Using a modelling approach to evaluate two options for improving animal nitrogen use efficiency and...

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Using a modelling approach to evaluate two options for

improving animal nitrogen use efficiency and reducing nitrous oxide emissions on dairy farms in southern Australia

Journal: Animal Production Science

Manuscript ID: AN14436.R2

Manuscript Type: Research paper

Date Submitted by the Author: 18-Jul-2014

Complete List of Authors: Christie, Karen; University of Tasmania, Tasmanian Institute of Agriculture

Rawnsley, Richard; University of Tasmania , Harrison, Matthew; University of Tasmania , Eckard, Richard; The University of Melbourne, School of Land and Environment

Keyword: Dairy cows, Greenhouse Gases, Nitrous oxide, Protein supplements, Milk quality

http://www.publish.csiro.au/nid/72.htm

Animal Production Science

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Using a modelling approach to evaluate two options for improving animal nitrogen use 1

efficiency and reducing nitrous oxide emissions on dairy farms in southern Australia 2

K.M. ChristieAC, R.P. RawnsleyA, M.T. Harrison

A, R.J. Eckard

B 3

4

A Tasmanian Institute of Agriculture, University of Tasmania, Burnie, Tasmania 7320, 5

Australia 6

B Department of Agriculture and Food Systems, University of Melbourne, Victoria 3010, 7

Australia. 8

9

C Corresponding author: Tasmanian Institute of Agriculture, Private Bag 3523, Burnie, 10

Tasmania 7320, Australia. Tel: +61 3 6430 4921. Fax: +61 3 6430 4959. Email address: 11

[email protected] 12

13

Short title: Dairy animal N use efficiency & N2O emissions 14

Keywords: DairyMod, dairy cows, milk protein, modelling, supplementary feeding, whole 15

farm systems 16

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Abstract 17

Ruminant livestock are generally considered inefficient converters of dietary nitrogen (N) 18

into animal product. Animal nitrogen use efficiency (NUE) is a measure of the relative 19

transformation of feed N into product and in dairy systems this is often expressed as milk N 20

per unit of N intake (g milk N/100 g N intake). This study was a theoretical exercise to 21

explore the relative potential efficacy and value proposition of breeding versus feeding to 22

improve NUE, reduce urinary N excretion and associated environmental impact in pasture 23

based dairy systems. The biophysical whole farm systems model DairyMod was used across 24

three dairying regions of south-eastern Australia representing a high rainfall/cool temperate 25

climate (HRCT), a high rainfall/ temperate climate (HRT) and a medium rainfall/ temperate 26

climate (MRT) to examine the two theoretical approaches of: (1) maintaining the same 27

amount of N exported in milk from a reduced N intake and (2) increasing the amount of N 28

exported in milk for the same amount of dietary N intake. Sixteen scenarios were explored 29

for each site: four supplementary feed N (SN) concentrations (ranging from 1 to 4% N) 30

combined with four milk N (MN) concentrations (ranging from 0.50 to 0.65% N). Reducing 31

the SN concentration from 4 to 1% increased the 30-year mean model-predicted NUEs from 32

~ 16 g milk N/100 g N intake at all three sites to between 23 and 28 g milk N/100 g N intake, 33

with the least and greatest improvements in NUE occurring for the HRCT and MRT sites, 34

respectively. Corresponding to this improved NUE through reduced SN concentrations, 35

model-predicted N2O emissions declined from 3.0 to 1.3 t carbon dioxide equivalents (CO2-36

e)/ha.annum for the HRCT site, from 4.2 to 2.1 t CO2-e/ha.annum for the HRT site and from 37

4.4 to 2.1 t CO2-e/ha.annum for the MRT site; representing a decline of by between 50 and 38

57%. In contrast, increasing the MN concentration from 0.50 to 0.65% increased the 30-year 39

mean model-predicted NUEs from 17 to 22 g milk N/100 g N intake for the HRCT site, from 40

18 to 23 g milk N/100 g N intake for the HRT site and from 18 to 24 g milk N/100 g N intake 41

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for the MRT site. Corresponding to the improved NUE through increased MN concentrations, 42

model-predicted N2O emissions declined from 2.3 to 2.0 t CO2-e/ha.annum for the HRCT site, 43

from 3.3 to 3.1 t CO2-e/ha.annum for the HRT site and from 3.4 to 3.2 t CO2-e/ha.annum for 44

the MRT site; representing a decline of by between 7 and 11%. These results suggest that 45

improving animal NUE to reduce associated N2O losses holds much more promise if 46

achieved through a reduction in the amount of N in supplementary feed than through 47

increasing N exported in milk. This is an important finding for the Australian dairy industry, 48

since manipulation of dietary N to better balance the energy to protein ratio would be much 49

easier to implement than manipulation of N concentration in milk through genetics. 50

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Introduction 51

Ruminant livestock are generally considered limited in their ability to convert dietary N into 52

animal product, with generally less than 30% of lifetime dietary N utilised for the production 53

of meat, milk or fibre (Whitehead, 1995). Given that the concentration of N in faeces and 54

products remains relatively constant over a range of N intakes above minimum metabolic 55

requirements, excess N is predominantly excreted in urine (Whitehead, 1995). Any strategy 56

that reduces the amount of excess N being consumed and excreted by livestock will generally 57

have environmental benefits. Nitrous oxide (N2O), a greenhouse gas (GHG) with 310 times 58

the global warming potential of carbon dioxide (CO2; DCCEE, 2011), is emitted to the 59

environment through the process of denitrification, and to a lesser extent, nitrification within 60

the soil (Dalal et al., 2003; de Klein and Eckard, 2008). In addition, a proportion of N lost 61

