As a growing number of organizations sponsor regenerative agriculture programs to reduce their Scope 3 emissions or generate carbon credits, a persistent challenge remains: recruiting and enrolling growers. Growers face much uncertainty when it comes to choosing and participating in regenerative ag cost share and incentive programs. One major frustration for growers and their advisors is wasting time providing detailed field history data. This is especially problematic if they go through an extensive process only to find out they don’t qualify for a program. CIBO Impact solves this problem through the platform’s prequalification process that integrates remote sensing, modeling and scaled data into a simple user interface.
The Benefits of Prequalification in CIBO Impact
Prequalification saves time and reduces data entry burden on growers and their trusted advisors in three ways: Integrating remote sensing; eliminating common disqualifiers up front; and using modeling to give advisors and growers an understanding of the return they can expect if they enroll in a program.
1. Removing Data Entry Burden with Remote Sensing
CIBO Impact reduces the burden on growers and advisors to spend unnecessary time supplying data on their farm practice history. When a grower or their trusted advisor draws or uploads a field boundary, the platform instantly shows that field’s cash crop, tillage and cover crop history. The user simply needs to review and confirm or update the information. This provides a handful of benefits:
- No immediate grower action: A trusted advisor with knowledge of the grower’s field boundaries can prequalify a field, only involving the grower if needed to verify practice history
- Easy verification: Users simply review, confirm or edit practices that CIBO Impact remotely sensed; the process is quick and only takes a few clicks
- Reduce duplicate effort: If/when the grower decides to enroll in a program, the prequalification data is pre-populated into their enrollment form; program sponsors may ask for additional information for enrollment, but the nearly-universally asked questions have already been answered and populated when the user reaches enrollment
2. Eliminating Common Disqualifiers Up Front
The remotely sensed data that populates the prequalification workflow in CIBO Impact includes some of the most common reasons that fields are not qualified to be enrolled in a program. This saves advisors and growers the inconvenience of combing through program paperwork or, worse, thinking the field will qualify only to spend time on enrollment and find that an easily verified detail disqualifies the field. These common disqualifiers include:
- Location: For example, program is only available in certain states and/or counties, but field is outside program area
- Cash crop: For example, program is for corn growers, but field history is entirely cotton
- Cover crop history: For example, program is for new cover crop adopters only, but field has been cover cropped the past three growing seasons
- Tillage history: For example, program is only for new adopters of no-till, but field has been no-tilled the past three growing seasons
Should the remotely sensed data occasionally be inaccurate, the prequalification workflow’s first step is to display the data and allow the user to edit it or add missing information.
3. Rapid Understanding of Program Options and Returns
Imagine an advisor has uploaded a field boundary, verified the practice history, and sees that the field qualifies for programs. Great! But how much will the grower stand to earn if they decide to enroll? Having an estimate of the return on a program is one of the factors that will go into the decision to enroll. CIBO Impact provides users with an up-front understanding of:
- Program matching: CIBO Impact recommends available programs based on the field’s location and practice history, making it simpler to compare program options
- Potential return: Based on the practice history and CIBO’s modeling capabilities, CIBO Impact can also display an estimate of the ROI and payouts the grower could earn if they enroll, such as the total dollar amount for a pay-for-practice program based on the grower’s acreage, or the number of carbon credits they could earn and sell
Conclusion
By integrating remote sensing and modeling while eliminating common disqualifiers up front, the prequalification process in CIBO Impact helps growers and their trusted advisors save time and choose to go through the enrollment process with more information and transparency.
If you’re ready to see a demo of the prequalification process in CIBO Impact, contact us. [link https://www.cibotechnologies.com/request-a-demo/ ]
