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FORAGE & GRAZINGLANDS

Alfalfa Fiber Estimation in Mixed Stands and Its Relationship to Plant Morphology

Dep. of Crop and Soil Sci., Cornell Univ., Ithaca, NY 14853

^* Corresponding author (jhc5{at}cornell.edu)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
In New York, most alfalfa (Medicago sativa L.) is grown in mixed stands with grass, and models for estimating neutral detergent fiber (NDF) are not used. The objectives of this experiment were to test the suitability of existing equations for estimating NDF of the alfalfa component of mixed stands, and to better understand how alfalfa is affected by the presence of grass. Stands of first-cut alfalfa and grass (10–90% grass) were sampled at two experimental sites and producers' fields in 19 New York counties during May and June 2004 and 2005. A range of plant measurements and environmental characteristics were recorded. The predictive equations for alfalfa quality (PEAQ) and other models were examined for applicability to mixed stand alfalfa NDF estimation. The R² values ranged from 0.80 to 0.87 and RMSE ranged from 20.5 to 25.2 g kg^–1. The most biased model was PEAQ, possibly due to the lower cutting height used to generate the PEAQ equation than the cutting height used in this study. For producer fields, a model based on alfalfa height was the best one-variable model, with R² of 0.84 and RMSE of 22.7 g kg^–1. Presence of grass increases the number of nodes and increases alfalfa height; however, the relationship between alfalfa height and NDF is not changed, suggesting that the predictive ability of models based on alfalfa height is not affected by the percentage of grass in the sward.

Abbreviations: GCANOPY, height of the grass canopy in the sample area • GDD, growing degree day • GRASS%, actual percentage of grass in the sample • LC, lack of correlation • MAXHT, height of the tallest alfalfa stem in the sample area • MSD, mean squared deviation • NDF, neutral detergent fiber • NU, nonunity slope • PEAQ, predictive equations for alfalfa quality • RMSE, root mean square error • SB, squared bias • SBC, Schwarz Bayesian criterion

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
A PROPERLY-TIMED SPRING ALFALFA HARVEST sets the stage for good harvest management throughout the rest of the season. Timing of spring forage harvest should be based on NDF (Cherney et al., 2006). Methods of estimating NDF for use as a harvest decision aid must be quick, simple, and reasonably accurate. Models based on weather, chronological age, and plant morphology (Fick et al., 1994) have been developed to estimate alfalfa NDF. The most commonly used of these are the PEAQ equations (Hintz and Albrecht, 1991). Equations using the tallest stem and maturity of the most mature stem in the sample gave acceptable RMSE compared with more complex methods involving mean stage determinations (Kalu and Fick, 1981; Allen and Fick, 1990; Fick and Janson, 1990). The Wisconsin PEAQ equations have been modified for other regions of the USA, including Ohio (Sulc, 1996; Sulc et al., 1997) and New York (Cherney, 1995). The original PEAQ equations have been evaluated in New York, Pennsylvania, Ohio, California, and Wisconsin (Sulc et al., 1997), and prediction errors were small enough to suggest the PEAQ equations are robust across a range of environments.

