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Precision Feeding Dairy Cow Pens by Predicting and Formulating for the Distribution of Dry Matter Intake /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Precision Feeding Dairy Cow Pens by Predicting and Formulating for the Distribution of Dry Matter Intake // Padraig Lucey.
Author:	Lucey, Padraig,
Description:	1 electronic resource (119 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Animal sciences. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31558337
ISBN:	9798384482956

Precision Feeding Dairy Cow Pens by Predicting and Formulating for the Distribution of Dry Matter Intake /
Lucey, Padraig,

Precision Feeding Dairy Cow Pens by Predicting and Formulating for the Distribution of Dry Matter Intake /Padraig Lucey. - 1 electronic resource (119 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

Dairy cows are managed and fed in pens. These pens are constructed for uniform handling and management, and cluster cows on stage of lactation, reproduction status, or milk production. The variation in nutrient requirement of the cows is not accounted for, and different distributions of dry matter intake (DMI) occur depending on cow selection. These pens are fed by a ration supplied collectively. This ration is solved to meet a nutrient requirement and scaled to the number of cows in the given pen. But the solution cannot account for within pen variation because DMI is not individually recorded. The common measurement is the average nutrient or DMI per cow, dividing the quantity of ration consumed by the number of cows present. Another approach is predicting a DMI value for a hypothetical mean cow to represent the pen. Both systems result in one DMI value, rather than a distribution. The only scenario where this would be considered precise or accurate nutrition is if the cows within the pen were completely uniform in their requirements. Failing this, if the distribution of the pen were normal, and the tails symmetrical, the lower requirement cows could have their feed consumed by the higher requirement cows, striking a crude balance.The first chapter of this work examined this assumption. Its hypothesis was the DMI of cow pens are not normally distributed, and the total pen DMI can be better calculated by describing it with an appropriate distribution shape. Cow DMI was first described by week of lactation for skew and kurtosis, then a large dataset of cow pens representing Fresh, High, and Low milk yield lactation stages were assembled from individual observations. The distribution of DMI for each pen was fit to the best distribution shape, then the total pen DMI was calculated from the area under its curve. This was compared to a second model that calculated the total DMI by applying an empirical equation to the pen's mean values. The Beta distribution shape was the most common best fit of all pens, and the distribution shape predicted pen DMI with less than 1 % error, and more accurately than the comparison model, with an error of 11 - 22 %.The second chapter predicted the distribution shape of DMI. In chapter I the distribution was demonstrated as a good calculation of pen DMI, but the shape is only known when described from individual DMI values. To build a prediction model in the case of unknown individual DMI, we trained machine learning algorithms to a known dataset. A large dataset of pens was assembled, and these were described with their best fit distribution, either the Beta or Generalized Normal distribution shape. Machine learning algorithms were trained to classify these pens to their best shape and predict DMI values that fit the distribution; the Distribution Shape Model (DSM). A second model was contrasted to this that predicted pen DMI by applying the mean descriptive statistics of the pen to an empirical equation, the NASEM equation model (NSM). Both models were validated for their DMI prediction performance on a naive dataset and compared by model diagnostics. The DSM was very precise and accurate, with low error, or bias, and outperformed the NSM in consistently predicting pen DMI.The last chapter of this work performed precision nutrition with the distribution shape of DMI for ration formulation. Three pens were considered: Fresh, High and Low. Each pen had its DMI predicted by the DSM, and by applying an empirical equation to each cow's characteristics, individual NASEM equation model (iNSM). These DMI values were used to formulate individual rations for every cow, and compared by cost, DMI, and metabolizable energy. As individual rations cannot be practically applied on dairies, precision nutrition pen rations were also solved. Each pen had a nutrient requirement calculated by summing its constituent cow's requirements, and rations were solved with these totals. The DSM and the iNSM were compared to an ideal ration solved to the observed DMI values (TRU) to evaluate these two precision nutrition approaches. Two imprecise approaches were also considered. One where an empirical equation was applied to the mean of each pen, the average NASEM equation model (aNSM), and one where it was applied to the 75th of the pen, the adjustment factor NASEM equation (fNSM). In the individual, and pen ration solutions, the DSM was the only model that was not significantly different to the TRU. It was also cheaper and less nutrient wasting than all other ration models, as its solution was almost identical to the TRU for individual cows, and pen rations.

