1

A whole farm modelling approach to

evaluate the economic viability of a

dairy farm in a sensitive catchment.

A dissertation presented in partial fulfilment of the requirements

for the Degree of Bachelor of Agriculture Science with Honours

at

Massey University, Palmerston North, New Zealand.

Palmerston North, New Zealand

Mr Trevor W Sulzberger 2014

2

ABSTRACT The Horizons One Plan recognises the significant impact that nutrient discharges from agricultural

activities can have on water quality and regulates existing intensive farming activities for individual

farms including dairy in targeted catchments. This is achieved by allocating nitrogen leaching

allowances based on Land Use Capability class (LUC). Existing dairy farms in target water

management sub-zones will either meet nitrogen (N) leaching targets (Limits), according to the Land

Use Capability (LUC) of the farms or, where they cannot, then consent will be granted subject to a

reduction in nutrient loss from farm land.

Horizons One Plan specifies use of the Overseer® nutrient budget program for calculating estimated

nutrient discharges from individual properties.

The objective of this research is to explore the complexity of calculating an N-loss measurement of a

case study farm in a sensitive catchment and to evaluate the mitigating strategies to reduce N-loss set

limits while maintaining the economic viability of a case study farm using a whole farm modelling

approach.

Three consultant’s Overseer® data from a single case study farm was used and evaluated. The

research highlights that there is considerable variation between operators of Overseer®, resulting in

different N-loss estimates. Industry validation and/or audit of consultant’s work and regular

professional training in Overseer® is recommended for better accuracy.

The research highlights that soil type is not always considered an important part of making up the

blocks within Overseer® and climate/location information is critical in accurately calculating N-loss.

A detailed soil and landscape capability survey at the paddock level be undertaken by trained Soil

Pedologist is recommended. This information is critical in defining blocks and improving accuracy of

the calculated N-loss of each block.

The research also identified poor communication between professionals. Better communication across

professionals in different fields would improve the accuracy of the outcome and single data entry is

required so farm data can go into the database and then be freely available to trained consultants. The Grazing Systems Limited Linear Program (GSL LP) is a bio-economic model in that resources

have economic values that drive optimisation, and provides an opportunity to distinguish the changes

that are required to optimise operating surplus, this is where marginal cost equals marginal revenue

(MC=MR) and to minimise N-loss of the farming system.

The results showed that six of the nine runs out performed the base system with farm surplus and

eight out of nine runs showed lower N-loss than the 20 year N-loss limit, with run five giving the

highest profit and run ten the lowest N-loss. Run five reduced cow numbers by 23% to 2.2 cows/ha,

removed imported supplements, N fertiliser and 15 ha of winter Oats. The results showed run five

increased profits by 14% and decreased N-loss by 43% over the base system; this would make the

farm meet the 20 year set limit imposed by the One Plan by 39% (N-loss). The research highlights that the farm system needs to de-intensify, reduce stocking rate, remove or

reduce imported supplements and remove or reduce nitrogen fertiliser, thus increasing profitably of

the farm system and reducing the environmental impact. This study found that the GSL LP whole farm modelling tool to be very effective when used with

Overseer®, to identify profitable options for reducing N-loss off the case study farm. The combination of a whole farm system model and Overseer® provides a decision-making tool

which leads to a complete picture which should then lead to better decisions as opposed to any one of

these in isolation.

3

TITLE: A whole farm modelling approach to evaluate the economic viability of a dairy farm in a

sensitive catchment. DEGREE: Bachelor of AgriScience (Honours) NAME: Mr Trevor W Sulzberger B. AgriScience (Massey)

YEAR: 2014 KEYWORDS: Dairy farm, sensitive catchment, whole farm modelling, linear programming,

nitrogen leaching, mitigation strategies.

4

ACKNOWLEDGEMENTS

This honours thesis has been a learning experience, with many hours of my time taken up

researching and writing. I would like to take this time to thank my supervisors Tom Phillips

and Nicola Shadbolt for their help in preparing this thesis, for giving me the opportunity to

undertake this research and for their guidance and support over this time.

I would like to thank the host farm, consultants, Soil Pedologist, Massey No.1 farm project

manager and GSL team, all contributed in making this research project possible.

I would also like to thank my father John for our long discussions on this topic, my wife

Belinda for supporting me over the time period and my kids Troy and Nina with which I have

missed many weekends due to working on my thesis.

Thank You.

Trevor W Sulzberger

I

Contents 1 Introduction .................................................................................................................................... 1

1.1 New Zealand’s National Identity ............................................................................................. 2

1.2 Dairy and Clean Streams Accord ............................................................................................. 3

1.3 The Resource Management Act & Local Government Act ..................................................... 3

1.4 The Horizons Regional Council ................................................................................................ 4

1.4.1 Land Use Capability (LUC) ............................................................................................... 7

1.5 Purpose of the study ............................................................................................................... 8

1.6 Research Question .................................................................................................................. 9

1.7 Research Objectives ................................................................................................................ 9

1.8 Dissertation outline ................................................................................................................. 9

2 Literature Review .......................................................................................................................... 10

2.1 Introduction .......................................................................................................................... 10

2.2 Mitigating N Leaching ........................................................................................................... 10

2.2.1 Standoff facilities .......................................................................................................... 10

2.2.2 Fertiliser ........................................................................................................................ 11

2.2.3 N Fixation ...................................................................................................................... 11

2.2.4 Diet based strategies ..................................................................................................... 12

2.2.5 Milk urea content .......................................................................................................... 13

2.2.6 Genetic merit ................................................................................................................ 14

2.2.7 Whole farm approach ................................................................................................... 15

2.3 Economic reporting ............................................................................................................... 16

2.3.1 DairyBase ...................................................................................................................... 16

2.3.2 Economic Viability ......................................................................................................... 16

2.3.3 Marginal decision making ............................................................................................. 17

2.4 Whole Farm Modelling ......................................................................................................... 19

2.4.1 Simulation Models ........................................................................................................ 19

2.4.2 Optimisation Models ..................................................................................................... 19

2.4.3 Modelling approach ...................................................................................................... 20

2.4.4 Existing Cost Benefit Analysis From Mitigating N Leaching .......................................... 20

2.4.5 OVERSEER® .................................................................................................................... 21

2.4.6 DairyNZ Whole Farm Model & Molly ............................................................................ 31

2.4.7 UDDER ........................................................................................................................... 32

II

2.4.8 Farmax® Dairy Pro ......................................................................................................... 33

2.4.9 Grazing Systems Limited ............................................................................................... 35

2.4.10 Economic optimum & N conversion efficiency using Whole farm model .................... 38

2.4.11 GSL model on Massey No.1 dairy unit .......................................................................... 39

2.5 Farmers' Decision Making ..................................................................................................... 47

2.6 Concluding Remarks .............................................................................................................. 49

3 Methodology ................................................................................................................................. 50

3.1 Introduction .......................................................................................................................... 50

3.2 Research methods ................................................................................................................ 50

3.3 Case selection ....................................................................................................................... 51

3.4 Data collection ...................................................................................................................... 51

3.4.1 Documentation ............................................................................................................. 52

3.4.2 Interview ....................................................................................................................... 52

3.5 Data analysis ......................................................................................................................... 52

3.5.1 Initial analysis ................................................................................................................ 52

3.6 Ethical considerations ........................................................................................................... 52

3.7 Summary ............................................................................................................................... 53

4 Contextual Information to Cases .................................................................................................. 54

4.1 Introduction .......................................................................................................................... 54

4.2 General location .................................................................................................................... 54

4.3 Farm description ................................................................................................................... 55

4.3.1 Climate data .................................................................................................................. 55

4.3.2 Land and production ..................................................................................................... 55

4.3.3 Feed supply ................................................................................................................... 55

4.3.4 Fertiliser ........................................................................................................................ 55

4.3.5 Effluent & Irrigation ...................................................................................................... 55

4.3.6 Soil resources ................................................................................................................ 55

4.3.7 Permissible N-Loss limits ............................................................................................... 57

5 Results ........................................................................................................................................... 58

5.1 Introduction .......................................................................................................................... 58

5.2 Obtaining an N-loss using Overseer® .................................................................................... 58

5.2.1 Introduction .................................................................................................................. 58

5.2.2 Setting up nutrient blocks within Overseer® ................................................................ 58

5.2.3 Summary of consultant’s data ...................................................................................... 64

III

5.2.4 Effects of climate change in Overseer .......................................................................... 64

5.2.5 Effects through changes in Overseer® versions ............................................................ 65

5.3 Whole farm modelling approach .......................................................................................... 66

5.3.1 Introduction .................................................................................................................. 66

5.3.2 GSL scenarios explained ................................................................................................ 67

5.3.3 Chart comparison to run one ........................................................................................ 72

5.3.4 Summary ....................................................................................................................... 76

5.4 Comparison with changes in milk solid pay-out ................................................................... 76

5.4.2 Summary ....................................................................................................................... 78

6 Discussion ...................................................................................................................................... 81

6.1 Obtaining an N-loss using Overseer® .................................................................................... 81

6.1.1 Effects of climate change in Overseer .......................................................................... 83

6.1.2 Effects through changes in Overseer® versions ............................................................ 83

6.2 Whole farm modelling approach .......................................................................................... 84

7 Conclusion ..................................................................................................................................... 88

7.1 Introduction .......................................................................................................................... 88

7.2 Main findings ........................................................................................................................ 88

7.2.1 Obtaining an N-loss using Overseer® ............................................................................ 88

7.2.2 Whole farm models ....................................................................................................... 89

7.3 Implications of the research ................................................................................................. 90

7.4 Further research ................................................................................................................... 90

8 References .................................................................................................................................... 91

APPENDICES ........................................................................................................................................ 105

APPENDIX A ..................................................................................................................................... 105

APPENDIX B: GSL DATA FROM CASE STUDY FARM ......................................................................... 110

IV

Tables

Table 1: Twenty year nitrogen leaching allowances for each land use capability class (Table 13.2,

Horizon, 2013 Proposed One Plan) ........................................................................................................ 7

Table 2: The key assumptions used in Overseer are: (Wheeler & Shepard, 2013, p. 4)...................... 23

Table 3: Regional Plan Approaches to Overseer Versions within nutrient rules (Park, 2014). ............ 29

Table 4: Comparison of Farmax and GSL predictions for current performance of Farms 1, 3 and 4.

Note Farm 2 was not modelled on Farmax (McCall, 2012, p. 16 Table 1 ) ........................................... 39

Table 5: Massey No.1 dairy farm permissible N-Loss limits ................................................................. 40

Table 6: GSL data from each run. .......................................................................................................... 45

Table 7: Soil names and drainage status............................................................................................... 57

Table 8: Permissible N-Loss limits for the case study farm................................................................... 57

Table 9: C1 & C2: Nutrient blocks and land area, resulting in N-loss with Overseer® .......................... 59

Table 10: C3: Nutrient blocks and land area, resulting in N-loss with Overseer® ................................ 60

Table 11: Consultant 1: Soil data used in Overseer® ............................................................................ 61

Table 12: Consultant 2: Soil data used in Overseer® ............................................................................ 61

Table 13: Consultant 3: Soil data used in Overseer® ............................................................................ 62

Table 14: Animal data used in Overseer® ............................................................................................. 63

Table 15: Climate data used in Overseer® ............................................................................................ 63

Table 16: Summary of land area for all consultants with N-loss calculated ......................................... 64

Table 17: Comparison changing climate data in Overseer® ................................................................. 65

Table 18: the effects of Overseer® version changes on the case study farm ....................................... 66

Table 19: Case study farm GSL run. ...................................................................................................... 71

Table 20: Ten year milk solid payout .................................................................................................... 76

Table 21: GSL Comparison with different MS Pay-out.......................................................................... 79

V

Figures Figure 1: Total cows milked with an increase in stocking rate (LIC & DairyNZ, 2013) ............................ 1

Figure 2: Increasing limitations to use and decreasing versatility of use from LUC Class 1 to LUC Class

8 (Figure 2: Lynn et al., 2009, p. 9) .......................................................................................................... 8

Figure 3: Marginal cost curve (Gans, King, & Mankiw, 2011) ............................................................... 18

Figure 4: A diagrammatic example of a nutrient budget (AgResearch. et al., 2013). .......................... 21

Figure 5: An illustration of the changes to model uncertainty as the conditions move from those used

for calibration: based on (Loucks et al., 2005). ..................................................................................... 27

Figure 6: Changing from Overseer® 5.4 to 6.0 (NL kg/ha/yr). Adapted from (Bell, 2013a) .................. 30

Figure 7: Schematic representation of modelling and assessment process (Monaghan et al., 2004). 33

Figure 8: N leached & profitability farm modelling (Bowler & McCarthy, 2013, p. 8) ......................... 34

Figure 9: Optimise using LP model (Bell, 2013a). ................................................................................. 36

Figure 10: Comparison between Zones – Tararua. Adapted from (Bell, 2013a, p. 16) ......................... 37

Figure 11: Comparison between Zones - West Coast. Adapted from (Bell, 2013a, p. 16) .................... 37

Figure 12: GSL plotted runs with comparison of N leaching, cow numbers and production and N

efficiency ............................................................................................................................................... 46

Figure 13: GSL plotted runs with comparison of N leaching, N efficiency and cash surplus ................ 47

Figure 14: GSL plotted runs with comparison of N leaching, cow numbers and production and N

efficiency ............................................................................................................................................... 75

Figure 15: GSL plotted runs with comparison of N leaching, N efficiency and cash surplus ................ 75

Figure 16: GSL Case study farm surplus/ha for each run ...................................................................... 76

Figure 17: GSL Comparison with different MS Pay-out ........................................................................ 80

Figure 18: GSL Comparison with different MS Pay-out /ha .................................................................. 80

VI

Maps Map 1: Water quality indicator for nitrate by catchment (State of the Environment of the Manawatu

Wanganui Region, Horizon, 2005, p. 62) ................................................................................................ 6

Map 2: Massey No.1 Dairy unit LUC class farm map ............................................................................ 40

Map 3: Coastal Rangitikei (Rang_4) (Horizon, 2014) ............................................................................ 54

Map 4: Soil map .................................................................................................................................... 56

1

1 Introduction

New Zealand has a land area of 27 million ha, 12 million hectares of pastoral land of which

1.6 million occupies dairy farming (Statistics New Zealand, 2012). Dairy cattle numbers in

New Zealand were approximately 2.9 million in 1980, 3.5 million in 1992, 4.2 million in

2000, 5.3 million in 2007 and 6.4 million in 2012 (MPI, 2013a; Statistics New Zealand,

2012), with total cow numbers on the rise so has the intensification of cows per hectare

(Figure 1). In 2012 New Zealand dairy farming had an average stocking rate of 2.83 cows per

hectare, with 63% of all cows in the North Island, 24.6% in the Waikato region and 4.6% in

the Manawatu region (Statistics New Zealand, 2012).

Figure 1: Total cows milked with an increase in stocking rate (LIC & DairyNZ, 2013)

Agriculture contributes approximately 50% of New Zealand's export earnings (Statistics New

Zealand, 2012) and the industry as a whole, particularly dairying, has been growing faster

than many other economic sectors (MPI., 2013c; The Treasury, 2013). The scale and

intensity of dairy farming in New Zealand has been driven by global economic circumstances

that influence the industry as a marketing and manufacturing enterprise.

The main source of feed for ruminant livestock in New Zealand is grazed grass. In New

Zealand, the key to economic low cost milk production is to maximize milk produced from

grazed grass (Dillon et al. 1995). Grassland-based agricultural production is the most

important component of livestock production systems in New Zealand because of the

competitive economic advantage of grazed grass within such a system (Dillon et al., 2005;

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Total cows milked & stocking rate

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2

Moreau et al., 2012). In New Zealand, intensive grass-based milk production systems

generally relies on inputs of nitrogen (N) in the form of chemical fertiliser to produce

sufficient herbage (grazed grass or grass silage), improving the overall quality of pasture

grown, increasing MJME and strategic supplementation during times of herbage deficit, to

sustain milk output per hectare at economically viable levels (Ball & Ryden, 1984; Cuttle &

Scholefield, 1995; Ryan et al., 2012).

This gain in productivity has been accompanied by increasing farm inputs. In the same period

(Figure 1), for example, the use of phosphorus (P) fertiliser in New Zealand has doubled and

the use of nitrogen (N) fertiliser increased five-fold (MPI, 2013b; Statistics New Zealand,

2012). This increase in fertiliser inputs, and consequently in stocking rates, can lead to

elevated nutrient losses from farms (Cichota & Snow, 2009). The high use of fertilisers under

intensive dairying along with the return of nitrogen (N) via urine is regarded as a major cause

of ground-water contamination and eutrophication of surface water bodies adjacent to

agricultural areas (Ball & Ryden, 1984; Cichota & Snow, 2009; Cichota et al., 2013; Di &

Cameron, 2002; Edgar, 2008; Goodlass et al., 2003; Haynes & Williams, 1993; Monaghan et

al., 2009). New Zealand’s Parliamentary Commissioner for the Environment has identified a

number of concerns with the way intensive farming is negatively impacting on the country’s

‘clean and green’ image Growing for good (PCE, 2004).

1.1 New Zealand’s National Identity

Freshwater ecosystems such as streams, rivers and lakes are intrinsically linked to cultural,

recreational and economic activities, forming an integral part of our national identity (Cullen

et al., 2006; Edgar, 2008; Smith et al., 1993). The country’s lakes and rivers have influenced

patterns of settlement, supported economic development, and helped to shape the national

identity (Ministry for the Environment, 2005). Similarly, agricultural practices are deeply

entrenched in New Zealand’s heritage and contribute significantly to our economic

foundation. However, public perception in New Zealand would appear to differ. In 2001 Fish

and Game New Zealand launched what has become known as the ‘‘Dirty Dairying’’

campaign (Cullen et al., 2006). Fish and Game New Zealand is an angler and game bird

hunter organisation that has a statutory duty to help protect New Zealand’s fresh water sports

fish fisheries and game bird hunting. The ‘‘Dirty Dairying’’ campaign was designed to

highlight the adverse consequences of dairying to the nation’s waterways and has been very

successful in raising awareness of the issues, particularly within the urban community (Bell,

2012; Robson & Edmeades, 2010).

3

1.2 Dairy and Clean Streams Accord

In May 2003, partly in response to the campaign, the dairy industry, regional councils and

government signed the Dairy and Clean Streams Accord (Jay, 2007). The accord sets targets

including fencing off and bridging 90% of waterways from cows by 2012; establishing farm

nutrient budgets to put appropriate fertiliser quantities on farm; and requesting that farmers

comply with their permits for managing effluent (Cowie et al., 2006; Fonterra et al., 2003).

The Accord, which expired in December 2012, has been an important voluntary instrument

that helped raise environmental awareness among dairy farmers (Ministry for the

Environment, 2013). A new accord has been developed to update and succeed the Clean

Streams Accord, this Water Accord is a broader and more comprehensive commitment than

the previous Clean Streams Accord, it includes commitments to targeted riparian planting

plans, effluent management, comprehensive standards for new dairy farms and measures to

improve the efficiency of water and nutrient use on farms (DairyNZ, 2013b).

The Sustainable Dairying: Water Accord has been developed under the oversight of the Dairy

Environment Leadership Group (DELG). DELG includes representatives from farmers, dairy

companies, central government, regional councils and the Federation of Māori Authorities

(DairyNZ, 2013b).

The partners committed to actions and targets in the Water Accord: DairyNZ, Dairy

Companies Association of New Zealand, Fonterra, Open Country, Miraka, Synlait, Tatua,

Fertiliser Association of New Zealand, Ballance Agri-Nutrients, Ravensdown Fertiliser Co-

operative, Federated Farmers Dairy Section, Irrigation New Zealand and NZ Institute of

Primary Industry Management. The friends who are supportive of the purpose of the Accord

and committed to contribute to its success, Westland Milk Products, The Federation of Māori

Authorities, Ministry for Primary Industries and Ministry for the Environment (DairyNZ,

2013b).

1.3 The Resource Management Act & Local Government Act

While these policies and programmes provide broad strategies for helping move towards

more sustainable development and environmental management, the main task of delivering

sustainable development and environmental management falls under the aegis of two key

pieces of New Zealand legislation: the Resource Management Act (RMA) and the Local

Government Act of 2002 (LGA). As a set, the RMA and LGA establish the basic governance

structures for deciding how to manage the allocation and use of New Zealand's natural

4

resources and operationalising the broader principles of sustainable management of the

environment (Rutledge et al., 2008; Rutledge et al., 2007). The LGA has an integrative

component, requiring consideration of social, economic, environmental and cultural well-

being of their communities (Jackson & Dixon, 2007) and creates the territorial authority

system of regions and districts (and unitary authorities) which gives them the power to carry

out services within their jurisdiction. Similarly, the RMA provides an overall framework

within which territorial authorities plan for and manage the natural resources within their

jurisdictions (Harris & Atkins, 2004; Jackson & Dixon, 2007). Because they operate at the

interface of policy, knowledge, management, education, and communication, territorial

authorities within New Zealand have the opportunity to take a lead role as enablers of

sustainable development and environmental management within New Zealand (Rutledge et

al., 2007).

1.4 The Horizons Regional Council

Horizons uses these two Acts of Parliament to provide the framework for policy making.

Two of the specific roles given to regional councils by the RMA are: the control of land use

for the purposes of maintaining and enhancing water quality, and the control of discharges

onto land or into water. As mechanisms to achieve sustainable management, Horizons is

required to develop objectives, policies, rules and other methods in Regional Policy

Statements and Regional Plans (Roygard & McArthur, 2008).

In 2007, Horizons Regional Council which manages Manawatu, Rangitikei and Wanganui

river catchments, proposed a legislation called One Plan (Horizon, 2013). The One Plan can

be described as a “one-stop-shop” regional planning document that defines how the natural

and physical resources of the Region (including fresh air, clean water, productive land and

natural ecosystems) will be cared for and managed by the Regional Council in partnership

with Territorial Authorities and the community (Horizon, 2013). The Proposed One Plan

(Manawatu-Wanganui Regional Council’s combined Regional Plan and Policy Statement)

will eventually supersede the Manawatu Catchment Water Quality Regional Plan. The One

Plan is focussed on four issues in the region - water quality, water demand, hill country land

use and threatened habitats. In some catchments, rivers and streams with elevated Nitrogen

(N) and Phosphate (P) concentrations have algal and weed growth problems that lead to

deterioration in water quality, preventing recreational use.

Technical and expert advice received during the development of nutrient standards for the

5

One Plan highlighted the need to manage both Nitrogen (N) and Phosphate (P) (Wilcock et

al., 2007). Limiting only one nutrient was not deemed a reliable tool to reduce periphyton

growth over the wide range of environmental conditions in the rivers of the region, given that

nutrient limitation has been found to vary over time and in different reaches of some New

Zealand streams (Biggs, 2000; Francoeur et al., 1999; Quinn et al., 1997; Wilcock et al.

2007).

Consequently, through the objectives and policies of the One Plan, water quality standards

have been proposed for both N and P in all waterways of the region at all flows less than the

20th exceedance percentile, to maintain the ecosystem, recreational and cultural values of the

rivers (McArthur, 2011; McArthur et al., 2010).

Until the 1970s, the major cause of deterioration in water quality in New Zealand was the

direct discharge (point source) of pollutants from poorly treated sewage, stock effluent and

other wastes from primary production and industry directly into water bodies. However,

stricter controls on discharge practices were introduced with the Water and Soil Conservation

Act 1967 and the Resource Management Act 1991 resulting in a continuing trend towards

farmers applying stock effluent to land. The main source of waterway pollution in New

Zealand now comes from diffuse sources, i.e. non-point-source pollutants with no single

point of origin (e.g. land runoff) (Ministry for the Environment, 2014).

Controlling N requires the control of land uses, such as intensive agriculture, which

contribute non-point source of soluble N per hectare (Clothier et al., 2007; McDowell et al.,

2009; Monaghan et al., 2007a, 2007b; Wilcock et al., 1999). The One Plan proposes rules

controlling N input from dairy farming, intensive sheep and beef, cropping and horticulture,

in combination with greater controls on point source inputs to meet nutrient concentration

standards in the river (for both N and P).

Horizons One Plan reflect a move towards catchment-based water management that seeks to

manage the effects of all land uses and activities within that catchment (Parfitt et al., 2013).

The One Plan recognises the significant impact that nutrient discharges from agricultural

activities can have on water quality and the growing scepticism that voluntary measures to

mitigate nutrient discharges, whilst well intended, will not significantly reduce nutrient

discharges without having measurable and enforceable standards in a regional plan.

Horizons Regional Council have identified a number of sensitive management zones that

have an adverse impact on the quality of rivers, monitoring of water quality throughout the

Region is achieved through State of the Environment (SOE) monitoring, compliance

monitoring and targeted science investigations. Nitrate contamination was particularly

6

prevalent in the upper Manawatu, Mangatainoka, Makuri, Waikawa, Lake Horowhenua and

Tutaenui catchments (Map 1) (Roygard & McArthur, 2008).

Map 1: Water quality indicator for nitrate by catchment (State of the Environment of the Manawatu Wanganui Region,

Horizon, 2005, p. 62)

The One Plan regulates existing intensive farming activities for individual farms including

dairy in targeted catchments (sensitive catchment zones, water management sub-zones), this

is achieved by allocating nitrogen leaching allowances based on Land Use Capability class

(LUC). Existing dairy farms in target water management sub-zones will either meet nitrogen

(N) leaching targets (Limits), according to the Land Use Capability (LUC) of the farms or

where they cannot then consent will be granted subject to a reduction in nutrient loss from

farm land (Bell et al., 2013b). The nitrogen leaching allowances for each land class are

presented in Table 1. Over the 20 years following implementation, the nitrogen leaching

allowances will be reduced on classes I to VII.

7

Table 1: Twenty year nitrogen leaching allowances for each land use capability class (Table 13.2, Horizon, 2013

Proposed One Plan)

LUC I LUC II LUC III LUC IV LUC V LUC VI LUC VII LUC VIII

Year 1 (kg of N/ha/year)

32 29 22 16 13 10 6 2

Year 5 (kg of N/ha/year)

27 25 21 16 13 10 6 2

Year 10 (kg of N/ha/year)

26 22 19 14 13 10 6 2

Year 20 (kg of N/ha/year)

25 21 18 13 12 10 6 2

1.4.1 Land Use Capability (LUC)

Land use capability (LUC) has been a keystone for rural planning in New Zealand since

1952, and used to give policy makers an idea of where productivity comes from. LUC is

currently being used as a surrogate in assessments for which it was not designed. It has

limitations and in some cases detailed soil data or some other combination of primary

attributes could be used to derive and model more appropriate suitability indices, e.g.

irrigation suitability, a leaching index, suitability for dairy effluent and septic tank disposal

(Barringer et al., 2012).

