---
title: "Regrow's SAI Context Analysis: Alignment with RTF"
description: Analysis on how Regrow's scoring lines up with SAI's qualitative bands, and the data sources behind each criterion needed for Context Analysis reporting
---

[Skip to content](https://help.regrow.ag/regrows-scontext-analysis-#main-content)

English

Show submenu for translations

[Customer portal](https://help.regrow.ag/support?hsLang=en)

![Regrow\_logo-1.png\]](https://help.regrow.ag/hs-fs/hubfs/03.%20Logos/Regrow_logo-1.png?height=40&name=Regrow_logo-1.png)

Open main navigation

Close main navigation

- English
  
  Show submenu for translations
- [Customer portal](https://help.regrow.ag/support)
- [Go to regrow.ag](https://www.regrow.ag/)

[Go to regrow.ag](https://www.regrow.ag/)

 How can we help you?

- There are no suggestions because the search field is empty.

1. [Knowledge Base](https://help.regrow.ag/?hsLang=en)
2. [Sustainability Insights](https://help.regrow.ag/sustainability-insights?hsLang=en)
3. [SAI Outcomes](https://help.regrow.ag/sustainability-insights?hsLang=en#sai-outcomes)

# Regrow's SAI Context Analysis: Alignment with RTF

### Alignment with SAI RTF Principles

| SAI principle (Context Analysis Guide §) | Regrow alignment |
| --- | --- |
| §1 — Group-level CA is permitted; the implementation group can be a supply shed or sourcing area. | Regrow delivers CA at the supply-shed level, which maps directly to SAI's "implementation group." |
| §1 — Whole-farm assessment is the default unit, regardless of sourced crop. | Regrow's Context Analysis is reported at the supply-shed level, but the underlying analysis is performed at the **field level** and aggregated up to the shed. Resolving the structural risk factors field by field, rather than averaging them across a whole farm or region, meets and exceeds SAI's whole-farm assessment intent. |
| §2 — Risk = structural exposure × mitigating/exacerbating management practices. | Regrow's scoring follows the same hazard / exposure / vulnerability framing SAI uses, combining structural exposure (climate, soils, hydrology) with management practices. Regrow incorporates management-practice data where it is available — including crop type, cover cropping, tillage, and fertilizer inputs — and uses outcome-based proxies (such as soil organic carbon for cumulative residue retention, conservation tillage, and organic amendments) for practices not yet directly observed. Broadening direct practice signals across every criterion is a continuing area of investment. |
| §3 — Quantitative thresholds are "indicative reference values to ensure consistency" — defined, predictable, transparently documented. | Each Regrow criterion has documented quantitative thresholds (see per-criterion sections); scoring formula and weights are published in the report Appendix. |
| §4 — Evidence hierarchy: Level 1 farm → Level 2 supply shed → Level 3 regional/global. | The report now surfaces each criterion's Evidence Level (L1 / L2 / L3) explicitly, and assigns it by the granularity of the underlying dataset. Field-differentiated sources — 30 m SSURGO soil properties, per-field CDL/Monitor-derived crop rotation, per-field irrigated-area, and field-level DNDC emissions modelling — are tagged **Level 1**; gridded climate and soil-loss surfaces are **Level 2**; county / country / global datasets are **Level 3**. **Note:** a Level 1 tag reflects the *granularity* of a public or modelled dataset computed per field, not grower-submitted farm documentation; CA is still delivered at the supply-shed level and aggregates these field-level values up to the shed. |
| §6 — Each score supported by a justification covering: structural exposure factors, management practices, data source/proxy, data quality level. | The report now includes a per-criterion narrative Justification field for every criterion, matching the Assessment Tool's score + justification structure. Each justification names the structural exposure factors, notes where management-practice signals are unavailable and what proxy stands in, identifies the data source, and states the evidence level. |
| §7 — Non-compensation between criteria: independent interpretation; no offsetting across criteria. | Regrow reports each of the 12 criteria independently with its own risk band. No cross-criterion roll-up score is produced. Within-criterion combination of evidence (hazard / exposure / vulnerability) is permitted under SAI's scoring logic in §2 and is distinct from inter-criterion compensation. |

---

### Per-Criterion Mapping

**How Regrow scores risk within each criterion: the H × E × V framework**

SAI's scoring logic (Context Analysis Guide) defines risk as **structural exposure × management practices that mitigate or exacerbate that exposure**.

Regrow uses a hazard / exposure / vulnerability (H × E × V) decomposition that mirrors this framing inside each criterion:

- **Hazard (H)** — the external force or stressor driving the risk (e.g., erosive rainfall, drought, climate exposure).
- **Exposure (E)** — how much the production system is exposed to that force (e.g., bare soil, irrigation reliance, intensive inputs).
- **Vulnerability (V)** — how susceptible the system is given its inherent properties (e.g., erodible soil, sandy texture, poor drainage).

The criterion's score is a weighted geometric mean of the available H, E, V components, binned to a 1 / 2 / 3 (Low / Medium / High) output. 

**On management practices:** SAI's scoring logic weights management practices heavily. Regrow incorporates practice data where it is available, including crop type, cover cropping, tillage, and fertilizer inputs, and uses outcome-based or land-cover-based proxies where a direct practice signal is not yet available for a given criterion (e.g., soil organic carbon as evidence of cumulative residue retention and conservation tillage; rotation diversity as evidence of crop-system design; satellite-derived irrigation extent as evidence of water-management intensity). Broadening direct practice signals across every criterion is a continuing area of investment.

**On the reporting period:** Every annually-varying input is now averaged over the **trailing 3-year reporting period** ending in the report year (e.g., 2023–2025 for a 2025 report). A year with no value for an input is dropped from that input's average, and a criterion falls back to Medium only when the entire 3-year window is empty for it. Static properties (soil texture, drainage, erodibility) are not time-averaged. This replaces the earlier single-year basis and smooths year-to-year weather and management variation out of the structural-risk signal.