Although most of the alfalfa in the USA is grown in pure stands, this is not the case in the northeastern USA. In New York it is estimated that >80% of the alfalfa land area is seeded with mixtures of alfalfa and grasses (Cherney et al., 2006). Added difficulties with mixtures include estimating the proportion of grass in the stand, estimating the NDF of the grass portion, and knowing how the grass portion affects the NDF of the alfalfa. Parsons et al. (2006b) has suggested models for estimating total sward NDF of mixed stands based primarily on alfalfa height and the percentage of grass in the stand. Informal observations of mixed stands in New York suggest that alfalfa height is positively correlated with the percentage of grass in the stand. The PEAQ equations have been tested and recommended for use only with pure stands of alfalfa that contain no grass or weeds (Sulc et al., 1997). Because the equations rely on alfalfa height, if other plants in the sward affect alfalfa height, then the validity of the models are uncertain. As a result, equations for estimation of alfalfa NDF have not been used to estimate the NDF of the alfalfa component of mixed stands. The objectives of this study were to (i) test existing alfalfa NDF models for their ability to estimate alfalfa NDF in mixed stands; (ii) develop alternative models for estimating the alfalfa NDF of mixed stands; (iii) understand how the presence of grass in the mixed stand affects the morphology of alfalfa; and (iv) determine the validity of applying equations for pure alfalfa to mixed stands.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Sampling of Mixed Stands
Alfalfa–grass mixed stands were sampled in the spring at two experimental sites and 150 producers' fields in 19 New York counties during May and June 2004 and 2005, as described in Parsons et al. (2006b). The experimental sites were the Cornell University Caldwell Field Research Farm (42°27'1'' N, 76°27'35'' E, 276 m, 0–2% slope) near Ithaca, NY, and Mount Pleasant Research Farm (42°27'36'' N, 76°22'12'' E, 520 m, 0–6% slope) near Dryden, NY. The soil at Caldwell Field was a Niagara silt loam (fine-silty, mixed, active, mesic Aeric Endoaqualfs) and the soil at Mount Pleasant was a Mardin silt loam (coarse-loamy, mixed, active, mesic Typic Fragiudepts). The experimental design at each site was a randomized complete block design with four blocks. Each block included three different alfalfa–grass species mixtures at two grass seeding rates, and one plot of pure-sown alfalfa, giving a total of 28 plots. The term pure-sown is used here to denote a stand that was planted as pure alfalfa and which may or may not have been invaded by grass species. Each plot measured 2.7 by 6 m (16.2 m²) with 0.15 m between plots and 0.3-m alleys between blocks. Plots were seeded on 19 May 2003 at Caldwell Field and 23 May 2003 at Mount Pleasant. All plots were seeded at Caldwell Field with ‘Hytest 340PLH’ alfalfa at 13.4 kg ha^–1 and at Mount Pleasant with ‘Hytest 104PLH’ alfalfa at 13.4 kg ha^–1 using a Brillion seeder (Brillion Farm Equipment, Brillion, WI). Grass plots were seeded using a Carter seeder (Carter Mfg., Brookston, IN). ‘Richmond’ timothy (Phleum pratense L.) was seeded at 3.4 and 6.7 kg ha^–1, ‘Okay’ orchardgrass (Dactylis glomerata L.) was seeded at 4.5 and 9.0 kg ha^–1, and ‘Rival’ reed canarygrass (Phalaris arundinaceae L.) was seeded at 6.7 and 13.4 kg ha^–1. Seeding rates were based on pure live seed. Lime, P, and K were applied based on soil test results.

Producer fields and plots were sampled when alfalfa height reached or exceeded 30 cm. To define a portion of the field or plot as the sample area, an area of 1 m² was visually identified in 2004, and in 2005 a circular quadrat of comparable area was used. The data collected and variable abbreviations are summarized in Table 1. Height of the tallest alfalfa stem in the sample area was measured to the terminal bud (MAXHT). The alfalfa maturity categories of Kalu and Fick (1981) were used to assign a numerical value to the most mature stem in the sample area (MAXSTAGE). The major grass species was recorded (GSPECIES). The height of the tallest grass tiller in the sample area was measured by fully extending the leaf (GMAXHT). The average grass canopy height of the sample area was measured with no extension of leaves (GCANOPY). The developmental stage of the most mature grass tiller in the sample area was determined using the staging system of Moore and Moser (1995) (GMAXNDX). Determination of the index number using this system requires knowledge of the total number of leaves or nodes that will appear before reaching the next development stage. Because this system requires prior knowledge of development norms for each member species, a simplified grass staging system described by Parsons et al. (2006b) was also used (GMAXSTG).

Samples were separated into alfalfa and grass fractions, oven dried at 60°C until a constant dry weight was reached, and the actual percentage of grass in the sample (GRASS%) was calculated. Samples with GRASS% < 10 or > 90 were not used for further analysis. Samples were ground to pass through a 1-mm screen. Samples (0.25 g) were analyzed for NDF concentration using the procedure described by Van Soest et al. (1991), using the ANKOM (Macedon, NY) fiber analyzer with filter bags. Sodium sulfite and -amylase were used in the NDF procedure.

