Impact of climate change on agriculture may be underestimated — ScienceDaily

“The changes in cropping that we quantified with remotely sensed data were stunning,” Mustard said. “We can use those satellite data to better understand what’s happening from a climate, economic, and sociological standpoint.”

The study showed that temperature increases of 1 degree Celsius were associated with substantial decreases in both total crop area and double cropping. In fact, those decreases accounted for 70 percent of the overall loss in production found in the study. Only the remaining 30 percent was attributable to crop yield.

“Had we looked at yield alone, as most studies do, we would have missed the production losses associated with these other variables,” VanWey said.

Taken together, the results suggest that traditional studies “may be underestimating the magnitude of the link between climate and agricultural production,” Cohn said.

That’s especially true in places like Brazil, where agricultural subsidies are scarce compared with places like the U.S.

“This is an agricultural frontier in the tropics in a middle-income country,” VanWey said. “This is where the vast majority of agricultural development is going to happen in the next 30 to 50 years. So understanding how people respond in this kind of environment is going to be really important.”

VanWey said a next step for this line of research might be to repeat it in the U.S. to see if increased subsidies or insurance help to guard against these kinds of shocks. If so, it might inform policy decisions in emerging agricultural regions like Mato Grosso.

Source: Impact of climate change on agriculture may be underestimated — ScienceDaily

Salvation Army Is Measuring Poverty In Real Time

Here is an example of an innovative community-level data project that will lead to better policy decisions and increased support for the many services that SA offers. I can easily relate this to farmers markets data collection and found particular interest in the key indicators chosen and in this line: “a result due in part to the lack of information in the public domain, but also, the lack of uniform reporting that exists on both a national and local scale”, which also remains a problem for grassroots markets and other food initiatives.

This story also illustrates what could very well be the next step to Farmers Market Metrics: collected indexes that show a larger system inequity or effect,  possible once we establish which metrics are best compiled for different system measures. Those system measures can come from the work done by Center for Whole Communities and their Whole Measures, which were part of the initial research done for FMM.

 

The Salvation Army has rolled out a new tool for measuring poverty across time and geographic regions.

Out of the 600-plus services that the Salvation Army tracks — everything from the number of toys given out at centers across the country — the team of 30 researchers selected seven indicators of poverty for the index: meals, groceries, assistance for medical needs, utility payments, furniture, clothing and housing…

The index enlists Salvation Army data collected from the roughly 30 million Americans who receive assistance with the organization each year. Out of the 600-plus services that the Salvation Army tracks — everything from the number of toys given out at centers across the country — the team of 30 researchers selected seven indicators of poverty for the index: meals, groceries, assistance for medical needs, utility payments, furniture, clothing and housing. Together, these data points form a score within the index that’s been plotted to show month-to-month changes both nationwide and within each state, between 2004 and 2015. The result is a highly interactive graphic that shows a number of interesting trends, including which states haven’t returned to pre-recession levels of need: Pennsylvania, Indiana, Nevada, Michigan Kansas and Minnesota…

A decade’s worth of data in the index revealed a number of curious trends, including a consistent uptick in demand for assistance on utility bills in springtime. “You’d think it’d be the opposite, because it’s warmer weather,” says Osili. The data analysts at Indiana University went to their colleagues at the Salvation Army, who soon enough arrived at an explanation. “In many communities, particularly in the Northern part of the United States, it’s against the law to turn off somebody’s utilities during cold weather,” says Lt. Colonel Ron Busroe of the Salvation Army. “So in April, when it is springtime, people are coming to us to get their utilities paid.”

Another observation illuminated by the index: a September back-to-school bump in service needs at Salvation Army centers everywhere. Those are just a couple of the discoveries that have Dr. Osili excited about what could blossom from this collaboration between university researchers and a sprawling service provider. “It was a revelation and showed the need for these kinds of partnerships, because on our own we would not have immediately or intuitively thought of that.”

Source: Salvation Army Is Measuring Poverty In Real Time – Next City

Big data doesn’t have to be Big Brother

This article easily says what I attempted to do in my 3-part Big Data, Little Farmers Markets posts earlier in the year.

