Updated October 14, 2026: corrected the titles and years of two cited studies (Poděbradská et al. and Bundy et al.), defined the Honest Cattle Forage Score (HCFS), removed internal drafting notes and a repeated score table, and noted RAP’s September 2026 production-data update. Updated October 15, 2026: labeled HCFS as experimental, listed the parts of the method not yet published, noted how the live county dashboard describes its inputs, and added that the score does not predict a ranch’s calf weights or cattle prices, after an external review; aligned the method description with the live score.

Honest Cattle thesis: A forage score built without pasture visits is scientifically credible when it is presented as a county-scale forage-condition signal based on vegetation response, growing-season moisture, drought stress, and local usability. It is not credible as a pasture-level forage inventory, AUM calculation, stocking-rate recommendation, ecological-site assessment, or substitute for local range inspection.

Executive Summary

Honest Cattle’s rangeland forage score does not claim to know how much grass is standing in a specific pasture. That requires local knowledge, management history, and ground inspection. But the scientific literature does support a county-scale forage condition signal when it combines climate, soil moisture, drought, and satellite-observed vegetation response.

The revised Honest Cattle method is:

HC Rangeland Forage Condition Score = 0.40(VR) + 0.35(GSM) + 0.15(DS) + 0.10(UM)

Where VR is Vegetation Response, GSM is Growing-Season Moisture, DS is Drought Stress, and UM is a Usability Modifier. On this site we call the result the Honest Cattle Forage Score (HCFS).

These four parts and weights are what the live score on the county dashboards and the Montana Counties map uses. It replaces the early snowpack-heavy presentation with a more defensible rangeland condition model. Snowpack no longer drives the score: it is used only as a backup moisture input when the NOAA rainfall rank is unavailable (see “How the live score is calculated” below).

The reason is straightforward: much of Montana’s rangeland forage production is driven by spring precipitation, soil moisture, evaporative demand, and plant response during the early growing season. Smart et al. found that April, May, and June precipitation accurately predicted annual forage production across central and northern Great Plains rangelands. Vermeire, Heitschmidt, and Rinella found that in southeast Montana mixed-grass prairie, 90% of aboveground net primary production was completed by July 1.

Key Findings at a Glance

1. A county-scale forage condition score is scientifically credible. A 2023 systematic review of 85 peer-reviewed forage prediction studies found that vegetation indices, precipitation, soil moisture, and climate variables are common inputs for forage and grass yield prediction.
2. The score must be honest about scale. Large-scale forage tools may not capture field- or pasture-level variability, so HCFS is presented as a county-scale early-warning and comparison tool, not a pasture inventory.
3. Grass-Cast is the closest scientific precedent. Grass-Cast uses more than 30 years of weather data, satellite-derived NDVI, ecosystem modeling, and precipitation forecasts to estimate county-level aboveground net primary production as below normal, near normal, or above normal.
4. RAP is a strong vegetation-response benchmark. The Rangeland Analysis Platform provides annual and 16-day herbaceous rangeland production estimates at 30-meter resolution across the western United States.
5. Snowpack should not dominate the statewide score. Snowpack is important in mountain, foothill, irrigated, and snowmelt-fed systems, but Montana dryland rangeland forage conditions are often more directly tied to April–June precipitation, soil moisture, drought stress, and observed vegetation response.
6. Standing forage and forage production are not the same thing. Remote sensing can estimate vegetation response and biomass patterns, but usable pasture forage is affected by grazing, trampling, wildlife, insects, hail, fire, senescence, weeds, and water access.
7. The website score is relative to local normal. A good year in Powder River County and a good year in Gallatin County are not the same in absolute forage production, but both can be scored relative to their own county history.

The Method Honest Cattle Is Using on the Website

This is the method Honest Cattle publishes and uses for the website score:

HC Rangeland Forage Condition Score = 0.40(VR) + 0.35(GSM) + 0.15(DS) + 0.10(UM)

The public score remains 0–100.

How the live score is calculated

Each of the four parts is put on a 0–100 scale, multiplied by its weight, and added up. The total is rounded to a whole number between 0 and 100 and placed in the bands above. The scores refresh each morning.

