Relationships of Forage Nutritive Value to Cool-Season Grass Canopy ...

RESEARCH

Relationships of Forage Nutritive Value to Cool-Season Grass Canopy Characteristics Renata La Guardia Nave,* R. Mark Sulc, and David J. Barker

ABSTRACT In-field assessment of forage nutritive value could help producers manage forage harvesting or grazing based on potential feed value to ruminants. The objective of this research was to evaluate the relationship of forage nutritive value to canopy characteristics of mixed cool-season grass forage to identify potential indicators of forage nutritive value that can be assessed in real time. The research was conducted at Columbus, OH, from April to October 2009 and 2010 in a mixed cool-season grass forage stand. Growth periods were initiated in April, May, June, July, and August and allowed to continue unharvested for the remainder of the growing season, with weekly sampling for nutritive value and morphological composition. The proportions of dead material and lamina, age (days of growth), and herbage mass had the highest correlations with forage neutral detergent fiber digestibility (NDFD) and were higher than the correlations of the canopy characteristics with neutral detergent fiber (NDF). A linear model was fit to predict NDFD (g kg 1) from herbage mass (kg dry matter [DM] ha 1), in which NDFD = ( 0.05 × herbage mass) + 746 (root mean square error = 44.1, r2 = 0.81, P < 0.001). The relationship between herbage mass and NDFD was consistent over the growing season and across both years.

R.L.G. Nave, R.M. Sulc, and D.J. Barker, Dep. of Horticulture and Crop Science, The Ohio State Univ., 2021 Coffey Rd., Columbus, OH 43210. Salary and research support provided in part by state and federal funds appropriated to the Ohio Agric. Res. and Dev. Ctr. (OARDC) and The Ohio State Univ. Partial financial support was also provided by the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service (now National Inst. for Food and Agriculture), grant no. 2006-55618-17025. Published as OARDC Journal Article HCS12-13. Received 11 Apr. 2012. *Corresponding author ([email protected]). Abbreviations: DM, dry matter; HAR, herbage accumulation rate; IVTD, in vitro true digestibility; NDF, neutral detergent fiber; NDFD, neutral detergent fiber digestibility; RMSE, root mean square error.

O

ne of the most important measurements for pasturebased livestock producers is herbage mass. This measurement can help producers to ensure that pastures are managed so as to avoid under- and overuse by grazing animals and to help calculate animal intake. It is important to maintain herbage mass within an appropriate range to avoid both a reduction in the forage accumulation rate and forage availability to the animal (Barker et al., 2010). However, when feeding animals with high nutritional requirements (e.g., dairy), the nutritive value of the forage must also be considered. Nutritive value is defined as the in vitro disappearance and chemical composition of forage (Mott and Moore, 1985) and is often expressed using crude protein, in vitro dry matter (DM) (or organic matter) disappearance, neutral detergent fiber (NDF), acid detergent fiber, and lignin concentrations (Sollenberger and Vanzant, 2001). The digestibility of cool-season grasses declines as they mature during spring (Minson et al., 1960). The development

Published in Crop Sci. 53:341–348 (2013). doi: 10.2135/cropsci2012.04.0236 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. CROP SCIENCE, VOL. 53, JANUARY– FEBRUARY 2013

