Sid Bosworth, Associate Extension Professor,
Jimenez-Serrano, Graduate Student, and John Aleong, Professor,
Plant & Soil Science Department, University of Vermont firstname.lastname@example.org
Duration: 2000 - 2004
The ideal NDF level of perennial forage for a dairy ration (when 50% of TMR is concentrate) is considered to be about 38 - 40% for alfalfa and 50% for grasses. On the average, ADF levels should be at about 30% for both legumes and grasses. The time in which forages reach these ADF and NDF levels in the field during the spring growth can vary by two weeks or more from one year to the next. The major variable that affects these differences is temperature. It affects both plant maturation and lignin synthesis. The proportion of legumes to grass as well as leaf to stem ratio also can vary tremendously. Being able to better predict when forages reach their ideal ADF and NDF levels could help dairy producers improve overall forage quality, thus, reducing concentrate costs and/or improving milk performance.
Prediction methods have been developed and tested for alfalfa but little has been done with grasses or mixtures. The three most common methods for alfalfa include the following:
1. Scissor cutting method - involves sampling fields once a week two to three weeks prior to normal harvest until target NDF is reached. This method is expensive, time consuming and works best across a region that is fairly uniform in soils and climate.
2. PEAQ - Predictive Equations for Alfalfa Quality - A relationship that predicts alfalfa ADF, NDF and RFV based on plant maturity and plant height. PEAQ works well with pure alfalfa but not with alfalfa grass mixtures.
3. GDD - Growing Degree Days (700 to 750 GDD to reach a NDF of 40%) - Calculated by averaging the maximum and minimum temperature (o F) for a given day (24-h period) and subtracting the base temperature of 41o F. GDD is good for first cut but is not reliable when water becomes limiting factor. The relationship of total GDD with NDF has not been consistent across environments; however, the relationship of changes in NDF with daily GDD has been reported to be somewhat consistent (average of 21GDD for every change in NDF for alfalfa in central New York).
of A Prediction Method for Vermont’s Grasses and Mixtures
The goal of this project was to develop an affordable, practical and reliable pre-harvest method for predicting forage quality of the first harvest that can be used as a management tool by dairy and livestock producers in Vermont. Since forages grown in Vermont are often made of mixtures, it would be very difficult to use specific models that have been developed using GDD’s or other parameters. Therefore, our approach uses a combination of methods that includes 1) a pre-harvest sampling to determine a baseline for ADF and NDF and 2) GDD to then predict changes in ADF and NDF to determine a target date for harvest.
Our first objective was to establish GDD relationships of various grass species and types. In the spring of 2001, two field experiments were established in South Burlington, VT and East Montpelier, VT, respectively. The South Burlington site contained six grass/variety treatments. The grass treatments included 'Pizza', 'Barindana' and 'Pennlate' orchardgrass, 'Sunrise' and 'Sunset' timothy (early and intermediate, respectively), and 'Palaton' reed canarygrass. The East Montpelier site contained the same six grasses but also included these in combination with alfalfa plus one pure alfalfa treatment (all in a replicated design). All sites were treated with fertilizer amendments according to University of Vermont soil test recommendations.
In 2002 and 2003, forage samples were collected once per week during the spring growth period (approximately from late April to mid-June) starting when the earliest grass variety was about 8 -10 inches tall. Data collected included plant height, stage of development, tiller population, acid detergent fiber (ADF) and neutral detergent fiber (NDF), aerial, temperature and precipitation.
Our second objective was to develop a simple prediction
model based on Growing Degree Day relationships with ADF and NDF of the
various grasses and alfalfa and to validate this model with on-farm fields
The dates in which the grasses reached a target ADF of 30% or NDF of 50% varied by grass/cultivar treatment, year and location. 'Pennlate' orchardgrass and 'Palaton' reed canarygrass were consistently the earliest to reach target ADF and NDF values. The other orchargrass cultivars reached target values next with the timothy cultivars being last. 'Sunset' timothy was generally, but not always consistently, the last to reach target levels.
Agronomists have often recommended using stage of maturity as a way to determine optimum cutting time. When using first visible seed head as a measure of maturity, we found that varieties were not always consistent from one year to the next or across locations. Based on our results, using boot to early heading as an indicator to cut grasses was generally too late to achieve a quality goal of 30% ADF and 50% NDF.
Changes in ADF either on a “per day” or “per GDD” basis were similar for the six grasses and alfalfa. Changes in NDF were similar for the six grasses but alfalfa had a lower rate of NDF change as compared to the grasses. This was consistent with UVM Forage Lab data that has shown NDF of grasses to be about 10% units higher than alfalfa when measured at the same ADF levels.
When we compared the changes in ADF and NDF across sites and years, we found GDD to be much more consistent than daily change . Changes in ADF were fairly consistent between the grasses and alfalfa; however, there was a large difference for NDF. For mixtures that include grasses and alfalfa, ADF would be a better measurement to use.
On Farm Studies
From 2002 to 2004, 17 farm fields were evaluated using the proposed method for predicting an optimum harvest date. A baseline sampling was generally collected either the first or second week of May and a temperature gauge was placed in the field until a final collection was made just prior to the field being harvested.
Out of 10 fields of grasses or mixtures, GDD predicted ADF within 2% units in 8 of the fields (Figure 1). Out of seven alfalfa fields, we were able to predict ADF within 2% units 6 times. Therefore, this method does show promise as a management tool for predicting forage quality.
This site is maintained by Sid.Bosworth@uvm.edu, Plant & Soil Science Department, University of Vermont.
Issued in furtherance of Cooperative Extension work, Acts of May 8 and June 30, 1914, in cooperation with the United States Department of Agriculture. University of Vermont Extension, Burlington, Vermont.University of Vermont Extension and U.S. Department of Agriculture, cooperating, offer education and employment to everyone without regard to race, color, national origin, sex, religion, age, disability, political beliefs, or marital or familial status
Last modified December 13 2005 10:22 AM