• Vegetation response (40%): RAP’s herbaceous production estimate (annual plus perennial grasses and forbs), added up from April 1 to date over rangeland only (shrubland and grassland; hay ground and cropland are left out), then ranked against the same stretch of the season in the county’s other years in a rolling 10-year window. RAP figures more than three weeks old are not used. Until RAP publishes the current season — the case for every county as of mid-October 2026 — a stand-in is used: Montana Mesonet shallow soil moisture (65%) and the NOAA three-month rainfall departure from normal (35%), each sorted into a 0–100 band. If neither is available, this part is set to a neutral 50.
• Growing-season moisture (35%): the county’s NOAA NCEI three-month precipitation rank, turned into a percentile against the full NCEI record (the driest year on record sits near 0, the wettest near 100). When that rank is unavailable, a backup blend is used: precipitation as a percent of normal (45%; NOAA’s latest month if available, otherwise the SNOTEL water-year figure), snowpack as a percent of median (35%) and SNOTEL water-year precipitation as a percent of median (20%), with a neutral value where a precipitation reading is missing. In that backup, a county with no snowpack — including any county in summer and fall — gets a low snowpack value, which pulls this part down.
• Drought stress (15%): 100 minus a weighted share of county area in drought (abnormally dry D0 counts 10%, D1 30%, D2 55%, D3 80%, D4 100%), kept between 5 and 95, makes up 60% of this part. USDA NASS’s weekly Montana pasture and range condition rating makes up the other 40%; when NASS has no current rating, a neutral 50 is used. Neither the drought classes nor the statewide NASS rating is measured against the county’s own history.
• Usability modifier (10%): held at a neutral 50 for every county for now, so it adds the same 5 points everywhere.
• Missing data: the weights are not rebalanced when an input is missing. The backup or neutral value stands in, and the county dashboard’s Confidence rating is lowered when key inputs are substituted.

The public county data files also carry an older score field from an earlier five-part formula. The dashboards and the map do not display it.

What changed from the early version

The early Honest Cattle Forage Score was described primarily as a blend of snowpack, soil moisture, and drought, and the early internal formula included Structural Potential, Moisture Input, Vegetation Response, Drought/Condition Overlay, and Local Usability. That structure was directionally reasonable, but the revised website method makes one important change:

Current condition and long-term potential should not be mixed into the same score.

A low-potential dryland county can have an excellent year relative to its normal. A high-potential mountain or irrigated county can have a poor year relative to its normal. For that reason, Honest Cattle keeps long-term forage potential out of the current-year forage condition score.

Why This Approach Is Scientifically Credible

1. Remote forage prediction is an established research field

A systematic review published in Agriculture examined 85 peer-reviewed studies on forage prediction. It found that vegetation indices, climate variables, and soil parameters are widely used to predict forage and grass yield. NDVI, precipitation, soil moisture, and temperature were among the most frequent variables used in forage prediction models.

That supports the basic Honest Cattle approach: do not rely on one weather number. Blend plant response, precipitation, soil moisture, and drought stress.

The same review also warns that large-scale prediction tools may not capture variability within individual fields or pastures. That warning is built into how Honest Cattle describes the score.

2. Grass-Cast provides a direct precedent

Grass-Cast is the closest scientific precedent for Honest Cattle’s county-scale score. Hartman et al. developed Grass-Cast to provide county-level forecasts of grassland aboveground net primary production in the Great Plains. It uses long-term weather records, satellite-derived NDVI, ecosystem modeling, and seasonal precipitation forecasts to classify county grass production as below normal, near normal, or above normal.

Grass-Cast is important because it shows that a regional forage signal does not need to be a pasture-level measurement to be useful. It can still help ranchers, lenders, and landowners understand whether a county is moving toward poor, normal, or favorable forage conditions.

3. RAP provides a production benchmark

The Rangeland Analysis Platform provides herbaceous rangeland production estimates at 30-meter resolution, annually and every 16 days, across western U.S. rangelands. Jones et al. describe these estimates as useful for decision-making when used as part of a multiple-lines-of-evidence approach.

That is the right way to use RAP inside Honest Cattle. RAP should not be treated as the only answer, but it is an excellent benchmark for the Vegetation Response component and for back-testing the county score.

Note (October 2026): on September 28, 2026, RAP released version 3.0 of its production estimates, replacing the 2024 and 2025 annual figures and the provisional 2026 16-day figures through late August (RAP changelog). Benchmark comparisons and back-tests that used the earlier RAP figures need to be re-run against the new data.

4. Standing forage is harder than vegetation response

Kearney et al. explain the key difference between production and standing forage. Production estimates do not automatically account for losses from grazing, trampling, insects, fire, hail, senescence, or other disturbances. Their remote-sensing work shows that standing herbaceous biomass can be monitored with satellite imagery and ground calibration, but it also reinforces why local conditions and validation matter.

This is why Honest Cattle does not claim to know pasture-level forage availability. The scientifically defensible claim is narrower: HCFS estimates county-scale forage-growing conditions relative to local normal.

