Transcript of Webinar
Dawn Thilmany: This is all made possible because the USDA Ag Marketing Service had the vision to realize there was an increasing interest in not just food systems but try to do a set of evaluations about how food system innovations may affect different parts of society. We were the team they actually brought together to specifically look at economics. And without being able to spend the time to list all their names but you’ll see I had a very rich team to draw on. So although we’re reporting the outcome realize that this was a team effort of a lot of clever minds and some good teamwork to try to give you guys the best practices. And that’s exactly what today is going to be about. The Ag Marketing Service gave us the pretty lofty charge of giving them a better insight and standardized approach that they could offer to their stakeholders and clientele to actually analysis what the economic implications of some of their investments might be. Obviously this is framed by the fact [inaudible] a really nice new set of resources and initiatives that they’ve been able to give investments into communities with and their increasing capacity to offer some technical assistance to communities as well. And so, again, they wanted our insight as kind of the people out in the field who’ve been thinking hard about this for a while, about how they might best do that in partnership with land grants but also community leaders like many of you probably on the call. So when we first met as a team, we decided to kind of break this into modules. We’re quite aware as the polls show that people are coming into this all the way from a beginner perspective to someone who’s been doing a lot of economic impact evaluation, but perhaps not in this particular sector, or how to be really careful and weary about how food systems initiatives might look different. So the first four modules which I’ll be covering quickly here today basically just even guides the process you would want to do to do an assessment. And a lot of you are very clever and savvy to the fact that when you’re evaluating projects some of these things have to happen in all cases. We’ll try to highlight mostly today how those processes might vary a bit when we’re talking about food systems. And then Becca will continue on with some of the more in depth modules that are offered up and fully available online as PowerPoints now and there will be publications that start getting into the real technical aspects of when you’re starting to put numbers to these economic implications. So the whole toolkit is available. And we realize you all might use it in parts but also might find [inaudible] the whole thing useful. The first thing that we really covered setting up Module 1 is that the reason food systems probably deserves its own little kind of a toolkit is because they’re so diverse and so nebulous in a way and they’re so [inaudible] in nature that although we can give you some rules of thumb and best practices they’re all things where your community itself has to dictate how some of it is framed. So the first thing we urge in the toolkit is to assemble a very diverse project team that recognizes anything you say is part of your food system whether it’s all the way back to water and land, all the way up to public health and consumer choices, however you’re framing that food system, you need to have other team members that represent those stakeholders, establishing a realistic timeline, and we gave you one example at the bottom from a project that was done to allow people to start having an understanding of when they would be chiming in. And then also scoping that study appropriately. Once you have the diverse project team you can look at what all you’re going to include, and because of maybe budget or time constraints, what you might not be able to include at least that first phase. Modules 2 and 3 I go through pretty quickly here. But we do have a fairly extensive discussion and list of first secondary data sources. Many of you might know some of these but I know I learned a lot that for different pockets of data you might need depending on how you scope this story they’re all out there. But sometimes it’s the navigation of finding them so we try to give a one-stop shop where we list all those, talk about some of their strengths and weaknesses, but then also do dive into what primary data is probably going to be at least partially necessary. Again, something this [inaudible]-based. It’s very likely all the information you need to answer your questions won’t be available. And we divide that discussion a little bit into both qualitative and quantitative methods you might use. We even gave some examples of some surveying and interview methods that have been used by other communities. Module 4 we turn into a discussion of basically data interpretation. Sometimes we amass a lot of information, we might make a nice chart or a graph, but we really have to think hard about what we’re going to let the data do for our team. And so we talk a little bit about what’s commonly used which is comparative of benchmarking analysis. Comparative would be that a place in the country that maybe you want to be more like and emulate and compared to where their numbers are on access to healthy foods or land conserved for food production. Or if you see this being a long-term project for your community starting to do benchmarking. Where are we now? Where will we look like five years from now? All those are important things you might to do but, again, this would be the point in the assessment when that conversation could probably come to life. And also where you can start talking about linkages across the system. Although the data’s generally assembled in buckets once people see what the data looks like from maybe their core interest area you can start looking for some linkages about where there might be connections between some of the land issues along with the farmer issues, or farmers on the ground relative to what your supply chains look like. We do give some words of caution in the toolkit about what was correlation versus causality. Although you might see some clear connections we’ll caution you that every difference you see across time or across places may not be significant but it’s at least worth a good conversation. And then we’ll even give some of you who aren’t familiar with them some interpretations of how you might use spatial analysis techniques and give both some descriptions and some great examples of cluster mapping, location quotients and some other terminology that if nothing else you should be aware of. And it’d be great if you actually might have a chance to actually explore some of those methods in your assessment as well. And, again, I hope it was okay that I went through those quickly. We’re trying to make up some time. But we will have questions at the end as well. For now I’d like to turn it over to Becca Jablonski who will do the remainder of this. Are you okay?