English

ISBN: 9798384482956Subjects--Topical Terms:

3174829
Animal sciences.
Subjects--Index Terms:

Dairy cattle

Precision Feeding Dairy Cow Pens by Predicting and Formulating for the Distribution of Dry Matter Intake /
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520 $a Dairy cows are managed and fed in pens. These pens are constructed for uniform handling and management, and cluster cows on stage of lactation, reproduction status, or milk production. The variation in nutrient requirement of the cows is not accounted for, and different distributions of dry matter intake (DMI) occur depending on cow selection. These pens are fed by a ration supplied collectively. This ration is solved to meet a nutrient requirement and scaled to the number of cows in the given pen. But the solution cannot account for within pen variation because DMI is not individually recorded. The common measurement is the average nutrient or DMI per cow, dividing the quantity of ration consumed by the number of cows present. Another approach is predicting a DMI value for a hypothetical mean cow to represent the pen. Both systems result in one DMI value, rather than a distribution. The only scenario where this would be considered precise or accurate nutrition is if the cows within the pen were completely uniform in their requirements. Failing this, if the distribution of the pen were normal, and the tails symmetrical, the lower requirement cows could have their feed consumed by the higher requirement cows, striking a crude balance.The first chapter of this work examined this assumption. Its hypothesis was the DMI of cow pens are not normally distributed, and the total pen DMI can be better calculated by describing it with an appropriate distribution shape. Cow DMI was first described by week of lactation for skew and kurtosis, then a large dataset of cow pens representing Fresh, High, and Low milk yield lactation stages were assembled from individual observations. The distribution of DMI for each pen was fit to the best distribution shape, then the total pen DMI was calculated from the area under its curve. This was compared to a second model that calculated the total DMI by applying an empirical equation to the pen's mean values. The Beta distribution shape was the most common best fit of all pens, and the distribution shape predicted pen DMI with less than 1 % error, and more accurately than the comparison model, with an error of 11 - 22 %.The second chapter predicted the distribution shape of DMI. In chapter I the distribution was demonstrated as a good calculation of pen DMI, but the shape is only known when described from individual DMI values. To build a prediction model in the case of unknown individual DMI, we trained machine learning algorithms to a known dataset. A large dataset of pens was assembled, and these were described with their best fit distribution, either the Beta or Generalized Normal distribution shape. Machine learning algorithms were trained to classify these pens to their best shape and predict DMI values that fit the distribution; the Distribution Shape Model (DSM). A second model was contrasted to this that predicted pen DMI by applying the mean descriptive statistics of the pen to an empirical equation, the NASEM equation model (NSM). Both models were validated for their DMI prediction performance on a naive dataset and compared by model diagnostics. The DSM was very precise and accurate, with low error, or bias, and outperformed the NSM in consistently predicting pen DMI.The last chapter of this work performed precision nutrition with the distribution shape of DMI for ration formulation. Three pens were considered: Fresh, High and Low. Each pen had its DMI predicted by the DSM, and by applying an empirical equation to each cow's characteristics, individual NASEM equation model (iNSM). These DMI values were used to formulate individual rations for every cow, and compared by cost, DMI, and metabolizable energy. As individual rations cannot be practically applied on dairies, precision nutrition pen rations were also solved. Each pen had a nutrient requirement calculated by summing its constituent cow's requirements, and rations were solved with these totals. The DSM and the iNSM were compared to an ideal ration solved to the observed DMI values (TRU) to evaluate these two precision nutrition approaches. Two imprecise approaches were also considered. One where an empirical equation was applied to the mean of each pen, the average NASEM equation model (aNSM), and one where it was applied to the 75th of the pen, the adjustment factor NASEM equation (fNSM). In the individual, and pen ration solutions, the DSM was the only model that was not significantly different to the TRU. It was also cheaper and less nutrient wasting than all other ration models, as its solution was almost identical to the TRU for individual cows, and pen rations.
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