through the volatilisation of ammonia (NH3) and the leaching/runoff of nitrate (NO3) will be 62

re-deposited onto land by rainfall or through waterways and also result in indirect N2O 63

emissions (DCCEE, 2011). 64

Animal nitrogen use efficiency (NUE) is a measure of the relative transformation of feed N 65

into product. In dairy systems, animal NUE can be expressed as milk N per unit N intake (g 66

milk N/100 g N intake). While many studies have explored the NUE of confinement feeding 67

herds (Powell et al., 2010), fewer studies have been undertaken in the pasture-based grazing 68

systems in the southern hemisphere where pasture forms a large component of the diet, with 69

supplementary feed used to fill feed deficits at times of the year when pasture supply does not 70

match animal demand (Vibart et al., 2009; Gourley et al., 2012). Gourley et al. (2011) found 71

that measured NUEs varied between 15 and 35 g N milk/ 100 g N intake across 17 farms, 72

where grazed pastures constituted the majority of the diet. They concluded that the highest 73

NUE was generally obtained at the lowest level of feed N intake. In addition, numerous 74

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reviews have highlighted that improving NUEs is one pathway to reducing N2O emissions 75

from livestock (de Klein and Eckard, 2008; Eckard et al., 2010). 76

From a practical perspective , measuring N dynamics on farm is difficult, time consuming, 77

expensive and can only consider a small combination of variables (e.g. soils, climate and 78

management for a limited timeframe) (Bryant et al. 2011; Smith and Western, 2013). 79

Dynamic biophysical models provide an alternative for exploring these interactions over a 80

longer timeframe and for varying farm management practices, climates and locations. The 81

aim of this study was to use a dynamic biophysical whole-farm system model to undertake a 82

theoretical exercise to explore the relative potential efficacy and value proposition of 83

breeding versus feeding to improve NUE, reduce urinary N losses and associated N2O 84

emissions with variations in supplementary feed N (SN) concentrations and milk N (MN) 85

concentrations for dairy farms in the temperate climates of south eastern Australia. 86

87

Materials and methods 88

DairyMod 89

The biophysical model DairyMod (version 4.8.16; Johnson et al., 2008) was used to simulate 90

a 100 ha dairy farm, with animals grazing rain-fed perennial ryegrass (Lolium perenne L.) 91

pasture sward in three south eastern regions of Australia (Elliott, -41.1°N, 145.8°E, Ellinbank 92

-38.3°N, 146.0°E and Terang -38.3°N, 142.6°E). Daily weather data for each site were 93

accessed as Patched Point Datasets from the Bureau of Meteorology SILO database (Jeffrey 94

et al., 2001). The daily weather data used were rainfall (mm), minimum and maximum 95

temperature (°C), minimum and maximum relative humidity (%), total solar radiation (MJ/m2) 96

and potential evapotranspiration (mm). Simulations were run between 1st July 1901 and 30

th 97

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June 2000, with the financial years 1970/71 to 1999/00 used for analysis. Over the 98

simulation period, as an annual average, the daily 30-year mean temperature at Elliott was 99

15.7°C compared to 18.5 and 18.6°C at Ellinbank and Terang, respectively. The 30-year 100

mean (range) annual rainfall was 1,220 (864 – 1,860), 1,083 (846 – 1,339) and 787 (590 – 101

1,006) mm at Elliott, Ellinbank and Terang, respectively. Based on these temperature and 102

rainfall means, the three sites of Elliott, Ellinbank and Terang are representative of and herein 103

referred to as a High Rainfall Cool Temperate (HRCT) site, a High Rainfall Temperate (HRT) 104

site and a Medium Rainfall Temperate (MRT) site, respectively. 105

DairyMod is a recognised whole farm systems-level computer based model used for analyses 106

of pasture based dairy systems in Australia and New Zealand (Cullen et al., 2008; White et al., 107

2008; Smith and Western; 2013). Several authors have compared simulated pasture 108

production estimates with measured data for the environments examined in this study, finding 109

close agreement between the model-predicted and observed values (Chapman et al. 2008a; 110

Cullen et al., 2008). Taking into consideration the many factors that can influence pasture 111

growth rates (e.g. grazing management, N fertiliser inputs), and the difficulty of accurately 112

measuring net herbage accumulation rate, it is not realistic to expect an exact match between 113

predicted and observed growth rates (Chapman et al., 2008a). Based on the model evaluation 114

statistics work of Tedeschi (2006), Cullen et al. (2008) found the r2, bias correction and mean 115

prediction error to be 0.88, 1.0 and 10.8%, respectively, for 31 modelled and observed annual 116

pasture yields across several temperate and sub-tropical environments, with varying ryegrass 117

cultivars, soils types and management systems in Australian and New Zealand. Similar 118

results have been found by other authors for some of the sites explored here giving rise to 119

confidence in DairyMod predicting dairy farm systems for south eastern Australia. 120

DairyMod includes modules for pasture growth and subsequent utilisation by grazing animals, 121

water and N dynamics, animal and plant physiology and production with a range of options 122

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for pasture management, irrigation and N fertiliser application. The animal module simulates 123

animal production based on the metabolisable energy (ME) intake that is utilised for 124

maintenance, lactation, pregnancy and growth as appropriate. The module has a significant 125

role in predicting nutrient dynamics through the recycling of faeces and urine (herein referred 126

to as excreta when summed together). The user defines the proportion of animal excreta that 127

is returned to the paddock (see section titled Soil parameters, N fertiliser and animal waste 128

management). While DairyMod assumes an even distribution of excreta across paddocks, 129

this is unlikely to be the case in reality. There is also the ability to define the nutrient 130

composition of milk and this function was implemented to define the four MN concentrations 131

examined in this study (see section titled Milk N concentrations and supplementary feed N 132

concentrations). Information on how N2O emissions are estimated in DairyMod is provided 133

in the Nitrous oxide emissions section. For more model details see Johnson (2013, 134

