Pasture Assessment In The Northeast United States

Matt A. Sanderson and Sarah C. Goslee, USDA Agricultural Research Service, Pasture Systems and Watershed Management Research Unit, University Park, PA 16802; and James B. Cropper, USDA Natural Resources Conservation Service, East National Technology Support Center, Greensboro, NC 27401

Abstract

Many livestock producers have intensified management of pastures in the Northeast and need assessment and monitoring tools to determine how management has influenced pastures. The Pasture Condition Score (PCS) system, developed by the NRCS, was used to assess 108 pastures on 31 farms across the Northeast. We examined the applicability of the system to identify potential problems with its uses and obtain a snapshot of pastureland status. None of the pastures evaluated scored in the lowest category (PCS < 15), and only a few pastures scored in the highest category (PCS > 45). More than 40% of the pastures scored in the category where only minor changes to management were needed (PCS = 36 to 45) and another 44% fell into the category where some improvements were needed (PCS = 26 to 35). About 15% of pastures scored 16 to 25, indicating immediate management changes were needed. The indicator "percent legume" scored lowest of all the indicators. The low rating for legume content suggests that producers should focus management on establishing and maintaining legumes, which contribute valuable N and high quality forage to the pasture system. Pasture condition score was negatively related to plant species richness. Pastures with the highest species richness generally had many weedy species. This indicates that focusing on increasing the number of species in a pasture without regard to the species composition may not be wise. The PCS system was readily implemented on most pastures. Producers would benefit by observing individual pasture condition indicators at regular intervals to track trends and inform management decisions.

Introduction

Pasture occupies 120 million acres of land in the USA (15). Assessment and monitoring tools are needed for some aspects of pasture management such as forage budgeting; stocking rate or stocking density decisions; nutrient management plans; and meeting regulatory requirements of governmental programs such as the Grassland Reserve Program (6), cropland insurance for risk management; the Conservation Security Program (7), and the national organic livestock production standards (14).

In the USA, much attention has been focused on assessing and monitoring rangeland health (11). Less emphasis has been placed on tools for assessing and monitoring pastureland. Early efforts toward developing such tools for pastures simply adapted early tools for rangeland condition assessment (4). Rangeland and pastureland, however, differ in many important features. Rangelands mostly occur in semiarid or arid regions and are managed as a native ecosystem with few inputs. Pasturelands typically occur in higher rainfall areas or are irrigated and rely on introduced forage species, which frequently receive agronomic inputs such as seed, fertilizer, and pesticides. Thus, different criteria and indicators are required for assessing and monitoring pastureland compared with rangeland.

The USDA-NRCS in cooperation with the University of Wisconsin released a new tool, the pasture condition score system (2,16) (Fig. 1). Pasture condition is defined as "the status of the plant community and the soil in a pasture in relation to its highest possible condition under ideal management" (3). The tool is used for assessing and monitoring the condition of pastureland for the purposes of (i) evaluating current pasture productivity and the stability of its plant community, soil, and water resources and (ii) identifying what treatments, if any, are required to improve a pasture’s productivity and to protect soil, water, and air quality (2,3). The tool is designed for land managers, private consultants, or public agency personnel. Ten key indicators of grazing land status are evaluated (Tables 1 and 2) along with causative factors explaining reasons for low condition scores.

Fig. 1. Example of a pasture condition score sheet. The score sheet and the Guide to Pasture Condition Scoring can be downloaded from the Grazing Lands Technical Institute website (16).

Table 1. Ten indicators used in the pasture condition score system along with a brief description of each [from Cosgrove et. al (2)].

