Intensification of Australian pasture-based dairy ...

Intensification of Australian pasture-based dairy farm systems: biophysical, economic and environmental analysis

by

Santiago Rafael Fariña

A Thesis Submitted in Fulfilment of the Requirements of a Degree of Doctor of Philosophy

2010

Faculty of Veterinary Science

Dedicated to Magdalena and Diegui

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ABSTRACT Australian dairy farmers are facing declining terms of trade and decreasing availability of land and water. To remain profitable in this context, they will need to increase total factor productivity. However, productivity growth has been very low in the last 20 years, mainly due to an increased reliance on expensive purchased feed to increase milk production. In addition, there is an increasing pressure from the communities to provide evidence of meeting environmental standards. Hence, farmers need systems to increase productivity by producing more milk per ha without having to rely on additional purchased feed, and without increasing environmental impact. A review of the literature (Chapter 1) concluded that an intensification system comprising pasture and forage crops, with small inputs of concentrates, could increase milk production per ha from home-grown feed beyond the potential of systems based on pasture plus supplements, and in a more economically and environmentally sustainable way. To test this general hypothesis, intensification systems based on pasture plus supplements (Study 1) or a pasture plus forage crops (Study 2) were evaluated at the whole farm level in terms of: 1) biophysical productivity; 2) operating profit and business risk (climate and price risk); and 3) potential environmental pollution (greenhouse gas emissions and nitrogen balance). In Study 1, systems increasing stocking rate or milk yield per cow or both were compared. A 2-year farmlet study (Chapter 2) showed that, whereas pasture utilisation was similar across all systems due to the management rules imposed, increasing stocking rate was a more effective way to increase milk per ha and use supplements more efficiently at the whole system level. This system also showed a lower exposure to price risk (Chapter 3). However, the increased milk yield per cow system showed lower greenhouse gas emissions per unit of milk (Chapter 4). In Study 2, a whole farm experiment, comprising a complementary forage system (CFS) combining pasture and forage crop rotations, achieved 27,800 litres of milk/ha from home-grown feed (Chapter 5), which is well beyond the limits of any pasture only system. Compared to a system intensified through higher use of purchased feed, which was modelled based on the results of the whole farm study, the CFS achieved a lower business risk (Chapter 6). This was due to a lower exposure to both concentrates price variation and the effects of climate variability on forage production through a more diversified forage base. The CFS also showed less potential N pollution and greenhouse gas emissions per unit of milk that the other option (Chapter 7), mainly due to its lower use of purchased feed. This thesis has demonstrated that intensification systems integrating pasture and forage crops, with small inputs of concentrates, can increase milk production per ha from home-grown feed beyond the potential of systems based on pasture plus supplements and with a lower business risk and environmental impact. v

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ACKNOWLEDGEMENTS I want to share the joy of completing this piece of work with all the people that helped me along the journey. First of all I want to say thank you to Yani García, my main supervisor. His generosity to invest so much time and energies to give me this opportunity under difficult circumstances, and support me right from the beginning, having ALWAYS an open door to discuss anything and help me out, is something I will be always grateful for. With him not only I learnt a great deal about pasture management and feeding systems, but also how to think as a scientist. Outside the academic life, I feel fortunate to have his honest friendship and the one of his family (Valeria, Oli and Juan thank you also). I am also very grateful to my second supervisor, Bill Fulkerson. His wisdom and practical knowledge is something I will always admire, and I feel very fortunate to have had his support and his very valuable contribution to my thesis. I also want to acknowledge Andrew Alford, who had a key role in the economic analysis in this thesis, and was always approachable to discuss ideas. The people at the MC Franklin Lab, the place where I spent most of the time during my work, were a bit like a family to me. Very special thanks to Ajantha, my “mum in Australia”! Your caring and generous nature inspired me every day. Thanks to all the great people at the lab: Kendra Davies, Sherry Catt, Michelle Heward, Noelene West, Rafiq Islam and Pietro Celi. Also to the students, Ravneet Jhajj (thanks for your advise and everyday happiness!), Mariana Pedernera, Daniel Dickeson and Helen Smith. Sergio Suarez and Davinder Singh were the key players that did all the hard work sampling and setting up fences at the Costorphine trial, and I am very grateful for their work, which was always done so responsibly and with great enthusiasm. The honour students with who I shared frosty mornings at the paddock cutting forage rape or watching the cows for hours, and were still so enthusiastic at all times: Damien Tanner, Edward Stefanski, Douglas Macintosh, Arne van Schot and Fabian Pyra. A big thank you to the people at the Costorphine dairy: Kim McKean, who had so much patience to cope with us, “the academics”, and from who I also learnt things outside dairying. Thank you Kim, you are a legend! I want to acknowledge all and

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each of the people that gave me a hand at the farm in these years: John Garrod, George Zammit, Ian Chapman, Jim Marsh, Tony Pace, Kathy Curran and Colin Spinks. I also would like to acknowledge the people that worked in the farmlet study at Number 9 dairy. First, Amy Rogers, who did so much work at the beginning of the study and showed me the ropes when I was freshly arrived (and could not understand the “aussie” English!). Thank you also to Mark Holdsworth, Kristine Riley, Cassandra Palmer, Norman Collins and Rose Sinclair. There are persons that were not involved in the research, but definitely helped me a lot to finish this experience successfully: those were my friends in Australia. My “vet” friends from the very beginning: Tim, Sian, Mich, Jen, Rob, Matt and Adele. My “agger” friends that came soon after: Dave, Tony, Ainslie, Kiri, Markuzzi and Marica. And the other bunch: Juan, Andrea (thanks for the editing!), Kane, Marta, Nico and Azul. Thank you so much for all the good times and for being there for me. I also want to say thanks to those friends that were backing me up from Argentina, and always showed me their support. My almost brother Lucas, Manolo and Tony. Jota, my dear friend. “La banda” from Tandil: Enano, Nacho, Santi, Juan, Cartero, Andres, Babo, Chirola and Cuis. And the ones from Uni: Tomas, Picu, Esteban, Iki, Pelado, Dabi, Boqui and Conrado. The love for farming and its people is what drove me to be studying here, and it all started in “El Tajamar” cattle station, in Ayacucho, where I grew up. From those days, I would like to acknowledge the people from who I first learned work ethics, respect and sacrifice: Don Juan Carlos Isasi, Don Vicente Ripoll, Don Jose Ripoll and Don Jose Luis Morales. And finally, my biggest thanks to the ones closest to my heart. Mamá, who suffered and prayed but always kept smiling. Papá, my role model, who has always made me see the important things in life. Cipri, to who I owe much time as a brother. Antonia, who, before I knew, has become an adult. Mariana, who is not just my sister but also my good friend. And Estanis, my big brother, I am now realising how great it is to have him as my brother. Los extraño mas de lo que me permito, y los quiero mucho.

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PREFACE

The content of this dissertation has not been previously submitted for any degree to this University or alternative Institution. The studies herein are original and were conducted by the author, unless otherwise specified in the acknowledgements.

Santiago Rafael Fariña Bsc. Agr.

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TABLE OF CONTENTS ABSTRACT..............................................................................................................V ACKNOWLEDGEMENTS ......................................................................................VII PREFACE ...............................................................................................................IX TABLE OF CONTENTS ..........................................................................................XI LIST OF ABBREVIATIONS ................................................................................ XVII LIST OF FIGURES .............................................................................................. XXI LIST OF TABLES ............................................................................................. XXVII LIST OF PUBLICATIONS FROM THIS THESIS ............................................... XXXI GENERAL INTRODUCTION ................................................................................... 1 Thesis structure .............................................................................................. 6 REFERENCES ......................................................................................... 7 CHAPTER 1 OPPORTUNITIES AND CHALLENGES TO INCREASE MILK PRODUCTION FROM HOME-GROWN FEED IN AUSTRALIAN DAIRY FARMS: A REVIEW OF THE LITERATURE ................................................... 9 INTRODUCTION ........................................................................................... 11 The Australian dairy industry ....................................................................... 11 MILK PRODUCTION FROM PASTURE ..................................................... 12 Strategies to increase milk production from pasture .................................. 13 THE POTENTIAL OF HOME-GROWN FEED TO INCREASE MILK PRODUCTION ....................................................................................... 17 The potential of pasture ............................................................................ 18 The potential of forage crops .................................................................... 19 The potential of forage crop rotations ....................................................... 22 SUSTAINABILITY OF SYSTEMS THAT INCREASE MILK PRODUCTION FROM HOME-GROWN FEED ...................................... 23 1. Social sustainability .............................................................................. 23 2. Economic sustainability ........................................................................ 24 3. Environmental sustainability................................................................. 28 CONCLUSIONS ............................................................................................ 38 REFERENCES ....................................................................................... 39 STUDY 1 FARM SYSTEM INTENSIFICATION BASED ON PASTURE PLUS SUPPLEMENTS ................................................................................................... 51 CHAPTER 2 INTENSIFICATION OF A PASTURE-BASED DAIRY FARM SYSTEM THROUGH STOCKING RATE OR MILK YIELD PER COW OR BOTH: BIOPHYSICAL PRODUCTIVITY ............................................................... 53 INTRODUCTION .................................................................................... 55 MATERIALS AND METHODS ................................................................ 57 xi

Experimental design and treatments .............................................................57 Management .................................................................................................59 Measurements ..............................................................................................61 Calculations .................................................................................................62 Statistical analyses........................................................................................63 RESULTS ............................................................................................... 65 Animal responses .........................................................................................65 Pasture growth rate and utilisation ................................................................71 Nutritive value of pasture .............................................................................73 Feed intake and nutritive value of the diets ...................................................75 Regression Analyses.....................................................................................78 Relationship between ME density, other nutrient value variables and feed sources DMI .............................................................................................78 Relationship between nutritive value of diet and DMI ..............................79 Feed conversion efficiency ...........................................................................80 DISCUSSION.......................................................................................... 82 CONCLUSIONS ...................................................................................... 87 REFERENCES ....................................................................................... 87 CHAPTER 3 INTENSIFICATION OF A PASTURE-BASED DAIRY FARM SYSTEM THROUGH STOCKING RATE OR MILK YIELD PER COW OR BOTH: BUSINESS RISK........................................................................................ 93 INTRODUCTION .................................................................................... 95 MATERIALS AND METHODS ................................................................ 97 The farmlet study .........................................................................................97 Whole farm budget .......................................................................................97 Risk analysis ................................................................................................99 RESULTS ............................................................................................. 103 Whole farm budget .................................................................................... 103 Risk analysis ............................................................................................. 104 Cumulative distribution of operating profit ............................................ 104 Milk, concentrates and urea fertiliser price risk ...................................... 107 Integrated price risk ............................................................................... 110 Example of safety-first approach ........................................................... 111 DISCUSSION........................................................................................ 112 CONCLUSIONS .................................................................................... 116 REFERENCES ..................................................................................... 116 CHAPTER 4 INTENSIFICATION OF A PASTURE-BASED DAIRY FARM SYSTEM THROUGH STOCKING RATE OR MILK YIELD PER COW OR BOTH: ENVIRONMENTAL IMPACT.................................................................... 121 INTRODUCTION .................................................................................. 123 MATERIALS AND METHODS .............................................................. 125 The farmlet study ...................................................................................... 125 Farm-gate N balance ................................................................................. 125 Nitrogen inputs ...................................................................................... 125 Nitrogen outputs .................................................................................... 125 Greenhouse gas emissions ......................................................................... 126

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Statistical analysis ..................................................................................... 126 RESULTS ............................................................................................. 126 Farm-gate N balance ................................................................................. 126 Greenhouse gas emissions ......................................................................... 127 DISCUSSION ....................................................................................... 128 CONCLUSIONS ................................................................................... 130 REFERENCES ..................................................................................... 131 STUDY 2 INTENSIFICATION BASED ON PASTURE PLUS FORAGE CROPS... ............ 137 CHAPTER 5 INTENSIFICATION OF A PASTURE-BASED DAIRY FARM THROUGH A COMPLEMENTARY FORAGE SYSTEM: BIOPHYSICAL PRODUCTIVITY ................................................................................................................. 139 INTRODUCTION .................................................................................. 141 MATERIALS AND METHODS .............................................................. 143 Farmlet description.................................................................................... 143 Establishment of pasture and CFR crops.................................................... 144 Fertiliser and irrigation management ......................................................... 145 Feeding management................................................................................. 146 Pasture grazing management. ................................................................ 146 Grazing management of CFR crops ....................................................... 147 Management of supplementary feeds ..................................................... 148 Animal management ................................................................................. 148 Measurements ........................................................................................... 148 Daily pasture utilised ............................................................................. 148 Weekly estimates of pasture mass.......................................................... 149 Daily forage crop utilised ...................................................................... 149 Daily feeds consumed by the cows ........................................................ 149 Chemical analysis of feeds .................................................................... 150 Milk yield, milk composition, body weight and body condition score .... 150 Calculations .............................................................................................. 150 Statistical analysis ..................................................................................... 151 RESULTS ............................................................................................. 153 Annual utilised yield and nutritive value of forage..................................... 153 Monthly pre-and post-grazing mass, utilised yield and nutritive value of forages ...................................................................................................... 154 Feed intake and nutritive value of the diet ................................................. 158 Milk yield.................................................................................................. 160 Body condition score and body weight ...................................................... 162 Reproductive efficiency............................................................................. 163 Feed conversion efficiency ........................................................................ 163 Regression analysis ................................................................................... 164 Effect of season of calving on animal performance .................................... 165 DISCUSSION ....................................................................................... 169 CONCLUSIONS ................................................................................... 172 REFERENCES ..................................................................................... 172

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CHAPTER 6 INTENSIFICATION OF A PASTURE-BASED DAIRY FARM THROUGH A COMPLEMENTARY FORAGE SYSTEM: BUSINESS RISK ................... 177 INTRODUCTION .................................................................................. 179 MATERIALS AND METHODS .............................................................. 180 The CFS field study................................................................................... 180 Scenarios (farm systems) ........................................................................... 180 Economic analysis ..................................................................................... 181 Incorporation of climate variability............................................................ 183 Pasture simulation ................................................................................. 183 Forage crops simulation ......................................................................... 185 Pasture and forage crop yields probability distribution ........................... 185 Incorporation of inputs-outputs price variability ........................................ 186 Risk analysis ............................................................................................. 187 RESULTS ............................................................................................. 188 Physical and economic performance of farm systems ................................ 188 Effect of climate variability on forage yield ............................................... 190 Stochastic budgeting.................................................................................. 193 Stochastic efficiency with respect to a function ......................................... 197 DISCUSSION........................................................................................ 199 CONCLUSIONS .................................................................................... 203 REFERENCES ..................................................................................... 204 CHAPTER 7 INTENSIFICATION OF A PASTURE-BASED DAIRY FARM THROUGH A COMPLEMENTARY FORAGE SYSTEM: ENVIRONMENTAL IMPACT . 209 INTRODUCTION .................................................................................. 211 MATERIALS AND METHODS .............................................................. 213 The CFS field study................................................................................... 213 Temporal and spatial deposition of N excreta ........................................ 214 Nitrogen balance at the field level .......................................................... 214 Modelling farm intensification systems ..................................................... 216 Nitrogen balance at the farm-gate level .................................................. 217 Greenhouse gas emissions ..................................................................... 218 Statistical analysis ..................................................................................... 218 RESULTS ............................................................................................. 219 The CFS field study................................................................................... 219 Temporal and spatial deposition of N excreta ........................................ 219 Relationship between time spent on an area and quantity of excreta ....... 219 Nitrogen inputs and outputs at the field level ......................................... 220 The farm intensification systems modelled ................................................ 223 Nitrogen balance at the farm-gate level .................................................. 223 Greenhouse gas emissions ..................................................................... 224 DISCUSSION........................................................................................ 225 CONCLUSIONS .................................................................................... 230 REFERENCES ..................................................................................... 231

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GENERAL DISCUSSION .................................................................................... 237 1. Biophysical productivity of intensification systems ............................... 240 2. Business risk of intensification systems ................................................. 242 3. Environmental impact of intensification systems ................................... 245 Nitrogen balance ................................................................................... 245 Greenhouse gas emissions ..................................................................... 246 CONCLUSIONS ................................................................................... 248 REFERENCES ..................................................................................... 249