DairyMod and the SGS Pasture Model: a mathematical description of the biophysical model 135

structure. IMJ Consultants, Dorrigo, NSW, Australia, 136

http://imj.com.au/consultancy/ModelDocumentation/ModelDocumentation.html). 137

Farm system 138

Milking herd, grazing management and supplementary feed 139

The same farm system was simulated at each site to avoid confounding comparison of results 140

between sites. The farm system was a herd calving 1st August (late winter) with a target milk 141

production of 6,500 L/cow per 300 day lactation. This amount of milk production per cow 142

was similar to regional mean values taken from the Victorian Department of Primary 143

Industries’ Dairy Industry Farm Monitor Project and Tasmanian Dairy Business of the Year 144

awards (access the Victorian data at http://www.depi.vic.gov.au/agriculture-and-145

food/dairy/business-management/farm-monitoring-dairy and the Tasmanian data at 146

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http://www.tia.tas.edu.au/centres/dairy-centre/). The N concentration of the milk was an 147

experimental variable for this study and is detailed in the Milk N concentrations and 148

supplementary feed N concentrations section below. The herd rotationally grazed 20 five-149

hectare paddocks down to a biomass residual of 1.4 t dry matter (DM)/ha once the pasture 150

reached the 2.5 leaf regrowth stage or if the pasture biomass had reached 2.5 t DM/ha, grazed 151

once the pasture reach the 2.0 leaf stage of regrowth. 152

The stocking rate was intentionally set high, at 3.5 cows/ha, to ensure a demand for 153

supplementary feeding in all years. When daily pasture biomass was insufficient for daily 154

herd ME intake requirements, supplement with an ME concentration of 12.5 MJ/kg DM was 155

supplied to meet the herd’s energy demand. Supplementary feed N concentration was the 156

other experimental variable for this study and is detailed in the Milk N concentrations and 157

supplementary feed N concentrations section below. DairyMod assumes 100% utilisation of 158

all pastures grown and supplementary feed supplied to the herd (i.e. no spoilage due to 159

trampling, excreta deposition on pastures etc). 160

Soil parameters, N fertiliser and animal waste management 161

A generic clay loam soil with a bulk density value of 1.3g/ cm3 and a saturated hydraulic 162

conductivity of 2.8mm/hour was used for each site. The wilting point, field capacity and 163

saturation point of the generic clay loam were 19%, 40%, and 48% by volume, respectively, 164

with the perennial ryegrass pasture having a rooting depth of 400mm. All sites were rain-fed, 165

and given the winter-dominant rainfall pattern at all sites, N fertiliser was applied at a rate of 166

30 kg N/ha in the form of urea on the 1st of April, 1

st of May, 1

st of August, 1

st of September 167

and 1st of October every year to correspond with times of the year when response rate to the 168

N fertiliser was most likely to be maximised. This N fertilisation rate was similar to regional 169

benchmarking averages of ~ 120 to 130 kg N/ha.annum for Victorian dairy farms and ~ 170 170

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kg N/ha.annum for Tasmanian dairy farms (access the Victorian data at 171

http://www.depi.vic.gov.au/agriculture-and-food/dairy/business-management/farm-172

monitoring-dairy and the Tasmanian data at http://www.tia.tas.edu.au/centres/dairy-centre/ ). 173

In addition, 90% of animal excreta were returned to the paddocks, with the remaining 10% 174

deposited in the dairy during milking. This reflects typical farm practices in Australia where 175

the time cows spend grazing pastures is maximised and is equivalent to cows spending 176

between 60 and 90 minutes a milking at the dairy and associated holding areas. To minimise 177

any inconsistency with how the remaining 10% of animal excreta deposited at the dairy could 178

be stored/managed between sites (e.g. flushed to a storage pond/lagoon system and spread at 179

a later date or flushed to a sump and spread daily through a sprinkler system on pastures), and 180

consequently alter the N2O emissions from this stored excreta, a consistent approach was 181

taken with these emissions effectively removed from the N2O estimations. Similar 182

approaches have been undertaken by others to explore N dynamics using DairyMod (e.g. 183

Smith and Western, 2013). 184

Milk N concentrations and supplementary feed N concentrations 185

Sixteen scenarios were examined for each site: four MN concentrations of 0.50, 0.55, 0.60 186

and 0.65% N, representing 3.1, 3.4, 3.8 and 4.1% milk protein, combined with four SN 187

concentrations of 1, 2, 3 and 4% N, representing 6.3, 12.5, 18.8 and 25.0% crude protein in 188

the supplement. Milk nitrogen concentrations of either 0.50 or 0.55% N are typical of 189

Holstein-Friesian cattle where increased milk volume generally results in a dilution of milk 190

protein concentrations (Robinson, 2014; 191

http://animalscience.ucdavis.edu/faculty/robinson/Articles/FullText/Pdf/Web199908.PDF). 192

In contrast MN concentrations of 0.60% N are typical of Jersey cattle, and to a lesser extent 193

Guernseys, Ayrshires and cross-bred cattle, where there is generally a decrease in milk 194

volume resulting in a higher concentration of milk protein (Robinson, 2014; 195

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The fourth 0.65% MN was included to examine an extreme level of MN concentration. 197

Long-term increases in MN concentrations are generally only possible through changes in 198

cattle breed, and to a lesser extent genetic selection within breeds, with changes in feeding 199

regimes only resulting in transitory changes in MN concentrations (Robinson, 2014; 200


