker and Peters2021). Under the circumstances, there exist opportunities to expand local beef production in grass-based ruminant production systems in the northeast USA.
Grass-finished beef offers a range of benefits, including ecological sustainability, environmental protection, improved quality of life, and increased economic viability (Conner, Campbell-Arvai and Hamm, Reference Conner, Campbell-Arvai and Hamm2008; Markiewicz-Keszycka et al., Reference Markiewicz-Keszycka, Carter, O'Brien, Henchion, Mooney and Hynds2022). Many research and implementation projects have since been undertaken to examine the potential to expand the local production of grass-finished beef to revive local foods and enhance food system sustainability (Gwin, Reference Gwin2009; Winrock International, 2012; Hayek and Garrett, Reference Hayek and Garrett2018; Thorn, Baker and Peters, Reference Thorn, Baker and Peters2021). In the northeast, the regional grass-finished beef expansion has fundamental implications for animal slaughter and processing. In the region, the majority of existing plants serving the cattle production system is often local, small, and dispersed. This infrastructure may be a barrier to expanded grass-finished beef production (Lewis and Peters, Reference Lewis and Peters2012; Waro et al., Reference Waro, Gómez, Kalaitzandonakes, Peters, Baker and Conard2019; Zezima, Reference Zezima2010). To date, there is insufficient information on bottlenecks for beef cattle slaughter and processing in the region and how the handling system should be adjusted to accommodate service demand stemming from the production expansion.
To characterize grass-finished beef production and examine supply chain barriers for expansion, we limit our study area to the contiguous counties of the six New England states plus New York, 129 counties in total. Beef cattle raised, slaughtered, processed, and sold in the region is about 40,000 heads (USDA, Reference GE2012a). Beef consumption in the region heavily relies on imports. While New York and New England (NYNE hereafter) agriculture officials look to expand the local beef industry with an aim to make the region more self-sufficient, many producers expressed similar hopeful sentiments for providing local beef to the community (Lewis, Reference Lewis2014; NYS Department of Agriculture and Markets, 2018). Given the availability of high-quality forage, pasture land, and markets, there is a significant opportunity for beef producers in the region to shift to grass-based finishing systems.
Scaling up grass-finished beef production requires additional land and higher yields for pasture and perennial forage crops. Given the inherent biological capacity of the region's natural resource base, we develop three possible scenarios for expanding production without converting land to agricultural use or displacing current agricultural commodity production. Each scenario associates with a different level of grass-finished beef production expansion. Based on the three scenarios, by solving an optimization model, we explore the spatial structure of NYNE beef cattle assembly, slaughter, processing, and distribution system that might result if these supply chain activities were regionally coordinated. From this, we examine the capacity bottleneck problem in the region and identify the optimal capacity expansion plans to address any discovered bottleneck problems. We answer two questions: (1) Does the physical capacity of existing infrastructure in NYNE meet the cattle slaughtering and processing demand in the three scenarios? (2) If there are bottlenecks for slaughter or processing, to what extent should existing plant slaughter or processing capacity be expanded to meet the service demand while minimizing the total operating costs of the beef supply chain system?
The seasonality of beef cattle production determines the seasonal operational patterns and operating cost dynamics for facilities in the region. To account for the geographic and seasonal variation of livestock production, the optimization problem is formulated as a monthly model. The volume of beef cattle available for slaughter is typically highest in October and November (USDA/NASS, 2013). The bottleneck problem is likely pronounced in these 2 months. While our analysis encompasses all 12 months, we focus on these 2 months when investigating the monthly bottleneck problem. We predict that if there is a processing or slaughter bottleneck, it will likely be more noticeable in these 2 months than it will be in other months. Just as our results indicate, the optimal capacity expansion solution in October or November is sufficient to solve the bottleneck problem in any other month.
The remainder of the paper is structured as follows: we start by describing the three grass-finished cattle production expansion scenarios (second section). Next, we introduce the structure of the beef supply chain and specify the optimization model. The sources of data are described (third section). Subsequently, the fourth section solves the model to examine the capacity bottlenecks and identify the optimal paths for capacity expansion. These results are then visualized and analyzed in the fifth section. Implications for improving supply chain performance are provided in the penultimate section. Finally, some directions for future research are presented in a concluding discussion.
Description of beef production expansion scenarios
There is a significant latent biological capacity to feed ruminant animals on perennial forage and pasture in the humid, temperate northeast region. In this study, we develop three grass-finished beef cattle expansion scenarios, accounting for the forage requirements of current cattle production as well as the expansion of sectors associated with grass-finishing, such as cow–calf operations and hay production. The scenarios are based on the assumption that expansion of forage demand, to feed grass-finished cattle, catalyzes additional increases in forage productivity (yield gap) and a growth in agricultural activities on unused pasture land. In each scenario, the production expansion will not expand regional agricultural land or force out already established agricultural businesses. For ease of interpretation, the three scenarios as follows are based on a historical benchmark, the number of beef calves produced in NYNE in 2012.