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LIST OF ABBREVIATIONS ABV

Australian Breeding Value

ADF

acid detergent fibre

AI

artificial insemination

am

ante meridian

APSIM

agricultural production systems simulator

AU$

Australian dollar

BCS

body condition score

BW

body weight

C

control

c

cents

©

copyright

CE

certainty equivalent

CFR

complementary forage rotation

CFS

complementary forage system

cm

centimetre

CP

crude protein

cv

cultivar

DM

dry matter

DMI

dry matter intake

DPI

department of primary industries

E

east

ECM

energy corrected milk

FCE

feed conversion efficiency

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g

gram

ha

hectare

HH

high stocking rate and high milk yield per cow system

HMY

high milk yield per cow system

HSR

high stocking rate system

K

potassium

kg

kilogram

km

kilometre

L

litre

LCA

life cycle analysis

max

maximum

ME

metabolisable energy

mg

milligram

min

minimum

MJ

mega joule

ML

mega litre

mm

millimetre

MOTAD

minimisation of total absolute income deviation

MUN

milk urea nitrogen

N

nitrogen

n/a

not applicable

NDF

neutral detergent fibre

NI

nitrogen intake

NSW

New South Wales

P

phosphorus

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PG

pasture plus grain system

PGR

pasture growth rate

Ph

hydrogen ion concentration,-log 10 of

pm

post meridian

PMR

partial mixed ration



registered

RAC

risk aversion coefficient

REML

restricted maximum likelihood

RPM

rapid plate meter

S

south

sd

standard deviation

SED

standard error of the difference

SEM

standard error of the mean

SERF

stochastic efficiency with respect to a function

t

tonnes

WSC

water soluble carbohydrates

WUE

water use efficiency

%

percentage

°C

degrees centigrade

α

alpha

β

beta

λ

lamda

µ

mu

σ

sigma

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LIST OF FIGURES CHAPTER 1 Figure 1. Mean milk production (L/ha.year) from home-grown feed (pasture) obtained in farmlet studies (Chapman et al., 2007a; Fulkerson et al., 2008a; Valentine et al., 2009; Chataway et al., 2010) and a commercial farms survey (Linehan et al., 2004) in Australia. Symbols indicate maximum () and minimum (◊) for each study. ............................................................................................ 13 Figure 2. Relationship between stocking rate (milking cows/ha) and utilised pasture (t DM/ha/year) in the studies by Armstrong et al. (2000)(•), Chapman et al. (2007a) (■), Holmes and Parker (1992)(×; ), Linehan et al. (2004)(▲), Macdonald et al. (2008)(). ................................................................................................................. 14 Figure 3. Relationship between milk yield (L/cow.year) and utilised pasture (kg DM/ha/year) in the studies by Fulkerson et al. (2008a)(•; ○), Chapman et al. (2007a)(■), Holmes and Parker (1992)(×), Linehan et al. (2004)(▲) and Macdonald et al. (2008) (). ............................................................................................ 16 Figure 4. Conceptual improvement in milk production from pasture (L/ha.year) by increasing conversion efficiency (Kolver et al., 2002; Kolver, 2007) or pasture yield (Garcia and Fulkerson, 2005; Beca, 2008) from the Australian industry average to their on-farm potential. ................................................................................................ 17 Figure 5. Mean metabolisable energy (ME) value (MJ/ kg DM) (a; ▲), neutral detergent fibre (NDF; %DM) (b; ) and yield (t DM/ha.year) (c; bars) of various forages from data obtained from farm and plot studies reviewed (Mason and Pritchard, 1987; Pritchard, 1987; Moate et al., 1998; Eckard et al., 2001; Jacobs et al., 2001; Jacobs et al., 2002; Garcia et al., 2006; Neal et al., 2006; Shrestha et al., 2006; Fulkerson et al., 2007; Garcia et al., 2007; Jacobs and Ward, 2007; Shrestha, 2007; Fulkerson et al., 2008b; Garcia et al., 2008; Fulkerson, 2009; Jacobs et al., 2009). Vertical bars indicate range of values. .............................................................................. 21 Figure 6. Whole farm N inputs and N surplus estimated at the farm-gate level for Australia (■) (Eckard et al., 2007), New Zealand () (Ledgard et al., 1998; Ledgard et al., 1999; Ledgard and Luo, 2008), the Netherlands (•) (Aarts et al., 1992; Hanegraaf and den Boer, 2003; Oenema et al., 2003), England () (Jarvis, 1993), Belgium (+) (Mulier et al., 2003), Switzerland (□) (Thomet and Pitt, 1997)and Germany (○) (Haas et al., 2001)....................................................................................... 31 Figure 7. Relationship between milk yield (L/cow.year) and total farm greenhouse gas emissions (kg CO2 equivalent /L milk) in the studies by Christie et al. (2009)(•), Beukes et al. (2010) (■), Basset-Mens et al. (2009)(), Verge et al. (2007)(▲), Haas et al. (2001)(), Thomassen et al. (2008)() and Casey and Holden (2005) (□).............. 36

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CHAPTER 2 Figure 1. Mean lactation curves (L/cow.day; adjusted to 305 days) for control (C;○), high stocking rate (HSR; ●), high milk yield per cow (HMY; ∆) and high stocking rate and milk yield per cow (HH;▲) systems. Vertical bar indicates the average standard error of the mean. ............................................................................................... 68 Figure 2. Mean monthly daily milk yields (L/cow) for control (C;○;--), high stocking rate (HSR; ●;—), high milk yield per cow (HMY; ∆;▪▪▪) and high stocking rate and milk yield per cow (HH;▲;▬) systems. Lines represent spline smoothing function.Vertical bar indicates the average standard error of the mean. ............................ 69 Figure 3. Monthly body weight (BW; kg; a) and body condition score (BCS; scale 18; b) for control (C;○;--), high stocking rate (HSR;●;—), high milk yield per cow (HMY;∆;▪▪▪) and high stocking rate and milk yield per cow (HH;▲;▬) systems. Values are the means of 2 years. Lines represent spline smoothing function. Vertical bars indicate the average standard error of the mean......................................................... 70 Figure 4. Pre- and post-grazing pasture dry matter mass (kg DM/ha) for control (C;○;--), high stocking rate (HSR; ●;—), high milk yield per cow (HMY; ∆;▪▪▪) and high stocking rate and milk yield per cow (HH;▲;▬) systems. Lines represent spline smoothing function. Vertical bars indicate the average standard error of the mean. .......... 72 Figure 5. Mean pasture growth rate (PGR; kg DM/ha.day; area under curve) for the four systems, and dry matter intake (DMI; kg DM/ha.day) for control (C;○;--), high stocking rate (HSR; ●;—), high milk yield per cow (HMY; ∆;▪▪▪) and high stocking rate and milk yield per cow (HH;▲;▬) systems. Horizontal arrows indicate the period of dominance of each pasture species. Lines represent spline smoothing function. Vertical bars indicate the average standard error of the mean............................. 73 Figure 6. Metabolisable energy density (ME; MJ/kg DM; a), crude protein concentration (CP; % DM; b), neutral-detergent fibre concentration (NDF; % DM; c), and water soluble carbohydrates concentration (WSC; % DM; d) of pasture. The data points are monthly means for the four systems for the two years of the study. Lines represent spline smoothing function. Vertical bars indicate standard errors of the mean. ............................................................................................................................... 74 Figure 7. Total intake (kg DM/cow.day) for control (C;○;--), high stocking rate (HSR; ●;—), high milk yield per cow (HMY; ∆;▪▪▪) and high stocking rate and milk yield per cow (HH;▲;▬) systems. The data points are means of 2 years. Lines represent spline smoothing function. Vertical bar indicates the average standard error of the mean. ..................................................................................................................... 77 Figure 8. Relation between whole diet metabolisable energy (ME) density (MJ/kg DM) and milk yield (L/cow. day) for control (C;○;--), high stocking rate (HSR; ●;—), high milk yield per cow (HMY; ∆;▪▪▪) and high stocking rate and milk yield per cow (HH;▲;▬) systems. The data points are monthly means for each system in each year. .... 78 Figure 9. Relation between intake (DMI; kg DM/cow.day) of each source of feed [ryegrass (a), kikuyu grass (b), concentrates (c) and fodder (d)] and whole diet

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metabolisable energy (ME) density (MJ/kg DM) for all systems. The data points are monthly means for each system in each year. ................................................................... 79 Figure 10. Relation between neutral-detergent fibre (NDF; % DM) content and intake (DMI; kg DM/cow.day) in summer-autumn (a) and winter-spring (b) for control (C;○), high stocking rate (HSR;●), high milk yield per cow (HMY;∆) and high stocking rate and milk yield per cow (HH;▲) systems. The data points are monthly means for each system in each year. ................................................................................. 80 Figure 11. Feed conversion efficiency (FCE; L milk/kg DM fed) for control (C;○;--), high stocking rate (HSR; ●;—), high milk yield per cow (HMY;∆;▪▪▪) and high stocking rate and milk yield per cow (HH;▲;▬) systems. The data points are monthly means of 2 years. Lines represent spline smoothing function. Vertical bars indicate the average standard error of the mean. ............................................................... 81 Figure 12. Marginal feed conversion efficiency (FCE; L milk/MJ ME; bars) of supplements fed above the control (C) for the high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems. All values are means of the 2 years. Vertical bars indicate standard error of the mean. ..... 82

CHAPTER 3 Figure 1. Cumulative distribution of operating profit (AU$/year) for control (C;--), high stocking rate (HSR; —), high milk yield per cow (HMY;▪▪▪) and high stocking rate and milk yield per cow (HH;▬) when price of milk (a), concentrates (b) or urea fertiliser (c) are simulated to vary stochastically. ............................................................ 106 Figure 2. Cumulative distribution of operating profit (AU$/year) for control (C;--), high stocking rate (HSR; —), high milk yield per cow (HMY;▪▪▪) and high stocking rate and milk yield per cow (HH;▬) for the integrated stochastic simulation of prices of milk, concentrates and urea fertiliser. ......................................................................... 107 Figure 3. Stochastic Efficiency with Respect to a Function (SERF) certainty equivalents of operating profit (AU$/year) for control (C;--), high stocking rate (HSR; —), high milk yield per cow (HMY;▪▪▪) and high stocking rate and milk yield per cow (HH;▬) at different levels of risk aversion of the decision maker [0 (risk neutral) to 4 (extremely risk averse)], when price of milk (a), concentrates (b) or urea fertiliser (c) are simulated to vary stochastically. ............................................................................... 108 Figure 4. Stochastic Efficiency with Respect to a Function (SERF) certainty equivalents of operating profit (AU$/year) for control (C;--), high stocking rate (HSR; —), high milk yield per cow (HMY;▪▪▪) and high stocking rate and milk yield per cow (HH;▬) at different levels of risk aversion of the decision maker, when price of milk, concentrates and urea fertiliser were simulated to vary stochastically. ............................ 111

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CHAPTER 5 Figure 1. Mean monthly pre- (♦) and post- (▲) grazing pasture mass (kg DM/ha) meaned over the two years of the study. Vertical bars indicate standard error of the difference……...………………………………………………………..………….……..1565 Figure 2. Mean monthly pasture growth rate (PGR; kg DM/ha.day; ▬ ) and pasture dry matter intake (DMI; kg DM/ha.day; ;▪▪▪) meaned over the two years of study. Vertical bars indicate standard error of the mean (SEM). ............................................... 156 Figure 3. Mean monthly content (% DM) of neutral detergent fibre (a; NDF), crude protein (b; CP), water soluble carbohydrates (c; WSC) and metabolisable energy (d; MJ ME/kg DM) for the Pasture section of the CFS study. Vertical bars indicate standard error of the difference. ..................................................................................... 157 Figure 4. Mean monthly dry matter intake (DMI) (kg DM/cow.day) of pasture ( ), CFR grazeable crops ( ), silage ( ) and concentrate ( ) as mean of the 2 years of study. Vertical bar indicates standard error of the difference for total DMI..................... 158 Figure 5. Mean weighed monthly content (% DM) of neutral detergent fibre (a; NDF), crude protein (b; CP), water soluble carbohydrates (c; WSC) and value (MJ/kg DM) for metabolisable energy (d; ME) of the total diet of cows meaned over two years. Vertical bars indicate standard error of the difference........................................... 159 Figure 6. Mean monthly milk yield (a ;L/cow.day) milk fat (b ; %) and milk protein ( c; %) meaned over the two years of study. Vertical bars indicate standard error of the difference. ...................................................................................................................... 161 Figure 7. Mean monthly body condition score (BCS; a; scale from 1 to 8) and body weight (BW; b; kg/cow) for milking cows, meaned over the 2 years of the study. Vertical bars indicate standard error of the difference..................................................... 162 Figure 8. Mean monthly feed conversion efficiency [FCE; L energy corrected milk (ECM)/ kg DM] over the two years of study. The vertical bar indicates standard error of the difference. ............................................................................................................ 163 Figure 9. Relationship between DMI of ryegrass (kg/cow.day) and milk yield (L/cow.day) during autumn to early spring. The data points are monthly means for each year. ....................................................................................................................... 164 Figure 10. Relation between ME (metabolisable energy as MJ/kg DM) on the diet and BCS (body condition score, scale 1 to 8) during the complete year. The data points are monthly means for each year.......................................................................... 165 Figure 11. Monthly mean of daily milk production (L/cow.day) averaged over the two years of study for cows calving in autumn (♦) or spring (■). Vertical bar indicates standard error of the difference. ..................................................................................... 166

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Figure 12. Mean milk yield (L/cow) in each week of lactation for cows calving in autumn (♦) or spring (■). Vertical bar indicates standard error of the difference. ............ 167 Figure 13. Mean monthly body condition score (BCS; a; scale from 1 to 8) and body weight (BW; b; kg/cow) for cows calving in autumn (♦) or spring (■) over the two years of study. Vertical bars indicate standard error of the difference. ............................ 168

CHAPTER 6 Figure 1. Total utilised yield (t DM/ha/year) of (a) pasture from DairyMod and (b) forage crops from APSIM [maize (∆), forage rape (♦) and field peas (▲)] for a fixed allocation of 4.5 ML of irrigation water/ha.year simulated for 100 years. ....................... 191 Figure 2. Relationship between simulated total utilised forage yield (t DM/ha.year) and annual rainfall (mm) for forage crops (■) and pasture (▲) of the CFS. ................... 192 Figure 3. Relationship between simulated total utilised forage yield (t DM/ha.year) and rainfall (mm) during the growing period for maize (), forage rape (▲) and field peas (•). ......................................................................................................................... 193 Figure 4. Cumulative probability distribution of operating profit (AU$/year) for Base (·····), CFS (―) and PG (---), when price of milk (a), concentrates (b), urea fertiliser (c), irrigation water (d) or yield of forages (e) are simulated to vary stochastically. ........ 195 Figure 5. Cumulative probability distribution of operating profit (AU$/year) for Base (·····), CFS (―) and PG (---) systems when price of milk , urea fertiliser, concentrates, irrigation water or yield of forages are simulated to vary stochastically. ......................... 197 Figure 6. Stochastic Efficiency with Respect to a Function (SERF) certainty equivalents of operating profit (AU$/year) for Base (□), CFS (•) and PG (▲) systems at different levels of risk aversion of the decision maker, when price of milk, urea fertiliser, concentrates, irrigation water and yield of forages are simulated to vary stochastically. ................................................................................................................ 198

CHAPTER 7 Figure 1. Mean daily urinary N excretion (g N/cow.day) in the Complementary Forage System (CFS) whole farm study in each month from May 2008 to April 2009. .. 219 Figure 2. Field level amounts and sources of N inputs (bars; positive values), outputs (bars; negative values) and surplus/deficit (line) as kg of N during autumn (a), winter (b), spring (c) and summer (d) for laneway/water trough (LN), dairy yard/milking platform (Y), feedpad (FP), Pasture (P), Double crop (DC) and Triple crop (TC) management units within the Complementary Forage System (CFS) whole farm study from May 2008 to April 2009. ....................................................................................... 223

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GENERAL DISCUSSION Figure 1. Whole farm N inputs and N surplus estimated at the farm-gate level for Australia (■) (Eckard et al., 2007), New Zealand () (Ledgard et al., 1998; Ledgard et al., 1999; Ledgard and Luo, 2008), the Netherlands (•) (Aarts et al., 1992; Hanegraaf and den Boer, 2003; Oenema et al., 2003), Ireland () (Humphreys et al., 2008), England () (Jarvis, 1993), Germany (○) (Haas et al., 2001), Belgium (+) (Mulier et al., 2003) and Switzerland (□) (Thomet and Pitt, 1997) and the intensification systems evaluated in this study in Chapter 4 ( ) and Chapter 7 ( ). .... 246 Figure 2. Relationship between milk yield (L/cow.year) and total farm greenhouse gas emissions (kg CO2 equivalent /L milk) in the studies by Christie et al. (2009) (•), Beukes et al. (2010)(■), Basset-Mens et al. (2009)(), Verge et al. (2007)(▲), Haas et al. (2001)(), Thomassen et al. (2008)() and Casey and Holden (2005) (□) and the intensification systems evaluated in this study in Chapter 4 ( ) and Chapter 7 ( ). ................................................................................................................................ 247

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LIST OF TABLES CHAPTER 1 Table 1 - Range in Yield (t DM/ha), ME (MJ/kg DM), CP(%DM), NDF (%DM), ADF (%DM), period available (grazing or cut) and water use efficiency (WUE; kg DM/mm irrigation + rainfall) of annual crops as potential forages for dairy cows and evaluated in field studies in Australia. .............................................................................. 20 Table 2. Mean annual greenhouse gas emissions (t CO2 equivalents), stocking rate (cows/ha) and milk yield (L/ha) from farm studies and commercial farms surveys. .......... 35