The SN concentrations of either 3 or 4% N in the current study are reflective of an all-pasture 202

diet; replacing grazed pastures with supplementary feed of a similar N concentration and 203

therefore essentially not varying the overall diet N concentration by any substantial amount. 204

In comparison the SN concentrations of 2% N reflects a pasture-based diet supplemented 205

with grain-based concentrates while the 1% SN reflects a pasture-based diet supplemented 206

with a low N concentration forage such as maize (Zea mays L) silage. 207

Nitrous oxide emissions 208

Denitrification losses are from the soil N2O pool, which is supplied either through direct 209

application of NO3 fertiliser or from the nitrification of ammonium (NH4). Ammonium 210

inputs are from organic matter breakdown (including dung decay), inputs from NH4 fertiliser 211

and urine inputs. Nitrogen in the diet is either retained in body tissue growth and milk 212

production, or is excreted. It is assumed that the milk has a fixed N concentration throughout 213

the simulations and we altered this fixed concentration to create the four MN concentrations. 214

Partitioning of N between dung and urine is related to the N concentration of the diet and 215

proportion of N that is excreted. The general behaviour of DairyMod is that the proportion of 216

excreted N in urine increases as the N content of the diet increases (Johnson et al., 2008). 217

Nitrification of NH4 is defined using a rectangular hyperbola in response to NH4 218

concentration, and is also affected by soil water status, temperature and soil microbial pools 219

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(Johnson et al., 2008). The rate of denitrification is defined using a rectangular hyperbola in 220

response to soil NO3- concentration, as well as temperature and soil C status, with soil C 221

status varied through the depth of the soil profile as defined by the ratio of labile soil C in 222

each soil layer to that in the surface layer (Johnson et al., 2008). Partitioning of 223

denitrification losses between N2O and N2 is affected by water filled pore space (WFPS) 224

based on the model proposed by Granli and Bøckman (1994). Denitrification of N2O 225

commenced and ceased at a WFPS as 60 and 90%, respectively. Denitrification of N2 226

commenced at a WFPS of 80%. 227

Indirect N2O losses, which are the proportion of leached NO3 and volatilisation of NH3 being 228

converted into N2O at a later date, were estimated using an inventory approach. Several 229

studies have found consistency with the model-prediction of N leaching, volatilisation and/or 230

denitrification within DairyMod compared to measured data (Bryant et al., 2011; 231

Hoogendoorn et al., 2011; Smith and Western, 2013) giving the model credibility in 232

estimating N dynamics for pasture-based grazing systems. Simulated losses of N through 233

leaching and volatilisation were multiplied by emissions factors of 0.01 and 0.0125, 234

respectively, as used in the Australian National GHG Inventory (DCCEE, 2011). A global 235

warming potential of 310 was used to convert N2O losses to GHG carbon dioxide equivalent 236

emissions (kg CO2e; DCCEE, 2011). For more details on DairyMod see Johnson (2013, 237

DairyMod and the SGS Pasture Model: a mathematical description of the biophysical model 238

structure. IMJ Consultants, Dorrigo, NSW, Australia, 239

http://imj.com.au/consultancy/ModelDocumentation/ModelDocumentation.html). 240

Model outputs 241

Model-predicted annual data for pasture intake (t DM/ha), supplementary feed intake (t 242

DM/ha), milk production (litres/ha), N fertiliser applied (kg N/ha), N exported in milk (kg 243

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N/ha), animal N intake (kg N/ha), animal excreta returned to paddocks (kg N/ha), direct N2O 244

emissions (kg CO2e/ha), leached N (kg N/ha) and volatilised N (kg N/ha) were collated for 245

each SN by MN scenario and site over the 30 year period to estimate the NUEs and N2O 246

emissions. 247

NUE and N2O emissions estimations 248

Annual NUE was determined for each of the 16 scenarios by dividing the annual total N 249

exported in milk by the annual total N consumed by the cows (g milk N / 100 g N intake) and 250

from this, a 30-year mean annual NUE was determined for each scenario and site. Thirty-251

year mean NUEs were estimated for each of the four SN concentrations by averaging the 252

mean NUEs for the four MNs within each SN concentration (e.g. average of the 30-year 253

mean NUEs of the four MNs where SN was 1%, 2% etc). The process was repeated for the 254

four MN concentrations by averaging the mean NUEs for the four SNs within each MN 255

concentration (e.g. average of the 30-year mean NUEs of the four SNs where MN was 0.50%, 256

0.55% etc). In addition, 30-year mean N2O emissions (directly as N2O and indirectly through 257

leaching and volatilisation) for each of the four SN concentrations (average of the four MNs) 258

and each of the four MN concentrations (average of the four SNs) were estimated for each 259

site using the same process as above. 260

Statistical analysis 261

Thirty-year means and standard deviations (SDs) were estimated for annual pasture 262

production and supplementary feed intakes for each site across all 16 scenarios. In addition, 263

30-year means and SDs were estimated for each of the four mean SN and MN concentration 264

scenarios with respect to N inputs (dietary N intake) and outputs (N deposited on pastures 265

from excreta, N exported in milk, N lost through denitrification, leaching, volatilisation and 266

total N2O losses) for each site. 267

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Results 268

Pasture production, supplementary feed intakes, diet N intakes, animal excreta and milk 269

production 270

The simulated 30-year mean (± SD) annual pasture production was 11.6 (± 1.7), 9.4 (± 1.9) 271

and 8.3 (± 1.6) t DM/ha for the HRCT, HRT and MRT sites, respectively. These mean 272

annual pasture yields align with previous model validation studies at these sites (Cullen et al., 273