The same data and algorithms that wreak havoc on workers’ lives could just as easily be repurposed to improve them. Worker cooperatives or strong, radical unions could use the same algorithms to maximize workers’ well being…

…Big data, like all technology, is imbued within social relations. Despite the rhetoric of its boosters and detractors, there is nothing inherently progressive or draconian about big data. Like all technology, its uses reflect the values of the society we live in.

Under our present system, the military and government use big data to suppress populations and spy on civilians. Corporations use it to boost profits, increase productivity, and extend the process of commodification ever deeper into our lives. But data and statistical algorithms don’t produce these outcomes — capitalism does. To realize the potentially amazing benefits of big data, we must fight against the undemocratic forces that seek to turn it into a tool of commodification and oppression.

Big Data article

Big Data on Shopping and Transportation

Some of you may remember the 3-part Big Data and farmers market posts I did a few months back. Here is another article from Next City that talks of a Big Data partnership for city planners to understand how people move about, to shop and to work.

What is great about this type of big data is that many markets can be a part of this data since a significant number of them accept MasterCard cards, either at the Welcome Booth or among vendors. Think about a funder doing this, using the number of and dollar amount of transaction metrics to analyze with transportation data; it could illuminate the need for more buses during market hours or simply to show the cluster patterns of market shopping among card users. That data could then pinpoint outreach.

By combining MasterCard’s transactions data (they process 43 billion per year) with Cubic’s transportation data, analytics and visualization technology, the platform — dubbed the Urbanomics Mobility Project — will yield insight into the way transit and economic activity are linked in cities.

https://nextcity.org/daily/entry/how-human-is-big-datas-newest-city-planning-tool

Big Data and Little Farmers Markets, Part 3

I used these examples in Part 2 of this series, but wanted to use them again for this post. To review:

Market A (which runs on Saturday morning downtown) is asked by its city to participate in a traffic planning project that will offer recommendations for car-free weekend days in the city center. The city will also review the requirement for parking lots in every new downtown development and possibly recalibrate where parking meters are located. To do this, the city will add driving strips to the areas around the market to count the cars and will monitor the meters and parking lot uses over the weekend. The market is being asked for its farmers to track their driving for all trips to the city and ask shoppers to do Dot Surveys on their driving experiences to the market on the weekend. Public transportation use will be gathered by university students.

Market B is partnering with an agricultural organization and other environmental organizations to measure the level of knowledge and awareness about farming in the greater metropolitan area. For one summer month, the market and other organizations will ask their supporters and farmers to use the hashtag #Junefarminfo on social media to share any news about markets, farm visits, gardening data or any other seasonal agricultural news.

Market C is working with its Main Street stores to understand shopping patterns by gathering data on average sales for credit and debit users. The Chamber of Commerce will also set up observation stations at key intersections to monitor Main Street shopper behavior such as where they congregate.

Market D has a grant with a health care corporation to offer incentives and will ask those voucher users to track their personal health care stats and their purchase and consumption of fresh foods. The users will get digital tools such as cameras to record their meals, voice recorders to record their children’s opinions about the menus (to upload on an online log) with their health stats such as BP, exercise regimen. That data will be compared to the larger Census population.

So all those ideas show how markets and their partners might be able to begin to use the world of Big Data. In those examples, one can see how the market benefits from having data that is (mostly) collected without a lot of work on the market’s part and yet is useful for them and for the larger community that the market also serves.

However, one of the best ways that markets can benefit from Big Data is slightly closer to home and even more useful to the stability and growth of the market itself. That is: to analyze and map the networks that markets foster and maintain, which is also known as network theory.
Network theory is a relatively new science that rose to prominence in the 1980s and 1990s and is about exploring and defining the relationships that a person or a community has and how, through their influence, their behavior is altered. What’s especially exciting about this work is that it combines many disciplines from mathematics to economics to social sciences.

