Nutrient Management Solution
Apply Nutrition when Needed and Reduce Leaching
USDA ranks fertilizer as the second most expensive input for US grain farmers. Overuse of fertilizer reduces soil health and increases GHG emissions and water pollution. Our nutrient management solution optimizes production output, by considering uptake, and nutrient usage, to maximize productivity. For the producer, this reduces costs, improves the environment and reduces pollution, while maintaining output profit. Seed companies may use our platform to economically evaluate the performance of their varieties in different soil profiles and fertilization applications, and augment their in-field trials.
Holos Benefits
Holos’ agricultural water management solution will benefit the:
Producer community: meet profit and sustainability goals for near-term and long-term, and specific zones:
- Single-season: recommend optimal variety,planting dates, irrigation schedules, harvesting timelines to be implemented.
- In-season; monitor crop growth and recommend adjustments to prevent stress and maximize stage based uptake.
- Multi-season (under development): recommend an optimal inter-crop and/or fallow sequence and estimated water usage during those periods.
Water agencies: develop the following at different spatial resolutions:
- Higher accuracy water demand based on characteristics of cultivated crops rather than broad regional ET observations/modeling.
- Efficient water use agricultural plans that can be presented to their constituents for practice.
How we do what we do
Holos’ water management solution is based on a hybrid platform, combining the advantages of mechanistic crop science models and AI models resulting in a high accuracy, dynamic, customizable, responsive decision making system.
- Dynamic and research calibrated science models: exercise multiple “treatments” on localized environments of lower spatial resolution (large farms, landscape). These models require detailed and complex inputs. The outputs are general, but multi-faceted crop responses of high accuracy due to calibration, and are used to train our AI models for specific organizational or individual goals.
- Customizable & Responsive AI models: are friendlier on the input requirements. Once trained, they predict an outcome or a recommendation given information at higher spatial resolution (parcel location, and parcel level monitored data).
Although science models are powerful, they are compute intensive while AI models are near real-time responsive. Water agencies may tend to use the science models, while producers may tend to use the AI models.
Efficient Water Management Case Study
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