Suitability Analysis: Agricultural Supply Chain Efficiency

Fernanda Argueta is currently a M.S. GIS graduate student at the University of Maryland. She serves as a GIS Specialist for the Department of State Climate Security and Resilience (CS&R) Program.

Prior to her current role, between 2020 and 2023, Fernanda made significant contributions to scientific publications alongside Dr. Louis Giglio and Dr. Joanne V. Hall. Her work involved research, data collection, quantitative data analysis, satellite imagery analysis, and the creation of data visualizations. Notable projects included research aimed at improving global cropland burned area using remote sensing techniques and validating geostationary active fire products. Additionally, she worked as a part of the NASA Harvest Developer team supporting research in agricultural supply chain and food-security.

Agriculture plays a vital role in Maryland's economy, with a reported combined value of approximately $721 million for corn and soybean production alone, according to the latest NASS USDA State Agriculture Overview (2022 State Agriculture Overview). Per the Overview report, there is a significant soybean acreage of around 510,000 acres, accompanied by 380,000 acres of corn totaling 44% of Maryland’s farmland (Williams, 2022). In Maryland’s Eastern Shore region, data fromthe Center for a Livable Future reveals the active involvement of approximately 1,400 farms in the cultivation of corn and soybeans (Johns Hopkins Center for A Livable Future).

Despite the prevalence of these crops on the eastern shore, the journey from farm to table is far from straightforward. Eastern Shore farmers face a unique challenge – the lack of easily accessible distribution and processing centers for their harvested crops. This critical infrastructure gap disrupts the supply chain, creating obstacles that prevent farmers from efficiently reaching consumers. The consequences are twofold: increased food waste and a negative impact on the profitability of farmers, underscoring the urgency of addressing this issue.

To mitigate the issue, the objective is to employ a suitability model that highlights and assesses the four most suitable locations for a processing and distribution center (P&DC) on the Eastern Shore. The four locations will meet six distinct criterion which are directly relevant to the supply chain process of soybeans and corn.

The suitability analysis focused on the eastern shore which encompasses the following 9 counties: Kent, Queen Anne’s, Talbot, Caroline, Dorchester, Wicomico, Somerset and Worcester.

1. Create suitability model which will locate areas on the eastern shore where a proposed crop processing and distribution center (P&DC) would mitigate current supply chain issues.
2. Perform sensitivity analysis to evaluate suitability model created.

The methods outlined below were completed in ArcGIS Pro version 3.1.2

All data layers were pre-processed, projected to NAD_1983_2011_StatePlane_Maryland_FIPS_1900_Meters_US, filtered, and masked to only include the eastern shore extent and to exclude Land Conservation areas.

Each data layer was reclassified using scale from Most Suitable (5) to least suitable (1). The reclassification of the data layers was accomplished using the Rescale by Function tool in ArcGIS Pro.

The rank sum method was used to determine the weight (w) for each criterion (table 3) and used in the suitability equation.

P&DC Baseline Suitability = c ₁ ∗0.143 + c ₂ ∗0.95 + c ₃ ∗0.286 + c ₄ ∗0.238 + c ₅ ∗0.190 + c ₆  ∗0.048

The P&DC Baseline suitability equation was run using Raster Calculator in ArcGIS Pro where each c _n  is the reclassed data layer.

The output of the suitability equation is a raster with suitable areas for a P&DC. The raster was the converted to a polygon where areas for each polygon were calculated and those less than 8 acres removed.

The sensitivity of the P&DC suitability model was analyzed using One-factor-at-a-time (OAT) method. Each baseline weight shown in Table 3 was increased by 20% and the remaining weights addressed accordingly. The suitable area output of each weight scenario was calculated and the percent change from the baseline scenario is shown in Table 4.

The analysis revealed areas on the Eastern Shore suitable for a processing and distribution center (P&DC) - those exceeding 8 acres with suitability scores above 4. Among the nine counties in the region, only four had suitable areas, as depicted in Figure 2. Talbot County led with 2348 acres, followed by Caroline County with 2135 acres, Queen Anne’s with 1235 acres, and Kent with 760 acres based on the baseline suitability equation. To pinpoint the best four locations for the PD&C, the fields were ranked by area, selecting the largest suitable area in each county, as shown in Figure 3.

Furthermore, the sensitivity analysis highlighted that the Distance to grain elevator (c ₃ ) criterion was the most influential, contributing to a 2.52% increase in suitable areas compared to the baseline scenario after the increased weight.

The Eastern Shore of Maryland, a hub for soybean and corn cultivation, faces a pressing challenge in its agricultural supply chain due to the absence of efficient processing and distribution centers. This shortage disrupts the journey from farm to table, resulting in increased food waste and diminished profitability for local farmers. To address this issue, a suitability analysis was conducted, aiming to pinpoint optimal locations for a processing and distribution center (P&DC). By assessing various criterion pertinent to soybean and corn supply chains, this model identified four suitable areas across Talbot, Caroline, Queen Anne’s, and Kent counties. However, limitations exist. The sensitivity analysis highlighted the significant impact of the distance to grain elevator on suitability, signaling the need for a more nuanced approach in future assessments. Additionally, the study focused solely on areas larger than 8 acres, potentially overlooking smaller but viable locations. Moving forward, refining the model's criteria and incorporating finer-scale data could enhance its accuracy. Exploring ways to account for smaller areas and considering additional factors like infrastructure accessibility and market proximity would render the analysis more comprehensive. Engaging farmers and local experts would also enrich the evaluation process, ensuring a more actionable outcome. Ultimately, this analysis represents a pivotal step toward mitigating supply chain issues in Eastern Shore agriculture.

1. 2022 State Agriculture Overview. USDA/NASS 2022 State Agriculture Overview for Maryland. (n.d.). https://www.nass.usda.gov/Quick_Stats/Ag_Overview/stateOverview.php?state=MARYL AND
2. Johns Hopkins Center for A Livable Future. (2023). Data associated with: The Maryland Food System Map [dataset]. Johns Hopkins Research Data Repository. https://doi.org/10.7281/T1/QUDBC6
3. Maryland at a glance. Maryland Agriculture, Farming. (n.d.). https://msa.maryland.gov/msa/mdmanual/01glance/html/agri.html#:~:text=In%202022%2 C%20field%20crops%20were,production%20value%20of%20%246.5%20million.
4. Williams, S. (2022). (rep.). 2021 Maryland Farms and Land in Farms Unchanged from Previous Year. Annapolis, MD: NATIONAL AGRICULTURAL STATISTICS SERVICE.