A social network perspective can mean that data about relationships between the individuals can be as useful as the data about individuals themselves. Some people talk about this work in terms of strong ties and weak ties. Strong ties are the close relationships that we use with greater frequency and offer support and weak ties are those acquaintances who offer new information and connect us to other networks. The key is that in order to really understand a network, it is important to analyze the behavior of any member of the network in relation to other members action. This has a lot to do with incentives, which is obviously something markets have a lot of interest in.

From the book Networks, Crowds, and Markets: Reasoning about a Highly Connected World. By David Easley and Jon Kleinberg. Cambridge University Press, 2010. Complete preprint on-line at http://www.cs.cornell.edu/home/kleinber/networks-book/

From the book Networks, Crowds, and Markets: Reasoning about a Highly Connected World.
By David Easley and Jon Kleinberg. Cambridge University Press, 2010.
Complete preprint on-line at http://www.cs.cornell.edu/home/kleinber/networks-book/

From the foodsystemsnetwork.org website

From the foodsystemsnetwork.org website

network analysis

network analysis

I could go on and on about different theories and updates and critiques on these ideas, but the point to make here is this is science that is so very useful to the type of networks that food systems are propagating. Almost all of the work that farmers markets do rely on network theory without directly ascribing to it.

Think about a typical market day: a market could map each vendors booth to understand what people come to each table, using Dot Surveys or intercept surveys. That data could assist the vendor and the market. The market will benefit in knowing which are the anchor vendors of the market, which vendors constantly attract new shoppers, which vendors share shoppers etc. The market could also find out who among their shoppers bring information and ideas into the market and who carrries them out to the larger world from the market. All of this data would be mapped visually and would allow the market to be strategic with its efforts, connecting the appropriate type of shoppers to the vendors, expanding the product list for the shoppers likely to purchase new goods and so on.

Network theory would be quite beneficial to markets in their work to expand the reach to benefit program users and in the use of incentives. Since these market pilots began around 2005/2006, it has been a struggle to understand how to create a regular, return user of markets among those who have many barriers to adding this style of health and civic engagement. Those early markets created campaigns designed to offer the multiple and unique benefits of markets as a reason for benefit program shoppers to spend their few dollars there. Those markets also worked to reduce the barriers whenever possible by working with agencies on providing shuttles, offering activities for children while shopping, and adding non-traditional hours and locations for markets. Those efforts in New York, Arizona, California, Maryland, Massachusetts and Louisiana (among others) were positive but the early results were very small, attracting only a few of the shoppers desired. When the outcomes were analyzed by those organizations, it seemed that a few issues were cropping up again and again:
1. The agency that distributed the news of these market programs didn’t understand markets or did not have a relationship of trust with their clients that encouraged introduction of new ideas or acceptance of advice in changing their habits.
2. The market itself was not ready to welcome new benefit program shoppers- too few items were available or the market was not always welcoming to new shoppers who required extra steps and new payment systems.
3. Targeting the right group of “early adopters” among the large benefit program shopping base was impossible to decipher.
4. Some barriers remained and were too large for markets alone to address (lack of transportation or distance for example).
4. Finding the time for staff to do all of that work.

Over time, markets did their best to address these concerns, which has led to the expansion of these systems into every state and a combined impact in the millions for SNAP purchases at markets alone. The cash incentives assisted a great deal, especially with #2 and #4. However, this work would be made so much easier and the impact so much larger if network theory was applied.
Consider:
Market A is going to add a centralized card processing system and has funds to offer a cash incentive. But how to spend it? And how to prepare the market for the program?

If the market joined forces with a public health agency and a social science research team from a nearby university, it might begin by mapping the networks in that market to understand the strong and weak ties it contains as well as the structural holes in its network. It might find out that its vendors attract few new shoppers regularly or that the market’s staff is not connected to many outside actors in the larger network, thereby reducing the chance for information to flow.
It might also see that younger shoppers are not coming to the market and therefore conclude that focusing its efforts on attracting older benefit program shoppers (especially at first) might be a strategic move. If the market has a great many low-income shoppers using FMNP coupons already, the mapping of those shoppers may offer much data about how the market supports benefit program shoppers already and how it might expand with an audience already at market
The public health agency might do the same mapping for the agencies that are meant to offer the news of the market’s program. That mapping might find certain agencies or centers are better at introducing new ideas or have a population that is aligned already with the market’s demographic and therefore likely to feel welcomed.