CHAPTER 2 Table 1. Target milk production (L/cow.lactation and L/ha), stocking rate (cows/ha), proportion of pasture and concentrates in diet for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems. .................................................................................................................. 59 Table 2. Milk (L/ha.year) and milk fat and protein (kg/ha.year) yield for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems............................................................................... 66 Table 3. Effect of treatment on 305 day milk yield per cow (L), milk composition (%), body condition score (BCS; scale 1 to 8; mean and change between start and end of lactation), body weight (BW; kg; mean and change between start and end of lactation), and reproductive performance for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems. .................................................................................................................. 67 Table 4. Mean annual utilised pasture (kg DM/ha) (grazed and conserved), and pre and post-grazing pasture mass (kg DM/ha) for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems…………………………………………………………………………....…71 Table 5. Mean daily feed consumption (kg DM/cow.day offered) and metabolisable energy value, crude protein, neutral-detergent fibre, acid-detergent fibre and water soluble carbohydrate content of the whole diet for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems. .................................................................................................................. 76

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CHAPTER 3 Table 1. Mean feed costs (AU$/t DM) incurred during the study (all concentrates, hay, limestone and urea) or calculated from budgeting (pasture, maize silage and pasture silage) for 2005/06. .............................................................................................. 98 Table 2. Capital value assumptions for “steady state” whole farm analysis. ..................... 99 Table 3. Price distributions used in the risk model ......................................................... 102 Table 4. Whole farm budget results (AU$/year) for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) modeled farms. Systems assume a “steady state” production and average capital values, and 0.35 AU$/L net milk price received. ............................................................ 104

CHAPTER 4 Table 1. Annual farm-gate N balance (kg N/ha.year) for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems (mean for 2 years). ........................................................................... 127 Table 2. Greenhouse gas emissions ( t CO2 equivalents/year) for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems (mean for 2 years). ............................................................. 128 Table 3. Farm-gate N balance components (kg N/ha.year) as average of treatments from farm studies. .......................................................................................................... 129

CHAPTER 5 Table1. Mean ± standard error of the mean (SEM) forage yield (t DM/ha) for each forage section of the farmlet and total for the whole farmlet for years 1 and 2. ............... 153 Table 2. Mean weighed nutritive value of forage for each forage section of the farmlet and mean value for the whole farmlet (CFS). ................................................................. 154 Table 3. Mean ± standard error of the mean of milk yield (L/cow.lactation corrected to 305 days and L/ha.year) and milk composition from total and from home-grown feed meaned over the two years of study. ....................................................................... 160

CHAPTER 6 Table 1. Price distributions parameters used in the risk model. ...................................... 187 Table 2. Mean annual forage and milk yields (per ha and per cow) and economic indicators (AU$/ha.year) for the modelled systems based on the two- year farmlet study at Costorphine Dairy, Camden…..................……………..……………………... ..189

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Table 3. Parameters of cumulative probability distribution of operating profit (AU$/year) for Base, CFS and PG systems, when price of milk, concentrates, urea fertiliser, irrigation water or yield of forages are simulated to vary stochastically. .......... 196

CHAPTER 7 Table 1. Mean utilised yields of forage crops and pasture (t DM/ha.year), concentrates fed (t DM/cow.year), stocking rate (cows/ha), annual milk yields (L) per cow and per ha, and milk components (%) for the Base, CFS and PG farm systems modelled on the results of year 2 of the Complementary Forage System (CFS) whole farm study at Costorphine Dairy, Camden. ..................................................................... 217 Table 2. Mean percentage of time spent and quantity of defecations and urinations at four management units within the Complementary Forage System (CFS) whole-farm study in winter (August) and summer (December). ........................................................ 220 Table 3. Field level N balance, including inputs, outputs and surplus/deficit (t/year) for each management unit within the Complementary Forage System (CFS) whole farm study from May 2008 to April 2009. ...................................................................... 221 Table 4. Annual farm-gate N balance (t N/ha.year) for the three farm systems modelled based on the Complementary Forage System (CFS) field study. ..................... 224 Table 5. Greenhouse gas emissions (t CO2 eq/year) for the three farm systems modelled based on the Complementary Forage System (CFS) field study. ..................... 225

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LIST OF PUBLICATIONS FROM THIS THESIS

Refereed Scientific Journals

1. FARIÑA S. R., GARCIA S. C. and FULKERSON W. J. (2011) A complementary forage system whole farm study: forage utilisation and milk production. Animal Production Science, 51 (in press). 2. FARIÑA S. R., GARCIA S. C., FULKERSON W. J. and BARCHIA I. M. (2011) Pasture-based dairy farm systems increasing milk production through stocking rate or milk yield per cow: pasture and animal responses. Grass and Forage Science (in press). 3. FARIÑA S. R., ALFORD A., GARCIA S. C. and FULKERSON W. J. (2011) Business risk of pasture-based dairy intensification through higher use of concentrates or a Complementary Forage System (CFS). Agricultural systems (submitted for publication).

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Conference Proceedings 1. FARIÑA S. R., GARCIA S. C. and FULKERSON W. J. (2007) Maximising milk production/ha from home grown feed: over 30,000 L/ha from a Complementary Forage Rotation/Pasture System. In: Fulkerson W. J. (ed). Proceedings of the Dairy Research Foundation Symposium, Camden, NSW, Australia, pp. 47-51. 2. FARIÑA S. R. (2007) Maximising milk production/ha from home-grown feed. In: Proceedings of the Faculty of Veterinary Science Postgraduate Conference, Sydney, NSW, Australia, pp.21 3. FARIÑA S. R. (2008) Making the most of water and land: 2000 Kg. milk solids/ha from home-grown feed. In: Proceedings of the Australian Dairy Conference, Launceston, TAS, Australia, pp. 129-130. 4. FARIÑA S. R., GARCIA S. C. and FULKERSON W. J. (2008) More milk from home grown feed: more profits? In: Fulkerson W. J. (ed). Proceedings of the Dairy Research Foundation Symposium, Camden, NSW, Australia, pp. 58-62. 5. FARIÑA S. R. (2008) Maximising milk production from home-grown feed: is that a profitable option? In: Proceedings of the Faculty of Veterinary Science Postgraduate Conference, Camden, NSW, Australia, pp.18. 6. FARIÑA S. R. (2009) Strategies to maximise intake per ha in a pasture based system. In: Fulkerson W. J. (ed). Proceedings of the Dairy Research Foundation Symposium, Camden, NSW, Australia, pp. 33-42. 7. FARIÑA S. R. (2009) Comparative effects of increasing stocking rate or milk per cow on a pasture based farm. Proceedings of the Faculty of Veterinary Science Postgraduate Conference, Camden, NSW, Australia, pp.15. 8. FARIÑA S. R. (2010) Little grain, big profits: milking 28,000 L/ha from homegrown forage. In: Proceedings of the Australian Dairy Conference, Wollongong, NSW, Australia, pp. 80-82. (Awarded the Department of Agriculture Fisheries and Forestry Young Dairy Scientist Communication Award)

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Industry publications

1. FARIÑA S. R. (2010) Forage system beats grain prices. New Zealand Dairy Exporter. April, p. 56. 2. FARIÑA S. R. and GARCIA S. C. (2010) Less carbon emission from intense dairy systems. Australian Farm Journal, 20, p. 50. 3. FARIÑA S. R. and GARCIA S. C. (2009) Less carbon emission from intense dairy systems. The Australian Dairyfarmer. December, p. 15. 4. FARIÑA S. R. (2010) Less grain and more gains: milking from home-grown forage. In: Proceedings of the Western Dairy Innovation Day, Scott River, WA, Australia.

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GENERAL INTRODUCTION


GENERAL INTRODUCTION Milk production in Australia has increased by 4.7 per cent a year between 1988 and 2008 (ABARE, 2010). However, most of this improvement was accomplished through increased use of grain-based concentrates (Fulkerson and Doyle, 2001; Thorrold and Doyle, 2007; ABARE, 2010). As prices of inputs, such as concentrates, water and fertilisers, have increased at a faster rate than the price of milk, total factor productivity growth in the last 20 years has only been 0.8 %/annum (ABARE, 2010). Over 75% of Australian dairy farmers supply companies that export the major part of their dairy production (Dairy Australia, 2009). The international price for milk, as most agricultural commodities, is exposed to a long-term real price decline (FAO, 2009). Importantly, world trade in dairy products is relatively small, leading to significant shifts in prices over short time frames, which in addition to the effects of volatile international exchange rates, means that prices received at the farm gate can be highly variable (Wales et al., 2006). The dairy industry in Australia uses 40% of the irrigation water destined for agricultural use (Bethune and Armstrong, 2004). Changes in water policy and low inflows into water storages over the past decade have seen substantial increases in water price and low water allocations in some regions such as northern Victoria (Wales et al., 2006). Climate change has been identified as the major risk to water availability in the irrigation dairy regions (CSIRO, 2007). In this context, farmers have needed to increase the use of bought-in feed to maintain milk production in their systems. In 2008, purchased feed represented the highest single input cost on dairy farms, accounting for 36% of total cash costs (ABARE, 2008a). The widespread adoption of higher intensity feeding regimes has increased the exposure of the industry to a new set of risks, since the international price for feed grain is likely to increase in the future due to higher demand for biofuel production and growing demand for human consumption in developing countries (Little, 2008). Acquisition of adjoining farm land has been an effective path to achieve productivity growth in the past (ABARE, 2006; Malcolm and Sinnett, 2007) but increasing demand from urban development and other agricultural enterprises has reduced the availability and increased the price of land suitable for dairying (Barr, 2007; Thorrold and Doyle, 2007; ABARE, 2008b). Under these circumstances, most farmers do not see an increase in size or changing location as viable options, the latter being both a high risk and a socially unattractive option (Barr, 2007). Therefore, most farming

3


families would prefer to continue to farm in their location and explore other paths to increase productivity (Barr, 2007). In this context, Australian dairy farmers will need to improve productivity by producing more milk per ha without having to rely on additional purchased feed. To achieve this there would appear to be two options of intensification systems: 1. Since perennial pasture is the cheapest source of feed on most dairy farms in Australia (Fulkerson and Doyle, 2001), it follows that an increase in milk production from this source will be needed. However, it is not clear if this should be achieved through an increase in stocking rate or milk yield per cow or both. 2. Another alternative is the use of annual forage crops, which often have a higher seasonal yield, can be more water use efficient than perennial pasture and can be used in combination with pasture. There are no studies in Australia that have evaluated, at the whole farm level, the integration of pasture and different forage crops on irrigated dairy systems. However, increased farm productivity, achieved with either option, will also need to show an increase in profitability that could be sustained in the long term, to cope with the high volatility of milk, grain and irrigation water prices as well as with a changing climate. The economic sustainability of the intensification of pasture-based dairy farms has been analysed under dryland conditions (Chapman et al., 2008; Armstrong et al., 2010) and considering either production or price risk, but not both sources of risk integrated. These authors found that systems based on a higher proportion of home-grown feed and lower use of purchased feed were more economically robust. For these intensification systems to be sustainable, they should be implemented without increasing the impact on the environment. As dairy farming intensifies, so does the communities’ pressure on farmers to provide evidence of meeting environmental standards (Gourley et al., 2007). The two major areas of potential pollution are nutrient losses to the environment and greenhouse gas emissions (Cameron et al., 2007; Basset-Mens et al., 2009). The information available about the potential environmental impact of different intensification systems for Australian conditions is scarce (Gourley et al., 2007; Christie et al., 2009), and there is no published research based on the impacts at the whole farm level. However, studies conducted on pasture-based dairy farms (Basset-Mens et al., 2009; Christie et al., 2009) have shown that intensification systems aimed at increasing productivity tend to reduce the environmental impact per unit of milk.

4


Therefore, the general objective of this research was to evaluate, at the whole farm level, intensification systems based on pasture as the only source of home-grown feed, or based on pasture plus forage crops, in terms of: 1. Biophysical productivity. 2. Farm profit margin and business risk (price and climate risk). 3. Potential environmental pollution (greenhouse gas emissions and nutrients balance). The general hypothesis of this research was that an intensification system integrating pasture and forage crops, with small inputs of concentrates, could increase milk production per ha from home-grown feed beyond the potential of systems based on pasture as the only source of home-grown feed, in a more economically and environmentally sustainable way. The focus of this thesis was on the evaluation of farm systems. The first study (Study 1) compared intensification systems based on pasture plus supplements. This study was based on a 2-year field experiment where systems to increase milk per ha by increasing stocking rate or milk yield per cow or both were compared. The second study (Study 2) evaluated an intensification system based on pasture plus forage crops. This study was based on a 2-year field experiment, comprising a farm system that combined pasture with forage crop rotations, which was compared to other intensification systems that were modelled based on the results of the field experiment. In both studies, the approach was to: 1. Evaluate the feasibility of implementation and biophysical productivity at the whole-farm level from the field experiments. 2. Evaluate the business risk of each system with the integration of different analytical tools, and using historical data of inputs and outputs prices and climate variability. 3. Estimate the potential environmental impact of each system in terms of greenhouse gas emissions and nitrogen (N) inputs and outputs.

5


Thesis structure In Chapter 1, a review of literature was conducted to investigate the potential to increase milk production per hectare on Australian pasture-based dairy farms. The review focused on farmlet and commercial farm studies to evaluate the role of pasture and forage crops alone or in rotation, the effects of farm intensification on business profitability and risk, and the whole farm level estimations of nutrients balances and greenhouse gas emissions as measures of potential environmental pollution. The Study 1 investigated intensification systems based on pasture plus supplements. This section compared systems aimed at increasing milk per ha by increasing stocking rate, milk yield per cow or both. In Chapter 2 the comparative biophysical production of each system was evaluated on a farmlet study, including pasture utilisation, efficiency of supplements use and animal responses. From these results a whole farm economic budget was constructed in Chapter 3, and the sensitivity of each system to variations in the price of key inputs and outputs was analysed. In Chapter 4, the biophysical results of each farm system were used to estimate the potential N pollution, through the development of a N balance, and the production of greenhouse gas emissions in relation to the intensification process. The Study 2 investigated an intensification system aimed at increasing milk per ha beyond the potential of pasture through the inclusion of forage crops. In Chapter 5 this concept was evaluated on a whole farm study, which comprised a proportion of the farm sown to two different forage crop rotations and was aimed at using a low amount of concentrates per cow as the only brought-in feed of the system. The purpose was to assess the feasibility of the implementation and biophysical productivity, with measurement of pasture and forage crops utilisation, feeds use efficiency and animal responses. In Chapter 6 the results of the whole farm study were used as a base to model alternative intensification systems. In turn, the sensitivity of these systems to climate variability (through its effect on forage yield) and variability of prices of key inputs and outputs was tested and compared between systems. An innovative integration of different analytical tools was used for this purpose. Finally, in Chapter 7, the potential N pollution and greenhouse gas emissions of these systems was evaluated in a similar way as outlined above for Chapter 4. However, a more detailed N balance for different sections within the farm and for each season of the year was also carried out in Chapter 7. The thesis concluded with an integrative General Discussion of the results of all chapters and the implications for the dairy industry.

6


The 7 chapters of this thesis have been written in a manuscript style. However, Chapters 2 and 5 have been accepted for publication in refereed journals and Chapter 6 has been submitted for publication too. Hence, each of these sections is presented as a stand-alone unit, although some minor changes were made in order to avoid excessive repetition across the thesis. For the same reason, references have been kept on each individual chapter.

REFERENCES ABARE (2006) Australian dairy: production systems, productivity and profit. Canberra, Australia: Australian Bureau of Agricultural and Resource Economics. ABARE (2008a) Financial performance of dairy farms, 2005-06 to 2007-08. In: Mackinnon D. (ed.). Canberra, Australia: Australian Bureau of Agricultural and Resource Economics. ABARE (2008b) Australian dairy industry: Use of technology and management practices on dairy farms, 1991-92 to 2006-07. In: Ashton D. and Mackinnon D. (eds.). Canberra, Australia: Australian Bureau of Agricultural and Resource Economics. ABARE (2010) Productivity growth: trends, drivers and opportunities for broadacre and dairy industries. In: Nossan K. and Sheng Y. (eds.). Canberra, Australia: Australian Bureau of Agricultural and Resource Economics. ARMSTRONG D. P., TARRANT K. A., HO C. K. M., MALCOLM L. R. and WALES W. J. (2010) Evaluating development options for a rain-fed dairy farm in Gippsland. Animal Production Science, 50, 363-370. BARR N. (2007) The changing social landscape of Rural Victoria. Proceedings of the 48th Annual Conference of the Grassland Society of Southern Australia, Murray Bridge, Australia, 13-14 June 2007, pp. 1-13. BASSET-MENS C., LEDGARD S. and BOYES M. (2009) Eco-efficiency of intensification scenarios for milk production in New Zealand. Ecological Economics, 68, 1615-1625. BETHUNE M. and ARMSTRONG D. P. (2004) Overview of the irrigated dairy industry in Australia. Australian Journal of Experimental Agriculture, 44, 127-129. CAMERON K. C., HEDLEY H., CLARK H. and DI H. J. (2007) Impact of pasture and supplement feeding on the environment. In: Rattray P. V., Brookes I. M. and Nicol A. M. (eds.) Pasture and supplements for grazing animals, pp. 287-309. Hamilton, New Zealand: New Zealand Society of Animal Production. CHAPMAN D. F., KENNY S. N., BECA D. and JOHNSON I. R. (2008) Pasture and forage crop systems for non-irrigated dairy farms in southern Australia. 2. Interannual variation in forage supply, and business risk. Agricultural Systems, 97, 126-138. CHRISTIE K. M., RAWNSLEY R. P. and ECKARD R. J. (2009) A whole farm system analysis of Greenhouse gas emissions from simulated dairy farm systems in Australia. Proceedings of the Greenhouse 2009 Conference Climate Change & Resources, Perth, Australia. 7


CSIRO (2007) Climate change in Australia. In: Pearce K., Holper P., Hopkins M., Bouma W., Whetton P., Hennessy K. and Power S. (eds.): CSIRO. DAIRY AUSTRALIA (2009) Australian Dairy Industry In Focus 2009. pp. 1-56. Southbank, Victoria, Australia: Dairy Australia. FAO (2009) The state of food and agriculture 2009. Rome, Italy: Food and Agriculture Organization of the United Nations. FULKERSON W. J. and DOYLE P. (2001) The Australian Dairy Industry. Victoria, Australia: Victorian Department of National Resources and Environment. GOURLEY C. J. P., POWELL J. M., DOUGHERTY W. J. and WEAVER D. M. (2007) Nutrient budgeting as an approach to improving nutrient management on Australian dairy farms. Australian Journal of Experimental Agriculture, 47, 1064-1074. LITTLE S. (2008) A new industry-wide approach to securing greater returns from grain and concentrate feeding. Proceedings of the The Australian Dairy Conference 2008, Launceston, Australia, pp. 109-114. MALCOLM W. and SINNETT A. (2007) Future Productivity and Growth in Dairy Farm Businesses in New Zealand: the Status Quo is not an option. Australian Agribusiness Perspectives: 2007. THORROLD B. and DOYLE P. T. (2007) Nature or nurture - forces shaping the current and future state of dairy farming in New Zealand and Australia. In: Chapman D. F., Clark D. A., Macmillan K. L. and Nation D. P. (eds). Proceedings of the Australasian Dairy Science Symposium 2007, Melbourne, Australia, pp. 450460. WALES W. J., HEARD J. W., HO C. K. M., LEDDIN C. M., STOCKDALE C. R., WALKER G. P. and DOYLE P. T. (2006) Profitable feeding of dairy cows on irrigated dairy farms in northern Victoria. Australian Journal of Experimental Agriculture, 46, 743-752.

