Limitations to Estimating Production on Rangelands

For rangeland management purposes, production refers to the annual production of above-ground biomass, typically expressed in lbs./acre of dry-weight matter. For example, when estimating production of herbaceous perennial plants (grasses and forbs), the portion of the standing biomass that grew during the year would be measured and any decadent remnants from the previous year’s growth that remain attached would be discarded. When measuring production for shrubs and trees, production would be measured only on the new leaves and twigs that grew during the year, which can often be difficult to separate, particularly on evergreens. Ideally, production data is collected when plants are at their peak standing crop, usually in late September through October in warm-season dominated sites in Arizona. However, collecting production data during peak standing crop is a challenge.

Rangelands are highly variable across time and space. Rangelands have different vegetation types, ecological sites, plant communities with different growth cycles and life forms (e.g., cool and warm season herbaceous plants, deciduous and evergreen shrubs, cacti, etc.), heterogeneity or a “patchy” distribution of species and vigor, etc. In addition to landscape variability, sampling during peak standing crop will almost always underestimate total annual production because it misses production that occurs before or after the sampling date as well as the portions of the plants (e.g. seeds and flowers) that fall from the plant (Biondini et al. 1991; Smith 2022). The extent of this underestimate can be significant and variable. Thus, it is important to recognize that peak standing crop does not equal total annual production, rather it is a measurement of what is “standing” at the time of sampling.

As with most range monitoring methods, production data is only one tool in the toolbox and should be complemented with other monitoring methods or tools. One reason to estimate annual production is to compare ecological site productivity between wet and dry years. Ecological Site Descriptions (ESDs) plant community data is based on a range of production values to characterize the abundance or dominance of plant life forms on the site. Land managers may compare production data from a site against the range of production values of the reference site for the same ESD. Similarly, when production is used with another method such as Dry Weight Rank, percent composition can be estimated by species weight as an indicator of range condition, range health, or ecological condition.

Another reason to measure production is to estimate the effectiveness of land treatments such as brush control, reseeding, burning, etc. on forage production. Often, one of the goals of grassland restoration is to remove overstory to incentivize increased herbaceous growth for the benefit of wildlife and livestock. Moreover, determining the effectiveness of land treatments may be more of a research application, for example determining whether one restoration strategy is more beneficial than another.

One of the more common uses for production data is to estimate the amount of forage available for grazing animals. This practice is best used as an initial basis for estimating carrying capacity, stocking rate, or allocating forage among different animal species (e.g., cattle and elk) when no previous data exists. However, there are several very important limitations of using production data to estimate carrying capacity, stocking rate, and forage availability or allocation that will be discussed later.

There are a variety of approved standard methods to measure production. Detailed descriptions, procedures, ground rules, etc. for specific production methods can be found in the Interagency Technical Reference, Sampling Vegetation Attributes (1999).

The most accurate and only direct, quantitative measurement of production is the harvest method (Bonham 1989; Catchpole and Wheeler 1992). The harvest method is relatively straightforward, requires minimal training, and is usually the most accurate method of estimating production. As the name implies, this method involves clipping and weighing the above-ground herbaceous biomass within a quadrat. The herbaceous material is bagged and either weighed in the field with conversion factors applied, or, more accurately dried in an oven and subsequently measured for dry weight (Interagency Technical Reference 1999).

One of the major disadvantages of the harvest method is that it can be very labor-intensive and time-consuming, making it expensive and impractical for application across broad scales (Olsoy et al. 2014). Collecting the number of samples required to estimate production across a variable landscape accurately is expensive. Furthermore, despite the harvest method being the most accurate, significant sampling errors may be introduced by differences in the experience of observers or application of techniques. For example, there may be differences in clipping heights, identification of current year’s growth, decisions about whether material lies in or out of the sampling quadrat, or sampling errors associated with weighing or drying procedures (Smith et al. 2012)

Other indirect sampling methods, such as the double- weight sampling or comparative yield methods where sub- samples are harvested for calibration or to establish reference plots, attempt to increase sample size while reducing time and labor required to make observations, but an associated trade- off may be reduced accuracy. These methods also require more advanced training and experience before observers are consistently accurate. As such, indirect methods introduce other types of errors such as personal bias, correction factors, etc. If these errors are controlled (through training and adhering to peer-reviewed sampling protocols and ground rules), these methods can provide adequate estimates of production because sampling errors may be offset somewhat by a larger sample size. More information and details about these methods can be found in the Interagency Technical Reference (1999), or Smith et al. Guide to Rangeland Monitoring and Assessment (2012).

Non-destructive methods for collecting production data include remote sensing and modeling methods such as Light Detection and Ranging (LiDAR), Rangeland Production Monitoring Service (RPMS), Rangeland Analysis Platform (RAP), and others. LiDAR uses lasers to measure distances and then models a high-density point cloud to create detailed terrain and surface models to calculate aboveground biomass (Yao et al. 2011; Olsoy et al. 2014). RPMS and RAP use satellite imagery and Normalized Difference Vegetation Index (NDVI) products to estimate annual production. However, these non- destructive methods also introduce calibration and resolution errors that result in coarse production estimates which may vary widely from production data collected with the more granular methods discussed above. For example, the RPMS averages biomass over 30-meter pixels (Reeves 2019) which produces estimates that are not as accurate nor precise as other methods that require on-site samples.

