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Part 3 is the last (for now) in the series on Smart Farming - focusing on projects and programs in the area of growing and distributing foods. As I wrote it, I realized that it had to be further decomposed into Parts 3a and Part 3B. The decomposition theme continues here - in a WBS sort of way. Read on, brave project leaders, read on.
In Part 1, I covered agrivoltaics - the practice of installing solar photovoltaic panels on farmland in a way that primary agricultural activities (such as animal grazing, insect resourcing (honey production) and crop/vegetable production) can continue. In Part 2, we shift upwards - WAY upwards, to focus on satellite imagery and using data to discover and potentially repair problems with topsoil.
In Part 3, we bring our attention to the food that is grown on farms and projects surrounding its distribution. Again from UMass Magazine, there is a piece about Farm to Institution New England, a network backbone that connects farms to institutions, such as universities and hospitals. Their mission?
"Our mission is to mobilize the power of New England institutions to transform our food system."
A good mission statement deserves a vision that drives it. We know this as project (and especially Program and Portfolio) leaders.
"By 2030, we envision New England institutions and the FINE network playing leadership roles in cultivating a region that is moving towards self-reliance. We envision an equitable and just food system that provides access to healthy and abundant food for all New Englanders, and is defined by sustainable and productive land and ocean ecosystems."
This is a project-oriented organization. For a glimpse at some of their work, visit their projects page by clicking here. I was fascinated by the projects surrounding University dining. As a long-ago graduate of UMass Amherst, I am of course proud of the UMass year-after-year number one ranking for campus food (very, very different from when I attended - can you say "cube steak"?). FINE has published (amongst many other items) this interesting report about the supply chain of food from local farms to university campuses, called Campus Dining 201 (click on the link or the image below for an immediate download).
In it, you will find a treasure trove of data (D) advanced into information (I) and knowledge (K), providing wisdom (W) (see the Part 2 post of this series to learn about the DIKW Pyramid). Amongst the gems in this report, and of particular interest to project leaders, is the pie chart (see figure below) which talks about the definition of "local food". We know that in a Work Breakdown Structure (WBS), we need a WBS Dictionary to tell us what we mean when we say (for example) "Complete Electrical Wiring", and whether or not that includes installation of light fixtures. It's similar to the idea of defining project success, so that we know when we're done - but on a work package level.
To even begin to understand the food supply chain and the element of 'local food', what do we mean by the term 'local food'? The pie chart below tells us that we have some work to do in that area:
I was amazed by the fact that almost 3/4 of the schools don't define or know what is meant by local food. So it seems some work is in order to provide the equivalent of a WBS Dictionary for terms such as this. Otherwise we are in danger of compiling lots of data and creating lovely charts that are based on undefined or unknown inputs - a formula for disaster. Work being done by groups such as FINE are helping us avert this disaster by providing some definition.
Do your projects have concise and clear definition around the work to be done? It's worth some background work on your part as a project leader.
In Part 3b, I'll close out this series with more about projects focused on the food supply chain and advancing data into information, knowledge, and wisdom in the area of just how that Christmas fruitcake from Auntie Catherine made it from ... wherever fruitcakes come from ... to your holiday table.
Big Data. Analytics. It’s hot now, and for good reason. The ability to apply machine learning and Artificial Intelligence (AI) to vast amounts of data to, for example, decide to put up an advert of a certain athletic shoe on your desktop, to decide whether a competitor may be worth acquiring, or to choose between investments.
And although money is important, AI can be applied to much, much more than money. Think about the data of the Earth. Well, yes, the planet Earth, but also literally, the earth - the soil - on which you are standing (or the building on which you are standing … is standing).
What’s under you? Soil, roots, worms.
There is a laboratory in the Swiss Federal Institute of Technology, led by a man named Thomas Crowther. That laboratory has embarked on a project, which, in a way, is an accounting project. The thing for which it is doing the accounting is, well, it’s the Earth.
Crowther’s lab is funded for 10+ years to collect individual observations (many, MANY of them) and use AI to reach conclusions about the count of trees, fungi, and, for example, nematode worms.
So far, his lab has concluded that there are 3 trillion trees and 0.4 sextillion nematode worms. We'll come back to these little wigglers later.
Why do this?
Well, as project managers we know about baselines. If we are to make improvements and/or to understand the changes taking place so that we can make corrections or note the effect of attempted corrections, we need that baseline.
All of this comes mainly from a cover story in the most recent edition of Nature magazine, in an article called, “The Everything Mapper”, by Aisling Irwin. It’s a fascinating story – partially because it’s a fascinating project. The project has already realized benefits, and has some lessons learned for project managers. For starters, when Crowther was getting started, he was at Yale and proposed the idea of using ground data from actual tree counts (satellite data can’t peer below the canopy). To do this, he needed to get scientists from different institutions to collaborate and share their data. He had to build a team from disparate organizations. Sound familiar? The professors around him though it was a ridiculous idea but he managed to do it, to the point where he had data representing an area the size of a US state. Granted, the state was Rhode Island, but still – quite an accomplishment.
He then worked with data scientist Henry Glick to compare the ground-level counts with the satellite imagery to make informed decisions about how many trees there really were.
The benefit realized was that the mapping done by Crowther and Glick (and others) was used to build the first global model of tree density – and the figure of “3 Trillion Trees”, which in turn changed the name of the UN’s “Billion Tree Campaign” to the “Trillion Tree Campaign”. Their database continues to serve the Forest Biodiversity Initiative, which studies and manages the world's largest tree-level forest inventory database. A snapshot of the status of the Trillion Tree Campaign is shown below.
Another outcome – an important one – is a conclusion that “tree planting is easily the best way to remove carbon from the atmosphere, and could be the key to slowing global warming”.
This is a conclusion that obviously spawns many new projects, but that’s another story.
Let’s get back to nematodes for a bit. They're usually tiny, around 50 micrometers thick and 1 millimeter long - but the nasty parasitic kinds (this is sort of sickening) can be up to 3 feet long. They actually play an interesting role in solving climate change. This recent article from Brigham Young University covers that aspect. One thing of interest to note is that the biomass of the nematodes of the planet is almost equal to our weight. That is, add up the weight of all the nematodes and you have 80% of the weight of the entire human population! The relationship to carbon is summed up here:
“Knowing where these tiny worms live matters because nematodes play a critical role in the cycling of carbon and nutrients and heavily influence CO2 emissions. An important finding of the paper is that nematode abundance is strongly correlated with soil carbon (more carbon = more worms). Understanding the little organisms at a global level is critical if humans are going to understand and address climate change.”
Below is a figure from the Nature article summarizing the data from Crowther's research for trees, nematodes and fungi.
In Part 2, I will talk about more lessons learned for project management and more about the connection between AI and Earth.