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of reliable predictors of changes in forage nutritive value during the growing cycle would allow targeting of harvests to desired levels of nutritive value to meet specific animal requirements (Valente et al., 2000). Plant maturity is the primary determinant of morphological development and forage nutritive value within a species (Moore and Moser, 1995). Knowledge of the relationship of nutritive value with morphological development may aid in making decisions such as when to harvest hay or graze pastures (Hill et al., 1995). Limited attention has been given to developing methods for field assessment of forage nutritive value. It is impractical to use conventional laboratory analyses of forage nutritive value for making real-time harvesting or grazing decisions because they are time consuming and costly (Starks et al., 2006). There is a need for quick and accurate methods for in-field estimation of forage nutritive value (Parsons et al., 2009). An ideal method of estimating nutritive value in the field was described as one providing accurate results while being cost effective and consistent across all harvests during the season and across a wide range of environments (Cherney and Sulc, 1997). A number of methods have been developed to estimate alfalfa (Medicago sativa L.) NDF in the field, including models based on weather, chronological age, and plant morphology (Fick et al., 1994). A widely used method involves predictive equations for alfalfa quality based on the height of the tallest stem and the maturity of the most mature stem in the sample to predict NDF quickly and easily (Hintz and Albrecht, 1991). That method has been validated and proven to be useful over a wide a range of environments in the United States and abroad (Sulc et al., 1997; Hakl et al., 2010). Limited work has been conducted to develop similar reliable estimates of grass nutritive value. Researchers in New York demonstrated that predictive models for NDF based on direct plant measurements were less sensitive to yearly variation than models that included seasonally dependent explanatory variables such as growing degree days (Parsons et al., 2006). The objective of this research was to evaluate the relationships of forage nutritive value to canopy characteristics of a mixed cool-season grass forage stand to determine plant-related variables that might serve as reliable estimators of forage nutritive value. The goal was to integrate herbage accumulation targets (maximizing herbage production) with nutritive value targets for animal production and develop a simple model that can be applied in the field to manage the use of cool-season forage grasses based on herbage accumulation (yield) and nutritive value.

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MATERIALS AND METHODS Site Description

The research was conducted at a field site near The Ohio State University, Columbus, OH (40°04 N, 83°05 W, and 274 m altitude) from April to October in 2009 and 2010 in a mixed forage stand (more than 10 yr old) comprising predominantly (73%) tall fescue (Festuca arundinacea Schreb.) and Kentucky bluegrass (Poa pratensis L.) (15%). The soil was a Kokomo silty clay loam (a fine, mixed, superactive, mesic Typic Argiaquolls). Urea was applied at 78 kg N ha 1 on 2 Apr. and 1 June 2009. In 2010, urea was applied at 50 kg N ha 1 on 4 April and at 22 kg N ha 1 on 1 June and 13 July.

Measurements Different initiation dates for herbage accumulation were imposed during the growing seasons of 2009 and 2010. In 2009, initiation dates were early and mid April, early and mid May, early June, early July, and early August. In 2010, initiation dates occurred on a monthly basis beginning the first week of April and ending the first week of August. On each initiation date, the forage was clipped to a 5-cm stubble height and allowed to grow unharvested for the remainder of the growing season. The initiation dates were replicated four times in a randomized complete block design. Individual plots (experimental units) were 5 by 9 m (45 m 2). Canopy height was monitored each week using the sward stick method (Barthram, 1986). The sward stick was a 45-cm metal ruler with 0.5-cm graduations and a sleeve supporting a 2 by 1-cm piece of clear acrylic glass (i.e., transparent thermoplastic). The ruler was held vertically and the sleeve was lowered until the Perspex touched the first piece of green nonflowering vegetation. The height in 0.5 cm graduations was recorded with a mechanical counter. Each measurement comprised 15 points (“hits”) per plot. Samples to characterize morphological composition, herbage mass, and nutritive value of the forage canopy were randomly collected from a 0.1 m 2 area within each experimental unit on a weekly basis. Forage samples were collected above a 5-cm stubble height. Samples were separated into the three categories: dead material, stem plus leaf sheath, and green lamina. All samples were dried at 60°C to constant weight. Herbage mass (kg DM ha 1) was recorded for each component and summed to provide the total dry weight of each sample collected. Morphological components for each plot were then combined and ground through a 1-mm screen in a shear mill (Thomas-Wiley Laboratory Mill Model 4, H. Thomas Co.) for laboratory analyses using the ANKOM A200 Fiber Analyzer and DaisyII Incubator (ANKOM Technology Corporation). Neutral detergent fiber was determined using the method of Van Soest and Robertson (1980) with modifications for a filter bag system according to Method 6 of ANKOM (2011), which included the use of -amylase and Na 2SO3 (Sigma no. A3306, Sigma Chemical Co.). For in vitro true digestibility (IVTD) we used a method similar to that described by Vogel et al. (1999), in that IVTD was determined according to Method 3 of ANKOM (2005), which uses Stage 1 of the procedure described by Marten and Barnes (1980), including use of Kansas State buffer in a 48-h incubation with rumen fluid,