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CHAPTER 1 OPPORTUNITIES AND CHALLENGES TO INCREASE MILK PRODUCTION FROM HOMEGROWN FEED IN AUSTRALIAN DAIRY FARMS: A REVIEW OF THE LITERATURE

Literature review

INTRODUCTION The Australian dairy industry The dairy industry is the third most important agricultural industry in Australia in terms of value at the farmgate. In the past ten years, Australia produced between 8 and 11 billion litres (L) of milk and exported 40 to 60% of that production (Dairy Australia, 2009). This positioned Australia as the fourth largest exporter of dairy products in the world (Dairy Australia, 2009). International prices are the major factor determining the milk price in Australia, since dairy farmers operate in a deregulated and open market (Dairy Australia, 2009). Thus, Australian dairy farmers receive a comparatively lower price than many major producing countries, and therefore need to maintain a low cost of production (Dairy Australia, 2009) to remain competitive internationally. In this regard, the high proportion of grazed pasture in the diet of dairy cows has allowed Australian dairy farmers to maintain lower costs than all their international competitors, except New Zealand (Dillon, 2006). Hence, dairy farm systems in Australia are predominantly pasture-based, with approximately 75% of feed requirements coming from pasture (Armstrong et al., 2000; Dairy Australia, 2009). As a consequence, 80% of the milk is produced in the south-eastern coastal areas (mainly in the states of Victoria, South Australia and Tasmania) where the temperate climate provides a longer pasture growing season. However, the low and variable rainfall has resulted in the need for the use of irrigation, particularly in northern Victoria and southern New South Wales. Some supplementary feed is fed on most dairy farms to fill seasonal feed gaps, with cereal-based grain being the major source due to its relatively low price (Fulkerson and Doyle, 2001). In 2009, an average of 1.5 t of grain-based concentrates was fed to cows on 93% of dairy farms. Holstein Friesian is the predominant dairy breed in Australia, followed by Jersey, comprising 67 and 10% of all dairy cattle, respectively (Dairy Australia, 2009). The Australian dairy industry is facing long term declining terms of trade, reduced land and water availability and increasing pressure to minimise environmental pollution. In this context, for dairy farmers to remain profitable, it will be necessary to develop systems that will allow them to increase milk production from home-grown feed (Garcia and Fulkerson, 2005; Chapman et al., 2008a) in an economically and environmentally sustainable way.

11

CHAPTER 1

The aim of this review is to identify the current opportunities and challenges for pasture-based farm systems in Australia to increase milk production from homegrown feed in an economically and environmentally sustainable way. This review focuses on information obtained from farmlet studies, whole farm studies and surveys of commercial dairy farms, although data from component research is included where information from studies of a larger scale were not available. In the first section, the potential for farm systems to achieve an increase in milk production per ha from home-grown feed based on pasture and supplements is reviewed. In the second section, the role of different pasture and forage crop species used in Australian dairy farm systems is reviewed. In the final section, the current knowledge on the social, economic and environmental sustainability of intensification of dairy farms based on home-grown feed is reviewed.

MILK PRODUCTION FROM PASTURE Milk production in Australia has increasingly focused on the use of grazed pasture, and this has been even more pronounced since the domestic market for milk was deregulated in 2000. However, there is considerable variation between and within regions in the proportion of milk produced from pasture on commercial farms (Rawnsley et al., 2007; Thorrold and Doyle, 2007). In terms of farmlet studies in Australia where at least one year of data is available, milk production from pasture ranging from 3,800 to 16,600 L milk/ha has been reported (Linehan et al., 2004; Chapman et al., 2007a; Fulkerson et al., 2008a; Valentine et al., 2009; Chataway et al., 2010; Figure 1). A pasture-based system of dairy farming is defined as one in which grazed pasture is the largest single feedstuff in the diet of cows or comprises at least 50% of total annual dry matter (DM) consumed/cow.year (Garcia and Fulkerson, 2005). The total milk production from pasture in the studies reviewed was estimated by deducting the milk produced from brought-in supplements. For this purpose, the total dry matter intake (DMI) of feed was converted to units of metabolisable energy (ME, in MJ).

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Milk from home-grown feed (L/ha.year)

.

Literature review

18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Linehan et et al. al. Linehan 2004 2004

Chapman et et al. al. Fulkerson Fulkerson et et al. al. Valentine Valentine et et al. al. Chataway Chapman Chataway et et al. al. 2007 2008 2009 2010 2009 2010 2008

Figure 1. Mean milk production (L/ha.year) from home-grown feed (pasture) obtained in farmlet studies (Chapman et al., 2007a; Fulkerson et al., 2008a; Valentine et al., 2009; Chataway et al., 2010) and a commercial farms survey (Linehan et al., 2004) in Australia. Symbols indicate maximum ( ) and minimum (◊ ◊) for each study.

The large dispersion in the levels of milk yield per ha achieved in these farmlet studies and commercial farm surveys is the result of a wide range of combinations of stocking rate and milk yield per cow. Strategies to increase milk production from pasture There is widely differing opinion as to the best strategy to increase milk production per ha from pasture. The two main strategies are to increase stocking rate (number of milking cows per ha of land) or increase milk yield per cow (L or kg of milksolids per cow/lactation). Increased stocking rate Increasing stocking rate has been shown to improve pasture utilisation and quality in farmlet system studies both with (Baker and Leaver, 1986; Fales et al., 1995) and without (Stockdale and King, 1980; Holmes and Parker, 1992; Macdonald et al., 2008) the use of supplements, as well as on commercial farms (Armstrong et al., 2000; Kellaway and Harrington, 2004). At higher stocking rates more pasture can be consumed per ha and there would be fewer opportunities for pasture to be 13

CHAPTER 1

wasted (Holmes and Roche, 2007). In this way, as higher stocking rate can lead to a lower proportion of mature pasture available, there could be an associated increase in nutritive value (Holmes and Roche, 2007). In addition, since higher stocking rates normally result in lower pasture allowances (kg DM offered per cow), substitution effects (amount of pasture left uneaten due to extra supplementary feed consumed) are reduced, resulting in a more efficient use of supplements at the system level (Wales et al., 1999; Kellaway and Harrington, 2004; Penno et al., 2006). In this way, a positive association between stocking rate and pasture utilised per ha was found in each of five studies reviewed (Holmes and Parker, 1992; Armstrong et al., 2000; Linehan et al., 2004; Chapman et al., 2007a; Macdonald et al., 2008) and across all of them (see Figure 2). In contrast, a recent farmlet study conducted in southern Australia by Valentine et al. (2009) compared five levels of stocking rate in dryland and four levels under irrigation over four years and found no association between stocking rate and pasture utilisation and only small improvements in nutritive value as stocking rate increased. This was because, in their study, grazing and supplementation management was adjusted to ensure full utilisation of pasture, even at the lowest stocking rates.

Pasture utilised (t DM/ha.year) .

18 16 14 12 10 8 6 4 2 0 1

1.5

2

2.5

3

3.5

4

4.5

Stocking rate (milking cows/ha)

Figure 2. Relationship between stocking rate (milking cows/ha) and utilised pasture (t DM/ha/year) in the studies by Armstrong et al. (2000)(••), Chapman et al. (2007a) (■), Holmes and Parker (1992)(× ×; ), Linehan et al. (2004)(▲) and Macdonald et al. (2008)( ).

14

Literature review

On the other hand, if stocking rates are too high, cow performance can be reduced, particularly in systems where no additional supplementary feed is used (King and Stockdale, 1980; Holmes and Parker, 1992; Dillon et al., 1995; Macdonald et al., 2008). In addition, researchers in New Zealand (McCall and Clark, 1999; Holmes et al., 2002; Macdonald et al., 2008) maintain that, as stocking rate increases, feed conversion efficiency (FCE; defined as kg of milk of standardised composition produced per kg feed DM consumed) tends to decline under pasture-based farming conditions. However, some authors (Holmes and Parker, 1992; Fales et al., 1995; Kellaway and Harrington, 2004; Valentine et al., 2009) proposed that with improved feeding management (i.e. including supplementation when pasture availability is limiting) farmers could avoid reductions in feed intake and milk yield per cow, and therefore in FCE (Tozer et al., 2004). Increased milk yield per cow Alternatively, milk production per ha can be increased by increasing milk yield per cow. This could be achieved through increased feed intake of individual cows, which can exploit more completely their genetic potential (Edwards and Parker, 1994; Fulkerson et al., 2008a). In this regard, a positive effect of increasing feed intake on FCE has been shown (Beever and Doyle, 2007; Kolver, 2007) although this strategy has several limitations in pasture-based systems, as discussed below. First, modern high genetic merit Holstein-Friesian cows cannot achieve their production potential from grazed pasture alone (Kolver and Muller, 1998; Doyle et al., 2001; Kolver et al., 2002) even when they are supplemented with grain (Bargo et al., 2002; Fulkerson et al., 2008a). Second, increasing the level of supplements fed to cows grazing pasture will lead, at some point, to a decline in marginal milk responses due to either substitution, negative associative effects between feeds or to partitioning of energy towards body tissue (Walker et al., 2001; Doyle et al., 2004; Wales et al., 2006; Beever and Doyle, 2007). In this regard, a negative association between milk production per cow and amount of pasture utilised per ha was shown in all of the five farmlet studies reviewed (Holmes and Parker, 1992; Linehan et al., 2004; Chapman et al., 2007a; Fulkerson et al., 2008a; Macdonald et al., 2008; Figure 3).

15

CHAPTER 1

18 Pasture utilised (t DM/ha.year) .

16 14 12 10 8 6 4 2 0 0

1,000 2,000 3,000 4,000

5,000 6,000

7,000 8,000

9,000

Milk yield (L/ cow.year) Figure 3. Relationship between milk yield (L/cow.year) and utilised pasture (kg DM/ha/year) in the studies by Fulkerson et al. (2008a)(••; ○), Chapman et al. (2007a)(■), Holmes and Parker (1992)(× ×), Linehan et al. (2004)(▲) and Macdonald et al. (2008) ( ).

In contrast to the trend shown in Figure 3, Holmes and Parker (1992) argued that, based on their studies, “the achievement of minimum pasture wastage with well-fed high-producing cows will require very skilful management, but will probably maximise productivity”. In other words, it should be possible to achieve high levels of pasture utilisation with high yielding cows, but management will need to be more skilful. In summary, although the effects of increasing stocking rate or milk yield per cow have been examined separately in extensive pasture-based studies, it has not been determined how they compare at the whole farm system level.

16

Literature review

THE POTENTIAL OF HOME-GROWN FEED TO INCREASE MILK PRODUCTION The amount of milk produced from home-grown feed is related to the product of the amount of forage utilised per year and the efficiency of its conversion into milk. In this way it is possible to establish, for both these parameters, the gap between the industry average and the potential (represented here by the results obtained at the research farms in Australia and New Zealand). To illustrate this concept, the FCE will be used as an estimate of the efficiency of conversion of pasture into milk, even though FCE represents the conversion efficiency of the overall diet (including supplementary feed). In this way, whereas FCE may increase from 1.01 to 1.38 L/kg DM (Kolver et al., 2002; Kolver, 2007), the yield of pasture utilised may increase from ~7,000 to ~17,000 kg DM/ha.year (Garcia and Fulkerson, 2005; Beca, 2008), on irrigated dairy farms. Based on this, there is clearly greater scope to increase milk production per ha by improving the amount of pasture utilised per ha (140% potential increase) than by increasing conversion efficiency (39% potential increase; Figure 4). Hence, this section of the review will focus on identifying the opportunities and challenges to increase the level of home-grown feed utilised and its nutritive value on Australian pasture-based dairy farms.

Milk from pasture (L/ha.year) .

18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Industry average

Improved conversion efficiency (from 1 to 1.4 L ECM/kg DM)

Improved pasture utilization (from 7 to 17 t DM/ha)

Figure 4. Conceptual improvement in milk production from pasture (L/ha.year) by increasing conversion efficiency (Kolver et al., 2002; Kolver, 2007) or pasture yield (Garcia and Fulkerson, 2005; Beca, 2008) from the Australian industry average to their on-farm potential.

17

CHAPTER 1

The potential of pasture Armstrong et al. (2000) identified a three-fold difference in the level of pasture utilised between irrigated dairy farms, in five different irrigation districts in northern Victoria. In a survey of 298 dairy farms in Australia, Beca (2008) has reported an average of 8 t DM/ha.year of utilised pasture, whereas Linehan et al. (2004) and Spain (2005) reported maximum on-farm yields of utilised pasture of over 20 t DM/ha.year. Similarly, Garcia et al. (2008) reported 17.3 t DM/ha of utilised pasture from a three-year paddock study in Australia. Rawnsley et al. (2007), under ideal conditions, modelled potential utilised pasture per hectare for the main dairy regions of Australia and New Zealand and found levels of 18.1 and 21 t DM/ha.year in Australia, and 19.5 and 23.4 t DM/ha.year in New Zealand, for dryland and fully irrigated conditions, respectively. Similarly, Edwards and Parker (1994) reported a maximum value of utilised pasture per year of 20 t DM/ha.year in an extensive review of the New Zealand dairy industry. These authors also stated that little further progress in terms of pasture yields was expected unless alternative forage crops were introduced. Similarly, both Rawnsley et al. (2007) and Thorrold and Doyle (2007) suggested that profitable pasture-based milk production in Australia may have had reached a ceiling. Besides total yield of pasture utilised, a further limitation of pasture as the only source of home-grown feed is the marked seasonality of its growth. Perennial ryegrass (Lolium perenne L.), the most common dairy pasture species in Australia, produces approximately 60% of its annual yield during spring, and less than 10% in winter. This causes a seasonal shortage of feed that is particularly problematic on nonseasonal calving farms, which represent 58% of the dairy farms in Australia (ABARE, 2009b). In addition, the low persistency of the perennial ryegrass in most dairy environments in Australia have created the need to over-sowing these pastures every one to two years (Fulkerson et al., 2003). Nutritive value of pasture is also influenced by season and is often sub-optimal with the neutral detergent fibre (NDF) and crude protein (CP) content of pasture usually exceeding the requirements of lactating cows (Doyle, et al., 2001). The NDF content is normally inversely related to digestibility of forage and can impose a limitation on the daily DM intake of dairy cows. An excessive concentration of CP in the diet can carry associated energy costs related with the excretion of excess N (Trevaskis and Fulkerson, 1999), and, occasionally, reduced reproductive performance (McCormick et al., 1999).

18

Literature review

The potential of forage crops Annual forage crops are often able to overcome some of the limitations of perennial pastures in terms of total and seasonal DM yield, and nutritive value. Table 1 depicts the most common species of annual forage crops utilised in Australian dairy farms and their main characteristics in terms of yield, seasonality, nutritive value and water use efficiency, reported in field studies. Typically, C4 summer crops such as maize or sorghum, are able to reach the highest yields per ha (Table 1), well above the potential of perennial pastures. However, the lower metabolisable energy and higher fibre content of these crops normally means that their intake can limit the production of lactating cows. There is a positive association of fibre content and a negative association of ME concentration with yields as shown in Figure 5. Hence, it would not be possible to grow a single forage crop that could produce a higher DM yield than pasture and also have a nutritive value sufficient to fulfil the requirements of the modern high producing dairy cow. However, a combination in an annual rotation of high yielding C4 lower quality summer forage with high quality autumn-winter crops (Figure 5), could outyield perennial pasture species in terms of potential DM yield per ha with an improved nutritive value.

