Using Growing Degree Days to Predict Harvest Timing of ...
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References Fick, G.W., P.W. Wilkens, and J.H. Cherney. 1994. Modeling Forage Quality Changes in the Growing Crop. In: Forage Quality, Evaluation, and Utilization American Society of Agronomy, Madison, WI. Joosse, D. 1997. Manitoba Green Gold - An extensio project linking high quality hay with high profits. Proceedings of the XVII International Grassland Congress. Turnbull, G.W., D.W. Claypool, and E.G. Dudley. 1982. Performance of lactating cows fed alfalfa hays graded by relative feed value system. J. Dairy Scienc 65: 1205-1211 Shaykewich, C.F. 2000. Estimating relative feed valu of alfalfa from GDD accumulation. Unpublished data Using Growing Degree Days to Predict Harvest Timing of First Cut Alfalfa D.Green 1 , A.Nadler 2 , and M.Walsh 1 1 Soils & Crops, 65-3rd Avenue, Carman MB R0G 0J0 Email: [email protected]; 2 Agrometeorological Centre of Excellence, 40-2nd St NE, Carman MB R0G 0J0 Email: [email protected] bjective velop a model to predict the standing crop relative d value of pure alfalfa stands in real time so oducers can gauge when field clippings and harvest erations 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 5 o C to calculate the daily accumulation of heat units. • In this context GDD is used as an index of crop development. • 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 Moosehorn Moosehorn 140 140 Carman Carman 122 122 St. Malo St. Malo 136 136 Snowflake Snowflake 172 172 Moosehorn Moosehorn 188 188 Carman Carman 147 147 St. Malo St. Malo 207 207 Snowflake Snowflake 181 181 Moosehorn Moosehorn 211 211 Carman Carman 174 174 St. Malo St. Malo 188 188 What was Learned in 2001 In 2001 data was accumulated from several dates to compare with RFV prediction through the GDD model. Figure 6 demonstrates a comparison of the regression equation after one outlying point was removed (n = 100). This comparison would indicate that the model used in 2001 had a tendency to over-predict in-field RFV, which is consistent with field observations in Figure 5. Figures 7 and 8 compare error and predictions of RFV using the two regression models listed. Program in 2001 In the 2001 growing season the RFV model was introduced by ACE as a product under development (Figure 5). Maps were produced twice per week and provide the most current information available. RFV forecasting was provided in 15 RFV point increments to account for variability which had been observed previously (Fick et al, 1994). Due to an expected 15-20 point decline from the time the crop is cut until it is harvested, it is recommended that harvest operations commence when in-field RFV is 15-20 points above the forage quality RFV production goal. b) Impact of Alfalfa Quality on Milk Production Alfalfa fed at 45% of dairy ration (Source: Journal of Dairy Science, 1982) (Turnball et al, 1982) ) Impact of Forage Quality on Beef Production Forage Quality Needs of Cattle troduction lative Feed Value (RFV) is the industry accepted ex of forage quality in alfalfa. Based on the Acid tergent Fibre (ADF) and Neutral Detergent Fibre DF) content of the forage, RFV is an indicator of both age digestibility and expected dry matter intake. sitive correlation of beef and dairy production with V have been documented [Figure 1(a) and (b)]. timal forage quality varies with the target market that alfa production is focused on [Figure 1(c)]. e goal of timing alfalfa cuts is to ensure that forage ality and production match the production uirements of the farm [Figure 1(d)]. ackground e Green Gold program was a successful extension ogram that assisted producers with understanding the timum cutting time for their alfalfa crops (Joosse, 97). Through this program, regional and year to year riability was observed, leading to the development of eal time index to predict in-field forage quality. e development of the Agrometeorological Centre of cellence (ACE) has resulted in the opportunity to velop a model for use in conjunction with a weather twork across Manitoba. Conclusions The RFV model for real time prediction of forage quality of alfalfa stand has potential to provide information on standing crop quality of alfalfa. With increasing model precision error rates can reach acceptable levels (Table 1). Results are supported by equations developed by Shaykewich (2000). The late model will predict the correct RFV within + 16 RFV points, 66% of the time. Further work will refine the model by assessing the accuracy of the near infrared test used to measure field samples. Acknowledgements Thanks are extended to Covering New Ground, the Manitoba Forage Council, and ACE for their support this 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 alfalfa yield & 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 of locations in Manitoba in 1999 and 2000. Figure 2. Variability of optimal cutting date based on regional and annual variability and accumulation of GDD at selected sites in 2000. RFV - 0.0004 (GDD)^2 - 0.5703 (GDD) + 337.8 Mean 16.3 15 11 S.D. 16.3 15.6 16.3 RFV - 0.0006 (GDD)^2 - 0.7447 (GDD) + 358.2 Mean 1.25 -1 -4 S.D. 16.1 16 16.1 Quadratic Tested Error Day Before Day of Day A GDD Used in Quadratic Re to 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 Moosehorn Moosehorn 130 130 Carman Carman 116 116 St. Malo St. Malo 132 132 Snowflake Snowflake 165 165 igure 1. Forage quality effects on livestock roduction and agronomic effects of cutting date n 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 formula and 3 dates of prediction relative to day of sampling Figure 6. Correlation of RFV with GDD at a number of locations in 2001 (n = 100).
Transcript of 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: [email protected]; 2Agrometeorological Centre of Excellence, 40-2nd St NE, Carman MB R0G 0J0 Email: [email protected]
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).