 

---

#### **Soil erosion**

**SAI Objective**  
Assess the structural risk of topsoil loss due to water and wind erosion at farm level, considering both biophysical exposure and soil management practices.

**SAI Description**  
Soil erosion refers to the loss or displacement of topsoil caused by water runoff or wind action. This criterion evaluates the structural risk of erosion at farm level based on biophysical conditions and management practices, including water and wind erosion, soil cover, tillage intensity and structural mitigation measures.

**Linked SAI Outcome(s)**O1 (Increased soil health & fertility) `++`; O5 (Enhanced farmland ecological integrity) `+`.

**Risk narrative (Regrow)**  
*Steep slopes and erosive rainfall create the force for erosion (**hazard**). Low residue or crop cover exposes soil to that force (**exposure**). Erodible soils are more easily detached (**vulnerability**). The criterion rates the land's structural susceptibility to erosion before management, so on-farm mitigation (contour farming, cover cropping, reduced tillage) is deliberately not an input — the cover-and-management term inside the soil-loss model is the only practice-adjacent signal it carries.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current structural conditions and management practices; where available, erosion trends over the past 3–5 years are recommended. | Rainfall behavior averaged over the trailing 3-year reporting period; soil/landscape characteristics are static. Annual soil-loss estimate is modeled with current inputs. |
| **What we look at** | Biophysical exposure (slope, rainfall intensity, soil erodibility, wind), soil cover and vegetation, tillage intensity, and structural mitigation measures. | Two indicators combined as `H0.40 × V0.60`: - **Rainfall behavior (Hazard).** A standardised precipitation index — essentially how unusual recent rainfall is compared to the long-term local norm. Anomalously wet periods are a signal of erosion-driving rain events. - **Modelled annual soil loss (Vulnerability).** Tonnes of soil lost per hectare per year, estimated using the Revised Universal Soil Loss Equation (RUSLE). RUSLE produces a soil-loss number by combining *rainfall erosivity*, *soil erodibility*, *slope length and steepness*, *cover and management*, and *support practices* — the same biophysical and management factors SAI's guidance describes. |
| **Score 1 — Low** | Low structural exposure and/or strong mitigation practices in place — e.g., permanent or frequent soil cover, minimal or conservation tillage, stable structure, no visible erosion. | Modeled annual soil loss is well below the tolerable rate (\<1 t/ha/yr) and rainfall is not anomalously erosive. Composite 1.00–\<1.75 → **Low**. |
| **Score 2 — Medium** | Moderate exposure and/or partial mitigation — e.g., seasonal bare soil, conventional tillage, localized erosion signs. | Soil-loss estimate is in the tolerable-to-elevated range (1–\<5 t/ha/yr) or rainfall behavior is moderately anomalous. Composite 1.75–\<2.25 → **Medium**. |
| **Score 3 — High** | High exposure and insufficient mitigation — e.g., frequent or prolonged bare soil, intensive tillage on slopes, visible rill or gully erosion. | Soil-loss estimate exceeds sustainable thresholds (≥5 t/ha/yr) or rainfall is strongly anomalous. Composite 2.25–3.0 → **High**. |
| **Evidence Level** | L1 farm documentation · L2 supplyshed proxy · L3 public regional erosion maps. | **Level 2.** The RUSLE soil-loss surface and rainfall SPI are gridded products coarser than a field but finer than a county. Surfaced in the report. |
| **Databases** | GloSEM (JRC), FAO Global Soil Erosion Map, ISRIC SoilGrids. | Regrow Climate Risk dataset (rainfall anomaly); Regrow RUSLE soil-loss model (proprietary); USDA SSURGO (soil erodibility inputs, USA). |

 

---

#### **Soil fertility loss**

**SAI Objective**  
Assess the structural risk of long-term soil fertility decline at farm level, based on soil organic matter management, soil disturbance intensity, biomass balance and crop system design.

**SAI Description**  
Soil fertility refers to the soil's capacity to sustain crop productivity through adequate organic matter, biological activity, soil structure stability and nutrient cycling. This criterion evaluates structural risk of fertility decline based on residue retention, organic amendments, tillage intensity and crop-system design.

**Linked SAI Outcome(s)**O1 (Increased soil health & fertility) `++`; O2 (Increased nutrient use efficiency) `+`.

**Risk narrative (Regrow)**  
*Soils that consistently lose more nitrogen than they receive are under pressure to mine fertility from organic matter (**hazard**). Simple rotations and continuous cropping expose the soil to nutrient mining and biomass deficit (**exposure**). Soils that already carry low organic carbon stocks are less resilient to that pressure (**vulnerability**). Diversified rotations, residue retention, and amendments rebuild organic matter and reduce both exposure and vulnerability.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current structural management; trends over the past 3–5 years strengthen the assessment. | Nitrogen flows averaged over the trailing 3-year reporting period; 5-year crop rotation history; current soil organic carbon. |
| **What we look at** | Soil organic matter management (residue retention, organic amendments, cover crops); soil disturbance intensity (tillage practices); biomass balance and crop-system design (rotation diversity). | Three indicators combined as `H0.25 × E0.50 × V0.25`: - **Net nitrogen balance (Hazard).** The difference between nitrogen added to the field (through fertilizer, manure, and biological fixation) and nitrogen removed by the harvested crop. A persistent negative balance means the system is mining soil nitrogen — an early warning for fertility decline. - **Crop rotation diversity over 5 years (Exposure).** The number of distinct crops grown on a field over a 5-year window. Simple monocultures expose soil to repeated nutrient demand on the same biomass pathway; diverse rotations spread that demand. Note: the rotation-diversity threshold function is shared with Criterion 07 (Crop Diversity Loss) — any tightening of that threshold will move this criterion's score as well. - **Soil organic carbon (Vulnerability — proxy for management).** Used here as a *proxy* for the cumulative effect of the management practices SAI lists (residue retention, organic amendments, conservation tillage). Soils with persistently high SOC are evidence that those practices are happening over time; soils with low SOC suggest the opposite. |
| **Score 1 — Low** | Practices structurally maintain soil fertility — residue retention or organic amendments, frequent cover crops, conservation tillage, diversified rotations. | Nitrogen inputs at least match crop removal · ≥3 distinct crops in the 5-yr rotation · soil organic carbon ≥2.0% (evidence of strong long-term management). Composite → **Low**. |
| **Score 2 — Medium** | Partial implementation of fertility-supporting practices — inconsistent residue retention, conventional tillage, limited cover crops. | Mild negative N balance (−20 to \<0 kg N/ha) · 2.0–\<3.0 distinct crops · SOC 1.0–\<2.0%. Composite → **Medium**. |
| **Score 3 — High** | Practices likely to degrade fertility — frequent residue removal, intensive disturbance, low diversity. | Strongly negative N balance (\< −20 kg N/ha) · \<2 distinct crops · SOC \<1.0%. Composite → **High**. |
| **Evidence Level** | L1 farm management documents · L2 supplyshed practice data · L3 regional practice / modeled. | **Level 1.** All three inputs are field-differentiated — per-field agronomic N-balance model, per-field CDL rotation history, and 30 m SSURGO soil organic carbon. Surfaced in the report. |
| **Databases** | ISRIC SoilGrids, FAO GSOCmap, FAO GLASOD. | USDA SSURGO (SOC, USA); USDA CDL (5-yr crop rotation); Regrow agronomic data (nitrogen application). |