(1) Scenario 1 assumes that the region feeds all available beef calves for grass-finished meat. Calves for the grass-finished system come from existing cow–calf operations.
(2) Scenario 2 assumes the region produces twice the number of calves currently available in the region for grass-finished meat. Calves for the grass-finished system come from existing cow–calf operations and from new cow–calf operations in a 1:1 ratio. New cow–calf operations are either adding cow–calf operations to the region or bringing in calves from outside the region.
(3) Scenario 3 assumes the region produces three times the number of calves currently available in the region for grass-finished meat. Calves for the grass-finished system come from existing cow–calf operations and from new cow–calf operations in a 1:2 ratio.
The area of available land, the productivity of that land for growing animal feed, and the efficiency with which cattle convert feed into a marketable product all influence a region's biological capacity to produce an animal product. Using an integrated modeling approach (Thorn, Baker and Peters, Reference Thorn, Baker and Peters2021), we estimated the extent of potentially available forage land based on land in grass cover, primarily hay and pasture (Cropland Data Layer, USDA-NASS, 2012b). We used a ‘yield gap’ approach to compare historical yields to predicted yields for the underlying soils under prudent management from the Soil Survey Geographic database (Soil Survey Staff, 2017) to assess the potential for increasing crop and pasture yields. (Global analysis of yield gaps in maize, rice, and wheat suggests that actual yields appear to top out at 70–80% of yield potential (Lobell et al., Reference Lobell, Cassman and Field2009).) Using the Large Ruminant Nutrition System model (LRNS, 2022) and the COWHERD model (Fox, Rasmussen and Baker, Reference Fox, Rasmussen and Baker1999), two models of livestock energy balance, we estimate feed efficiency for grass-finished production for both the cow–calf and finishing phases of the system. Our calculation of the annual capacity to produce grass-finished beef for the three scenarios relies on assumptions as follows:
(1) the area of land used for hay and haylage production remains constant;
(2) except for cow–calf operations, the area of pasture on existing farms is in use to support existing livestock and is unavailable to grass-finished beef production (existing livestock includes grain-finished cattle, grass-finished cattle, dairy cows, sheep, pigs, and horses);
(3) the grass-finished beef system may have access to all remaining land in grass cover;
(4) regional hay productivity increases to accommodate the needs of additional grass-finished cattle but cannot exceed 80% of expected crop yields reported in the Soil Survey Geographic (SSURGO) database (Soil Survey Staff, 2017). (Given that current hay yields in the region are just 54% of expected yields, considerable room exists to increase yield with available technology by intensifying the current level of management (Thorn et al., Reference Thorn, Baker and Peters2021));
(5) grazing productivity on grass-finished operations gradually increases to close the yield gap, reaching 80% of expected yields (Soil Survey Staff, 2017).
In the three scenarios, biological capacity was measured as the number of market animals finished per year and equals the available land area for growing perennial forages divided by the land area required per finished animal, accounting for the needs of both market animals and the cow–calf herd. Note that in all scenarios, the land requirements for the cow–calf operations needed for the first 113,250 calves are captured by the pasture requirements of existing farm operations, which include existing cow–calf farms. The land area needed for each finished cattle equals the total amount of the land areas required for pasture and hay, which equals annual dry matter requirements for the grass-finished cattle system from pasture or hay divided by the yield of pasture or hay.
Based on the above, we estimate the annual capacity to produce grass-finished beef under the three aforementioned scenarios based on the biological potential for production. All expansion outcomes were well below the estimated biological maximum potential of roughly 900,000 finished cattle year. We assume that production would increase proportionately to each county's biological potential. By distributing the overall regional expansion among the 129 counties in NYNE in proportion to each county's share of the total regional biological potential for grass-finished beef production, county-level estimates of the expansion of grass-finished beef under the three scenarios were estimated. The three scenarios include 113,250, 226,500 and 339,750 grass-finished beef cattle in addition to existing beef cattle grown in the region. The expanded cattle production each scenario represents about 2.8, 5.6, and 8.4% of overall beef consumption in NYNE respectively. (Given the NYNE population (34.13 million) (U.S. Census Bureau, 2022) and beef consumption per capita (57 pounds) (USDA, 2022), we calculate the total consumption amount. Given the production of grass-finished cattle under three scenarios, live weight of cattle (1240 pounds), and dressing percentage (70% of live weight to carcasses and 55% of carcasses to final product) (Cheung and McMahon, Reference Cheung and McMahon2017), we calculate the amount of final grass-finished beef production.) Given that about 4% of US beef retail and food service sales are made up of grass-finished beef, these expansion scenarios provide typical examples of regional large-scale progressive shifts toward grass-based finishing systems.
Data
Cattle production in the northeast typically consists of three stages: cow–calf, stocker, and finishing. During the cow–calf stage, breeding cows give birth to calves, which are normally weaned at 7 months old. The animals then begin the stocker stage, which typically lasts another 5 months, bringing them to an age of 12 months. For both grain-finished and grass-finished cattle, the initial two stages of production are always pasture-based. Following the stocker phase, they enter the third and final production stage. The most obvious distinction between grass-finished and grain-finished beef production occurs during the finishing stage. For grain-fed cattle, animals are confined during the finishing phase and fed a grain-based diet for 5 months. Rather than being sent to feedlots, grass-finished cattle spend 12 months in pastures and finish on a diet primarily composed of grasses or other forages.