Indicator	Brief description and relevance to pasture ecosystem function
Percent desirable plants	Proportion of forage in the sward contributed by commonly sown and agronomically useful grasses and legumes.
Plant cover	Percentage of the ground surface covered by the vertical projection of live vegetation. An indicator of the hydrologic condition of a pasture.
Plant diversity	The number of well-represented forage species and functional groups in a sward.
Plant residue	The undecomposed or partially decomposed vegetation on the soil surface or in the sward (includes both standing dead vegetation and litter).
Plant vigor	Describes the relative size and health of desirable and intermediate quality forage plants relative to those in a non-limiting environment.
Percent legume	The average amount of legume in the sward as a percentage of the sward mass.
Uniformity of use	A pattern in the vegetation formed by differential grazing across the pasture. The indicator depicts the variation in sward height and reflects the utilization rate of forage and also reflects the area of pasture or amount of forage rejected by grazing animals.
Livestock concentration areas	Indicates places where livestock congregate and denude the pasture. The area and location of soil surface degraded by treading damage from livestock congregation. Nutrient build-up common in these areas.
Soil compaction	Indicator of impaired water infiltration capacity. Also inhibits root growth.
Erosion	Indicates presence and severity of different types of wind and water erosion.

Table 2. Explanation of pasture condition score categories (from 2).

Pasture condition score		Management change suggested
Overall	Individual	Management change suggested
45-50	5	No changes in management needed at this time
35-45	4	Minor changes would enhance, do most beneficial first
25-35	3	Improvements benefit productivity and/or environment
15-25	2	Needs immediate management changes, high return likely
10-15	1	Major effort required in time, management, and expense.

We applied the PCS system on selected farms across the northeast USA. Our goals were to examine the applicability of the system, to identify potential problems in its use, and obtain a snapshot of the status of pastureland in this region.

On-Farm Survey and Application of Pasture Condition Score

Thirty-one farms were surveyed across the Northeast during 1997-2004. Nine farms were beef operations, 19 farms were dairies, two were dairy-heifer operations, and one was a sheep farm (Table 3). Six of the 31 farms practiced continuous stocking, and the remainder practiced rotational stocking. The farms represented a range of locations, soils, and managements. Farms were selected and producers contacted with the assistance of the local Cooperative Extension Service and USDA-NRCS personnel.

Table 3. Brief description of the 31 farms sampled in this survey.

Farm	State	County	Farm type	Predominant soils
1	MA	Franklin	Dairy	Charlton fine sandy loam
2	MD	Harford	Beef	Chester very stony silt loam
3	MD	Frederick	Dairy	Chester silt loam
4	ME	Cumberland	Sheep	Buxton silt loam
5	ME	Cumberland	Beef	Scantic silt loam
6	ME	Kennebec	Beef	Paxton fine sandy loam
7	ME	Penobscot	Organic dairy	Boothbay silt loam
8	ME	Penobscot	Dairy heifers	Dixmont silt loam/Thorndike silt loam
9	ME	Waldo	Dairy	Boothbay silt loam
10	ME	Waldo	Beef	Scantic silt loam
11	NY	Chenango	Dairy	Mardin channery silt loam/Valois gravely loam
12	NY	Chenango	Dairy	Mardin channery silt loam
13	NY	Chenango	Dairy	Volusia channery silt loam
14	NY	Delaware	Dairy	Lewbeach silt loam
15	NY	Delaware	Dairy	Willowemoc clay loam/Onteora loam
16	NY	Schuyler	Beef	Langford silt loam
17	NY	Tompkins	Dairy	Howard gravelly loam
18	PA	Berks	Dairy heifers	Berks/Weikert channery silt loam
19	PA	Centre	Beef	Hagerstown silt loam
20	PA	Centre	Beef	Hagerstown silty clay loam
21	PA	Chester	Beef	Glenelg channery silt Loam
22	PA	Franklin	Dairy	Purdy silty clay Loam
23	PA	Juniata	Dairy	Edom clay loam
24	PA	Lancaster	Beef	Hagerstown silt loam
25	PA	Northumberland	Beef	Alvira silt loam
26	PA	Northumberland	Dairy	Hartleton channery silt loam
27	PA	Northumberland	Dairy	Hartleton channery silt loam
28	PA	Schuykill	Dairy	Weikert channery silt loam
29	PA	Somerset	Dairy	Hazleton channery sandy loam
30	PA	Somerset	Dairy	Wharton silt loam
31	VT	Chittenden	Dairy	Vergennes clay/Covington silty clay

On each farm, two to eight pastures in different landscape positions were surveyed. In total, 108 pastures were surveyed. Each pasture was rated by the same person following the PCS System (Tables 1 and 2). Each indicator was scored on a 1 to 5 scale, with 1 representing an unacceptable condition and 5 representing the optimal condition (Table 2). Scores for the indicators were then summed to give an overall score for the pasture. Individual PCSs were regressed on the total number of plant species identified in each pasture to examine the relationships with plant species richness.