 

---

#### **Soil salinity**

**SAI Objective**  
Assess the structural risk of soil salinity accumulation at farm level, primarily based on regional exposure and moderated by irrigation and drainage management practices.

**SAI Description**  
Soil salinity refers to the accumulation of soluble salts in the soil profile that may impair crop growth and long-term productivity. This criterion evaluates the structural risk of salinity accumulation, considering regional exposure and irrigation/drainage management.

**Linked SAI Outcome(s)**O1 (Increased soil health & fertility) `++`; O2 (Increased nutrient use efficiency) `+`.

**Risk narrative (Regrow)**  
*Dry climates concentrate salts as water evaporates from the soil surface (**hazard**). Irrigation in those regions delivers more salt-laden water, and the more of the landscape that is irrigated, the more exposure (**exposure**). Soils that already carry measurable salts or that cannot drain well are more vulnerable to salt accumulation (**vulnerability**). Improved drainage and irrigation management flush salts and reduce exposure.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current structural exposure and management; recent trends in irrigation or water quality strengthen. | Climate dryness and irrigation extent averaged over the trailing 3-year reporting period; soil salt content and drainage are static. |
| **What we look at** | Regional salinity exposure (maps, classifications); irrigation dependency and water source quality; drainage conditions; salt-leaching practices. | Combined as `H0.25 × E0.50 × Vavg0.25`: - **Climate dryness / aridity index (Hazard).** The ratio of rainfall to evaporative demand (AI = P/PET) — i.e., how much rain falls compared to how much water the atmosphere would pull from a fully wet surface. The drier the climate, the more salts concentrate at the surface. - **Share of land irrigated (Exposure).** Percent of the supply shed under irrigation. More irrigation in a dry region = more salt delivery. - **Soil salt content (Vulnerability).** Measured electrical conductivity of the soil — a direct read on existing salt accumulation. - **Drainage class (Vulnerability).** Whether the soil can move excess water (and dissolved salts) downward. Poor drainage leaves salts in the root zone. |
| **Score 1 — Low** | Regional exposure low or negligible, OR exposure is moderate but effectively mitigated through irrigation and drainage practices. | Climate is humid (AI \> 0.65), little irrigation (\<10% of land), low existing salts (\<2 dS/m), well-drained soils → **Low**. |
| **Score 2 — Medium** | Regional exposure present with partial mitigation; irrigation-dependent in moderate-risk zones. | Semi-arid climate (AI 0.20–0.65), moderate irrigation reliance (10–30%), moderate soil salt (2–4 dS/m), moderately drained → **Medium**. |
| **Score 3 — High** | Regional exposure high; management insufficient; irrigation in arid or high-salinity zones. | Arid climate (AI \< 0.20), extensive irrigation (\>30%), elevated soil salt (\>4 dS/m), poorly drained soils → **High**. |
| **Evidence Level** | L1 farm EC tests · L2 regional maps + farm management · L3 regional only. | **Level 1 / 2.** Soil EC and drainage come from 30 m SSURGO (L1); supply-shed irrigation extent (L1/L2); the aridity index is a gridded climate surface (L2). Surfaced in the report. |
| **Databases** | FAO GSASmap, ISRIC SoilGrids, OpenLandMap. | ERA5-Land rainfall + Hargreaves-Samani evaporative-demand model (aridity index); USGS MIRAD / LGRIP30 (irrigation extent); USDA SSURGO (salt content + drainage, USA). |

 

---

#### **Soil compaction**

**SAI Objective**  
Assess the structural risk of soil compaction at farm level, considering soil sensitivity, mechanical pressure from field operations, and mitigation practices.

**SAI Description**  
Soil compaction refers to the compression of soil particles that reduces pore space, limits water infiltration and air exchange, and restricts root development. This criterion evaluates the structural risk of compaction based on soil sensitivity, mechanical pressure from field operations, and mitigation practices.