As for incentives, what markets and their partners routinely tell me is more money is not always the answer. Not knowing what is expected from the use of the incentives or how to reach the best audience for that incentive is exhausting them or at least, puzzling them.
If markets knew their networks and knew where the holes were, they could use their incentive dollars much more efficiently and run their markets without burning out their staff or partners.
They might offer different incentives for their different locations, based on the barriers or offerings for each location. (They may also offer incentives to their vendors to test out new crops.)
If connectors are seen in large numbers in a market, then a “bring a friend” incentive might be offered, or if the mapping shows a large number of families entering the system in that area, then an incentive for a family level shopping experience may be useful.
One of the most important hypotheses that markets should use in their incentive strategy is how can they create a regular shopper through the use of the incentive. Of course, it is not the only hypothesis for a market; a large flagship market might identify their role as introducing new shoppers to their markets every month and use their funds to do just that. But for many markets with limited staff and small populations in and around the market, a never-ending cycle of new shoppers coming in for a few months and then not returning may not be the most efficient way to spend those dollars or their time. So this is also where network theory could be helpful.
By asking those using their EBT card to tell in detail where and how they heard about the program and by also tracking the number of visits they have after their introduction, we could begin to see which introductions work the best. Or by asking a small group of new EBT shoppers to be members of a long-term shopping focus group to track what happens during their visit (how many vendors they purchase from and how long they stay) and after (see Market D example at the top), we could learn about what EBT shoppers in that area value in their market experience. We may also find out that the market has few long-term return shoppers from the EBT population or we may find out that connectors become easy to spot and therefore they can be rewarded when sharing information on the market’s behalf.
In all of these cases, it will be easier for the staff to know what to do and when to do it if they understand their networks both in and around the market.
And of course, mapping the larger food systems around the markets’ systems would be exciting and could move policy issues to action sooner and allow funding to be increased for initiatives to fill the holes found.

However markets do it, what seems necessary is to know specifically who is using markets and how and why they decided to begin to use them and to whom those folks are connected. Network theory can be the best and widest use of the world of Big Data, especially to accomplish what Farmers Market Coalition has set as their call to action: that markets are for everyone.

Some reading, if you are interested:

http://www.foodsystemnetworks.org

The Tipping Point

http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book-ch03.pdf

http://www.sciencemag.org/content/301/5634/827.full.pdf

http://melander335.wdfiles.com/local–files/reading-history/kadushin.pdf

Big data, little farmers markets Part 2: The minefield of analyzing Big Data

In the first installment of this series, I introduced the idea of Big Data, the Internet of things (IoT) and what social media has promised and what it has delivered. I promised some thoughts on analysis next. here goes:

•Big Data is partly defined by its resistance to analysis. The volume, velocity and variety of Big Data makes problems for easy collection and analysis. This story on the struggle among safe street advocates to find good data speaks to that issue.

•Big Data is probably more appealing to advertisers than to our often shadowy government at this point but still, we should keep an eye on both of them and their analysis/use of Big Data.

•Lastly, as put so well by the author of Dataclysm: Who We Are (When We Think No One is Looking), much of behavioral science research is based on WEIRD research: White, educated, industrialized, rich and democratic nation’s subjects. Big Data may help to offset that issue.

Markets already intersect with Big Data across many different sectors, such as health care, the public sector, agriculture and retail. So let’s think about how this could play out for markets:
What if a researcher used the total dollars spent at markets on SNAP and compared it to grocery store SNAP sales on a map, not adjusting for hours open or the number of goods or markets available or fixed costs to offer those goods? Or how about the decrease in certification for organic farmers among market vendors – What if that was just a graph showing the decrease year after year, without the analysis that many farmers stated that they feel they do not need certification while they sell directly to shoppers and are therefore able to explain their practices? What if those maps/graphs were what influenced policymakers?