19

20

Table 1 - Range in Yield (t DM/ha), ME (MJ/kg DM), CP(%DM), NDF (%DM), ADF (%DM), period available (grazing or cut) and water use efficiency (WUE; kg DM/mm irrigation + rainfall) of annual crops as potential forages for dairy cows and in field studies in Australia.

Annual Ryegrass Oats

Lolium multiflorum Avena Sativa

Triticale

Agropyron caninum Triticum aestivum

Wheat

CP

NDF

ADF

Period available (grazing or cut)

Yield

ME

(t DM /ha) 13.7-13.9

(MJ/kg DM) 10.6

12-26.6

46.1-52.3

24.6

May

Nov

(kg DM /mm) 27.3

6.1-8

9.8-12.5

5.7-22.5

44.2-44.4

24

Apr

Nov

19.2

10.6-14.5

8.7-10.3

7.2-21.9

40.7-55.8

20.9

Jun

Nov

28.5

(Mason and Pritchard, 1987; Eckard et al., 2001; Neal et al., 2006; Fulkerson et al., 2008b; Jacobs et al., 2009) (Neal et al., 2006; Fulkerson et al., 2008b; Jacobs et al., 2009)

10.7-20.6

9.1-10.2

10.1-23.5

42.7-53.3

22.4

May

Nov

34.4

(Neal et al., 2006; Fulkerson et al., 2008b; Jacobs et al., 2009)

4.5-13.3

9.8-10.7

24.1-27

36.6

23

Feb

Nov

19-23

10.4-13.6

9.5-10.8

24.3-26.5

35.7

23.2

Apr

Nov

19-23

(Garcia et al., 2006; Neal et al., 2006; Shrestha et al., 2006; Fulkerson et al., 2007) (Neal et al., 2006; Shrestha et al., 2006; Fulkerson et al., 2007)

9.7-13.9

9.5-10

23.9-28.2

39.4

20.9

Apr

Nov

17.7-18

(Neal et al., 2006; Shrestha et al., 2006; Fulkerson et al., 2007)

Aug

Nov

26.7-44

(Garcia et al., 2006; Neal et al., 2006; Fulkerson et al., 2007)

Nov

Apr

36-54.6

(Mason and Pritchard, 1987; Pritchard, 1987; Neal et al., 2006; Shrestha, 2007; Fulkerson et al., 2008b; Garcia et al., 2008) (Pritchard, 1987; Neal et al., 2006; Fulkerson et al., 2008b)

(%DM)

From

To

WUE

Persian clover Balansa clover Berseem clover Maple pea Maize

Trifolium resupinatum Trifolium michelanium Trifolium alexandrium Pisum sativum Zea mays

19.2-29

9.1-9.7

6.8-8.2

Sorghum

Sorghum bicolour

17.6-20.1

8.3-8.5

6.9-19.3

56.2

34.3

Nov

Apr

30.2

Millet

Echinocloa utilis

5.2-16.3

9.2-11.1

11.9-20.9

50.1

30.7

Dec

Mar

5.5-22.7

Turnips

Brassica rapa

3.5-14

13-13.4

7.3-16.2

15.4-22.6

Oct

Jan

Forage rape Pasja

Brassica napus

4-14.4

10.6-13.1

10-27

21.9

Nov

Apr

18.520.2 16-29.9

Brassica campestris x napus Lablab purpureus

3.5-4.1

12.6

9.5

Nov

Mar

21

8.3-13

8.5

16.3-17.8

Nov

Mar

18.6

Lab lab

3.8-6.9

45.3

20.2

40.4

References

(Neal et al., 2006; Fulkerson, 2009; Jacobs et al., 2009)

(Pritchard, 1987; Eckard et al., 2001; Neal et al., 2006; Fulkerson et al., 2008b) (Moate et al., 1998; Eckard et al., 2001; Jacobs et al., 2001; Jacobs et al., 2002; Jacobs and Ward, 2007) (Eckard et al., 2001; Neal et al., 2006; Shrestha et al., 2006; Jacobs and Ward, 2007; Fulkerson et al., 2008b; Garcia et al., 2008) (Eckard et al., 2001; Jacobs et al., 2002) (Mason and Pritchard, 1987; Neal et al., 2006; Fulkerson et al., 2007)

ME, metabolisable energy; CP, crude protein; NDF, neutral detergent fibre; ADF, acid detergent fibre.

Literature review

15.0

a)

14.0

ME (MJ/kg DM)

13.0 12.0 11.0 10.0 9.0 8.0 7.0 68.0

b)

58.0

NDF (%DM)

48.0 38.0 28.0 18.0 8.0 30.0

Yield (t DM/ha.year) .

25.0

c)

20.0 15.0 10.0 5.0 0.0 Maize

Sorghum

Wheat

Annual Triticale Balansa Berseem Millet ryegrass clover clover

Lab lab

Persian clover

Forage rape

Turnips

Oats

Maple pea

Pasja

Figure 5. Mean metabolisable energy (ME) value (MJ/ kg DM) (a; ▲), neutral detergent fibre (NDF; %DM) (b; ) and yield (t DM/ha.year) (c; bars) of various forages from data obtained from farm and plot studies reviewed (Mason and Pritchard, 1987; Pritchard, 1987; Moate et al., 1998; Eckard et al., 2001; Jacobs et al., 2001; Jacobs et al., 2002; Garcia et al., 2006; Neal et al., 2006; Shrestha et al., 2006; Fulkerson et al., 2007; Garcia et al., 2007; Jacobs and Ward, 2007; Shrestha, 2007; Fulkerson et al., 2008b; Garcia et al., 2008; Fulkerson, 2009; Jacobs et al., 2009). Vertical bars indicate range of values.

21

CHAPTER 1

The potential of forage crop rotations Garcia et al. (2007) carried out a comprehensive review of field studies on forage crop rotations for dairy systems in Australia and New Zealand. These authors identified the opportunity to increase the ceiling yield of DM per ha, and increase the efficiency of use of water and N, by implementing forage crop rotations that were complementary to each other. They argued that this complementarity should be achieved at three levels: soil-plant level, animal-feeding level and whole-system level. The complementarity at the soil-plant level means that the crops can mutually benefit from each other mainly in terms of nutrient availability, soil structure and soil health. An example of this complementarity is the use of brassica, as a break crop which can reduce soil-borne pathogens, increase soil aeration and capture N more quickly and deeper in the soil profile than most grasses (Garcia et al., 2007). The complementarity at the animal-feeding level means that the forages in the rotation can improve the nutritional match with each other and with a typical pasture. For example, fiber content of annual clovers and brassicas are low, and this can be complemented by feeding pasture, in which fibre is normally in excess. The high protein of clovers and brassicas complements CP intake, which is typically low in pastures during summer (Garcia et al., 2007). The complementarity at the whole-system level means that the nutrients provided by the forage crop rotation should be able to combine with the pasture availability in order to sustain an all-year round high milk production per ha with minimal use of bought-in feed. This implies the effective integration of forage crop rotations on a pasture-based farm system, and this would require an understanding of the implications of this process on the various grazing, harvesting, conservation and feeding management practices. Garcia and Fulkerson (2005) designed a complementary forage rotation (CFR) which aimed at achieve the three levels of complementarity described above. This CFR comprised an annual sequence of three crops [maize (Zea mays L.), forage rape (Brassica napus L.) and a legume (Persian clover, Trifolium resupinatum L. or maple pea, Pisum sativum L.)] under full irrigation. The complementarity of this CFR at the soil-plant (Shrestha, 2007; Garcia et al., 2008; Kabore, 2008)and animal-feeding levels (Kaur et al., 2009; Kaur et al., 2010) has been documented but not at the whole-system level.

22

Literature review

Chapman et al. (2007a) conducted a farmlet comparison under dryland conditions in south west Victoria, where a 100% perennial ryegrass farm was compared to a farm that included a double crop CFR on 15% of the farm area. Even though the farm system that included the CFR reached a higher yield of home-grown feed in both years of study, the operating profit during the drought year was lower than the ryegrass only farm system. However, Chapman et al. (2008a; 2008b) conducted a modelling study to evaluate various CFR options under variable climatic scenarios and found that the inclusion of CFRs increased the diversification of the farm systems forage base, which seemed to smooth out the inter-annual variability in production and profit. Nevertheless, these authors concluded that further field studies were necessary to evaluate and confirm the feasibility of managing such combination of crops and feeds at the whole-farm level.

SUSTAINABILITY OF SYSTEMS THAT INCREASE MILK PRODUCTION FROM HOME-GROWN FEED “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs” according to the worldwide accepted definition by the World Commission on Environment and Development (WCED, 1987). For any new technology or change in an industry to succeed, the sustainability of its implementation should be assessed. The “triple bottom line of sustainability”, including social, economic and environmental aspects of the impact of a technology is an approach that has been applied worldwide for the development of policies and projects, both in the private and in the public sector (Nevens et al., 2007; Meul et al., 2008). This approach is adopted in this section of the review to analyse the sustainability of increasing milk production per ha from home-grown feed on Australian dairy farms.

1. Social sustainability The intensification of dairy farms can have an impact on labour and lifestyle issues. The milk harvesting process requires the largest proportion (40 to 50%) of the total labour used in pasture-based dairies (Mein and Smolenaars, 2001). For this reason, the research around labour and lifestyle issues on Australian dairy farms focuses mainly on milk harvesting practices and systems.

23

CHAPTER 1

However, potential issues could arise due to the increased labour demand from the implementation of intensive forage cropping involved in some of the systems described earlier. For instance, the multiple forage crop systems studied by Chapman et al. (2008a) or Garcia et al. (2008) require the sowing and management of crops two or more times every year, plus the use of specific machinery for the harvesting of maize and its conservation as silage. As Garcia et al. (2007) argued, the use of a triple crop system has the challenge of balancing animal requirements with a diverse forage base and special grazing management conditions, which could require a high level of management skills. On the other hand, some of these practices can be carried out by specialised service providers (contractors), who are having an increasingly important role in farming activities in Australia. The assessment of the labour and lifestyle issues associated with the intensification of pasture-based dairy farms in Australia is clearly beyond the scope of the present review, although it might be an area of interest for further social research studies.

2. Economic sustainability The economic sustainability of the intensification of dairy farms has been a matter of discrepancy. According to the results of a national survey conducted by ABARE (2008) “larger farms tend to generate higher rates of return on total farm capital: as herd size increases, overhead costs can be spread over more units of output”. However, the findings of such survey could be masked by the fact that the most efficient farmers are the ones that have been able to increase the size of their business over time and now have a larger dairy herd. Therefore their higher returns are a consequence of a more efficient management rather than a dilution of overhead costs per se. In this way Kompas and Che (2006), in a more comprehensive analysis of the ABARE surveys data from NSW and Victoria, found no evidence of economies of scale related to farm area in dairying, a finding that was consistent with previous study by Jarofullah and Devlin (1996) in New Zealand. Doonan (2008) discussed that the large profitable farms reached an increased size of the business due to management skills and not because of pure economies of scale. Beca (2006), analysing commercial pasture-based dairy farms in Victoria, Tasmania and South Australia within the Red Sky database, found that only 2% of all dairying expenses were fixed, whereas 70% of them were directly correlated to variation in land and 28% depended on number of cows. Since an increase in the size of a dairy business normally relates to an increase in either land or cows, this author would not expect a dilution of fixed costs when reaching a higher size of the dairy operation. Beca (2006) found that only farms with fewer than 150 cows had a substantial impact of fixed

24

Literature review

costs, mainly related to infrastructure and the cost a of a capable dairy manager for such a small business. Hence, only on farms below that size, an impact of economies of scale could be expected. However, the author also suggested that, in larger farms, there could be a positive although different impact of economies of scale if the manager is able to increase the business size (and thus, its associated costs) without a decrease in the rate of return to the total capital. Then, in a simplistic way, the total amount of money available for the owner will be higher. The concept of farm development was recently evaluated for dryland pasture-based dairy farms in southern Australia by Armstrong et al. (2010) on a case study modelling research. These authors found that expanding the milking area was more profitable than increasing stocking rate, since the former option would allow farmers to increase the proportion of home-grown feed in the diet and reduce the amount of more expensive purchased feed. The effects of changes in stocking rate on farm profit are conditioned by the initial level of stocking rate and the capacity of a farm system to produce home-grown feed. In another case study modelling research conducted by Doyle et al. (2004) based on a pasture-based dairy farm in southern Australia, increasing milk yield per cow led to a greater profit than increasing stocking rate. However, since the initial level was of 4.4 cows/ha with pasture covering 60% of their requirements, a substantial increase in expensive brought-in feed was needed to increase stocking rate. Another relevant consideration is the investment required to implement these changes in stocking rate. In the study by Armstrong et al. (2010), a substantial additional investment was assumed to be required when increasing stocking rate in order to cover feedpad and effluent upgrades, which might not be necessary when increasing stocking rate on dairy farms of different characteristics. Furthermore, in both the work by Doyle et al. (2004) and by Armstrong et al. (2010), the pasture, supplements use and milk production of each scenario were simulated by modelling and/or based on the perceptions of a steering group. Hence, a comparison of strategies for farm development where all inputs and outputs of the system are measured could provide a better basis for the assessment of their effects on farm profit.

Business risk Because farming is a dynamic phenomenon, production and price uncertainty can affect the expected production and profitability outcomes (Antle, 1983; Ford et al., 1995). In this way, risk perception has been considered one of the major factors limiting the adoption of new technologies (Marra et al., 2003; Ghadim et al., 2005). The risk aversion of Australian farmers is well reported (Bardsley and Harris, 1991;

25

CHAPTER 1

Kingwell, 1994; Marra et al., 2003; Ghadim et al., 2005). Business risk is the aggregate effect of uncertainty related to production (environment), price, institutional and human factors on the profit of a farm, independently of how the operation is financed (financial risk; Hardaker et al., 2004b). If technologies involving some degree of intensification are to be considered for adoption by farmers, the economic performance and business risk of implementing such innovations would need to be assessed (Wales et al., 2006; Chapman et al., 2007b; Chapman et al., 2008b). There are several methodologies to assess business risk, including mean variance analysis, quadratic risk programming, minimisation of total absolute income deviation (MOTAD), and different types of simulation analysis (Young, 1984). A commonly applied method in agriculture is stochastic simulation analysis. This approach can be useful to assess risk from variability in either production (through the effect of the environment) or prices of inputs and outputs (Antle, 1983; Cacho et al., 1999). These variables incorporate random components (by constructing a probability distribution) in order to reflect the uncertainty of economic outcome for a real farm system (Hardaker et al., 2004a). In Australia, the variability of both climate (production risk) and prices of inputs and outputs (price risk) comprises the major source of business risk for dairy farms.

Production risk Production risk results from the natural factors that can affect output yields on a farm system, such as weather or pests (Antle, 1983; Chapman et al., 2007b). The impact of inter-annual climate variability on a variable of production (such as forage or milk production) and, in turn, the effects of this variable on profit, can be simulated using dynamic models (Antle, 1983; Chapman et al., 2007b). Using a deterministic approach, Chapman et al. (2008b) investigated the effect of climate variability on production and profit of non-irrigated dairy farm systems of southeast Australia using different forage bases. They found that diversifying the forage base by using multiple forage crops led to better profit than a 100% perennial ryegrass system. However, a few assumptions were adopted for this evaluation. First, it was assumed that these multiple forage crops could be effectively integrated into a year-round feeding system to produce milk. Second, the growth rates used were obtained from cutting studies and extrapolated to grazing conditions. Third, the risk of complete crop failure was not considered in their simulations. In this way, Chapman et al. (2008b) stated that there was a clear opportunity to address these issues when designing future studies. Antle (1983) suggested that dynamic models that include uncertainty may be more useful than conventional static (deterministic) models for understanding the role

26

Literature review

production risk plays in farm management. For example, Cacho et al. (1999) used a stochastic simulation approach to assess the production risk originated from the effect of climate variability on pasture growth rates for sheep grazing farms in New Zealand.

Price risk Price risk refers to the impact on the farm profit of the variation in the prices of inputs and outputs (Antle, 1983; Chapman et al., 2007b). It can be assessed by estimating probability distribution of prices from the analysis of a historical series of prices for the relevant period of time (Cacho et al., 1999). In a similar way as the analysis of production risk described above, price risk can be estimated by incorporating uncertainty through a stochastic simulation analysis. For this purpose, historic data forecasting accuracy is a better method to incorporate risk in programming models than the use of current or conditional future price predictions (Ford et al., 1995). This approach has been used by Shalloo et al. (2004) to conduct a stochastic budgeting type analysis of the risk of milk and concentrate price on profitability of pasture-base dairy farms in Ireland.