Vegetation and Soil Assessment

A modified Whittaker plot technique (13) was used to quantify the richness and cover of plant species in pastures on 29 of the 31 farms. In each pasture, one 66-×-164-ft (20-×-50-m) plot was established in a random location. Nested within this large plot were ten 11-ft² (1 m²) plots, two 107-ft² (10 m²) plots, and one 1076-ft² (100 m²) plot. Percentage cover of each species plus bare ground was recorded in each 11-ft² plot. The larger plots were then searched successively for plant species and species lists were developed, but plant cover was not evaluated for these plots. A composite soil sample (10 cores to a 6-inch depth) was taken in each modified Whittaker plot area and analyzed for pH, P, K, and organic matter at the Agricultural Analytical Laboratory at the Pennsylvania State University.

Simple statistics were used to summarize the PCS data. Means and standard deviations of aggregate scores for each pasture and the individual indicators were calculated. Correlation analysis was used to examine relationships between PCS and plant species diversity measurements and soil characteristics.

Overall Pasture Condition Scores

None of the evaluated pastures scored in the lowest category (PCS < 15) and only a few pastures scored in the highest category (PCS >45; Fig. 2). More than 40% of the pastures scored in the category where only minor changes to management were needed (PCS = 36 to 45) and another 44% scored in the category where some improvements were needed (PCS = 26 to 35). About 15% of pastures scored 16 to 25, indicating immediate changes were needed to improve sustainability.

Fig. 2. Distribution of pasture condition scores for 108 pastures surveyed on 31 farms in the northeastern USA.

Individual Indicator Scores

The pattern of scores for the 10 indicators was similar across the PCS categories (Fig. 3). Averaged for all pastures and farms, the two indicators with the lowest scores included "plant diversity" and "percent legume." The largest change in indicator scores between the highest and lowest score categories occurred for proportion of desirable plants (48% reduction), percentage legume (50%), plant cover (60%), and uniformity of use (47%). The score for plant diversity averaged about 2, which, according to the scoring criteria, indicated that the pasture was dominated by one functional group of plants. In most instances the dominant functional group was cool-season grasses. Correspondingly, scores for the "percent legume" indicator were low (average of 1.7) as well. A rating of 2 corresponds to an estimated legume content of 10 to 19% of dry weight according to the scoring criteria. Legumes contribute valuable N to the pasture system via N₂ fixation and are highly nutritious to grazing livestock. The optimum content of legume in pasture swards is often recommended to be about 30 to 35% of the sward dry matter (8) or as high as 40 to 60% (17). Based on the indicator scores across all farms, management changes should include establishing and maintaining legumes in the pasture.

Fig. 3. Distribution of individual indicator scores among pasture condition score categories. Bars indicate standard deviations.

When we examine the individual indicators for the group of pastures with the lowest scores, it is clear that plant diversity and percentage legume influence scores heavily, along with "uniformity of use" (Fig. 3). Legumes were almost nonexistent in these pastures and the pastures were typically dominated by one grass species. Uniformity of use was low on these pastures because they were largely overgrown and there was a great deal of spot grazing. The excess growth probably shaded legumes partly accounting for their low proportion in pastures (1). A uniformity of use score of 2 corresponds to 25 to 50% of the pasture area being ungrazed or grazed very little.

Other indicators (e.g., soil erosion and compaction, livestock concentration areas) were mid-range and were not driving the low scores. This indicates that many of the problems on the pastures in this region would be relatively straightforward to resolve.