The LUC Unit is the most detailed component of the Land Use Capability classification.

LUC Units group together land inventory units which require essentially the same kind of

management, the same kind and intensity of conservation treatment, and are suitable for the

same kind of crops, pasture or forestry species with similar potential yields, in effect, an

assessment of the land’s capacity for sustained productive use taking into account physical

limitations of the land, soil conservation needs and management requirements (Hendy et al.,

2007; Lynn et al., 2009; Walker et al., 2008). ‘Physical limitations’ refer to land

characteristics which have an adverse effect on the capability of land. These limitations can

be permanent, removable, or modifiable. (Lynn et al., 2009).

LUC has eight classes from the NZLRI (New Zealand Land Resource Inventory; held by

Landcare Research). The NZLRI is a spatial database of 100,000 polygons (land parcels)

covering the whole of New Zealand. The characteristics or attributes (e.g. rock, soil, slope,

erosion, vegetation, LUC) of each parcel of land is described (Lynn et al., 2009; Walker et

al., 2008). LUC is denoted by Arabic numerals, with limitations to use increasing, and

8

versatility of use decreasing, from LUC Class 1 to LUC Class 8 (Figure 2) (Lynn et al.,

2009).

LUC Classes 1 to 4 are suitable for arable cropping (including vegetable cropping),

horticultural (including vineyards and berry fields), pastoral grazing, tree crop or production

forestry use. Classes 5 to 7 are not suitable for arable cropping but are suitable for pastoral

grazing, tree crop or production forestry use, and in some cases vineyards and berry fields.

The limitations to use reach a maximum with LUC Class 8. Class 8 land is unsuitable for

grazing or production forestry, and is best managed for catchment protection and/or

conservation or biodiversity (Lynn et al., 2009).

Figure 2: Increasing limitations to use and decreasing versatility of use from LUC Class 1 to LUC Class 8 (Figure 2: Lynn et

al., 2009, p. 9)

1.5 Purpose of the study

The One Plan imposes limits on nutrient losses from farming systems. It does not suggest

how to change a dairy enterprise or to comply with the set nutrient loss limits that are

required in a dairy system or determine the economic outcomes of such changes.

The purpose of this study is to analyse a farm system in a sensitive catchment using a whole-

farm modelling approach, scenarios will be first developed around mitigating N-leaching to

within the allocated N-leaching limits on different LUC class types soils. A whole-farm

modelling approach will be used to incorporate the different scenarios and compared against

each other to evaluate which option best suits the stakeholder and is economically viable

under the One Plan.

9

1.6 Research Question

The main purpose of this research is to answer the following question:

• Is it possible for a dairy farm in a sensitive catchment to have acceptable N leaching and

make a profit?

1.7 Research Objectives In order to answer the research question the following objectives were developed:

• To define the method by which the sensitive catchments regulate N leaching

• To define the methods of mitigation.

• Describe the combination of overseer and whole farm system analysis.

• Evaluate mitigation options for a case study farm using the whole farm system and

Overseer® models.

1.8 Dissertation outline

This dissertation reports on methods used to mitigate N leaching and modelling techniques

that will define a path to evaluate a whole system change and to analyse a dairy farm’s

economic viability. Chapter Two, provides an overview of the literature on the methods of

mitigating N-loss, describes system analysis techniques and, determine mitigation for a case

study farm using the whole farm system techniques. In Chapter Three, the methodology used

for this case study research is explained. In Chapter Four, the contextual information of the

cases study farm is defined. A case description and the results of this study are presented in

Chapter Five. In Chapter Six, discussion is presented when the results are compared to the

literature and then discussed. Chapter Six encompasses the conclusions from this research,

implications that lead from this research and suggestions for areas of future research.

10

2 Literature Review

2.1 Introduction

New Zealand and international literature is reviewed in relation to a dairy enterprise and

mitigating N-leaching and modelling techniques used. The aim of this review is not to

complete a comprehensive review of literature associated with mitigating N-leaching and

modelling techniques or to provide a synopsis of all definitions for mitigating N-leaching and

modelling techniques. Rather it seeks to provide a background to the concepts and ideas

relevant to the dairy enterprises in New Zealand which can be used to provide references

relevant to the research been undertaken and identify gaps or disparities in the literature.

2.2 Mitigating N Leaching

2.2.1 Standoff facilities

The deposition of nitrogen in small concentrated urine and dung patches by grazing cows is

considered to be the main cause of nitrate leaching from dairy farms (Burden, 1982; Jarvis,

1992). Studies in the UK showed that the amount of urine N remaining in the soil at the start

of the drainage season was directly related to the time of the year that the urine was deposited

(Cuttle & Bourne, 1993; Sherwood, 1986). Of the urine N deposited in late spring and early

summer, 5-13% remained in the soil profile in late autumn/early winter, while from late

summer onwards 30-50% of urine deposited was still present in the soil in late autumn. Since

plant uptake in autumn and winter is limited by low temperatures, and excess rain is expected

in these seasons, any nitrate N remaining in the soil in late autumn is liable to be lost by

leaching (de Klein & Ledgard, 2001).

The construction of cow housing or feedpad facilities along with an effluent storage facility,

provides the option to prevent soil compaction and treading damage to the soil and sward

during wet periods of the year, effluent can be redistributed to the soil evenly and in lower N

concentrations, reducing N fertiliser application and reduces nitrate-N leaching losses to the

environment (Christensen et al., 2010, 2011; de Klein, 2001; de Klein & Ledgard, 2001; de

Klein et al., 2005c). De Klein & Ledgard (2001) showed that management systems in which

grazing is avoided throughout the year (nil grazing), or during autumn/winter when the risk

of nitrate leaching is highest (restricted grazing), have the potential to reduce nitrate leaching

by 50-60%. Christensen et al., (2011), in a three year study found that duration-controlled

grazing (DG; 4 hour day or night graze) reduced NO3--N leaching by more than 50% on dairy

11

farms, though no financial analysis of the system change was undertaken. If slurry return can

be managed so as to optimise pasture growth on DG plots, then DG has the potential to be a

very useful nitrate leaching mitigation strategy for the dairy industry, particularly in nitrogen

sensitive catchments (Christensen et al., 2011). De Klein & Monaghan (2005) used a

wintering pad and reduced N-leaching losses by 14 – 44%, with the largest reductions

achieved in the South Island catchments, where off-farm wintering is common practice.

2.2.2 Fertiliser

Nitrogen fertiliser recommendations on temperate grassland vary because of growing season

length, water availability, and fertilisation philosophy (Russelle, 1997; Vellinga et al., 2001).

Proper timing of fertiliser application can be important in promoting efficient utilisation.

Greater fertiliser N uptake by cool-season grasses occurs during spring growth than in

autumn growth (Stout & Jung, 1992). Luxury absorption of nitrogen during spring growth,

however, can raise nitrate concentrations in forage with high fertiliser N application (Vetsch

et al., 1999). Tactical N fertilisation, where N application rates and timing are adjusted

according to measured soil nitrate at harvest, can reduce N surpluses (Titchen & Scholefield,

1992). This reduction, however, may come at the expense of animal and plant production

(Laws et al., 2000) and it may not reliably reduce fertiliser N application requirements

(Kowalenko & Bittman, 2000). To maximise economic returns from nutrient addition,

management intensity must increase to utilise improved pasture production (Davison et al.,

1985; Teitzel et al., 1991). Groot et al. (2003) concluded that long-term N losses can be

reduced only by improving N use efficiency by both plants and livestock.

2.2.3 N Fixation

Ledgard, (1991) estimated that N2 fixed by legumes in pasture range from 10 to 270 kg N ha-

1, but most estimates fall between 100 and 200 kg N ha-1 for typical forage legumes grown

with grasses (West & Mallarino, 1996). Ledgard (1991) found that N transferred from white

clover (Trifolium repens L.) to perennial ryegrass (Lolium perenne L.) was similar below

ground (70 kg N ha-1) and through excreta (60 kg N ha-1). Together these transfers

represented nearly one-half of the total N2 fixed by the clover (270 kg N ha-1). The proportion

of legume in a sward necessary to provide sufficient N to the nonlegume varies with legume

species and forage utilisation by the livestock, which depends on stocking rate, grazing

management, and forage palatability. In New Zealand, pasture yields were similar when

white clover comprised 9 to 30% of the sward (Ledgard, 1991). At low to moderate N rates,

12

pasture yield can increase without reduction in legume stands (Whitehead, 1995), but this

response is affected by soil moisture conditions, grazing intensity, and other factors that

affect competition for limiting resources (Rotz et al., 2005).

2.2.4 Diet based strategies

The intake of high-quality temperate forages, such as perennial ryegrass and white clover

pastures, are of high nutritive value, with ME concentrations typically greater than 11.5 MJ

of ME/kg of DM and a crude protein (CP) content of up to 30% (Waghorn et al., 2007). This

is in excess of dietary CP requirements (% CP in DM) for grazing animals which are 11% for

maintenance, 14% for growing cattle and 18% for young or lactating animals (Pacheco &

Waghorn, 2008). This high-protein diet ultimately provides an excess of N relative to animal

requirements (Totty et al., 2013), and dairy cows excrete between 65-75% of the N they eat,

of which approximately 50% is excreted as urine (UN) (Bannink et al., 1999; Broderick,

2005).

The major dietary strategies to reduce urinary N losses are to decrease the concentration of

dietary protein, by increasing non-protein substrates in the diet (often carbohydrates), or to

decrease the rate of degradation of protein in the rumen to better balance energy and N supply

to rumen microbes (Dijkstra et al., 2011). Options for reducing urinary N excretion in grazing

systems include using ryegrass cultivars with a lower CP concentration (Miller et al., 2001;

Moorby et al., 2006) or using plant species with higher rumen undegraded protein (RUP),

which divert the dietary N away from urine (Woodward et al., 2009). Alternative forages may

provide opportunities for altering N partitioning within the animal. Alternative pasture

species that can be incorporated into pasture mixes include high-sugar ryegrasses, chicory,

plantain, and the lotus genus (Totty et al., 2013).

Newly developed grasses, such as high-sugar ryegrass, have been bred to contain a higher

content of water-soluble carbohydrates (WSC) and have been proposed as another method to

increase the efficiency of N use (Edwards et al., 2007). This is due to an improved rumen

supply and synchrony of energy with CP to increase microbial protein synthesis. Some

alternative forages, such as lotus, also contain condensed tannins (CT).

Condensed tannins are phenolic compounds found in plant cells that bind to dietary protein,

making it unavailable for animal absorption (Barry & McNabb, 1999; Waghorn et al., 1994)

and thus increasing the fecal:urinary N ratio (Totty et al., 2013). A study by Totty et al.,

(2013) concluded that their results demonstrate a role for the use of high diversity pastures

containing herbs as a mitigation strategy to reduce the environmental impact of dairying and

13

their results showed no disadvantage to milk production on these diverse pastures in late

lactation.

Other models have been used to evaluate the effect of changing CP by growing and feeding

more maize as forage (Del Prado & Scholefield, 2008; Schils et al., 2007) and showed that

for dairy cows, shifting towards more maize and less concentrate and grass silage, increased

total starch, fat and low degradable protein content in the diet, decreasing energy and CP.

This resulted in less N excretion (Cardenas et al., 2011). However, an increase in nitrate

leaching losses would be expected through the increased proportion of arable land to produce

maize within a sensitive catchment zone, mitigating nitrate leaching losses could be achieved

by growing maize outside of the sensitive catchment zone or with soils that are classed as low

risk to N leaching.

2.2.5 Milk urea content

In previous studies Ciszuk & Gebregziabher, (1994) found positive relationships that exist

between the concentration of urea in milk (MUC; mg/dL) or in blood plasma (PUC; mg/dL)

and excretion of N in urine. Because of this positive relationship, milk urea content (MUC)

has been used to predict urinary N excretion and ammonia emission (Burgos et al., 2007;

Nousiainen et al., 2004; van Duinkerken et al., 2011). However, the relationship is affected,

among other factors, by diurnal dynamics in MUC, which in turn, is largely influenced by

feed intake pattern (Gustafsson & Palmquist, 1993) and transport characteristics of urea from

blood to milk and vice versa (Spek et al., 2012).

For dairy cows, conversion efficiencies of dietary N into milk N range from 25% to 35%,

with the highest efficiencies usually associated with high levels of milk production

(Flachowsky & Lebzien, 2006; Gourley et al., 2012; Powell et al., 2006). In a summary of 26

N balance studies of lactating dairy cows including 103 treatment means, Santos (2003)

reported that N not secreted in milk is excreted on average approximately equally in urine

(35% of total N intake; range, 13%–51%) and feces (35% of total N intake; range, 26%–

52%).

Most feed N not secreted in milk is excreted approximately equally in feces and urine,

although this ratio varies with the amount and form of feed N consumed (Broderick, 2003;

Broderick et al., 2008; Olmos & Broderick, 2006b).

As dietary CP increases and N intake exceeds requirement, feed N use efficiency declines

and the excretion of urinary N increases without gains in milk N secretion (Broderick &

Clayton, 1997; Nousiainen et al., 2004). A well-balanced diet that contains about 164 g

14

(16.4%) of CP/kg of DM maximises milk production and minimises urinary urea N (UUN)

excretion by dairy cows (Broderick, 2003, 2009), this is in contrast to Pacheco & Waghorn,

(2008), were Broderick, (2003, 2009) is not only maximising milk production but also

minimising urinary urea N. After excretion, urea N comprises 55% to 82% of total urinary N

when dietary CP ranges from 135 g to 194 g of CP/kg of DM (Olmos & Broderick, 2006b).

Also related to the amount and form of feed N consumed is the proportion of total urinary N

(UN) that is urinary urea N (UUN). Marini and Van Amburgh (2005) reported that UUN

increased from 23% to 96% of UN when dietary crude protein (CP) increased from 9% to

21% of dry matter (DM) intake.

Milk urea (MU) is an indicator of the amount of protein in the diet of the cow, the more

protein in the diet the higher the MU. MU levels provides an indication of rumen efficiency,

this helps us understand the carbohydrate input relative to protein. MU values are now being

supplied to New Zealand dairy farmers, this now allows farmers to analyse the cows dietary

requirements and provides another pathway in reducing urinary N as excess N is ‘wasted’ as

urine and creates risks in terms of N leaching on the platform.

2.2.6 Genetic merit

Van Es (1961) showed little variation exists between animals in the ability to digest a given

diet. Grieve et al. (1976) found no relationship between genetic merit for milk yield and the

apparent digestibilities of ration components in 24 Canadian Holstein heifers. This agreed

with the results from Davey et al. (1983) who compared high and low genetic merit Friesians.

Similarly, Grainger et al. (1985) found no difference between high and low merit genotypes

in their ability to metabolise the gross energy of the food, or in the individual losses of energy

in the faeces, urine and methane. Also, Custodio et al. (1983) suggested that cows of different

genetic merit for milk did not differ in their ability to digest fibre and starch. No differences

in the partitioning of gross energy into digestible or metabolisable energy were found by

L’Huillier et al. (1988) and Tyrrel et al. (1990) in experiments comparing Holstein and Jersey

cows. In contrast, Freeman (1975) indicated that large differences in digestibility have been

observed among cows, and Trigg and Parr (1981) found that high genetic merit animals

digested a greater proportion of the gross energy eaten.

The association between genetic merit and milk urea may be explained by the fact that cows

with a high milk production ate a higher proportion of pasture relative to a constant level of

supplementation than lower producing cows (Fulkerson & Trevaskis, 1997). The physiology

15

of dairy cows in late lactation is quite different to that of dairy cows in early lactation, with a

greater use of nutrients for foetal development and body condition replenishment than in

dairy cows in early lactation. Milk production by dairy cows in early lactation is increased

with the provision of spring grass herbage, compared with the feeding of grass silage (Dillon,

Crosse, O'Brien, & Mayes, 2002).

2.2.7 Whole farm approach

While there is potential to apply diet based strategies into the whole farm system to reduce N-

leaching, it is not a common approach used. Instead nitrogen fertiliser usage application rates

and timing, effluent storage and irrigation management are used. These strategies alone are

generally not enough. Cardenas et al., (2011), found the most effective measures to reduce

leaching for the dairy system were in general those involving reduction in stocking rates,

duration of grazing, improving the timing of fertiliser application and accounting for manure

N (to avoid surplus N application). The implementation of these mitigation measures resulted

in a reduction of up to 45% in leaching for the dairy system. They also found the most

effective measures (reducing stocking rates and duration of grazing) were all associated with

relatively high costs with implementation of zero grazing having the highest costs due to the

assumption made that machines used to cut and carry fresh forage would have to be financed,

maintained and run. They suggest a reduction in animal numbers would need to be

compensated for by income derived from a more valuable product and/or greater

environmental benefits (Cardenas et al., 2011). It is unlikely that reduction in grazing,

specifically in zero grazing conditions, would result in higher value products. Studies of cow

on pasture verses cows in housing found, cows grazing on pasture may produce milk with

higher concentrations of polyunsaturated fatty acids (PUFA) (Elgersma et al., 2006) and have

a decreased risk of suffering from lameness, which is generally associated with walking on

hard concrete surfaces covered in slurry (Phillips, 2008). This is relevant to research looking

at the difference between housed and grazed systems.

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2.3 Economic reporting

2.3.1 DairyBase

DairyBase is a web-based package that records and reports standardised dairy farm business

information - both physical and financial. DairyBase® is owned and managed by DairyNZ on

behalf of the dairy farmers of New Zealand.

The purpose of DairyBase® is to improve the financial understanding and performance of

dairy farmers using a benchmarking approach and is designed to link the production and

financial performance of farms. DairyBase® contains financial data from annual farm

accounts as well as physical data supplied by the farmer and estimated current market values

of fixed assets. Farmers wishing to benchmark their farm performance have access to a wide

range of statistics in DairyBase® including (where numbers permit) regional, district, herd

sizes and production system data. Accredited accountants and other rural professionals enter

the data on behalf of their clients and the data is validated within DairyBase® (DairyNZ,

2010b).

2.3.2 Economic Viability

Dairy farmers are challenged to comply with stricter environmental regulations and remain

economically viable. The challenge is to determine a system change in relation to liquidity,

profitability and wealth. This is a breakdown of the grouping that is also used in DairyBase

(Shadbolt et al., 2007).

Liquidity - Cash

Liquidly in a business means having sufficient cash available to meet commitments as they

arise, and, over the year, ensuring cash outflows are not greater than cash inflows (Shadbolt

& Martin, 2005). When considering repayment capacity, the ability to service debts is

important.

Profitability - Efficiency

One measure of profit is net profit in the Statement of Financial Performance, used most

frequently to determine tax liability, the other operating profit comes from ‘management

accounts’ (Shadbolt & Martin, 2005). From which three profitability ratios calculated include

rate of return on farm assets (ROA), rate-of-return on farm equity (ROE), and operating profit

margin ratio (Barry & Ellinger, 2012).

Wealth – Value

Equity is a measure of wealth, the capacity of a business to withstand adversity and to cope

with risk. An accurate valuation of assets, particularly for land and buildings, and to lesser

17

extent livestock, is essential in determining equity growth and other wealth indicators

(Shadbolt & Martin, 2005).

Agricultural land is valued for its productivity-bearing characteristics, including soil

characteristics, climate conditions, and topographical attributes (Samarasinghe &

Greenhalgh, 2013). Soil, climate, and water resources are among the most relevant elements

of natural capital in the context of agricultural production (Lipper, 2001).

Farmland buyers and sellers will incorporate into the land price the impacts of attributes that

affect the productivity of land, provided they have appropriate information about those

attributes available to them and they perceive the importance of those attributes (Ervin &

Mill, 1985; Ma & Swinton, 2011).

Maddison (2000) used hedonic techniques to measure the productivity of farmland

characteristics including, land quality and climate variables in England and Wales, and found

that the implicit values for land quality and climate were embedded in farmland prices.

2.3.3 Marginal decision making

In the dairy industry, it is almost always much easier to focus on the income side rather than

to try to decrease expenses. However, farmers can dilute their fixed costs by increasing

production, MS/cow or increasing stocking rate (Eicker, 2006).

When facing an economic choice, farmers should base their analysis on the marginal impact

of the decision, not on the farm’s average performance. Any economic estimates that

involved increase milk production needs to account for the increase feed costs. These must be

calculated using marginal costs, not average feed costs (Eicker, 2006).

Averages can be a useful measure of a farm’s status, but it is only one measure. When

making specific management decisions, averages can be misleading sources of information.

Averages themselves may not accurately reflect the farm’s real status, as averages are

vulnerable to several types of error, significant lag or bias; averages also are deficient in

characterising a farm because they, of necessity, express only the central point on a

distribution. Averages can be particularly dangerous when used in making economic

decisions. Economic decisions based on averages can be seductively appealing; at first glance

they can seem like “common sense”. In fact, farmers often make decisions based on such

“common sense”. Often, these common sense decisions are wrong, and very costly (Eicker,

2006).

Many dairy farmers forego very significant profit opportunities in the false pursuit of

reducing the costs of inputs. By focusing on the costs of inputs and not the inputs’ marginal

18

impact on revenue (milk) and therefore profit, many dairy producers box themselves into a

cycle of poor investment decisions, poor profitability, and a poor lifestyle (Eicker, 2006).

The law of diminishing marginal returns dictates that as more of an input is added to the fixed

resources of the farm, the addition to output eventually declines (called diminishing marginal

product). The effect of diminishing marginal product is to cause average production per unit

of input to decline. The profit maximising rule for the use of inputs is to use inputs up to the

level where marginal cost (MC) from an extra unit of input nearly equals the marginal return

(MR) (Figure 3). This level of input use will be somewhere between the level of input use

where the average product of the input (total product/total input) is maximum and where the

total production reaches a maximum and the marginal product of an extra unit of the input

becomes negative. Between these two levels of input use – where average product is

maximum and marginal product is zero, any level of technical efficiency (total output/total

input) could be the most profitable, depending on the prices of the input and the output

(Melsen et al., 2006).

Figure 3: Marginal cost curve (Gans, King, & Mankiw, 2011)

Figure 3 shows the marginal-cost curve (MC), the average-total-cost curve (ATC) and the

average-variable cost curve (AVC). It also shows the market price (P), which equals marginal

revenue (MR) and average revenue (AR). At the quantity Q1, marginal revenue MR1, exceeds

marginal cost MC1, so rising production increases profit. At the quantity Q2, marginal costs

MC2 is above marginal revenue MR2, so reducing production increases profit. The profit-

maximising quantity Qmax is found when the horizontal price line intersects the marginal-cost

curve (Gans et al., 2011).

19

2.4 Whole Farm Modelling

2.4.1 Simulation Models

Stochastic simulation models use some sort of randomisation giving multiple outputs that are

unoptimised, this is a valuable tools in the analysis of farming systems, particularly for

assessing impacts of climatic variability and the long-term consequences of alternative

management strategies. Models are complementary to experimentation which is invariably

constrained by the prevailing seasonal conditions, limitations in the treatments imposed, and

the duration of the experimentation. They are a means of extrapolation of knowledge, derived

from experimentation, to other situations-other seasons, other soils, and different

managements such as crop sequences, tillage, and residue management practices. To do this,

the simulation package must deal credibly with the season-to-season variability in production

and the long-term trends in production in response to changes in the soil resource (Probert,

Dimes, Keating, Dalal, & Strong, 1998).

2.4.2 Optimisation Models

Optimisation models are a key tool for the analysis of emerging policies, prices, and

technologies within grazing systems. Optimisation allows the efficient identification of

profitable system configurations, which can be time consuming if manual trial-and-error is

used, particularly in complex farming systems (Doole et al., 2013c).

Deterministic farm models generally use mathematical programming and do not have a

random number generator, this is often based on Linear programming (LP) (Janssen & van

Ittersum, 2007). Linear programming represents the farm as a linear combination of so-called

‘activities’. An activity is a coherent set of operations with corresponding inputs and outputs,

resulting in e.g. the delivery of a marketable product, the restoration of soil fertility, or the

production of feedstuffs for on-farm use (Ten Berge et al., 2000).

Linear programming based models does optimise, however only gives one answer, this can

be a devise alternative management choices that maximise (minimise) an objective function

according to a set of restrictions (Hardaker et al., 2004). Linear programming models have

been widely used in analysis of farming systems, since 1958 (Cabrera et al., 2005). The

discipline of this type of modelling is that the systems and the individual components that

make up each system must be clearly defined (Ridler et al., 2001).

20

2.4.3 Modelling approach

Dairy farmers face important issues related to improving efficiency, lowering costs, and

increasing productivity while being cognizant of issues related to the environment, animal

welfare, and food safety. The complex interrelationship between a large number of factors in

a dairy system makes it difficult to determine the costs and benefits of implementing various

management or technological alternatives (Shalloo et al., 2004). Systems modelling involves

representing what seem to be the key features of a relevant system in mathematical "models",

and then using these models to make inferences about the system. There are a range of

modelling approaches based on different forms of mathematical representation and methods

of analysis. In addition, some important issues that influence decision making by farmers,

such as practical skill levels, family goals, cultural constraints, habits, changing personal

worldviews, values, and interests, are difficult to represent in a computer model (Woodward

et al., 2008).

Bywater and Cacho (1994) noted that mathematical models were increasingly being used in

animal research both independently and in conjunction with experimental research. Farm

simulation models can make a major contribution to guiding experimental research in a

variety of ways. These range from identification of critical gaps in knowledge or data to

interpretation of experimental results and the development of improved systems of

production (Bywater & Cacho, 1994). Farm simulation models also have a role as direct

extension and management tools (i.e., evaluating alternative systems) (Shalloo et al., 2004).

