This is my summary of the work of a large number of people, some whose names I did not catch (some people were working virtually) but there is a few names on our note pad (http://titanpad.com/lkXZuphYpS). I do not speak for the group but merely speak as a voice of the group and have written down my impressions. While I think the data we found was interesting, this report is by no means a good case for any policy, yet it may be an on ramp for another individual or organization to look further. So in the spirit of the Hackathon and Open Data, here is the summary, for what it's worth (stay tuned for upcoming post "Open Data Hackathon: For what it's worth").

Disclaimer : None of the calculations here have been peer reviewed or double checked. Do not quote this work as fact unless you plan on double checking the calculations yourself.

Update 1 : I've been working on tracking down our group members. I'll keep posting the names as they come in.
R.J. Steinert
Albert Chao
Dominic DiFranzo
Diane Hatz
Ann Middleton
Harlan Harris
Aurie Ben-Ezri-Raven

Update 2 : New version of the 'cow chart' with sources, cc logo, and farm bill hack logo. Also found that our data was from 2009, not 2010 (but published in 2010 ;) )!

Click to see the full version

The question

Do our Farm Bill policies foster a healthy lending environment for young farms?

Why we were concerned

I think Dominic DiFranzo's point that was made during our presentation was an interesting one. His point was that a Goverment's lending priority should be the young farms without the credit history because older farms may have an easier time of getting loans at private/cooperative lending banks. I personally was thinking back to the Vermont Law School Agriculture Law and Policy Conference from January of 2011 where I heard stories from New Hampshire that there were not enough new farmers and farms to make up for the old farmers that were leaving the industry.

The data we found

The first dataset that really caught our eye was a report we found on USDA's site called "FARM SERVICE AGENCY - LENDING TO BEGINNING FARMERS - As of February 28, 2010" (See PDF here). From that data set we were able to calculate on a per state basis, the number of Beginning Farms (10 years or younger) that received FSA Loans and the number of Older Farms (older than ten years) that received FSA Loans. We did not think that just seeing how many new and old farms received FSA Loans is a fair comparison because if a state has 50x more old farmers than young farms, then it's not surprising that older farms would have a higher quantity of loans because there are more older farms applying for loans. Luckily, in the Census Data we were able to find data on the total number of new farms and old farms in each state. This allowed us to calculate "Percent on a State Level of Old Farmers Who Received Loans" and "Percent on a State Level of New Farmers Who Received Loans". We decided this made for an thought provoking comparison. Using these calculations, we made three graphs.

For each State, the percents of all Newer Farms who received loans and the the percent of all Older Farms that received loans

The first graph we made was of our calculated values for old and new farms.

Average Percent on a State Level of New and Old Farmers Who Received Loans

We then averaged all of those states' percentage together (this means each state's percentages have equal weight on the average) to get a national average. We found that being an older farm, you were five times more likely to have received a loan from the FSA than if you were a young farm. Note this DOESN'T mean older farms were five times more likely to succeed in the loan application process, it may be the case that young farms are not applying for loans from the FSA.

Discrepancy between "Percent on a State Level of Old Farmers Who Received Loans" and "Percent on a State Level of New Farmers Who Received Loans"

After looking at the "Percents on a State Level of New and Old Farmers Who Received Loans" graph and noticing a lot of states with large discrepancies between new and old, we decided to figure out if there were regions where the discrepancies where higher. To calculate the discrepancy I calculated:

(Percent on a State Level of Old Farmers Who Received Loans) - (Percent on a State Level of New Farmers Who Received Loans) = Discrepancy

Using Google Fusion Table (see a tutorial on how I did this) I was able to make a Choropleth map where the darker states have larger discrepencies and the lighter states have less discrepancy between new and older farms.

API and Linked Data source

I did not get the chance to fully grok this, but Dominic DiFranzo compiled our calculated data into a triple store (a type of database spiced with semantic data) and gave it an API using something I believe is called SPARQL Proxy. He produced the following Chloropleth map but I'm not exactly certain what he's using to calculate his values. There is more info on the last slide of our presentation (see below).

Presentation Slides

Here are the slides from our presentation at the end of the Farm Bill Hack event.

Other Important Links

Wiki page for this project -> http://farmbillhack.wikispaces.com/young_farmers
TitanPad (it's like a google doc, it has a lot of our notes) -> http://titanpad.com/lkXZuphYpS