Using Growing Degree Days to Predict Harvest Timing of

ReferencesFick, G.W., P.W. Wilkens, and J.H. Cherney. 1994.Modeling Forage Quality Changes in the GrowingCrop. In: Forage Quality, Evaluation, and Utilization.American Society of Agronomy, Madison, WI.

Joosse, D. 1997. Manitoba Green Gold - An extensionproject linking high quality hay with high profits.Proceedings of the XVII International GrasslandCongress.

Turnbull, G.W., D.W. Claypool, and E.G. Dudley.1982. Performance of lactating cows fed alfalfa haysgraded by relative feed value system. J. Dairy Science.65: 1205-1211

Shaykewich, C.F. 2000. Estimating relative feed valueof alfalfa from GDD accumulation. Unpublished data.

Using Growing Degree Days to Predict Harvest Timing of First Cut AlfalfaD.Green1, A.Nadler2, and M.Walsh1

1Soils & Crops, 65-3rd Avenue, Carman MB R0G 0J0 Email: dogreen@gov.mb.ca; 2Agrometeorological Centre of Excellence, 40-2nd St NE, Carman MB R0G 0J0 Email: anadler@aceweather.ca

ObjectiveDevelop a model to predict the standing crop relativefeed value of pure alfalfa stands in real time soproducers can gauge when field clippings and harvestoperations are to commence.

What is a Growing Degree Day?• Growing Degree Days (GDD) are a commonly used

measurement of heat unit accumulation.• The model uses Tmax, Tmin and a crop specific base

temperature of 5oC to calculate the dailyaccumulation of heat units.

• In this context GDD is used as an index of cropdevelopment.

• Example: (25+8)/2 - 5 = 11.5 GDD

Advantages of GDD• Accounts for year to year variation in weather• Accounts for regional differences in weather• Effective for providing advance notice of alfalfa quality• Reduce analysis costs of scissors-clipped sampling

Year to Year Variation Regional Variability

2000 Results by Date GDD Accumulation - 2000

www.aceweather.ca

MoosehornMoosehorn140140

CarmanCarman122122 St. MaloSt. Malo

136136SnowflakeSnowflake172172

MoosehornMoosehorn188188

CarmanCarman147147 St. MaloSt. Malo

207207SnowflakeSnowflake181181

MoosehornMoosehorn211211

CarmanCarman174174 St. MaloSt. Malo

188188

What was Learned in 2001In 2001 data was accumulated from several dates tocompare with RFV prediction through the GDD model.Figure 6 demonstrates a comparison of the regressionequation after one outlying point was removed (n =100). This comparison would indicate that the modelused in 2001 had a tendency to over-predict in-fieldRFV, which is consistent with field observations inFigure 5. Figures 7 and 8 compare error andpredictions of RFV using the two regression modelslisted.

Program in 2001In the 2001 growing season the RFV model was introduced by ACE as a product under development (Figure 5). Mapswere produced twice per week and provide the most current information available. RFV forecasting was provided in15 RFV point increments to account for variability which had been observed previously (Fick et al, 1994). Due to anexpected 15-20 point decline from the time the crop is cut until it is harvested, it is recommended that harvestoperations commence when in-field RFV is 15-20 points above the forage quality RFV production goal.

b) Impact of Alfalfa Quality onMilk Production

Alfalfa fed at 45% of dairy ration(Source: Journal of Dairy Science, 1982)(Turnball et al, 1982)

a) Impact of Forage Quality onBeef Production

c) Forage Quality Needs of Cattle

IntroductionRelative Feed Value (RFV) is the industry acceptedindex of forage quality in alfalfa. Based on the AcidDetergent Fibre (ADF) and Neutral Detergent Fibre(NDF) content of the forage, RFV is an indicator of bothforage digestibility and expected dry matter intake.Positive correlation of beef and dairy production withRFV have been documented [Figure 1(a) and (b)].

Optimal forage quality varies with the target market thatalfalfa production is focused on [Figure 1(c)].

The goal of timing alfalfa cuts is to ensure that foragequality and production match the productionrequirements of the farm [Figure 1(d)].

BackgroundThe Green Gold program was a successful extensionprogram that assisted producers with understanding theoptimum cutting time for their alfalfa crops (Joosse,1997). Through this program, regional and year to yearvariability was observed, leading to the development ofa real time index to predict in-field forage quality.

The development of the Agrometeorological Centre ofExcellence (ACE) has resulted in the opportunity todevelop a model for use in conjunction with a weathernetwork across Manitoba.

ConclusionsThe RFV model for real time prediction of foragequality of alfalfa stand has potential to provideinformation on standing crop quality of alfalfa. Withincreasing model precision error rates can reachacceptable levels (Table 1). Results are supported byequations developed by Shaykewich (2000). The latestmodel will predict the correct RFV within + 16 RFVpoints, 66% of the time. Further work will refine themodel by assessing the accuracy of the near infraredtest used to measure field samples.

AcknowledgementsThanks are extended to Covering New Ground, theManitoba Forage Council, and ACE for their support ofthis project. Thanks also to: John McGregor(Steinbach), Mark Sloane (Pilot Mound), Ray Bittner(Ashern), Stephanie Jersak (The Pas), Dan Roche(Fisher Branch), Shane Dobson (Melita), Earl Hjelte(Carman), Kira Rowat (St. Pierre) and Brian Nedohin(Morden).

d) Influence of weekly cut on alfalfayield & quality at Carman in 2000

Figure 4. Influence of site specific GDD on scissors-clipped RFV of alfalfa at 5 locations in Manitoba, 2000.Mean + 95% confidence interval.

Figure 3. Correlation of RFV with GDD at a number oflocations in Manitoba in 1999 and 2000.

Figure 2. Variability of optimal cutting date based onregional and annual variability and accumulation ofGDD at selected sites in 2000.

RFV - 0.0004 (GDD)^2 - 0.5703 (GDD) + 337.8 Mean 16.3 15 11S.D. 16.3 15.6 16.3

RFV - 0.0006 (GDD)^2 - 0.7447 (GDD) + 358.2 Mean 1.25 -1 -4S.D. 16.1 16 16.1

Quadratic Tested Error

Day Before Day of Day After

GDD Used in Quadratic Relativeto Day of Clipping

Figure 8

Figure 7

Amount of Error in RFV Points Model: RFV = 0.004 (GDD)^2 - 0.5703 (GDD) +337.8

Amount of Error in RFV Points Model:RFV = o.oo6 (GDD)^2 - 0.7447 (GDD) +358.17

MoosehornMoosehorn130130

CarmanCarman116116 St. MaloSt. Malo

132132SnowflakeSnowflake165165

Figure 1. Forage quality effects on livestockproduction and agronomic effects of cutting dateon alfalfa production.

Figure 5. Four weeks of RFV prediction maps and actual field measurements of RFV at selected locations in 2001.

Table 1. Mean and standard deviation of error(Predicted-Actual) of RFV for 3 regression formulaeand 3 dates of prediction relative to day of sampling.

Figure 6. Correlation of RFV with GDD at anumber of locations in 2001 (n = 100).