**Linked SAI Outcome(s)**O1 (Increased soil health & fertility) `++`.

**Risk narrative (Regrow)**  
*Wet field conditions during machinery operations are when compaction happens (**hazard**). The more often a system has anomalously wet operating windows, the more exposure (**exposure** — captured by the same rainfall-anomaly signal used elsewhere). Clay-rich, poorly draining soils are inherently more vulnerable to having their pore structure crushed (**vulnerability**). Controlled traffic, lighter equipment, and avoiding wet-soil operations reduce exposure.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current structural management; recurring patterns (waterlogging, rutting) strengthen. | Rainfall behavior averaged over the trailing 3-year reporting period; soil properties are static. |
| **What we look at** | Soil sensitivity (heavy or poorly drained soils); mechanical pressure (machinery weight, traffic frequency, ops under wet conditions); mitigation (controlled traffic, residue, structure-building practices). | Combined as `H0.25 × Vavg0.75`: - **Rainfall anomaly (Hazard).** Same signal as Soil Erosion — wetter-than-normal periods coincide with field operations that would otherwise be avoided. - **Soil clay content (Vulnerability).** Heavy, clay-rich soils have weaker structural resistance to mechanical pressure. - **Drainage class (Vulnerability).** Poorly drained soils stay wet longer and compact more easily. **Gap from SAI's framing:** the mechanical-pressure axis (machinery weight, traffic frequency, controlled traffic) is not directly quantified in the current build. Vulnerability proxies indicate where compaction is *most likely* to occur given the rainfall and soil context, but do not measure operator practices directly. Closing this gap is on the roadmap. |
| **Score 1 — Low** | Risk is structurally limited — controlled or reduced traffic, ops avoided in wet soil, residue and structure-building practices. | Low rainfall anomaly · clay content \<20% · well-drained soils → **Low**. |
| **Score 2 — Medium** | Mechanical pressure present, partially mitigated. | Moderate rainfall anomaly · clay 20–35% · moderately drained → **Medium**. |
| **Score 3 — High** | Significant mechanical pressure with insufficient mitigation; visible compaction signs. | High rainfall anomaly · clay \>35% · poorly drained → **High**. |
| **Evidence Level** | L1 farm machinery use docs · L2 supplyshed practice · L3 regional. | **Level 1 / 2.** Clay fraction and drainage from 30 m SSURGO (L1); rainfall SPI is a gridded surface (L2). Surfaced in the report. |
| **Databases** | ISRIC SoilGrids, ESDAC Soil Compaction Risk. | USDA SSURGO (clay %, drainage, USA); Regrow Climate Risk (rainfall anomaly). |

 

---

#### **Groundwater depletion**

**SAI Objective**  
Assess the structural risk of groundwater depletion associated with agricultural irrigation practices, considering regional water availability and irrigation pressure.

**SAI Description**  
Groundwater depletion refers to the long-term decline of groundwater resources due to water withdrawals exceeding natural recharge. This criterion evaluates the potential contribution of agricultural irrigation to groundwater depletion based on water availability and irrigation pressure.

**Linked SAI Outcome(s)**O1 (Increased soil health & fertility) `+`; O4 (Enhanced water resource stewardship) `++`.

**Risk narrative (Regrow)**  
*Persistent drought conditions reduce the natural rainfall that recharges aquifers (**hazard**). Irrigated agriculture in that setting pulls more water from the same aquifers, so the more of the landscape under irrigation, the higher the exposure (**exposure**). The intersection — drought + reliance on irrigation — is the structural risk SAI is asking about.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current irrigation practices and hydrology; 3–5 yr irrigation trends strengthen. | Drought severity and irrigation extent averaged over the trailing 3-year reporting period. |
| **What we look at** | Regional groundwater stress; presence and extent of irrigation; source of irrigation water. | Combined as `H0.33 × E0.67`: - **Drought severity (Hazard).** A standardised precipitation index oriented to the dry side — how persistently below-normal recent rainfall has been. Sustained drought is what stresses aquifers. - **Share of land irrigated (Exposure).** Percent of the supply shed under irrigation. A direct read on the magnitude of agricultural water demand on the local water system. |
| **Score 1 — Low** | No irrigation, or irrigation not from groundwater, or groundwater availability strong. | Limited drought stress and little irrigation (\<10% of land) → **Low**. |
| **Score 2 — Medium** | Irrigation partially reliant on groundwater in moderate-stress regions. | Moderate drought and moderate irrigation reliance (10–30%) → **Medium**. |
| **Score 3 — High** | Intensive groundwater use in water-stressed regions. | Sustained drought and extensive irrigation (\>30%) → **High**. |
| **Evidence Level** | L1 farm irrigation docs · L2 supplyshed + regional groundwater stress · L3 global groundwater stress. | **Level 1 / 2.** Per-field irrigated area (L1) combined with a gridded drought signal (L2). Surfaced in the report. |
| **Databases** | WRI Aqueduct, IGRAC, FAO Aquastat. | USGS MIRAD / LGRIP30 (irrigation extent, USA + global); Regrow Climate Risk (drought severity). |

---

#### **Surface water depletion**

**SAI Objective**  
Assess the structural risk of surface water depletion associated with agricultural irrigation practices, considering regional water availability and irrigation pressure on rivers, lakes, and reservoirs.

**SAI Description**  
Surface water depletion refers to the reduction of water availability in rivers, lakes, or reservoirs due to withdrawals exceeding natural or managed replenishment. This criterion evaluates the potential contribution of agricultural irrigation to surface-water depletion.