Relation of PCS to Soil Fertility and Species Richness

Pasture soil fertility was generally similar among the different score categories (Fig. 4). An exception was the lower values for the highest PCS category (score of 45 to 50). Because there were only two pastures in the highest category, this average may have been skewed by the small number of observations. Nevertheless, these results indicate that high soil fertility does not guarantee high PCS.

Fig. 4. Soil P, K, pH, and organic matter levels (to a 6-inch depth) among pasture condition score categories on 108 pastures in the northeastern USA. Boxes show the distribution of values from the 25th to 75th percentiles (i.e., 50% of the data points fall within the box), whiskers indicate the 10th and 90th percentiles (i.e., 90% of the data fall between the whisker ends), and the line inside boxes indicates the median value. Individual data points indicate outliers. There were only two values in the highest pasture condition score category, thus the box shows only the range of those two values.

Soil pH was below 6.0 for most pastures, which may partly account for the low legume abundance. Soil P levels were near or above 200 ppm in a few pastures, which is considered by some as an environmental threshold indicating risk of phosphorus loss and water quality degradation from surface water runoff. However, other risk factors such as manure application rates, landscape position, and hydrologic connections must be considered as well (5). Soil K was also very high in some pastures. High soil potassium may result in high forage potassium concentrations, which can cause metabolic problems in cattle (12). Soil organic matter values were consistently high for northeastern US soils especially if compared with tilled cropland soils. Our data indicate that graziers need to monitor soil fertility for nutrient management considerations.

Pasture condition score was negatively correlated (r = -0.34, P < 0.05) with plant species richness (Fig. 5). Although variable, the relationship indicates that focusing strictly on increasing the number of species in a pasture without regard to the species composition may not be wise. In this survey, pastures with the highest species richness generally had a large number of weed species that were indicative of lax management. In a project on 10 sites in southern Australian temperate grazing lands, greater plant species diversity on pastures was associated with lower soil fertility and greater disturbance (9). Botanical composition is considered a robust indictor of pasture sustainability (10). The criterion set as the ideal for plant diversity in the pasture condition score sheet is a moderate diversity of four to five species from three basic functional groups (2). The criterion was based on maintaining consistent (not maximizing) pasture forage production during the grazing season. Low diversity or high diversity of forage species (not including weedy species) both receive low scores. Both total plant cover and cover of forage species were positively related (P < 0.01) to PCS (data not shown). Thus, maintaining vegetative cover is more important in maintaining pasture condition than is increasing or maintaining plant species richness.

Fig. 5. Relationship between species richness and pasture condition scores on 108 pastures in the northeastern USA. Boxes show the distribution of values from the 25th to 75th percentiles (i.e., 50% of the data points fall within the box), whiskers indicate the 10th and 90th percentiles (i.e., 90% of the data fall between the whisker ends), and the line inside boxes indicates the median value. Individual data points indicate outliers. There were only two values in the highest pasture condition score category, thus the box shows only the range of those two values.

Evaluation of the Pasture Condition Score System

The PCS system was readily implemented on most pastures. We did not evaluate variation among operators in scoring. Informal assessments with research technicians, NRCS technical assistants, and Cooperative Extension Service agents, however, indicated subjectivity and confusion in the interpretation of some of the scoring criteria. For example, the indicator "plant vigor" incorporates observations of multiple conditions including plant recovery rate after grazing, plant color, presence of insect or disease damage, and degree of plant wilting due to drought stress. Thus, training of field technical assistants or technical service providers is essential along with training aids that illustrate the criteria. A second difficulty with visually scoring some indicators, such as soil compaction, indicates the need for rapid and objective methods that account for spatial variability. Although the system is meant mainly for technical assistants, highly skilled producers could implement the system with training. Producers would probably benefit more from the process of observing pasture condition indicators at regular intervals within and among grazing seasons than from focusing on individual or composite scores. Scores should be recorded to track trends and to inform management decisions.

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