Alfalfa Morphology Experiment
Measurements of alfalfa morphology were obtained from the Caldwell Field experimental plots described above. The low and high seeding rates defined in the original experiment were not distinguished as separate treatment levels. The plots were harvested on 20 May and 8 June 2005. From each plot, single 625-cm² quadrats were harvested with hand shears to a height of 10 cm. Alfalfa and grass were separated and the grass portions were oven dried to a constant weight at 60°C to determine the concentration of dry matter in each sample. The five tallest alfalfa stems from each sample were selected for further measurements and the remaining alfalfa portions were oven dried to a constant weight at 60°C to determine the concentration of dry matter. The following measurements were made on the five tallest stems. Alfalfa stem height was measured to the terminal bud. Alfalfa stem diameter was measured with digital callipers at 2.5 cm above the cutting height. Easily separable nodes above the cutting height were counted. Lengths of the first two internodes at the 20 May harvest, and the first four internodes at the 8 June harvest, were measured. Leaf-to-stem mass ratios were determined by hand separating leaf from stem for the combined five tallest stems, oven drying at 60°C, and weighing. Replicates of leaf and stem fractions were combined and analyzed for NDF concentration. The procedure described by Van Soest et al. (1991) was followed using the ANKOM fiber analyzer with filter bags. Sodium sulfite and -amylase were used in the NDF procedure.

Predictive Equations and Statistical Analysis
The original PEAQ equation for NDF estimation developed by Hintz and Albrecht (1991) is NDF = 168.9 + 2.7(MAXHT) + 8.1(MAXSTAGE). Cherney and Sulc (1997) developed an equation for NDF estimation in New York fields, based only on alfalfa height. This equation, labeled NYPQ, is NDF = 122.7 + 3.1(MAXHT). Parsons et al. (2006a) developed a similar equation, also based only on alfalfa height. This equation, labeled NYHT, is NDF = 67.7 + 4.1(MAXHT). In addition, Parsons et al. (2006a) developed an equation based on alfalfa height and GDDs. This equation, labeled NYGD, is NDF = 68.9 + 3.4(MAXHT) + 0.14(GDD5). Model validation was performed by regressing actual NDF values on the predicted NDF values. A number of parameters were used in evaluating the equations because no single statistical test can adequately describe the goodness of fit of the model. The coefficient of determination (r² or R²) and RMSE were determined. Root mean square error has the same units as the variable predicted, and in model validation is the prediction error of the model. All regression equations were tested for intercepts at the origin (a = 0) and unitary coefficients (b = 1). Kobayashi and Salam (2000) presented reasons why these parameters are not entirely satisfactory for model evaluation and promoted the use of mean squared deviation (MSD) and its components as more informative parameters. The MSD reflects discrepancy between a model and the data and is a direct measure of predictive success. Gauch et al. (2003) proposed the partitioning of MSD into the components of squared bias (SB), nonunity slope (NU), and lack of correlation (LC), to provide further insight into model performance. The three components have distinct meanings and simple geometric interpretation, with SB relating to translation, NU relating to rotation, and LC relating to scatter.

For model development, the PROC RSQUARE variable selection procedure of SAS, v. 9.1 software (SAS Inst., 2003) was used to identify models that maximized the coefficient of determination (r² or R²) and minimized RMSE and the Schwarz Bayesian criterion (SBC). In model development, RMSE is the calibration error of the model. The SBC is a better indicator of predictive accuracy than R² or RMSE. PROC GLM in SAS, v. 9.1 software (SAS Inst., 2003) was used to determine whether the resultant F value from the addition of the second explanatory variable in a two-variable model was statistically significant.

Alfalfa morphology data were analyzed using PROC GLM in SAS, v. 9.1 software (SAS Inst., 2003). Plant species, seeding rates, and harvest dates were considered fixed effects and replicates were considered random effects. Dunnett's test was used to compare pure alfalfa (the control) with each alfalfa–grass mix. Principal component scores were derived using PROC PRINCOMP in SAS, v. 9.1 software (SAS Inst., 2003), and standardized by variable. A biplot for the first two principal components was constructed using the method of Gabriel (1971).

