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 should 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.

This is the method Honest Cattle should use and describe on the website. It replaces the early snowpack-heavy presentation with a more defensible rangeland condition model. Snowpack remains in the score where it matters hydrologically, but it should not dominate a statewide forage score.

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 should be described 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 should be 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 should publish and use 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.

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 should display long-term forage potential separately from 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 should be built into the website language.

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.

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 should 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

Recommended Website Language

Use this language on Honest Cattle county pages and the research page.

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, growing-season moisture, soil moisture, drought stress, snowpack where hydrologically relevant, and grazeable-rangeland usability factors.

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

Score categories:

• 0–25 Poor — elevated risk of below-normal forage conditions
• 26–50 Fair — below-normal to near-normal forage signals
• 51–75 Good — near-normal to above-normal forage signals
• 76–100 Excellent — strongly favorable forage signals relative to local history

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: satellite-observed greenness or herbaceous production response relative to historical normal.
• Growing-Season Moisture: spring precipitation, root-zone soil moisture, evaporative demand, and snowpack where snowpack is hydrologically relevant.
• Drought Stress: drought severity and short-term moisture deficits.
• Usability Modifier: grazeable-rangeland masks and local constraints that affect whether forage is likely available to livestock.

Snowpack is included where it matters, especially in mountain, foothill, irrigated, and snowmelt-fed systems. It is not treated as a statewide substitute for spring precipitation, soil moisture, or observed vegetation response.

Validation Plan

Honest Cattle should validate HCFS before calling it a validated forage-production model.

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.

Minimum validation statistics should include:

• Correlation with RAP production anomaly
• Classification accuracy versus Grass-Cast categories
• Confusion matrix for Poor/Fair/Good/Excellent categories
• Separate performance by western mountain counties, central foothill counties, and eastern plains counties
• A review of known drought years and false-positive or false-negative county scores

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. “Expected Ecosystem Performance May Inform Rangeland Forage Production Forecasts and Increase Drought Preparedness.” 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. 2025. “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.