Local Agricultural Supply Chain ImprovementLindsey Arita, Joseph Lee, Julia Mahon, Jay Raghavan

Advisor: Dr. Eric BruunESE Senior Design 2009-2010

Demo TimesApril 22nd, 2010

9-9:30 AM9:30-10 AM

10-10:30 AM1:30-2 PM

Abstract Successful operation and maintenance of small-scale farms is becoming increasingly difficult. Escalating farmland prices, price competition from corporate farms, and increased costs of agricultural production have forced many farmers to mass-produce for corporations or sell their farms. The objective of this project was to design and apply a model that allows individual farmers in Pennsylvania to identify factors that help increase profitability. The key steps in the construction of this model included determining the most significant contributors to a farm’s profit through stepwise regressions on extensive census data and understanding how these profit indicators could be realistically improved. This system helps farmers understand specific opportunities for improvement by comparing their unique data to optimal performance levels across statistically significant factors. The final product is a self-assessment model and an information hub for local farmers, which includes a website that offers a downloadable copy of the model and analysis of extensive research on the agricultural dynamics in Pennsylvania. The individual utilization of this model and increased accessibility to regional agricultural data ultimately contributes to the long-term viability of local agriculture. This allows farmers to avoid resorting to growing crops strictly for corporations, which tends to reduce the farmers’ margins and decreases product quality for the end-consumer. Local consumers further benefit from this supply chain improvement by gaining more access to locally grown, higher quality agricultural goods.

Model DescriptionThe model is a self assessment and predictive tool that farmers run within Excel:• The farmer inputs his or her input variables and the model calculates how the farmer is doing relative to all the

other farmers in Pennsylvania. • The model calculates the farmer’s predicted net income using the linear regression obtained from the stepwise

algorithm.• Each variable has a detailed definition of what goes into calculating the variable and also tailored responses to

how well the farmer is performing in that particular variable. • Since not all farmers are familiar with statistical analysis, text explanations and analysis are offered for each

variable as well.• The model aims to help farmers by allowing them to evaluate themselves in comparison to their competitors.

While much of the data is publicly available, the data analysis and results have been refined to provide the maximum utility to farmers.

User Portal (Website) DescriptionThe website is designed as a portal for local farmers to gain access to valuable research on the agricultural dynamics in Pennsylvania. On the website, farmers can:

· Download a copy of the model for personal use and verification;· Access detailed maps of Pennsylvania by county displaying: · Market Value (Total Sales) per Farm by County, · Profit per Farm vs. Concentration of Farms, · Government Subsidies vs. Type AND Volume of Sales;· Access a map of all Farmers’ Markets in Pennsylvania, including hours of

operation and contact information;· Access to links and resources for further assistance and information.

ResultsNine variables were found to be statistically significant variables in farm profit:Certain kinds of products contribute more to profit than others;• Sale of livestock (i.e. milk products), for example, tends to contribute more to

profit than vegetables and potatoes;Some variables have significant correlation but not cause;• More acres of woodland may mean larger farms with higher margins, but no

farm can instantaneously acquire more acres of woodland;• The negative correlation with the number of farmers’ markets is likely because

counties with greater opportunity for improvement attract the formation of more farmers’ markets, not that farmers’ markets are detrimental to profit.

Recommended Next Steps• Increased farmer interaction to institute continuous improvement of model

and a feedback loop• Initialize a rigorous data gathering process to include individual farm data• Develop network of farmers to facilitate development and distribution of

model

User Flow Chart

Validation• Organizations like the Food Trust and Delaware Valley Regional Planning

Committee (DVRPC) have offered support of the project's endeavors and have provided references of farmers to work with on improving the model;

• 9 out of 14 farmers expressed interest in such a system after trying the model;

• Feedback from regional planners and farmers have been incorporated to make the model easier to understand, more user-friendly, and more applicable.

Average Market Value (Total Sales) Per FarmIn Pennsylvania, by County

Farm Profitability vs. Concentration of FarmsIn Pennsylvania , by County

Example Statistics from Model Interface

Output Report• Explanation of

location’s most significant contributors to farm profit

• Comparison of farm performance relative to others in PA region

• Customized recommendations for improved profitability