An integrative approach The combined effect of climate variability and price risk on profit can be integrated in dynamic models to obtain a closer representation of the real risk of a farming activity, as it has been stated by Antle (1983) and Cacho et al. (1999), and further demonstrated by Behrendt et al. (2006). However, no single model can simulate, at the same time, climate and price sources of profit variation, their effect on the whole farm business, and also produce a stochastic distribution of profit. Therefore, more than one model has to be used in integration for the above described analysis to be carried out. Using three different analytical tools “in series”, Chapman et al. (2008a; 2008b) modelled the business risk of dryland farms in southern Australia using different forage crops integrated into a typical perennial pasture, and compared these to a system based on 100% perennial ryegrass. This study assumed a constant milk production every year, without accounting for adjustments that are normally made by the farmer as a reaction to weather or market conditions each year (Kingwell, 1994). However, attempting to model farmer’s tactical decision making in relation to the multiple factors involved in such decisions, has been shown to be frequently misleading and tending to mask the genuine differences between farming systems (Antle, 1983). Hence, the assumption of constant milk production between years adopted by Chapman et al. (2008b) seems to be an appropriate approach.

27

CHAPTER 1

The research by Chapman et al. (2008b) evaluated business risk without taking into account price of inputs and outputs as a source of risk. Shalloo et al. (2004) also conducted a risk analysis of dairy businesses based on stochastic budgeting and considered the price of milk and concentrates, as well as the possible variation in the quality of silage and number of grazing days as sources of risk, which are variables of interest for the systems typical in Ireland, where the study was conducted. Armstrong et al. (2010) also evaluated the effect of milk and supplements price, as well as pasture yield, on the economic performance of different modelled development options. However, the pasture yield distribution for the risk simulation was determined based on anecdotal evidence and the perception of a steering committee. If forage crop systems such as the one evaluated by Chapman et al. (2008b), were to be implemented on irrigated dairies, they would be exposed to a different set and magnitude of risks, including, for instance, water prices. Cacho et

al. (1999) conducted a study analysing production risk of a farm system and concluded that a more comprehensive approach would have been to evaluate the combined effects of both production and price risk. Following this recommendations, Behrendt et al. (2006) outlined a methodology for the assessment of farmlet performance through a stochastic simulation of the impact of pasture persistence, climate risk and commodity prices on the performance of a sheep enterprise in Australia. This type of stochastic simulations taking into account both production and price risk appear as comprehensive approach to be considered for the evaluation of the economic sustainability of further innovative forage systems to be implemented on dairy farms. 3. Environmental sustainability Satisfying the increasing demand for animal food products, while at the same time sustaining the natural resource base and coping with climate change and vulnerability, is one of the major challenges facing world agriculture today (FAO, 2009). The intensification of pasture-based dairy farms normally increases the risk of adverse environmental impact mainly through nutrient pollution and increased greenhouse gas emissions (Cameron et al., 2007; Basset-Mens et al., 2009). The likelihood and extent of this impact should be assessed before any system aimed at increasing milk production from home-grown feed is to be considered for adoption (Chapman et al., 2008b).

Nutrients pollution

28

Literature review

Pastoral agriculture is considered a major contributor of nutrients pollution of land, air and water (Rotz et al., 2005; Cameron et al., 2007; Gourley et al., 2007). The main concern has been the nitrate contamination of potable water, as a potential threat to human health, and the disproportionate enrichment of nutrients (eutrophication) of rivers, water storages and lakes (Rotz et al., 2005; Cameron et al., 2007; Gourley et al., 2007). The assessment of nutrients pollution on a farm system can be done at two levels: the potential and the actual level. The potential level is determined by the balance between nutrient inputs (mainly feed and fertiliser) and nutrient outputs (mainly milk and livestock). This has been defined as the farm-gate nutrient balance (Oborn et al., 2003; Oenema et al., 2003; Gourley et al., 2007). According to this concept, a substantial nutrient surplus could eventually cause a significant nutrient accumulation on the farm and, therefore, a potentially greater risk of nutrient loss to the environment (Whitehead, 2000; Gourley et al., 2007). Thus, the greater the nutrient surplus, the greater the potential risk of pollution. Nitrogen and phosphorus (P) are the major farm nutrients of environmental concern (Rotz et al., 2005; Cameron et al., 2007; Gourley et al., 2007). However, losses of P are considerably fewer and lower than for N under grazed pasture conditions (Rotz et al., 2005; Gourley et al., 2007) and not related to system intensification or higher production of home-grown feed to the same extent as losses of N. Hence, this section of the review will focus on the assessment of the environmental impact of N. In theory, once production of a pasture system is near equilibrium with respect to soil organic N, all further N inputs are lost to the environment (Rotz et al., 2005). Hence, the N losses will increase with production intensification, since there are inherent limits to the efficiency of milk production (Rotz et al., 2005). As described before in this review, intensification of pasture-based dairy farms can be achieved by increasing milk yield per cow or stocking rate. At the farm level, increases in milk yield per cow are usually achieved by increasing feed intake. The total amount of N excreted by each cow increases in direct proportion to the N intake (Castillo et al., 2000). Thus, it is expected that cows with higher feed intakes and milk yields would excrete more N. Moreover, cows fed to attain their genetic potential for milk yield normally need additional bought-in feed, which increases the N inputs at the farm-gate level, and hence the chances of higher N surplus (Rotz et al., 2005). However, if that supplementary feed helped reducing the high concentration of N typical of intensively managed pastures, the N excretion per cow could be lowered (Broderick and Clayton, 1997; Ledgard et al., 1999; Castillo et al., 2000; Rotz et al., 2005).

29

CHAPTER 1

Systems with increased stocking rates and greater inputs have higher chances of showing N surpluses (Schröder et al., 2003; Rotz et al., 2005; Gourley et al., 2007) and, therefore, an increased risk of causing N pollution. A recent comparison of farmlets under contrasting stocking rates conducted by Valentine et al. (2009) in southern Australia, indicated that the surplus of N increased linearly with stocking rate, and raised some concern regarding the sustainability of systems with very high stocking rate (up to 7 cows/ha). The aggregated observation of several farm studies across the world shows a clear linear relationship between N inputs and N surplus at the “farm-gate level” (Figure 6). From the information reviewed here, it seems that, regardless of the characteristics of the farm system, there is a constant and relatively low proportion (16%) of the total N entering the farm that is transformed into farm outputs (mainly milk). Similarly, a recent survey of 43 commercial dairy farms of different regions of Australia (Gourley et al., 2010) reported an average value of 20.8% N ouputs/N inputs. This association suggests that dairy industries across the world pursuing sustainable intensification will face the challenge of finding alternative systems that could reduce the N surplus per each additional input of N brought into the farm. From a more global perspective, the potential impact of N pollution per unit of food produced by a farm (i.e. milk) will be another variable of great interest. In this way, it is still not clear if there are on-farm advantages that can be captured from focusing on either increasing milk yield per cow or increasing stocking rate, as the two main strategies to increase milk production per ha. Clearly, further whole farm system research is needed to investigate these effects and to explore alternatives that can reduce the N surplus per each unit of N input for the sustainable intensification of pasture-based systems.

30

Literature review

600 N surplus (kg/ha.year) .

y = 0.84x - 42.09

500 400 300 200 100 0 0

100

200

300

400

500

600

N inputs (kg/ha.year) Figure 6. Whole farm N inputs and N surplus estimated at the farm-gate level for Australia (■) (Eckard et al., 2007), New Zealand ( ) (Ledgard et al., 1998; Ledgard et al., 1999; Ledgard and Luo, 2008), the Netherlands (••) (Aarts et al., 1992; Hanegraaf and den Boer, 2003; Oenema et al., 2003), England ( ) (Jarvis, 1993), Belgium (+) (Mulier et al., 2003), Switzerland (□) (Thomet and Pitt, 1997)and Germany (○) (Haas et al., 2001).

Several authors have found a linear association between farm-gate N surplus and nitrate leaching (Hanegraaf and den Boer, 2003; Bobe et al., 2004; Trott et al., 2004; Wachendorf et al., 2004). However, the presence of nutrient surpluses will only give an indication of the potential presence of a nutrient loss to the environment but would not confirm the actual loss or the level of its impact on the environment (Oborn et al., 2003; Oenema et al., 2003). Supporting this concept, several authors have shown very weak relationships between N balance and leaching losses at the catchment scale (Jansons et al., 2003; Schröder et al., 2003; Watson et al., 2003). This is because the farm-gate N surplus does not give any indication of how this amount of N is distributed within the farm system and throughout the year (e.g. it would be very pollutant if the surplus were occurring all at once in 1 ha of land without any crop and during a period of high rainfall). Furthermore, there are many factors affecting nutrient loss and nitrate leaching in particular. One of the factors is the concentration of nitrates in the soil, which depends more on the short term balance between N mineralisation, immobilisation and plant demand than on amount of total N accumulated in the soil (Oborn et al., 2003). In this regard, grasses have a larger capacity to uptake nitrate than legumes, whereas legumes have the ability of fixing N

31

CHAPTER 1

through the symbiotic association with bacteria (Ryzhobia), reducing the need of N input as fertiliser. Some break crops, such as forage rape, are known to be very effective in rapidly uptaking nitrate from the soil profile and therefore reducing nutrient loss. However, regardless of the forage sward, urine patches from livestock cause nitrates concentration to exceed the plants uptake capacity (Whitehead, 1995) for a certain period of time. As a consequence, the magnitude of the N losses by leaching in grazing conditions is closely related to the spatial (White et al., 2001) and temporal distribution of excreta. Thus, when working on farm scale systems, the temporal and spatial variability of the processes regulating nutrients dynamics have to be understood in order to determine the risk of nutrient pollution. There are no reported studies in Australia evaluating a farm N balance with detail of distribution of N surplus within the whole farm system in terms of time and space. One of the limitations for the implementation of this assessment in the past has been the lack of a validated methodology for the determination of excreta distribution within a dairy farm. Although White et al. (2001) established the relationship between time spent and cow excreta for pasture-based farms in the United States, this is not directly applicable to farms in Australia due to substantial differences in climate and farm set-up, and deserves further investigation for this particular conditions. Beyond the potential environmental impact evaluation, the outcomes of such exercise could be also of particular value to explore management practices for a more efficient use of the N in dairy farm systems.

Greenhouse gas emissions The production of greenhouse gas emissions is another source of potential environmental impact of dairy farming. In Australia, more than 65% of agriculture’s greenhouse gas emissions come from methane as a result of enteric fermentation by livestock (ABARE, 2009a). Recently, a planned reduction in greenhouse gas emissions from agriculture has been sought throughout the world, and also in Australia (Parker, 2008b). To achieve this, the implementation of on-farm emissions accounting systems has been considered as a step towards the application of either a taxing system or trading scheme. The potential impact of the Emissions Trading Scheme (ETS) proposed by the Australian government on farm businesses has been analysed using a modelling approach by Keogh and Thompson (2008) and ABARE (2009a). Even though this trading scheme has not yet been established in Australia, these modelling studies have shown that the cost to farmers from these

32

Literature review

schemes can be reduced by implementing farm management strategies aimed at increasing productivity (Keogh and Thompson, 2008). On dairy farms, the production of methane by animals is normally the main source of greenhouse gas emissions. Methane is a product of ruminal fermentation, representing a pathway for the disposal of metabolic hydrogen produced during the microbial oxidation of carbohydrates from the diet (McAllister and Newbold, 2008). The methane emissions from ruminants depend on animal size, production level and on the amount and composition of the feed (van Groenigen et al., 2008; Place and Mitloehner, 2010). Hence, these emissions increase when a farm system increases stocking rate or production per cow, especially when the latter is driven by a higher feed intake. With world population expected to increase from 6.8 billion persons to 9.1 billion in 2050 and no prospects of significant increases in agricultural land, food security is an issue of increasing interest (FAO, 2009; Place and Mitloehner, 2010). Hence, greenhouse gas emissions mitigation in dairies to address environmental concerns needs to be balanced with the growing global demand for dairy products, along with profitability of individual dairy farmers (Place and Mitloehner, 2010). In this way, mitigation strategies should be analysed in terms of their capacity to reduce emissions per unit of milk (Place and Mitloehner, 2010; Rotz et al., 2010). The use of ruminant modifiers to inhibit methanogenesis in order to mitigate methane emissions has not proved to be fully successful yet. McAllister and Newbold (2008) reviewed several approaches to reduce ruminal methanogenesis and found that, although methane production can be inhibited for short periods, it frequently reverts back to its initial levels through adaptive mechanisms. On the other hand, a recent review by Place and Mitloehner (2010) found that improvement of production efficiency is an effective way to reduce emissions per unit of milk. These authors identified heifer management, herd health, nutrition and feed production and reproduction as the most effective strategies to reduce greenhouse gas emissions per unit of milk. Parker (2008a) had also suggested some of these areas of work for the reduction of emissions in Australia and New Zealand. A recent comprehensive review of 20 metabolic studies has found that methane emissions per unit of milk production and energy intake can be reduced by increasing milk yield per cow (Yan et al., 2010). It is believed that it will be beneficial in terms of methane emissions to produce a fixed amount of milk from a smaller number of high producing animals than a large number of low producing cows (Cameron et al., 2007; Place and Mitloehner, 2010). In simplified terms, it can be said that there is a relatively fixed amount of methane

33

CHAPTER 1

output associated to the portion of the diet that covers cows’ maintenance, and a variable emission in relation to the level of animal production (Howden and Reyenga, 1999; Cameron et al., 2007). Recent

whole-farm

modelling

comparisons

for

pasture-based

dairies

in

Australia (Christie et al., 2009) and New Zealand (Basset-Mens et al., 2009) have shown that farm intensification strategies that involved an improved nutrition of the cow tended to reduce the greenhouse gas footprint per unit of milk produced (Table 2). Greenhouse gas emissions are expressed in terms of tonnes of CO2 equivalents. Because of their different global warming potential, carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are multiplied by 1, 21 and 310, respectively, to convert them to tonnes of CO2 equivalents.

34

35 Table 2. Mean annual greenhouse gas emissions (t CO2 equivalents), stocking rate (cows/ha) and milk yield (L/ha) from farm studies and commercial farms surveys. Country

Stocking rate

(cow/ha)

Milk yield

(L/cow)

Greenhouse gas emissions

(L/ha)

References

Per farm

Per ha

Per L milk

( t CO2 eq)

( t CO2 eq)

( kg CO2 eq)

743

3.1

Australia*

1.2

(Keogh and Thompson, 2008)

Australia

2.0

7,750

12,768

2,257

10.9

1.05

(Christie et al., 2009)

New Zealand

2.6

5,114

13,296

232

9.3

0.70

(Beukes, et al., 2010)

New Zealand

3.3

4,575

15,276

1,059

9.2

0.77

(Basset-Mens et al., 2009)

US

1.83

7,880

14,417

876

10.0

1.09

(Phetteplace et al., 2001)

Canada

9,380

517

1.02

(Verge et al., 2007)

Ireland *

4822

341

1.5

(Casey and Holden, 2005)

Ireland

6,000

263

1.15

(Casey and Holden, 2005)

Sweden

7,127

1.05

(Cederberg and Stadig, 2003)

5.7

1.1

(Cederberg, 1998)

Sweden

0.67

7,800

5,195

Netherlands

2.1

7,991

13,860

906

19.0

1.4

(Thomassen et al., 2008)

Germany

2.0

6,141

12,344

238

7.6

1.2

(Haas et al., 2001)

* industry average

CHAPTER 1

1.6 Emissions (kg CO2 eq/L milk)

1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0

2000

4000

6000

8000

10000

12000

Milk yield (L/ cow.year)

Figure 7. Relationship between milk yield (L/cow.year) and total farm greenhouse gas emissions (kg CO2 equivalent /L milk) in the studies by Christie et al. (2009)(••), Beukes et al. (2010) (■), Basset-Mens et al. (2009)( ), Verge et al. (2007)(▲), Haas et al. (2001)( ), Thomassen et al. (2008)( ) and Casey and Holden (2005) (□).

In line with this, a consistent negative association between milk yield per cow and the greenhouse gas emissions/L of milk was detected when each farm system study was analysed separately (Figure 7). This was despite a lack of relationship found previously when regressing all studies together, which was due to the large differences between the production systems evaluated. Clearly, the farm studies of New Zealand (Basset-Mens et al., 2009; Beukes et al., 2010) achieved substantially lower total emissions per unit of milk produced than the other studies. The main difference between the studies in New Zealand and the other studies is that, except from the one in Australia (Christie et al., 2009), these latter studies were conducted on dairy farms where the cows were housed during most of the year, whereas in New Zealand, cows graze outdoors all year round. In the intensive housing systems, there is a substantial amount of emissions originated from the process of harvesting, conditioning, transportation and feeding of forage from the paddocks to the indoor feeding areas. In addition, there are emissions embedded in the feed brought-in from

36

Literature review

outside the farm, and the emissions originated from the collection, storage and spreading of manure. In the grazing systems such as the one in New Zealand, cows ingest their feed from and deposit their excreta directly on the pasture land, in a short cycle that does not require fossil energy consumption, manure management, and reduces the exposure of manure to the atmosphere, hence reducing also the chance of producing greenhouse gas emissions (Basset-Mens et al., 2009). For example, in the Irish study by Casey and Holden (2005) cows spent 47% of the year housed, and concentrates fed and manure management represented 14 and 10% of the total farm emissions, respectively. In contrast, in the two New Zealand studies (Basset-Mens, et

al., 2009; Beukes, et al., 2010) no concentrates were used and manure management represented less than 1% of total emissions, with the only exception of a treatment which evaluated a very high intensification system, where manure management was 9% of total emissions (Basset-Mens et al., 2009). Interestingly, the Australian study (Christie et al., 2009), which was also in grazing conditions, had a higher level of emission per L of milk, even for the treatment where pasture represented 89% of the diet. It is important to note that, whereas most studies (Haas et al., 2001; Casey and Holden, 2005; Thomassen et al., 2008; Basset-Mens et al., 2009; Beukes et al., 2010) used the life cycle analysis (SETAC, 1993) methodology to estimate emissions, which is an internationally standardised method that assesses the environmental impact of a product “from cradle to grave”, others (Verge, et al., 2007; Christie, et al., 2009) used a simpler standardised inventory method. In this way, Basset-Mens et al. (2009) and De Boer (2003) suggested that caution must be taken when comparing life cycle analysis (LCA) studies, since different methodologies and assumptions can be used in each of them. This could be one of the reasons for the differences between the New Zealand and Australian studies. In summary, even though it appears from the literature that increased milk yield per cow leads to lower emissions per unit of milk, it seems that aiming to achieve these increased yields by increasing the amount of bought-in feed in the diet could increase the emissions at the whole farm level, and potentially diluting the benefits obtained by the high milk yield per cow. Since it is not possible to draw a general relationship between milk yield and greenhouse gas emissions/L of milk and the type of system considered seems to have a high influence on this variable, further whole farm evaluations will be needed in order to determine the production of greenhouse gas/L of milk from Australian pasture-based systems.