**Linked SAI Outcome(s)**O1 (Increased soil health & fertility) `+`; O4 (Enhanced water resource stewardship) `++`.

**Risk narrative (Regrow)**  
*Below-normal rainfall reduces flows into rivers, lakes, and reservoirs (**hazard**). Irrigation in that setting pulls from those surface-water bodies, with more exposure as more of the landscape comes under irrigation (**exposure**). The intersection of dry conditions and high irrigation share is the structural risk.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current irrigation and hydrology; 3–5 yr irrigation trends strengthen. | Rainfall behavior and irrigation extent averaged over the trailing 3-year reporting period. |
| **What we look at** | Regional water stress; presence and extent of irrigation; source of irrigation water (rivers, lakes, reservoirs); efficiency practices. | Combined as `H0.33 × E0.67`: - **Rainfall anomaly (Hazard).** Standardised precipitation signal indicating whether recent rainfall is unusually low compared to local norms. - **Share of land irrigated (Exposure).** Same indicator as the groundwater criterion; here it captures total agricultural draw on the local water system. |
| **Score 1 — Low** | No irrigation, or non-surface-water source, or low stress with efficient practices. | Limited rainfall anomaly · \<10% of land irrigated → **Low**. |
| **Score 2 — Medium** | Partial reliance on surface water in moderate-stress regions. | Moderate rainfall anomaly · 10–30% irrigated → **Medium**. |
| **Score 3 — High** | Extensive surface-water irrigation in water-stressed regions. | Strong rainfall anomaly · \>30% irrigated → **High**. |
| **Evidence Level** | L1 farm docs · L2 supplyshed irrigation + regional stress · L3 global datasets. | **Level 1 / 2.** Per-field irrigated area (L1) combined with a gridded rainfall signal (L2). Surfaced in the report. |
| **Databases** | WRI Aqueduct, JRC Global Surface Water Explorer, HydroSHEDS. | USGS MIRAD / LGRIP30 (irrigation extent); Regrow Climate Risk (rainfall anomaly). |

 

---

**Crop diversity loss**

**SAI Objective**  
Assess structural crop concentration risk at farm level and its potential implications for agroecosystem resilience, ecological function and production stability.

**SAI Description**  
Crop diversity refers to the diversity of cultivated crop species within a defined assessment period at farm level. It reflects the degree of crop concentration and its influence on resilience to pests, diseases, climate, and on ecological function and production stability.

**Linked SAI Outcome(s)**O3 (Optimised crop protection) `+`; O5 (Enhanced farmland ecological integrity) `+`; O6 (Increased cultivated crop and pasture diversity) `++`.

**Risk narrative (Regrow)**  
*This criterion is measured directly rather than through the H × E × V framing: the indicator is the system characteristic SAI is assessing. Where one or two crops dominate the landscape, the entire supply shed is exposed to the same pests, diseases, weather sensitivities, and market shocks. Diversified rotations spread that risk.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Annual systems: minimum 3-year rotation (5-year recommended). Perennials: current composition. | 5-year window, matching SAI's recommended extended option. Annual cropping systems only in current build. |
| **What we look at** | Number of cultivated species representing ≥5% of the cropped area over the assessment period; share occupied by the dominant crop. | Distinct crops grown per field over the 5-year window, weighted by area share. Derived from CDL crop classifications by field — cash crops only (cover crops are not currently visible to the count). |
| **Score 1 — Low** | ≥5 crop species (annual rotation) OR ≥2 perennial species with diversification; no single crop \>~60% of productive area. | ≥3 distinct crops per field over 5 years → **Low**. **Note on thresholds:** SAI's indicative Low band is ≥5 species, so Regrow's band is intentionally more permissive. Regrow's count is derived from cash-crop classifications and does not yet include cover crops that may be present in the rotation. Because counting those cover crops would raise the number of distinct species in many rotations, applying SAI's ≥5-species threshold to a cash-crop-only count would overstate crop-concentration risk — so Regrow uses the ≥3 cash-crop threshold to avoid that bias. |
| **Score 2 — Medium** | 3–4 species (annual) OR dominant perennial with partial diversification; main crop 60–80% of area. | 2.0–\<3.0 distinct crops over 5 years → **Medium**. |
| **Score 3 — High** | 1–2 species (annual) OR monoperennial without diversification; main crop \>80% of area. | \<2 distinct crops over 5 years → **High**. |
| **Evidence Level** | L1 farm crop plan (≥3 yr) · L2 supplyshed / cooperative records · L3 public regional stats. | **Level 1.** Per-field rotation histories derived from CDL, aggregated to the shed. Surfaced in the report. |
| **Databases** | FAOSTAT, EarthStat, ESA WorldCover / Copernicus Land Cover. | USDA CDL (USA, 5-yr rotation history). |

---

#### **Habitat loss**

**SAI Objective**  
Assess the risk of recent habitat loss associated with agricultural land conversion and evaluate whether natural or semi-natural habitats are retained within agricultural landscapes.

**SAI Description**  
Habitat loss refers to the conversion of natural or semi-natural habitats into agricultural land. This criterion evaluates the risk of habitat loss primarily through evidence of recent land conversion and the retention of habitat elements within agricultural landscapes.

**Linked SAI Outcome(s)**O5 (Enhanced farmland ecological integrity) `++`.

**Risk narrative (Regrow)**  
*This criterion is measured directly. Where natural or semi-natural land has been converted to agriculture in the recent past, the supply shed carries habitat-loss risk regardless of how the land is now managed. The signal is the rate of conversion observable in the historical land-cover record.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Recent conversion: past 10 years, extendable to 20 years. | 20-year land-use change window — uses SAI's extended option. |
| **What we look at** | (1) Recent land conversion of natural or semi-natural habitat to agriculture; (2) presence of retained natural/semi-natural habitat elements within agricultural landscapes. | Percent of supply-shed area that converted from natural land cover (forest, native pasture, semi-natural) to cropland or managed pasture over the prior 20 years. Derived from satellite land-cover histories. **Gap from SAI's framing:** the second component — retention of natural/semi-natural habitat *within* agricultural landscapes (hedgerows, buffers, wetlands, fallows) — is not currently scored. **Threshold note:** the \<2% / 2–5% / ≥5% bands are drawn from the SAI RTF Context Analysis Brief (no external calibration source), and the metric measures the land-use-change share of the shed's own field area rather than regional natural-habitat conversion; the source / definition for these thresholds is an open item for review. |
| **Score 1 — Low** | No evidence of recent conversion + habitat elements retained on farm. | \<2% of supply-shed area converted in the past 20 years → **Low**. |
| **Score 2 — Medium** | Limited / small-scale conversion or uncertain evidence; limited retained habitat elements. | 2–\<5% converted → **Medium**. |
| **Score 3 — High** | Evidence of recent conversion; few or no retained habitat elements. | ≥5% converted → **High**. |
| **Evidence Level** | L1 farm LUC maps / imagery · L2 regional + farm habitat data · L3 global LUC datasets. | **Level 1.** The land-use-change share is computed over the shed's own field geometries from satellite land-cover histories. Surfaced in the report. |
| **Databases** | Global Forest Watch, ESA WorldCover / Copernicus Land Cover, WWF Biodiversity Risk Filter. | Regrow LUC dataset, which integrates **Global Forest Watch** (forest-loss detection) and **Global Pasture Watch** (native-pasture / grassland conversion). |

---

#### **Pesticide leaching**

**SAI Objective**  
Assess the structural risk of pesticide transfer to water bodies through leaching or runoff, considering pesticide management practices and environmental vulnerability.