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Despite the popularity of using production for rangeland management decisions, several limitations should be considered when assessing production data for management decisions. To illustrate the limitations of using production for management decisions, an example using the harvest method is provided below that attempts to estimate production for a site in northern Arizona (Figure 1). Recall that the harvest method is widely considered to be the most accurate method of estimating production.

Harvest Method 

A 9.60 ft2 quadrat was used as prior sampling demonstrated a 0.96 ft2 quadrat was too small for the area to be sampled. As a general rule, if more than 5% of the quadrats do not contain any plants, a larger quadrat should be used, and the average weight per quadrat should be at least 5-10 grams (Smith et al. 2012). Production was clipped at peak standing crop of perennial herbaceous forage. Ten quadrats (two transects of 5 quadrats each) were sampled at the site. Perennial herbaceous forage was clipped, bagged, oven-dried, weighed, and summarized to have an estimated production value of approximately 371 lbs./acre (Table 1). Data were further analyzed with an 80% confidence interval (CI) to estimate production with a range of approximately 250 lbs./acre – 500 lbs./acre (Table 2).

An 80% CI was used to reduce the number of plots sampled in exchange for a lower probability of certainty (e.g. 10 plots instead of 20 – 30 plots). The Society for Range Management (SRM) Range Inventory Standardization Committee (1983) recommends a probability level of 80% for most rangeland monitoring studies unless there is a demonstrated need for more precise results (Smith et al. 2012). Probabilities at or above 90% may be selected when more precise estimates are deemed necessary (e.g., when conducting research studies).

Table 1. Summary of production data at a site in northern Arizona

Table 2. Statistical analysis for estimating annual production at a site in northern Arizona at the 80% Confidence Interval (CI).

Carrying capacity and stocking rate are common rangeland calculations that require some degree of knowledge about the forage or production estimation. Carrying capacity is a long-term estimate of the average number of livestock and/ or wildlife that may be sustained without inducing damage to vegetation or related resources. Stocking rate is a shorter-term decision about the number of animals a manager decides to place within a management unit (e.g., pasture) for a specific time period (SRM Task Force 1998). One of the limitations that should be considered if implementing management decisions such as stocking rate based on production data alone is the inaccuracy of production estimates.

A question often asked is how many animals can graze a pasture? If only production data is used, the estimate of how many animals may vary greatly. For example, if the data in Table 2 is representative of a 100-acre pasture, one may want to know how many cows can graze on it for a month. An Animal Unit month (AUM) is the amount of dry matter forage a 1,000 lb. cow will consume over 30 days. If we assume a 1,000 lb. cow will consume 900 lbs. of forage in 1 month (assuming an average of 30 lbs. of forage per head per day over 30 days), this site could support approximately 13 – 27 cows, a range of 14 cows (See Box 1). Doubling the stocking rate in our small 100-acre pasture between the minimum and maximum production values could have severe impacts on rangeland resources if our initial estimates are inaccurate. The simple exercise above demonstrates how unreliable using production data alone can be for implementing management decisions such as stocking rate.

In reality, most pastures are much larger and more complex than the sample site above with multiple ecological sites, vegetation growth cycles, life forms, distribution, climate, etc. There are additional issues with using production estimates to estimate stocking rate. One of the issues is often only perennial herbaceous species are clipped. The harvest method protocol above does not account for grazing other plants such as annuals, grasses, forbs, and browse (i.e., shrubs and trees) which do contribute to available forage. Another factor is the assumed forage consumption per AUM which varies considerably depending on the physiological stage of the animal and is often unknown during a given grazing season. More detail on additional available forage and AUM assumptions can be found in: Assessment of US Forest Service Methods for Determining Livestock Grazing Capacity on National Forests in Arizona: Report to Gov. Jane D. Hull (Rangeland Technical Advisory Council, 2001).

To avoid injuring plants, only a portion of the plants should be harvested by grazing. A general rule of thumb is the “take half, leave half”, which has been used as a standard in the rangeland management profession for years. Therefore, we will implement a 50% proper use factor (PUF) for the estimated stocking rate calculation. The estimated stocking rate will be calculated twice to get the minimum number of cows based on our low estimate of production and a second time to calculate the maximum to know the range of stocking rate. The total number of AUMs that can be grazed is calculated as follows:

Total forage production (lbs./acre) x proper use factor (%) = available forage (lbs./acre)
250 lbs./acre x 50% PUF = 125 lbs./acre available forage
500 lbs./acre x 50% PUF = 250 lbs./acre available forage
Available forage (lbs./acre) x pasture size (acres) = available forage in the pasture (lbs.)
125 lbs./acre x 100 acres = 12,500 (lbs.) available forage
250 lbs./acre x 100 acres = 25,000 (lbs.) available forage
Available forage in pasture (lbs.) / 900 lbs. forage per AUM = AUMs in pasture
12,500 (lbs.) / 900 lbs. per AUM = 13.9 AUMs or approximately 13 head of 1,000 lbs. cows.
25,000 (lbs.) / 900 lbs. per AUM = 27.8 AUMs or approximately 27 head of 1,000 lbs. cows.