2008). On a per cow basis, the model-predicted 30-year mean pasture intakes equated to 3.3 274

(± 0.5), 2.7 (± 0.5) and 2.4 (± 0.5) t DM/cow.annum for the HRCT, HRT and MRT sites, 275

respectively (Fig 1). Due to the inter-annual variation in pasture production, the model-276

predicted 30-year mean (± SD) supplementary feed intakes were 2.2 (± 1.6), 2.8 (± 1.8) and 277

3.1 (±1.6) t DM/cow.annum for the HRCT, HRT and MRT sites, respectively (Fig 1). The 278

amount of supplementary feeding was greater than industry averages. However, this was due 279

to the high stocking rate which was specifically selected such that there was a requirement for 280

supplementary feed even for high pasture production years. The percentage of total diet 281

from supplementary feed, as a mean (± SD) of all 16 scenarios across all 30 years, were 40 (± 282

8), 50 (± 10) and 57% (± 8) for the HRCT, HRT and MRT sites, respectively. 283

Total diet N concentration varied between 3.1 and 3.3% N among the four model-predicted 284

30-year mean MN concentrations. When fed a supplement with an N concentration of 1%, 285

mean (± SD) dietary N concentrations were 2.5 (± 0.3), 2.4 (± 0.2) and 2.2 (± 0.3) % for the 286

HRCT, HRT and MRT sites, respectively (Figs 2 to 4). This increased to 4.1% N across all 287

sites when the SN concentration was increased to 4% N and was comparative to that of the 288

pasture N concentrations (Figs 2 to 4). Not dismissing the complexity of microbial N 289

dynamics within the cow, optimum concentrations of N in the diet for high-yielding dairy 290

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cows fed a well-balanced diet is generally accepted within the range of 2.6 to 2.9% (Olmos 291

Colmenero and Broderick, 2006), making the 1% SN diet marginal at all three sites. 292

[Insert Figure 1 near here] 293

The model-predicted 30-year mean (± SD) annual N intakes was 539 (± 48), 483 (± 52) and 294

456 (± 49) kg N/ha for the HRCT, HRT and MRT sites, respectively, when the SN was 1%. 295

For every 1% increase in SN concentrations, the 30-year mean total diet N intakes increased 296

by ~ 90, 100 and 110 kg N/ha.annum for the HRCT, HRT and MRT sites, respectively. In 297

addition, the inter-annual variation in dietary N intakes declined considerably as SN 298

increased to 4% (Figs 2 to 4). In contrast, there was very little (< 5 kg N/ha.annum) variation 299

in the total diet N intakes across the four MN scenarios (Figs 2 to 4). 300

[Insert Figures 2 to 4 near here] 301

The overall 30-year model-predicted mean milk production across all 16 scenarios was ~ 302

6,190 litres/cow.lactation at all three sites, close to the initial target of 6,500 303

litres/cow.lactation, with little inter-annual variation within and between sites. This resulted 304

in similar amounts of N exported in milk each year, with 30-year model-predicted means of 305

108, 119, 130 and 140 kg N/ha.annum (SDs < 1 kg N/ha.annum at all three sites) when the 306

MN concentrations were 0.50, 0.55, 0.60 and 0.65%, respectively. For every 0.01% increase 307

in MN concentrations, the model-predicted 30-year mean milk N exports (averaged over the 308

four SN scenarios) increased by ~ 2.2 kg N/ha.annum. 309

As SN concentrations increased from 1 to 4%, so too did the amount of N returned to the 310

paddock in excreta. There was substantial differences in N excreta among sites when the SN 311

concentration was 1% N as shown with a model-predicted 30-year means (± SD) of 359 (± 312

45), 309 (± 49) and 285 (± 45) kg N/ha for the HRCT, HRT and MRT sites, respectively 313

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(Figs 2 to 4). These results align with measured studies where intakes similar to those 314

achieved with the SN 1% diets reported above resulted in between ~ 250 and 315 g N 315

excreted/cow.day (Kebreab et al., 2011). However, once the SN concentration was either 3 316

or 4% N, there was less variation among sites in the amount of N returned to the paddock in 317

excreta, best illustrated with the 4% SN, with means (± SD) of 590 (± 13), 576 (± 13) and 318

580 (± 12) kg N/ha for the HRCT, HRT and MRT sites, respectively (Figs 2 to 4). Increasing 319

the MN concentration from 0.50 to 0.65% resulted in a reduction in N lost via excreta. 320

However this decline was relatively insignificant (around 10 kg N/ha.annum per 0.05% MN 321

increase) compared to the rate of decline in excreta N with declining SN concentrations 322

(around 70 to 100 kg N/ha.annum per 1% SN decline). 323

Nitrogen use efficiency 324

As SN concentrations increased, mean NUE decreased. In contrast, NUE increased with 325

increasing MN concentrations (Figs 2 to 4). The 30-year mean NUEs varied between 14 and 326

31 g milk N/ 100 g N intake (Figs 2 to 4). There was little difference in NUEs between sites 327

when the SN concentration was 3 or 4% (Figs 2 to 4). However, when the SN concentration 328

declined to 1%, NUE differences became more apparent, with mean NUEs (± SD) of 23 (± 3), 329

26 (± 4) and 28 (± 4) g milk N/ 100 g N intake for the HRCT, HRT and MRT sites, 330

respectively (Figs 2 to 4). 331

N lost through denitrification, leaching and volatilisation 332

When the SN was 1%, mean (± SD) nitrogen lost to the environment directly through N2O 333

denitrification was 1.1 (± 0.4), 2.3 (± 0.8) and 2.4 (± 0.6) kg N/ha for the HRCT, HRT and 334