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and then undigested residues were treated with NDF solution. Rumen fluid was obtained from a nonlactating, ruminally fistulated dairy cow (Bos taurus) that was offered a diet of orchardgrass (Dactylis glomerata L.) hay. The neutral detergent fiber digestibility (NDFD) was calculated using the following (all units g kg 1): NDFD = {1 – [(1000 – IVTD)/NDF]} × 1000.

Statistical Analysis All statistical analyses were based on the means of four replications (means of the four experimental units sampled per initiation date). Data from the cut in early August 2009 and 2010 were omitted from the analysis procedures due to dry weather and limited regrowth resulting in a low number of observations for those accumulation periods. Several procedures of SAS (SAS Institute, 2008) were used to test the correlations between nutritive value and canopy measurements (morphological components, height, and herbage mass). The CORR procedure was used to determine simple correlations between plant-related variables such as proportions of green lamina, stem plus sheath, dead material, herbage mass, canopy height, and age (accumulation period in days) versus forage nutritive value variables (NDF, IVTD, and NDFD). The REG procedure with the STEPWISE option was used to determine the introduction of a plant-related variable into the model based on its significance for predicting the nutritive value variable being evaluated. The REG procedure was used to develop a linear regression equation between the most highly correlated variables based on the REG stepwise and CORR analyses. The MIXED procedure was used to test differences among regression coefficients and intercepts within and across years for the relationships between plant variables and nutritive value variables. The CANCORR procedure was used to examine the potential relationships between the group of nutritive value variables and a group of plant variables (morphological composition, height, age, and herbage mass). By means of this procedure, the Pearson correlation coefficient matrix of nutritive value and canopy characteristics was interpreted, evaluating the maximum global multivariate relationship between both groups of parameters. Herbage mass during each accumulation period (H, a dependent variable) was fitted to time (t, an independent variable) using the Gompertz equation Eq. [1] with PROC NLIN of SAS (SAS Institute, 2008) to best fit the data. These models were developed in order for herbage mass to be integrated with nutritive value data collected across the season. Parameter estimation by PROC NLIN had less error when the three-parameter model Eq. [1] was used rather than the four parameter model used by Barker et al. (2010):

H

bt

H e ae ,

Weather Data

Rainfall was adequate for pasture growth from April through July in both years and averaged 95 mm mo 1, which was similar to the 30-yr average (data not shown). Late summer and autumn herbage accumulation was reduced by dry weather in both years. In 2009, August to October rainfall averaged 72 mm mo 1, which was 4% below the 30-yr average, and in 2010 it averaged 42 mm mo 1, which was 44% below the 30-yr average. The mean air temperature was 1.2°C below and 0.9°C above the 30-yr average in 2009 and 2010, respectively. The April to October average air temperature was 17.2 and 19.3°C in 2009 and 2010, respectively.

Herbage Accumulation Herbage accumulation ranged from 184 and 6693 kg DM ha 1 across the growing season (Table 1) and was reliably predicted by Gompertz equations (Fig. 1 and 2). The parameters for the herbage mass curves varied considerably during the growing season (Table 2). The asymptotic herbage mass showed the greatest seasonal variation in Table 1. Summary statistics for all variables analyzed above stubble (5 cm) of a cool- season mixed grass canopy during the growing season in two consecutive years (2009 and 2010). Variable Herbage mass, kg DM ha Canopy height, cm Age, d ‡

Lamina proportion, g kg Stem proportion, g kg Dead proportion, g kg NDF§, g kg 1 IVTD§, g kg 1 NDFD§, g kg 1

1 1

1

1

n†

MIN†

MAX†

Mean

SD

81 69 81

184 6.5 19

6693 60.2 175

2606 20.3 66

1684 11 38.1

75 75 75 73 73 73

303 0 0 465 575 329

1000 328 680 641 888 780

761 74 164 559 775 601

193 96 165 43 66 99

†

n, number of observations; MIN, minimum observation; MAX, maximum observation. Each observation was the average of four replicates.