Note: 50% PUF is only a guideline and may need to change depending on the goals and objectives of a management plan. Similarly, the amount of dry matter a cow will consume will change based on the physiological status of the cow (dry, pregnant, lactating, etc.).

Estimating production on rangelands is a common monitoring method but difficult to accurately estimate. However, production can be a useful tool when used appropriately. Some appropriate uses for estimating production are to characterize various ecological sites during wet or dry years; estimate percent composition by species weight as an indicator of range condition or ecological condition; estimate the effect of land restoration treatments; and as a starting point for estimating carrying capacity or stocking rate when no previous data exists. However, there are several issues with using production alone to estimate carrying capacity.

Though there are a variety of methods to collect production data, it is difficult to obtain an accurate estimate because of the high degree of variability. The harvest method is the most accurate method to sample production, however, it is labor- intensive, time-consuming, and requires many samples to account for the variability across pastures and landscapes. Indirect methods can increase sample size but introduce additional errors such as personal bias and calibration errors. These errors may lead to an estimate of production that is unsuitable for formulating management decisions such as carrying capacity or stocking rate.

Range management is a challenging endeavor that requires land stewards to make appropriate and timely decisions based on ever-changing conditions. Management decisions should be based on the best available science. In some cases, production data may be appropriate for initial carrying capacity or stocking rate decisions; however, decision-makers should be aware of the limitations of the data informing those decisions. Implementing management decisions based on unreliable data may have severe ecological impacts.

Bonham, C. D. 1989. Measurements for terrestrial vegetation. New York, NY, USA. John Wiley and Sons, p 352

Byrne, K. M., Lauenroth, W. K., Alder, P.B, and Byrne, C. M. 2011. Estimating aboveground net primary production in grasslands: a comparison of nondestructive methods. Journal of Rangeland Ecology and Management 64: 498- 505.

Biondini, M. E., Lauenroth, W. K., and Sala, O. E. 1991. Correcting estimates of net primary production: are we overestimating plant production in rangelands. Journal of Rangeland Ecology and Management 44:498-505.

Catchpole W. and Wheeler, C. 1992. Estimating plant biomass: a review of techniques. Australian Journal of Ecology 17:121-131.

Interagency Technical Reference. 1999. Sampling Vegetation Attributes. BLM Technical Reference 1734-4.

Olsoy, P. J., Glenn, N. F., & Clark, P. E. 2014. Estimating sagebrush biomass using terrestrial laser scanning. Rangeland Ecology & Management, 67: 224–228.

Society for Range Management (SRM) Range Inventory Standardization Committee. Guidelines and terminology for range inventories and monitoring. 1983.

SRM Task Force. 1998. Glossary of terms used in range management, 4th edition. Society for Range Management.

Sprinkle, J. and Bailey, D. 2004. How many animals can I graze on my pasture. The University of Arizona Cooperative Extension AZ1352.

Rangeland Technical Advisory Council. 2001. Assessment of US Forest Service Methods for Determining Livestock Grazing Capacity on National Forests in Arizona: Report to Gov. Jane D. Hull

Reeves, M.C., Hanberry, B.B., Wilmer, H., Kaplan, N.E. and Lauenroth, W.K., 2021. An assessment of production trends on the Great Plains from 1984 to 2017. Rangeland Ecology & Management, 78, pp.165-179. An assessment of production trends on the Great Plains from 1984 to 2017. Rangeland Ecology and Management 78:165-179.

Smith, L., Ruyle, G., Dyess, J., Meyer, W., Barker, S., Lane, C.B., Williams, S.M., Maynard, J.L., Bell, D., Stewart, D. and Coulloudon, A. 2012. Guide to rangeland monitoring and assessment, basic concepts for collecting, interpreting and use of rangeland data for management planning and decisions. Arizona Grazing Lands Conservation Association, Tucson, Arizona, USA.

Smith, L., 2022. Production and utilization. Presentation. Range 101 Workshop. Prescott, AZ. May 26, 2022.

ao, T., Yang, X., Zhao, F., Wang, Z., Zhang, Q., Jupp, D., Lovell, J., Culvenor, D., Newnham, G., Ni-Meister, W. and Schaaf, C. 2011. Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar. Remote Sensing of Environment 115:2965-2974.

This author would like to thank Dr. Lamar Smith and Dr. Larry Howery for their input for this publication. This publication incorporated many concepts from their unpublished manuscripts and presentations.