Some scenarios to ponder:

    •Market A (which runs on Saturday morning downtown) is asked by its city to participate in a traffic planning project that will offer recommendations for car-free weekend days in the city center. The city will also review the requirement for parking lots in every new downtown development and possibly recalibrate where parking meters are located. To do this, the city will add driving strips to the areas around the market to count the auto traffic and will monitor the meters and parking lot uses over the weekend. The market is being asked for its farmers to track their driving for all trips to the city and ask shoppers to do Dot Surveys on their driving experiences to the market on the weekend. Public transportation use will be gathered by university students.

    •Market B is partnering with an agricultural organization and other environmental organizations to measure the level of knowledge and awareness about farming in the greater metropolitan area. For one summer month, the market and other organizations will ask their supporters and farmers to use the hashtag #Junefarminfo on social media to share any news about markets, farm visits, gardening data or any other seasonal agricultural news.

    •Market C is working with its Main Street stores to understand shopping patterns by gathering data on average sales for credit and debit users. The Chamber of Commerce will also set up observation stations at key intersections to capture visual data on visitor behavior.

    •Market D has a grant with a health care corporation to offer incentives and will ask those voucher users to track their personal health care stats and their purchase and consumption of fresh foods. The users will get digital tools such as cameras to record their meals, voice recorders to record their children’s opinions about the menus (to upload on an online log) with their health stats such as BP, exercise regimen. That data will be compared to the larger Census population.

In all of these cases, the data to be collected crosses sectors and systems, meaning that no one entity has all of the raw data at their disposal at all times. That boils down into Analysis Issue #1

In all of these cases, the data to be collected has many ways to be interpreted, based on which entity is interpreting the data. Analysis Issue #2

In most of these cases, the data collected requires some self-reporting. Analysis Issue #3

In some of these cases, privacy controls must be strictly managed and will affect how much analysis can be done. Analysis Issue #4

from the New York Times:
“The first thing to note is that although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, it never tells us which correlations are meaningful (italics added). Analysis Issue #5

Check out this site for fun examples of how matching correlations doesn’t always add up to good conclusions.

The thing we should be able to agree on: all partners should be involved with the analysis and should receive access to the raw data. That means markets participating in just the data collection piece is not enough. They need to be involved in the analysis because if not, the context of markets will be lost.
Yet we know that just collecting the data is be a massive undertaking for low-capacity markets (even assume some funding is offered in all of these cases for the partners to staff the collection of the data), not even adding in the time and effort it takes to analyze it. What might help is to have some analysis prepared ahead of time and to prepare the market community for participation.
1. This means that every market association, or group of markets or markets themselves should keep information about each market’s history, size, structure and staffing in separate PDFs. This, by the way, is a resource that Farmers Market Coalition (for whom I am a consultant) is working on with one of their university partners, the University of Wisconsin to pilot for their AFRI Indicators for Impact project . Hopefully, the Market Profile will be available online for all markets to test in 2015- stay tuned!
2. Markets need to know the area’s current demographic and other relevant details. Check the census to know what the larger population’s stats are and make friends with real estate professionals to keep up on trends in the neighborhood.
3. Do a Dot Survey or Bean Poll a few times a year asking shoppers to tell you what zip code they live in, how they come to the market, things like that and keep track of that data. Maybe a big dry wipe calendar on the wall to add all data collected?
4. Market boards and advisors should keep any data already collected and the Profile information to be able to share it as needed in any meeting they happen to attend in their own professional lives.
5. When researchers do come to your market with an offer to help with data collection, be ready to ask for data you want. How about asking for focus group data so that a market can begin to build “persona profiles” of those who come to the market? Or ask for added analysis for numbers that you think might be important for the market: those who know me have heard my song about finding a way to track the number of return SNAP shoppers and how I think it that metric is so useful for markets and possibly even more useful than total SNAP dollars, in terms of analysis.

5. Encourage city or county public health agencies to offer a semi-annual breakfast for those entities that work on community interventions (like markets, health clinics, social service entities, university programs, youth outreach etc) to share news about what they are seeing in their field and to share any data informally. If meetings are impossible, then a regular email would work. In other words, stay in touch with other data collection efforts in your community.

