Phil Garnsworthy

Prof. Phil Garnsworthy

orcid

Research Focus & Metrics

Academic Career Length

45Years (1982-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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5 July 2019 in Science Advances
Verified
Journal Article
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RWR. John Wallace
R. John Wallace
GSGoor Sasson
Goor Sasson
PGPhil Garnsworthy
Phil Garnsworthy
ITIlma Tapio
Ilma Tapio
EGEmma Gregson
Emma Gregson
IMItzhak Mizrahi
Itzhak Mizrahi
Cow genes dictate environmentally friendly microbiomes: a step towards reducing greenhouse gas emissions.

16 February 2018 in Global Change Biology
Verified
Journal Article
Open access
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MNMutian Niu
Mutian Niu
EKErmias Kebreab
Ermias Kebreab
AHAlexander N. Hristov
Alexander N. Hristov
JOJoonpyo Oh
Joonpyo Oh
CAClaudia Arndt
Claudia Arndt
ZYZhongtang Yu
Zhongtang Yu
Enteric methane (CH4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

1 March 2004 in Livestock Production Science
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Journal Article
Closed access
JPJ.E. Pryce
J.E. Pryce
MRM.D. Royal
M.D. Royal
PGPhil Garnsworthy
Phil Garnsworthy
IMI.L. Mao
I.L. Mao

14 September 2018 in Nature Sustainability
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ABAndrew Balmford
Andrew Balmford
TATatsuya Amano
Tatsuya Amano
HBHarriet Bartlett
Harriet Bartlett
DCDave Chadwick
Dave Chadwick
Adie CollinsAdie Collins
Adie Collins
RERowan Eisner
Rowan Eisner

1 January 2003 in Livestock Production Science
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Journal Article
Closed access
ALA.L Lock
A.L Lock
PGPhil Garnsworthy
Phil Garnsworthy

1 August 1982 in Animal Science
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PGPhil Garnsworthy
Phil Garnsworthy
JTJ. H. Topps
J. H. Topps
In two trials, three groups of eight dairy cows were fed for 2 months before calving to achieve condition scores at calving of 1·5 to 2 (low), 2·5 to 3 (medium) and 3·5 to 4 (high). For 16 weeks after calving, all cows were given a complete diet composed of silage, malt distillers' grains, barley, swedes and soya bean meal (metabolizable energy 12·25 and 12·35 MJ/kg dry matter in trials 1 and 2 respectively).In both trials, significant differences were found between groups in dry-matter intake after calving. Cows that had higher condition scores at calving ate less and reached maximum intake of dry matter later than cows with lower condition scores (high, medium and low condition-score cattle attained maximum intakes in 14·5, 12·7 and 9·3 weeks in trial 1, and in 141, 9·6 and 7·9 weeks in trial 2). In trial 1, no difference was found in milk yield but, in trial 2, cows that had lower condition scores at calving produced slightly more milk. In both trials, cows that had higher condition scores at calving lost more body weight and condition, over a longer period, and started to regain the losses later than cows with lower condition scores. The biological efficiencies of milk production (energy output/energy input) from 8 weeks before calving until 16 weeks after calving were 0·302, 0·299 and 0·295 in trial 1, and 0·312, 0·290 and 0·306 in trial 2, for the low, medium and high groups respectively.It was concluded that cows with lower condition scores at calving produced more milk directly from food rather than via body fat, were in positive energy balance earlier in lactation and over the total period were biologically more efficient than cows with higher condition scores. There appears to be no benefit from feeding cows to achieve a condition score greater than 1·5 to 2 at calving if high-energy complete diets are offered in early lactation.