The capability of simulating whole dairy farm systems is a challenge that has long been

recognised (Cabrera et al., 2006). The complexity of dairy farms that include livestock,

waste, feed, crops, and their interactions, justifies the creation of a whole-farm model,

integrating several disciplines and modelling approaches, in order to better analyse these

systems (Herrero et al., 2000). However, currently there are few tools available for predicting

how dairy farm systems will respond to management changes from environmental and

economic perspectives. Those tools that are available are typically applied in isolation, with

the net result that a double bottom line analysis (environmental and economic) is seldom

considered (Monaghan et al., 2004).

2.4.4 Existing Cost Benefit Analysis From Mitigating N Leaching

De Klein (2001) suggested that, for an average New Zealand dairy farm, nil and restricted

grazing systems may increase pasture production by about 18% and 2-8%, respectively.

21

Although the construction of a feed pad and associated effluent storage and effluent

application facilities will require capital investment, a cost benefit analysis by de Klein

(2001) suggested that the potential increase in pasture production through a more efficient use

of excreta N alone can off-set these costs, it was also noted any additional production gains

due to reduced soil and sward damage would then be a direct cost-saving. However in a later

study, De Klein & Monaghan (2005) found In terms of financial performance, the wintering

pad option had a slightly negative impact on farm profits in three of four catchments

analysed, these increased costs were largely associated with the increase in imported feed and

the capital and operational costs associated with the wintering pad, this study did not take into

account any potential benefits of reduced soil physical damage from grazing in wet winter

conditions, that was accounted for by de Klein (2001). De Klein et al., (2005c) noted that the

estimated reductions in environmental emissions will only be achieved at the current stocking

rate and farming intensity of the current farming operation. However, if farmers use the

availability of a feed pad to increase stocking rate, supplement usage and/or fertiliser inputs,

the strategic use of a feed pad might not necessarily reduce NO3 leaching losses.

2.4.5 OVERSEER®

The Overseer® nutrient budgets program is a decision support model to help users develop

nutrient budgets (Wheeler et al., 2003). A nutrient budget is a table of inputs and outputs of a

nutrient, into and from a particular physical identity (AgResearch. et al., 2013). It calculates a

nutrient budget for a farm and for management blocks within the farm, taking into account

inputs and outputs and internal cycling of nutrients around the farm (Figure 4) (Cichota &

Snow, 2009; Wheeler et al., 2003; Wheeler et al., 2006).

Figure 4: A diagrammatic example of a nutrient budget (AgResearch. et al., 2013).

22

The vision for Overseer® is a robust, science-based decision support tool and policy support

tool that is widely used for improving farm profitability, optimising nutrient use and

minimising impacts on air, soil and water quality (Shepherd & Wheeler, 2012). Models can

assist here by indicating when high N-leaching risk occurs. For example, recent work

estimated that restricting the duration of grazing at particular times of the year can markedly

reduce leaching and nitrous oxide emissions (de Klein et al., 2006; Luo et a.l, 2008;

Christensen et a.l, 2010).

The nutrient modelling software Overseer® has been used to evaluate N leaching with

effluent (Wheeler et al., 2003; Hanly, 2012), to estimate on-farm greenhouse gas emissions

(Wheeler et al., 2008), to capturing the effect of the time of urine deposition on N leaching

from urine patches (Cichota et al., 2013).

Regional authorities are developing regional plans aimed at reducing agriculturally derived

nitrate and other nutrient levels in surface and ground water. The favoured approach is

regulation of nutrient losses from farmland rather than capping nutrient inputs. This output-

based approach has the potential to offer more flexibility for farmers to use nutrients

efficiently and maintain or improve productivity than input limits. Determining nutrient

losses is more difficult than monitoring inputs. As measurements of losses is impractical at

present, a modelling approach is needed and Overseer® is set to be the model of choice

(Williams et al., 2011).

At present, Environment Canterbury, Otago Regional Council, Environment Southland,

Waikato Regional Council, Horizon Regional Council and Environment Bay of Plenty

specify use of Overseer® for calculating estimated nutrient discharges from individual

properties. However, the application of Overseer® in regional authority water policy requires

greater transparency regarding the scientific basis of the model and in the software

development and validation processes (Williams et al., 2011).

2.4.5.1 Overseer® ground rules

When the model is used as a component in developing nutrient management plans, an

understanding of the model is helpful. Like all models, the quality of the input data is

important. The Overseer® model requires actual farm data, as assumptions are made about

farm efficiency. The main assumptions (Table 2) underpinning the model are that: it uses

long-term annual averages, i.e. the model assumes a “steady state”; the system is in quasi-

equilibrium (inputs commensurate with production levels on the farm); users supply actual

and reasonable inputs; and management practice implemented on the farm follows good

23

practice (Shepherd et al., 2013). This is done to reduce the number of inputs, and to use data

that most users have or where suitable defaults are available. For example, the model uses

estimates of farm productivity to calculate animal intake, rather than estimates of pasture

production, utilisation, and grazing management. Using this model structure does have

implications when using the model to look at mitigation options (Wheeler et al., 2007).

Table 2: The key assumptions used in Overseer are: (Wheeler & Shepard, 2013, p. 4)

Assumption Notes

Quasi-equilibrium The model assumes that inputs and farm management practices described

are in quasi-equilibrium with the farm productivity.

Long-term average For a given farm system, the nutrient budget estimates the long-term annual

average outputs if the management system described remained in place.

Actual and reasonable inputs The model assumes that inputs including animal productivity are correct. There is

no checking on whether an inputted farm system is practicable, possible or viable.

Mitigations The quasi-equilibrium and actual and reasonable assumptions means that any

management changes or mitigation changes must also include changes in animal

productivity

Management practices Assumes ‘good management practices’ have been implemented

on the farm

The model assumes that Good Management Practices (GMP) are followed, especially for

storage and application of effluent, fertiliser, and irrigation. Under GMP, there are a number

of practices that farmers could adopt to improve water quality. For example, if fertiliser or

effluent are applied, Overseer® assumes the stated rate is applied evenly at the time stated

i.e., there is no ‘poor management’ that would result in ‘large’ discharges. GMP reflects

complying with supplier regulations and local government law, such as those in the Fertiliser

Code of Practice, Best Management Practices (BMP) and Regional Council guidelines on

effluent management (Everest, 2013; Park, 2014; Paterson et al., 2014; Wheeler & Shepard,

2013). Research has demonstrated that the use of GMP such as deferred irrigation (pond

storage during periods of high soil moisture) and low application rate/intensity technology

has been effective in decreasing or avoiding the direct losses of Farm Dairy Effluent (FDE)

from land application (Everest, 2013; Longhurst et al., 2013).

It is reasonable to assume that, in the absence of full regulation, the practices adopted will

initially be those that have low cost or little impact on farm profitability, or, are those

required under supply agreements with processing companies (Bell et al., 2013b).

24

When these practices are not being followed, the model is likely to underestimate nutrient

losses. Thus, in developing plans, a method is required to identify these breeches, and in most

cases these should be remedied first as they are usually easiest to do (Wheeler et al., 2007).

The model uses long-term annual average input data and loss predictions. The variation

between years in nutrient flows and losses, as affected by climatic variability, are

encompassed within the long-term annual average. This reduces the need for specific daily

climate data and a large amount of extra detail in the model, which is more appropriate for

detailed research models or those used by expert users. In most cases, the user does not need

to specify within-year nutrient management information, although some model components

account for the effects of timing on management practices (e.g. timing of fertiliser use,

animal winter management and fodder crop management). Many of the effects of poor timing

of application or placement of fertiliser or effluent are covered by BMP recommendations

(Wheeler et al., 2007).

2.4.5.2 Overseer® Best Practice Data Input Standards

The ground rules listed above are a set of guidelines to assist expert users to define data

inputs that consistently achieve the most accurate nutrient budget of a farm for nutrient

management purposes. In 2013, the Overseer® owners (Ministry for Primary industry (MPI),

Fertiliser Association of New Zealand (FANZ) and AgResearch) brought together a

Stakeholders Advisory Group (SAG) to scope out the need for, and requirements of an input

user guide. The new input user guide, called “Best Practice Data Input Standards” was

finalised in late August 2013 and published on the Overseer® websites in December 2013,

since updated April 2014 and the latest August 2014. The purpose of providing a ‘Best

Practice Data Input Standards’ is to reduce inconsistencies between different users when

operating Overseer® to model individual farm systems (Roberts & Watkins, 2014).

2.4.5.3 Verification and validation

Verification is the general process used to decide whether a method in question is capable of

producing accurate and reliable data. Validation is an experimental process involving external

corroboration by other laboratories (internal or external) or methods or the use of reference

materials to evaluate the suitability of methodology. Neither principle addresses the

relevance, applicability, usefulness, or legality of an environmental measurement. The

reliability and acceptability of environmental analytical measurements depend upon rigorous

completion of all the requirements stipulated in a well-de-fined protocol (Keith et al., 1983).

25

Precision describes the degree to which data generated from replicate or repetitive

measurements differ from one another. Statistically this concept is referred to as dispersion.

Accuracy refers to the correctness of the data. Unfortunately, in spite of its importance, there

is no general agreement as to how accuracy is evaluated. Inaccuracy results from imprecision

(random error) and bias (systematic error) in the measurement process, high precision does

not imply high accuracy and vice versa. Unless the true value is known, or can be assumed,

accuracy cannot be evaluated. Bias can only be estimated from the results of measurements

of samples of known composition (Keith et al., 1983).

Overseer® operates at a block level, blocks are set up within the property, usually according

to variations in soil type and/or management history of the farm. The primary aim of

Overseer® is to calculate a long term average nutrient balances and nutrient loss estimates at

both the block and property level. With this, Overseer® has evolved from a decision support

system designed for on-farm fertiliser and nutrient management advice to a tool being used to

implement regional policy and regulations in relation to nutrient losses from agriculture.

Horizon Regional Council requires the development of a farm environmental plan for the

consent process. However, for detailed farm nutrient management and development of

management measures, each farm must have constructed a robust individual farm Overseer®

nutrient budget model, that must be prepared by or validated by a suitably qualified person.

Therefore, all users of Overseer® must appreciate its limitations and must have a good

understanding of the uncertainties in Overseer® estimates (GHD, 2009; Stafford & Peyroux,

2013; Williams et al., 2011).

The main inputs that have the most influence on nutrient loss estimates in Overseer® are

those that influence the size of source of a nutrient (e.g., stocking rate, fertiliser inputs), and

those that influence the transport of a nutrient (e.g., soil, drainage, slope for P). Drainage is a

key driver of N (and P) losses and it is therefore important to recognise that this calculation is

sensitive to climate inputs, predominately rainfall, potential evapotranspiration, soil

characteristics that affect available water capacity such as soil order, texture, sand or stony

subsoils, and the depth to those subsoils, and irrigation rate and method, and (less important)

crop cover (Wheeler & Shepard, 2013).

The challenge continually is being able to model the transfer and fate of nutrients around the

farm system whilst maintaining a level of user input that is practical and achievable

(Shepherd & Wheeler, 2010). Amongst other outputs, Overseer® calculates the long-term

annual average N leaching from the management block(s) and the farm. Thus, the model has

26

to respond to the full range of inputs that Overseer® has (e.g. stocking rate, soil type, and

rainfall) and it has to be driven by parameters that the user knows, or suitable defaults need to

be available (Wheeler et al., 2011). Therefore, there are differences between measured and

modelled values, for example N leaching, are an expression of the certainty/uncertainty

arising from attempting to model complex biological processes with a minimum set of readily

available farm data inputs (Williams et al., 2011). Further uncertainty are associated with the

accuracy and appropriateness of data inputs, as Overseer® users must have access to good

quality farm data that accurately reflect management practices on farm (Williams et al.,

2011).

Clear protocols are now available “Best Practice Data Input Standards” to ensure a consistent

and fair approach is taken across farm systems. However, setting up a farm system in

Overseer® still requires a reasonable amount of interpretation and judgement by the user.

The major limitation to improving precision can be potential differences in inputs entered by

users. For example, model parameters such as soil properties, weather and/or climatic data

always contain errors. Some of these may be “human error” or mistakes, and it is important to

minimise this type of error (Shepherd et al., 2013; Wheeler et al., 2014).

When interpreting a model’s predictive abilities, it is important to know whether the model

has been calibrated. This is the process of adjusting model parameter values to maximise the

agreement between a given set of data and the model outputs (Refsgaard, 2001; Trucano et

al., 2006). The next step in the application of a model like Overseer is to validate the model

to provide a method of assessing the confidence that can be had in the modelled outputs (i.e.,

testing to see how well the model outputs fit a set of data (Jorgensen, 2006)).

It is also important to appreciate that the uncertainty will increase significantly the more a

situation moves from the information used to develop and calibrate a model such as overseer

(Wheeler & Shepard, 2013). This is illustrated in Figure 5.

27

Figure 5: An illustration of the changes to model uncertainty as the conditions move from those used for calibration:

based on (Loucks et al., 2005).

Overseer® can only be calibrated and/or validated against measured data where trials have

been carried out. Calibrate and/or validate is limited by the range of soils, climates and time

to undertake these field trials, it would be extremely resource intensive to test all

combinations of soils, climate and regional variation. For instance, no N leaching trials have

been undertaken in Northland, on peat soils or under high rainfall (>1200mm /yr) (Wheeler &

Shepard, 2013). More data for calibration/validation data will be required to decrease any

uncertainty, most notably for, clay and shallow and light textured soil types; and locations

with high (>1200 mm) rainfall (Shepherd et al., 2013).

Policy makers should consider the accuracy of Overseer® and the uncertainty associated with

the inputs and outputs from the model. Placing Overseer® into a policy setting where the

outputs are regarded as a fixed and absolute number may instigate legal challenges and

experts could be drawn in to discussion over the appropriateness or otherwise of input

variables used in any given farm situation (Edmeades et al., 2013).

2.4.5.4 Soils mapping

The need for reliable soil information to develop farm environmental plan for the Horizon

Regional Council consent process has never been greater. There is, however, uncertainty

about what soil information is required to meet the needs of both councils and the farming

industry – in terms of the appropriate scale and types of soil attribute information required, as

28

well as the information accuracy and uncertainty appropriate to the resolution (scale) of farm

management (Carrick et al., 2014).

Soil survey in New Zealand from 1938 to 2001 has resulted in a set of soil maps of varying

quality at varying scales. By the end of 2001, the whole country was covered by 1:253,440

scale soil maps. In addition, just over 50% of the country is covered by more detailed maps

(with scales ranging from 1:126,720 through to 1:10,000) (Lilburne et al., 2012).

Soil information is available from a range of sources, produced using a variety of methods,

and varies in the degree of fitness for purpose. Farm soil maps may be provided at any

nominal scale, with no quality indication as regards the accuracy or uncertainty of the

mapping. The level of detail needed to resolve the soil pattern in areas with significant risk of

leaching or runoff depends upon the nature of soil variability (Carrick et al., 2014). In a

highly variable floodplain we would expect significant improvement in the accuracy of

leaching and runoff predictions with soil maps at finer scales, with 1:10,000 map scale often

suggested as an appropriate standard (Manderson & Palmer, 2006).

Soil maps are often confused with single-attribute mapping; e.g. the use of detailed

electromagnetic induction survey to predict soil water holding capacity. Extrapolation of

single-attribute maps beyond their original purpose may be inappropriate; e.g. the use of a

soil water holding capacity map for farm dairy effluent system design, which is strongly

affected by a number of other soil attributes such as soil drainage, infiltration rate, subsoil

permeability and bypass flow vulnerability. Likewise soil survey can be confused with other

farm-scale assessment, such as land-use capability (LUC) mapping or farm plan assessment.

Soil information is a crucial underpinning component of these assessments, rather than a

direct derivative (Carrick et al., 2014).

S-map is the new digital soil spatial database that aims to provide a seamless digital 1:50,000

scale (or better) soil map coverage for New Zealand. S-map has being created as part of the

government-funded Spatial Information programme run by Landcare Research (Landcare-

Research., 2014). S-map is not just a map but, rather, is an integrated and dynamic soil

information system. The S-map system has been designed to accommodate soil data at any

scale, and be adaptable to both changing soil science knowledge and end-user needs. Up to

now, soil data generation has been funded by regional councils, with priority to meet regional

and catchment-level policy needs, and to digitise the historical soil surveys. As a result the

resolution of the spatial soil data (soil maps) is mostly 1:50,000 scale, although there are

finer-resolution data in some areas (Carrick et al., 2014).

29

There are a number of key S-map development initiatives at each level of the information

system that can support farm-scale mapping. The flexibility of the factsheet generator allows

the information provided on the soil factsheets to be customised to meet end-user needs; e.g.

the development of a factsheet page for each soil type, with targeted soil information for

inputs into Overseer® Nutrient Budget Model (Carrick et al., 2014). The S-map database,

due to underlying scale limitations, at a scale of 1:50,000, this resolution may be too coarse to

be useful and then there is the lack of on-farm verification, there is still likely to be reliability

issues in this database, especially when applied at paddock-scale or sub paddock scale

management (Stafford & Peyroux, 2013). Carrick et al., (2014), defines quality for soil

mapping, a scale of 1:50,000 is classed as poor quality, while farm-scale soil map at 1:10,000

is of good quality and premium quality if farm-scale soil map can demonstrate high

confidence (e.g. ± 10% variance in area of each soil type).

2.4.5.5 Overseer versions within rules

Overseer® is typically updated several times per year to reflect new science, new farm

systems, new mitigations, user interface improvements and bug fixes. Exceptions can be

made by agreement with the Overseer® owners, notably the ongoing support of Version 5.4.3

for regulatory use in the Taupo catchment. As more Overseer® users migrate to the web-

based version (with cloud storage of farm files) it appears likely that using the latest version

will become more “automatic” (Park, 2014).

Particular challenges in referencing Overseer® within RMA plans (and rules) are the

restrictions imposed by RMA Schedule 1 Clauses 30-35. These clauses enable external

documents and “methods” to be incorporated in a plan rule by reference but emphasise the

need for a specific document title and date to provide certainty for plan users. Useful

guidance is provided at the Quality Planning website, notably that any updated versions of

documents and methods can only have legal effect via a specific plan change process i.e.

there is no automatic update (Park, 2014).

The review of 10 regional plans across seven regional councils revealed a range of

approaches to Overseer version issues, as shown in Table 3 below:

Table 3: Regional Plan Approaches to Overseer Versions within nutrient rules (Park, 2014).

Option Number of plans Which regional plans?

No reference 5 Waikato, BoP RWLP, Horizons POP, Hurunui-Waiau, Southland RWP

Latest version 3 Tukituki, Canterbury NRRP, Canterbury L&WP

Specified version 2 Taupo RPV5, Otago RWP Change 6A

30

Horizons did not specify any Overseer® version. The High Court in 2013 found that although

Overseer® was a “method” in terms of Schedule 1, the relevant rule, Table 1 (Table 13.2,

Horizon, 2013 Proposed One Plan) focused primarily on Nutrient Management Plans

(NMPs) with Overseer® one element of such NMPs. Further, NMPs are linked to an external

document i.e. NMPs must comply with the 2007 Nutrient Management Code of Practice

(Park, 2014).

2.4.5.6 Overseer® version changes and impact

The One Plan was initially formulated with Overseer® version 5.4 and implemented using

Overseer® version 6.0. The change in Overseer® from V5.4 to V6.0 did not result in the

Council revising the LUC controlled limits, which has significant implications for some

farmers. Overseer® V6.0 differs from Overseer® V5.4 in a number of ways. One of the main

changes in V6.0 is that soil type, particularly the drainage aspect of soils, is given more

weight than in V5.4 The result is that for free draining soils the level of N leaching using

Overseer® V6.0 is likely to be higher than when Overseer® V5.4 is used. For example, Bell

(2013b) working with a farmer at the north end of the Mangatainoka catchment on LUC III

class land who had an average N loss of 28 kg N loss/ha/year under Overseer® V5.4, had this

revised up to 44 kg N loss/ha/year under Overseer® V6.0, the farm’s limits, according to the

regional plan, was to be 22 kg N/ha/year. This meant his required reduction in year 1 to

achieve controlled activity status increased from 21% (28 kg N loss/ha/year to 22 kg N

loss/ha/year) to 50% (44 kg N loss/ha/year to 22 kg N loss/ha/year), therefore, the required

reduction in nutrient losses went from 6 kg N/ha/year to 22 kg N/ha/year with the change in

version and for year 20 from 35% (28 kg N loss/ha/year to 18 kg N loss/ha/year) to 59% (44

kg N loss/ha/year to 18 kg N loss/ha/year) to achieve the limit (Figure 6).

Figure 6: Changing from Overseer® 5.4 to 6.0 (NL kg/ha/yr). Adapted from (Bell, 2013a)

Base Year 1 Year 5 Year 10 Year 20

O 5.4 28 22 21 19 18

O 6.0 44 22 21 19 18

GMP 44 35 33 30 28

0

10

20

30

40

50

kg

N l

oss

/ha

/ye

ar

Comparison of N leaching under Overseer® 5.4 and 6.0

31

The Council recognises however, that in practice there has been no change in the operating

system of the farm from that existing under Overseer® V5.4 and therefore consent can be

granted. Bell (2013b) concluded that the change from a current estimated 28 kg N

loss/ha/year to 44 kg N loss/ha/year did not change the actual amount of N load in-river and

the contribution this farm makes to the total load in-river has not changed, but the required

reduction to achieve controlled activity level by year 20 has gone from 35% to 59%.

Applying, the same percentage change, i.e. 35% would result in moving from 44 kg N

loss/ha/year to 29 kg N loss/ha/year (not 18 kg N loss/ha/year) as this would achieve the same

load in-river at year 20 as before the change to Overseer® (Bell et al., 2013b). Consent is

required under both the Controlled Activity process where a farm meets Table 1 (Table 13.2,

Horizon, 2013 Proposed One Plan) and the Restricted Discretionary process. Recognising

that leaching off farm has not altered means that the farmer will get consent (Bell et al.,

2013b).

Importantly, with each new version of Overseer®, more or less leaching could be calculated

from the same model inputs. These outcomes could derive from changes made in the model,

which depend on what science is done (which is dependent on a range of variables, for

example, research findings) and what decisions are made by the model’s governance group

on what should be incorporated into the model. Second, they could arise from improvements

in import information to overseer, for example soil and climate data, with new understanding

and calculations, lapilli and pumice soils have a higher capacity to hold water, it was

previously concluded that these soils were high nitrogen leaching soils. This now means that

nutrients are held for longer in the soil, making these nutrients more available to plants rather

than running through the soil profile (depending on climate conditions and weather events).

Consequently, the leaching potential of these soils would reduce with implications for model

outputs with the reduction of leaching losses and how far away a farm business would stand

relative to a farm scale limit (Duncan, 2014).

2.4.6 DairyNZ Whole Farm Model & Molly

The DairyNZ Whole Farm Model (WFM) is a computer model of a dairy farm that simulates

individual cows and paddocks, with pasture growth driven by observed climate data and with

management policies guiding decisions on a daily basis. The model was specifically designed

to extend farmlet trial results and to simulate trial designs before implementation, and has

been tested extensively against observed data from farmlet trials and commercial farms

32

(Beukes et al., 2005a; Beukes et al., 2004; Beukes et al., 2005b). As the WFM has developed,

DairyNZ has also used it to contribute to extension programmes and farm systems analysis

for policy setting (DairyNZ, 2013). The WFM assists with analysis and design of farm

systems and component experiments involving complex interactions over time (Gregorini et

al., 2010). The economics component in the WFM consists of a simplified profit and loss

statement, balance sheet and return on assets (Beukes et al., 2011a). The WFM consists of an

aggregation of dynamic and mechanistic sub models, including a new version (Hanigan et al.,

2009) of the cow model, Molly (Baldwin, 1995). Molly is a mechanistic and dynamic model

representing the digestion, metabolism and milk production of a dairy cow and predicts

various aspects of digestion and metabolism in the cow, including nutrient partitioning

between milk and body stores (Hanigan et al., 2007). Molly in WFM dynamically interacts

with changes in quantity and quality of feed and metabolic capacity to absorb and convert

nutrients into milk determined by cow genotype (Gregorini et al., 2010).

2.4.7 UDDER

The UDDER simulator models a single dairy farm which predicts the flow of energy within

the dairy production system by calculating the consumption and output of energy by different

classes of cattle on the farm (Hart et al., 1998). UDDER provides an optimisation system

based on the simplex algorithm (Nelder & Mead, 1965), but the robustness of this relatively

simple algorithm with such a complex problem has been questioned (Hart et al., 1998). The

key strength of UDDER is the ability to investigate interactions related to feed supply and

demand and milk production, such as stocking rate, calving pattern, supplementary feed, and

pasture growth rates. The simulations of different options/scenarios become particularly

useful when several years of actual data have been analysed to ensure the model fits the

individual farm. Gross margins are generated by UDDER, so actual economic analysis

requires another model (Armstrong, 2012).

Monaghan et al., (2004) developed a conceptual framework that could evaluate both farm

productivity issues and the wider environmental impacts of future dairy farm systems. To

achieve this goal, the farm systems tool UDDER and the OVERSEER® nutrient budgets

model were applied in tandem (Figure 7) to case study farms within each of the catchments.

The desktop modelling of scenarios to reduce N leaching from the Wharenui dairy farm were

estimated using UDDER and OVERSEER® models and used to simulate various

management options for effects on productivity, profitability and minimising nitrate leaching

(Ledgard et al., 2008). It was also used in calculating economics of the system.

33

Figure 7: Schematic representation of modelling and assessment process (Monaghan et al., 2004).

2.4.8 Farmax® Dairy Pro

The farm-scale simulation model Farmax® Dairy Pro (Farmax, 2013) (www.farmax.co.nz) is

a whole-farm decision support model that uses monthly estimates of pasture growth, farm and

herd information to determine the production and economic outcomes of managerial

decisions. Farmax® can either be run in the short-term mode for designing farm management

for the forthcoming season, or in a long-term mode for describing the farm's average year

(Vogeler et al., 2014). The model is a Windows application developed using Delphi®.

Farmax® Dairy Pro is a combination of the pasture module of Farmax® (originally called

Stockpol (Marshall et al., 1991; Webby et al., 1995)), the animal components of MOOSIM

(Bryant et al., 2008a) with recently developed animal representations, management options,

cash flow and profitability (Bryant et al., 2010). Farmax® Dairy Pro enables the comparison

of financial and physical performance of farms and scenarios. It helps users explore pasture

and supplement feeding options, and purchasing and drying off decisions and see the

34

expected impact on pasture substitution, milk production, condition score, pasture cover and

operating profit (Armstrong, 2012).The model can be used to predict the outcome of farm

management changes on animal performance, pasture cover and total yields (Bryant et al.,

2010).