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Evaluation of Existing Equations
Results of fitting model estimates from PEAQ, NYPQ, NYHT, and NYGD to observed NDF values are contained in Table 2. The range of values for R² (0.80–0.87) and RMSE (20.5–25.2 g kg^–1) are of comparable magnitude to the PEAQ equations of Hintz and Albrecht (1991). The NYGD equation has the best goodness of fit, with R² of 0.87 and RMSE of 20.5 g kg^–1. The other equations have lower goodness of fit. For PEAQ and NYPQ, the b values were significantly >1 and the a values were significantly <0. For NYHT and NYGD, the b values were significantly <1 and the a values were significantly >0. The order of b values ranging from closest to 1 to furthest from 1 is NYPQ, NYGD, NYHT, and PEAQ. Similarly, the order of a values ranging from closest to 0 to furthest from 0 was NYPQ, NYGD, NYHT, and PEAQ.

View larger version (14K): [in this window] [in a new window]   Fig. 1. Components of mean squared deviation (MSD) of regression models used to estimate the neutral detergent fiber of the alfalfa component of mixed stands. The equations used are PEAQ (Hintz and Albrecht, 1991), NYPQ (Cherney and Sulc, 1997), NYHT (Parsons et al., 2006a), and NYGD (Parsons et al., 2006a). LC, lack of correlation; NU, nonunity slope; SB, squared bias.

View larger version (18K): [in this window] [in a new window]   Fig. 2. Comparison of regression models used to estimate the neutral detergent fiber (NDF) of the alfalfa component of mixed stands. The equations used are PEAQ (Hintz and Albrecht, 1991), NYPQ (Cherney and Sulc, 1997), NYHT (Parsons et al., 2006a), and NYGD (Parsons et al., 2006a). The line from lower left to upper right is the 1:1 unity line indicating a perfect fit.

Development of New Equations
The data were used to develop new equations to estimate the alfalfa NDF of mixed stands. Table 3 shows the variable selection results, grouped according to location. Locations included the Mount Pleasant and Caldwell Field experimental sites, producer fields, and a combination of all locations. For each location, three equations are presented: (i) the best one-variable model, (ii) the best two-variable model including only plant-derived variables in the analysis, and (iii) the best two-variable model including all variables in the analysis. Models were selected on the basis of having the lowest SBC.

View this table: [in this window] [in a new window]   Table 3. Selected regression models to estimate alfalfa neutral detergent fiber in mixed stands.

For the producer field samples, the best one-variable model (Eq. [7]) was based on MAXHT. The best two-variable models were based on MAXHT and MAXSTAGE (Eq. [8]), and MAXHT and GDD5 (Eq. [9]). When all samples were combined, the best one-variable model (Eq. [10]) was also based on MAXHT. The best two-variable models were based on MAXHT and GCANOPY (Eq. [11]), and MAXHT and GDD5 (Eq. [12]).

For both Mount Pleasant and Caldwell Field, a GDD variable was the best single explanatory variable. Fick et al. (1994) detail numerous studies that demonstrate the use of GDD for estimation of alfalfa NDF. Our results confirm this relationship; however, they also show that GDD had less predictive power for the producer fields than for the experimental fields. Both Mount Pleasant and Caldwell Field had a weather station on site, ensuring that GDD data was accurate. In contrast, the nearest weather station for individual producer fields could be a considerable distance away, and the difference in altitude (ALTD) might also be great. Allen and Beck (1996) reported decreased goodness of fit for GDD models as distance from weather stations increased. The excellent results for Caldwell Field suggested that GDD has potential as a tool for NDF estimation if accurate local weather data can be obtained. However, Parsons et al. (2006b) showed that models based on GDD data were less successful when used to predict alfalfa NDF for years on which the model was not built. Therefore, although calibration errors may be low (encouraging use of the model), prediction errors may be inflated when an existing model is used on new data.

The goodness of fit for the Mount Pleasant models is less than for the Caldwell Field models, but comparable with other studies such as the PEAQ equations of Hintz and Albrecht (1991). The poorer results at Mount Pleasant may be reflective of the stands, which were generally uneven in plant distribution and contained more weeds. Although the Caldwell Field plots were better, they were not free of weeds. These observations suggest that equations for estimating NDF of the alfalfa component of mixed stands may be somewhat robust to the presence of weeds. The effect of weeds on alfalfa NDF may depend on both the competitive ability and density of the weeds. Further investigation of this observation would be a practical area of study, particularly in light of the fact that in New York very few stands of alfalfa or alfalfa–grass are free of weeds.