MRT sites, respectively. As SN increased from 1 to 4% N, the amount of N lost as N2O 335

increased at all three sites but with a diminishing rate of increase. For example, for the MRT 336

site the mean N2O model-predicted to be lost to the environment was 2.4, 2.9, 3.3 and 3.5 kg 337

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N/ha.annum for SNs of 1, 2, 3 and 4%, respectively. As MN increased, there was only a 338

small reduction in the amount of N lost as N2O, with a maximum difference of only 0.2 kg 339

N/ha.annum between and MN of 0.50 and 0.65%. 340

Nitrogen was also lost to the environment through leaching and, to a lesser extent, 341

volatilisation. Given that the simulated volatilisation losses were only ~ 10% of those losses 342

from leaching, only the leaching results are presented here. However, the amount of N2O 343

attributed to volatilisation losses is included in the total N2O losses presented in the NUE and 344

N2O emissions estimation section below. Similarly to N lost as N2O through denitrification, 345

the amount of N lost through leaching was the lowest for the HRCT site and greatest at the 346

MRT site. When SNs were 1%, the 30-year mean (± SD) leached N was 109 (± 52), 147 (± 347

61) and 148 (± 63) kg N/ha.annum for the HRCT, HRT and MRT sites, respectively. As SN 348

increased from 1 to 4%, the model-predicted 30-year mean leached N increased by 181, 227 349

and 257 kg N/ha for the HRCT, HRT and MRT sites, respectively. In contrast, as MN 350

increased from 0.50% to 0.65%, the 30-year mean leached N declined by between 24 and 25 351

kg N/ha across the three sites. 352

Total nitrous oxide emissions 353

Total N2O emissions, as the sum of the N2O lost directly and through the conversion of a 354

proportion of N lost through leaching and volatilisation into N2O, varied between scenarios 355

and sites. When the SN was 4% N the model-predicted 30-year mean (± SD) total N2O 356

emission was 3.0 (± 0.9), 4.2 (± 1.0) and 4.4 (± 1.1) t CO2e/ha.annum for the HRCT, HRT 357

and MRT sites, respectively (Figs 2 to 4). This declined to model-predicted 30-year mean (± 358

SD) total N2O emissions of 1.3 (± 0.5), 2.1 (± 0.6) and 2.1 (± 0.4) t CO2e/ha.annum for the 359

HRCT, HRT and MRT sites, respectively, when SN was 1% N (Figs 2 to 4). When the MN 360

concentration was 0.50% N, the model-predicted 30-year mean (± SD) total N2O emission 361

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was 2.3 (± 0.9), 3.3 (± 1.1) and 3.4 (± 1.1) t CO2e/ha.annum for the HRCT, HRT and MRT 362

sites, respectively (Figs 2 to 4). This declined to 2.0 (± 0.9), 3.1 (± 1.1) and 3.2 (± 1.1) t 363

CO2e/ha.annum when the MN concentration was increased to 0.65% N (Figs 2 to 4). 364

Direct N2O emissions, as a mean of the 16 scenarios, were model-predicted as 0.39, 0.48 and 365

0.48 of total N2O emissions for the HRCT, HRT and MRT sites, respectively, with the 366

balance attributed to indirect N2O emissions associated with leaching and volatilisation. 367

These proportions are similar to those of the national inventory (DCCEE, 2011), giving 368

additional weight to the accuracy of the model’s outputs. 369

Discussion 370

Based on the assumptions inherent in DairyMod, this study suggests that decreasing dietary N 371

supply to better balance the dietary N requirements of a dairy cow leads to improvements in 372

NUE and results in greater reduction in N2O emissions than strategies that are targeting a 373

greater amount N in the animal product. In practice, improving the balance between protein 374

and energy can be achieved by several processes. The most obvious is to provide a diet that 375

matches N requirement, varying throughout the year depending on animal requirements for 376

lactation and pregnancy. However, for pasture-based systems typical of southern Australia, 377

where there can be large seasonal variations in pasture N concentrations, this is somewhat 378

more difficult to achieve (Powell et al., 2010). Options for pasture-based dairy systems 379

include selecting forage species with a higher energy to protein ratio, strategically feeding 380

animals based on changes in N concentration with plant phenology, or providing supplements 381

with a low N concentration (de Klein and Eckard, 2008). Such feeding strategies can be 382

more easily adapted in Australia’s pastures based dairy systems than longer term options such 383

as selection of dairy cows for a higher concentration of milk protein (Pablo Keim and 384

Anrique, 2011), especially given that the assumed theoretical increases in milk N 385

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concentration presented here resulted in minimal estimated potential reductions in N2O 386

emissions. 387

In the current study, the 30-year mean NUEs for individual scenario were model-predicted to 388

vary between 14 and 31 g milk N/ 100 g N intake. These results are similar to measured 389

results of Gourley et al. (2012) who found that measured NUEs of 17 dairy farms located 390

throughout Victoria , including some farms located in the HRT and MTR regions explored in 391

this study, varied between 15 and 35 g milk N/ 100 g N intake. In a review of NUEs by 392