Wedderburn et al., (2011) used Farmax® Dairy Pro and Overseer® nutrient budget model as

a modelling approach to connect an integrated and participatory exploration of future

scenarios to estimate the impact of long-term projections and to test adaptive strategies for

uncertain and complex futures.

The challenge to reduce N leaching/ha in a sensitive catchment while maintaining or

increasing profit has become a topic of interest, Bowler & McCarthy, (2013) demonstrated

system optimisation using Farmax® Dairy Pro and Overseer® to evaluate opportunities to

use existing resources more efficiently through optimisation & innovation. This required a

whole farm assessment to ensure the balance is right between economic, environmental and

social outcomes. With a whole farm assessment, a holistic view, business and life goals along

with detailed analysis of any capital investment needed need to be well understood. A system

change involved investigating supplements used, N fertiliser rates, stocking rates and

infrastructure, (effluent, feedpad, herd shelter) (Bowler & McCarthy, 2013). Optimisation

modelling is an effective way to analyse many different scenarios, with the aim to reduce N-

leaching while increasing profit/ha (Figure 8).

Figure 8: N leached & profitability farm modelling (Bowler & McCarthy, 2013, p. 8)

Bowler & McCarthy, (2013) demonstrated a base system on 218 ha with 619 cows (2.8

cows/ha), producing 1,261 kg/ha or 448 kg/cow and using 200 kg N fertiliser and with 50%

of the cows wintering off resulted in 31 kg N/ha leached. In an optimised model reducing

cows from 619 cows to 599 cows (2.8 cows/ha to 2.7 cows/ha), increasing production to 459

35

kg/cow, reducing N fertiliser to 160 kg N/ha and the timing applied while keeping 50% of the

dry cows on farm resulted in N leaching of 26 kg N/ha a reduction of 16% and operating

profit increase of $23/ha. Opportunity to use existing resources more efficiently do exist, the

use of modelling along with financial analysis can be used to help farmers feel in control.

2.4.9 Grazing Systems Limited

The Grazing Systems Limited (GSL) Model is a Linear Program model for pastoral farm

systems developed by Barrie Ridler. It optimises animal production needs against dry matter

feeds (energy) – pasture, crops and supplements. It is a bio-economic model in that resources

have economic values that drive optimisation (Riden, 2009). The GSL LP optimisation model

allowed selected resources to be constrained, primarily cow number and production per cow,

but it allowed the addition of other resources such as supplementary feeds, nitrogen and

grazing off. The model depended primarily on relationships involving feed energy and its

cost (Anderson & Ridler, 2010). The ability to substitute use and management of specific

resources as part of the optimisation process provides the difference between this and other

more deterministic simulation methods. The use of GSL LP is highly educational to those

involved, it is not restricted to thinking inside the square and will often provide answers that

are sensible but not necessarily intuitive (Riden, 2009).

When used as a modelling tool GSL LP generally takes an established model representing an

optimised farm system and varies a single input parameter about the optimal –typically herd

size but it can also be herd structure, calving dates, animal or pasture production, or

management decisions on culling or drying off/sales. This provides data to build the system

production function, and marginal cost and/or revenue curve (Riden, 2009).

The production level where operating surplus is maximised (marginal cost equals marginal

revenue - MC=MR) is simple to determine. Setting production at the operating surplus

maximising point (MC=MR) on a production function is common wisdom and infers

allocation efficiency. That there are a wide range of possible production functions for the

same farm should be no surprise given the complexity of pastoral farming and the diversity of

farm managers and farm management system (Riden, 2009). It is also assumed that except in

unusual circumstances, no rational manager would continue to use the input or resource at a

level beyond the point where MR = MC, the profit-maximising point. Critically, this is also

the point of optimal or efficient resource allocation. Calculation of a ratio such as the average

revenue or a Gross Margin does not indicate when that profit-maximising ‘tipping point’ has

been reached (Ridler et al., 2010).

36

The effect of optimising a dairy system based on farm profit and N leaching is demonstrated

Figure 9: Optimise using LP model (Bell, 2013a). Many iterations can be run with a single

change to determine the outcome and compare this to the next.

Figure 9: Optimise using LP model (Bell, 2013a).

Bell, (2013) demonstrated estimating impact of the One Plan on a dairy enterprise using

Overseer® 6.0, Scenario 1 (Sc1) (Limits), assumes all farms attempt to meet controlled

activity limits, Scenario 2 (Sc2), (System Change), assumes all farmers will maximise N

leaching reduction without reducing profit by more than 10% with restricted discretionary

consents (RDC) of 80%, and optimised using GSL LP model, Scenario 3 (Sc3), (Within

System) assumes farmers will adopt management practices to reduce N leaching while

maintaining production and profit, using Farmax® Dairy Pro. Scenarios 2 and 3 were

designed to model possible implications of Horizons Regional Council providing a RDC.

Cashflows are considered over a 20-year project life, the period of the One Plan. Cashflows

are discounted at three rates 2%, 5% and 8% real 2012 dollars. The milk solids (MS) price

per kg is standardised at $6.70 which is the average of the last four years in 2012 dollar terms

and includes Fonterra dividends. The results are then plotted, with Tararua (Figure 10) and

West Coast (Figure 11), using representative farm nutrient limit (NL), revenue (Rev), &

expenditure (Exp.) $/ha/yr.

37

Figure 10: Comparison between Zones – Tararua. Adapted from (Bell, 2013a, p. 16)

Figure 11: Comparison between Zones - West Coast. Adapted from (Bell, 2013a, p. 16)

Bell, (2013) results found the difference between the two districts widens when the

Controlled Activity limits are compared. Tararua shows a 30% reduction in N leached with

revenue down 33% and expenditure down 26%. This compares with 30%, 9% and 10%

respectively in the West Coast district. Most dramatically, farm business profit is expected to

fall by 45% in the Tararua compared with a fall of only 5% in the West Coast when farmers

achieve the controlled limits.

The analysis highlighted how policies if not robust and based on good science can have a

financial impact on the farm, impacts on communities at the district and regional level, also

how support tools can affect a system change over time. Bell, (2013) concluded, economic

analysis highlights the risks of getting the policy wrong, collaborative process needs to be

embodied in the implementation.

NL t Rev $m Exp $m Profit $m

Sc3 WS -17% 0% 0% -2%

SC2 SC -22% -11% -15% 2%

Sc1 Limits -33% -30% -26% -45%

-50%

-40%

-30%

-20%

-10%

0%

10%%

ch

an

ge

co

mp

are

d w

ith

Ba

seTararua N leaching and dairy farm profitability

NL t Rev $m Exp $m Profit $m

Sc3 WS -20% 0% 2% -6%

SC2 SC -26% -8% -9% -3%

Sc1 Limits -30% -9% -10% -5%

-35%-30%-25%-20%-15%-10%

-5%0%5%

% c

ha

ng

e c

om

pa

red

wit

h B

ase

West Coast N leaching and dairy farm profitability

38

2.4.10 Economic optimum & N conversion efficiency using Whole farm model

In a statement of evidence in regards the proposed plan ‘Hurunui and Waiau River Regional

Plan’, McCall, (2012), that existing Hurunui dairy farms can reduce nitrogen leaching in

order to create the headroom for anticipated irrigation development in the catchment to

maintain the current nitrogen footprint employing good practice and reducing waste. In

summary of evidence, McCall, (2012) stated that the headroom for land use development can

be achieved by driving the efficiency of resource use, including nitrogen use, in existing dairy

farm businesses to its economic optimum1 for each farm and so improve nitrogen conversion

efficiency2 and reduce nitrogen waste (nitrogen surplus) and thus leaching.

In order to provide evidence for this approach and to calculate headroom potential from high

nitrogen use efficiency, McCall, (2012), summited data that was gathered from 32 farms in

the Hurunui catchment. These 32 farms were processed through Overseer version 5.4.10 and

separated into four quadrants (shown in Figure 4) based on their estimated leaching loss and

nitrogen conversion efficiency (NCE). McCall, (2012), demonstrated that the data in Figure 4

shows that, across the Hurunui catchment dairy farms, as the efficiency of nitrogen use

increases (NCE), the nitrogen leaching loss decreases. From the 32 farms, four farms were

chosen to calculate the opportunity to reduce nitrogen leaching by optimising technical

efficiency and thus nitrogen conversion efficiency. These farms approximately mapped one

to each of the four quadrants in Figure 4. The four farms were analysed through the GSL

linear programming (LP) model3. The model calculates the maximum profit for a farm for a

given level of input-resource use. Resulting leaching loss predictions were calculated on

Overseer version 5.4.10. McCall, (2012) demonstrated the reduction in nitrogen leaching by

1 The economic optimum for a farm is found by maximising the efficiency of resource use on that farm (e.g. application of fertiliser, use of feed, number of animals and use of different irrigation systems) by eliminating waste. In determining the economic optimum for a farm the level of resource use is progressively constrained below its current level until profit is maximised. The technically efficient resource use corresponds to the resource-use that achieves maximum profit. 2 Nitrogen conversion efficiency is a measure of the percentage of nitrogen input to a farm that is captured in product (either meat or milk, e.g. 30%). The greater the conversion efficiency the greater the percentage of nitrogen that is exported as product. 3 The GSL model was chosen over Farmax (which was used for the calculations presented in Brown et al, (2011), and of which the author of this evidence was a developer). This was because GSL is more efficient at finding optimal resource use allocations due to it being an optimising, rather than a simulation model. With simulation models (such as Farmax) the definition of optimal resource use requires the user to iterate their way to an optimum solution. This iteration is time consuming, not always full-proof and optima may be missed. Predictions from Farmax and GSL are very close, given similar resource inputs. This is shown in Table 4 where predicted outputs for the current configuration for three of the farms which had previously been loaded into Farmax by another user, were compared with predictions by GSL. It means that the only significant difference between the models is in the model structure (optimising – GSL, versus simulation - Farmax).

39

optimising nitrogen and other resource use efficiency to its economic optimum was 21%, 0%,

23% and 12% respectively for each farm modelled. McCall, (2012) concluded, the results

show the ability for positive outcomes from technical efficiency gains giving increased

profitability and reduced nitrogen leaching loss on three of the four farms. The positive

outcome occurs because the farms are not currently operating at their economic optimum for

nitrogen input and other resource use efficiency.

Table 4: Comparison of Farmax and GSL predictions for current performance of Farms 1, 3 and 4. Note Farm 2 was not

modelled on Farmax (McCall, 2012, p. 16 Table 1 )

Farm Farm 1 Farm 3 Farm 4

Model Farmax GSL Farmax GSL Farmax GSL

Farm area (ha) 141 141 297 297 190 190

Cow numbers 503 503 1087 1080 620 620

Milksolids to factory (kg ms) 232,831 232,487 541,640 541,413 266,604 266,630

Production per cow (kg ms / cow)

463 462 498 501 430 430

Hay/silage fed (tonnes) 228 212 210 200 41 40

PKE fed (tonnes) 89 90 0 0 0 0

Other feeds fed (tonnes) Straw Maize silage Molasses Wheat

43 0 0 0

40 0 0 0

75 207 44 814

45 200 45 920

40 0 0 357 Barley

40 0 0 336 Barley

Silage conserved (tonnes) 132 72 60 125 40 0

Nitrogen fertiliser (kgN/ha) 320 315 257 256 232 232

Expenses ($/ha) $7,042 $7,068 $7,573 $7,586 $5,347 $5,410

Crop area (ha) 0 0 0 0 15 15

Cow graze-off All 63 days

All 65 days

All 63 days

All 65 days

370 63 days

350 65 days

Replacement Graze-off From weaning

From weaning

From weaning

From weaning

From weaning

From weaning

2.4.11 GSL model on Massey No.1 dairy unit

Massey University No.1 Dairy research farm is on the banks of the Manawatu River, the farm

is not in a sensitive catchment and complies with resource consents. However, the farm is

under development to have a system change, based on environmental restriction set by

Horizon regional council One Plan. Tom Phillips (No.1 Dairy Farm Project Manager) aim

was to improve profitability, but not at the expense of the environment. The GSL model was

used to evaluate options, each scenario is run through Overseer® to assess the N-leaching

component. The GSL LP model identifies the point at which any input no longer improves

the economic outcome, demonstrated in Figure 12. The area coverage of different LUC

classes on the farm is displayed in Map 2. LUC I area is 61.75 ha with a for sensitive zones

40

N-Limits of 30 kg N/ha/year, LUC IV area is 55 ha with a One Plan N-Limits of 18 kg

N/ha/year for this LUC class.

Map 2: Massey No.1 Dairy unit LUC class farm map

The calculated total Permissible N-Loss limits for sensitive zones of 24 Kg N/ha/year (Table

5), the original farm system had average N loss of 42 kg N loss/ha/year under Overseer®

V6.0, the farm’s limits, according to the regional plan, was to be 24 kg N/ha/year, resulting in

a 18 Kg N/ha/year difference between the original system and the farm’s limits.

Table 5: Massey No.1 dairy farm permissible N-Loss limits

LUC One Plan N-Limits Dairy platform

Class (kg N/ha/year) Area (ha) (kg N)

LUC Class I 30 61.75 1853

LUC Class II 27 2.25 61

LUC Class III 24 0 0

LUC Class IV 18 55 990

LUC Class V 16 0 0

LUC Class VI 15 0

LUC Class VII 8 0 0

LUC Class VIII 2 0

Totals 119 2903

Permissible N-Loss limits (Kg N/ha/year) 24

41

2.4.11.1 Brief description of the changed farm system

No.1 Dairy unit is now a self-contained spring calving farm system based on Once a Day

(OAD) milking with dry cows & young stock being run off the farm. Currently about 235

cows are OAD for 300 days and 26ha is irrigated. Cropping includes 7ha of Lucerne and

7.2ha of Chicory/Plantain/clover mix which will provide an alternative nutrient mix. The

budget based on 85,110 Milk solids with an operating profit target 40% of gross farm

Income.

A system change was initially designed using criteria set by a steering committee. The system

change had being developed using Overseer® V6 and an Excel spreadsheet feed budget, the

farm’s limits, according to the regional plan, was to be 24 kg N/ha/year, this was the target to

achieve. The next step was to validate the system change to see if the option was viable and if

other options might be better suited. The GSL software was chosen for the validation process

and 14 optimised runs performed (Table 6), with the first run (run one) being our chosen

option.

Once data has been entered into the GSL model it is simple to run the model with different

scenarios.

2.4.11.2 GSL scenarios explained

The GSL software requires real and accurate data to provide the best possible solution.

However, there appeared to be no accurate or validated pasture growth data on the Massey

No.1 dairy farm, so synthetic data was used to create a feed growth budget, this was then

used as the base system and represented as run one. It was, however recommended that a

comprehensive monitoring system be instigated to ensure regular and timely monitoring of all

critical components.

The use of the GSL software was to provide an understanding of different scenarios to

ultimately reduce N loss over the whole farm. Each run, shows how the system responded to

the system change, from this a more in-depth analysis could be performed on the desired

options. The GSL software gave the team a snapshot of the physical resources and the

possible financial outcome; it does not provide information on how the selected option is

managed, protocols implemented or the unrealised resource constraints of the selected

system. This is up to the management team on how successful the selected option will

perform.

42

2.4.11.2.1 Run one

The GSL software was constrained by the production details given and then run. This farm

runs 235 cows, with once a day milking, production is 85,010 kg MS/year, producing 362 kg

MS/cow at a stocking rate of 2.3 cows/ha. Cropping is on 40 ha with only 690 kg’s of maize

silage being required, no supplements are made on the farm. All cows are off farm over

winter with most of the replacements off farm all year. Pasture growth rates and ME of

pasture have been classed as lower that what would be expected, this is the basis of run No.

two. The result is an N-loss of 24 kg N/ha/year over the whole farm with an N conversion

efficiency of 52%. Income is $536,112 with costs of $364,472 result in a profit of $171,639.

2.4.11.2.2 Run two

The GSL software was constrained by the production details given and then run. This run is

similar to run one, with pasture and ME adjusted upwards to reflect actual data. With 235

cows and an increase in production of 156 kg MS/year over run one, no supplements are

made or used on the farm. This resulted in an N-loss of 25 kg N/ha/year over the whole farm

with an N conversion efficiency of 37%. Income is $537,065 with costs of $324,005 result in

a profit of $213,060, this is up $41,221 compared to run one.

2.4.11.2.3 Run three

The GSL software was optimised by cow numbers, this resulted with an increase in cow

numbers to 258 cows, production of 93,221 kg MS/year, producing 362 kg MS/cow at a

stocking rate of 2.5 cows/ha no supplements are made or used on the farm. This resulted in an

N-loss of 24 kg N/ha/year over the whole farm with an N conversion efficiency of 66%.

Income is $587,872 with costs of $368,968 result in a profit of $219,004, this is up $47,365

compared to run one.

2.4.11.2.4 Run four

Run 4 adopts a slightly different management strategy for the 40 ha of at risk soils. 20 ha of

the 40 ha is assumed as “graze on graze off” system of about 2 - 4 hours .The GSL software

was optimised by cow numbers with a decrease in cropping by 20 ha which was now used for

“graze on graze off”. Cow numbers increased to 274 cows with an increase in production of

98,942 kg MS/year, producing 362 kg MS/cow at a stocking rate of 2.6 cows/ha no

supplements are made or used on the farm. This resulted in an N-loss of 24 kg N/ha/year over

the whole farm with an N conversion efficiency of 71%. Income is $624,040 with costs of

$339,773 result in a profit of $284,266, this is up $112,627 compared to run one.

43

2.4.11.2.5 Run five

Run 5 adopts a policy of twice a day milking until end of December, then OAD from there

on, this begins a process of increasing productivity within the herd. The GSL software was

optimised by cow numbers and production per cow set at 380 kg MS/cow with 20 ha of

cropping (no cup and carry). Cow numbers increased to 284 cows with an increase in

production of 108,123 kg MS/year, producing 380 kg MS/cow at a stocking rate of 2.7

cows/ha no supplements are made, however, 500 kg is of maize silage is used. This resulted

in an N-loss of 29 kg N/ha/year over the whole farm with an N conversion efficiency of 43%.

Income is $679,465 with costs of $369,921 result in a profit of $309,544, this is up $137,905

compared to run one. This change in efficiency plus a more managed approach to the 40 ha

increases surplus and herd number but also results in increased N loss.

2.4.11.2.6 Run six

The GSL software was optimised by cow numbers and production per cow set at 393 kg

MS/cow, with 20 ha of cropping. Cow numbers decreased to 278 cows with an increase in

production of 109,403 kg MS/year, producing 393 kg MS/cow at a stocking rate of 2.7

cows/ha no supplements are made, however, 380 kg is of maize silage is used. This resulted

in an N-loss of 30 kg N/ha/year over the whole farm with an N conversion efficiency of 42%.

Income is $682,557 with costs of $353,446 result in a profit of $329,111, this is up $157,472

compared to run one. These changes add considerably to the surplus with minimal apparent

increase to N loss.

2.4.11.2.7 Run seven

The GSL software was optimised by cow age preference and cow numbers, this is achieved

by improve productivity by reducing replacement and increasing herd longevity, with 20 ha

of cropping. Cow numbers decreased to 260 cows with an increase in production of 110,321

kg MS/year, producing 424 kg MS/cow at a stocking rate of 2.5 cows/ha no supplements are

made or used. This resulted in an N-loss of 25 kg N/ha/year over the whole farm with an N

conversion efficiency of 72%. Income is $678,630 with costs of $334,168 result in a profit of

$353,466, this is up $181,827 compared to run one. These changes add considerably to the

surplus with minimal apparent increase to N loss.

2.4.11.2.8 Run eight to run twelve

Runs 8, 9, 10, 11, 12 are variations of Runs relating to Run 2, 5, 7, 10, 10 where output

constraints are applied within the GSL

44

2.4.11.2.9 Run thirteen

The GSL software was optimised by N excreted (or “Nx”), which has been shown to be

correlated to Overseer® N loss provided soils, region and climate are the same (an in farm

comparison). NX is set to 29,500. Cow numbers decreased to 213 cows with production of

90,406 kg MS/year, producing 424 kg MS/cow at a stocking rate of 2.0 cows/ha. 45 tonnes of

grass silage is made with 19 tonnes discarded, no supplements are used. This resulted in an

N-loss of 38 kg N/ha/year over the whole farm with an N conversion efficiency of 46%.

Income is $563,471 with costs of $395,740 result in a profit of $167,730, this is down $3909

compared to run one.

2.4.11.2.10 Run fourteen

Run 14 is based on run one with some adjustments. The GSL software was optimised by Nx

at a value of 37,000. Cow numbers decreased to 213 cows with production of 97,300 kg

MS/year, producing 229 kg MS/cow at a stocking rate of 2.2 cows/ha. 55 tonnes of grass

silage is made with 146 tonnes discarded, no supplements are used. This resulted in an N-loss

of 30 kg N/ha/year over the whole farm with an N conversion efficiency of 50%. Income is

$606,416 with costs of $326,338 result in a profit of $280,178, this is up $108,539 compared

to run one.

45

Table 6: GSL data from each run.

Description Initial

run Run2 Run3 Run4 Run5 Run6 Run7 Run8 Run9 Run10 Run11 Run12 Run13 Run14

Base IncMJME Optcows` 20haC&C 380MS .18/2/08 +ageperf

Run2adj Run5adj Run7adj RUN10 Lmtconc Nx

29500 Nx37000

CO2 opt CO2 opt CO2 opt CO2 1100

CO2 1100

Equiv Run 1

Run no. 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0

Hectare 64+40 64+40 64+40 64+40 64+40 64+40 64+40 64+40 64+40 64+40 64+40 64+40 64+40 64+40

Cows No. 235 235 258 274 284 278 260 228 210 197 169 169 213 229

kgMS/cow 362 362 362 362 380 393 424 362 382 426 425 425 424 425

Supp made (Silage) 0 0 0 0 0 0 0 0 0 0 0 0 450000 55000

Discard (Pasture) 0 60000 59000 435000 515000 190000 (~14 ha) 146000

Concentrate (Maize silage)

690 0 0 0 500 380 0 0 0 0 114000 100000 0 0

PKE 95500

MS Prodn Kg/Ms 85010 85166 93221 98942 108123 109403 110321 82293 80230 83870 71901 71901 90406 97300

$ Income 536112 537065 587872 624040 679465 682557 687630 519028 504122 522720 448173 448173 563471 606416

$ Costs 364472 324005 368968 339773 369921 353446 334163 343892 289938 277882 331603 364500 395740 326238

$ Surplus 171639 213060 219004 284266 309544 329111 353466 175136 214183 244737 116570 83673 167730 280178

N retained 6103 6659 6659 7067 7615 7590 7589 5878 5982 5877 5354 5354 6237 6700

N excreted (urine) 37550 38789 39039 42882 42947 42951 42942 34888 35784 35785 30246 29491 29500 37000

N leached Kg/N/Ha 24 25 24 24 29 30 25 24 29 28 35 36 38 30

N Conversion Efficiency

52 37 66 71 43 42 72 37 43 45 65 66 46 50

Total GHG 2.3 1.9 2 2.1 2.3 2.4 2.2 1.8 2.2 2.1 2.2 2.2 2.3 2.2

Crop area grown 40 40 40 20 20 20 20 40 20 20 20 20 20 20

Total KG/Dm 1146510 1187725 1187085 1265438 1265708 1265666 1265376 1052106 1038009 1037593 993957 998975 1119584 1145949

Graze offcows All All All All All All All Most Most 31 0 28 80 121

Graze off replacements Most Most Most Most Most Most Most All All R1yr - No R2yr All 0 0 All All

46

Run one, three, four, eight meet the first year N leaching limits. Run one with 235 cows

producing 85,010 kg MS, 362 kg MS/cow with an N conversion efficiency of 52% and a

surplus of $171,639, run three with 258 cows producing 93,221 kg MS, 362 kg MS/cow with

an N conversion efficiency of 66% and a surplus is up $47,365 compared to run one and run

eight with 285 cows producing 82,293 kg MS, 362 kg MS/cow with an N conversion

efficiency of 37% and a surplus is up $3,497 compared to run one. Production Figure 12 and

Financial Figure 13.

Figure 12: GSL plotted runs with comparison of N leaching, cow numbers and production and N efficiency

The four runs resulted with an N leaching of 24 kg N/ha/year over the farm. Run one system

results, 85,010 kg MS with 235 cows producing 362 kg MS/ha with a surplus of $171,639,

run three results, 93,221 kg MS with 258 cows producing 362 kg MS per cow with a surplus

of $219,004, run four results, 98,942 kg MS with 274 cows producing 362 kg MS per cow

with a surplus of $284,266, and run eight results, 82,293 kg MS with 228 cows producing

362 kg MS per cow with a surplus of $175,136.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

NoCows 235 235 258 274 284 278 260 228 210 197 169 169 213 229

kgMS/cow 362 362 362 362 380 393 424 362 382 426 425 425 424 425

N leached Kg/N/Ha 24 25 24 24 29 30 25 24 29 28 35 36 38 30

One Plan N-Limits 24 24 24 24 24 24 24 24 24 24 24 24 24 24

N Conversion Efficiency 52 37 66 71 43 42 72 37 43 45 65 66 46 50

24 25 24 24

29 30

25 24

29 28

35 3638

30

52

37

66

71

43 42

72

37

4345

65 66

4650

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

150

200

250

300

350

400

450

kg

N l

oss

/ha

/ye

ar

& %

N e

ffic

ien

cy

Pro

du

ctio

n K

g/C

ow

an

d C

ow

No

.

Cow numbers and production with N-loss on No.1 dairy farm

47

Figure 13: GSL plotted runs with comparison of N leaching, N efficiency and cash surplus The base system has been implemented on No.1 dairy unit and monitoring will continue to

see how the dairy unit system change performs compared to the GSL model.

2.5 Farmers' Decision Making

Decision-making in dairy farming has focused mainly on the decision-making process,

characteristics of the farmer as a decision-maker and risk management. According to a large

body of research, the decisions farmers make to adjust to new circumstances are not only led

by economic factors, but also by socio-economic and psychological factors (Gow & Stayner,

1995; Traoré et al., 1998; Willock et al., 1999).