I’ll end this post with some of the lovely words of Dataclysm author Christian Rudder who was talking about the Vietnam Memorial’s physical self versus its online database self:

“A web page can’t replace granite. It can’t replace friendship or love or family either. But what it can do – as a conduit for our shared experience – is help us understand ourselves and our lives. The era of data is here; we are now recorded. That, like all change is frightening, but between the gunmetal gray of the government and the hot pink of product offers we just can’t refuse, there is an open and ungarish way. To use data to know yet not manipulate, to explore but not to pry, to protect but not to smother, to see yet never expose, and, above all, to repay that priceless gift we bequeath to the world when we share our lives so that other lives may be better – and to fulfill for everyone that oldest of human hopes, from Gilgamesh to Ramses to today:that our names be remembered not only in stone but as part of memory itself.”
I think I’ll adopt that bit as my mantra.

Big data and little farmers markets, Part 1

Recently, I have been reading a few books and articles on the new world looming over the next bend. This new world is called many things and includes shiny named ideas and tools to make it so. Here are some of those titles in case anyone needs some bedside reading:

•Collaborative Commons (Rifkin, (The Zero Marginal Costs Society)
•Disruption (Next City 2012, Fortune 2014 “Next up for disruption: The grocery business”, Urbanophile 2014, Disrupting the Disruptors )
•Flattened economy (Friedman The World Is Flat: A Brief History of the Twenty-First Century 2005)
•Spiky Economy (Florida, “The World Is Spiky” 2005)
•Alternative Economics, Community-Supported Industry (Anderberg 2012, Schumacher Center for New Economics)
•Social impact bonds (Jacobin Magazine Issue 15–16 “Friendly Fire”)
•Placemaking/Livable Places (PPS, Tactical Urbanism, CityLab)
•Human-Centered Design (LUMA, Ideo)

and then bunches on how to measure this stuff:
•Measuring Urban Design: Metrics for Livable Places (Ewing, Clemente 2013)
•3 Keys To Better Data-Driven Decisions (Technology Evaluation Centers)
•Five Borough Farm II: Growing the Benefits of Urban Agriculture in NYC (Design Trust for Public Space 2014)
•Data Infoactive (Chiasson, Gregory 2013)
•Disruption Index (Next City 2012)
•Livability Index (livability.com 2014)

and so on. (and please feel free to send me any that you find useful).

Much of this discussion of the new economy and its infrastructure centers around the use of technology to allow data (usually known as Big Data) produced by every system, sensor, and mobile device to be shared across sectors and users – aka the Internet of Things (IoT). Big Data and IoT are representative of what is both good and bad about the new world; they pressure public entities to adopt private sector characteristics and measures, and conversely, ask private entities to add public sector transparency as a mode of operating in this new world. Additionally, both sectors must respond immediately to any trends or innovations. This can be good and bad.
 (The intersection of public and private is what the non-profit sector is supposed to exist and, increasingly how it participates in Big Data, is a measure of its ability to do just that. I’ll come back to that very idea later in this series.)

Examples of Big Data:
Think of how that grocery store loyalty card transmits information about what, when and where customers purchase goods. Or citizen used tools to measure and report pollution, or how that electronic parking card tells the city the peak parking hours, letting planners know the need for more (or less) parking facilities. Or, the sensors that are timed to go off for irrigation to start for food production.
For food system advocates, the connection to data sharing is mostly through the public health sector at this point, but the planning and design sector of governments will be wanting data from us too and then, you can expect the line to form from other sectors after that.

Social media is not the center of Big Data, but it’s already helping to study the behavior of its millions of users. In the interdisciplinary Cornell University course entitled “Networks, Crowds and Markets” taught by professors David Easley, Jon Kleinberg, and Éva Tardos, they use data from online networks to talk about “strong and weak ties” and “bridges” and to map the patterns of why, how and when connections are made and what impact those connections have in the fields of economics, social sciences, and public health, among others. Since social media is mostly networking, informal updates, and chatter, (constant and sometimes as cheerfully mindless as an acquaintance’s wave from across the street), it may seem without value, but it is certainly changing the way that we communicate.
Social media can also power revolutions, allow for professional development and offer small businesses appealingly designed, low-cost online faces for their already-developed customer base. This blog you are reading is part of social media and as such, is written to be ephemeral and chatty opinion with links to other information sources rather than hosting peer-reviewed reports.