37

CHAPTER 1

An integrative approach New potential risks of environmental impact arise when the whole farm system is considered. Comprehensive dairy farm modelling studies conducted recently in the Netherlands (van Groenigen et al., 2008), the United Kingdom (del Prado et al., 2010), the United States (Rotz et al., 2010) and New Zealand (Basset-Mens et al., 2009) illustrated very clearly the need for integral measures of both nutrient and greenhouse gas pollution when assessing environmental impact of farm practices, and the importance of considering different scales in the analysis. This is needed since, for example, greenhouse gas mitigation methods may, in some cases, increase emissions of other forms of pollution as nitrates leaching (del Prado et al., 2010).

CONCLUSIONS There is not a clear consensus from the current literature about which is the most effective farm strategy to increase milk production per ha from pasture for the Australian conditions. Moreover, the comparative sustainability of the different strategies has been broadly overlooked, either with regard to their economic performance or their impact on the environment. On the other hand, clear opportunities have been identified to increase milk production per ha from home-grown feed beyond the potential of pasture alone. In this way, the role of forage crops in rotation has showed promising production results from paddock studies under irrigation, and whole farm scale studies on dryland conditions. However, it is still unknown if these forage crop rotations can be effectively integrated into irrigated pasture-based dairy systems. This review has also highlighted the need for a whole farm comprehensive assessment of both the climatic and price risk if these intensified systems are to be considered for adoption by farmers. For pasture-based farms intensification to be environmentally sustainable, it appears that a high milk yield per cow strategy with low proportion of bought-in feed in the diet should be sought if aiming to achieve a low level of emissions and N pollution per unit of milk produced. The feasibility of this concept is yet to be proven.

38

Literature review

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BECA D. A. (2006) Are there economies of scale in dairying? If so, what is the most economic size? In: Fulkerson W. J. (ed). Proceedings of the Dairy Research Foundation Symposium, Camden, Australia, pp. 15-25. BECA D. A. (2008) Tri-nations revisited: profitability variations between the average and the best in South Africa, Australia and New Zealand. Warrnambool, Australia: Red Sky Agricultural Pty Ltd. BEEVER D. E. and DOYLE P. T. (2007) Feed conversion efficiency as a key determinant of dairy herd performance: a review. Australian Journal of Experimental Agriculture, 47, 645-657. BEHRENDT K., CACHO O., SCOTT J. M. and JONES R. (2006) Methodology for assessing optimal rates of pasture improvement in the high rainfall temperate pasture zone. Australian Journal of Experimental Agriculture, 46, 845-849. BEUKES P. C., GREGORINI P., ROMERA A. J., LEVY G. and WAGHORN G. C. (2010) Improving production efficiency as a strategy to mitigate greenhouse gas emissions on pastoral dairy farms in New Zealand. Agriculture Ecosystems & Environment, 136, 358-365. BOBE J., WACHENDORF M., BUCHTER M. and TAUBE F. (2004) Nitrate leaching losses under a forage crop rotation. Proceedings of the 20th General Meeting of the European Grassland Federation. Land use systems in grassland dominated regions., Luzern, Switzerland, pp. 346-348. BRODERICK G. A. and CLAYTON M. K. (1997) A Statistical Evaluation of Animal and Nutritional Factors Influencing Concentrations of Milk Urea Nitrogen. Journal of Dairy Science, 80, 2964-2971. CACHO O. J., BYWATER A. C. and DILLON J. L. (1999) Assessment of production risk in grazing models. Agricultural Systems, 60, 87-98. CAMERON K. C., HEDLEY H., CLARK H. and DI H. J. (2007) Impact of pasture and supplement feeding on the environment. In: Rattray P. V., Brookes I. M. and Nicol A. M. (eds.) Pasture and supplements for grazing animals, pp. 287-309. Hamilton, New Zealand: New Zealand Society of Animal Production. CASEY J. W. and HOLDEN N. M. (2005) Analysis of greenhouse gas emissions from the average Irish milk production system. Agricultural Systems, 86, 97-114. CASTILLO A. R., KEBREAB E., BEEVER D. E. and FRANCE J. (2000) A review of efficiency of nitrogen utilisation in lactating dairy cows and its relationship with environmental pollution. Journal of Animal and Feed Sciences, 9, 1-32. CEDERBERG C. (1998) Life cycle assessment of milk production - a comparison of conventional and organic farming. SIK Rapport, p. 86. Goteborg, Sweden: Swedish Food Institute (SIK). CEDERBERG C. and STADIG M. (2003) System expansion and allocation in life cycle assessment of milk and beef production. International Journal of Life Cycle Assessment, 8, 350-356.

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DEL

BOER I. J. M. (2003) Environmental impact assessment of conventional and organic milk production. Livestock Production Science, 80, 69-77. PRADO A., CHADWICK D., CARDENAS L., MISSELBROOK T., SCHOLEFIELD D. and MERINO P. (2010) Exploring systems responses to mitigation of GHG in UK dairy farms. Agriculture, Ecosystems & Environment, 136, 318-332.

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DOONAN B. M. (2008) How the manager of a pasture system delivers the goods. Proceedings of the The Australian Dairy Conference 2008, Launceston, Australia, pp. 36-41. DOYLE P. T., STOCKDALE C. R., WALES W. J., WALKER G. P. and HEARD J. W. (2001) Limits to and optimising of milk production and composition from pastures. Recent Advances in Animal Nutrition in Australia, 13, 9-17. DOYLE P. T., HO C., ARMSTRONG D. P. and MALCOLM L. R. (2004) Modelling feeding system efficiency and profitability of an irrigated dairy farm. Animal Production in Australia, 25, 45-48. ECKARD R. J., SALARDINI A. A., HANNAH M. and FRANKS D. R. (2001) The yield, quality and irrigation response of summer forage crops suitable for a dairy pasture renovation program in north-western Tasmania. Australian Journal of Experimental Agriculture, 41, 37-44. ECKARD R. J., CHAPMAN D. F. and WHITE R. E. (2007) Nitrogen balances in temperate perennial grass and clover dairy pastures in south-eastern Australia. Australian Journal of Agricultural Research, 58, 1167-1173. EDWARDS N. J. and PARKER W. J. (1994) Increasing per cow milk solids production in a pasture-based dairy system by manipulating the diet: A review. Proceedings of the New Zealand Society of Animal Production, Lincoln University, pp. 267-274. FALES S. L., MULLER L. D., FORD S. A., O'SULLIVAN M., HOOVER R. J., HOLDEN L. A., LANYON L. E. and BUCKMASTER D. R. (1995) Stocking rate affects production and profitability in a rotationally grazed pasture system. Journal of production agriculture, 8, 88-96. FAO (2009) The state of food and agriculture 2009. Rome, Italy: Food and Agriculture Organization of the United Nations. FORD S. A., FORD B. P. and SPREEN T. H. (1995) Evaluation of alternative risk specifications in farm programming models. Agricultural and Resource Economics Review, 24, 25-35. FULKERSON W. J. and DOYLE P. (2001) The Australian Dairy Industry. Victoria, Australia: Victorian Department of National Resources and Environment. FULKERSON W. J., SLACK K., BRYANT R. and WILSON F. (2003) Selection for more persistent perennial ryegrass (Lolium perenne) cultivars for subtropical/warm temperate dairy regions of Australia. Australian Journal of Experimental Agriculture, 43, 1083-1091. FULKERSON W. J., NEAL J. S., CLARK C. F., HORADAGODA A., NANDRA K. S. and BARCHIA I. (2007) Nutritive value of forage species grown in the warm temperate climate of Australia for dairy cows: Grasses and legumes. Livestock Science, 107, 253-264.

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WALES W. J., HEARD J. W., HO C. K. M., LEDDIN C. M., STOCKDALE C. R., WALKER G. P. and DOYLE P. T. (2006) Profitable feeding of dairy cows on irrigated dairy farms in northern Victoria. Australian Journal of Experimental Agriculture, 46, 743-752. WALKER G. P., STOCKDALE C. R., WALES W. J., DOYLE P. T. and DELLOW D. W. (2001) Effect of level of grain supplementation on milk production responses of dairy cows in mid–late lactation when grazing irrigated pastures high in paspalum (Paspalum dilatatum Poir.). Australian Journal of Experimental Agriculture, 41, 1-11. WATSON C. A., ATKINS T., BENTO S., EDWARDS A. C. and EDWARDS S. A. (2003) Appropriateness of nutrient budgets for environmental risk assessment: a case study of outdoor pig production. European Journal of Agronomy, 20, 117-126. WCED (1987) Our Common Future. World Commission on Environment and Development Oxford, United Kingdom. WHITE S. L., SHEFFIELD R. E., WASHBURN S. P., KING L. D. and GREEN J. T., JR. (2001) Spatial and time distribution of dairy cattle excreta in an intensive pasture system. Journal of Environmental Quality, 30, 2180-2187. WHITEHEAD D. C. (1995) Grassland nitrogen. Wallingford, UK: CABI International. WHITEHEAD D. C. (2000) Nutrient elements in grassland: soil-plant-animal relationships. Wallingford, United Kingdom: CABI International. YAN T., MAYNE C. S., GORDON F. G., PORTER M. G., AGNEW R. E., PATTERSON D. C., FERRIS C. P. and KILPATRICK D. J. (2010) Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows. Journal of Dairy Science, 93, 2630-2638. YOUNG D. L. (1984) Risk concepts and measures for decision analysis. In: Barry P. J. (ed.) Risk management in agriculture, pp. 31-42. Ames, United States: Iowa State University Press.

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STUDY 1: FARM SYSTEM INTENSIFICATION BASED ON PASTURE PLUS SUPPLEMENTS

CHAPTER 2 INTENSIFICATION OF A PASTURE-BASED DAIRY FARM SYSTEM THROUGH STOCKING RATE OR MILK YIELD PER COW OR BOTH: BIOPHYSICAL PRODUCTIVITY

Intensification on pasture – Biophysical productivity

INTRODUCTION Rising costs of land and water, volatility of international milk prices, and increasing cost of grain have necessitated dairy farmers in Australia to look for strategies that will improve productivity and profitability (Thorrold and Doyle, 2007). In the past, farmers have attained productivity gains by increasing the size of their farm area (Sinnett and Malcolm, 2006; ABARE, 2008). However, the increasing cost and reduced availability of land suitable for dairying has meant that this option is often limited or non existent. Hence, farmers must increase milk output per unit of land, by increasing either production per cow or number of cows per hectare, or both (Garcia and Fulkerson, 2005). Since pasture is the cheapest source of feed in these systems (Fulkerson and Doyle, 2001; Doyle et al., 2004) the benefits of adopting either of these strategies will depend on the effect on pasture utilisation. On the other hand, as purchased feed remains the highest component of total cash costs (Doyle et al., 2004; ABARE, 2005) it will be important to determine which strategy optimises the efficiency of conversion of these feeds into milk (Doyle et al., 2004; Wales et al., 2006; Beever and Doyle, 2007). Stocking rate is commonly defined as the number of milking cows per unit of effective hectare (i.e. cows/ha). Increasing stocking rate has been shown to improve pasture utilisation and quality in farmlet studies both with the use of supplements (Baker and Leaver, 1986; Fales et al., 1995) and without (Stockdale and King, 1980; Holmes and Parker, 1992; Macdonald et al., 2008), as well as on commercial farms (Armstrong et al., 2000; Kellaway and Harrington, 2004). At higher stocking rates more pasture is consumed per ha and, therefore, less pasture is wasted and there is an associated increase in nutritive value (Holmes and Roche, 2007). In contrast, a recent farmlet study in southern Australia (Valentine et al., 2009) found no effects of increasing stocking rate on pasture utilisation or nutritive value when grazing and supplementation management was aimed at maximising pasture utilisation. In addition, since a higher stocking rate normally results in lower pasture allowances (kg DM offered per cow), substitution effects (amount of pasture left uneaten due to extra supplementary feed) could be reduced, resulting in a more efficient use of supplements at the system level (Wales et al., 1999; Kellaway and Harrington, 2004). On the other hand, high stocking rates on pasture-based systems can reduce cow performance (King and Stockdale, 1980; Holmes and Parker, 1992; Dillon et al., 1995; Macdonald et al., 2008). In addition, it has been argued (McCall and Clark,

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1999; Holmes et al., 2002; Macdonald et al., 2008) that a trade off exists between stocking rate and FCE under pasture-based farm conditions. These effects were associated with reductions in the individual cow intake of pasture as stocking rate increased, which is likely to occur if supplementary feed does not compensate for the reduction in pasture allowance. In this way, some authors (Holmes and Parker, 1992; Fales et al., 1995; Kellaway and Harrington, 2004; Valentine et al., 2009) proposed that with improved feeding management (i.e. including supplementation when pasture availability is limiting) farmers could avoid reductions in feed intake and milk yield per cow, and therefore in FCE (Tozer et al., 2004). Alternatively, milk production per ha could be increased by increasing milk yield per cow. This can be achieved by increasing individual feed intake, which can more fully exploit the genetic potential of the cows (Edwards and Parker, 1994; Fulkerson et al., 2008) and also gives the opportunity to dilute maintenance requirements (Armstrong

et al., 2000; Beever and Doyle, 2007). In this regard, a positive effect of increasing feed intake on FCE has been shown (Beever and Doyle, 2007; Kolver, 2007). Moreover, a more appropriate level of feeding may improve reproductive performance of the typical Australian Holstein Friesian dairy cow (Fulkerson et al., 2008). However, pasture-based systems present some limitations to this strategy. First, it is accepted that high genetic merit Holstein-Friesian cows cannot achieve their production potential from grazed pasture alone (Kolver and Muller, 1998; Doyle et al., 2001; Kolver, 2003) or even when they are supplemented with grain only at milking (Bargo et al., 2002; Fulkerson et al., 2008). Second, increasing the level of supplements fed to grazing cows will lead, at some point, to declining marginal responses in milk due to either substitution, negative associative effects between feeds or partition of energy towards body tissue (Walker et al., 2001; Doyle et al., 2004; Wales et al., 2006; Beever and Doyle, 2007). Thus, although some key effects of increasing stocking rate or milk yield per cow on pasture-based systems have been reported, a comparison between these two strategies to lift milk production per ha at the system level has not been undertaken yet. Such a comparison needs to be carried out within a whole farm system approach with equitable management rules in order to remove bias towards one or other system (Macdonald and Penno, 1998). Therefore, the objective of this study was to compare pasture-based dairy farm systems increasing milk production per ha by increasing either stocking rate, milk yield per cow, or both, in terms of animal response, pasture utilisation and efficiency of use of supplements. Since this evaluation includes the use of supplementary feed, it should be approached as a comparison of systems rather than a comparison of the effects of changes in the

56


number of cows per ha, as it has been reported in the past (Stockdale and King, 1980; Kennedy et al., 2006; Macdonald et al., 2008), or in the milk yield per cow. To achieve this, a two year whole farm study was conducted using non-replicated farmlets where the same grazing and management principles were applied to all farmlets. The hypothesis was that, under equitable management, a system with an increased stocking rate and a moderate milk yield per cow would lead to a greater increase in milk production per ha and a more efficient use of supplements than a system that increases milk yield per cow only.

MATERIALS AND METHODS The experiment was undertaken at No. 9 dairy, Elizabeth Macarthur Agricultural Institute, Department of Primary Industries, Menangle, NSW, Australia (34° 07’ S, 150° 42 ‘ E, 100 m above sea level) between March 2006 and March 2008. The climate is warm-temperate with warm to hot summers (mean monthly maximum temperature in January of 29.3 °C) and mild to cool winters (mean monthly minimum temperature in July of 2.9 °C). Long-term mean annual rainfall is 740 mm/year. The soil types are a combination of brown chromosols and black vertisols (halpic epipedal endocalcareous), in the Australian Soil Classification (Isbell 2002). The proportion of the experimental area sown to perennial ryegrass (Lolium perenne L.) and kikuyu grass (Penisetum clandestinum Hochst. ex Chiov.) was 0.4 and 0.6, respectively. Both these perennial pastures were over-sown (direct drill) with short-rotation ryegrass (Lolium multiflorum Lam.) in March-April each year. The use of the animals in this experiment was approved by the Animal Ethics Committee of Industry and Investment New South Wales (NSW) (formerly NSW Department of Primary Industries).