‡

DM, dry matter.

§

NDF, neutral detergent fiber; IVTD, in vitro true digestibility; NDFD, neutral detergent fiber digestibility.

[1]

in which H was the maximum (asymptotic) herbage mass and a and b were parameters that determined the shape of the Gompertz curve.


RESULTS AND DISCUSSION

Figure 1. Herbage mass (above 5 cm stubble) for seven initiation dates with a fitted Gompertz curve in 2009. Values are the mean of four replicates. DM, dry matter.

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Forage Nutritive Value and Plant-Related Variables

Figure 2. Herbage mass (above 5 cm stubble) for five initiation dates with a fitted Gompertz curve in 2010. Values are the mean of four replicates. DM, dry matter. Table 2. Parameters for Gompertz curves predicting herbage mass accumulation patterns (Fig. 1 and 2). Initiation date April 2009 Mid April 2009 May 2009 Mid May 2009 June 2009 July 2009 August 2009 April 2010 May 2010 June 2010 July 2010 August 2010

Asymptotic herbage mass (H )† kg DM‡ ha 6450 5736 3660 3199 3063 2854 2606 6693 5443 4004 3075 689

Shape Curvature coefficient (a)† coefficient (b)†

1

5.31 3.04 2.98 2.92 2.96 3.64 3.01 3.54 3.07 2.40 3.96 3.45

0.035 0.032 0.024 0.025 0.018 0.025 0.023 0.030 0.028 0.022 0.035 0.064

†

H , maximum (asymptotic) herbage mass. Parameter symbols refer to Eq. [1].

‡

DM, dry matter.

both years. Values were higher for the accumulation periods beginning in April to June (mean = 4421 kg DM ha 1 in 2009 and 5380 kg DM ha 1 in 2010) and decreased to their lowest values in the accumulation periods initiated during July to August (mean = 2730 kg DM ha 1 in 2009 and 1882 kg DM ha 1 in 2010) (Fig. 1 and 2). In 2009, the greatest value for the curvature coefficient was for the curve fit to the forage accumulation period that began in April, when the growth rate was greatest (mean = 0.034). The lowest value for the curvature coefficient occurred for herbage accumulation periods that began in midsummer ( July mean = 0.025). For the initiation date starting in April 2010, the mean for the curvature coefficient was 0.030, with a slight decrease in May and June (mean 0.025), and increased substantially in autumn with the highest value occurring in August (0.064) (Fig. 1 and 2).

