Top Grower Questions About Sustainability

We developed a very simple way for a grower to enroll in the CIBO marketplace or join a customer’s enterprise private working space. Growers upload a field and through our simplified process, we can calculate within that moment how many credits they can generate based on their practices. Once a farmer has completed his or her enrollment, we use our own computer vision technology to verify their practices. Our platform gives estimates of how many carbon credits they can sell. Farmers input their practices and we are able to give them a quick and easy number. We make it as simple and easy for farmers to input and access their information in one place. It also gives them access to the full breadth of data about land they just enrolled.

CIBO uses the SALUS model and it was developed by one of our co-founders, Dr. Bruno Basso. It models the growth of a crop over 300 different parameters from planting date, inputs, crop type, etc. Some of the other things that come out of that model are the carbon equivalent to a particular crop growing on a piece of land.

We use a process-based model called SALUS, which models the growing crop as well as the ecosystem surrounding it. This model is well-documented in the scientific literature. Find out more about the science behind CIBO.

We use a process-based model so we simulate the entire ecosystem. It’s an academic model that we transform into a scalable model that can be used commercially. We use published studies of soil carbon to test and calibrate our model. Here are some published results on model accuracy.

We use SSURGO soil samples and publicly available soil maps as well as remote sensing. It allows us to get the details on the infield variability analysis. Our yield simulation is based on a crop modeling approach based on how a crop behaves over a growing season. What is its performance and how is it changing? It is also based on the specific understanding of that field’s conditions. CIBO combines modeling and simulation as one pillar to benchmark against county averages. Then, we combine AI and remote sensing to detect what is actually happening inside the field in the last day, week or specific time period. By combining both simulation and modeling with computervison and AI, CIBO is able to bring in the public, private, and proprietary data to produce scores, in field variability, crop identification. We blend it all together to make it bigger than the sum of its parts.

CIBO has built a very flexible pipeline for ecosystem modeling. As a general rule, we use as much farmer data as we have available. We then supplement that data with inferences based on Computer Vision, as well as our database of common grower practices in different geographies. When we estimate past yields and historical practices on a field, we use recorded weather data. For in-season forecasting, we use recorded weather data up to the present, and a sheaf of weather forecasts (projections) for the remainder of the growing season.

We do not have our own satellites. We rely currently on a few public satellites, such as Landsat 8 (which produces an image every few weeks), and the newer Sentinel 2 (which produces an image every few days). We have also worked with private satellites. Some images have to be discarded, due to technical glitches in the satellites or cloud coverage obscuring the image. Taking all this into account, we generally have 3 or 4 images/month of a given field throughout the year.

It’s true that the SSURGO soils database (from USGS) contains inaccuracies. When possible, we compare SSURGO data to actual soil samples. We’ve found that SSURGO is generally pretty accurate, although it may miss some small-scale details. We like to use soil samples from growers when those are available, to supplement or replace the larger-scale databases.

We take a lot of public data sources that are relevant to CIBO like USDA valuation, historical practices, soil maps, etc. We take this data and put it in one place. On top of all this data, we layer it with remote vision, AI and modeling to provide a deeper understanding of the information. Visit CIBO’s blog for more information about our specific valuation approaches. When we take a look at the publicly available data, we use our own understanding of performance, stability, and valuation to find the difference between each piece of land in order to find its own unique agronomic value. CIBO has built a robust data infrastructure at a county level. We have agronomists who go through public information to validate it. We add our remote sensing data to this information and apply algorithms on top of it about the specific crops (our own IP). Together, with our agronomists, we give validation to our practices and agronomic data. We do license some data from third parties. This includes our owner information but we do not sell this information to others.

The purpose of models such as CIBO’s is not to replace the expertise of a grower who is walking through his own fields. That person is always going to know more about the soil, the weather, the specific variety of a crop planted, etc, and can probably make a good educated guess as to when the crop will mature and what the yield will be. The purpose of our models is to give the grower (and others) insight on a broader basis:  to have a better understanding of what is happening in his neighbors’ fields, in other counties, and other states.  Additionally, we wish to provide insights into things that can’t be observed directly, like the grower’s impact on soil health and carbon footprint. We are in a constant process of testing our models against measured data and making improvements where we see discrepancies.

We don’t use these types of data at present, although it’s an idea we have considered.  There are a couple of complications to take into account: Each of these image types would have to be processed differently from satellite imagery. We would have to develop completely separate processes to use such images. We’re trying to develop solutions that scale across the country, and potentially across the world. With satellite data, we can get information on any field in the world. Someday, tractor video images may be so common that we can rely on them for up-to-date information, but that seems to be far in the future.

Yes. Our models and interfaces allow the farmer, as well as the scientist, to ask “what if” questions around planting and management. Our interface provides projections of yield and readiness for harvest, as well as soil health metrics, on the basis of a management scenario.

At present, no. The kinds of weather phenomena that cause crop loss are things like hail and tornadoes, which tend to be hyper-local.  We have not found any sources that reliably predict such phenomena on a local scale.

In addition to soil and weather, we consider a suite of management practices: choice of cultivar, planting dates, harvest dates, fertilizer practices, tillage practices, and cover cropping. All of these factors interact with each other in complicated ways that add up to the total carbon footprint of a farm. Over time, we intend to add additional factors, as well as creating more detailed and nuanced models of how these factors interact.

Every field is different. In our model, tillage affects the soil characteristics, such a compaction and aeration, and indirectly affects the amount of soil carbon available in different soil layers. The net effect of this, in conjunction with the soil of the field, the weather, and other management practices, can have either a positive or negative impact on productivity, depending on the circumstances.

Yes. That is something very important to us, as we are focused on scaling regenerative ag across the U.S. Our ecosystem models estimate the beneficial effects of applying additional fertilizer, as well as the direct results in nitrogen leaching and nitrous oxide emissions. We also estimate the indirect costs of producing nitrogen fertilizer, which is itself a significant contributor to greenhouse gases.

We agree that there’s a trend toward more frequent and higher-resolution imagery. However, it’s a big leap from the few images a month that we currently have, to a live view with 24/7 imagery. Realistically, we doubt this will be achieved any time soon, as it would be very expensive, and market forces don’t seem strong enough. When it is a reality, it will make our jobs easier, but not fundamentally different. Some of the challenges in Computer Vision are related to image acquisition, but many are centered around the interpretation of those images. When we have more and better images, the challenges of interpretation will remain, and may even become larger.