Experimental design and treatments The farmlet study was conducted as a complete randomised design with two levels of stocking rate (2.5 and 3.8 cows/h) by two levels of milk yield per cow (target of 6,000 and 9,000 L/cow.lactation) treatments (systems). The systems were non-replicated, however, individual cows and paddocks were considered experimental units for either animal variables or utilised forage, respectively (Garcia, 2000). Each system comprised ten paddocks evenly distributed across the whole area of the experimental

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farm in order to balance the four systems in terms of soil type location, distance from the milking parlour, pasture species, pasture growth and soil nutrient status based on previous data collected in a three year study conducted on the same site (E. Havilagh, DPI NSW, pers. comm.). The target levels of milk yield per cow were selected to represent a typical production of Holstein-Friesian cow in Australia (6,000 L/cow.lactation) and about the maximum achievable with a pasture-based diet with optimum supplementation (9,000 L/cow.lactation; Pedernera et al., 2008). The stocking rates chosen were the result of a modelling evaluation undertaken to assess the feasibility of each system based on 12 t DM/ha.year of utilised pasture and a minimum of 30 – 35% of grazed pasture in the total diet of any herd. Stocking rate was defined on the basis of an average 600 kg body weight (BW) cow. In January, 2005, 240 Holstein Friesian cows from the existing dairy herd were paired into two herds, based on their mean (± sd) Australian Breeding Values (ABV) for milk fat plus milk protein (-6.3 ± 21.5), parity (3.6 ± 2.5 lactation) and milk production in the previous lactation (5,698 ± 882 L/cow.lactation). An adaptation period of one year was then used to develop two groups of cows that were fed and managed to achieve either a target of 9,000 L/cow or 6,000 L/cow.lactation. During the adaptation year, the two groups of cows averaged 8,466 ± 1,162 and 6,748 ± 787 L/cow.lactation for the higher and lower milk yield per cow targets, respectively (Pedernera et al., 2008). Cows from each of these two groups were categorised number of lactation and milk yield, and within category, randomly assigned to systems. Thirty cows from the lower production group were assigned to the control system (C) with a stocking rate of 2.5 cows/ha, and 30 cows to the high stocking rate system (HSR) with a stocking rate of 3.8 cows/ha. From the higher milk yield per cow group, 30 cows were assigned to the system with a stocking rate of 2.5 cows/ha (HMY), and 30 cows to the higher stocking rate system (HH) with a stocking rate of 3.8 cows/ha. A description of system characteristics is given in Table 1. A total area of 9.5 and 6.5 ha was allocated to the systems stocked at a lower and higher stocking rate, respectively.

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Table 1. Target milk production (L/cow.lactation and L/ha), stocking rate (cows/ha), proportion of pasture and concentrates in diet for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems. System C

HSR

HMY

HH

6,000 6,000 9,000 9,000 Target milk yield (L/cow.lactation) 1 2.5 3.8 2.5 3.8 Stocking rate (cows/ha) 15,000 22,500 22,500 34,200 Target milk yield (L/ha) 0.6-0.7 0.4-0.5 0.35-0.4 0.3-0.35 Target pasture proportion in the diet Target concentrates proportion in 0.15-0.2 0.2-0.25 0.4-0.45 0.5-0.55 the diet 1 Stocking rate is defined as average number of lactating cows per unit of effective hectare

Management All systems were managed according to the same grazing and cow management guidelines (Garcia, 2000). Pasture was grazed using rotational grazing of daily strips within paddocks, which were subdivided using electrified fences. The grazing management decision rules were based on matching daily pasture consumption with pasture growth rate (PGR) (Macdonald and Penno, 1998; Holmes and Roche, 2007) to maintain a whole system pasture cover (i.e. the average pasture mass of all paddocks in the system) of approximately 2,000 kg DM/ha (± 200 kg); and pre- and post-grazing pasture mass of 2,400 and 1,500 kg DM/ha (± 200 kg), respectively (Garcia and Holmes, 2005). Following this objective, the step-process to allocate pasture and supplements to each system was as follows: Weekly, the pasture cover was monitored on each paddock using a calibrated rising plate meter (RPM) (Earle and McGowan, 1979) (calibrated from a previous study in the same experimental site and pastures) in year 1 and a calibrated Rapid Pasture Meter (C-Dax Systems Ltd., Palmerston North, New Zealand; Yule et al., 2007) in year 2. Average pasture cover for the day of monitoring, and PGR, of previous seven days, were calculated for each system. Pasture allocation was calculated based on the following: Pasture allocation (kg DM/cow.day) = PGR (kg DM/ha.day)/Actual stocking rate (cows/ha).

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CHAPTER 2

Where: Actual stocking rate (cows/ha) = Number of cows in system during the week / Total system area (ha). Cows on all systems grazed pasture for most of the day and were fed concentrates twice-a-day at milking. For the C and HSR cows, silage or hay were the only supplements fed in addition to concentrates at milking. A total intake of ~19 kg DM/cow.day was targeted in these two systems and, for this purpose, pasture and supplements were allocated on a weekly basis, aiming to provide about 200 MJ of ME/cow.day and a minimum CP concentration of 16% of DM fed. In contrast, for the systems with a higher target of milk yield per cow (HMY and HH), a partial mixed ration (PMR) strategy was used to give a total ration of ~23 kg DM/cow.day, formulated to provide about 250 MJ ME/cow.day and a minimum CP concentration of 17% of DM fed. The Cornell Net Carbohydrate and Protein System model v. 4.0 (Fox et al., 2004) was used to formulate the ration. The difference between pasture allocation and cows total target intake (~19 or 23 kg DM/cow.day for C and HSR or HMY and HH, respectively) was covered with supplements taking into account the formulation parameters described above. Occasionally, when actual pasture cover was different from target, pasture allocation (kg DM/cow.day) was decreased or increased accordingly, until pasture cover target levels were reached again. When PGR exceeded cow requirements, an area of the system equivalent to the proportion of pasture growth in excess of cow requirements was cut and conserved as silage. This was kept separately and used to supplement cows of the same system in which the silage was produced. In addition to the management based on PGR and pasture cover described above, the number of leaves per tiller (ryegrass: between two and three; kikuyu grass: between three and four and a half) was monitored weekly to ensure the grazing interval was adequate to maximise growth and persistence of ryegrass (Fulkerson and Donaghy, 2001), nutritive value of kikuyu grass (Reeves et al., 1996b) and utilisation of both pasture species by stock. Mechanical cutting (slashing) of paddocks occurred systematically at one time in early autumn, prior to the sowing of the short rotation ryegrass (in March). During the summer months, occasional post-grazing slashing was undertaken when deemed necessary to maintain kikuyu grass quality. All systems were fertilised with 100 - 150 kg urea (46% N)/ha every second grazing. This practice was based on experimental results from similar pastures at the same site as the present study, which were managed to maximise yield of utilised

60


pasture (Garcia et al., 2008). A fertiliser mix [15% N, 8% P, 16% potassium (K) and 8% sulphur (S)] was applied in autumn (March – April - May) and winter (June-JulyAugust) of both years at a rate of approximately 300 kg/ha per application on all systems. This resulted in total N input being similar across all systems and ranging between 350 and 450 kg N/ha.year. Irrigation water was limited, particularly during the dry summer of 2006 - 2007 (year 1), although total irrigation water applied was similar across all systems [6.2 megalitres (ML)/ha in year 1 and 3 ML/ha in year 2]. Thus, during the summer drought of 2006-2007, when application ranged from 4 ML/ha, in paddocks excluded from irrigation, to 7.5 ML/ha in paddocks prioritised to receive a better irrigation during summer. Approximately half of the cows calved in autumn (February – March - April) and half in spring (July – August - September). After calving, cows had a voluntary waiting period of 50 days before artificial insemination (AI). Oestrus was detected by observation of oestrus behaviour at the time of milking (twice-a-day) and by using tail paint or heat mount detectors. If cows were not pregnant by 120 days of lactation and were still within the breeding period, they received hormonal treatment to synchronise their oestrus cycles. Pregnancy was diagnosed by rectal palpation at least six weeks after the last artificial insemination. All cows were dried off about 60 days before the expected date of calving, or earlier if daily milk production dropped below 12 L/cow.day for the C or HSR system and 17 L/cow.day for the HMY or HH systems. Every year, at least 20% of the cows from each system were replaced with primiparous cows (half on each calving season). Cows were culled based on reproductive failure, health or production. Due to the low number of cows per system, cows that showed problems clearly unrelated to the treatments imposed (e.g. behaviour issues) were replaced by a cow of similar status (i.e. days in milk, parity and milk production during the last lactation).

Measurements Pasture DMI for each daily grazing was estimated in all systems by measuring the pre- and post-grazing pasture mass with a RPM (Earle and McGowan, 1979) within 2 days before and after each grazing. The RPM measurements were converted into kg DM/ha using a calibration developed for the same pasture type at the same site in the previous year (Garcia et al., 2008). The weekly farm walks in year 2 were carried out using both the rising plate meter and the Rapid Pasture Meter on the same track, twice each season. The Rapid Pasture Meter is a sensor which can be attached to the back of a vehicle to estimate pasture height based on the frequency with which a light

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CHAPTER 2

beam between multiple pairs of receptors is being interrupted. The weekly walk allowed developing a conversion factor to convert the Rapid Pasture Meter measurements into rising plate meter units and, in turn, into kg DM/ha. Daily pregrazing pasture samples were taken from each paddock to be grazed within two days before each grazing by hand-plucking to simulate grazing height (Beever et al., 1986). The daily amount of concentrates fed per cow at each milking was automatically registered by the Alpro system (DeLaval, Tumba, Sweden). The rest of the supplements fed alone or as part of the partial mixed ration were individually weighed daily. Samples of all supplements used in each system were taken weekly. All forage samples were pooled on a monthly basis, oven dried at 60 °C for 48 h and ground to pass through a 1.0 mm sieve to be later analysed for NDF and ADF (Van Soest et al., 1991); water-soluble carbohydrates (WSC) (Smith, 1969); N and nitrate-N by combustion using a Leco_ FP-428 Nitrogen Determinator (Leco_ Corporation, St. Joseph, MI, USA) content and in vitro digestibility of DM and organic matter (Tilley and Terry, 1963). Crude protein content was estimated as 6.25 x N content. The ME was estimated from in vitro digestibility of DM (SCA, 1990). The milk yield of individual cows was automatically recorded daily using flow meters (DeLaval, Tumba, Sweden). Fortnightly, individual milk samples were taken at a.m. and p.m. milkings and analysed for milk fat, milk protein and somatic cell count by a Milko-Scan instrument (Foss Electric, Hillerød, Denmark). Body weight of all cows was recorded weekly and body condition score (BCS) [scale 1 to 8 as described by Earle (1976)] was measured fortnightly by the same trained operator, following the a.m. milking. All animal events (reproduction, health and treatments) were recorded for each individual cow.

Calculations Annual values of cumulative grazed, conserved pasture and pre- and post-grazing pasture mass for each system were calculated for each paddock as the sum of daily measurements conducted every time that paddock was grazed or cut for conservation. Daily data of pasture and supplement consumption were averaged on a weekly basis, and, in turn, monthly means were obtained. Monthly means of the nutritive value characteristics of the whole diet were calculated from the weighed average of DMI of each feed source consumed daily. Cumulative production of milk, milk fat and milk protein per ha was calculated adding on a year basis, each system’s weekly average of daily individual yields and,

62


in turn, multiplying this by the number of milking cows per ha grazing in that system for each year. Individual lactation data were adjusted to 305 days in milk. Because some cows on each system had incomplete lactations within the experimental period, only lactations with more than 200 days in milk recorded were considered for this calculation. The missing daily milk yields of cows with less than 305 days in milk were calculated in relation to the persistency of each individual cow. This resulted in a total of at least 40 lactations analysed per system. Milk fat and milk protein percentages, somatic cell counts and lactation length were calculated as the mean of each cow’s lactation. Days in milk was considered as a covariate for the calculation of monthly evolution of BCS, BW and DMI in each system. The FCE for each system was evaluated as kg of energy-corrected milk (ECM) kg/ DM of feed (Beever and Doyle, 2007), where ECM is calculated as per the following formula (Tyrrell and Reid, 1965): ECM = kg milk × (383 × milk fat% + 242 × milk protein% + 783.2)/3140. With the exception of the concentrate fed at milking, all other supplementary feeds were fed on a feedpad and, due to this, only amounts of supplementary feed on offer were recorded, without estimation of refusal, which was always kept at a minimum. Hence, in this study FCE is an integrated measure of both biological and farm efficiency (Beever and Doyle, 2007; Kolver, 2007). All illustrations showing monthly evolution depict April as the initial month, since this is when the field study commenced.

Statistical Analyses Data were fitted with linear mixed models and all parameters were estimated using the Restricted Maximum Likelihood (REML) procedure of GENSTAT for WINDOWS version 11 (VSN International, Hemel Hempstead, UK; Payne et al., 2008). The year was considered a random effect for all pasture and intake related variables, as the environmental conditions are assumed to vary randomly from year to year and the possible cumulative effects of the application of the same treatment for two years were not of interest for this study (Hansen et al., 2000). Individual paddocks were considered as the experimental unit for pasture related variables (except pasture intake and nutritive value). Animals were considered as the experimental unit for animal

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CHAPTER 2

related variables, as it has been done in the past for whole farm studies where animals are normally group-fed in order to represent whole farm systems realistically (Dillon

et al., 1995; Garcia et al., 1998; Jonsson et al., 1999; Horan et al., 2005; Fulkerson et al., 2008; Macdonald et al., 2008). The standard error of the predicted means will include estimates of random variation as well as variation due to the repeat observations on the same paddock or animal, in the models where one or the other are used as the experimental unit. For pasture-related variables (Yijk) the mathematical model used was:

Yijk = µ + Si + Tj + STij + eijk

(Model I)

Where Yijk = the pasture related variable in system i of year j and paddock k; µ = the general mean; Si = the fixed effect of ith System (i=C, HSR, HMY, HH); Tj = the random effect of jth year (j=1,2) and eijk = the residual error term. For animal-related variables (Yil) the model was: Yil = µ + Si +Ul + eil

(Model II)

Where Yil = the animal related variable in system i of animal l; µ = the general mean; Si = the fixed effect of ith System (i=C, HSR, HMY, HH); Ul= the random effect of lth animal and eil = the residual error term. The effect of year was not included in Model II because, since all farmlets comprised split-calving herds, the data corresponding to each cow’s 305-day lactation did not always correspond to year 1 or 2 respectively. In order to examine the seasonal effects on pasture or animal response, the response variables were fitted with a cubic spline smoothing function (Verbyla et al., 1999), with the exception of total intake/cow.day data. Such function is effective in modelling non linear trend and covariance structure for longitudinal data as presented in this study. A curvature model fitted for each system (C, HSR, HMY, HH) followed a spline function below: Yt = a + b1(t-t1) +b2(t-t2)+ …+ bn(t-tn)+ et Where Yt = the interest variable of month t; a = the intercept and t1,t2…tn = the knots (month at which growth rate changes), b1,b2…bn = the growth rates of the specific time intervals that constitute the entire spline, and et = the residual error. For variables which the above curvature model was fitted to the above spline function, the model was: 64


Yijm = µ + Si + Tj + Vm+ SVjm +STij + eijm

(Model III)

Where Yijm = the variable in system i of year j and month m; µ = the general mean; Si = the fixed effect of ith System (i=C, HSR, HMY, HH); Tj = the random effect of jth year (j=1,2); Vm= the fixed effect of mth month (l=1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) and eijm = the residual error term. The analysis of these variables was run on ASReml statistical software (Gilmour et al., 2006). With the exception of individual cows’ lactation data, all values presented are means of the two years of study. Standard error of the difference (SED) is reported for all data presented as total per year, and standard error of the mean (SEM) for all data describing the monthly evolution of a variable. For clarity of illustration, only the average SEM is presented. A multiple regression analysis was conducted in order to evaluate the capacity of total diet nutritive value variables measured (%DM) and DMI of each feed (kg/cow.day) to explain changes in milk yield/cow.day. An additional multiple regression analysis was performed to evaluate the capacity of different parameters of the diet nutritive value (%DM) and each feed DMI (kg/cow.day) to explain changes in total DMI (kg DM/cow.day).

RESULTS Animal responses The milk, milk fat and milk protein yields per ha of systems increasing stocking rate or milk yield per cow are shown in Table 2.

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CHAPTER 2

Table 2. Milk (L/ha.year) and milk fat and protein (kg/ha.year) yield for the control (C), high stocking rate (HSR), high milk yield per cow (HMY) and high stocking rate and milk yield per cow (HH) systems. System C Milk (L/ha.year) Milk fat (kg/ha.year) Milk protein (kg/ ha.year) abc

HMY

HH

20,895c

31,143b

22,975c

34,583a

1,067