Sustainability Insights for Romania

Overview of Regrow's Q4 2024 platform data enhancements with insights for Romania

Introduction

In December 2024, Regrow Ag continued its expansion to Europe with the launch of the Romania dataset. This expansion offers our customers greater precision in making informed Scope 3 investment decisions and reporting on Scope 3 emissions outcomes in a broader range of their supply sheds.

Over the past two years, Regrow has invested heavily in ongoing research and development, resulting in more accurate and dependable outputs. We've also expanded our ground-truth data coverage, incorporating a larger number of fields to ensure that Regrow’s solutions can reliably predict outcomes and monitor changes on the field.

What’s New: 

This release of the Romania data includes advancements in Regrow’s models:

• Enhanced Regrow remote sensing capabilities by improving models, better data processing capabilities, and expanded use of Sentinel-2 sensors 
  • New in-house crop detection model, MonitorML
  • New field-boundary model, Parcel ID 
• Improved cover crop detection accuracy
• Reclassified tillage practices to better align with global standards
• Made improvements to Regrow’s DNDC model that better incorporate soil nitrogen and carbon cycling to enhance emissions modeling accuracy 
• Reporting on a cultivation cycle basis, versus a strict annual timeframe

Remote Sensing and Monitor Model Updates

Crop Detection

Introducing MonitorML: 

MonitorML is Regrow’s in-house crop detection model, built on remote sensing technology. MonitorML is trained and validated using a combination of ground truth data collected from field surveys, grower reported data and public data sets such as LPIS. Using this mix of public and high quality data for model training and evaluation, we are able to provide accurate classification, especially in the crops most relevant to our customers. By leveraging machine learning for crop detection and advanced algorithms, Regrow can now provide:

• Increased cropland coverage 
• Enhanced identification of fields with hay, improving programs involving livestock or silage 
• Improved detection of additional cover crops 
• Better insights into field boundaries 
• Identification of non-agricultural land through Regrow’s Parcel ID model 

As we incorporate more data inputs, we continuously refine our ML models to provide targeted insights and emissions impact data, ensuring our customers receive the most relevant and up-to-date information.

Supported Crops 

Crop	SI Availability
Barley	Report & Plan
Corn	Report & Plan
Corn Silage	Report & Plan
Canola (Rapeseed)	Report & Plan
Fallow	Report Data Only - Practices & Emissions (Monitor & Measure)
Millet	Report & Plan
Oats	Report & Plan
Peas	Report & Plan
Potatoes	Report & Plan
Rice	Report (Monitor) Data Only
Rye	Report Data Only
Sorghum	Report & Plan
Soybeans	Report & Plan
Sugar Beets	Report & Plan
Sunflower	Report & Plan
Spring Wheat	Report & Plan
Triticale	Report & Plan
Winter Wheat	Report & Plan

 

Field Delineation

Delineated field boundaries that represent crop land are essential parameters for remote sensing, and serve as the basis for DNDC to quantify emissions of an area. 

Regrow has developed an in-house field delineation model called Parcel ID, which generates precise field boundaries, crucial for evaluating key performance indicators, baseline emissions, and abatement potential in SI.

Benefits of using Regrow Generated Field Boundaries: 

Regrow's Parcel ID uses satellite imagery to accurately delineate agricultural field boundaries within a region. This is crucial for generating SI data, as it provides a detailed list of fields, which are processed through Regrow’s Monitor and Measure APIs. Parcel ID simplifies MRV farmer enrollment by pre-filling field boundaries, making it easier for farmers to select their fields. It also improves the accuracy of covered area identification and the exclusion of non-agricultural land. Unlike outdated CLUs, which can complicate farmer pre-fill and credit auditing in MRV programs, Regrow's Parcel ID offers precise and current field delineations, enabling more efficient credit auditing. 

Cover Crop Classification

Regrow’s cover crop classification methodology uses a dynamic time window and geographically specific thresholds that reflect climate variability, allowing for the assessment of cover crop presence at any time when no commodity crop is present on the field. This dynamic approach enables the detection of cover crops, including winter commodity systems.

The thresholds for cover crop presence are set dynamically by assessing the average greenness of nearby perennial grass areas during the cover crop season. The updated methodology for dynamic thresholds increases the overall accuracy of cover crop detection, especially in areas with temperate climates including:

1. Minimum of 8 weeks required between main commodity crop harvest and the following crop planting to make a green cover determination
   1. This results in green cover determinations that exclude volunteers, weeds, and short-lived commodity re-growth as possible cover crops
2. Regional greenness thresholds are used to inform the cover crop determination. In practice this means in order for a determination of cover crop to be made, the field must have enough living vegetation on the field to cover the soil for the majority of the cover cropping period.  
   1. This means that colder regions where there is less green cover may have lower greenness thresholds for classifying a cover crop.

Tillage Classification

Conservation tillage is defined as having a minimum of 30% remaining crop residue on a field after a tillage event. This definition aligns with accepted global definitions of conservation tillage, defined by leaving at least 30% crop residue or more on the field.  By aligning our classifications to observed residue cover, the result is a more conservative account of tillage practices that removes the ambiguity in tillage intensity definition. 

