Alvaro Romera

Dr. Alvaro Romera

orcid

Research Focus & Metrics

Academic Career Length

29Years (1998-Present)

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

Representative Publications

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1 March 2010 in Agriculture, Ecosystems & Environment
Verified
Journal Article
Closed access
PBPierre Beukes
Pierre Beukes
PGPablo Gregorini
Pablo Gregorini
ARAlvaro Romera
Alvaro Romera
GLG. Levy
G. Levy
GWG.C. Waghorn
G.C. Waghorn

1 February 2004 in Computers and Electronics in Agriculture
Verified
Journal Article
Closed access
ARAlvaro Romera
Alvaro Romera
SMS.T Morris
S.T Morris
JHJ Hodgson
J Hodgson
WSW.D Stirling
W.D Stirling
SWS.J.R Woodward
S.J.R Woodward


1 January 2012 in Journal of Environmental Management
Verified
Journal Article
Closed access
PBPierre Beukes
Pierre Beukes
MSM.R. Scarsbrook
M.R. Scarsbrook
PGPablo Gregorini
Pablo Gregorini
ARAlvaro Romera
Alvaro Romera
DCD.A. Clark
D.A. Clark
WCW. Catto
W. Catto

1 September 2009 in Journal of Dairy Science
Verified
Journal Article
Open access
PDF file
PGPablo Gregorini
Pablo Gregorini
CCCameron Clark
Cameron Clark
JJJ.G. Jago
J.G. Jago
CGC.B. Glassey
C.B. Glassey
KMK.L.M. McLeod
K.L.M. McLeod
ARAlvaro Romera
Alvaro Romera
This study investigated the effect of restricting grazing time on circulating concentrations of ghrelin, nonesterified fatty acids (NEFA), and glucose before, and foraging behavior of dairy cows during, the first grazing session of the day (GS, 0800-1200 h). Forty-eight Holstein-Friesian cows (470 +/- 47 kg of BW; 35 +/- 9 d in milk) were strip-grazed on a perennial ryegrass pasture for either 4 h after each milking (2 x 4), 8 h between milkings (1 x 8), or the 24-h period excluding milking times (CTL). Cows were bled before the GS; plasma was analyzed for ghrelin and serum for glucose and NEFA. Herbage mass was measured pregrazing (0730 h), during and at the end of the GS (1200 h), and postgrazing (24 h after the first measurement). Herbage mass data were fitted to a model to estimate herbage disappearance rates. Herbage intake and bite mass were calculated using herbage mass disappearance and behavioral measurements. Bite rate, eating, searching, ruminating, and idling time were determined during the GS for each cow. No difference in glucose concentration was found between treatments. Concentrations of NEFA and ghrelin were the greatest for cows in the 1 x 8 treatment. Daily herbage intake did not differ between treatments; however, during the GS 1 x 8 had a greater herbage intake than 2 x 4 and CTL. Bite mass differed between treatments and throughout the GS. Bite mass was smallest for CTL during the first 60 min and greatest during the last 90 min, when cows in the 2 x 4 treatment had the smallest bite mass. Cows in 1 x 8 spent the longest time eating and the least time searching and ruminating. Eating time was greatest for 1 x 8 during the first 60 and last 90 min of the GS. Searching time only differed in the second 60 min, when it was the lowest for 1 x 8. Cows from all treatments did not ruminate during the first 120 min. Cows in CTL had the greatest rumination time during the last 90 min. The model fitted to represent dynamics of herbage mass disappearance presented differences in the fractional herbage disappearance rate. There was an interaction between treatment and time in herbage depletion rate. The results of this study present a fuller picture of foraging dynamics during the first 4 h of grazing and its potential relationship with physiological markers of hunger as affected by grazing management.

2 January 2018 in Animal Production Science
Verified
Journal Article
Closed access
PDF file
PBPierre Beukes
Pierre Beukes
SMS. McCarthy
S. McCarthy
CWC. M. Wims
C. M. Wims
PGP. Gregorini
P. Gregorini
ARAlvaro Romera
Alvaro Romera
Paddock selection is an important component of grazing management and is based on either an estimate of herbage mass, or the interval since last grazing for each paddock. Obtaining estimates of herbage mass to guide grazing management can be a time consuming task. A value proposition could therefore assist farmers in deciding whether to invest resources in obtaining such information. A farm-scale simulation exercise was designed to estimate the effect of three levels of knowledge of individual paddock herbage mass on profitability of two typical pasture-based dairy systems in New Zealand; a medium input system stocked at 3.2 Friesian-Jersey cross bred cows/ha with ~15% imported feed, and a high input system stocked at 4.5 Friesian cows/ha with ~40% imported feed. The three levels of knowledge were: (1) ‘perfect knowledge’, where herbage mass per paddock is known with perfect accuracy, (2) ‘imperfect knowledge’, where herbage mass per paddock is estimated with an average error of 15%, (3) ‘low knowledge’, where herbage mass is not known, and paddocks are selected based on longest time since last grazing. In both systems, grazing management based on imperfect knowledge increased farm operating profit by ~NZ$385/ha at a milk price of NZ$6.33/kg milksolids (fat + protein) compared with low knowledge. Perfect knowledge added a further NZ$155/ha to profit. The main driver of these results is the level of accuracy in daily feed allocation, which increases with improved knowledge of herbage availability. This allows feed supply and demand to be better matched, resulting in less incidence of under- and over-feeding, higher milk production, and more optimal post-grazing residual herbage mass to maximise herbage regrowth.