All decision-makers have to deal with risk and uncertainty, which are often used

interchangeably, with risk being an uncertainty that can be approximated by (subjective)

probabilities and the magnitude of the consequences, it is imperfect knowledge about

alternative outcomes and its likelihoods that creates uncertainty (Botterill & Mazur, 2004;

Hardaker et al., 2004). According to Bernstein (1996: 35) data based in the past constitute a

sequence of events rather than a set of independent observations that are required in the laws

of probability. Bernstein (1996) points out that the challenges for probability are the

contrasting tasks of having to look into the future while interpreting the past and balance

opinions with what is ‘known’.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

$ Surplus 171,639 213,060 219,004 284,266 309,544 329,111 353,466 175,136 214,183 244,737 116,570 83,673 167,730 280,178

MS Prodn Kg/Ms 85,010 85,166 93,221 98,942 108,123 109,403 110,321 82,293 80,230 83,870 71,901 71,901 90,406 97,300

N leached Kg/N/Ha 24 25 24 24 29 30 25 24 29 28 35 36 38 30

One Plan N-Limits 24 24 24 24 24 24 24 24 24 24 24 24 24 24

N Conversion Efficiency 52 37 66 71 43 42 72 37 43 45 65 66 46 50

24 25 24 24

29 30

25 24

29 28

35 3638

30

52

37

66

71

43 42

72

37

4345

65 66

46

50

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

50,000

75,000

100,000

125,000

150,000

175,000

200,000

225,000

250,000

275,000

300,000

325,000

350,000

375,000

400,000

kg

N l

oss

/ha

/ye

ar

& %

N e

ffic

ien

cy

$ S

urp

lus

& P

rod

uct

ion

Kg

/MS

N-loss with $ surplus and milk production on No.1 dairy unit

48

A range of situational factors and knowledge, beliefs and attitudes influence the perceived

risk of innovation. Guerin & Guerin’s (1994) review of adoption found that farmers were less

likely to take up innovation when they did not understand the nature of the risk and its

associated circumstances; could not easily compare new alternatives with old practices; had a

diminished sense of personal control over agricultural production; and had bad past

experiences (Botterill & Mazur, 2004). As farmers are seeking to reduce the risk of adopting

innovation, new practices are taken up more quickly when they can be readily observed,

trailed, are less complex (Cary, Webb, & Barr, 2002, p. viii) are perceived to be profitable,

appropriate, consistent with existing goals and can easily be integrated into existing practices

(Botterill & Mazur, 2004; Guerin & Guerin, 1994).

The dairy farmer has to contend with trading off production with environmental objectives,

this adds to the complexity of farm planning and decision making in a farming system.

Therefore, a framework to evaluate farming opportunities that a farmer can use to trade-off

financial, production and environmental risk at the whole farm level would greatly assist

pastoral farmers to better manage their business operation at a time when production goals

must include a greater evaluation of environmental impacts (Dake et al., 2005).

In the past five years there has been a significant increase in the amount of supplementary

feeding on dairy farms around the country, resulting in a shift for many farms up the intensity

scale. This has been both intentional and unintentional (systems creep), driven mainly by

adverse weather conditions, particularly droughts, and the availability of palm kernel. High-

input systems have greater variability because of additional complexity and more decisions

required on a daily basis, including decisions on supplement usage, integrating supplements

with pasture management, maximising the conversion of supplements to milk, and sourcing

the right supplements at the right time for the right price (Kloeten, 2014).

We can never assume farmers make decisions about farm systems based on profitability as

many decisions about farm systems, infrastructure, and intensity are based on non-economic

reasons. This doesn't mean they are poor decisions, if the long-term viability of the business

is maintained, it is important farmers are clearly aware of the implications through an

instinctive response to a system change because of a short-term change in milk price

(Kloeten, 2014).

49

The use of agricultural decision support models by farm managers was noted to be minimal

(McCown, 2002) and this is still largely the case. Reasons for the lack of uptake include

overly complex models that are not easy to use, insufficient evaluation of model predictions,

lack of involvement of users in the design and refinement of the models, lack of

demonstration of their value to business and lack of training (Borenstein, 1998; Bryant et al.,

2010; Cox, 1996; McCown, 2002).

2.6 Concluding Remarks

There is a need to undertake this research with an opportunity to evaluate the complexity of

getting an N-loss measurement that reflect a dairy farm in a sensitive catchment and then

evaluate the mitigating strategies that will be required to reduce N-loss set limits while

maintaining the economic viability of the farm.

50

3 Methodology

3.1 Introduction

The purpose of this study is to evaluate the complexity of getting an N-loss measurement of a

case study farm in a sense of catchment and then investigate what effects reducing N leaching

to the first year targets of the One Plan has on the economic outcome of a dairy farm. One

key concept is having a nutrient budget for the farm, as Horizon Regional Council has based

the One Plan around the outcomes of Overseer® results, Overseer® will be an integral tool in

determining the N-leaching status of the farm.

Decisions about managing farms involves a complex combination of human, production,

environmental, economic and financial of the business will be included in this study

(Malcolm et al., 2005b). To examine the economic performance of a range of development

options or system changes for a case study dairy farm, annual whole-farm budgets will be

developed using GSL for both the biophysical and economic modelling provide an in-depth

analysis of a whole farm business (Armstrong et al., 2010; Heard et al., 2012; Malcolm et al.,

2005a; Malcolm et al., 2005b).

An iterative procedure is recommended whereby mitigation options are considered based on

initial Overseer® output, farm financial and management implications are investigated and

the mitigation options modified where necessary before re-running the Overseer® model

(Wheeler et al., 2007).

This chapter describes the research methods used to collect and analyse information to

achieve this objective. The method of research used in this study is described in Section 3.2.

Section 3.3 describes the selection of the case. The data collection protocols are presented in

Section 3.4 and in Section 3.5 the data analysis techniques are described. Section 3.6 outlines

ethical considerations relevant to the study.

3.2 Research methods

The type of research methods depends on the objectives and purpose of the research (Yin,

2008). Yin, (2008) considers that five major alternative research strategies available to

researchers in the social science fields, experiments, surveys, histories, archival analysis and

case studies. Each strategy has its advantages, disadvantages and distant characteristics and

the strategies do overlap. The goal is therefore to choose the strategy which provides the most

advantages and few disadvantages (Yin, 2008). Case study research can deal with a variety of

evidence that includes documentation, artefacts, direct observations and participant

51

observation (Yin, 2008). Research questions can be categorised into the familiar series who,

what, where, how and why questions (Yin, 2008). A case study approach was selected to

answer the ‘how’ and ‘why’ questions in this study as it provided an in-depth understanding

of the current process used by the farm owner, along with documentation of financials and

physical data to develop a whole farm model that allows the evaluation of the farm’s

economic viability from a system change.

Case study research can be undertaken using a single case, or multiple case studies (Yin,

2008). Case studies provide an in-depth source of information about a single or limited

number of cases (O'Leary, 2005). As an in-depth understanding of the current process used

by farm owner is required to answer the research question a single case study was used in this

research.

3.3 Case selection

This study identified different approaches that are available and used to mitigate N leaching

and to analyse the economic viability of a farm system. Time available and the scope of this

study meant that only a few modelling methods could be used.

The objective of this study is to investigate the effects of reducing N leaching to the first year

targets set by the One Plan and how the reduction will impact on the economic viability of

that farm. To achieve this objective, the main criteria for a case selection was that the farm is

in a sensitive catchment, Overseer® has been run with available data and 3 years of data has

been input into DairyBase. Secondary criteria is the farm owner’s accessibility and

willingness to be involved in the study.

3.4 Data collection

Case studies can use multiple sources of evidence to answer research questions (Yin, 2008).

Sources of evidence include documents, artefacts, interviews, direct observations and

participant observations. It was decided that documents would be the primary source of

evidence. This was because a detailed understanding of the process was required and this

would not be obtained through either direct observation or the collection of artefacts and

interviews. A secondary source on information will be through interviews as this will provide

the how and why answers to questions.

52

3.4.1 Documentation

Documentation (Data) from Overseer® will be used for the initial nitrogen loading of the

farm and provides a base to explore mitigation options to reduce N leaching. Documents

relating to physical and financial information from DairyBase will be collected and used to

build a whole farm model. This data will be gathered before the start of the interview process,

allowing for gaps in the data to be filled.

3.4.2 Interview

A suitable time for the farm owner will be selected and the farmer will be asked permission

for the interview to be recorded. The data gathered is then analysed so any questions relating

to gaps or clarification of the physical and financial information can be obtained. The

interview will then proceed with questions relating to any processes undertaken relating to

the research. On completion of the interview the farm owner was thanked for their time and

informed if further information was required or if any data was needed for the project.

3.5 Data analysis

3.5.1 Initial analysis

If after the initial analysis, important information is found to be missing, a second interview

will be arranged. The initial data from Overseer® is the base system, mitigating techniques in

the literature will be used to change the system to be within the first years N leaching limits

of the farm. The physical and financial data gathered will first be used to develop a whole

farm model of the base system. The recorded interview will be transcribed verbatim, this is

achieved using Microsoft Word and provide information on how and why current processes

are achieved. A second whole farm model will be developed using the changes made in

Overseer®, this will include any physical and financial changes required over the base

system.

3.6 Ethical considerations

The study undertaken involves the collection of data and interviewing the farm owner so a

number of ethical considerations must be accounted for. This includes receiving informed

consent from the farm owner, preserving the farm owner’s confidentiality, and protecting the

farm owner from harm. Ethical consideration of data collected, the use and storage of the data

on the case study farm have been identified, and appropriate handling and disposal of the data

has been taken into consideration. Ethical considerations and processes required by the

53

Massey University Human Ethics committee were followed. The project was assessed as

low-risk and the information sheet and informed consent form are attached in Appendix A.

Prior to commencing data collection, the farm owner is given a brief of the project and upon

agreeing to participate his informed consent was obtained. Throughout the research process

the farm owner’s confidentiality is preserved as neither the name of the farm owner nor their

place of employment will be disclosed throughout the research process to those outside of the

research team. Throughout the research process the confidentiality of the three consultants

and their data was preserved as neither the name nor their place of employment or their data

will be disclosed throughout the research process to those outside of the research team.

3.7 Summary

A case study is the research strategy used in this research because to answer the research

question a rich and in-depth understanding of the current whole farm system used by a farm

owner is required. One farm was selected due to the desired depth of the information sought,

this would not be achievable in the same timeframe had multiple cases been used. The data

was analysed, and used to perform a system change, the interview data was transcribed and

used to fill any gaps on current processes. The whole farm model provided economic data

from the system change; this could then be used to compare the economic viability of a

system change against the base system. The final results were then compared to that of the

literature and then published.

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4 Contextual Information to Cases

4.1 Introduction

A single case study farm has been selected to represent a dairy farm in a sensitive catchment.

This section will outline the characteristics of the case study farm within the region of a

sensitive of catchment as outlined by the Horizon regional council with respect to the One

Plan.

4.2 General location

The case study farm borders the Rangitikei River and is in the Coastal Rangitikei (Rang_4)

catchment (Map 3).

Map 3: Coastal Rangitikei (Rang_4) (Horizon, 2014)

55

4.3 Farm description

4.3.1 Climate data

The case study farm is situated approximately 10 km from the coast. A virtual climate station

in close proximity to the farm has been used to provide climate data for the farm as no other

means of measurements are available, the mean rainfall is 880 mm per annum with a mean

temperature of 13.5°C with potential evapotranspiration (PET) of 1024 mm per annum and

PET seasonal variation of low.

4.3.2 Land and production

The milking platform is 238.3 ha or 217 ha effective. The herd is prominently Friesian with

an average weight of 520 kg, at peak 620 cows are milked producing approximately 275,124

kg MS each year, equating to 443.75 kg MS/cow or 1,268 kg MS/ha, stocking rate is 2.86

cows/ha. In winter half the herd is removed from the farm for six to eight weeks during June

and July with all excess stock and replacement grazed off farm.

4.3.3 Feed supply

Pasture production is not measured so it has been estimated (synthetic data) to be around

13,000 Kg /ha/year across the farm, supplements are used to fill any feed deficit, this includes

220,000 kg’s palm kernel extract (PKE) and 240,000 kg’s maize silage, pasture silage is only

cut when the farm has a feed surplus. Pasture renewal is through crop rotation, this consists of

25 ha of Chicory and clover mix, producing roughly 13,000 kg/ha yield and a winter crop of

Oats with an area of 15 ha producing a yield of about 6,000 kg/ha.

4.3.4 Fertiliser

Nitrogen fertiliser is applied with four equal application of 25 kg N/ha over the whole farm

during April, August, September and October while the crops get an initial application when

sown of 40 kg N/ha.

4.3.5 Effluent & Irrigation

The effluent system is a sump and pump system with an effluent application area of 38 ha

and irrigation covering approximately 98 ha, 55 ha from central pivot and 43 ha from K-line

pods. The pods are moved approximately every three days around the 43 ha, the central pivot

is used in two locations, location one for about three days in the location to two for about five

days.

4.3.6 Soil resources A detail soil and landscape capability survey at the paddock level was undertaken for the

dairy unit within a sensitive catchment, completed earlier in 2014 by a trained Soil

56

Pedologist. Its purpose is for determining farm scale soil and the LUC resources for

calculating nitrogen loss limits.

The complexity of the soil types underlining the farm shown in Map 4: Soil map. Effluent,

irrigation and cropping crossover multiple soil types, when the farm floods, as silt is heavier

will be the first to settle, sand will continue to flow and settle further afield, the farm also had

a riverbed through part of the property which has now been covered up by farmland.

Map 4: Soil map

The farm has 12 different soil types (Table 7) on the farm; they are mainly silt, silt loams, sandy loams and sand, with drainage status of well drained, moderately well drained, and imperfectly drained to poorly drained.

57

Table 7: Soil names and drainage status

No. Ledgend Name Drainage status

1 R1 Rangitikei silt Moderately well to imperfectly drained

2 R2 Rangitikei silt loam Moderately well drained

3 Pa Parewanui silt Imperfectly to poorly drained

4 M1 Manawatu silt loam Moderately well to well-drained

5 M2 Manawatu fin sandy loam Moderately well drained

6 Kt1 Karapoti sandy loam Moderately well to well-drained

7 Kt2 Karapoti black silt loam Moderately well to well-drained

8 H Hokowhitu silt loam Imperfectly drained

9 K Kariranga silt loam Imperfectly to poorly drained

10 Ahs Ashurst stony loam Well drained

11 F Foxton brown sand Well drained

12 Hm Himatangi sand Well drained

4.3.7 Permissible N-Loss limits

The permissible N-loss limits were calculated using farm scale maps on a 1:6,000 scale. With

ten different LUC units recorded on this case study dairy farm, the data was then used to

calculate the permissible N-loss (Table 8), results show year one permissible N-Loss limits of

26.9 kg N/ha/year through to, year twenty at 21.2 kg N/ha/year.

Table 8: Permissible N-Loss limits for the case study farm

One Plan N-Limits Farm N-limits

Area Year 1 Year 5 Year 10 Year 20 Year 1 Year 5 Year 10 Year 20

LUC (ha) (Kg

N/ha/yr.) (Kg

N/ha/yr.) (Kg

N/ha/yr.) (Kg

N/ha/yr.) (Kg N) (Kg N) (Kg N) (Kg N)

LUC Class I 64.7 30 27 26 25 1941 1747 1682 1618

LUC Class II 126.9 27 25 22 21 3426 3173 2792 2665

LUC Class III 36.6 24 21 19 18 878 769 695 659

LUC Class IV 1.2 18 16 14 13 22 19 17 16

LUC Class V 16 13 13 12 0 0 0 0

LUC Class VI 8.9 15 10 10 10 134 89 89 89 LUC Class VII 8 6 6 6 0 0 0 0 LUC Class VIII 2 2 2 2 0 0 0 0

Totals 238.3 6401 5796 5275 5046

Permissible N-Loss limits for Dairy Unit = N/ha/year 26.9 24.3 22.1 21.2

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5 Results

5.1 Introduction

This purpose of this chapter is to present the results of the case study farm research

undertaken in relation to the research question.

The combination of a whole farm system model and Overseer® provides will provide the

basis of this research. However, in the process of using the model Overseer® a robust N-loss

must be first obtained.

5.2 Obtaining an N-loss using Overseer®

5.2.1 Introduction

To evaluate the robustness of the calculated N-loss using Overseer®, three consultants

Overseer® files have been obtained representing the case study farm. Consultant 1, with farm

data dated 2013, Consultant 2 with farm data dated 2013 and Consultant 3, completed

recently using 2013-2014 seasonal farm data and up to date farm soil mapping. It is duly

noted that Consultant 1 (C1) and Consultant 2 (C2) would not have had reference to the new

input user guide “Best Practice Data Input Standards” as this was only published on the

Overseer® websites in December 2013. However, Consultant 3 (C3) used this guide as it was

intended, to reduce inconsistencies between different users when operating Overseer® to

model individual farm systems as outlined by Roberts & Watkins, (2014).

This section is to show the basis around the complexity of getting an N-loss limit and

accuracy required.

5.2.2 Setting up nutrient blocks within Overseer®

The Overseer® Best practice data input standards manual (BPDIS), published by Overseer

Management Services Limited on behalf of the owners of Overseer® has been used as a

reference for the impact and recommendation (noted in order) on data entry within

Overseer® (OMSL, 2014).

Williams et al., (2011) noted, Overseer® users are to supply actual and reasonable inputs and

management practices of the farm, this removes uncertainty associated with the accuracy and

appropriateness of data inputs as Overseer® requires actual farm data as assumptions are

made about farm efficiency, this is outlined in Overseer® ground rules, section 2.4.5.1.

All the consultants had the opportunity to communicate the information required by the

farmer; information available included DairyBase, supplying physical and financial

information, virtual climate data from NIWA’s web site and soil information from s-maps,

59

this data is at a regional scale of 1:50,000. However, Consultant 3 had access to detailed soil

and landscape capability survey at the paddock level 1:6,000 scale. A pasture block is defined

as an area that has not irrigation or effluent applied to the soils.

5.2.2.1 Block Data

Impact: - BPDIS

It is critical to get the farm area and block areas within the farm defined as accurately as

possible to truly represent the farm being modelled.

Recommendation: - BPDIS

Blocks should be defined based on land uses, management systems (i.e. effluent and/or sludge

applied, irrigation applied, cut and carry, support block/runoff), soils, topography and

enterprise.

Consultant 1 split the blocks based on irrigation, effluent, non-irrigated and non-productive

land (Table 9), this resulted in a total area of 232 ha with 131.5 ha irrigated, 40 ha effluent,

54.7 ha pasture and 5.8 ha non-productive, this equates to 226.2 ha effective area on the

milking platform.

Consultant 2 split the blocks based on irrigation, effluent, non-irrigated and non-productive or

stock excluded with fodder crop ‘Kale’ rotating through block 1, 2 and 3 (Table 9), this

resulted in a total area of 230 ha with 110 ha irrigated, 40 ha effluent, 68 ha pasture and 12 ha

non-productive or stock excluded, this equates to 218 ha effective area on the milking

platform.

Table 9: C1 & C2: Nutrient blocks and land area, resulting in N-loss with Overseer®

No. Consultant 1 Effective No. Consultant 2 Effective

Blocks Block Name Area (ha) Blocks Block Name Area (ha)

1 Home (Non-Irrigated) 54.7 1 Upper Flats FD Irrigated 55

2 New (Irrigated) 104.8 2 Riverside Flats 68

3 Effluent Block 40 3 Effluent Block 40

4 Home (Irrigated) 26.7 1, 2,3 Fodder Crop 1 18

5 Non Productive 5.8 4 Upper Flats ID Irrigated 55

5 Stock Excluded 12

Effective area 226.2 Effective area 218

Total 232 Total Area 230

Consultant 3 split the blocks up based on irrigation, effluent, non-irrigated, non-productive

and then split it further based on soil drainage properties of the soil with fodder crop chicory

and Oats rotating through block 4,5,6,8,9,10,11 (Table 10). The results, a total area of 238 ha

with 98 ha irrigated, 38 ha effluent (Note. irrigation and effluent are combined in block 3), 86

60

ha pasture and 21 ha non-productive or stock excluded, this equates to 217 ha effective area

on the milking platform.

Table 10: C3: Nutrient blocks and land area, resulting in N-loss with Overseer®

No. Consultant 3 Effective

Blocks Block Name Area (ha)

1 Effluent - Poorly Drained 19

2 Effluent - Silt-stony 13.9

3 Effluent & Irrigation - Poorly Drained 4.6

4 Irrigation - Sandy-stony 14.3

5 Irrigation - Mod well drained 50.5

6 Irrigation - Poorly Drained 21.9

7 Irrigation - Sand Plains 6.3

8 Pastoral - Mod Well Drained 28.8

9 Pasture - Silt-stony 19.8

10 Pasture - Mod well drained 15.1

11 Pasture - Poorly Drained 22.4

Going through 4,5,6,8,9,10,11 Chicory 25.0

Going through 4,5,6,8,9,10,11 Oats 15.0

12 Stop Banks -Non productive 4.7

13 Stock Excluded 8.8

14 Non Productive 7.9

Effective area (ha) 217

Total area (ha) 238

5.2.2.2 SOIL DESCRIPTION

Impact: - BPDIS

The soil description is a key driver of soil nutrient losses, particularly nutrient leaching due to

the impact of the Available Water Capacity (AWC).

Recommendation: - BPDIS

1. Use farm specific soil map, produced by a trained Soil Pedologist.

2. Soil Order data – sourced from S-map Online Factsheet – section “Soil information for

Overseer (page 3 of the Factsheet)” (smap.landcareresearch.co.nz).

Dairy Industry: Select ‘Occasionally” for all soil types for Susceptibility to pugging or treading damage.

Consultant 1 has defined three soil types, Marton, has a top soil texture defined as Sandy

loam with a lower profile defined as a stony mix, the second soil, Rangitiki has a top soil

texture defined as Sandy loam with a lower profile defined as a stony mix and the third soil,

Parewanui has a top soil texture defined as silt loam with a lower profile defined as a stony

mix. All soils have a drainage status of ‘well’ and ‘occasionally’ for pugging (Table 11).

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Table 11: Consultant 1: Soil data used in Overseer®

No. Consultant 1 Soil Top Soil Soil Lower Profile Susceptibility

Blocks Block Name Description texture Lower Profile Soil texture group Drainage to pugging

1 Home (Non-Irrigated) Marton Sandy Loam Stony Matrix - Well Occasional

2 New (Irrigated) Rangitikei Sandy Loam Stony Matrix Medium Well Occasional

3 Effluent Rangitikei Sandy Loam Stony Matrix Medium Well Occasional

4 Home (Irrigated) Parewanui Silt Loam Stony Matrix - Well Occasional

5 Non Productive NA NA NA NA NA NA

Consultant 2 has defined two soil types, Rangitikei, has a top soil texture defined as silt loam

with a lower profile not defined, the second soil, Manawatu has a top soil texture defined as

silt loam with a lower profile not defined. All soils have a drainage status of ‘imperfect’ and

‘occasionally’ for pugging (Table 12).

Table 12: Consultant 2: Soil data used in Overseer®

No. Consultant 2 Soil Top Soil Soil Lower Profile Susceptibility

Blocks Block Name Description texture Lower Profile Soil texture group Drainage to pugging

1 Upper Flats FD Irrigated Rangitikei Silt Loam - Medium Imperfect Occasional

2 Riverside Flats Rangitikei Silt Loam - Medium Imperfect Occasional

3 Effluent Block Manawatu Silt Loam - Medium Imperfect Occasional

Fodder Crop 1

4 Upper Flats ID Irrigated Manawatu Silt Loam - Medium Well Occasional

5 Stock Excluded NA NA NA NA NA NA

Consultant 3 made use of the Soil Pedologist data to define soils and land management use of

the farm (Table 13).

The management blocks of the farm were defined by the 12 different soil types along with

irrigation, effluent and pasture for its land management use. Not all soil types could be

defined by its series as some of the series were not listed in Overseer®, for this reason ‘soil

by order’ was used.

The effluent area is located on Gley and Parewanui soils, Gley soil has a soil texture of silt

loam and a drainage status of poor and Parewanui soils with soil texture of silt loam and a

drainage status of moderately well drained. The irrigated area is located on Gley and Recent

soils, Gley soil has soil texture of silt loam and a drainage status of poor, Recent soils have a

soil texture of silt loam, sandy loam stony profile and sand with a drainage status of poorly

drained, moderately well drained and well drained for sand. The pasture area is located on

Gley, Recent and Rangitikei soils, Gley soil have a soil texture of silt loam and a drainage

status of poor, Recent soils have soil texture of silt loam with a drainage status of moderately

well drained and Rangitikei soils with soil texture of silt and silt loam with a stony profile

with a drainage status of moderately well drained.

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Table 13: Consultant 3: Soil data used in Overseer®

No. Consultant 3

Soil Description

Top Soil texture Soil Lower Profile

Lower Profile

Soil texture group

Susceptibility to pugging Blocks Block Name Drainage

1 Effluent - Poorly Drained Gley Silt Loam - - Poor Occasional

2 Effluent - Silt-stony Parewanui Silt Loam - - Moderately Well Occasional

3 Effluent & Irrigation - Poorly Drained Gley Silt Loam - - Poor Occasional

4 Irrigation - Sandy-stony Recent Sandy Loam - Stony - Medium Moderately Well Occasional

5 Irrigation - Mod well drained Recent Sandy Loam - Medium Moderately Well Occasional

6 Irrigation - Poorly Drained Gley Silt Loam - - Poor Occasional

7 Irrigation - Sand Plains Recent Sand - Light Well Occasional

8 Pastoral - Mod Well Drained Rangitikei Silt - Medium Moderately Well Occasional

9 Pasture - Silt-stony Rangitikei Silt Loam - Stony - Medium Moderately Well Occasional

10 Pasture - Mod well drained Recent Silt Loam - Medium Moderately Well

11 Pasture - Poorly Drained Gley Silt Loam - - Poor Occasional

4,5,6,8,9,10,11 Chicory 25ha

4,5,6,8,9,10,11 Oats 15ha

12 Stop Banks -Non productive NA NA NA NA NA NA

13 Stock Excluded NA NA NA NA NA NA

14 Non Productive NA NA NA NA NA NA

5.2.2.3 Enterprises

Impact: - BPDIS

The type and amount of animals on farm, their weight and the associated maintenance,

growth, gestation, lactation and production has a direct influence on metabolisable energy

requirements, which is used to determine pasture dry matter intake, which in turn directly

influences nutrient cycling between animals and pasture.

