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Alfalfa Management Research at the
Klamath Experiment Station
Randy Dove1 1, David Hannaway 2, and Steve Orloff
INTRODUCTION
Alfalfa is grown on almost 40,000 acres in Klamath County, and accounts for about
25 percent of total crop sales in the region. Alfalfa research at KES currently involves
variety testing (pages 109-111) and several management studies. Research on alfalfa
management includes the alfalfa weed management study (pages 118-123), a date of
planting study, and alfalfa phenology research. Although planting date and phenological
studies are incomplete, a brief description of studies in progress may be of interest to
producers and colleagues.
Planting Date Trial
Alfalfa field establishment timing can have significant effects on stand density,
seedling development, weed competition, yields, and ultimately, profitability. Common
practice in the area includes a wide range of planting dates. Most establishment occurs
from March to early June, or in mid- to late August. The warmest portion of the season
is avoided, probably for concern about moisture stress. Early season planting is intended
to produce a crop during the establishment year. Locally derived data on crop response
to establishment timing has not been available.
University of California research has recently resulted in the development of a
model to predict optimum time for alfalfa establishment. The model is based on alfalfa
response to photoperiod and soil temperatures. The model predicts that under Klamath
Basin conditions the optimum time to plant alfalfa occurs in the last two weeks of July,
a time carefully avoided by local producers. Research in Yolo and Fresno Counties has
validated the model for conditions in that part of California. Through the establishment
and two subsequent years, yields were reduced by 1 ton/A in each year, when planting
was delayed one month past the optimum time predicted by the model. Experience at
the Intermountain Research and Extension Center (IREC) also tends to support the
model; however, formal studies specifically addressing this question have not been
conducted at IREC.
Experiments were established at KES and IREC in 1992 to evaluate the economics
of time of planting for alfalfa under local conditions. A split-plot design includes eight
planting dates in three-week intervals from early April through late August as main-plot
treatments, and three varieties with dormancy ratings of 2 to 4 (typical of varieties
planted locally) as split-plot treatments. Crops will be harvested individually when plants
1
2
3
/ Assistant Professor, Klamath Experiment Station, Klamath Falls, OR.
/ Associate Professor, Department of Crop and Soil Sciences, Oregon State University,
Corvallis, OR.
/ Farm Advisor, Cooperative Extension, Siskiyou County, CA.
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reach the early bud stage. Forage yield data and economics of crop production will be
monitored through three years. Stand counts will be made after the first cutting in 1993
to evaluate stand establishment success. Preliminary observations of stand and vigor in
the KES trial, indicate poor establishment of the late August planting. All other planting
dates experienced good alfalfa survival in the first winter.
Alfalfa Phenology Studies
Alfalfa Phenology and Forage Quality
Cutting management of alfalfa requires a balance between competing interests in
yield and quality. Dairy quality alfalfa commands price premiums, but at a significant
yield sacrifice. Producers require reliable information on the relationship between yield
and quality. They also need easily measured criteria for cutting management decisionmaking to meet individual producer marketing goals. Recent research in other regions
has devised systems for predicting forage quality, based on simple procedures. The
validity of these procedures for local conditions remains to be determined.
Phenology is the study of the development of an organism as influenced by genotype
and the total environment. Alfalfa forage quality is greatly affected by age or growth
stage. A 10-stage classification system has been developed to accurately determine the
growth stage of alfalfa. The phenological stage of individual stems is determined and
assigned a value. The mean stage by count (MSC) procedure estimates the mean stage
as the average of observed stages weighted for the number of shoots in each stage. MSC
is a relatively simple procedure that can be performed in the field. By correlating stage
and forage quality values, the MSC has provided a quick way to estimate forage
quality of growing alfalfa in some areas. However, initial studies in Oregon do not
support the correlation found elsewhere. It may be necessary to modify the equation for
Oregon conditions to accurately estimate forage quality using MSC values.
A second method for evaluating and predicting growth stage is the mean stage by
weight (MSW) procedure. This system estimates the average of observed stages,
weighted by the dry weight of shoots in each stage. The procedure is more time
consuming and expensive than the MSC procedure; however, it may be more accurate as
a research tool.
A cooperative study has been initiated to determine the relationship of MSC to
forage quality under Oregon conditions. Samples will be collected from alfalfa fields
throughout Oregon, and MSC and forage quality will be determined for each sample.
Quality parameters to be determined include acid detergent fiber (ADF) and crude
protein (CP). Data has been collected at KES for two years on nine alfalfa varieties.
More extensive sampling at other locations will be used to compare findings at KES and
other Oregon sites.
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Alfalfa Phenological Models
Stage determination using MSC requires destructive sampling and processing to
estimate current alfalfa phenological stages. In addition, rate of future development
cannot be predicted using this method. Accurate prediction of alfalfa development
stages is important in scheduling management practices such as planting, pesticide
applications, irrigation periods, and harvest. Computer models have been developed to
predict alfalfa phenological stage using a number of environmental measurements.
Although alfalfa models have been quite successful in predicting alfalfa stage, they are
still considered too complex for commercial use. Growers and crop modelers need a
simple and reliable index for predicting alfalfa development. The model should be
based on temperature, and accurately describe alfalfa development over a range of
environmental conditions.
Temperature is the most important variable influencing alfalfa phenological
development, and it is easily monitored. Other factors include photoperiod, soil
moisture, solar radiation, soil conditions, and genotype. Phenological development may
also vary according to fall dormancy classification, since alfalfa cultivars differ in their
growth response to temperature. The concept of growing degree days (GDD) has been
advanced to describe the effect of temperature on the rate of progress toward maturity
for crop species. The heat unit system has found widespread use in predicting
development of several cultivated crops, including wheat, cotton, corn, peas, and beans.
Several studies have indicated that temperature indices (GDD) can account for more
than 95 percent of the variability for corn and sorghum development.
Linear GDD models have been used to predict growth and development of alfalfa.
Since alfalfa is a perennial crop, phenological models are needed for both new plantings
and perennial fields. Work at OSU to further develop these models is currently
underway. Validation of these models in southern Oregon with varieties from a range of
dormancy groups is needed.
A study examining the phenological development of nine alfalfa varieties has been
underway at KES since 1990. Three varieties were very dormant (Maverick, Spreador 2,
and Vernal); three were moderately dormant (Apollo II, WL-320, and Vemema); and
three were non-dormant (Florida 77, WL-605, and Madera). Phenological stage of plots
was determined by both MSC and MSW procedures. A new trial was established each
year, and phenological stage of the newly planted seedlings was monitored approximately
every two weeks. In 1991 and 1992, perennial plots established the previous year were
also sampled, and phenological stage was determined. Forage samples used to estimate
phenological stage in each plot were analyzed for both ADF and CP concentration.
Perennial plots were cut to a 2-inch height as close to the new planting date as possible.
Phenological stage of regrowth after cutting was monitored. Forage quality and
computer analysis is currently underway. Results will be used to determine the validity
of existing models for Klamath Basin conditions, and perhaps they will lead to a simple
model predicting alfalfa performance on the basis of easily monitored weather
conditions.