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The NDF concentration ranged from 465 to 641 g kg 1, IVTD ranged from 575 to 888 g kg 1, and NDFD ranged from 329 to 780 g kg 1 (Table 1). Those results were typical of values found in irrigated tall fescue pastures where the NDF averaged 468 g kg 1 and IVTD averaged 860 g kg 1 (Asay et al., 2002). Changes in nutritive value in our study were attributed to changes in the tissue age and maturity of the forage as the samples were collected throughout the growing season from April to October. Reid et al. (1959), studying stage of maturity effects on forage nutritive value, observed a reduction in IVTD for spring grown forages of 4.8 g kg 1 for each day that harvesting was delayed. Simple Correlations Most correlation coefficients of forage nutritive value variables with canopy characteristics (except stem proportion) were significant (P < 0.005; Table 3) across years. Proportion of dead material, proportion of lamina, age, and herbage mass were highly correlated with forage NDF, IVTD, and NDFD concentrations. The results indicated that nutritive value variables and canopy characteristics in a cool-season grass forage stand were related and those correlations were similar within individual years (data not shown). Our results were in agreement with previous studies that showed a strong relationship between forage nutritive value and morphological components. For example, in an evaluation of grazing influences on mass and nutritive value of stockpiled tall fescue, Burns et al. (2006) concluded that reduced nutritive value was attributed to a large decrease in green leaf tissue (of greatest nutritive value) and a concomitant increase in dead tissue (least nutritive value). Although most canopy characteristics were highly correlated with nutritive value variables, the relationships were not as consistent for some as for others. For example, coefficients for correlations between NDFD and age decreased steadily as initiation date advanced as r = 0.92, 0.82, 0.50, and 0.37 for initiation dates starting in April, May, June, and July, respectively (Table 3). Coefficients for correlations between age and NDF ranged from 0.73 to 0.46 across the different initiation dates (Table 3). Likewise, coefficients for correlations of age with IVTD ranged from 0.94 to 0.51. Karn et al. (2006) concluded that IVTD consistently declined with advances in maturity of four perennial grasses but NDF showed a less consistent pattern of change as maturity increased. The most consistent relationships occurred between NDFD and herbage mass, lamina proportion, and dead proportion. Herbage mass had the highest overall correlation with NDFD (r = 0.89), and r was 0.94, 0.88, 0.82, and 0.78 for initiation dates of April, May, June,

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Table 3. Pearson correlation coefficients between forage nutritive value variables and morphological component variables above stubble (5 cm) of a cool-season grass canopy during the growing season over 2 yr. Canopy characteristic variables

Nutritive value variables

Initiation date

n

NDF†

IVTD†

NDFD†

April May June July Overall

24 18 17 14 73

0.71* 0.63* 0.73* 0.46 0.66*

0.94* 0.89* 0.76* 0.51 0.87*

0.92* 0.82* 0.50* 0.37 0.81*


24 18 17 14 73

0.74* 0.36 0.25 0.11 0.53*

0.94* 0.88* 0.80* 0.71* 0.90*

0.94* 0.88* 0.82* 0.78* 0.89*

April May June

16 16 15

0.67* 0.72* 0.24

0.84* 0.68* 0.72*

0.86* 0.55* 0.72*

July Overall

14 61

0.01 0.47*

0.67* 0.59*

0.79* 0.53*

Lamina proportion


21 16 16 14 67

0.70* 0.44 0.67* 0.20 0.56*

0.81* 0.87* 0.82* 0.82* 0.78*

0.79* 0.84* 0.64* 0.86* 0.75*

Dead proportion


21 16 16 14 67

0.65* 0.48 0.71* 0.39 0.57*

0.85* 0.84* 0.88* 0.76* 0.83*

0.82* 0.79* 0.67* 0.68* 0.78*

Stem proportion


21 16 16 14 67

0.17 0.14 0.31

0.38

0.35

0.56* 0.39 0.61* 0.14

0.60* 0.30 0.84* 0.15

Age

Mass

Canopy height

0.21 0.12

*Significant at P < 0.005. †

Table 4. Standardized canonical coefficients (loadings) and correlation coefficients of the standardized traits between their first canonical variables for forage nutritive value traits and canopy characteristics.


and July, respectively (Table 3). The NDFD is an important determinant of animal performance, and being able to estimate it from herbage mass would provide a convenient and relatively easy method to help producers better manage their pastures to obtain greater animal performance. Feeding diets that provide greater levels of NDFD can increase energy intake in early lactating dairy cows (Oba and Allen, 1999). Analyzing forages for NDFD is an additional tool to predict forage nutritive value and use by dairy cattle (Kendall et al., 2009). Canonical Correlation Canonical correlation describes the relationship between multiple sets of independent variables and multiple sets of dependent measures (Hair et al., 1998). In this method, CROP SCIENCE, VOL. 53, JANUARY– FEBRUARY 2013

