Trees offer an abundance of gifts. Both wild and tended trees provide food, fuel, fiber, animal feed, medicines, and a range of co-benefits, including clean water and wellness. Much more has been said on this, and there is more to say. For now, I zoom in.
How can we reconnect with trees for basic needs? One way I’m drawn to is that of a sprouting organization called the Keystone Tree Crop Cooperative (KTCC). KTCC aims to gather food from existing nut trees and enable broader audiences to connect with these gifts from trees.
To catalyze the harvest of existing nut trees, gatherers will benefit from incentives (such as compensation for nuts), education on methods (including food quality standards and comfortable tools), and guidance on gathering locations (as in maps). This post is about the latter: mapping existing nut trees and identifying hotspots to efficiently harvest from, with an eye for the coming autumn 2021.
iNaturalist was homed in on as a pretty good platform for nut tree info. There are observations and observers already on iNaturalist. This site also has experts maintaining a cross-platform database of trees, which is useful to build on. While iNaturalist has some limitations discussed below, it is a good starting point – let’s take a look!
Asked how tree crop enthusiasts are keeping track of plantings and related info, I shared this info:
Metal tree tags on locust stakes are low-tech and work reliably. Next best is grease-pen or indentations on metal or vynil tags, attached to trees themselves. Overall, it’s great to be pro at plant ID, but that doesn’t always work to differentiate between cultivars.
For a more advanced, digital approach, I use QGIS. It offers the benefits described in a comment above about ArcMap, but it is free and open-source software. It has a little bit of a learning curve, but it is a very powerful tool and it can interface with other geospatial technology including GPS, Google Earth, and iNaturalist. Google Earth has an easier learning curve and has more than enough features for most users. If you want to go the QGIS route, I recommend trying the following steps to begin with, and feel free to ask questions in the comments or on gis.stackexchange.com.
Find and download raster files (.jpg, .tif) for overhead views of your Area of Interest (AOI). These files are referred to as aerial imagery or orthoimagery, and in the U.S. you can get them from county GIS websites or from https://nationalmap.gov.
Follow a basic tutorial about raster vs. vector file types, and creating shapefiles.
Create a polygon shapefile for your AOI boundary- Create a point shapefile for your individual plants.
Add new attribute fields to your ‘plant points’ shapefile to describe characteristics you want to keep track of. Here’s some fields I use (+ examples/explanation):
species (corylus spp.),
planted_date (fall 2019),
permanent (y/n in case it is to be transplanted),
measured (y/n to indicate if its location is precise or estimated),
source (to keep track of cultivars, purchases, etc),
updated (date for when this entry was last updated, since inevitably the records can get out of date; update this every time you update any other field for this data row)
notes (misc info that doesn’t fit cleanly in other fields, try to use this sparingly as it is better to have distinct fields in case later on you want to select or analyze plants based on some attribute)
Here’s what QGIS looks like in regular use for me. You can add shapes as points, lines, or polygons, which can represent individuals, linear plantings, or orchard blocks. This screenshot shows an individual tree point selected, with the kind of info I described above and more displayed on the right-side ‘attribute table’.
At this stage, you will have a powerful, interactive map of your AOI, with individual points or polygons to depict features of interest on your property, and those features can have a miniature (or massive) database of characteristics associated with them. You can have as many or as few fields as you’d like, and you can even associate fields (e.g. feature_ID) with other datasets, such as yield records or amendment history for an orchard block.
In practice, as I plant or inspect plantings, I jot notes in text messages to myself or on a muddy piece of paper, and then I digitize those notes by updating my QGIS project for the plantings. A text might be as cryptic as “purp os willow x4 3′ e of ne hazels” and I use those notes to enter four purple osier willows planted at 3-foot spacing starting east of the northeast hazelnut hedge. As long as I don’t wait too long between field notes and digitizing, it works well enough, and this could be much more precise if I wanted to take the time to GPS-locate each planting.
Lastly, if you are just getting started with GIS tools, I suggest exploring a basic tutorial about Coordinate Reference Systems (CRS) and about Georeferencing. That will help you get ahead of GIS software’s more confusing aspects, which many people don’t learn about until they are tangled in problems with coordinate systems. Using a CRS appropriate for your region and consistent for all layers in your project will help you avoid problems and make accurate spatial measurements and maps. Again feel free to drop questions in comments or visit the very helpful gis.stackexchange.com.
Here’s a finished map from QGIS. It is not as easy to make proper maps in QGIS but it is possible – a tutorial will save you a ton of time vs. winging it.
LoNo Computing. LoNo (short for low-power no-power) Computing is the idea of building a computer that is extremely low power. It is the attempt, inspired by nature, to build efficient computers by harnessing the natural behavior of natural systems.
I discovered this while exploring another concept I recently discovered, Solarpunk. Solarpunk was first presented to me via Firefox’s Pocket webpage recommendation of Nine Sci-Fi Subgenres to Help You Understand the Future.
Computers powered by natural forces, using very little power. I found this fascinating at first, then recognized with more fascination than usual the wonder of our current computing technology: the laptop I type this on is labeled as 65W, and online searches suggest it probably uses more like 15W; this is a fraction of the ~100W human body. Water has many interesting properties, as does electromagnetism. I’m curious about how hydraulics, pneumatics, and electronics compare as computing mediums, but it seems likely electronics will continue advancing the forefront of computing, especially as it is 1) an underlying, fundamental force essential to the behavior of hydraulics and pneumatics; and 2) entangled in all sorts of quantum strangeness.
Just today I’ve seen two blatantly fake and misleading news stories circulating my Facebook bubbles. As usual, they gain momentum on the fringes (political extremes) and become more widespread by grabbing the attention of any fish that’ll bite and get hooked. Let it go – it ain’t even food!
The problem highlighted by this article has two ferocious prongs:
> On one end, we have the “fake news” itself and all the misled steps that can follow it. I’ll let that speak for itself.
> On the other end, we have the potential for reasonable people to start casting broad and blind blankets of doubt over any information they encounter – perhaps rightfully so!
While the latter can seem safer at first, consider how disturbed the lives of extreme conspiracy theorists are by the single behavior of excessive skepticism. If you cannot have confidence in any data, you are left only to your biases or data-less decisions and conclusions. Skepticism is a key part of science, but science progresses with skepticism *AND* increased confidence through verification.
Verification is quite important then. How do we verify information we receive, from the news, from the Internet, from your own senses? Do we verify some things more than others? Do we do the meta- level task of considering _how much_ verification is needed for certain information?
For that matter, how can you verify that anything in this post has any basis in reality? And before we even go there, … is this food? Bon voyage.