As mentioned above, we estimate the potential for expanding production without converting any land to agricultural use or displacing existing agricultural industries. We mix the current production of beef cattle in 2012 with the prospective production under the three scenarios in our analysis to investigate the capacity bottleneck respectively. County-level annual production for beef cattle USDA/NASS's reports county-level annual production statistics for beef cattle that remained in local feedlots and were slaughtered and processed locally in NYNE are published by USDA/NASS (Reference GE2012a). (Over 1.5 million beef and dairy cattle are present in the NYNE (USDA-NASS, Reference GE2012a). Because it is more costly to fatten cattle in feedlots in the region, almost all dairy cattle in NYNE are mostly shipped to meat giants in Pennsylvania (e.g., Nicolas Meat Packing, JBS, and Cargill) for slaughter and processing.) Table 1 shows the cattle production we take into account when analyzing the bottleneck issue under the three scenarios. Here we list the current cattle production (year 2012) as a baseline scenario to facilitate comparison.
Annual cattle production in three scenarios (unit: head)
* The literature suggests that grass-finished cattle make up 7% of the beef cattle in 2012 in NYNE (Dillon, Rotz and Karsten, Reference Dillon, Rotz and Karsten2020; Bussard, Reference Bussard2016). Number of grass-finished and grain-finished animals in the baseline scenario derived from 2012 production totals.
Figure 1 shows the county-level beef cattle production, comprising both grain- and grass-finished animals, in 2012 (Fig. 1a) and the expanded grass-finished cattle production under scenarios 1–3 (Figs. 1b–d).
Distribution of annual beef cattle production across counties: (a) baseline production (year 2012), (b) production scenario 1, (c) production scenario 2, and (d) production scenario 3.
The annual production data are disaggregated into monthly data. To date, there were no publicly available estimates of monthly distributions of the grass-finished beef cattle population. The monthly distributions were estimated using slaughter statistics for all cattle from USDA/NASS (2013) (Fig. 2). Such an estimation of monthly distributions is consistent with the result of a survey conducted by Lozier, Rayburn and Shaw (Reference Lozier, Rayburn and Shaw2005). The survey indicates that the grass-finished beef producers slaughter in all 12 months of a year, with peaks in October and November, and lowest frequencies in February, March, and April.
Monthly cattle production distribution in NYNE.
Generally, regionally produced beef just meets the demand of the niche consumer market for local beef (Ge, Gómez and Peters, Reference Ge, Gómez and Peters2023). We assume the volume of grass-finished or grain-finished beef shipped to each county for consumption is proportional to the population of the county based on 2022 US Census Bureau data (U.S. Census Bureau, 2022). In reality, the influential determinants of consumers' beef preference are complicated and thus the consumption per capita differs across cohorts. While it is beyond the focus of this study, we know that the distribution of beef consumption across counties influences the model solution, thus it would be worthwhile to investigate the validity of this work once more actual consumption information is available.
We surveyed all NYNE's 62 USDA inspected red meat packing plants to investigate the slaughter and processing infrastructure, capacity, operations, and operating costs in the region. The survey was conducted in 2017 and 2018 using a questionnaire administered via telephone or through an in-person interview. Survey results were updated in 2022 by a short follow up via telephone. Slaughter and processing capacity of plants were collected assuming single-species days. That is, survey respondents were asked to quantify the volume of cattle that can be slaughtered or processed if plant capacity is used to exclusively handle cattle. We exclude the slaughter and processing capacity for other farm animals from the capacity for beef cattle. To do this, we convert each livestock into a single unit called ‘cattle equivalent’. Following Lewis and Peters (Reference Lewis and Peters2012), the average ratio of capacity for cattle to the capacity for each other type of livestock for each plant was calculated to estimate equivalence. The capacity that can be utilized for cattle slaughter or processing is the left capacity after excluding the capacity for handling other farm animals. Table 2 shows a summary of slaughter and processing capacity (monthly average), slaughter cost, and processing cost of 62 surveyed plants. The slaughter and processing costs vary widely across plants. Beef cattle harvest prices range from $30 to $130 per animal, with a $20 standard deviation. Beef processing ranges from $198 to $705 per animal with a $310 standard deviation.
Slaughter and processing capacity and cost
Source: Survey conducted in 2018 and 2019 and updated in 2022.
Our survey results suggest that processing usually occurs in the same facility as slaughter. Based on our survey, the daily slaughter capacity of a plant is greater than the processing capacity of the plant. The reason is that processing is more time-intensive than slaughtering. To handle this mismatch, normally plant workers spend less than 5 days per week for slaughtering of cattle. To this end, if there is a slaughter bottleneck in an area, the processing bottleneck problem will be more pronounced.