Recommendation: - BPDIS

Dairy Industry:

• Enter cows numbers by “Specify using peak cow number”.

• Do not check “Breeding numbers are constant”.

• Leave Mature cow weight as Overseer default (462 kg).

The difference in Cow numbers and production between consultants was not significant,

while replacement rates were the same. Consultant 1 and Consultant 2 defined the herd as

Jersey X Friesian while Consultant 3 defined the herd as Friesian. The cow’s weight was

defined by Consultant 1 as 500 kg, Consultant 2 as 525 kg and Consultant 3 used Overseer’s

defaults to define the cow’s weight, recommended by the ‘Best practice data input standards

manual’. The number of cows wintered of ranged from 34% by Consultant 1, 48% by

Consultant 2 and 50% by Consultant 3 (Table 14).

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Table 14: Animal data used in Overseer®

Consultant Cows Bread Weight Replacement Winter off Production Medium Calving

No. kg % % kg MS/yr Date

1 621 F x J 500 25 34 276,000 25-Jul

2 620 F x J 525 25 48 275,124 NA

3 620 F Default 25 50 275,124 18-Aug

5.2.2.4 Location & Climate

Impact: Location: - BPDIS

The location sets variable climate defaults and some animal characteristics

e.g. calving date.

Recommendation: Location: - BPDIS

1. Select location by region.

2. If your site has similar climatic conditions (i.e. temperature or rainfall) to the nearest town,

choose that option.

Impact: Climate: - BPDIS

Climatic variables such as rainfall and evaporation are critical inputs, which affect drainage

and therefore nutrient losses.

Recommendation: Climate: - BPDIS

Dairy Industry: Use latitude and longitude data from the farm dairy, enter to at least 3

decimal places.

Consultant 1 and Consultant 3 used option 1 for location; this required the region to be

selected for the location while Consultant 2 used option 2, nearest town (Table 15). For

rainfall Consultant 1 used 916mm, Consultant 2 used 937mm while Consultant 3 used

latitude and longitude data from the farm dairy location this also populated PET, mean temp,

recommended by the ‘Best practice data input standards manual’.

Table 15: Climate data used in Overseer®

Consultant Location Distance

from coast Daily rainfall pattern

settings Mean

Rainfall Mean Temp

PET (mm) PET

variation

1 Manawatu/Wanganui 5 731-1450 mm,low 916 12.2 - moderate

2 Palmerston North 10 - 937 13.3 801-950 mm/yr moderate

3 Manawatu/Wanganui 10 731-1450 mm,low 880 13.5 1024 Low

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5.2.3 Summary of consultant’s data

Consultant 1 and Consultant 2 used management of the farm area to define each block (Table

16), this was based on irrigation, effluent, non-irrigated and non-productive land, while

Consultant 3 defined the blocks using land uses, management systems and soil data, so the

total productive blocks went from four to eleven blocks and increase of seven blocks. The

irrigation, effluent, total farm land and effective farm area has not changed. However, there is

a large discrepancy in area used by all three consultants. The total N-loss across the whole

farm (Table 16) results in Consultant 1 with an N-loss to water 25 kg N/ha/year, Consultant 2

with an N-loss to water 32 kg N/ha/year and Consultant 3 with an N-loss to water 23 kg

N/ha/year.

Table 16: Summary of land area for all consultants with N-loss calculated

Consultant No. Blocks Irrigated Effluent Pasture Total area Effective area N loss to water

Productive ha ha ha ha ha kg N/ha/yr

1 4 131.5 40 54.7 232 226 25

2 4 110 40 68 230 218 32

3 11 98 38 86 238 217 23

5.2.4 Effects of climate change in Overseer

The calculation of N-loss of Consultant 1 and Consultant 2 was previously defined based on

individual interpretation of Overseer® ground rules. What impact does climate data have on

the calculation of N-loss of the whole farm? To answer this, consultant climate data was

changed to reflect the “Best Practice Data Input Standards” and used to calibrate 1 and 2

climate data. Consultant 1 went from 25 kg N/ha/year to 18 kg N/ha/year a reduction of 7 kg

N/ha/year or 28%, Consultant 2 went from 32 kg N/ha/year to 25 kg N/ha/year a reduction of

7 kg N/ha/year or 22%, and when fertiliser was adjusted 148 kg N/ha/year of N applied,

Consultant 2 reduced from 32 kg N/ha/year to 27 kg N/ha/year, changing climate data

reduced this further to 22 kg N/ha/year, a further reduction of 5 kg N/ha/year or 19%,

Consultant 3 used these input standards to define the Overseer® data with a result of 23 kg

N/ha/year (Table 17).

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Table 17: Comparison changing climate data in Overseer®

Consultant N loss to water kg N/ha/yr N loss to water kg N/ha/yr Change

Before Climate Change After Climate Change %

1 25 18 28%

2 32 25 22%

2 (fertiliser reduced) 27 22 19%

3 23 23 Default

5.2.5 Effects through changes in Overseer® versions

Over the course of this research there have been a number of Overseer® version changes and

version number formatting (e.g., V6.0 build 1, V6.1.3). To investigate the impact of N-loss

within Overseer®, three earlier versions and the current version of Overseer® have been

used. The versions are, Overseer® V6.0 build 1, Overseer® V6.0 build 3, Overseer® V6.1

build 1 and Overseer® V6.1.3. Two sets of each consultant’s data were used, the original

data and climate changed data. Consultant 1 with Overseer® V6.0 build 1 results in an N-loss

of 28 kg N/ha/year, Consultant 2 with 36 kg N/ha/year, while Consultant 3 produced an error,

this was a result of the cropping changes over the versions. The results are displayed in Table

18. Over the version changes and original data, Consultant 1 resulted in an N-loss reduction

of 10.7%, and Consultant 2 resulted in an N-loss reduction of 11.1%, with no change in

Consultant 3 N-loss. Using climate changed data, Consultant 1 resulted in an N-loss

reduction of 18.2%, and Consultant 2 resulted in an N-loss reduction of 16.7%, with no

change in Consultant 3 N-loss.

A comparison between data, original and climate change, this results in a drop in N-loss for

Consultant 1 of 35.7% and Consultant 2 a drop of N-loss of 30.6%.

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Table 18: the effects of Overseer® version changes on the case study farm

Before Climate Change C1 C2 C3

N loss to water kg N/ha/yr kg N/ha/yr kg N/ha/yr

Overseer® 6.0 Build 1 28 36 NA

Overseer® 6.0 Build 3 25 32 NA

Overseer® 6.1 build 1 25 32 23

Overseer® 6.1.3 25 32 23

Reduction by versions 10.7% 11.1% -

After Climate Change C1 C2 C3

N loss to water kg N/ha/yr kg N/ha/yr kg N/ha/yr

Overseer® 6.0 Build 1 22 30 NA

Overseer® 6.0 Build 3 20 30 NA

Overseer® 6.1 build 1 20 29 23

Overseer® 6.1.3 18 25 23

Reduction by versions 18.2% 16.7% -

Reduction from Before & after climate change 35.7% 30.6%

5.3 Whole farm modelling approach

5.3.1 Introduction

The combination of a whole farm system model and Overseer® provides a decision-making

tool that leads to a complete picture which should then lead to better decisions for the

stakeholder as opposed to any one of these tools in isolation.

The decision-making tool GSL LP has been chosen as the whole farm model. The main

reason for using the optimising model GSL LP was the potential to maximize profit by

determining optimal resource allocation using constraints and then perform as many snap

shots as required to compare and contrast the differences in performance economically and

environmentally.

Farm operating surplus (surplus) is defined as the difference between returns from milk sales

and working expenses (costs). Working expenses relate to direct costs of production and

exclude overheads and financial costs not normally quantified to specific activities of daily

farm production. Working expenses is converted into a per cow cost while resources that

influenced the optimisation is independently allocated as per unit increment when required,

this included fertiliser, purchase of off-farm feed, cropping, grazing, conservation of silage.

67

The analysis is based on a whole farm forecast, modelled covering the next 12 month period,

for this reason depreciation, family income, taxation, capital items and loan (principle and

interest) are not included in this analysis.

The model comprises a feed supply and a feed requirement component, with feed

management activities linking supply and consumption. The year is divided into 26 periods,

allowing management decisions to be made every 2 weeks.

A case study farm using 2013-2014 seasonal data was used and formed the basis to develop a

base system within the GSL LP. From this, a further nine runs could then be compared to the

base to see how the farm performs financially and environmentally. Consultant 3 Overseer®

data formed the bases to develop N-loss of the case study farm.

The permissible One Plan N-loss limits for the cases study farm (Table 8) outlines the N-loss

limits targets at specific time periods. The farm already meets year one N-limits of 26.9 kg

N/ha/year and year five N-limits of 24.3 kg N/ha/year with the current farm N-loss of 23 kg

N/ha/year. However the farm needs to reduce N-loss limits to meet the year 10 limits of 22.1

kg N/ha/year a reduction of 0.9 kg N/ha/year and year 20 limits of 21.2 kg N/ha/year a

reduction of 1.8 kg N/ha/year.

Over the last few years we have seen record paid-out for Milk solids (MS), this season (2014-

2015) there has been a sharp drop, therefore a pay-out of $6.00 kg/MS has been assumed,

with maize silage and PKE set at $350 per tonne.

5.3.2 GSL scenarios explained

5.3.2.1 Run one

The base was defined using farm inputs and financial data from the case study farm.

However, milk production and MS/cow results come in lower than the actual milk production

for the season. The reasoning for this, GSL required actual data, pasture data is synthetic as

no actual monitoring of pasture growth rates have been undertaken, supplements have also

been fixed so the model can only use feed based on limits given. This farm system runs 620

cows, production is 268,695 kg MS/year, producing 433 kg MS/cow at a stocking rate of 2.9

cows/ha. Cropping is on 25 ha with 15 ha been converted into winter Oats each season,

240,000 kg’s of maize silage and 220,000 kg’s palm kernel extract (PKE) being used, no

supplements are made on the farm. In winter 50% of the herd is wintered off farm and all

replacements are grazed off farm all year. Income is $1,740,322 (based on $6.00 MS pay-out)

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with costs of $1,077,834 result in a surplus of $662,489. This results in an N-loss of 23 kg

N/ha/year over the whole farm with an N conversion efficiency of 43%.

5.3.2.2 Run two

A reduction of N excreted (“Nx”) by 7.5% over the base system and optimised by cow

numbers was used for this run. This farm system runs 521 cows, production is 237,895 kg

MS/year, producing 457 kg MS/cow at a stocking rate of 2.4 cows/ha. Cropping is on 25 ha

with 15 ha been converted into winter Oats each season, no supplements are made on farm or

purchased. In winter, 50% of the herd is wintered off farm and all replacements are grazed off

farm all year. Income is $1,535,622 with costs of $771,651 result in a surplus of $763,971.

This results in an N-loss of 22 kg N/ha/year over the whole farm with an N conversion

efficiency of 45%.

5.3.2.3 Run three

A reduction of Nx by 15% over the base system, optimised by cow numbers and removing

spring nitrogen fertiliser from 217 ha was used for this run. This farm system runs 490 cows,

production is 223,579 kg MS/year, producing 456 kg MS/cow at a stocking rate of 2.3

cows/ha. Cropping is on 25 ha with 15 ha been converted into winter Oats each season, no

supplements are made on farm or purchased. In winter,50% of the herd is wintered off farm

and all replacements are grazed off farm all year. Income is $1,443,275 with costs of

$703,556 result in a surplus of $739,719. This results in an N-loss of 20 kg N/ha/year over

the whole farm with an N conversion efficiency of 54%.

5.3.2.4 Run four

A reduction of Nx by 15% over the base system and optimised by cow numbers, removing

Oats from the farm system and removing nitrogen fertiliser from 217 ha was used for this

run. This farm system runs 478 cows, production is 218,356 kg MS/year, producing 457 kg

MS/cow at a stocking rate of 2.2 cows/ha. Cropping is on 25 ha, 30,960 kg grass silage is

made on farm and 47,123 kg PKE is purchased. In winter, 50% of the herd is wintered off

farm and all replacements are grazed off farm all year. Income is $1,409,546 with costs of

$668,123 result in a surplus of $741,423. This results in an N-loss of 13 kg N/ha/year over

the whole farm with an N conversion efficiency of 54%.

5.3.2.5 Run five

A reduction of Nx by 15% over the base system and optimised by cow age preference and

cow numbers, removing Oats from the farm system and removing nitrogen fertiliser from 217

ha was used for this run. This farm system runs 479 cows, production is 218,477 kg MS/year,

69

producing 456 kg MS/cow at a stocking rate of 2.2 cows/ha. Cropping is on 25 ha; no other

supplements are made on farm or purchased. In winter, 50% of the herd is wintered off farm

and all replacements are grazed off farm all year. Income is $1,410,359 with costs of

$635,079 result in a surplus of $775,280. This results in an N-loss of 13 kg N/ha/year over

the whole farm with an N conversion efficiency of 53%.

5.3.2.6 Run six

A reduction of Nx by 20% over the base system and optimised by cow age preference and

cow numbers, removing all cropping from the farm system and removing nitrogen fertiliser

from 217 ha was used for this run. This farm system runs 426 cows, production is 194,654 kg

MS/year, producing 457 kg MS/cow at a stocking rate of 2.0 cows/ha. 59,676 kg grass silage

is made, no other supplements purchased. In winter, 50% of the herd is wintered off farm and

all replacements are grazed off farm all year. Income is $1,256,564 with costs of $498,343

result in a surplus of $758,221. This results in an N-loss of 9 kg N/ha/year over the whole

farm with an N conversion efficiency of 50%.

5.3.2.7 Run seven

A reduction of Nx by 25% over the base system and optimised by cow age preference and

cow numbers, removing all cropping from the farm system and removing nitrogen fertiliser

from 217 ha was used for this run. This farm system runs 406 cows, production is 185,423 kg

MS/year, producing 457 kg MS/cow at a stocking rate of 1.9 cows/ha. 10,766 kg grass silage

is made and 90,767 kg grass silage is discarded, no other supplements purchased. In winter,

50% of the herd is wintered off farm and all replacements are grazed off farm all year.

Income is $1,196,918 with costs of $473,431 result in a surplus of $723,487. This results in

an N-loss of 9 kg N/ha/year over the whole farm with an N conversion efficiency of 54%.

5.3.2.8 Run eight

A reduction of Nx by 30% over the base system and optimised by cow age preference and

cow numbers, removing all cropping from the farm system and removing nitrogen fertiliser

from 217 ha was used for this run. This farm system runs 379 cows, production is 172,888 kg

MS/year, producing 456 kg MS/cow at a stocking rate of 1.7 cows/ha. No grass silage is

made and 237,838 kg grass silage is discarded, no other supplements purchased. In winter,

50% of the herd is wintered off farm and all replacements are grazed off farm all year.

Income is $1,116,037 with costs of $454,792 result in a surplus of $661,245. This results in

an N-loss of 9 kg N/ha/year over the whole farm with an N conversion efficiency of 60%.

70

5.3.2.9 Run nine

A reduction of Nx by 35% over the base system and optimised by cow age preference and

cow numbers, removing all cropping from the farm system and removing nitrogen fertiliser

from 217 ha was used for this run. This farm system runs 351 cows, production is 160,359 kg

MS/year, producing 457 kg MS/cow at a stocking rate of 1.6 cows/ha. No grass silage is

made and 389,084 kg grass silage is discarded, no other supplements purchased. In winter,

50% of the herd is wintered off farm and all replacements are grazed off farm all year.

Income is $1,035,155 with costs of $438,723 result in a surplus of $596,432. This results in

an N-loss of 9 kg N/ha/year over the whole farm with an N conversion efficiency of 65%.

5.3.2.10 Run ten

A reduction of Nx by 40% over the base system and optimised by cow age preference and

cow numbers, removing all cropping from the farm system and removing nitrogen fertiliser

from 217 ha was used for this run. This farm system runs 319 cows, production is 145,625 kg

MS/year, producing 457 kg MS/cow at a stocking rate of 1.5 cows/ha. No grass silage is

made and 429,989 kg grass silage is discarded, no other supplements purchased. In winter,

50% of the herd is wintered off farm and all replacements are grazed off farm all year.

Income is $940,021 with costs of $409,395 result in a surplus of $530,626. This results in an

N-loss of 8 kg N/ha/year over the whole farm with an N conversion efficiency of 67%.

71

Table 19: Case study farm GSL run.

Production Scenarios

Case study farm analysis

Base Farm Nx - 7.5% Nx - 15% 4 Nx - 15% Nx -15% Nx - 20% Nx -25% Nx - 30% Nx -35% Nx -40%

118ha Non Irrg Opt Cows Opt Cows Opt Cows Opt Cows Opt Cows Opt cows Opt cows Opt cows Opt cows

99 ha Irrg No Oats No Oats No Oats, Chicory

No Oats, Chicory

No Oats, Chicory

No Oats, Chicory No Oats, Chicory

35 ha efflnt N appln100 kg/ha Opt N 1 - 100 Opt N 1 - 100 No N No N No N No N No N No N

89627 NX 82509 NxLmt 76183 NxLmt 76183 NxLmt 76183 NxLmt 70447 NxLmt 67220NxLmt 62739 NxLmt 58258 NxLmt 53776 NxLmt

Run 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

Total KG MilkSolids 268,695 237,895 223,579 218,356 218,477 194,654 185,423 172,888 160,359 145,625

Income $1,740,323 $1,535,622 $1,443,275 $1,409,546 $1,410,359 $1,256,564 $1,196,918 $1,116,037 $1,035,155 $940,021

Farm Working Exps $1,077,834 $771,651 $703,556 $668,123 $635,079 $498,343 $473,431 $454,792 $438,723 $409,395

Farm Operating Surplus $ $662,489 $763,971 $739,719 $741,423 $775,280 $758,221 $723,487 $661,245 $596,432 $530,626

Number Milking Cows 620 521 490 478 479 426 406 379 351 319

Milking Cows per Ha 433 457 456 457 456 457 457 456 457 457

Kg/Milk Solids/Cow 433 457 456 457 456 457 457 456 457 457

Farm Production Ratios per Hectare

Farm Operating Surplus /Ha $ 3,053 3,521 3,409 3,417 3,573 3,494 3,334 3,047 2,749 2,445

Milking Cows/Ha 2.9 2.4 2.3 2.2 2.2 2.0 1.9 1.7 1.6 1.5

Kg MS/Ha 1,238 1,096 1,030 1,006 1,007 897 854 797 739 671

Total N Excreted (Urine) Nx 89,627 68,991 76,183 76,183 70,447 70,447 67,220 62,739 58,258 53,776

Total N Retained 19,070 13,630 15,842 15,472 15,481 13,789 13,134 12,247 11,359 10,235

KG N Leach/Ha (Overseer) 23 22 20 13 13 9 9 9 9 8

N Conversion Efficiency % 43 45 54 54 53 50 54 60 65 67

Total GHG Emissions (CO2) 15580 13917 10936 10511 10478 9372 8916 8520 8139 7540

72

5.3.3 Chart comparison to run one

To show how the farm would perform over each run, the GSL LP data is plotted, this allowed

a comparison of each run using Figure 14, Figure 15 and Figure 16. The One Plan

permissible N-Loss of the farm has been set at the 20 year limit, 21.2 kg N/ha//year. The milk

solid production per cow went from 433 kg MS/cow and stabilised around the 456 kg

MS/cow to 457 kg MS/cow for the rest of the runs.

5.3.3.1 Run one – base system

The base system, run one, had the highest number of cow (620 cows or 2.9 cows/ha),

producing the most milk (268,695 kg/MS), with the highest income ($1,740,323), highest

expenses ($1,077,834) and only produced surpluses ($662,489) higher than run eight

($661,245), nine ($596,432) and ten ($530,626), while N-loss was the highest (23 kg

N/ha/year) and produced the lowest N conversion efficiency (43%) of all runs.

5.3.3.2 Run two

In comparison to run one, run two has a reduction of 99 cows (2.4 cows/ha), reduction in

farm working expenses by $306,183 and decreased production by 30,800 kg MS for the

season, this reduced income by $184,800 for MS and a further $19,901 in livestock sales,

total income dropped by $204,701 resulting in a surplus of $101,482, in comparison to the

base run. The N conversion efficiency increased by 2% to 45% and reduced N-loss by 1 kg

N/ha/year to 22 kg N/ha/year, the farm now surpasses the 10 year N-loss limits of 22.1 kg

N/ha/year by 0.1 kg N/ha/year.

5.3.3.3 Run three

In comparison to run one, run three has a reduction of 130 cows (2.3 cows/ha), reduction in

farm working expenses by $374,278 and decreased production by 45,116 kg MS for the

season, this reduced income by $270,696 for MS and a further $26,352 in livestock sales,

total income dropped by $297,048 resulting in a surplus of $77,320, in comparison to the

base run. The N conversion efficiency increased by 11% to 54% and reduced N-loss by 3 kg

N/ha/year to 20 kg N/ha/year, the farm now surpasses the 20 year N-loss limits of 21.2 kg

N/ha/year by 1.2 kg N/ha/year.

5.3.3.4 Run four

No nitrogen fertiliser utilised for the rest of the runs, 15 ha of Oats removed, 31 Tonnes of

grass silage made and 47 Tonnes of PKE purchased. In comparison to run one, run four has a

reduction of 142 cows (2.2 cows/ha), reduction in farm working expenses by $409,711 and

73

decreased production by 50,339 kg MS for the season, this reduced income by $302,034 for

MS and a further $28,743 in livestock sales, total income dropped by $330,777 resulting in a

surplus of $78,934, in comparison to the base run. The N conversion efficiency increased by

11% to 54% and reduced N-loss by 10 kg N/ha/year to 13 kg N/ha/year, the farm now

surpasses the 20 year N-loss limits of 21.2 kg N/ha/year by 11.2 kg N/ha/year.

5.3.3.5 Run five

Run five utilises grown pasture and 25 ha of chicory. In comparison to run one, run five has a

reduction of 141 cows (2.2 cows/ha), reduction in farm working expenses by $442,755 and

decreased production by 50,218 kg MS for the season, this reduced income by $301,308 for

MS and a further $28,656 in livestock sales, total income dropped by $329,964 resulting in a

surplus of $112,791, in comparison to the base run. The N conversion efficiency increased by

10% to 53% and reduced N-loss by 10 kg N/ha/year to 13 kg N/ha/year, the farm now

surpasses the 20 year N-loss limits of 21.2 kg N/ha/year by 11.2 kg N/ha/year.

5.3.3.6 Run six

All cropping has been removed for the rest of the runs, 60 Tonnes of grass silage is made. In

comparison to run one, run six has a reduction of 194 cows (2.0 cows/ha), reduction in farm

working expenses by $579,491 and decreased production by 74,041 kg MS for the season,

this reduced income by $444,246 for MS and a further $39,513 in livestock sales, total

income dropped by $483,759 resulting in a surplus of $95,732, in comparison to the base run.

The N conversion efficiency increased by 7% to 50% and reduced N-loss by 14 N/ha/year to

9 kg N/ha/year, the farm now surpasses the 20 year N-loss limits of 21.2 kg N/ha/year by

12.2 kg N/ha/year.

5.3.3.7 Run seven

11 Tonnes of grass silage is made and 91 Tonnes of pasture is discarded. In comparison to

run one, run seven has a reduction of 214 cows (1.9 cows/ha), reduction in farm working

expenses by $604,403 and decreased production by 83,272 kg MS for the season, this

reduced income by $499,632 for MS and a further $43,773 in livestock sales, total income

dropped by $543,405 resulting in a surplus of $60,998, in comparison to the base run. The N

conversion efficiency increased by 11% to 54% and reduced N-loss by 14 N/ha/year to 9 kg

N/ha/year, the farm now surpasses the 20 year N-loss limits of 21.2 kg N/ha/year by 12.2 kg

N/ha/year.

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5.3.3.8 Run eight

No grass silage is made and 238 Tonnes of pasture is discarded. In comparison to run one,

run eight has a reduction of 241 cows (1.7 cows/ha), reduction in farm working expenses by

$623,042 and decreased production by 95,807 kg MS for the season, this reduced income by

$574,842 for MS and a further $49,444 in livestock sales, total income dropped by $624,286

resulting in a deficit of $1,244, in comparison to the base run. The N conversion efficiency

increased by 17% to 60% and reduced N-loss by 14 N/ha/year to 9 kg N/ha/year, the farm

now surpasses the 20 year N-loss limits of 21.2 kg N/ha/year by 12.2 kg N/ha/year.

5.3.3.9 Run nine

No grass silage is made and 389 Tonnes of pasture is discarded. In comparison to run one,

run nine has a reduction of 269 cows (1.6 cows/ha), reduction in farm working expenses by

$639,111 and decreased production by 108,336 kg MS for the season, this reduced income by

$650,016 for MS and a further $55,152 in livestock sales, total income dropped by $705,168

resulting in a deficit of $66,057, in comparison to the base run. The N conversion efficiency

increased by 22% to 65% and reduced N-loss by 14 N/ha/year to 9 kg N/ha/year, the farm

now surpasses the 20 year N-loss limits of 21.2 kg N/ha/year by 12.2 kg N/ha/year.

5.3.3.10 Run ten

No grass silage is made and 430 Tonnes of pasture is discarded. In comparison to run one,

run ten has a reduction of 323cows (1.5 cows/ha), reduction in farm working expenses by

$668,439 and decreased production by 123,070 kg MS for the season, this reduced income by

$738,420 for MS and a further $61,882 in livestock sales, total income dropped by $800,302

resulting in a deficit of $131,863, in comparison to the base run. The N conversion efficiency

increased by 24% to 67% and reduced N-loss by 15 N/ha/year to 8 kg N/ha/year, the farm

now surpasses the 20 year N-loss limits of 21.2 kg N/ha/year by 13.2 kg N/ha/year.