**SAI Description**  
Pesticide leaching refers to the transfer of pesticide substances from agricultural land into groundwater or surface water through infiltration, runoff, or drainage processes. This criterion evaluates the risk based on pesticide management practices and the environmental vulnerability of the system.

**Linked SAI Outcome(s)**O1 (Increased soil health & fertility) `+`; O3 (Optimised crop protection) `++`; O4 (Enhanced water resource stewardship) `++`; O6 (Increased cultivated crop and pasture diversity) `+`.

**Risk narrative (Regrow)**  
*Heavy rainfall drives pesticide movement off-field and into groundwater (**hazard**). Crops that demand intensive crop-protection use create more exposure (**exposure**). Sandy, free-draining soils are more vulnerable because water (and dissolved actives) move through them quickly (**vulnerability**). IPM, application timing, and buffer practices reduce both exposure and vulnerability.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current pesticide management; 3–5 yr use patterns strengthen. | Crop type and rainfall behavior averaged over the trailing 3-year reporting period; soil texture is static. |
| **What we look at** | Pesticide management practices (IPM adoption, targeted applications, timing); environmental vulnerability (soil texture, slope, proximity to water, climate); mitigation (buffer zones, ecological compensations). | Combined as `H0.25 × E0.50 × V0.25`: - **Rainfall anomaly (Hazard).** Anomalously wet periods drive runoff and leaching events. - **Crop type as a pesticide-intensity proxy (Exposure).** In the absence of farm-level application records, the crop being grown is a strong predictor of total active-ingredient load — high-input crops (corn, potato, vegetables) versus moderate-input grains/oilseeds versus low-input legumes/perennials/forage. - **Soil texture (Vulnerability).** Sandy soils transmit water (and pesticides) quickly to groundwater; loams are intermediate; high-clay soils retain water at the surface. **Gap from SAI's framing:** direct IPM adoption and per-farm application practices are not available at the supply-shed level. Crop type serves as a proxy for total intensity but does not distinguish farms practicing IPM from those that are not. |
| **Score 1 — Low** | Strong IPM, targeted/reduced applications, buffer zones. | Low rainfall anomaly · low-input crops · high-clay soils → **Low**. |
| **Score 2 — Medium** | Regular use with limited mitigation; partial IPM. | Moderate rainfall anomaly · grains/oilseeds · loam soils → **Medium**. |
| **Score 3 — High** | Intensive use, little/no IPM, high-risk pesticides. | High rainfall anomaly · high-input crops · sandy soils → **High**. |
| **Evidence Level** | L1 farm mgmt docs · L2 farm/supplyshed mgmt · L3 global/national + company policies. | **Level 1 / 2 / 3.** Per-field crop type (L1) and SSURGO soil texture (L1); supply-shed aggregation (L2); crop-type pesticide-intensity lookup is a country/regional table (L3). Surfaced in the report. |
| **Databases** | FAOSTAT Pesticide Use. | Regrow crop-protection dataset (proprietary intensity-by-crop-type); USDA SSURGO (soil texture, USA); Regrow Climate Risk (rainfall anomaly). |

 

---

**Nutrient leaching**

**SAI Objective**  
Assess the structural risk of nutrient transfer to water bodies through leaching or runoff, based primarily on nutrient management practices and their efficiency.

**SAI Description**  
Nutrient leaching refers to the transfer of nutrients, primarily nitrogen and phosphorus, from agricultural land into groundwater or surface water through infiltration, runoff, or drainage processes. This criterion evaluates the risk based primarily on nutrient management practices and their efficiency.

**Linked SAI Outcome(s)**  
O1 (Increased soil health & fertility) `+`; O2 (Increased nutrient use efficiency) `++`; O4 (Enhanced water resource stewardship) `++`; O7 (Improved on-farm nutrient cycling from livestock production) `++`; O8 (Reduced greenhouse gas emissions) `+`.