A plant slaughters cattle and processes carcasses, subject to slaughter and processing capacity constraints. According to Goodsell and Stanton (Reference Goodsell and Stanton2011), the carcass makes up around 62% of the weight of the grain-finished live animal. The ratio is 55% for grass-finished cattle (Cheung and McMahon, Reference Cheung and McMahon2017). The carcass will hang in storage to dry for 2 weeks before being processed. Processing operations are concerned with such activities as cutting half or quarter carcasses into sub-primal cuts, turning sub-primal cuts into fixed-weight steaks, roasts, and other boneless and trimmed retail cuts, and packaging products according to buyer and customer preferences. The processed meat products account for 72% of the weight of the carcass for grain-finished cattle (Goodsell and Stanton, Reference Goodsell and Stanton2011) and 70% for grass-finished cattle (Cheung and McMahon, Reference Cheung and McMahon2017).
Processed beef products or carcasses must be shipped in refrigerated vehicles to ensure delivery and wholesomeness of those products while maintaining product safety. Average refrigerated truck transportation cost statistics are reported by USDA's Agricultural Marketing Service (2023). Livestock trailers are used to transport live animals from farms to slaughter and processing plants, based on a cost of $4 per loaded mile for a truck carrying up to 40 cattle, whether it is full or (nearly) empty.
To reflect the realistic beef cattle transportation operations, we restrict the maximum and average shipping distance for an animal's journey to a plant. According to our survey, the maximum shipping distance between farms and plants (assembly) is 230 miles. We set the maximum assembly distance in the model at 230 miles. Farmers also believe that a shipping distance of about 70 miles is appropriate. Accordingly, the model defines 70 miles as the constraint of average assembly distance.
Model specification
In the beef supply chain, cattle from different production locations are consolidated at plants, slaughtered and processed there, and then distributed to consumers in different demand nodes (counties), through either retail or foodservice channels. Similar to processed products, carcasses (called unprocessed product in this case) are also demanded. For example, retail butcher shops buy carcasses and do cutting and processing by themselves (NMPAN, 2024).
Given the beef supply chain structure and production levels, we examine whether there is a shortage of slaughter and processing capacity and identify the optimal solution for expanding plant capacity in case of bottlenecks. However, even a very small new facility requires substantial investment. If there is a need to expand regional slaughter and processing capacity, it appears that channeling new grass-finished production to existing plants oriented to conventional beef may be more efficient and effective than constructing a new, parallel supply chain (Cheung and McMahon, Reference Cheung and McMahon2017). From this point forward, in this study, the capacity utilization and expansion modeling only considers currently existing plants in the region. We assume that plants use the same facility to process grain-finished and grass-finished beef products. However, cleaning and sanitizing equipment and utensils are required to prevent adulteration in switching. Figure 3 shows the structure of the beef supply chain along with modeling activities (highlighted in blue) considered in this study.
Representation of cattle (products) movement in the supply chain.
Note: slau, slaughter; proc, processing; capa, capacity.
The study is relevant to production and distribution planning of supply chains, a field in which optimization models have shown promise for solving supply chain network design problems. Specifically, modeling and optimization of facility and resource utilization in agricultural supply chain systems have been growing steadily in the past decade (Ge et al., Reference Ge, Goetz, Gómez, Gray and Nolan2019, Reference Ge, Goetz, Cleary, Yi and Gómez2022a; Ge, Gómez and Peters, Reference Ge, Gómez and Peters2022b, Reference Ge, Gómez and Peters2022c, Reference Ge, Yi, Goetz, Cleary and Gómez2024; Colnenne-David and Dore, Reference Colnenne-David and Thierry2014; De Pue, Bral and Buysse, Reference De Pue, Bral and Buysse2019; Nategh et al., Reference Nategh, Banaeian, Gholamshah and Nosrati2021; Taifouris and Martín, Reference Taifouris and Martín2022). Aligning problem complexity with the mixed-integer optimization capacity, we present mixed-integer programming-based optimization algorithms for solving the optimal capacity utilization problem.
The capacity optimization problem is mathematically formulated as a linear programming model. The model involves determining values for a set of decision variables to minimize the objective function subject to a set of constraints. Table 3 shows the notations for the objective function and relative constraints.
Notation in the model
* Dressing percentage = Carcass weight/Live weight; Carcass cutting yield = Pounds of meat/Carcass weight.
Capacity bottleneck problem
The basic algorithms of the problem are summarized and presented by a linear objective function and a set of system constraints formulated as follows (Equations (1)–(10)):
The objective function is minimized subject to following constraints:
Equation (1) defines the cost function that will be minimized. Equation (2) ensures that the total quantity of cattle shipped from production locations (counties) to slaughter plants in a month is equal to (in case of no bottleneck) or less than (in case of a bottleneck) the total quantity produced in the locations. Equation (3) ensures that slaughtered animals at a plant are processed at the same plant. Equation (4) indicates the equivalence of a plant's inbound flow and outbound flow. Equation (5) states the total beef supply is equal to the total beef demand. Equations (6) and (7) define the threshold of a plant's slaughter and processing capacity. Equation (8) defines the maximum animal assembly distance. Equation (9) ensures that the average assembly transportation distance is within a given range. Equation (10) reflects the standard restrictions of non-negativity of shipments.