75

Figure 14: GSL plotted runs with comparison of N leaching, cow numbers and production and N efficiency

Figure 15: GSL plotted runs with comparison of N leaching, N efficiency and cash surplus

1 2 3 4 5 6 7 8 9 10

NoCows 620 521 490 478 479 426 406 379 351 319

kgMS/cow 433 457 456 457 456 457 457 456 457 457

N leached Kg/N/Ha 23 22 20 13 13 9 9 9 9 8

One Plan N-Limits 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2

N Conversion Efficiency 43 45 54 54 53 50 54 60 65 67

23 2220

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1 2 3 4 5 6 7 8 9 10

$ Farm Operating Surplus 662,489 763,971 739,719 741,423 775,280 758,221 723,487 661,245 596,432 530,626

MS Prodn Kg/Ms 268,695 237,895 223,579 218,356 218,477 194,654 185,423 172,888 160,359 145,625

N leached Kg/N/Ha 23 22 20 13 13 9 9 9 9 8

One Plan N-Limits 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2 21.2

N Conversion Efficiency 43 45 54 54 53 50 54 60 65 67

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N-loss with $ Farm Operating Surplus and milk production on case farm

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Figure 16: GSL Case study farm surplus/ha for each run

5.3.4 Summary

The completion of the ten runs demonstrated that six out of ten runs provided a higher surplus

and lower N-loss across the whole farm with run five providing the highest surplus of all

runs. For this reason run five has been selected to perform a comparison with different milk

solid pay-outs.

5.4 Comparison with changes in milk solid pay-out

The comparison was performed using a range of forecast milk solid pay-outs, based a range

of milk solid pay-out over the last ten years (Table 20), this included four dollars, six dollars

and eight dollars. A sensitivity analysis was not appropriate for this analysis as it cannot

optimise and allocate resources based on marginal returns. For this reason the GSL LP was

used to perform this analysis.

Table 20: Ten year milk solid payout

Season 2005-2006

2006-2007

2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

2012-2013

2013-2014

2014-2015

$ kg MS

4.10 4.46 7.66 5.20 6.37 7.90 6.40 6.16 8.50 5.65

The base run (one) and run five has been used with a change in PKE price to reflect the

correlation that has been seen with milk prices over the last few seasons. The base run was

run twice, this was to reflect the current 620 cows and then to optimise cows, also nitrogen

1 2 3 4 5 6 7 8 9 10

$ Surplus /Ha 3,053 3,521 3,409 3,417 3,573 3,494 3,334 3,047 2,749 2,445

KG N Leach/Ha (Overseer) 23 22 20 13 13 9 9 9 9 8

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N-loss with $ Farm Operating Surplus /Ha on case farm

77

fertiliser was restricted to a range of 0 kg N/ha to 25 kg N/ha, supplements import limits have

been opened up to 2,000 tonnes. The base system did not have a constraint on N excreted

(Nx), while run five had a maximum N excreted of 76,183.

Each run (Table 21) demonstrate how a change in the forecast milk solid pay-out impacts on

the whole farm system, financially and physically. The changes to the whole farm system was

then modelled using Overseer® and plotted to see the correlation to the farm operating

surplus over individual runs (Figure 17 and Figure 18).

5.4.1.1 Run one – base system: - Pay-out $6.00

The base system, run one used 240 Tonnes of maize silage and 220 Tonnes of PKE, with a

herd size of 620 cows (2.9 cows/ha), producing 268,695 kg/MS (1,238 kg MS/ha), with an

income $1,740,323, expenses $1,077,834 and a surplus of $662,489 ($3,052/ha), while N-

loss was 23 kg N/ha/year and N conversion efficiency 43%.

5.4.1.2 Run two– base system with optimal cows: - Pay-out $6.00

Run two did not require supplements and optimised the herd at 528 cows (2.4 cows/ha),

producing 241,133 kg/MS (1,111 kg MS/ha), with an income $1,556,556, expenses $771,194

and a surplus of $785,362 ($3,619/ha), while N-loss was 23 kg N/ha/year and N conversion

efficiency 42%.

5.4.1.3 Run three– Run five with optimal cows: - Pay-out $6.00

Run three did not require supplements and optimised the herd at 479 cows (2.2 cows/ha),

producing 218,477 kg/MS (1,007 kg MS/ha), with an income $1,410,359, expenses $635,079

and a surplus of $775,280 ($3,572/ha), while N-loss was 13 kg N/ha/year and N conversion

efficiency 53%.

5.4.1.4 Run four– base system: - Pay-out $8.00

Run four used 563,921 Tonnes of maize silage with a herd size of 620 cows (2.9 cows/ha),

producing 283,032 kg/MS (1,304 kg MS/ha), with an income $2,393,097, expenses

$1,114,611 and a surplus of $1,278,486 ($5,891/ha), while N-loss was 23 kg N/ha/year and N

conversion efficiency 44%.

5.4.1.5 Run five– base system with optimal cows: - Pay-out $8.00

Run five used 2,000,000 Tonnes of PKE and optimised the herd at 754 cows (3.5 cows/ha),

producing 344,370 kg/MS (1,587 kg MS/ha), with an income $2,911,699, expenses

$1,632,300 and a surplus of $1,279,399 ($5,895/ha), while N-loss was 24 kg N/ha/year and N

conversion efficiency 45%.

78

5.4.1.6 Run six– Run five with optimal cows: - Pay-out $8.00

Run six used 82 Tonnes of maize silage and optimised the herd at 482 cows (2.2 cows/ha),

producing 219,866 kg/MS (1,013 kg MS/ha), with an income $1,859,036, expenses $640,211

and a surplus of $1,218,825 ($5,616/ha), while N-loss was 13 kg N/ha/year and N conversion

efficiency 52%.

5.4.1.7 Run seven– base system: - Pay-out $4.00

Run seven used 435 Tonnes of PKE with a herd size of 620 cows (2.9 cows/ha), producing

274,739 kg/MS (1,266 kg MS/ha), with an income $1,227,323, expenses $1,021,097 and a

surplus of $206,226 ($950/ha), while N-loss was 24 kg N/ha/year and N conversion

efficiency 37%.

5.4.1.8 Run eight– base system with optimal cows: - Pay-out $4.00

Run eight did not require supplements and optimised the herd at 455 cows (2.1 cows/ha),

producing 207,881 kg/MS (958 kg MS/ha), with an income $926,144, expenses $580,363 and

a surplus of $345,781 ($1,593/ha), while N-loss was 21 kg N/ha/year and N conversion

efficiency 41%.

5.4.1.9 Run nine– Run five with optimal cows: - Pay-out $4.00

Run nine did not require supplements and optimised the herd at 427 cows (2.0 cows/ha),

producing 181,246 kg/MS (835 kg MS/ha), with an income $807,487, expenses $411,261 and

a surplus of $396,226 ($1,825/ha), while N-loss was 11 kg N/ha/year and N conversion

efficiency 53%.

5.4.2 Summary

The analysis demonstrates that some form of mitigation constraints need to be implemented

in the model to achieve reduced N-loss across the whole farm system. In comparison to the

base run there have been no costings for an increase/decrease in labour units on any of these

runs and no costings for any new infrastructure that might be required if the herd increases

above the base system.

79

Table 21: GSL Comparison with different MS Pay-out

Production Scenarios

Case Study Farm

Base-MS $6 Base MS $6 Run 5 MS $6 Base MS $8 Base MS $8 Run 5 MS $8 Base MS $4 Base MS $4 Run 5 MS $4

118ha Non Irrg Opt Cows Opt cows 620 Cows Opt Cows Opt cows 620 Cows Opt Cows Opt cows

99 ha Irrg No Oats, Chicory No Oats, Chicory No Oats, Chicory

35 ha efflnt Opt N 1 - 120 No N Opt N 1 - 120 Opt N 1 - 120 No N Opt N 1 - 120 Opt N 1 - 120 No N

89627 NX 76183 NxLmt 67220NxLmt

76183 NxLmt 76183 NxLmt 67220NxLmt

82509 NxLmt

70447 NxLmt 67220NxLmt

Run 1 2 3 4 5 6 7 8 9

Total KG MilkSolids 268,695 241,133 218,477 283,032 344,370 219,866 274,739 207,881 181,246

Income $1,740,323 1,556,556 $1,410,359 $2,393,097 $2,911,699 $1,859,036 $1,227,323 $926,144 $807,487

Farm Working Exps $1,077,834 $771,194 $635,079 $1,114,611 $1,632,300 $640,211 $1,021,097 $580,363 $411,261

Farm Operating Surplus $ $662,489 $785,362 $775,280 $1,278,486 $1,279,399 $1,218,825 $206,226 $345,781 $396,226

Livestock

Number Milking Cows 620 528 479 620 754 482 620 455 427

KG N Leach/Ha 23 23 13 23 24 13 24 21 11

N Conversion Efficiency % 43 42 53 44 45 52 37 41 53

Farm Production Ratios per Hectare

Farm Operating Surplus /Ha $ $3,052 $3,619 $3,572 $5,891 $5,895 $5,616 $950 $1,593 $1,825

Milking Cows/Ha 2.9 2.4 2.2 2.9 3.5 2.2 2.9 2.1 2.0

Kg MS/Ha 1,238 1,111 1,007 1,304 1,587 1,013 1,266 958 835

Maize Silages Purchased KG/DM 240,000 563,921 81,288

PKE Purchased KG DM 220,000 2,000,000 434,306

80

Figure 17: GSL Comparison with different MS Pay-out

Figure 18: GSL Comparison with different MS Pay-out /ha

Base-MS$6

Base MS$6

Run 5 MS$6

Base MS$8

Base MS$8

Run 5 MS$8

Base MS$4.0

Base MS$4.0

Run 5 MS$4

$ Surplus 662,489 785,362 775,280 1,278,486 1,279,399 1,218,825 206,226 345,781 396,226

N leachedKg/N/Ha

23 23 13 23 24 13 24 21 11

23 23

13

23 24

13

24

21

11

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

1,100,000

1,200,000

1,300,000

1,400,000

kg

N l

oss

/ha

/ye

ar

$ F

arm

Op

era

tin

g S

urp

lus

N-loss with $ Farm Operating Surplus on case farm

Base-MS$6

Base MS$6

Run 5 MS$6

Base MS$8

Base MS$8

Run 5 MS$8

Base MS$4.0

Base MS$4.0

Run 5 MS$4

$ Surplus /Ha $3,052.94 $3,619.18 $3,572.72 $5,891.64 $5,895.85 $5,616.71 $950.35 $1,593.46 $1,825.93

N leached Kg/N/Ha 23 23 13 23 24 13 24 21 11

23 23

13

23 24

13

24

21

11

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

$0.00

$500.00

$1,000.00

$1,500.00

$2,000.00

$2,500.00

$3,000.00

$3,500.00

$4,000.00

$4,500.00

$5,000.00

$5,500.00

$6,000.00

$6,500.00

kg

N l

oss

/ha

/ye

ar

$ F

arm

Op

era

tin

g S

urp

lus

/Ha

N-loss with $ Farm Operating Surplus /Ha on case farm

81

6 Discussion

6.1 Obtaining an N-loss using Overseer®

To evaluate the complexity of obtaining a N-loss measurement for the case study farm

three consultants’ Overseer® data files were evaluated. Shepherd et al., (2013) stated that

users supply actual and reasonable inputs, so all three consultants’ N-loss limits results

should be the same.

Overseer® operates at a block level according to variations in soil type and/or

management history of the farm. Wheeler & Shepard, (2013) stated that the main inputs

that have the most influence on nutrient loss estimates in Overseer® are those that

influence the transport of a nutrient, (e.g., soil, drainage, slope for P). Consultant 1 and

Consultant 2 used the management history of the farm to define the blocks within

Overseer®; soil data was only used within these blocks, and has not influenced how the

blocks are set up within farm level. This is in contrast to GHD, (2009); Stafford &

Peyroux, (2013) and Williams et al., (2011), who iterate that each farm must have

constructed a robust nutrient budget. One possible reason soil data was not used for the

development of the blocks could be the reliability, accuracy and scale of soil information

available. Carrick et al., (2014) stressed, that soil information is critical and can be

provided at any nominal scale, with no quality indication as regards the accuracy or

uncertainty of the mapping, the ‘Best Practice Data Input Standards’ does not enforce the

method to be used. S-map is typically 1:50,000 and possibly the only soil data that

Consultant 1 and Consultant 2 had access to, where Carrick et al., (2014), defines quality

for soil mapping, a scale of 1:50,000 is classed as poor quality, while farm-scale soil map

at 1:10,000 is of good quality. In contrast to Consultant 1 and Consultant 2, advocated by

Carrick et al., (2014), Manderson & Palmer, (2006) Consultant 3 had access to reliable

and accurate soil information on the farm at a scale of 1:6,000.

From Dairybase physical details season ending 2012 cow numbers were 620. This is

current today, with the herd listed as Friesian. Both Consultant 1 and Consultant 2, in

Table 14, selected Friesian X Jersey, this increased the N-loss using Consultant 1 data

from 25 kg N/ha/year to 26 kg kg N/ha/year but Consultant 2 N-loss was unchanged.

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Consultant 3 in comparison, recevied confirmation from the farmer, referred to the

DairyBase report and selected Friesian as the breed.

Everest, (2013), Park, (2014), Paterson et al., (2014), Wheeler & Shepard, (2013) and

Wheeler et al., (2007) stated that Good Management Practices (GMP) need to be

followed to avoid underestimating nutrient losses using the model so when developing

plans a method is required to identify any breaches and to remedy.

Consultant 1 used three even applications of 33 kg N/ha, resulting in 85 kg N/ha/year and

Consultant 3 used four applications of 25 kg N/ha, resulting in 81 kg N/ha/year. There

was not much difference in application rate between Consultant 1 and Consultant 3; this

could reflect different seasons requiring different application rates and/or frequencies.

Consultant 2 varied application rates, the highest application of 112 kg N/ha and the

lowest application of 8 kg N/ha in a given month, resulting in 179 kg N/ha/year. Good

Management Practices must reflect complying with supplier regulations and local

government law, such as those with the Fertiliser Code of Practice (Everest, 2013; Park,

2014; Paterson et al., 2014; Wheeler & Shepard, 2013; Wheeler et al., 2007). However,

Consultant 2 used high application rates which reflect ‘poor management’ that would

result in large discharges and does not reflect the current farm management system. If

Consultant 2 fertiliser application rates changed to 33 kg N/ha, resulting in 148 kg

N/ha/year of N applied, a reduction of 31 kg N/ha/year, N-loss over the farm would drop

from 32 kg N/ha/year to 27 kg N/ha/year, a drop of 5 kg N/ha/year, this is in line with the

farm owner management strategy and would be considered, actual and reasonable inputs

and within the compliance of the Fertiliser Code of Practice.

Climate data needs to reflect long term averages, Wheeler & Shepard, (2013) stated that

drainage is a key driver of N loss and it is important to recognise that this calculation is

sensitive to climate inputs; predominately rainfall and potential evapotranspiration. The

variation in rainfall used by Consultant 1 and Consultant 2 did not reflect the local virtual

weather station closest to the farm while Consultant 3 used the closest virtual station to

obtain 30 years of rainfall and PET. This now corresponds with the values that can be

obtained in the latest version of Overseer® climate station tool to obtain climate data.

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6.1.1 Effects of climate change in Overseer

Roberts & Watkins, (2014) advocated that the purpose of providing a ‘Best Practice Data

Input Standards’ is to reduce inconsistencies between different users, changing the

consultants climate data has changed the N-loss. However, without reliable and accurate

soil information, and using this soil information along with the management history of the

farm to define the blocks within Overseer® and different climate data, it is unlikely that

Consultant 1 and Consultant 2 would deliver the same results as Consultant 3. With

Overseer® V6.1.3 using ‘Best Practice Data Input Standards’, consultants now have

access to consistent climate data.

The case study farm has been using crops for a number of years. Consultant 1 refrained

from using crops as this would have had an impact on the lower N-loss recorded

compared to Consultant 2 and Consultant 3. This is in conflict with Overseer® ground

rules (Wheeler & Shepard, 2013, p. 4) and would result in underestimate of nutrient

losses. Users of Overseer® are responsible for providing accurate and reliable data but if

a consultant interprets information differently then Overseer® results will be

questionable. Keith et al., (1983) defines verification as the general process used to decide

whether a method in question is capable of producing accurate and reliable data. Keith et

al., (1983) also refers accuracy to the correctness of the data, and then he states, “There is

no general agreement as to how accuracy is evaluated”.

6.1.2 Effects through changes in Overseer® versions

The model is a science based decision tool (Shepherd & Wheeler, 2012), and is required

to be calibrated and/or validated against measured data. It is logical that over time, with

field trials, better understanding of science, new mitigations, bug fixes each version

change could produce different N-loss results; this is supported by Shepherd et al., (2013)

and Park, (2014) that uncertainty will decrease as more data is calibrated and/or validated.

This is demonstrated in this study, Consultant 1 and Consultant 2 from version 6.0 build 1

to version 6.1.3 a reduced N-loss by 10.7% and 11.1% respectively, and with the climate

data changed, N-loss drop between versions by 18.2% and 16.7%.

Bell, (2013), based his report on an earlier version of Overseer®, this study highlights, if

the analysis was run with the latest Overseer® V6.1.3, possible a different conclusion

84

would have presented itself. Shepherd & Wheeler, (2012) describe a vision of Overseer®

as a robust, science-based decision support tool and policy support tool, with an uncertain

N-loss with every release of Overseer®. Unreliable N-loss results across versions could

result in legal issues when Overseer® is placed as a policy tool (Edmeades et al., 2013).

6.2 Whole farm modelling approach

New Zealand has an average stocking rate of 2.83 cows/ha, the case study farm has a

stocking rate of 2.9 cows/ha, with high supplements and farm resources requirements.

With the current farm N-loss of 23 kg N/ha/year, and with the use of GSL LP

optimisation model, not all of the mitigation options would be required. There was no

additional expenditure on infrastructure; when an effluent storage system was included in

Overseer®, N-loss was unchanged, a feed pad or standoff shelter also was not considered,

due to the capital expenditure and the current N-loss of the farm.

There are numerous ways a farmer could reduce N-loss. For example, a standoff facility

with duration-controlled grazing, as advocated by Christensen et al., (2011), reduced

NO3--N leaching by more than 50%. However, no financial analysis is provided by

Christensen et al., (2011) of the costs associated with this N-loss. De Klein & Monaghan,

(2005) suggests monetary benefits do not necessary flow from such investments.

To provide a standoff shelter, including infrastructure on the case study farm would cost

approximately $1,125 per cow, with 620 cows, $697,500 and effluent system, $125,000

total cost of $822,500 would need to be spent. The GSL LP run five by comparison

achieved a reduction of 43% in N-loss, an increase in surplus of $112,791, plus in the first

year the proceeds of shares & cow of $543,036, a total surplus of $655,827 above the

base system.

It was difficult to measure the impact of fertiliser application rates and timing in this GSL

analysis. In most runs N fertiliser was completely removed, the focus was on improving

nitrogen use efficiency by both plants and livestock. As described by Groot et al., (2003),

Ledgard, (1991) and Stout & Jung, (1992), fertiliser was a factor in N-loss and long-term

N losses can be reduced by improving N use efficiency by both plants and livestock also

with changing fertiliser application rates and timing.

The GSL LP optimisation model eliminates inputs in order of their non-acceptance on an

economic or environmental consequence basis according to the constraint data applied,

not on the type of supplement, protein or carbohydrates in the diet. The farm produced

85

sufficient pasture at a cost lower than maize silage or PKE, this results in the removal of

supplements and an increased reliance on pasture with each run. The GSL LP

optimisation model demonstrated in accordance with Edwards et al., (2007) and in

contrast to Dijkstra et al., (2011) diet based strategies are not always required to

significantly reduce N-loss.

The GSL LP optimisation model was set up to optimise profit, this was achieved by

optimising/restricting stocking rate, cow numbers, cow age, N excreted (Nx), N fertiliser,

supplements, pasture by discarding or cutting for silage, wintering off, cropping area, this

whole farm approach as advocated by Cardenas et al., (2011) reduced N-loss by as much

as 65% in run ten. It also confirmed LP as a tool for model optimisation (Doole et al.,

2013).

It is difficult to compare and contrast other models against the GSL LP optimisation

model. To compare the GSL LP optimisation model to UDDER or Farmax® Dairy Pro

you would have to totally constrain GSL LP to give the desired answer, this also applies

in reverse, trying to compare UDDER or Farmax® Dairy Pro to GSL LP optimisation

model as explained by McCall, (2012).

A point emphasised by Eicker, (2006), Melsen et al., (2006), Monaghan et al., (2004),

Riden, (2009) and Ridler et al., (2010), UDDER and Farmax® Dairy Pro are simulation

models that will always average the additional costs and benefits over the entire farm’s

resources over the 12 month period. This is in contrast to the GSL LP optimisation model

as it uses margins, each year is divided into 26 periods, allowing management decisions

to be made every 2 weeks, where each cost/benefit change to each additional and/or

subtracted unit is applies.

Averages as mentioned by Eicker, (2006) can be particularly dangerous when used in

making economic decisions, any economic estimate that involve an increase and/or

decrease in milk production needs to account for the increase and/or decrease in feed

costs, this must be calculated using marginal costs, so any specific final financial answer

can be found from many different mixes of resources which means it is impossible to

identify exactly the same mix of resources (Anderson & Ridler, 2010; Eicker, 2006;

Riden, 2009; Ridler et al., 2010).

Optimisation models are an effective way to analyse many different scenarios, with the

aim to reduce N-leaching while increasing profit per hectare (Bell, 2013; Bowler &

McCarthy, 2013; McCall, 2012; Riden, 2009). Optimisation models can perform many

iterations quickly by changing a single input to determine the outcome, this can then be

86

compared to the next. This allows efficient identification of profitable systems and can be

time consuming if a manual trial-and-error approach is used (Bell, 2013; Doole et al.,

2013c; McCall, 2012; Riden, 2009).

In contrast to UDDER and Farmax® Dairy Pro, the GSL LP optimisation model provides

the user the ability to run initial scenarios, this can then be used to restrict inputs and/or

optimise resources. Not only does the GSL LP optimise a single run, it also provides a

snapshot where each run can then be compared (production levels, expenses and surplus)

based on resources used. This provides an opportunity to distinguish the changes that are

required to maximise operating surplus. This is where marginal cost equals marginal

revenue (MC=MR) and to minimise N-loss of the farming system (McCall, 2012; Riden,

2009). This has been demonstrated with the case study, run five, producing the highest

profit of all the 10 runs, while reducing N loss to 13 kg N/ha/year, surpassing the 20 year

N-loss limits by 11.2 kg N/ha/year.

In the same catchment and similar farm type Bowler & McCarthy, (2013) demonstrated

using a combination of Farmax® Dairy Pro and Overseer® to evaluate opportunities of

using existing resources more efficiently, demonstrated N-loss reduction of 16% and

operating profit increase of 0.6% ($23/ha). In parallel using GSL LP optimisation model

on the case study farm, run five resulted in a N-loss reduction of 43% and a surplus

increase of 14% ($520/ha). This demonstrates the strength of a whole farm optimisation

model set up to optimise profit, while some models fine tune system runs, others rely on

manual trial-and-error (Doole et al., 2013c; McCall, 2012).

The Overseer® nutrient model provided the environmental information in the form of N-

leaching and nitrogen conversion efficiency (NCE) for each run on the case study farm.

The base run had a N-loss of 23 kg N/ha/year and a NCE of 43% as the N-loss decreased

the NCE increased with run five N-loss of 13 Kg N/ha/year and NCE of 53%, so as

nitrogen conversion efficiency (NCE) increases, nitrogen leaching loss decreases.

A whole farm modelling approach using GSL LP optimising model provides information

on the physical and economic impact of each run, while Overseer® provides the

environmental impact of each run. The whole farm modelling in combination with

Overseer® provides the stakeholder with an invaluable insight into how a physical system

change will affect the economic viability and provide the environmental impact of the

farming system, as demonstrated by Bowler & McCarthy, (2013), Wedderburn et al.,

(2011), Monaghan et al., (2004), Bell, (2013), McCall, (2012). The same approach was

87

used at Massey No.1 dairy unit, to develop a whole farm system that was both profitable

and environmentally robust.

The case study farm is rated in DairyBase as a production system three, with 10% to 20%

of imported feed to extend lactation. The runs produced using GSL LP give the

stakeholder a snapshot of options, these are not all the runs that can be performed; this is

only the first step. The stakeholder has the ability to clarify runs that could potentially be

used; make further runs or drill down on a selected run, this allows price comparisons,

risk analysis, uncertainty or other potential variation of interest to be performed as

requested by the farmer. The next step is to select a run (option) and perform an in-depth

analysis; this could be financial, managerial, social, environmental or a combination.

Botterill & Mazur, (2004) and Hardaker et al., (2004) states that imperfect knowledge

about alternative outcomes creates uncertainty so improving knowledge through multiple

runs will improve that knowledge so you can actually feel better informed.

Run five was only selected based on its highest surplus and responsible N-loss over the

case study farm, not because it suits the stakeholder’s ability, skills, ideology, constraints

or resources.

High-input systems can have greater variability because of additional complexity and

more decisions required on a daily basis (Kloeten, 2014). This case study farm is a system

three farm. Removing imported supplements and fertiliser reduces the complexity but it

will require greater pasture management, monitoring, measuring and grazing skills

(system two farm). These are learnt farmer skills and cannot be assumed. There are

always going to be trade-offs when you have environmental objectives (Dake et al.,

2005).

88

7 Conclusion

7.1 Introduction

The objectives of this study were to gain an in-depth understanding of how the One Plan

imposes limits on nutrient losses and how mitigation strategies will affect the economic

viability of a dairy farm in a sensitive catchment. A case study farm in a sensitive

catchment was considered the most appropriate method for achieving this objective and

the combined GSL and Overseer® modelling approach was adopted. In this chapter, the

main research findings are described and the implications of this research discussed and

opportunities for future research outlined.

7.2 Main findings

7.2.1 Obtaining an N-loss using Overseer®

Horizons One Plan specifies the use of Overseer® nutrient budgets program for

calculating estimated nutrient discharges from individual properties.

7.2.1.1 This was observed.

• There is a difference in the interpretation of data between operators of

Overseer®, the outcome is a variation of calculated N-loss outputs across the

case study farm by operators.

• It is easier to define the blocks based on management history rather than soils

and management history.

• Soil information is critical in making up the blocks within Overseer®.

• Climate information and location is critical in accurately calculating N-loss.

• The ‘Best Practice Data Input Standards’ gives the operators too much scope to

choose an option.