Trait Nutritive value traits NDF† IVTD† NDFD† Canopy characteristics Herbage mass Age Height Lamina proportion Dead proportion Canonical correlation

Standardized canonical coefficients (loadings)

Correlation coefficient

0.50 0.63

0.47

1.36 0.66 0.19 0.20 0.07 0.30

0.66 0.72 0.72 0.64 0.43 0.57 0.60 0.80*

*Significant at P < 0.005. †


linear transformations of the original variables in each set creates two new sets of standardized variables (the canonical variables) such that the canonical variables are not correlated within the sets but that the correlation between the canonical variables is maximized (Calinsky and Krzysko, 2005; Revell and Harrison, 2008). The standardized correlation coefficients examined the loading of independent variables on the canonical discriminant variables of nutritive value and canopy characteristics (Table 4). Canonical correlation analysis revealed that the group of nutritive value variables (NDF, IVTD, and NDFD) and canopy characteristics (proportion of lamina, proportion of dead material, and herbage mass) were highly correlated (r = 0.80), indicating a strong overall association between the two groups. The single variable that contributed the most to the loading coefficients was NDFD (among the nutritive value group) with a value of 1.36 compared with the lower coefficients of 0.50 and 0.63 for NDF and IVTD, respectively. Among the group of canopy characteristics, herbage mass had the highest loading coefficient (Table 4). The highest correlation coefficient between the nutritive value group and the canopy characteristics group was 0.72 for NDFD and 0.72 for herbage mass, suggesting that the best representation of the relationship between those two groups is primarily due to the negative relationship between NDFD and herbage mass. As described earlier, NDFD was most strongly and inversely related to herbage mass (Table 3). This was probably because herbage accumulation was associated with advancing forage maturity. Likewise, herbage mass was inversely correlated with the nutritive value group (except for NDF), which was dominated by NDFD concentration (Table 4). These findings are consistent with previous reports in the literature, where canonical correlations were considered good indicators of the relationships between groups of quantitative

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variables of palisadegrass {Urochloa brizantha (Hochst. ex A. Rich.) R. D. Webster [syn. Brachiaria brizantha (Hochst. ex A. Rich.) Stapf ] ‘Xaraes’} showing highly significant relationships between morphological, physical, and chemical nutritive value variables (Nave et al., 2009).

Prediction of Neutral Detergent Fiber Disappearance from Herbage Mass Of the nutritive value variables, we chose to focus on predicting NDFD because it had a high correlation with the canopy characteristics and is an important forage nutritive value measurement to consider in predicting animal performance. Enhanced NDFD of forage improves DM intake and milk yield of dairy cows and is particularly useful to accurately rank forages that might vary in yield and different nutritive value traits (Oba and Allen, 1999). For example, with a 1 g kg 1 increase in NDFD there was an associated increase of 0.23 kg d 1 of milk yield from dairy cows (Oba and Allen, 1999). Stepwise regression analysis of NDFD concentration in our study resulted in a model that included proportion of dead material (g kg 1) and herbage mass (kg DM ha 1), in which NDFD (g kg 1) = 720 – 0.197D – 0.341H, root mean square error (RMSE) = 71.0, and r 2 = 0.61, with partial r 2 = 0.54 and P < 0.001 for herbage mass and partial r 2 = 0.07 and P < 0.001 (Fig. 3) for dead material. Regression equations were also developed to describe the relationship between NDFD and herbage mass alone, because determining the proportion of dead matter in a canopy is tedious and time consuming while herbage mass can be easily and quickly measured in the field and was highly and consistently correlated with NDFD. There was a good relationship between herbage mass (kg DM ha 1) and NDFD across all samples collected in both years. Regression equations did not differ (P > 0.05) among forage accumulation periods within years and between the 2 yr; therefore, data were combined to generate one regression equation across all sampling dates: NDFD (g kg 1) = 0.052H + 746, r 2 = 0.81, RMSE = 44.1, and P > 0.001 (Fig. 3). With this model (standard error of estimate = 9.95), 46% of the predicted NDFD values were within ±25 g kg 1 of the observed NDFD and 81% were within ±50 g kg 1 of the observed values.

