🏡
Farm Setup
Farm details followed by dedicated crop setup
🌦️
Climate & Soil
Auto-select climate by state and choose soil texture
🌾
Crop-by-Crop Entry
Select the cash crop and cover crop species for one crop at a time
📊
Instant Cover Crop Summary
Directional soil health estimates, species insights, seeding rates, CSV, and PDF
Sources & accuracy How this tool works Open to see the science, species data, management guidance, and accuracy notes behind the calculator.
⌄

This calculator draws on three independent scientific resources. Each plays a different role — statistical modelling, species-level agronomy, and management guidance.

01

Statistical model & soil health estimates

Jian, Lester, Du, Reiter & Stewart (2020) · Soil & Tillage Research 199, 104575 · Virginia Tech SPES

The average percentage change estimates for all 13 soil health and productivity parameters come from this peer-reviewed meta-analysis. The authors compiled paired treatment/control data from 269–281 published field studies across 38+ countries, then used one-sample t-tests and unbalanced analysis of variation (UANOVA) to identify which factors — climate type, soil texture, cover crop category, and cash crop type — most strongly determine how much each indicator responds. Those four factors became the structure of this calculator's five-step inputs. The companion dataset paper (Data in Brief 29, 105376) is fully open-access and includes the R code and raw data at github.com/jinshijian/SoilHealthCalculator. The original web tool was hosted by Virginia Tech's School of Plant and Environmental Sciences at soilhealth.spes.vt.edu.

200+field studies
38+countries
13parameters modelled
4driving factors
02

Species-level agronomy data

USDA-ARS Northern Great Plains Research Laboratory · Cover Crop Chart V4.0, April 2023 · Johnson & Liebig

Every species dropdown, C:N ratio, crude protein value, salinity tolerance, seeding depth, water use rating, and nitrogen-scavenging tier in Step 4 comes from the NRCS Cover Crop Chart — a compendium assembled by USDA-ARS from the Midwest Cover Crops Council, USDA-SARE, the USDA-NRCS PLANTS database, and peer-reviewed journal articles. The chart covers 50+ species across cool-season grasses, warm-season grasses, cool- and warm-season legumes, and broadleaf/brassica groups. Species modifiers applied to the base model values — for example, radish's strong effect on bulk density and infiltration, or hairy vetch's outsized nitrogen contribution — are derived from the trait ratings and C:N data in this chart. The chart is produced and freely distributed by the NGPRL in Mandan, ND.

50+species profiled
V4.0April 2023
USDA-ARSsource agency
NGPRLMandan ND
03

Management insights & agronomic guidance

Clark, A. (Ed.) (2007, 3rd printing 2012) · Managing Cover Crops Profitably, 3rd Ed. · SARE Handbook Series Book 9

The species insights shown after calculating — termination timing, allelopathy warnings, N credit guidance, cash crop compatibility notes, and C:N interpretation — are drawn from this USDA-SARE handbook. It synthesizes farmer trials, university extension research, and peer-reviewed studies to provide practical management guidance for each species. Key findings used here include: hairy vetch N credit ranges of 90–200 lb N/ac and its documented yield uplift in no-till corn; cereal rye allelopathic effects on soybean germination and appropriate termination windows; brassica bio-fumigant effects on nematodes and soilborne disease; the legume inoculation requirement for effective N fixation; and the 3-week termination buffer before planting for allelopathic species. The book is published by the Sustainable Agriculture Research & Education (SARE) program with funding from USDA-NIFA.

SAREUSDA program
3rd Ed.2007 / 2012
Growerguidance
On accuracy: The statistical estimates represent average change percentages across diverse geographies, management systems, and years of cover crop adoption. They should be read as directional guidance — the sign and rough magnitude of change a farmer might expect — not as field-specific quantitative predictions, guarantees, or measured soil test results. Local soil test data, extension advice, and on-farm observation remain essential complements to any modelled estimate. Soil texture groupings follow the Cornell Comprehensive Assessment of Soil Health (CASH) framework. Climate classification follows Köppen-Geiger (Kottek et al., 2006). Soil type lookup: USDA Web Soil Survey.

Resume where we left off

Upload the Complete ZIP from the previous Roll Up export. The tool will find the saved progress file inside the ZIP, auto-fill the form, and return to the saved step.

Step 1: Farm Details
Farm Details
Crop Setup
Cover Crop
Results

Step 1: Farm Details

Enter the basic farm logging information and upload field boundaries if available. Crop records are created in the next Crop Setup step.
Farm info

Logging information

The farm State is used as the default for every crop and automatically selects climate. A crop can be marked as grown in another state within Crop Setup. Years practicing cover crops is entered as a number, then grouped into benefit maturity ranges in results and exports; it does not change the average directional percentage estimates.

Farm-level spatial context

Field Boundary KML Upload

Upload a KML with field boundary polygons. Boundary names populate the crop setup dropdowns, and total acres / total fields are auto-populated from the KML.

No KML uploaded yet.
Step 2: Crop Setup
Farm Details
Crop Setup
Cover Crop
Results

Step 2: Crop Setup

Create each crop record, assign field boundaries, and confirm the crop-specific climate and soil information before continuing.
Crop Setup

Crop Setup

Create each crop record here. The farm State applies to all crops by default; use the per-crop alternate-state option only when a crop is grown in another state. Crop names, cash crops, acres, climate, soil texture, and selected KML field boundaries flow into the crop-by-crop calculator. Cash crop selection is completed here rather than in a separate calculator tab. Selected KML boundary acres automatically fill crop acres.

