If you wish to know crop yield in a given year on a given parcel in the corn belt, you could physically travel to that parcel and conduct a field experiment. You’d grow a crop, then measure the yield when the crop matures. This process would undoubtedly provide the desired answer. However, it couldn’t realistically be repeated for all parcels across the U.S. — and for all years of interest — because such experiments would simply be too expensive and impossible to manage.
That’s where crop modeling can come in. We create a digital model-plant, give it the main characteristics of a real plant (roots, leaves, flowers, grains, etc.), then ask a computer to “grow” the plant by essentially mimicking the physiological behavior of a real plant. Once we have a model-plant that grows on a given parcel in a given year, it becomes possible to expand this process to other parcels, other years, and other crops.
CIBO’s proprietary technology is able to provide crop yield predictions in virtually any environment, helping organizations preserve resources and gain insights at any scale and for various points in time. The applications for this insight are numerous. Yield modeling can help companies understand how regenerative agriculture practices could affect crop yields in the short and long term, for example. As more companies are setting net zero targets and seeking to decarbonize their supply chains, having this knowledge at hand becomes increasingly important.
CIBO’s yield simulations engage four components — soil properties, weather, management, and specific crop characteristics — to help compare different parcels’ productivity over time. . . all without planting a seed. How does CIBO’s proprietary technology develop these crop yield prediction models in virtually any environment? Download the whitepaper to explore the science behind our crop modeling.
Download the whitepaper to learn more