**Risk narrative (Regrow)**  
*Heavy rainfall drives nitrogen movement off-field (**hazard**). The more nitrogen applied per hectare, the more there is available to move (**exposure**). Sandy soils and irrigated systems transmit nitrogen to water more readily (**vulnerability**). Application timing, splitting, and 4R practices reduce exposure for the same total rate.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current nutrient management; 3–5 yr practice trends strengthen. | Nitrogen application, rainfall, and irrigation extent averaged over the trailing 3-year reporting period; soil texture is static. |
| **What we look at** | Nutrient management practices (timing, rate adjustment, splitting, manure management); environmental vulnerability; mitigation (buffer strips, cover crops). | Combined as `H0.25 × E0.50 × Vavg0.25`: - **Rainfall anomaly (Hazard).** Same signal as for erosion / pesticide leaching — wet periods drive nutrient runoff and leaching. - **Synthetic nitrogen application rate (Exposure).** Total kg N/ha applied as fertilizer. The simplest measure of how much nitrogen is in the system and available to leach. - **Soil texture (Vulnerability).** Sandy soils transmit water (and dissolved N) to groundwater more rapidly. - **Share of land irrigated (Vulnerability).** Irrigated systems have more water moving through the soil profile, increasing the leaching pathway. **Gap from SAI's framing:** timing of application, splitting, 4R adherence, and manure management are not directly quantified — total rate alone is used as the exposure indicator. |
| **Score 1 — Low** | Applications aligned with crop demand, structured plans, splitting, covered manure storage, cover crops. | Low rainfall anomaly · \<80 kg N/ha · high-clay soils · \<10% irrigated → **Low**. |
| **Score 2 — Medium** | Regular applications with limited optimization; partial mitigation. | Moderate rainfall anomaly · 80–120 kg N/ha · loam · 10–30% irrigated → **Medium**. |
| **Score 3 — High** | Frequent / poorly timed applications, limited mitigation, intensive systems. | High rainfall anomaly · \>120 kg N/ha · sandy soils · \>30% irrigated → **High**. |
| **Evidence Level** | L1 farm fertilization plans · L2 supplyshed nutrient mgmt · L3 global / company-policy proxies. | **Level 1 / 2.** Per-field nitrogen application rate and SSURGO soil texture (L1); supply-shed irrigation extent (L1/L2). Surfaced in the report. |
| **Databases** | FAOSTAT Fertilizer Use, WWF Water Risk Filter. | Regrow agronomic data (N application); USDA SSURGO (texture, USA); USGS MIRAD / LGRIP30 (irrigation); Regrow Climate Risk (rainfall anomaly). |

---

#### **Agricultural air pollution**

**SAI Objective**  
Assess the risk of local air pollution associated with agricultural practices at farm level, focusing on combustion-related and manure storage and management activities.

**SAI Description**  
This criterion evaluates the risk of local air pollution resulting from specific agricultural practices, particularly residue burning and other combustion-based activities, as well as manure storage and management practices — focusing on particulate matter and ammonia emissions rather than greenhouse gas emissions or carbon accounting.

**Linked SAI Outcome(s)**O7 (Improved on-farm nutrient cycling from livestock production) `++`.

**Risk narrative (Regrow)**  
*Residue burning is the local-air-quality practice SAI names first for this criterion. Regrow has no field-level observation of whether a given grower burns residue, so it uses the prevailing regional burning intensity for the areas the shed sources from, weighted by how much of the landscape is cropland. The score describes the burning environment the shed sits in, not any single field's practice.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current management; 3–5 yr burning-practice trends strengthen. | Regional burning intensity (treated as a stable regional characteristic), farmland-weighted for the supply shed; crop activity averaged over the trailing 3-year reporting period. |
| **What we look at** | Presence and frequency of residue burning, open-field combustion of crop residues, type of manure management, regional regulatory context — focusing on particulate matter and ammonia. | A single regional indicator, scored against an absolute band rather than H × E × V (only one data point is available): - **PM2.5 from crop-residue field burning (farmland-weighted).** Fine-particulate mass emitted by agricultural field burning across the regions the shed sources from, expressed per unit of county land area and weighted by cropland share. Inside the US, from each county's total agricultural-burning PM2.5; outside the US, from a country-by-crop residue-management table (share of residue burnt vs. left or removed). **Gap from SAI's framing:** manure storage / management and ammonia emissions are not yet included (manure is inventoried against animal operations, not farmland area, so it has no supply-shed join today). Burning data is regional, not field-level, so it represents regional norms rather than observed on-farm burning. |
| **Score 1 — Low** | Residue burning absent or legally prohibited and effectively enforced. | Farmland-weighted PM2.5 below the low-band threshold (\<1.74 tons/1,000 km²/yr) → **Low**. |
| **Score 2 — Medium** | Burning occasional or under specific conditions. | PM2.5 between the low and high thresholds (1.74–\<7.70 tons/1,000 km²/yr) → **Medium**. |
| **Score 3 — High** | Burning frequent and structurally embedded. | PM2.5 at or above the high-band threshold (≥7.70 tons/1,000 km²/yr) → **High**. |
| **Evidence Level** | L1 farm burning docs · L2 supplyshed/regional practice · L3 regulatory context. | **Level 2 / 3.** Supply-shed farmland weighting (L2) applied to county-/country-level burning inventories (L3). Surfaced in the report. |
| **Databases** | EDGAR, GFED. | US EPA National Emissions Inventory (2020 vintage, county agricultural field-burning PM2.5, USA); Regrow country-by-crop residue-management table (share of residue burnt — compiled from Smerald et al. 2023, IPCC 2006 guidelines, FAOSTAT, and the Global Fire Emissions Database) for non-US regions. |

---

#### **Agricultural GHG assessment and mitigation**

**SAI Objective**  
Assess whether agricultural supply chains measure and actively manage greenhouse gas emissions associated with agricultural production.

**SAI Description**  
Agricultural production contributes to greenhouse gas emissions through multiple sources including fertilizer use, soil emissions, energy consumption, and land use change. This criterion evaluates whether the implementation group has a structured GHG accounting and mitigation program in place. **Unlike other criteria in this grid, this criterion is assessed at implementation group, not at farm level.**

**Linked SAI Outcome(s)**O8 (Reduced greenhouse gas emissions) `++`.

**Risk narrative (Regrow)**  
*The more emissions the production system generates per unit of output, the higher the GHG exposure the group would need a program to manage. Regrow scores the modelled footprint against crop-specific absolute bands, so the result is comparable across sheds growing the same commodity.*

