Date of Award:


Document Type:


Degree Name:

Master of Science (MS)


Plants, Soils, and Climate

Department name when degree awarded

Plant Science

Committee Chair(s)

Ralph E. Whitesides


Ralph E. Whitesides


Robert C. Lamb


William F. Campbell


Knowledge of relationships between nutrient levels, yield, maturity, and environmental influences on alfalfa (Medicago sativa L.) are necessary to estimate when to harvest alfalfa to maximize quality and yield. Objectives were to document the change in nutrient content, yield, and growth stage of alfalfa grown in Utah as it matures and to develop a simple model to predict optimal harvest date. The study involved three locations in major alfalfa producing regions in Utah. Samples were collected from three commonly grown alfalfa varieties between 26 April and 26 September in 1987. Maximum and minimum levels of crude protein (CP) observed were 32.8% to 16.2%, acid detergent fiber (ADF) 39.4% to 14.0%, and dry matter (OM) 31.7% to 14.3%. Maximum yield of 7.0 Mg ha-1 for a single harvest was observed. The growth stage and average yield in Mg ha-1 for all varieties and harvests collected were: prebud 3.6; midbud 4.2; and late bud to early bloom 4.9. As alfalfa matured CP% declined, ADF% increased, and DM% increased. Criteria used to estimate optimal harvest date was achieving not less than 20.0% CP, at least 29.0% ADF, but not more than 31.0% ADF. The estimated optimal harvest date was determined 63.3% of the time by not exceeding 31.0% ADF. Early to midbud were characteristic growth stages of the estimated optimal harvest date occurring 34.4% and 41.0% respectively. Midbud stage was characterized by elongation of the peduncle at second and third axillary bud positions. Accumulated growing degree hours (AGDH) were calculated using the ASYMCUR modeling concept. Height models were developed by averaging AGDH at 5 cm increments of shoot height. Models developed from the Nephi site were used to predict data from other sites. These models made about 37% acceptable predictions ranging from 0-100%. The general model made 23% acceptable predictions, variety models 47%, harvest models 43%, and 36% from specific data models. The general model, with a coefficient of variation (CV) of 14.6%, made fewer acceptable predictions than specific models having CV of 11.1% and 6.0%. Height models generated using the ASYMCUR concept were inefficient in predicting growth of alfalfa. This may be due to inaccurate estimates of when regrowth began, inaccurate weather data, and a variety of temperature related stresses which reduces the growth rate of alfalfa per unit of growing degree hours. Development of stress factor in cosine equations, improved data collection, and additional model generation and testing, could reduce variability and increasing percentage of acceptable predictions.