Modeling the Influence of Cattle Management on Dry Matter Intake
Rick Grant*, Tom Tylutki†, and Peter Krawczel*
*William H. Miner Agricultural Research Institute, Chazy, NY
and†AMTS LLC, Cortland, NYPresented at 2010 ADSA/ASAS Conference, Denver, CO
William H. Miner AgriculturalResearch Institute
Predicting dry matter intake in cattle
Accurate prediction of DMI is key component of nutrition models Body weight, milk yield, stage of lactation
DMI predictions can be improved by including measures of physical and social environment
Physical Environment Social Environment
Resting
FeedingMeals
Meal lengthEating rate
Dry Matter Intake
Gut Fill
ChemostaticMechanisms
Control
Modulation
Feeding Environment
Physical environment inputs:Example CNCPS (Fox et al., 2004)
Temperature, relative humidity Current and previous
Wind speed Hours in sunlight Mud depth in lot Activity level Time standing Distance walked Potential to improve these inputs
Focus: Social Environment
When cattle are grouped, social behavior modifies DMI & productivity (Grant and Albright, 2001)
Future modeling efforts should focus on: Social factors of greatest importance to
feeding behavior stocking density grouping strategy interaction with physical environment
Stocking Density and Stocking Density and Behavioral Behavioral ResponsesResponses
Stocking density and cattle behavioral response
As stocking density increases: Greater frequency of aggressive interactions More displacements; altered time of feeding Faster eating rate Reduced latency to lie down Less lying time More standing in alley Decreased rumination activity
Question: What is effect on meal patterns, DMI, and other responses?
Stocking rate data base:lactating dairy cattle
14 studies that measured feeding behavior as well as DMI
TMR feeding and free-stall Pen and individual feeding studies Stocking density imposed on feed space
only or feed and free-stalls Feeding system varied by study
Feed bins Headlocks Post and rail
Greater feeding time ≠ Greater dry matter intake
Feeding time poorly correlated (r = 0.18) with total daily DMI (Kauffman et al., 2007)
Constitutes a major constraint on studies that only measure feeding behavior for quantitatively modeling DMI
Stocking density and DMI
y = 5.5x + 18.0R2 = 0.05
0
5
10
15
20
25
30
35
40
0 0.2 0.4 0.6
Manger space (m/ cow)
DM
I (k
g/d)
Weak short-term relationship between stocking density or manger space and DMI
Stocking density and eating rate
y = -80.9x + 134.5R2 = 0.43
40
60
80
100
120
140
160
0 0.2 0.4 0.6
Manger space (m/ cow)
Eati
ng r
ate
(g D
M/m
in)
Eating rate increases with increased stocking density, reduced feeding space
Stocking density and meals per day
y = 69.9x2 - 61.8x + 21.1R2 = 0.86
02468
101214161820
0 0.2 0.4 0.6
Manger space (m/ cow)
Meals
(n/d)
More meals, especially below ~0.4 m/cow (16 in)
Stocking density and meal size
y = -15.8x2 + 14.2x - 0.73R2 = 0.44
0123456789
10
0 0.2 0.4 0.6
Manger space (m/ cow)
Meal si
ze (
kg D
M/m
eal)
Smaller meal size, especially below ~0.40 m/cow
Stocking density and eating time
y = -625.6x2 + 601.5x + 105.3R2 = 0.42
100
150
200
250
300
350
400
450
500
0 0.2 0.4 0.6
Manger space (m/ cow)
Eati
ng t
ime (
min
/d)
Stocking density, grouping strategy, & DMI Group to increase homogeneity Primi- vs multiparous cows
DMI reduced by 10% Resting reduced by 20% Milk reduced by 9% (Kongaard and Krohn, 1980) Greater loss of BW by 30 DIM Reduced FCM/DMI by 30 DIM (Bach et al., 2006) Less drinking, rumination, and milk fat % (Bach
et al., 2007)
Interaction with stocking density?