| Field | SAI guidance | Regrow implementation |
| --- | --- | --- |
| **Timeframe** | Current GHG accounting and mitigation programs; 3–5 yr mitigation actions strengthen. | Modelled GHG footprint for the supply shed's dominant commodity, averaged over the trailing 3-year reporting period. |
| **What we look at** | Existence and maturity of a structured GHG accounting program; monitoring of emissions; mitigation actions; reduction targets and progress against them. | A single footprint metric, scored against crop-specific absolute bands: - **Total cradle-to-farm-gate GHG footprint (kg CO₂e/kg yield).** The sum of every on-field and upstream farm-gate emission category Regrow models — on-field soil emissions, fertilizer and seed production, transportation, machinery use, and irrigation infrastructure — *excluding* soil-carbon (SOC) change. |
| **Score 1 — Low** | Structured GHG accounting program implemented; mitigation actively deployed; reduction targets being met. | Footprint below the crop-specific low-band threshold (\<0.2500 kg CO₂e/kg yield for this commodity) → **Low**. |
| **Score 2 — Medium** | GHG accounting in place but mitigation not yet systematic; initial targets not met. | Footprint between the low and high thresholds → **Medium**. |
| **Score 3 — High** | No GHG accounting program; absence of footprint assessment; no mitigation initiatives. | Footprint at or above the crop-specific high-band threshold (≥0.5000 kg CO₂e/kg yield for this commodity) → **High**. |
| **Evidence Level** | L1 farm GHG assessments · L2 supplyshed carbon footprint · L3 corporate-level info. | **Level 1 / 3.** On-field emissions are modelled per field with DNDC (L1); upstream / infrastructure factors are regional defaults (L3). Surfaced in the report. |
| **Databases** | N/A — assessed from implementation-group documentation. | Regrow GHG model (DNDC) + Regrow proprietary upstream emission factors; crop-specific thresholds calibrated against published LCA literature (Qin et al. 2021 / GREET; Poore & Nemecek 2018; FAOSTAT Emissions Intensities; SBTi FLAG cross-check). |

 

- [Sustainability Insights](https://help.regrow.ag/sustainability-insights?hsLang=en#main-content)

    - [General](https://help.regrow.ag/sustainability-insights?hsLang=en#general)
    - [Configure](https://help.regrow.ag/sustainability-insights?hsLang=en#configure)
    - [Report](https://help.regrow.ag/sustainability-insights?hsLang=en#report)
    - [Plan](https://help.regrow.ag/sustainability-insights?hsLang=en#plan)
    - [Data updates](https://help.regrow.ag/sustainability-insights?hsLang=en#data-updates)
    - [Models](https://help.regrow.ag/sustainability-insights?hsLang=en#models)
    - [Integrations](https://help.regrow.ag/sustainability-insights?hsLang=en#integrations)
    - [SAI Outcomes](https://help.regrow.ag/sustainability-insights?hsLang=en#sai-outcomes)
- [MRV](https://help.regrow.ag/mrv?hsLang=en#main-content)

    - [Login and registration](https://help.regrow.ag/mrv?hsLang=en#login-and-registration)
    - [Field boundaries](https://help.regrow.ag/mrv?hsLang=en#field-boundaries)
    - [Data entry tips and guidelines](https://help.regrow.ag/mrv?hsLang=en#data-entry-tips-and-guidelines)
    - [Data entry accelerators](https://help.regrow.ag/mrv?hsLang=en#data-entry-accelerators)
    - [Soil sampling](https://help.regrow.ag/mrv?hsLang=en#soil-sampling)
    - [Program data review](https://help.regrow.ag/mrv?hsLang=en#program-data-review)
    - [Reporting](https://help.regrow.ag/mrv?hsLang=en#reporting)
- [Research and data credits](https://help.regrow.ag/research-and-data-credits?hsLang=en)
- [Measure API: Enteric](https://help.regrow.ag/measure-api-enteric?hsLang=en#main-content)

    - [Getting started](https://help.regrow.ag/measure-api-enteric?hsLang=en#getting-started)
- [Measure API: Intervention](https://help.regrow.ag/measure-api-intervention?hsLang=en#main-content)

    - [Introduction](https://help.regrow.ag/measure-api-intervention?hsLang=en#introduction)
    - [Getting started](https://help.regrow.ag/measure-api-intervention?hsLang=en#getting-started)
    - [Common Questions](https://help.regrow.ag/measure-api-intervention?hsLang=en#common-questions)
    - [Outcomes](https://help.regrow.ag/measure-api-intervention?hsLang=en#outcomes)
    - [Product Releases](https://help.regrow.ag/measure-api-intervention?hsLang=en#product-releases)
- [Measure API: Inventory](https://help.regrow.ag/measure-api-inventory?hsLang=en#main-content)

    - [Introduction](https://help.regrow.ag/measure-api-inventory?hsLang=en#introduction)
    - [Getting started](https://help.regrow.ag/measure-api-inventory?hsLang=en#getting-started)
    - [Outcomes](https://help.regrow.ag/measure-api-inventory?hsLang=en#outcomes)
- [Monitor API](https://help.regrow.ag/monitor-api?hsLang=en#main-content)

    - [Getting started](https://help.regrow.ag/monitor-api?hsLang=en#getting-started)
    - [Remote sensing technology](https://help.regrow.ag/monitor-api?hsLang=en#remote-sensing-technology)
    - [Integrate with Monitor API](https://help.regrow.ag/monitor-api?hsLang=en#integrate-with-monitor-api)
    - [Understanding Monitor field data](https://help.regrow.ag/monitor-api?hsLang=en#understanding-monitor-field-data)
- [Cradle to farm-gate emission factors](https://help.regrow.ag/cradle-to-farm-gate-emission-factors?hsLang=en#main-content)

    - [Getting started](https://help.regrow.ag/cradle-to-farm-gate-emission-factors?hsLang=en#getting-started)
    - [Methodology](https://help.regrow.ag/cradle-to-farm-gate-emission-factors?hsLang=en#methodology)
    - [On-field activity data sources](https://help.regrow.ag/cradle-to-farm-gate-emission-factors?hsLang=en#on-field-activity-data-sources)

[![Chill listening crop-3](https://help.regrow.ag/hs-fs/hubfs/03.%20Logos/Regrow_logo.png?width=96&height=24&name=Regrow_logo.png "Chill listening crop-3")](https://www.regrow.ag/)

Copyright © 2026, Regrow Agriculture Inc.