The GDD stuff is really cool and I believe it works really well. In part it depends on what it is you’ll be growing as there is a bit of an optimization period to adjust and much of it is based on the instrumentation quality and observation quality.
It took us a couple seasons to really get dialed in on an optimized number of degree days to achieve the perfect spec on product, further, it took us a while to kind of optimize out the measurement plans and get the math refined
Sentinel-2, much like many projects in our lifetimes, is collecting more data than has even begun to yield its ultimate value, the sheer volume of data could lend itself to a career in data science. The uncovered treasures of which are likely numerous.
As far as the time events I would think of anything that is not a point event would benefit from the ability to aggregate. Clearly as my example indicates, irrigation is really a time event. Right now - I have the ability to introduce a timestamp onto an input. Hydrologists and master irrigators would tell you the total amount of water, and the range of time it is applied depends on how deep your water penetrates.
I would think there are other events that would lend themselves to this type of thinking. Something like a danger event [getting too hot or too cold] would be nice to know how long heat was high or cold was low.
Further I can see this type of aggregated event being useful in other inputs or events as well - time to cool is big metric for me and my leafy greens. Quality increases significantly when the total uncooled time is kept low. This cannot really be thought of as a point event - rather the time between cut and cool needs to be treated as a collective unit.
In indoor agriculture I have a couple of other use cases where it could make sense as well - unlike your outdoor scenario - I can view light application times for purposes of GDD or other sunlight metrics as a block of time. For my analytics having the sun be a ramp with a temperature gradient simply doesnt apply so having this idea of on-off cycled block aggregates is a clean representation in the indoor world.
In my outdoors experience I can think of many other times when a timeblock would apply. For example, water applications and test time, presumptive tests, product wetting etc. I know there are scenarios where you cannot water or conduct other events like spray or applications based on certain time criterias. For example - If we test clean on our field of arugula - we cannot irrigate between now and harvest.
I shore this up almost like an execption - same thing if we sprayed - perhaps we have to wait a certain number of days before we can assume the insect repelling agent has taken affect. Similarly, perhaps a trap crop or other diversion technique is deemed effective for 30 days. Sure - the application date is the input date - but the event “crop protection” actually occurs for a thirty day period.
Kind of like a video game powerup type of thing.
I envision this allowing the FarmOS user several relationships to time. I think all farmers respect that we have a time based process, and events are not standalone point events. For the live look - as in sensors - it is logical to have that point-time relationship.
Conversely, I think about my time breaking new ground, or applying fish, compost, other nutrients, even GDD. We know that these have a point application Aug 3 2021 we applied the product. We also know that the efficacy of the application has a gradient, in my mind this means you need to have the concept of blocked time in order to represent - There will be x amount of this nutrient in the soil from the compost application y days from the point application.
I still dont exactly know how it is this SHOULD be dealt with. But I am now well into my journey on FarmOS, I have used Trimble Ag (big ag software), as well as the John Deere info stuff, ive seen farmers do it on excel, power bi etc etc etc, about any data capture scheme you can think of and the one thing I come back to all the systems missing:
The farm is an ecosystem, when I put nutrient in my hydro reservoir its not like my NPK is a square wave Nutriented or Un-Nutriented, testing and monitoring still only give you a point in time look at that, but what is really happening is time-blocks, this idea that an application isnt just a standalone event, it has a tip, a tail and geometry to it point in time analysis is simply unprepared to answer
This is one of the things that fascinated me so much about the intersection of big data and farming. Unlike my previous work in mortgage portfolios, big-banking, and financial markets, farms are organic and traditional mechanisms used to measure packetized loans, or stocks, or portfolios simply dont apply to the gradient-rich environment that is farming.
Anyways I will stop pontificating - if you are ever in Phoenix ill buy you a hot or cold beverage of your choice and we can nerd out on data-mining our farming operations