Stocking density and DMI by parity in mixed
groups
Interaction between parity and stocking density
Component of future models
y = -90.9x2 + 109.0x - 8.6R2 = 0.85
y = -64.2x2 + 68.8x + 6.7R2 = 0.82
15
17
19
21
23
25
27
29
0.3 0.4 0.5 0.6
Manger space (m/ cow)
Dry
matt
er
inta
ke (
kg/d)
MP
PP
Feeding environment: defined by social and physical environment
Typical feeding environment in US based on recent surveys: 3-row pens > 2-row pens Once daily feeding > multiple deliveries Post & rail > headlocks ~18 in/cow bunk space Feed push-up ~4 to 6x/d Feed refusal rate ~3.5% > clean bunk Mixed > group by parity
Stimulating feeding behavior: Priorities for modeling feeding strategy
Feed accessibility & periods of empty bunks Feed push-up
More important during the day rather than at night (DeVries et al., 2005)
Feeding frequency, delivery of fresh feed
Biggest driver of feeding behavior is delivery of fresh feed (DeVries et al., 2003; 2005)
Feeding frequency of TMR
Reference FF/d
Eating time %
DMI%
Milk%
Rest%
DeVries et al. 1 vs 2x2 vs 4x
+3.5+4.6
-2.0-3.0
NRNR
-0.80*
Mantysaari et al. 1 vs 5x + 7.0 -4.8 -1.0 -12.1
Phillips and Rind 1 vs 4x +11.0 -6.3 -4.7 -8.6
Greater FF may improve rumen fermentation, rumination time, and eating time, but often it reduces lying time and DMI
*17% decrease in latency to lie down
Role of time budgeting in ration formulation? Appears to be a requirement for
resting/lying down 12 to 13 h/d (Munksgaard et al., 2005)
11.5, 13.5 h/d (low, high milk; Grant, 2004)
11.4 to 13.7 h/d (Cook et al., 2005; Drissler et al., 2005)
12.9 h/d (Fregonesi et al., 2007)
Inelastic demand of 12-13 h/d (heifers; Jensen et al., 2005)
Baseline requirement ~12 h/d
Resting influences feeding behavior (Munksgaard et al., 2005)
Lying time has priority over eating when measured at all stages of lactation
Cows will sacrifice eating time to compensate for lost resting time
Cows may compensate for reduction in feeding time by increasing rate of feed consumption (they began to “slug-feed”) Not possible with lying behavior
Reduction in eating time with rest deprivation
Lying-deprived cows spend less time eating during period of lying deprivation & after deprivation (Cooper et al., 2007)
With situation of chronic rest deprivation, we speculate reduced eating time
Relationship between lost rest and eating time: For every 3.5 minutes of lost rest, cows sacrifice
1 minute of eating
Where could we go in next decade? Modeling approach
Time budgeting Cows have a minimum resting time
requirement Cows will adjust eating time to ensure
resting time requirement met DMI may or may not change depending
on meal size and number of meals Function of feed and feeding environment
Where do we go in next decade? Two modeling approaches
Theoretical dynamic model Non-steady state Based on relationships in data base
Simpler predictive model can be implemented in current formulation systems On-farm inputs, spreadsheet
Derived equations from database for feeding & resting can be incorporated into models
Resting time, min/d
Number of meals, n/d
Meal size, kg of DM/meal
Eating time, min/d
Eating rate, g of DM/min
DMI, kg/d
Directly and as a calculation using number of meals and meal size predictions
Resting time adjustment (%) based on feeding frequency
Resting Time, min/d
Coefficient
SEM P-value
Intercept 148.7 33.6 0.0004Bunk space, m/cow 275.4 57.0 0.0002
Base predicted DMI, kg/d
17.6 1.2 <0.001R-sq = 0.94, RMSE = 28.2
Number of meals, n/dCoefficien
tSEM P-value
Intercept 13.8 1.0<0.000
1Bunk space, m/cow 3.7 1.5 0.0349Resting time, min/d -0.012 0.002 0.0001R-sq = 0.82, RMSE = 0.6
Meal Size, kg DM/meal
Coefficient SEM P-value
Intercept 4.4 0.8 0.0007
Resting time, min/d 0.003 0 0.0018
Number of meals, n/d -0.5 0.1 <0.0001
(Meals, n/d - 8.3) x (Resting time, min/d -
591.2)-0.003 0 <0.0001
R-sq = 0.99, RMSE = 0.1
Eating time, min/dCoefficient SEM P-value
Intercept 243.2 33.6 <0.0001
Meal size, kg DM/meal -48.1 18.4 0.0204
Bunk space, m/cow 192.3 52.2 0.0024
(Bunk space, m/cow - 0.4)2 -1494.1 386.7 0.0017
(Meal size, kg DM/meal - 1.8)2 49.1 11.4 0.0008
R-sq = 0.77, RMSE = 32.2
Eating rate, g DM/min
Coefficient SEM P-value
Intercept 190.7 10.9 <0.0001
Meal size, kg DM/meal 7.0 3.5 0.0608
Bunk space, m/cow -44.4 17.1 0.0201
Eating time, min/d -0.4 0.1 <0.0001R-sq = 0.83, RMSE = 11.6
Dry matter intake, kg/d
Coefficient SEM P-value
Intercept -18.8 3.2 <0.0001
Meal size, kg DM/meal 5.5 0.6 <0.0001
Meals, n/d 1.9 0.2 <0.0001
Eating time, min/d 0.04 0.0 <0.0001
R-sq = 0.91, RMSE = 1.3
Resting time adjustment for feeding frequency, %
Coefficient
SEM P-value
Intercept 6.7 0.4 0.0363
Feeding frequency -3.8 0.1 0.0166
R-sq = 0.99 RMSE = 0.2
Looking to the future:Theoretical dynamic model
Existing components of CNCPSv6.1 used to determine initial values Cow group descriptors DMI, physical environment adjustments for DMI
Existing CNCPSv6.1 rumen sub-model Interactions between VFA production,
intake patterns, and cow health needed Incorporation of social environment
inputs needed
Vision for Dynamic Nutritional Model of the Future
Generic Overview of Stocking Rate and DM Intake Relationships
Group descriptions: calculations for basal DMI (NRC, 2001)
Adjusting basal DMI for pen physical environment
Cow time budgeting related to management, h/d
Interactions betweenresting time and eating time: most difficult to model
Impact on degradation, nutrient flows, and cow health; non-steady state
Short-term: simpler predictive model
Based on previously derived equations and relationships
Inputs easily collected on-farm Can be used to:
estimate DMI, eating behavior illustrate impact of limited bunk space
and variable feeding frequency on resting time and DMI
Excel spreadsheet implementation
Vision for nutrition models in next decade Social and physical environment define
the feeding environment that modulates DMI
Models must incorporate key inputs to predict feeding behavior and adjusted DMI
Time budget analysis (eating & resting) should become a routine part of DMI prediction and ration formulation