In contrast to the experimental plots, MAXHT was the variable that explained the most variation in alfalfa NDF for producer fields. Although goodness of fit for the model with MAXHT and MAXSTAGE is statistically greater (Table 2), we consider this increase to be not biologically significant and thus the extra measurement of alfalfa stage is not warranted. For pure-sown alfalfa stands in New York, Parsons et al. (2006a) found that that addition of alfalfa stage to a model containing alfalfa height was not statistically significant. In this study, producer fields were sampled over a wide geographic area, and included a range of alfalfa and weed densities. These results confirm the robustness of MAXHT and its usefulness in estimating alfalfa NDF not only in pure-sown stands, but also for the alfalfa component of mixed stands.

Alfalfa Morphology in Mixed Stands
Blocks and interactions between harvest date and species were nonsignificant for all variables at a probability level of 0.05. The effects of species and harvest date on alfalfa morphological characteristics are shown in Table 4. Species had a significant effect on alfalfa height. The height of alfalfa growing with each grass species was significantly greater than the height of pure-sown alfalfa. Alfalfa height increased from Harvest 1 to Harvest 2. There was no significant difference in Alfalfa stem diameter either across harvests or across species treatments.

View this table: [in this window] [in a new window]   Table 4. Treatment means for alfalfa height, alfalfa stem diameter, number of alfalfa nodes, alfalfa internode lengths (IN), alfalfa leaf-to-stem mass ratio (LSR), alfalfa yield, grass yield, and bluegrass yield.

Leaf-to-stem mass ratio decreased from Harvest 1 to Harvest 2. Pure-sown alfalfa had a greater leaf-to-stem mass ratio than alfalfa growing with orchardgrass or reed canarygrass, but was not significantly different from alfalfa growing with timothy. Alfalfa yield increased from Harvest 1 to Harvest 2. Yield of alfalfa was greater in pure-sown stands than in orchardgrass or timothy, but was not significantly different from alfalfa growing with reed canarygrass. Grass yield increased from Harvest 1 to Harvest 2. The yield of bluegrass (Poa spp.), a common weed of alfalfa stands, increased from Harvest 1 to Harvest 2. Alfalfa growing in pure-sown stands had a greater yield of bluegrass than alfalfa growing in mixed stands.

Principal component analysis was used to further clarify the relationship between the presence of grass and alfalfa morphology. The first two principal components accounted for 81% of the total variance. Figure 3 is the biplot for the first two principal components and shows the relationship between samples and variables. Combining replicates, there were four species and two harvest dates, giving a combination of eight samples. The eleven variables plotted include leaf-to-stem mass ratio, length of Internode 1, length of Internode 2, grass yield, alfalfa yield, bluegrass yield, alfalfa stem diameter, alfalfa height, number of alfalfa nodes, alfalfa leaf NDF, and alfalfa stem NDF. The cosine of the angle between two points of the same type (i.e., sample or variable) estimates the correlation between the two points; thus points that are clustered are highly correlated. Trends from the principle component analysis are not directly comparable with the analysis of variance results; however some commonalities are expected. There is a cluster of variables that are highly correlated, composed of leaf NDF, stem NDF, nodes, and height (Fig. 3). For example, the correlation between height and nodes is 0.994. The eigenvector for leaf-to-stem mass ratio points in the opposite direction; thus, the four clustered variables are highly negatively correlated to leaf-to-stem mass ratio. As an example, the correlation value between nodes and leaf-to-stem mass ratio is –0.991. This set of variables reflects alfalfa maturation. Moving from left to right in Fig. 3, as the alfalfa plant matures it increases in height, number of nodes, and stem and leaf NDF, whereas the leaf-to-stem ratio decreases.

View larger version (22K): [in this window] [in a new window]   Fig. 3. Biplot for sample means and variables. Sample points, signified by a letter and number, are composed of mixes of alfalfa and orchardgrass (O), alfalfa and timothy (T), alfalfa and reed canarygrass (R), pure-sown alfalfa (A), Harvest 1 (1), and Harvest 2 (2). The variables include leaf-to-stem mass ratio (LSR), length of Internode 1 (IN1), length of Internode 2 (IN2), grass yield (Grass), alfalfa yield (Alfalfa), bluegrass yield (Bluegrass), alfalfa stem diameter (Diameter), alfalfa height (Height), number of alfalfa nodes (Nodes), alfalfa leaf neutral detergent fiber (LNDF), and alfalfa stem neutral detergent fiber (SNDF).