By solving the monthly optimization problem stated above iteratively, we can identify the magnitude of the monthly slaughter and processing capacity bottlenecks if any. If there are bottlenecks, we further examine to what extent the currently existing plants' slaughter or processing capacity should be expanded to meet the service demand while minimizing the total operating costs of the system. We examine two capacity expansion plans, expansion 1 and expansion 2 (indicated in Fig. 3).
Capacity expansion plan—expansion 1
We identify the optimal capacity expansion solution under a condition that the capacity of currently existing plants is given utilization preference (expansion 1 shown in Fig. 3). For this capacity expansion problem, while using the same objective function, we add Equations (11)–(14) as new constraints while removing Equations (2), (6), and (7) and keep the remaining:
Equation (11) ensures that grass-finished and grain-finished cattle are slaughtered. Equations (12) and (13) ensure that all the existing plant capacity should be utilized before any capacity expansion. The three constraints ensure the existing capacity of slaughter and processing is utilized as much as possible before considering any expansion of slaughter and processing capacity. Equation (14) ensures that all slaughtered animals will be processed. While acknowledging that plants sometimes ship carcasses to wholesalers and retailers for processing into retail products, we set this constraint in order to identify the processing expansion plan. The capacity expansion model assumes the slaughter cost and processing cost per cattle remain unchanged no matter to what extent a plant expands its capacity. (Our survey suggests there is no evidence for the existence of scale effect on the slaughter and processing costs.) The same assumption applies to the capacity expansion plan 2 next.
We further solve for the optimal expansion solution without giving preference to the existing capacity of plants (expansion 2). For doing this, we remove the plant capacity constraints Equations (12) and (13) from system constraints and keep the remaining for modeling expansion 1, and solve the model. In this case, the model will select more efficient plants (better location for assembly/distribution, lower fees for slaughter/processing) for utilization and expansion and disregard the less efficient ones. In this case, the locations of utilized plants are endogenized. There is no guarantee that all existing plants are utilized.
Solutions and analysis
Using an algorithm to determine the optimal solutions of the model presented in the previous section, this section examines the slaughter and processing bottleneck and identifies the capacity expansion plans to solve the bottleneck problem.
Capacity bottleneck problem
By matching the supply seasonality and maximizing plant use capacity while minimizing total costs, the monthly model generates the efficient solution for plant utilization. The model is solved under a condition that all currently existing slaughter and processing capacity is utilized as much as possible. From this, we examine the capacity bottleneck. Table 4 shows a summary of slaughter and processing capacity utilization and emerging bottlenecks in a year, given levels of beef cattle production under three scenarios.
Summary of results of the production expansions scenarios under current capacity (unit: head)
The monthly details for slaughter and processing capacity utilization and bottlenecks are shown in Figure 4. In the three scenarios, there is no underutilized processing capacity.
Slaughter and processing capacity utilization and bottlenecks across scenarios over months.
The slaughter and processing bottleneck varies across months and across scenarios due to the variation in monthly cattle production and monthly plant capacity. In scenario 1, although the monthly cattle production does not exceed the sum of monthly slaughter capacity, unhandled cattle emerge in the months of January, October, and November. In the three months, while some producers suffer difficulty in accessing slaughterhouses in Western New York, plenty of slaughter capacity in other areas remains underutilized in other areas. As an example, while the underutilized slaughter capacity of scenario 1 in the peak production month of October represents 24% of the monthly total capacity, the percentage is even higher in other months. Producers are averse to shipping live animals to plants located further away. These producers may not find plants for slaughter services within a distance they deem appropriate, leading to the so-called slaughter bottlenecks in some areas. This phenomenon may address the dilemma in the NYNE beef industry nowadays. Livestock producers in the region often complain they have great difficulty in accessing slaughter services while slaughterhouse operators argue they often lack the steady, consistent flow of cattle required to keep skilled workers and expensive equipment utilized and remain profitable (Gwin, Reference Gwin2009; Waro et al., Reference Waro, Gómez, Kalaitzandonakes, Peters, Baker and Conard2019). In scenario 2, varying magnitudes of slaughter bottlenecks emerge in January, February, May, June, September, October, November, and December. Similarly, unhandled cattle coincide with underutilized slaughter capacity in months from February to August. The slaughter bottleneck problem becomes more pronounced in scenario 3 even if slaughter capacity is fully utilized. The monthly processing capacity bottleneck always exists across 12 months in three scenarios, most significant in October and November.
