'Smart' Winter Vegetables: Balancing Nutrients, Carbon Footprint and Cost

Transcript

My project explores how people in northern communities can make informed decisions when buying vegetables for winter.

I developed three questions:

First, how is the nutrient content of vegetables impacted by source and time during the winter

Second, what are the costs of different vegetables from different sources and does it change over time; and

Third, what is the carbon footprint of different sources and storage methods.

For nutrients, I tested sugar content and vitamin C of local, non-local and grocery store garlic, potatoes and kale, completing 5 test cycles over a period of 87 days for a total of 936 data points. I found no evidence that nutrients change based on time or source.

For cost, based on the data I collected, local was more than non-local at fall harvest.

For carbon footprint, I calculated carbon per kg and learned that transportation

I then used my results to create a simple decision tool to help consumers make informed decisions when buying vegetables.

As a northern athlete, I am personally interested in how nutrient levels in vegetables change during winter storage. However, people's food choices are not based on nutrients alone. There has been growing interest in eating locally due to concerns about sustainability and climate change. Northern communities face unique challenges due to short growing seasons and transportation distances. Because of this, I also wanted to examine the carbon footprint of different food options, particularly local versus non-local produce.

In addition, cost is an important factor for many consumers. Even if food is nutritionally superior, it may not be practical if it is too expensive. Therefore, I wanted to determine which option provides the best balance between nutrient retention, environmental impact, and affordability. By considering nutrients, and then analyzing cost and carbon footprint, this project aims to provide realistic and informed recommendations for consumers.

Hypothesis

My hypothesis is that locally grown food will have the most desirable trend in nutrient indicators (Vitamin C and Brix) throughout storage. I do not think there will be a large difference in nutrient indicators for non-local grocery store produce and non-local stored produce.

For cost, I believe for the initial purchase, local produce will be more expensive than non-local. I also expect grocery store prices to increase over winter.

For transportation carbon footprint, I believe non-local produce will be highest and local produce lowest. I believe that grocery storage will have a higher carbon footprint than home storage (cold storage vs freezer)

Experiment Design Process:

I began by selecting produce types. Garlic, potato, and kale were chosen as options northern consumers could access and store and they were compatible with testing methods: refractometer, vitamin C strips, and iodine titration. I used iodine titration to compare with vitamin C strips.

I defined vegetable sources as: local (<100 km), non-local  (>100km but <1000 km) and grocery store (purchased at each test cycle from the closest possible option). I chose storage methods based on research and consumer convenience; potatoes and garlic in paper bags and kale frozen in ziploc bags.

Procedure:

In total I completed 5 test cycles over 87 days.  At the start of each test cycle, I made observations on a labeled stored sample group.

Next I prepared the juice samples for all three test types. I juiced potatoes and garlic with an electric juicer and prepared kale using a mortar and pestle to liquify five grams of kale mixed with 30 grams of water. I tested with vitamin C test strips and refractometer and recorded results. At the school lab, I used iodine titration, measuring the iodine solution used and calculating vitamin C.

For test 2-5 I also purchased potatoes, garlic and kale from the grocery store as comparison samples, all prices were recorded.

Decision Tool:

I created a decision tool based on a weighted scoring system for nutrients, cost, and carbon footprint based on the data I collected and the user’s inputted ratings. The user inputs a rating (1-5) for each of nutrients, carbon footprint and cost and the tool multiplies each rating by its importance level and adds the totals. The highest score is recommended as the best match for the user’s priorities.

Statistics:

I chose simple linear regression as it allowed me to analyse how nutrient indicators change over time. The slope of the trendline showed if values were increasing or decreasing, while R² values showed the strength of the relationship between the two variables. P-values showed whether any observed relationship was statistically significant. Standard error for my error bars showed the uncertainty around the sample means. If clearer trends had been present in my data, ANCOVA could have been used to compare whether changes over time differed significantly between sources.