Pablo Keim and Anrique (2011), where pastures compromised the majority of the diet, 393

measured NUEs varied between 15 and 33 g milk N/ 100 g N intake. Similar measured 394

results were found by Vibart et al. (2009) with an average NUE of 24 g milk N/ 100g N 395

intake. 396

According to the definitions of varying NUE’s by Chase (2003), NUEs of < 25 g milk N/ 100 397

g N intake are considered to indicate room for substantial improvement. However, these 398

definitions by Chase (2003) were based on studies in which most cattle were involved in 399

confinement feeding practices as opposed to the majority of the diet consisting of grazed 400

pastures for dairy farms in southern Australia (Dairy Australia; 401

http://www.dairyaustralia.com.au/Animals-feed-and-environment/Feeding-and-402

nutrition/Feeding-systems.aspx). Compared to grazed pastures, confinement feeding allows 403

for more control of feed quantity and nutrient concentrations throughout the year, where the 404

energy to protein ratio can be monitored and rectified as required, the diet can be delivered in 405

smaller amounts throughout the day to increase daily dry matter intakes and milk production 406

per unit of feed intake can be maximised due to less energy being diverted to maintenance 407

and activity (Gourley et al., 2012; Powell et al., 2010). Any one of these would assist in 408

increasing the NUE of milk production for confinement feeding systems relative to grazed 409

pastures systems typical of southern Australia. 410

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When the SN concentration declined from 4 to 1%, reflecting a change in overall diet N 411

concentration from 4.1% down to a low of 2.2% depending on the site. N intakes also 412

declined from approximately 790 kg N/ha.annum at all three sites to between 460 and 540 kg 413

N/ha.annum depending on the site. This corresponded with an improvement in NUE from a 414

low of 16 g milk N/ 100 g N at all three sites to a high of between 23 and 28 g milk N/ 100 g 415

N. This relationship between N intake and NUEs has been shown in measured studies 416

highlighting that any practice that better balances N intake will improve NUE (Huhtanen and 417

Hristov, 2009; Powell et al., 2010). 418

When MN concentrations increased from 0.50% to 0.65%, N intakes remained relatively 419

stable and NUEs increased from approximately 17 to 23 g milk N/ 100 g N. More 420

importantly, this increase in NUE was lower than that achieved with reducing SNs. Milk N 421

concentrations of 0.50% N returned a 30-year mean of between 71 and 73% of total N intake 422

as excreta and only declined to between 66 and 68% of total N intake as excreta when the 423

MN concentration was increased to 0.65% N. Pablo Keim and Anrique (2011) suggest that 424

increasing the amount of N exported in milk is a positive means of improving NUE to reduce 425

N2O losses. However, the reduction in N2O corresponding with improved NUEs as a 426

consequence of increased MN concentrations, resulted in 0.2 to 0.3 t CO2-e/ha.annum 427

abatement depending on the site. This was a relatively insignificant abatement compared to 428

those achieved with declining SN concentrations. 429

Due to climatic differences, pasture consumption for the HRCT site was 11.6 t DM/ha.annum 430

which was discernibly higher than the 9.4 and 8.3 t DM/ha.annum achieved for the HRT and 431

MRT sites, respectively. This affected the N balance in two ways: more of the N inputs from 432

fertiliser and excreta were converted into pasture and less supplementary feed was required. 433

Both aspects would have contributed to less N being available for loss, either directly or 434

indirectly, highlighting that maximising pasture production and pasture utilisation is the goal 435

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of dairy farmers to not only better balance their herd’s feed requirements but also reduce 436

some of the environmental concerns of dairy farming. 437

Direct and indirect N2O emissions for the HRCT site were approximately half and one-third 438

those at the other two sites further confirming that converting N into pasture production 439

attributed to the reduced N2O losses for the HRCT site. The warmer soil temperatures 440

experienced at the two temperate climate sites may have also favoured increases in N2O 441

emissions relative to those at the cool temperate climate site even though the latter was 442

discernibly wetter over the winter months (Dalal et al., 2003). Therefore, for the temperate 443

climate sites the implementation of other mitigation strategies in addition to better balancing 444

of the diet’s energy to protein ratio could have some merit. Other potential strategies to 445

optimise pasture production in addition to reducing N2O emissions include management 446

practices such as improved drainage, better scheduling of irrigation events to minimise 447

through drainage and/or strategic grazing of pastures over winter to reduce soil conditions 448

conducive to N losses (de Klein and Eckard, 2008). 449

For many southern Australian dairy systems, the diets are predominantly grazed/conserved 450

pastures based on perennial ryegrass (Chapman et al., 2008a). This study has shown that the 451

introduction of some form of forage crop low in N concentration, such as maize grown for 452

silage, was advantageous in reducing N2O emissions. However, although the use of forage 453

crops to better balance the diet can be beneficial, it is critical to avoid inadvertently 454

introducing an energy deficit as some of these low N forages can also be below optimum 455

energy (e.g. sorghum (Sorghum bicolour L) and millet (Pennisetum glaucum L)), leading to a 456

compromise in milk production and subsequently increasing the NUEs. In addition, on-farm 457

adoption of forage crops is often low, as improvements in farm profitability can be minimal 458

(Chapman et al., 2008b; Rawnsley et al., 2013). In addition, their introduction could bring 459

changes to management practices for the farming system. For example, the inclusion of 460

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maize silage into the diet may require specialised harvesting equipment, concreted feeding 461

areas and mixer wagons for feeding. Therefore just replacing a proportion of the diet with a 462

low N concentration forage supplement has other considerations which need to be addressed 463

beyond their ability to reduce N2O losses. 464

This study has clearly demonstrated, and supports previous findings, that adopting feeding 465

strategies to better balance the diet so that N is not supplied in excess of animal requirements 466

will most likely result in improved NUE and consequently lower N2O emissions (Powell et 467

al. , 2010; Pablo Keim and Anrique, 2011). Identifying pasture/forage species that can 468

reduce N intakes without compromising milk production and farm profitability will have 469

positive outcomes for the environment. In recent years there has been an increasing focus on 470

the use of high sugar ryegrass to improve the energy to protein ratio of the cow’s diet (Pablo 471