The cattle production level and plant handling capacity jointly determine the magnitude of capacity bottleneck. The variation in the magnitude of bottleneck stems from the monthly variation of beef cattle production and the slaughter and processing capacity of plants. While cattle production in October is greater than that in November, the slaughter and processing capacity of plants in November is less than that in October due to the smaller number of workdays. As our results show, in scenarios 1 and 2, the bottleneck problem is more significant in November than in October. For scenario 3, the bottleneck problem is more significant in October because cattle production in October is 3800 heads more than that in November, much more than the capacity excess in November, 1000 heads. To facilitate our discussion, in the next subsection, we focus on typical peak production months of October (for scenarios 1 and 3) and November (for scenario 2) to examine the capacity expansion plan for addressing the capacity bottleneck problem. We hypothesize that, if there is a bottleneck in slaughter and processing, the constraint will likely appear more pronounced in these 2 months than that in other months. The optimal capacity expansion solution in October or November will suffice to solve the bottleneck problem in any other month.
Capacity expansion plan—expansion 1
The slaughter and processing bottleneck problem exists in all three scenarios, more or less. For scenario 1, only a limited quantity of cattle in Western New York remains unhandled in November. The bottleneck problem grows with cattle production in scenarios 2 and 3. For scenario 2, in November, producers in more districts of New York, such as Western New York, Finger Lakes, Central New York, and North Country, cannot find plants for slaughter services within an appropriate distance. For scenario 3, in October, the slaughter bottleneck problem spreads across more counties in Southern Tier, Western New York, Finger Lakes, Central New York, and North Country in New York. Besides, some producers in Aroostook County in Maine State suffer difficulty in accessing a plant for slaughter services.
If all cattle are slaughtered and processed locally in the region, slaughter and processing capacity must be expanded. We solve our capacity expansion model (expansion 1) to determine to what extent the slaughter or processing capacity of existing plants should be expanded to meet the maximum monthly service demand. As our results show, while all 62 plants are utilized, only a limited number of plants are selected for slaughter capacity expansion. Two plants in scenario 1 (Fig. 5a), four plants in scenario 2 (Fig. 5b), and six plants in scenario 3 (Fig. 5c) are selected for capacity expansion to provide additional slaughter services. These plants are either located in or adjacent to areas where there are cattle left unhandled. In scenario 2, the largest plant located in Tompkins County in New York State should expand its monthly slaughter capacity to additionally handle 2985 cattle. The second largest plant in St. Lawrence County should handle an additional 1656 heads. In scenario 3, these two plants still remain the largest and should additionally handle 8179 and 3801 heads respectively. Another plant in Maine should expand its monthly slaughter capacity by 752 heads. The expanded slaughter capacity of this plant is used to accommodate the volume of cattle shipped from Aroostook County.
Expansion 1—slaughter capacity expansion across three scenarios. Counties of the same color only utilize the plants within the same shade area where cattle remain unhandled. The exception to this rule is Aroostook County in Maine State: (a) scenario 1 (November), (b) scenario 2 (November), and (c) scenario 3 (October).
While the lack of slaughter capacity may not always be the limiting factor for local production across months, the lack of processing capacity represents a greater challenge for most areas. Given animal allocations among plants under three scenarios, all existing processing capacity of 62 plants is fully utilized and all plants need to expand their processing capacity (see Fig. A1 in Appendix A). We note that the slaughter capacity of a plant is greater than the processing capacity of a plant. This renders the processing capacity bottleneck more pronounced than the slaughter bottleneck.
Capacity expansion—expansion 2
The above capacity utilization solutions (Section ‘Capacity expansion plan—expansion 1’) give preference to the currently existing capacity. Any expansion plan is subject to the condition that the existing capacity must be utilized as much as possible regardless of the efficiency (location and cost) of a plant. Now we solve an optimal capacity utilization problem by releasing the slaughter and processing capacity constraint. We assume that each plant has sufficient capacity to slaughter and process cattle month to month, without giving preference to existing plant capacity.
We examine this location–allocation problem by focusing on the location of utilized plants and the allocation of animals among plants, given the geographical distribution of the animal population across production locations. We identify the largest monthly operating scale of each plant across 12 months. Figure 6 displays the identified size of utilized plants under three scenarios. Given the operating costs of plants in the region, logistics in coordinating plant utilization and deliveries of products suggests that a reduction in plant numbers, and an increase in the utilization rate of those remaining, would likely lead to lower supply chain costs. There are roughly 21, 19, and 18% cost reductions as compared to the costs generated by the capacity expansion plan 1. As shown in Figure 6, 41 and 40 among 42 plants are utilized in scenarios 1, 2, and 3 respectively to meet the slaughter and processing demand in the region (red spots). There are 21, 22, and 20 plants that are not utilized at all across the three scenarios (green spots). These plants are mainly located in New York State.
Expansion 2—optimal plant utilization across three scenarios: (a) scenario 1, (b) scenario 2, and (c) scenario 3.
