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7/23/2019 Philip Techniques Powerpoint

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Comparison of modeling techniquemilk-production forecasting

M. D. Murphy ,* M. J. OMahony , L. Shalloo ,* P. French ,* and

J. Upton * 1 *


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ABSTRACTThe objective of this study was to assess the

suitability of 3 different modeling techniques for tprediction of total daily herd milk yield from a her140 lactating pasture-based dairy cows over varyingforecast horions!

" nonlinear auto-regressive model with e#ogenousa static artificial neural network$ and a multiple lregression model were developed using 3 yr of histormilk-production data!


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INTRODCTION%ilk production from pasture-based dairy cows

susceptible to variation due to seasonality ofpasture production &"dediran et al!$ '01'($ graiconditions &)audracco et al!$ '01'($ disease&*ollard et al!$ '000($ nutritional interventions

&+olver and %uller$ 1,,($ and other disturbances&.lori et al!$ 1,,,/ Tekerli et al!$ '000(!


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INTRODCTION

The aim of this study was to assess thesuitability of a static neural network &SANN), a model, and a nonlinear auto regressive model withe#ogenous input &NARX) for the prediction of totaldaily herd milk yield &DHMY) over varying forecas

horions!The most successful model was selected accordi

to its abilities to generate the most accurateforecast using very limited training data in lowvolumes over a long- &30 d($ medium- &30 to 0 dand short-term &10 d( horion!


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!aterials and methodata

*ollection 1! ata were collected from a research farm in thesouth of 2reland for a period of 4 yrs &'00'010(!

'! aily herd milk yield &liters( and number of cowsmilked on that corresponding herd 2% was collected!

3! %ilk yields were recorded from a conventionalherringbone swingover milking parlor using 2nternation*ommittee for "nimal 5ecording approved milk meters!

4! The model was set at herd level and evaluated bycomparing daily milk yields across a herd of 140 pastubased 6olstein-7riesian cattle!

! The milking season of '010 was selected as thetarget prediction horion and the previous 3 yr of dat

were used to train the model!


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!aterials and method

%odel 2nputs2n previous studies certain variables were found to

have an influence on milk production8 season of calvingclimatic conditions$ number of 2%$ and stocking rate!

2n this study the farm graing area remained statiwhereas the number of cows graing varied throughout t

year! 9imilarly$ the season of calving &spring( was keptconstant in the herd over several years!

6ence the total herd milk production behaves in acyclical pattern!


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:eural:etworks"n ":: is a mathematical model whose operating

principle is based on biological neural networks!

The ":: architecture comprises a series ofinterconnected layered neurons through which inputs arprocessed!

These inputs values are multiplied by the synaptiweights$ which represent the strength of the neuralconnections!


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%ultiple ;inear 5egression %odel

;inear regression models characterie theassociation between a dependent and independentvariable!

The relationship between these variables can bee#pressed in a '-dimensional space!

6owever$ few outputs can be accurately profiledusing just one input!

%ost real world systems are controlled by multipleinputs!


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Result

Totalied forecast errors of the 3 models over thecycle with 4 different piecewise horions ranging fromd!


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Discussion

%odel model to correct its projected trajectory over thne#t horion based on information from the previoushorion!


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Discussion

%odel


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Discussion

%odel


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Discussion

%odel


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Conclusion

The :"5> was shown to be the most effective milk-production model! it was moreaccurate than the 9":: anfor each moving horion and over the majority of indivprediction ranges &3!? of ranges(!

7rom this research$ the :"5> model appears to be aalternative to conventional regression models!


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thanks"B#$ Carl %hilip Bumanlag

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