Practical Implications Relationships between herbage mass and daily herbage accumulation rate (HAR) could be calculated for each day of the growing season using the Gompertz curves for the accumulation periods that had been initiated up to that point in time. Representative data points were calculated for 1 May, 1 June, 1 July, 1 August, and 1 September of each year and the curves fitted using the equation of Barker et al. (2010) to show HAR as a function of herbage

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Figure 3. Relationship between neutral detergent fiber digestibility (NDFD) and herbage mass (above stubble) during the growing season in 2009 and 2010. Values are the mean of four replicates. The solid line is the regression line and the dotted lines are ±50 g kg 1 deviation from the regression. DM, dry matter.

Figure 4. Relationship between herbage mass (above 5 cm stubble), herbage accumulation rate (HAR), and neutral detergent fiber digestibility (NDFD) concentration during the growing season for five dates in 2009 and 2010. DM, dry matter.

mass (Fig. 4). Of potential interest to pasture managers is the relationship of HAR and NDFD as a function of herbage mass. The optimum herbage mass (with greatest HAR) varied during the season in both years. The optimum herbage mass could not be identified on 1 May of either year because there were no data points for herbage mass exceeding 1500 kg DM ha 1 on that date. As the season progressed, the relationships could be modeled for

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canopies with larger herbage mass; therefore, it was possible to model a wider range of responses and identify the herbage mass at which HAR was maximized. On 1 July, optimum pasture mass was 3500 to 4000 kg DM ha 1 and decreased during the season to 2500 kg DM ha 1 by 1 September (Fig. 4). Forage NDFD can range from approximately 400 to more than 750 g kg 1 for cool-season grasses (NRC, 2001). When the two relationships (i.e., HAR vs. herbage mass and NDFD vs. herbage mass) are plotted on the same graph, we can gain some insight into the effect on HAR when pastures are either managed to maximize yield or nutritive value. For example, cool-season grasses having NDFD ≈ 500 g kg 1, a suitable range for breeding cows, was achieved with a pasture mass not exceeding 4703 kg DM ha 1. That herbage mass was usually greater than the optimal mass (i.e., having maximum HAR) in June to September (Fig. 4), suggesting it is possible to manage for high forage production with better than adequate nutritive value for beef cows during most of the growing season. In contrast, cool-season grasses having NDFD ≥ 600 g kg 1, a more suitable range for high producing dairy cows, was achieved with herbage mass not exceeding 2791kg DM ha 1. That herbage mass was near the optimal mass (i.e., having maximum HAR) after early June, suggesting that forage can be harvested or grazed near maximum HAR when managing for high forage nutritive value, except in the spring when HAR was at a lower level (Fig. 4). Therefore, we would not expect a yield penalty for harvesting this cool-season grass canopy at an optimum nutritive value content for dairy cows; however, this needs to be validated experimentally with livestock at the field scale.

CONCLUSIONS Herbage mass is a fundamental measure of a production system. The NDFD concentration of a cool-season grass canopy was the single variable that had the highest canonical correlations within the nutritive value group evaluated in this study. The NDFD of a mixed cool-season grass canopy was highly correlated with herbage mass and we were able to develop a model to estimate NDFD from the herbage mass present. The relationship between herbage mass and NDFD was consistent over the growing season and across 2 yr. Although these results need to be validated at more locations and for other species, they demonstrate the importance for managers to monitor herbage mass, because it can be a useful guide to manage the optimal balance between HAR (to maintain forage productivity) and the nutritive value targets for the class of animals being fed.


Acknowledgments We thank John McCormick, Jason Rethman, and Gina Yapple for their assistance with field operations and data collection. We thank Dale Gelter for providing access and support at the field research site. We thank Dr. Kent Harrison, Dr. William Weiss, and four anonymous reviewers for their helpful critique of the article.

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