Nutrient Indicators:

Brix: There were no clear changes in Brix over time from any source of garlic, potatoes, or kale. Local and non-local garlic trendlines were flat over time, while grocery store garlic decreased slightly. For non-local and local potatoes, trendlines decreased slightly, whle grocery store potatoes increased. All relationships were weak (low R² values) and none of the trends were statistically significant (all p-values > 0.05). Kale results are shown in Figure 2.

Vitamin C: Vitamin C trendlines decreased for grocery store and non-local garlic but increased slightly for local garlic. Non-local and grocery store garlic had weak relationships (R² values=0.18, 0.03), local garlic had a moderate relationship (R² values=0.53). None of the trends were statistically significant (all p-values > 0.05).

For kale, grocery store and non-local kale had decreasing trendlines, while local increased slightly. All the R² values were weak with high p-values.

Vitamin C trendlines for potatoes tested with Vitamin C test strips decreased for local potatoes and increased for non-local and groceries store. R² values for grocery stores samples were weak (0.11), while local (0.52) and non-local (0.66) were moderate. None of the trends were considered statistically significant (p=0.53, 0.17, 0.09), though non-local potatoes were close.

Iodine titration results for potatoes are shown in Figure 7.

Observations of stored samples:

Weight: Non-local garlic has a strong relationship between time and storage (R²=0.96) and local garlic is moderate (R²=0.50). The weight loss in non-local garlic is statistically significant (p=0.02). Potato results are shown in Figure 3.

Texture: Garlic showed no clear changes in texture over time. Both local and non-local potatoes changed from firm to mushy over the storage period at slightly different rates. By test cycle 2 local were 20% firm and 80% soft while non-local were 10% firm and 90% soft. During test cycle 4, local were 20% soft and 80% mushy, while non-local stayed 100% soft.

Color: There were no clear changes in brightness over time for stored local or non-local garlic and potatoes, as measured by red, blue, and green values. I noticed that local potatoes decreased in brightness while non-local potatoes turned green, however, the data only captured a weak relationship between brightness and time (R² values=0.35, 0.19).

Additional considerations:

Cost: Cost results are shown in Figure 13 and 14.

Carbon footprint: Carbon results are shown in Figure 15.

Findings:

My results did not show that locally grown food has the most desirable trend in nutrients indicators; I found little difference in the three sources over the storage period for any vegetable.  I confirmed that local food is the most expensive at fall harvest but found out that grocery store kale, garlic and potatoes do not change significantly in cost over winter.

Limitations:

I spent a lot of time researching options and talking to mentors to decide on a procedure that would help me answer my questions, and that I could do at home. I had to balance access at home, cost, accuracy and compatibility of vegetables with testing methods.

I chose to add an observation component to my experiment design to see if weight, texture or brightness could help answer my questions, which added even more data collection to my project.

When buying vegetables, I was unable to say with certainty that all of my vegetables were from the same variety/cultivar.  Which limited me to looking at trends over time, not direct comparisons of amounts.

Real world application:

Consumers have to think about multiple factors to make an informed decision when purchasing vegetables.  My findings that there is no significant relationship between vegetable source, nutrients and storage time, influences cost and carbon as factors in consumer choice.

Taking this information, I created a decision tool, with the intention of creating a simple way for consumers to apply the results of my project to the real world.

To expand on my project I would like to collect more data to increase the capabilities of my decision tool. One idea I have is to add variety by collecting data from more types of vegetables. I would also like to look at additional storage methods such as storing in sand or sawdust in the cold storage, dehydration and canning. I also want to increase the amount of prices recorded.

I plan to explore options for communicating what I learned with my community, including sharing my decision tool.

I would like to thank a few people.

Karen for assisting me with analyzing my data and helping me determine the proper statistical tests to use.

Anne-Marie, for answering my many questions and providing valuable assistance throughout the project from my early thinking to the end.

Ms. Barber for being willing to help me figure out how to do Iodine titration and letting me use her lab equipment over and over again.

Thank you to Shawna for making science fair happen in my town and her constant support.

Thanks to everyone who filled out my survey for feedback on the decision tool.

And finally, my Mom, for supporting me through the entire process of completing this project. Thank you for being there during the many hours spent working to make it to CWSF.

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