Predicting First‐Cut Alfalfa Yields from Preceding Winter Weather (1995)

In the northern USA, reliable predictions of first‐cut alfalfa ( Medicago sativa L.) yields could assist producers in forecasting forage availability for the coming season and in deciding whether to replace an existing stand. A mathematical model using over‐winter (fall‐winter‐spring) weather variables was developed for predicting first‐cut yield of moderately winter‐hardy alfalfa cultivars. Models for the second year, third year, and combined second and third years after seeding were developed using regression analysis of first‐cut yields on antecedent over‐winter weather data from East Lansing, MI, between 1972 and 1988. Models explained 76% of variability for second year data, 69% for third year data, and 65% for combined data. The three models used either winter temperature cycles (based on fluctuations of daily mean temperature) or winter degree days and spring growing degree days to predict yield. Winter temperature cycles and winter degree days were negatively correlated with yield while spring growing degree days were positively correlated with yield. Validation predictions averaged ±15% of measured yields in 1989 to 1992 and 33% above measured yields in 1993 when conditions not included in the models (i.e., extremely wet fall and spring soil) were major yield determinants. Models like those developed in this study may provide an alfalfa management tool for producers, extension workers, and consultants. These models also may improve the simulation of winter injury in existing alfalfa growth models. Care must be exercised, however, in using any model developed at a single location to predict crop yields in other (especially dissimilar) locations. Research Question In areas of the northern USA where alfalfa stands may be injured by severe winters, the ability to estimate spring alfalfa yield would be helpful to farmers for estimating their potential forage shortfalls or to decide whether to maintain or replace an injured stand. Predictive models of first‐cut yield based on preceding over‐winter weather would be useful to farmers facing these decisions. In this study, models were developed for predicting first‐cut yield of moderately winter‐hardy alfalfa in the second and third years after seeding. The relative importance of over‐winter (fall‐winter‐spring) weather variables to first‐cut yield was evaluated as part of the model development process. Models were validated using an independent data set. Literature Summary Identified causes of alfalfa winter injury include temperature fluctuations, lack of snow cover, and persistent ice sheeting. Fluctuating temperatures are detrimental when warm temperatures cause over‐wintering alfalfa to initiate growth too early for normal spring development. Heaving injury, caused when plants are lifted from the soil by frost, also is associated with winter temperature fluctuations. Snow cover usually is beneficial, providing insulation from fluctuating and low temperatures. Ice sheeting may cause plants to smother in metabolic byproducts or to be injured by low temperatures due to the low insulation value of ice. Weather preceding and following winter may condition plants for, or promote recovery after, a severe winter. A previously reported study evaluated the relative importance of over‐winter variables to first‐cut yield in a three‐cut system in Minnesota. In that study, over‐winter weather variables relating to solar radiation, temperature, and precipitation summarized for discrete periods accounted for 40 to 50% of yield variability. Study Description First‐cut yields of moderately winter‐hardy alfalfa cultivars in a four‐cut system between 1972 and 1988 were obtained from Michigan State University variety trials in East Lansing, for model development. East Lansing yield data from 1989 to 1993 were used for validation. Weather data were obtained from the Michigan State University weather station. Multiple regression analysis of first‐cut yield on weather variables was used to develop predictive equations. The following prediction model was developed for the second and third years after seeding: YLD = 3.10 − 0.100 WTC 31,6 + 0.00122 SGDD 38.5 − 0.00703 HDAYS where YLD = tons dry matter per acre in first cutting of second or third year after seeding. WTC 31,6 = winter temperature cycles, where one‐half WTC was counted for each time the mean daily air temperature rose above or fell below 31 °F. Cycles were counted on days when snow depth was less than 6 in., beginning with the first occurrence of a minimum daily air temperature of 26 °F or less (late October) through 14 April. SGDD 38,5 = spring growing degree days above 38.5 °F accumulated from 15 April to first‐cut. HDAYS = days in hardening period, i.e., from the first occurrence of maximum daily air temperature of 59 °F or less (mid‐September) until first occurrence of a mean daily air temperature of 26 °F or less (late October). Applied Question Can first‐cut alfalfa yield be explained based on over‐winter weather conditions? Yes. In this study, 65% of variability in first‐cut yields in the second and third years after seeding was explained by the model. What over‐winter weather variables were most important in explaining first‐cut alfalfa yield? Winter temperature cycles (i.e., number of times the mean daily air temperature rose above and fell below 31 °F on days with less than 6 in. snow cover) and spring thermal day accumulation (i.e., growing degree days above a 38.5 °F base) were the most important over‐winter weather variables affecting variability of first‐cut yield of moderately winter‐hardy alfalfa cultivars in the second and third years after seeding. First‐cut yield was negatively affected by winter temperature cycles and positively affected by thermal day accumulations in the spring. Minimum daily winter temperature did not reduce first‐cut yields. How might this model provide timely, useful information for forage producers? Based on relationships observed in this study, a producer could predict the effect of winter injury on first‐cut yield by 15 April. For example, let's say that a producer counted 20 winter temperature cycles by 15 April. (The average winter during model development had 12.6 winter temperature cycles. Therefore, in this example winter, there are 7.4 more winter temperature cycles than average.) According to the relationship of −0.1 tons/acre per winter temperature cycle shown in the prediction equation, an additional 7.4 winter temperature cycles would be expected to decrease yield by 0.74 tons. With such a prediction, a forage producer would be better able to prepare for forage shortfalls or to decide whether to maintain or replace an injured stand. What are the model's limitations? Yield may be affected, either negatively or positively, by factors not considered by the model. When this happened with yield‐decreasing anaerobic soil conditions in fall 1992, the model overpredicted yields by 44%. Weather data may not be readily accessible. Applied Question Known and suspected relationships between over‐winter weather variables and first‐cut alfalfa yield were confirmed and quantified in this study. A clear negative relationship between fluctuating winter temperature and first‐cut yield was established. Based on this study, farmers should be prepared for forage yield decreases following over‐winter exposure of alfalfa to greater than normal temperature fluctuations when snow cover is less than 6 in. deep.