Step 1: Climate Type
Climate
Soil
Cover Crop
Results

Step 1: Climate Type

Climate is selected from the farm State or the crop-specific alternate State in Crop Setup.
Step 1

Climate type

Climate type is set from Crop Setup. You can still manually override it for local conditions. Look up your Köppen climate →

Auto climate: Select a farm State in Farm Details or an alternate State in Crop Setup.
Step 2: Soil Texture
Climate
Soil
Cover Crop
Results

Step 2: Soil Texture

Choose soil texture for this crop or use the optional sand / clay calculator.
Step 2

Soil texture

Coarse = sand/loamy sand/sandy loam · Medium = loam/silt loam · Fine = clay loam/silty clay
Find your soil type →

Sand 40%
Clay 20%
Silt (calculated) 40%
Sand + Clay cannot exceed 100%. Please adjust one of the values.
USDA texture class: Loam
→ Mapped to Medium texture group — chip updated above.
Step 3: Cover Crop
Farm Details
Crop Setup
Cover Crop
Results

Step 3: Cover Crop

Choose the cover crop type and species for this crop record. Crop acreage is carried forward from the KML boundaries selected in Crop Setup.
Step 1

Cover crop type & species

Cover Crop Planting Season
Warm season — typically planted when soil and air temperatures are warm
Cool season — typically planted in fall, winter, or early spring windows
Cool season selected: Cool season — typically planted in fall, winter, or early spring windows.
Cover Crop Goals
Select one or more goals for reference guidance. These do not change the calculator math.
Step 4: Results
Farm Details
Crop Setup
Cover Crop
Results

Step 4: Results

Results roll up all completed crop records using the existing model logic.
Step 5

Select average change percentage parameters to estimate

How to read these estimates: each selected output is an average change percentage from the study dataset. The numbers are directional guidance — they indicate likely increase/decrease and relative strength, not a field-specific quantitative prediction, guarantee, or measured soil test result.
Results show average change percentages from published field studies; acres are scale context only.

Configure your settings above and click Calculate to see results.

Methodology & sources

How this tool works

This calculator draws on three independent scientific resources. Each plays a different role — statistical modelling, species-level agronomy, and management guidance.

01
Statistical model & soil health estimates
Jian, Lester, Du, Reiter & Stewart (2020) · Soil & Tillage Research 199, 104575 · Virginia Tech SPES

The average percentage change estimates for all 13 soil health and productivity parameters come from this peer-reviewed meta-analysis. The authors compiled paired treatment/control data from 269–281 published field studies across 38+ countries, then used one-sample t-tests and unbalanced analysis of variation (UANOVA) to identify which factors — climate type, soil texture, cover crop category, and cash crop type — most strongly determine how much each indicator responds. Those four factors became the structure of this calculator's five-step inputs. The companion dataset paper (Data in Brief 29, 105376) is fully open-access and includes the R code and raw data at github.com/jinshijian/SoilHealthCalculator. The original web tool was hosted by Virginia Tech's School of Plant and Environmental Sciences at soilhealth.spes.vt.edu.

200+field studies
38+countries
13parameters modelled
4driving factors
02
Species-level agronomy data
USDA-ARS Northern Great Plains Research Laboratory · Cover Crop Chart V4.0, April 2023 · Johnson & Liebig

Every species dropdown, C:N ratio, crude protein value, salinity tolerance, seeding depth, water use rating, nitrogen-scavenging tier, and seeding-rate reference in Step 4 comes from the NRCS Cover Crop Chart and SARE guidance — a compendium assembled by USDA-ARS from the Midwest Cover Crops Council, USDA-SARE, the USDA-NRCS PLANTS database, and peer-reviewed journal articles. The chart covers 50+ species across cool-season grasses, warm-season grasses, cool- and warm-season legumes, and broadleaf/brassica groups. Species modifiers applied to the base model values — for example, radish's strong effect on bulk density and infiltration, or hairy vetch's outsized nitrogen contribution — are derived from the trait ratings and C:N data in this chart. The chart is produced and freely distributed by the NGPRL in Mandan, ND.

50+species profiled
V4.0April 2023
USDA-ARSNGPRL, Mandan ND
03
Management insights & agronomic guidance
Clark, A. (Ed.) (2007, 3rd printing 2012) · Managing Cover Crops Profitably, 3rd Ed. · SARE Handbook Series Book 9

The species insights shown after calculating — termination timing, allelopathy warnings, seeding-rate recommendations, N credit guidance, cash crop compatibility notes, and C:N interpretation — are drawn from this USDA-SARE handbook. It synthesizes farmer trials, university extension research, and peer-reviewed studies to provide practical management guidance for each species. Key findings used here include: hairy vetch N credit ranges of 90–200 lb N/ac and its documented yield uplift in no-till corn; cereal rye allelopathic effects on soybean germination and appropriate termination windows; brassica bio-fumigant effects on nematodes and soilborne disease; the legume inoculation requirement for effective N fixation; and the 3-week termination buffer before planting for allelopathic species. The book is published by the Sustainable Agriculture Research & Education (SARE) program with funding from USDA-NIFA.

SAREUSDA program
3rd Ed.2007 / 2012
Free PDFsare.org