Why Snowpack Alone Is Not Enough

Snowpack matters. Li et al. found that a substantial share of western U.S. runoff originates as snowmelt, especially in mountain regions. Snowpack can affect stock water, irrigation supply, runoff timing, soil moisture recharge, and western Montana forage conditions.

But snowpack is not a statewide substitute for spring precipitation or vegetation response. For much of central and eastern Montana rangeland, the main forage question is whether moisture arrives during the cool-season and early growing-season window.

Smart et al. found that April, May, and June precipitation — or combinations of those months — accurately predicted annual forage production across central and northern Great Plains rangelands. They also found Montana examples where April–May precipitation had strong correlations with annual forage production.

Vermeire, Heitschmidt, and Rinella found that southeast Montana mixed-grass prairie production was dominated by cool-season perennial grasses and that 90% of aboveground net primary production was completed by July 1.

What the Score Can and Cannot Tell a Producer

The last limitation is important. Interpreting Indicators of Rangeland Health is a site-scale protocol intended to evaluate soil/site stability, hydrologic function, and biotic integrity. HCFS does not do that. It is a county-scale synthesis layer, not an ecological-site assessment.

Model Components and Scientific Rationale

How We Describe the Score

HC Rangeland Forage Condition Score

The HC Rangeland Forage Condition Score is a 0–100 county-scale signal of rangeland forage conditions relative to local normal. The score blends satellite-observed vegetation response (with a soil-moisture stand-in until the season’s satellite data is published), growing-season rainfall, drought stress, and a usability part that is currently held neutral.

Higher scores indicate more favorable county-scale forage-growing conditions for the current grazing season.

The score categories (Poor, Fair, Good, Excellent) are the ones shown in the table in the method section above.

The score is designed for regional screening, early warning, and county-to-county comparison. It is not a pasture-level forage inventory, pounds-per-acre estimate, AUM calculation, stocking-rate recommendation, ecological-site assessment, or substitute for pasture inspection and local range knowledge.

Method Note

The HC Rangeland Forage Condition Score uses four weighted components:

0.40(VR) + 0.35(GSM) + 0.15(DS) + 0.10(UM)

• Vegetation Response: RAP herbaceous production on rangeland so far this season, ranked against the county’s recent years, or the soil-moisture stand-in until RAP publishes the season.
• Growing-Season Moisture: the NOAA NCEI three-month county rainfall rank; snowpack and percent-of-normal precipitation are used only as a backup when that rank is unavailable. Evaporative demand is not yet included.
• Drought Stress: U.S. Drought Monitor drought classes (60%) and USDA NASS’s statewide Montana pasture and range condition rating (40%).
• Usability Modifier: currently held at a neutral 50; the grazeable-rangeland filter is applied in the vegetation input.

Snowpack is not a regular input to the live score. It enters only through the backup moisture calculation, when the NOAA rainfall rank is unavailable.

Experimental (beta). HCFS is still experimental. The weights and score bands are published above. How each part is scaled, the baselines used, and how missing data are handled are set out under “How the live score is calculated” above. The Montana Counties map sums the score up more briefly as a blend of U.S. Drought Monitor status, growing-season rainfall (NOAA NCEI county rank), and satellite rangeland vegetation, and shows the date of its latest refresh (Montana Counties methods).

Validation Plan

HCFS has not yet been validated as a forage-production model. The independent sources below are what we will check it against.

Bundy, Gensini, and Ashley show that USDA NASS pasture and rangeland condition data can be used to build a grazing-season climatology and analyze pasture/rangeland condition trends from 1995–2022. That makes NASS condition data useful as one broad validation source, even though it is not a pasture-level measurement.

Next steps in validation: measure how closely the score tracks RAP production anomalies (above- or below-normal production), how often its categories match Grass-Cast’s, and how it performs separately in western mountain, central foothill, and eastern plains counties — including a check of known drought years for false alarms and misses.

Operator Quick Reference

Bottom Line for Producers

The HC Rangeland Forage Condition Score is not trying to replace a rancher’s eye. It is trying to put public scientific data into a practical county-scale format.

A low score should trigger caution. A high score should provide regional confidence. Neither score should replace walking pastures, checking water, reading utilization, or knowing the country.

The scientifically defensible claim is:

HC Rangeland Forage Condition Score estimates county-scale forage-growing conditions relative to local normal. It is an early-warning and comparison tool, not a pasture-level forage inventory or stocking-rate recommendation.

Sources & Data References

Subhashree, S. N., et al. 2023. “Tools for Predicting Forage Growth in Rangelands and Economic Analyses—A Systematic Review.” Agriculture, 13(2), 455. DOI: 10.3390/agriculture13020455. Used for the scientific basis that forage prediction models commonly use vegetation indices, precipitation, soil moisture, climate variables, and soil parameters.