There are 38 plants utilized consistently across the three scenarios. Conversely, 16 plants are not utilized. It is not surprising that the utilization of plants displays a very similar pattern regarding location and scale. Whenever operating conditions change, plants with superior operating conditions are always ideal candidates for capacity utilization and expansion. These plants are mainly located in either high production areas or near major metropolitan areas. Possible explanations for the concentration of utilized plants in these areas might be that these plants typically have either: (1) superior locations relative to other plants for cattle supply; (2) a strong consumer base that leads to great sales of beef products and can be reached at a short distance; or (3) lower slaughter and processing costs that increase the demand for services. Lower slaughter and processing costs expand the geographic radius of farmers seeking services. The difference between solutions across three scenarios traces to the location of a few small-scale plants primarily working with small-scale producers. These plants selected enter with less than full capacity.
In this case, the slaughter bottleneck across three scenarios becomes more pronounced because the existing slaughter capacity of some plants is disregarded. There is an increase in the number of plants that need to expand their slaughter capacity and the total expanded capacity as compared with that in expansion 1. Twelve plants in scenario 1, 16 plants in scenario 2, and 20 plants in scenario 3 expand their slaughter capacity to meet the service demand (see Fig. A2 in Appendix A). These plants are mainly located in New York State where beef production and demand are both higher than that in other states.
Under the assumption that all slaughtered animals at a plant must be processed on site, the cattle allocation for slaughtering among plants to some degree reflects the extent to which a plant needs to expand its processing capacity. More plants need to expand their processing capacity than those that need expanded slaughter capacity (see Fig. A3 in Appendix A). This is not surprising because, as our survey suggests, a plant's processing capacity is always less than its slaughter capacity.
There is a dramatic variation in the allocation of animals among plants. Our optimal plant utilization solutions suggest that 45–56% of all cattle are slaughtered and processed at large-sized plants (Table 5). Another 27–46% are slaughtered and processed at medium-sized plants while only 2–28% is done at small-sized plants. The results show a clear consolidation of slaughter and processing in large plants as cattle production expands. Slaughter and processing activities shift to plants with comparative cost advantages and better locations. The allocation of animals among plants with varying capacities reflects a long-term trend. Over the last 25 yrs, large plants have become vastly more important and there is a sharply increased concentration of cattle slaughter in large plants. A recent survey suggests the vast majority (55%) of livestock slaughter in the USA is done in a relatively small number of very large facilities (Johnson, Mati and Gwin, Reference Johnson, Mati and Gwin2012). In turn, 44% are slaughtered in medium-sized plants while small-sized plants process only a nominal amount. Such a distribution is almost consistent with that of scenario 3 in this study and likely reflects the evolution of cattle slaughter and processing patterns in NYNE if cattle production is expanded.
Distribution of slaughtered volume among plants by size across three production expansion scenarios
Discussion and policy implications
Economic and operational environments keep evolving, and agricultural supply chains are vulnerable to these conditions. Various changes during a slaughter/meat processing plant's lifetime, such as production or demand patterns of surrounding areas, or local transportation conditions may turn a good location in the past into a bad one today. For example, according to our survey, there are four plants closed in NYNE in 2019, three in Vermont, and one in Maine. Seasonal shortfalls in supply volume largely drive plant failure. Accordingly, our modeling methodology and model solutions may provide valuable insights into possible improvements in the overall efficiency of these networks as plant operators adapt to changed conditions of their operating environment. Such information is currently lacking and planning decisions on new facilities are not fully informed.
The utilization of plant capacity is not evenly distributed due to seasonal variation in production and the spatial distribution of slaughter capacity of plants. In scenarios 1 and 2, the slaughter bottleneck in New York coincides with underutilized slaughter capacity in New England. Given beef production expansion scenarios, a strategic adjustment in slaughter and processing capacity facilitates mitigating the bottleneck problem and improving the efficiency of resource use. Our findings help identify plants that have the highest potential for slaughter and processing capacity utilization and expansion, given levels of cattle production and the temporal and spatial distribution. For example, a combination of results of optimal expansion (expansion 2) might lead to improved decision making on facility investment. If the three scenarios confirm the utilization or/and capacity expansion of a plant, the plant should likely be an efficient one and deserve efforts to expand market participation. An abandoned plant across the three scenarios implies that the plant is likely redundant or less competitive as compared with others. While a lack of favorable location could lead to excessive competition or restricted access to supply, a higher price for services could also increase a plant's vulnerability. Given locations of these plants and the facing competition from neighboring plants, reducing operating costs and thus the price for services represents an effective way to gain a competitive advantage. Before making investment decisions, investors have to be effective in discerning the issues surrounding plant operation and determine whether the plant can sustain long-term viability and competitiveness.
The resulting temporal and spatial structure of the region's beef supply chain also reveals potential opportunities for new plants and farm business regarding physical locations. Our results suggest that the majority of capacity extension activities occur in New York State. New plants should be located in areas with few plants and near a cluster of farmers that can provide necessary quantities at desired times, e.g., the Finger Lakes and Central New York regions. (Our suggestion is supported by a follow-up survey we conducted in 2020. Among three newly opened plants in NYNE, two are located in NYS, i.e., ‘CUDAS Meats’ in the Finger Lakes region and ‘E&M Custom Meats’ in Central NYS.) This also facilitates producers' access to slaughter and processing services and thus mitigates the bottleneck problem in the region. New capacity should also be located in areas in favor of livestock production but limited in service capacity for producers to access. For example, producers in Northern Maine have to ship animals far away to access the USDA-inspected meat plants. The long journey not only gives rise to concern animal's welfare, but also significantly raises beef costs in the region. Allowing for the current production level and the high potential to expand production in the region, building facilities in Northern Maine would have a substantial impact on the region—restoring the viability of small farms, motivating farmers' incentives to increase local production, boosting local economy, benefiting the environment, and making locally grown beef available to consumers at a lower price. An understanding of current regional cattle supply and existing capacity of plants helps inform decisions for a business venture.