	Regrow’s Classification
Conventional till	<30% residue cover
Reduced Till	30% residue cover
Conservation Tillage	30% residue cover (equivalent to reduced tillage)
Conservation Till / No Till	60% residue cover

Cultivation Cycles

With the expansion into Europe, Sustainability Insights is moving towards a cultivation cycle reporting methodology. A cultivation cycle spans from the harvest of one commodity crop until the harvest of the next commodity crop. All practices that happened within this time period (after harvest of the preceding crop and in preparation for planting of the crop of interest) are associated with the crop of interest. All outcomes will be reported in the year the crop is harvested. Here is a visual representation of our reporting methodology:

What data validation framework does Regrow use for crop and practice determination models?

• Crop type models are trained on public data, validated on high quality data from growers, customers, and other 3rd party partners.
• Practice models are calibrated and tested at the field level using data from research partners & organizations, growers, and other 3rd party partners. We also compare our aggregate data to publicly reported data in EuroStat (eg NUTS 2 regional stats).

Emissions and DNDC Model Updates

Regrow's Denitrification-Decomposition (DNDC) model is a biogeochemical model that estimates nutrient cycling in soil and GHG emissions, considering changes from new farming practices. It supports over 100 crop types and has been validated by more than 500 scientific peer reviews. DNDC is calibrated for each crop, field, and region by sourcing the best available emissions data for specific crop and geographic combinations. 

DNDC Updates

We have incorporated some updates to our latest DNDC model version:

• Enhancing soil nitrogen cycling by improving the urea hydrolysis routines in the model
• Enhancing soil organic carbon cycling by introducing a biological mixing routine to represent influence of macrofauna on the soil system
• Amending problematic soil temperature methods which in turn improved emissions modeling

CO2 Conversion Factors 

Regrow has changed CO2 equivalent conversion factors for N2O and CH4 to reflect values published in the 2021 IPCC Report. 

The table below summarizes the changes between each version

Version	N2O Multiplier	CH4 Multiplier
AR5	44 / 28 * 265 = 416.429	16 / 12 * 28 = 37.33
AR6	44 / 28 * 273 = 429	16 / 12 * 29.8 = 39.733

Detailed values for AR6 can be found in Table 7.15 of the IPCC Technical Report, found here.

 

Impact of Weather on Outcomes 

Net GHGs decrease between 2019 and 2022 and increase between 2021 and 2023. GHG emissions are variable with the lowest emissions in 2022. dSOC has the largest SOC loss in 2019 that can be explained by a number of reasons ranging from Monitor crop detection and tillage intensity across Romania.

With respect to GHG emissions, there are some weather patterns in some major agricultural regions that can explain the peak in 2021. For TELEORMAN and CONSTANTA regions (two of the top 5 largest cultivated areas), 2021 has the highest average precipitation (mm/day) over the year. High amounts of rainfall can lead to increased nitrification and denitrification processes due to higher soil moisture content.

Additionally, Romania has fields with higher clay fraction in the south. Soils with high clay fractions generally lead to increased N2O emissions because the small pore spaces within clay particles create more anaerobic microenvironments, which are ideal conditions for the denitrification process that produces N2O.

As for dSOC, we ideally would like to see values in the negatives to indicate yearly sequestration. However, that is not currently the case likely due to a few reasons:

• MonitorML indicates that only 14.26% of fields on average between 2019-2023 are using cover crops, and over 66.55% of fields are using conventional till.

• A lack of cover cropping and a majority of conventional tillage, it is not surprising that we are seeing loss in SOC over the years. The 2019 year is particularly bad for SOC loss and it likely is being driven by Monitor outputs.

Quality Control and Assurance

Monitor Sustainability Insights QA

The primary aim of Monitor Sustainability Insights (SI) QA is to discover errors or problems in the modeled agronomic logic, so that solutions can be implemented before finalized data production. Additionally, it sheds light on assessment of the overall quality of the data in terms of alignment with reasonable cropping practices, temporal expectations of those practices, and regional-scale reports of practice adoption. Monitor SI QA is conducted on 1) Monitor results, aggregated with SI logic, to check for data validity and 2) Monitor results, aggregated with SI logic, as compared to LPIS data. The process consists of metrics, reporting, and analysis aggregated over time and geographic extent to check for valid data generation results. The QA process ensures data completeness by analyzing error rates, cropping period consistencies, and practice distributions. The concepts checked for data validity include:

• Measurement of return rate of associated field data over the supported time period
• Alignment of agronomic expectations for cropping period time windows
• Assessment of spatiotemporal distribution of management practices and crop types 

 

DNDC QA

The primary goal of DNDC SI QA is to identify abnormal values or outliers in our modeled GHG results that may indicate errors or problems within the model or input generation pipeline itself. DNDC SI QA aims to validate the GHG and EF numbers that are produced using a large suite of peer reviewed studies and LCA/EF databases. When conducting QA on the DNDC produced GHG values, data is aggregated such that results can be assessed on a country, regional, and crop level. EFs will also be aggregated in a similar fashion such that we can validate them against our known data sources. Validating data includes ensuring that GHG values are within reasonable bounds for individual emissions (SOC, N2O, and CH4) defined by a collection of peer reviewed studies and their corresponding in situ measurements. Similarly, crop EFs are validated by comparing values with Quantis data. If outliers are identified at either phase of QA, DNDC inputs such as weather, soil texture, fertilizer amounts, and crop rotations will be investigated to identify a reasonable explanation for the outlier or identify a problem within the model itself.