The negative correlation between grass yield and alfalfa stem diameter (–0.393) suggests that alfalfa may also respond to grass competition by decreasing stem diameter, an accompanying response to increasing internode length. In Fig. 3 it appears that this correlation is much stronger. However, Fig. 3 only takes into account Principle Components 1 and 2, whereas correlation matrix values take into account all dimensions. Volenec et al. (1987) reported a decrease in stem diameter as plant population increased in pure-sown stands of alfalfa. Although in this study there was no significant species effect on stem diameter in the ANOVA (Table 4), decreased stem diameter should not be disregarded as a possible response to competition from grass.

Bluegrass is positively correlated with alfalfa yield (0.722) and negatively correlated with grass yield (–0.546). This is consistent with observations in New York that bluegrass is widespread in fields of alfalfa, but less prolific where there is a significant sown grass component in the sward.

The location of sample data points (Fig. 3) also contributes to understanding the direction of growth processes in the sward. Markers for the first, less mature harvests are located in the left of the biplot, whereas markers for the second, more mature harvests are in the right. Markers for the first harvest are clustered, signifying that at this stage there is less competition, whereas markers for the second harvest are more dispersed. Particularly for the second harvest, markers for pure-sown alfalfa and alfalfa with reed canarygrass are located toward the top, and are associated with greater yields of alfalfa and bluegrass. This is consistent with observations that reed canarygrass is less competitive with alfalfa than other grasses grown in New York. Markers for alfalfa with orchardgrass and timothy are toward the bottom and signify high grass yield and strong grass competition. These observations are consistent with the typical pattern of growth of these species combinations in New York fields.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Existing models for pure-sown alfalfa NDF can be used to estimate the alfalfa component of mixed stands. This is significant for producers in New York and other locations where mixed stands are common. Although equations exist to estimate the total NDF of mixed stands (Parsons et al., 2006b), this finding is still pertinent. Previously, producers have been advised against using the PEAQ equation in fields where alfalfa may be dominant but a small amount of grass is present.

Acknowledging that PEAQ was originally calibrated and only advocated for pure stands of alfalfa cut at a 3.5-cm stubble height, an issue the results raise is the bias of PEAQ when it is used for alfalfa not cut at this height. In reality, producers cut at varying stubble heights, based on such factors as available machinery and stoniness of the field. Adjusting PEAQ (and other equations) for expected cutting height could help reduce the potential for biased NDF estimates.

The additional equations presented in this paper offer alternatives for predicting the NDF of the alfalfa component of mixed stands. The equations could be tested in other locations outside New York to assess their predictive ability. The results of this study should increase confidence in MAXHT as the premium explanatory variable for alfalfa NDF in both pure and mixed stands. Samples were taken from a wide geographical area and from fields differing in many respects. No attempt was made to exclude fields from the study because of a few weeds or to restrict the fields selected to having a narrow range of grass species.

The alfalfa morphology results suggest that the presence of grass causes an increase in alfalfa height by stimulating an increase in the number of nodes, consequently increasing NDF. It is possible that the presence of grass may also increase internode length and reduce stem diameter; however, these effects were less evident. Although grass causes a morphological response in alfalfa, the relationship between alfalfa height and NDF remained consistent across the height range in the study. This suggests that an alfalfa plant growing in a pure-sown stand is similar in terms of forage chemistry as an alfalfa plant of the same height growing in a mixed stand. It follows that for practical purposes the PEAQ equation and other alfalfa NDF predictive equations are applicable to the alfalfa component of mixed stands.

	ACKNOWLEDGMENTS

 
The authors thank Sam Beer, Kai Ming Zhao, Molly Lebowitz, Jen Beckman, Peter Barney, Aaron Gabriel, Jeff Miller, Bruce Tillapaugh, Rick Faucett, Michael Hunter, Michael Davis, Aysin Bilgili, and Leon Hatch for assistance with harvesting and analysis. This research was supported by a Kieckhefer Adirondack Fellowship.

Received for publication March 2, 2006.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

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