Baseline Description

The baseline scenario reflects the continuation of historical agricultural practices, specifically the application of synthetic fertilizers at business-as-usual (BAU) rates. Under this scenario, nitrous oxide (N₂O) emissions from soils are expected to remain higher compared to the reduced emissions achieved through project implementation. This baseline serves as a reference point to measure the project's impact and additionality by demonstrating that emission reductions would not have occurred without the project.

The baseline must confirm that synthetic fertilizers were historically used as part of the agricultural management practices on the project land. Baseline emissions represent the estimated amount of N₂O released during the project crediting period if these practices continued without change.

2.2.1 Baseline Validation

Developers must provide detailed records of synthetic fertilizer application, including type, quantity, and timing, for at least one year prior to project initiation. This data ensures that the baseline scenario accurately reflects historical farming practices.

• Baseline Period: To ensure an accurate representation of historical fertilizer use and practices, a minimum of one year of pre-project data must be collected. However, a longer period (e.g., 3–5 years) is recommended when possible to account for variations in weather, crop rotation, and farming practices. If only one year of data is available, supplementary sources such as regional agricultural records or farmer surveys should be used to strengthen the baseline assessment.
• Project Period: Post-project implementation data must span at least one year to demonstrate the reduction or replacement of synthetic fertilizers and the corresponding decrease in N₂O emissions.

2.2.2 Additionality

To demonstrate additionality in fertilizer reduction projects, it is essential to show that the emission reductions are real, measurable, and exceed business-as-usual (BAU) practices. This can be achieved by implementing actions that go beyond legal or regulatory requirements and represent a significant departure from standard agricultural practices. Key considerations for proving additionality include:

1. Clear Identification of Project Activities: The project should outline specific actions taken to reduce fertilizer use, such as transitioning to precision application, using slow-release fertilizers, switching to organic alternatives, or introducing nitrogen-fixing rhizobia. Rhizobia, used with leguminous crops, help convert atmospheric nitrogen into a form plants can use, reducing reliance on synthetic nitrogen fertilizers. These interventions must be significantly different from standard practices in the region to demonstrate a meaningful shift towards sustainable nutrient management.
2. Baseline Comparison and Emission Reduction Evidence: Evidence must be provided to show that, without the project, fertilizer application rates and the associated N₂O emissions would have remained at or near BAU levels. This may involve using historical data or modeling to estimate emissions under typical practices.
3. Technological and Financial Barriers: The project should address and overcome barriers that would otherwise prevent the adoption of the new practices, such as high upfront costs or limited knowledge and resources among farmers.
4. Regulatory Surplus: The project must demonstrate that reducing fertilizer use is not mandated by any law or regulation. The reductions must go beyond legal requirements, ensuring that the actions taken are voluntary and not a result of regulatory obligations.

2.2.3 Leakage

Leakage risks in fertilizer reduction projects are considered negligible due to the continued use of land for agricultural production. Historical evidence demonstrates that reducing nitrogen fertilizer to optimal economic levels does not compromise crop yields, eliminating incentives for production shifts that could increase emissions or reduce soil carbon pools outside the project boundary (Zhao, Lu, Zhang, Li, & Liu, 2017; Nasiro & Mohammednur, 2024; Hoben, Gehl, Millar, Grace, & Robertson, 2011).

To ensure thorough monitoring, Carbontribe can utilize advanced computer vision models to detect increased agricultural activities or emissions in neighboring areas, providing an additional safeguard against potential indirect impacts.