Recently, I had the good fortune (thanks to the Farmers Market Coalition) to be invited to a Knight Foundation technology gathering of social entrepreneurs and so heard many ideas for leashing the power of Twitter and other social media platforms to better aggregate data or reorganize news feeds. No doubt as new platforms are built on top of the first tier, there will be more usability and versatility, but for now, many people view it as a multi-platform address book to keep track of friends, colleagues, and friends of friends.

The ease of using social media is what was beguiling to many at first but the gossamer veil of privacy means that if not careful, one’s identity may be stolen or become the target of a bully. At that point, that once-enticing open entry can drive plenty away and that very fact is what is being argued about sites like Airbnb and Uber: 1) that the lack of regulation at city halls or public agencies allows for exemption of rules that their counterparts with physical outlets are not able to sidestep and 2) since there is often no face to face meeting between buyer and user, the perceived opportunity for criminal activity increases. My feeling is that the regulation needed for the IoT and online sites must be a new system rather than asking for adherence to the old since the old grey mare of city hall or the federal government is not suited for managing these (which sounds like what the community food system has been saying for the last few decades!)
The European Commission has already published a report outlined some best practices for architectural, ethics and governance of the IoT, highlighting social justice, privacy and opting out concerns (“consent activities” in designer language). Their early conclusions encourage better credential exchange systems and a deeper awareness of “reliance versus trust” parameters. In short, make sure most online relationships include a requirement for sharing some sort of identification and create some active boundaries between systems. Maybe the U.S. community food system can jump on these ideas, thereby leaping ahead in confidence levels to be able to share useful data more rapidly than other sectors.

Yet, even with the perception of these systems as being hackable, an increasing number of people in the Western world still participate regularly even while others hoot it down while they cling to their wall phone and postal stamps as their talismans against the new world of constant updates. Those folks are not likely to let us forget that social media is just a part of the communication sector and only the ephemeral part of it. We still read newspapers and books, meet people face to face and still have postal carriers and grocery store corkboards with lists of apartments to rent.
Therefore, how we use social media within community food systems has to be balanced far better than we early adopters have done so far. Plenty of markets and other food system initiatives use social media brilliantly within its limited use, but others often ignore traditional media entirely by not factoring in that those reached with social media are only a tiny portion of the audience that might be found. Or conversely for the Luddites among us, the need to adapt their thinking to understand that social media has worth for a low-capacity, face-to-face entity like a Saturday morning market.
What I have noticed is that social media helps drive farmers market or CSA sales for a single or a few products on a single day extremely well. It also does a passable to good job reminding its users that they are members of a larger community of doers and thinkers, which can extend the social and human capital of a market. It can connect producers to shoppers on non-market days (although I think less well than promised) and can do something akin to the Dot Survey method pioneered at market by Stephenson, Brewer and Lev: allow for an easy mood of the day give and take between market organizers and users. It also is that friendly wave from across the street that in our sped up world can stand in for reminder of community on a bad day and add a layer of connection. Let’s just not build our world entirely on chance meetings or depend on a small number of tools.

update This morning, I am sitting in a farmer workshop at Southern SSAWG conference listening to a 5th generation farmer talk about the open source free crop planning software system, sensors, and apps that he uses to run his direct marketing farm business; clearly, for some, the IoT is already here.

Coming Soon
Part 2 The minefield of analyzing Big Data
Part 3 Connecting farmers markets and food systems to Big Data
Part 4 Managing face to face and online communities in farmers markets

20150116-091953.jpg
Coming Soon
Part 2 The minefield of analyzing Big Data
Part 3 Connecting farmers markets and food systems to Big Data
Part 4 Managing face to face and online communities in farmers markets