Our results also suggest a necessity of introducing mobile slaughter units (MSUs), a self-contained facility that can travel from site to site for animal slaughter and processing. Our findings show that a few plants only slaughter and process less than 50 heads each month, underutilizing a large portion of their capacity. The necessity of small-scale plants stems from the small-scale cattle farms scattered in NYNE. However, this might indicate a misperception between perceived and real demand for small plants in particular areas. Even if real demand appears to exist in a county, that demand may not be sufficient for a small plant to be viable. In the case of grass-finished beef cattle production expansion, large throughput enables large-scale plants to take advantage of economies of scale in slaughtering, processing, distribution, and marketing. This may make small-scale plants obsolete. Limited throughput inhibits the plant's ability to benefit from economies of scale, which makes the plant not economically viable since more inputs are required per animal relative to larger fixed slaughter plants. MSUs can assist in meeting the need for geographically isolated small farms and ranches for slaughter and processing services (Amann, Reference Amann2017; NMPAN, 2011). MSUs can release the impediments to animal slaughter and processing for these small, locally-focused producers. This can increase the cost-effectiveness of the whole system. The introduction of MSUs tailored toward local markets may rule out the necessity of operating plants with insufficient throughput.
The uncertainty and complexity of logistics operations in the beef supply chain are determined by production seasonality and spatial distribution of dispersed farms with varying production levels and heterogeneous operating costs. This calls for the development of an all-inclusive coordinating framework for the supply chain. Forming a regional coordinated supply chain system requires a fundamental shift in the relationship between beef farmers and slaughter and processing plants away from a series of independent transactions to a long-term relationship recognizing the benefits of coordination. The implementation of a coordination mechanism requires not only to enhance communication among businesses in the supply chain but also to strengthen commitments between farmers and plant managers. To this end, this study sheds light on ways to establish and improve regional coordination mechanisms to enhance efficiency in regional food systems.
Conclusions
In this study, we investigated the slaughter and processing capacity bottleneck of plants in NYNE under three scenarios of expanding the production of grass-finished beef, given the inherent biological capacity of the region's natural resource base. The grass-finished beef expansion confronts slaughter and processing bottlenecks in some areas, mainly in New York State. The bottleneck varies in magnitude across locations and months. Comparatively, the processing bottleneck is more pronounced than the slaughter bottleneck. One way to eliminate the bottleneck problem is to expand the existing slaughter and processing capacity. Our results suggest that only a limited number of plants, mainly concentrated in New York State, are well-positioned for capacity expansion. If the beef supply chain structure can be reshaped, a reduction in plant numbers, and an increase in the utilization rate or expand the capacity of those remaining would likely lead to greater cost savings. Besides, for those counties with lower cattle production, introducing MSUs might be more cost-efficient for solving the capacity bottleneck problem than operating small-sized plants.
Given the growing demand for local and regional foods, our findings offer critical information to address the challenges and explore opportunities to expand the regional beef supply chain system in the northeastern USA. Our findings can be used by researchers, practitioners, and policy-makers concerned with developing nationally or regionally coordinated food supply chain systems, investing in local farms and facility infrastructure, developing initiatives to enhance local food systems and sustainable agriculture, and increasing facility operation efficiency to sustain sustainability and competitiveness. Although this framework focuses on a regional problem, it can easily be applied to other geographic scales, such as a national level or a subregional level, or applied to other supply chain systems to address related strategic decision-making issues elsewhere.
Data availability statement
The data that support the findings of this study are available from the corresponding author, Houtian Ge, upon reasonable request.
Acknowledgments
We appreciate the two anonymous referees' constructive comments. This research was supported in part by funding from the USDA National Institute of Food and Agriculture through the Agriculture and Food Research Initiative (Award No. 2016-68006-24744).
Funding statement
This research was supported by the National Institute for Food and Agriculture, USDA (grant no. 2016-68006-24744).
Competing interests
None.
Appendix A: Slaughter and processing capacity expansion
Expansion 1—processing capacity expansion across three scenarios: (a) scenario 1 (November), (b) scenario 2 (November), and (c) scenario 3 (October).
Expansion 2—optimal slaughter capacity expansion across three scenarios: (a) scenario 1, (b) scenario 2, and (c) scenario 3.
Expansion 2—optimal processing capacity expansion across three scenarios: (a) scenario 1, (b) scenario 2, and (c) scenario 3.
Open access