Smart, A. J., Harmoney, K., Scasta, J. D., Stephenson, M., Volesky, J., Vermeire, L. T., Mosley, J. C., Sedivec, K., Meehan, M., Haigh, T., Derner, J. D., and McClaran, M. P. 2021. “Critical Decision Dates for Drought Management in Central and Northern Great Plains Rangelands.” Rangeland Ecology & Management, 78, 191–200. DOI: 10.1016/j.rama.2019.09.005. Used for the importance of April–June precipitation and early drought decision timing in Great Plains forage production.

Vermeire, L. T., Heitschmidt, R. K., and Rinella, M. J. 2009. “Primary Productivity and Precipitation-Use Efficiency in Mixed-Grass Prairie: A Comparison of Northern and Southern US Sites.” Rangeland Ecology & Management, 62(3), 230–239. Used for Montana mixed-grass prairie production timing and the finding that most annual production at the Montana site occurred by July 1.

Hartman, M. D., Parton, W. J., Derner, J. D., Schulte, D. K., Smith, W. K., Peck, D. E., Day, K. A., Del Grosso, S. J., Lutz, S., Fuchs, B. A., Chen, M., and Gao, W. 2020. “Seasonal Grassland Productivity Forecast for the U.S. Great Plains Using Grass-Cast.” Ecosphere, 11(11). DOI: 10.1002/ecs2.3280. Used as the closest scientific precedent for a county-scale forage productivity signal.

Jones, M. O., Robinson, N. P., Naugle, D. E., Maestas, J. D., Reeves, M. C., Lankston, R. W., and Allred, B. W. 2021. “Annual and 16-Day Rangeland Production Estimates for the Western United States.” Rangeland Ecology & Management, 77, 112–117. DOI: 10.1016/j.rama.2021.04.003. Used for the Rangeland Analysis Platform as a vegetation-production benchmark.

Kearney, S. P., Porensky, L. M., Augustine, D. J., Gaffney, R., and Derner, J. D. 2022. “Monitoring Standing Herbaceous Biomass and Thresholds in Semiarid Rangelands from Harmonized Landsat 8 and Sentinel-2 Imagery to Support Within-Season Adaptive Management.” Remote Sensing of Environment, 271, 112907. DOI: 10.1016/j.rse.2022.112907. Used for the distinction between production estimates and standing forage biomass, and for the limits of remote sensing without ground calibration.

Poděbradská, M., et al. 2022. “Monitoring Climate Impacts on Annual Forage Production across U.S. Semi-Arid Grasslands.” Remote Sensing, 14(1), 4. DOI: 10.3390/rs14010004. Used for the concept of expected biomass, site-specific growth potential, climate-driven performance, and the limits of models that do not include management or disturbance.

Bundy, L. R., Gensini, V. A., and Ashley, W. S. 2024. “United States Pasture and Rangeland Conditions: 1995–2022.” Agronomy Journal, 117, e21736. DOI: 10.1002/agj2.21736. Used for NASS pasture and rangeland condition data as a broad validation source.

Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P. 2017. “How Much Runoff Originates as Snow in the Western United States, and How Will That Change in the Future?” Geophysical Research Letters, 44, 6163–6172. DOI: 10.1002/2017GL073551. Used for the hydrologic importance of snowpack and snowmelt in the western United States.

Pellant, M., Shaver, P. L., Pyke, D. A., Herrick, J. E., Lepak, N., Riegel, G., Kachergis, E. J., Newingham, B. A., Toledo, D. P., and Busby, F. E. 2020. Interpreting Indicators of Rangeland Health, Version 5. Bureau of Land Management Technical Reference 1734-6. Used to clarify that rangeland health assessment is a site-scale ecological assessment, not something replaced by a county forage score.

About the Author

Dirk Adams is the founder of Honest Cattle, a Montana-focused cattle market and ranch decision-support resource. He has ranched in Montana’s Shields River Valley for more than 40 years and publishes county-level cattle market, range, and forage condition tools for producers, lenders, and landowners.

Disclaimer

The HC Rangeland Forage Condition Score is a county-scale decision-support index based on public scientific and agricultural datasets. It is intended for regional screening, comparison, and early warning. It is not a pasture inspection, stocking-rate recommendation, AUM calculation, ecological-site assessment, financial advice, or substitute for professional range evaluation. Producers should use local knowledge, pasture inspection, forage measurements, and professional advice before making grazing, marketing, lease, or herd-retention decisions.

Prepared by Dirk Adams with the assistance of AI. More than forty years ranching in Montana’s Shields River Valley.