Keim and Anrique, 2011). The study has shown that any strategy that reduce the amount of 472

surplus N in the diet will result in improvements in NUE and consequently decrease N2O 473

emissions. This adoption of high sugar ryegrass in temperate dairying regions of Australia 474

requires further research to ascertain both the productive and environmental benefits. . 475

Conclusions 476

It is generally accepted that while there are several pathways to improve animal NUE for 477

confinement feeding of dairy cows, large uncertainty remains regarding pathways for 478

improving NUE in pasture-based dairy systems (Pablo Keim and Anrique, 2011; Gourley et 479

al., 2012). Finding approaches to achieving improvement in NUE to reduce associated N2O 480

emissions is considered critical to maintaining productivity growth whilst adhering to 481

environmental stewardship. This study explored two potential pathways for improving NUE 482

to assist in reducing N2O emissions: increasing the amount of N exported off-farm with 483

improved milk N concentrations and decreasing the overall N concentration of the diet with 484

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varying supplementary feed N concentrations. Both pathways improved NUE and reduced 485

associated N2O emissions. However reducing supplementary feed N concentrations reduced 486

N losses by an order of magnitude greater than achieved by increasing milk N concentrations. 487

It is also clear that no single strategy alone will achieve the desired improvement in NUE and 488

N2O abatement; however a combination of several complementary options may hold some 489

promise for the southern Australian dairy industry. 490

Acknowledgements 491

This project was supported by the Tasmanian Institute of Agriculture, the University of 492

Melbourne and the Victorian Department of Environment and Primary Industries, through 493

funding from the Australian Government Department of Agriculture, Dairy Australia, Meat 494

and Livestock Australia and Australian Wool Innovation. 495

References 496

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in climate and soil, choice of animal type and N fertilisation level on nitrogen leaching from 498

pastoral farming systems around Lake Taupo, New Zealand. Agricultural Systems 104, 271-499

280. doi:10.1016/j.agsy.2010.11.001. 500

Chapman, D.F., Kenny, S.N., Beca, D., Johnson, I.R., 2008a. Pasture and forage crop 501

systems for non-irrigated dairy farms in southern Australia. 1. Physical production and 502

economic performance. Agricultural Systems 97, 108-125. doi:10.1016/j.agsy.2008.02.001. 503

Chapman, D.F., Kenny, S.N., Beca, D., Johnson, I.R., 2008b. Pasture and forage crop 504

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Vibart, R.E., Koolaard, J., Barrett, B.A., Pacheco, D., 2009. Exploring the relationships 562

between plant chemical composition and nitrogen partitioning in lactating dairy cows fed 563

ryegrass-based diets. Proceedings of the New Zealand Society of Animal Production 69, 188-564

195. 565

White, T.A., Johnson, I.R., Snow, V.O., 2008. Comparison of outputs of a biophysical 566

simulation model for pasture growth and composition with measured data under dryland and 567

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571

Fig 1. Boxplots showing the estimated annual pasture intakes ( ) and supplementary feed 572

intakes ( ) at Elliott (high rainfall cool temperate climate), Ellinbank (high rainfall 573

temperate climate) and Terang (medium rainfall temperate climate) between 1971 and 2000. 574

(Boxplots represent 25th and 75

th percentiles, whiskers represent 10

th and 90

th percentiles, 575

outliers represent 5th and 95

th percentiles, solid lines represent medians and dotted lines 576

represent means). 577

Elliott Ellinbank Terang Elliott. Ellinbank. Terang.

Pasture and supplementary feed intakes

(t DM/cow.annum)

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

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578

Fig 2. Boxplots showing the estimated annual nitrogen intake, nitrogen excreted in dung and 579

urine, nitrogen use efficiency and nitrous oxide emissions for the high rainfall cool temperate 580

climate site between 1971 and 2000 with milk nitrogen (MN) concentrations of 0.50, 0.55, 581

0.60 and 0.65% and supplementary feed nitrogen (SN) concentrations of 1, 2, 3 and 4%. 582



th and 90

th percentiles, 583




MN 0.50%

SN 1% SN 2% SN 3% SN 4%

N intake (kg N/ha.annum)

300

400

500

600

700

800

900

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

N excreta (kg N

/ha.annum)

200

300

400

500

600

700

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

NUE (g m

ilk N/ 100g N

intake)

10

15

20

25

30

35

40

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

N2O emissions (t CO2e/ha.annum)

0

1

2

3

4

5

6

7

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

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586


urine, nitrogen use efficiency and nitrous oxide emissions for the high rainfall temperate 588





th and 90

th percentiles, 591




MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

N intake (kg N/ha.annum)

300

400

500

600

700

800

900

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

N excreta (kg N/ha.annum)

100

200

300

400

500

600

700

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

NUE (g m

ilk N

/ 100g N intake)

10

15

20

25

30

35

40

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%


0

1

2

3

4

5

6

7

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594


urine, nitrogen use efficiency and nitrous oxide emissions for the medium rainfall temperate 596





th and 90

th percentiles, 599




MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%


0

1

2

3

4

5

6

7

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

N excreta (kg N

/ha.annum)

100

200

300

400

500

600

700

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

NUE (g m

ilk N/ 100g N intake)

10

15

20

25

30

35

40

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

MN 0.50%

SN 1% SN 2% SN 3% SN 4%

N intake (kg N

/ha.annum)

300

400

500

600

700

800

900

MN 0.55%

SN 1% SN 2% SN 3% SN 4%

MN 0.60%

SN 1% SN 2% SN 3% SN 4%

MN 0.65%

SN 1% SN 2% SN 3% SN 4%

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