• Communication between professionals was poor.

• Version changes of Overseer® can produce different N-loss outputs.

7.2.1.2 This would suggest that.

A detailed soil type and landscape capability survey at the paddock level undertaken by a

trained Soil Pedologist is recommended.

89

A single data entry is required so farm data can be entered into the database and be freely

available to trained consultants.

The future goal is to provide a reliable service to the farming community and industry as

a whole, there needs to be regular professional training in Overseer® and a follow-up

audit process so people can become more confident the calculated N-loss is accurate.

7.2.2 Whole farm models

Foremost, grassland-based agricultural production is the most important component of

livestock production systems in New Zealand because of the competitive economic

advantage of grazed grass. This study found that GSL LP whole farm modelling tool to be

very effective when used with Overseer®, to identify profitable options for reducing N-

loss off the case study farm.

7.2.2.1 This was observed.

• Most of the analysis in mitigating N-loss currently completed has focused on

single issue or solution and in most cases without any financial analysis on the

impact on the farm system.

• Some whole farm models use averages, the selected whole farm model for this

case study farm used margins in its calculations.

• That whole farm management system analysis requires many iterations to get a

useful picture. The selected whole farm model for the case study farm optimises

its outputs on selected constraints giving greater accuracy and a more reliable

outcome for both financial and environmental analysis.

• Some whole farm models are better at fine tuning than others.

• The case study farm system needs to de-intensify, reduce stocking rate, remove

or reduce imported supplements and remove or reduce nitrogen fertiliser to

increase the farm surplus and reduce the environmental impact.

• Overseer® should not be used in isolation of whole farm modelling tools. A better

result is obtained if the two tools are used together.

90

7.2.2.2 This would suggest that.

This would suggest that it is possible for a dairy farm in a sensitive catchment to have

acceptable N leaching and make a profit.

7.3 Implications of the research

The Horizon Regional Council should have an auditing program in place to certify that

registered consultants are calculating N-loss based on good management practices, the

climate data is correctly used and the blocks correctly reflect the management history and

soils using farm scale mapping. There needs to be regular professional training for

consultants in the use of both Overseer® and whole farm modelling tools and how these

tools should be used together.

The research showed that de-intensifying the farming system for the case study farm can

increase profits, reduce the reliance on imported supplements and reduce the

environmental impact of farming.

7.4 Further research

This research becomes the basis for an in-depth study over multiple farms with a diverse

range of inputs and systems using the two tools to measure the economic and

environmental impact of various mitigation strategies for farms in a sensitive catchment.

91

8 References AgResearch., MPI., & FANZ. (2013). Technical Note No. 6, OVERSEER® Nutrient budgets., from

http://www.overseer.org.nz/Home.aspx Anderson, J. R., Dillon, J. L., & Hardaker, J. B. (1977). B.(1977), Agricultural Decision Analysis:

Iowa State University Press, Ames. Anderson, W. J., & Ridler, B. J. (2010). Application of resource allocation optimisation to provide

profitable options for dairy production systems. PROCEEDINGS- NEW ZEALAND SOCIETY

OF ANIMAL PRODUCTION, 70, 291-295. Armstrong, D. (2012). A ‘Guide’ for organisations investing in Farm Business Management tools.

www.dairyaustralia.com.au Armstrong, D., Tarrant, K. A., Ho, C. K. M., Malcolm, L. R., & Wales, W. J. (2010). Evaluating

development options for a rain-fed dairy farm in Gippsland. Animal Production Science,

50(6), 363-370. doi: http://dx.doi.org/10.1071/AN10009 Baldwin, B. L. (1995). Modeling ruminant digestion and metabolism: Springer. Ball, P. R., & Ryden, J. (1984). Nitrogen relationships in intensively managed temperate

grasslands. Plant and Soil, 76(1-3), 23-33. Bannink, A., Valk, H., & Van Vuuren, A. M. (1999). Intake and Excretion of Sodium, Potassium,

and Nitrogen and the Effects on Urine Production by Lactating Dairy Cows. Journal of

Dairy Science, 82(5), 1008-1018. doi: http://dx.doi.org/10.3168/jds.S0022-0302(99)75321-X

Barringer, J., Lynn, I., & Basher, L. (2012). Report on the 'Roadmap for the new zealand land resource inventory / land use capability' Landcare Reasearch.

Barry, P. J., & Ellinger, P. N. (2012). Financial management in agriculture / Peter J. Barry, Paul N.

Ellinger: Boston : Prentice Hall, 2012 7th ed. Barry, T., & McNabb, W. (1999). The implications of condensed tannins on the nutritive value of

temperate forages fed to ruminants. British journal of nutrition, 81, 263-272. Bell, B. (2012). Agricultural productivity and Environmental Sustainability Are we going to throw

the baby out with the bathwater? Paper presented at the 2012 Conference, August 31, 2012, Nelson, New Zealand.

Bell, B. (2013a). Impacts on Dairy from meeting Horizons One Plan requirements. Paper presented at the 2013 Conference, August 28-30, 2013, Christchurch, New Zealand.

Bell, B., Brook, B., McDonald, G., Fairgray, D., & Smith, N. (2013b). Cost Benefit and Economic Impact Analysis of the Horizons One Plan. New Zealand: Nimmo-Bell & Company LTD.

Bernstein, P. L. (1996). Against the gods: The remarkable story of risk: Wiley New York. Beukes, P. C., Gregorini, P., & Romera, A. J. (2011b). Estimating greenhouse gas emissions from

New Zealand dairy systems using a mechanistic whole farm model and inventory methodology. Animal Feed Science and Technology, 166–167(0), 708-720. doi: http://dx.doi.org/10.1016/j.anifeedsci.2011.04.050

Beukes, P. C., Gregorini, P., Romera, A. J., & Dalley, D. E. (2011a). The profitability and risk of dairy cow wintering strategies in the Southland region of New Zealand. Agricultural

Systems, 104(7), 541-550. doi: http://dx.doi.org/10.1016/j.agsy.2011.04.003 Beukes, P. C., Gregorini, P., Romera, A. J., Levy, G., & Waghorn, G. C. (2010b). Improving

production efficiency as a strategy to mitigate greenhouse gas emissions on pastoral dairy farms in New Zealand. Agriculture, ecosystems & environment, 136(3–4), 358-365. doi: http://dx.doi.org/10.1016/j.agee.2009.08.008

Beukes, P. C., Palliser, C. C., Prever, W., Serra, V., & Lancaster, J. (2005a). Use of a whole farm model for exploring management decisions in dairying. Dairy InSight (proyecto 10079) y

Foundation for Research Science and Technology (proyecto DRCX0301), Nueva Zelanda. Beukes, P. C., Thorrold, B. S., Wastney, M. E., Palliser, C. C., & Clark, D. A. (2004). Modelling farm

systems with once-a-day milking (Vol. 64): New Zealand Society of Animal Production.

92

Beukes, P. C., Thorrold, B. S., Wastney, M. E., Palliser, C. C., Macdonald, K. A., Bright, K. P., . . . Auldist, M. J. (2005b). Modelling the bi-peak lactation curves of summer calvers in New Zealand dairy farm systems. Australian Journal of Experimental Agriculture, 45(6), 643-649. doi: http://dx.doi.org/10.1071/EA03251

Biggs, B. J. F. (2000). New Zealand Periphyton Guideline - Prepared for the Ministry for the

Environment. Christchurch: NIWA Retrieved from www.mfe.govt.nz/publications/water/nz-periphyton-guide-jun00.pdf .

Borenstein, D. (1998). Towards a practical method to validate decision support systems. Decision

Support Systems, 23(3), 227-239. doi: http://dx.doi.org/10.1016/S0167-9236(98)00046-3 Botterill, L., & Mazur, N. (2004). Risk and risk perception: A literature review. Project No. BRR-8A,

Rural Industries Research and Development Corporation, Barton. Bowler, L., & McCarthy, S. (2013). N leaching and Profitability Massey University, September

12th Broderick, G. A. (2003). Effects of Varying Dietary Protein and Energy Levels on the Production of

Lactating Dairy Cows. Journal of Dairy Science, 86(4), 1370-1381. doi: http://dx.doi.org/10.3168/jds.S0022-0302(03)73721-7

Broderick, G. A. (2005). Feeding dairy cows to minimize nitrogen excretion. Paper presented at the Proc. Tri-State Dairy Nutrition Conference 2005.

Broderick, G. A. (2009). New perspectives on the efficiency of nitrogen use in ruminants. Paper presented at the II Simposio internacional avancos em techicas de pesquisa em nutricao de ruminantes. University of Viscoso, Brazil.

Broderick, G. A., & Clayton, M. K. (1997). A Statistical Evaluation of Animal and Nutritional Factors Influencing Concentrations of Milk Urea Nitrogen. Journal of Dairy Science,

80(11), 2964-2971. doi: http://dx.doi.org/10.3168/jds.S0022-0302(97)76262-3 Broderick, G. A., Stevenson, M. J., Patton, R. A., Lobos, N. E., & Olmos Colmenero, J. J. (2008).

Effect of Supplementing Rumen-Protected Methionine on Production and Nitrogen Excretion in Lactating Dairy Cows. Journal of Dairy Science, 91(3), 1092-1102. doi: http://dx.doi.org/10.3168/jds.2007-0769

Brown, I., Norton, N., Wedderburn, L., Monaghan, R., Harris, S., Hayward, S., & Ford, R. (2011). Nutrient management in Hurunui: A case study in identifying options and opportunities: Environment Canterbury.

Bryant, J. R., Lopez-Villalobos, N., Holmes, C. W., Pryce, J. E., Rossi, J., & Macdonald, K. (2008a). Development and evaluation of a pastoral simulation model that predicts dairy cattle performance based on animal genotype and environmental sensitivity information. Agricultural Systems, 97(1–2), 13-25. doi: http://dx.doi.org/10.1016/j.agsy.2007.10.007

Bryant, J. R., Ogle, G., Marshall, P. R., Glassey, C. B., Lancaster, J. A. S., García, S. C., & Holmes, C. W. (2010). Description and evaluation of the Farmax Dairy Pro decision support model. New Zealand Journal of Agricultural Research, 53(1), 13-28. doi: 10.1080/00288231003606054

Burden, R. J. (1982). Nitrate Contamination of New Zealand Aquifers: A Review. N. Z. J. SCI.,

25(3), 205-220. Burgos, S. A., Fadel, J. G., & DePeters, E. J. (2007). Prediction of Ammonia Emission from Dairy

Cattle Manure Based on Milk Urea Nitrogen: Relation of Milk Urea Nitrogen to Urine Urea Nitrogen Excretion. Journal of Dairy Science, 90(12), 5499-5508. doi: http://dx.doi.org/10.3168/jds.2007-0299

Bywater, A. C., & Cacho, O. J. (1994). Use of simulation models in research (Vol. 54): New Zealand Society of Animal Production.

Cabrera, V. E., Breuer, N. E., Hildebrand, P. E., & Letson, D. (2005). The dynamic North Florida dairy farm model: A user-friendly computerized tool for increasing profits while minimizing N leaching under varying climatic conditions. Computers and Electronics in

Agriculture, 49(2), 286-308. doi: http://dx.doi.org/10.1016/j.compag.2005.07.001

93

Cabrera, V. E., Hildebrand, P. E., Jones, J. W., Letson, D., & de Vries, A. (2006). An integrated North Florida dairy farm model to reduce environmental impacts under seasonal climate variability. Agriculture, ecosystems & environment, 113(1), 82-97.

Cardenas, L. M., Cuttle, S. P., Crabtree, B., Hopkins, A., Shepherd, A., Scholefield, D., & del Prado, A. (2011). Cost effectiveness of nitrate leaching mitigation measures for grassland livestock systems at locations in England and Wales. Science of The Total Environment,

409(6), 1104-1115. doi: http://dx.doi.org/10.1016/j.scitotenv.2010.12.006 Carrick, S., Hainsworth, S., Lilburne, L., & Fraser, S. (2014). S-MAP@ THE FARM-SCALE?

TOWARDS A NATIONAL PROTOCOL FOR SOIL MAPPING FOR FARM NUTRIENT BUDGETS. Nutrient management for the farm, catchment and community.

Cary, J., Webb, T., & Barr, N. (2002). Understanding landholders\'capacity to change to sustainable practices.

Christensen, C., Hanly, J., Hedley, M., & Horne, D. (2010). Reducing nitrate leaching losses by using duration-controlled grazing of dairy cows. Proceedings of the Farming's Future:

Minimising Footprints and Maximising Margins, 73. Christensen, C., Hanly, J., Hedley, M., & Horne, D. (2011). NITRATE LEACHING AND PASTURE

PRODUCTION FROM TWO YEARS OF DURATION CONTROLLED GRAZING. Fertilizer & Lime

Research Centre, Massey University. Christensen, C., Hanly, J., Hedley, M., & Horne, D. (2012). THREE YEARS OF DURATION -

CONTROLLED GRAZING: WHAT HAVE WE FOUND? Fertilizer and Lime Research Centre,

Massey University. Cichota, R., & Snow, V. O. (2009). Estimating nutrient loss to waterways—an overview of models

of relevance to New Zealand pastoral farms. New Zealand Journal of Agricultural

Research, 52(3), 239-260. doi: 10.1080/00288230909510509 Cichota, R., Snow, V. O., Vogeler, I., Wheeler, D. M., & Shepherd, M. A. (2013). Describing N

leaching from urine patches deposited at different times of the year with a transfer function. Soil Research, 50(8), 694-707. doi: http://dx.doi.org/10.1071/SR12208

Ciszuk, P., & Gebregziabher, T. (1994). Milk Urea as an Estimate of Urine Nitrogen of Dairy Cows and Goats. Acta Agriculturae Scandinavica, Section A – Animal Science, 44(2), 87-95. doi: 10.1080/09064709409410187

Clothier, B., Mackay, A., Carran, A., Gray, R., Parfitt, R., Francis, G., . . . Green, S. (2007). Stratagies for contaminant management, A report by SLURI, The Sustainable Land Use Research Initiative for Horizons Regional Council: AgResearch Ltd.

Cowie, B., van Voorthuysen, R., & Ridley, G. (2006). A Monitoring and Reporting Strategy for the Dairying and Clean Streams Accord.

Cox, P. G. (1996). Some issues in the design of agricultural decision support systems. Agricultural

Systems, 52(2–3), 355-381. doi: http://dx.doi.org/10.1016/0308-521X(96)00063-7 Cullen, R., Hughey, K., & Kerr, G. (2006). New Zealand freshwater management and agricultural

impacts. Australian Journal of Agricultural and Resource Economics, 50(3), 327-346. doi: 10.1111/j.1467-8489.2006.00338.x

Custodio, A. A., Blake, R. W., Dahm, P. F., Cartwright, T. C., Schelling, G. T., & Coppock, C. E. (1983). Relationships between Measures of Feed Efficiency and Transmitting Ability for Milk of Holstein Cows. Journal of Dairy Science, 66(9), 1937-1946. doi: http://dx.doi.org/10.3168/jds.S0022-0302(83)82032-3

Cuttle, S. P., & Bourne, P. C. (1993). Uptake and leaching of nitrogen from artificial urine applied to grassland on different dates during the growing season. Plant and Soil, 150(1), 77-86. doi: 10.1007/BF00779178

Cuttle, S. P., & Scholefield, D. (1995). Management options to limit nitrate leaching from grassland. Journal of Contaminant Hydrology, 20(3–4), 299-312. doi: http://dx.doi.org/10.1016/0169-7722(95)00075-5

DairyNZ. (2010b). DairyNZ Economic Survey-2009-2010. Hamilton: DairyNZ.

94

DairyNZ. (2013b). Sustainable Dairying: Water Accord. Retrieved 20/11, 2013, from http://www.dairynz.co.nz/page/pageid/2145879933/Sustainable_Dairying_Water_Accord

Dake, C., Mackay, A., & Manderson, A. (2005). Optimal trade-offs between financial and

environmental risks in pastoral farming. Paper presented at the MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand.

Davey, A., Grainger, C., Mackenzie, D., Flux, D., Wilson, G., Brookes, I., & Holmes, C. (1983). Nutritional and physiological studies of differences between Friesian cows of high and

low genetic merit. Paper presented at the Proceedings of the New Zealand Society of Animal Production.

Davison, T., Cowan, R., & Shepherd, R. (1985). Milk production from cows grazing on tropical grass pastures. 2. Effects of stocking rate and level of nitrogen fertilizer on milk yield and pasture-milk yield relationships. Australian Journal of Experimental Agriculture, 25(3), 515-523. doi: http://dx.doi.org/10.1071/EA9850515

de Klein, C. A. M. (2001). An analysis of environmental and economic implications of nil and restricted grazing systems designed to reduce nitrate leaching from New Zealand dairy farms. II. Pasture production and cost/benefit analysis. New Zealand Journal of

Agricultural Research, 44(2-3), 217-235. doi: 10.1080/00288233.2001.9513479 de Klein, C. A. M., & Eckard, R. J. (2008). Targeted technologies for nitrous oxide abatement from

animal agriculture. Animal Production Science, 48(2), 14-20. de Klein, C. A. M., & Ledgard, S. F. (2001). An analysis of environmental and economic

implications of nil and restricted grazing systems designed to reduce nitrate leaching from New Zealand dairy farms. I. Nitrogen losses. New Zealand Journal of Agricultural

Research, 44(2-3), 201-215. doi: 10.1080/00288233.2001.9513478 de Klein, C. A. M., & Monaghan, R. M. (2005b). The impact of potential nitrous oxide mitigation

strategies on the environmental and economic performance of dairy systems in four New Zealand catchments. Environmental Sciences, 2(2-3), 351-360. doi: 10.1080/15693430500402549

de Klein, C. A. M., Smith, L. C., & Monaghan, R. M. (2005c). Restricted autumn grazing to reduce nitrous oxide emissions from dairy pastures in Southland, New Zealand. Agriculture,

ecosystems & environment, 112(2–3), 192-199. doi: http://dx.doi.org/10.1016/j.agee.2005.08.019

Del Prado, A., & Scholefield, D. (2008). Use of SIMSDAIRY modelling framework system to compare the scope on the sustainability of a dairy farm of animal and plant genetic-based improvements with management-based changes. The Journal of Agricultural

Science, 146(Special Issue 02), 195-211. doi: doi:10.1017/S0021859608007727 Di, H. J., & Cameron, K. C. (2002). Nitrate leaching in temperate agroecosystems: sources, factors

and mitigating strategies. Nutrient Cycling in Agroecosystems, 64(3), 237-256. Dijkstra, J., Oenema, O., & Bannink, A. (2011). Dietary strategies to reducing N excretion from

cattle: implications for methane emissions. Current Opinion in Environmental

Sustainability, 3(5), 414-422. doi: http://dx.doi.org/10.1016/j.cosust.2011.07.008 Dillon, P., Crosse, S., O'Brien, B., & Mayes, R. W. (2002). The effect of forage type and level of

concentrate supplementation on the performance of spring-calving dairy cows in early lactation. Grass and Forage Science, 57(3), 212-223. doi: 10.1046/j.1365-2494.2002.00319.x

Dillon, P., Roche, J., Shalloo, L., & Horan, B. (2005). Optimising financial return from grazing in temperate pastures. Utilisation of grazed grass in temporal animal systems, 131-147.

Doole, G. J., Romera, A. J., & Adler, A. A. (2013c). An optimization model of a New Zealand dairy farm. Journal of Dairy Science, 96(4), 2147-2160. doi: http://dx.doi.org/10.3168/jds.2012-5488

95

Duncan, R. (2014). Regulating agricultural land use to manage water quality: The challenges for science and policy in enforcing limits on non-point source pollution in New Zealand. Land Use Policy, 41(0), 378-387. doi: http://dx.doi.org/10.1016/j.landusepol.2014.06.003

Edgar, N. B. (2008). Icon Lakes in New Zealand: Managing the Tension Between Land Development and Water Resource Protection. Society & Natural Resources, 22(1), 1-11. doi: 10.1080/08941920802223325

Edmeades, R. H., Metherell, A., Rahn, C., & Thorburn, P. (2013). A peer review of OVERSEER® in relation to modelling nutrient flows in arable crops.

Edwards, G. R., Parsons, A., Rasmussen, S., & Bryant, R. H. (2007). High sugar ryegrasses for

livestock systems in New Zealand. Paper presented at the Proceedings of the New Zealand Grassland Association.

Eicker, S. (2006). Marginal thinking: making money on a dairy farm. Paper presented at the Advances in dairy technology: proceedings of the... Western Canadian Dairy Seminar.

Ekman, S. (2005). Cost-effective farm-level nitrogen abatement in the presence of environmental and economic risk. Department of Economics, Swedish University of Agricultural Sciences, Box 7013, 75007 Uppsala, Sweden.

Elgersma, A., Wever, A., & Nalecz-Tarwacka, T. (2006). Grazing versus indoor feeding: effects on milk quality. Grassland Science in Europe, 11, 419-427.

Ervin, D. E., & Mill, J. W. (1985). Agricultural Land Markets and Soil Erosion: Policy Relevance and Conceptual Issues. American Journal of Agricultural Economics, 67(5), 938-942. doi: 10.2307/1241350

Everest, M. (2013). Hinds catchment nutrient and on-farm economic modelling. Farmax. (2013). Helping pastoral farmers make confident decisions that improve their

profitability. . Retrieved 10/11, 2013, from http://www.farmax.co.nz Flachowsky, G., & Lebzien, P. (2006). Possibilities reduction of nitrogen (N) excretion from

ruminants and the need for further research: A review. Landbauforsch. . Fonterra, Regional Councils, Ministry for the Environment, & Ministry of Agriculture & Forestry.

(2003). Dairying and Clean Streams ACCORD. Francoeur, S. N., Biggs, B. J. F., Smith, R. A., & Lowe, R. L. (1999). Nutrient Limitation of Algal

Biomass Accrual in Streams: Seasonal Patterns and a Comparison of Methods. Journal of

the North American Benthological Society, 18(2), 242-260. doi: 10.2307/1468463 Freeman, A. (1975). Genetic variation in nutrition of dairy cattle: National Research Council. Fulkerson, W., & Trevaskis, C. (1997). Limitations to milk production from pasture. Gans, J., King, S., & Mankiw, N. G. (2011). Principles of microeconomics: Cengage Learning. GHD. (2009). Cumulative Water Quality Effects of Nutrients from Agricultural Intensification in

The Upper Waitaki Catchment Summary Report. Goodlass, G., Halberg, N., & Verschuur, G. (2003). Input output accounting systems in the

European community—an appraisal of their usefulness in raising awareness of environmental problems. European Journal of Agronomy, 20(1–2), 17-24. doi: http://dx.doi.org/10.1016/S1161-0301(03)00068-6

Gourley, C. J. P., Aarons, S. R., & Powell, J. M. (2012). Nitrogen use efficiency and manure management practices in contrasting dairy production systems. Agriculture, ecosystems

& environment, 147(0), 73-81. doi: http://dx.doi.org/10.1016/j.agee.2011.05.011 Gow, J., & Stayner, R. (1995). The process of farm adjustment: a critical review. Review of

Marketing and Agricultural Economics, 63(02). Grainger, C., Holmes, C., & Moore, Y. (1985). Performance of Friesian cows with high and low

breeding indexes 2. Energy and nitrogen balance experiments with lactating and pregnant, non-lactating cows. Animal production, 40(03), 389-400.

Gregorini, P., Beukes, P., Bryant, R., & Romera, A. (2010). A brief overview and simulation of the

effects of some feeding strategies on nitrogen excretion and enteric methane emission

96

from grazing dairy cows. Paper presented at the Proceedings 4th Austrilian Dairy Science Symposium.

Grieve, D. G., Macleod, G. K., Batra, T. R., Burnside, E. B., & Stone, J. B. (1976). Relationship of Feed Intake and Ration Digestibility to Estimated Transmitting Ability, Body Weight, and Efficiency in First Lactation. Journal of Dairy Science, 59(7), 1312-1318. doi: http://dx.doi.org/10.3168/jds.S0022-0302(76)84361-5

Groot, J. C. J., Rossing, W. A. H., Lantinga, E. A., & Van Keulen, H. (2003). Exploring the potential for improved internal nutrient cycling in dairy farming systems, using an eco-mathematical model. NJAS - Wageningen Journal of Life Sciences, 51(1–2), 165-194. doi: http://dx.doi.org/10.1016/S1573-5214(03)80032-5

Guerin, L., & Guerin, T. (1994). Constraints to the adoption of innovations in agricultural research and environmental management: a review. Australian Journal of Experimental

Agriculture, 34(4), 549-571. doi: http://dx.doi.org/10.1071/EA9940549 Gustafsson, A. H., & Palmquist, D. L. (1993). Diurnal Variation of Rumen Ammonia, Serum Urea,

and Milk Urea in Dairy Cows at High and Low Yields. Journal of Dairy Science, 76(2), 475-484. doi: http://dx.doi.org/10.3168/jds.S0022-0302(93)77368-3

Hanigan, M. D., Palliser, C. C., & Gregorini, P. (2009). Altering the representation of hormones and adding consideration of gestational metabolism in a metabolic cow model reduced prediction errors. Journal of Dairy Science, 92(10), 5043-5056. doi: http://dx.doi.org/10.3168/jds.2008-1922

Hanigan, M. D., Rius, A. G., Kolver, E. S., & Palliser, C. C. (2007). A Redefinition of the Representation of Mammary Cells and Enzyme Activities in a Lactating Dairy Cow Model. Journal of Dairy Science, 90(8), 3816-3830. doi: http://dx.doi.org/10.3168/jds.2007-0028

Hardaker, J. B., Huirne, R. B. M., & Anderson, J. R. (2004). Coping with risk in agriculture

[electronic resource]: CABI. Harris, R., & Atkins, H. (2004). Development v protection, an introduction to RMA and related

laws. Handbook of Environmental Law, 56-76. Hart, R. P. S., Larcombe, M. T., Sherlock, R. A., & Smith, L. A. (1998). Optimisation techniques for

a computer simulation of a pastoral dairy farm. Computers and Electronics in Agriculture,

19(2), 129-153. doi: http://dx.doi.org/10.1016/S0168-1699(97)00039-2 Haynes, R. J., & Williams, P. H. (1993). Nutrient Cycling and Soil Fertility in the Grazed Pasture

Ecosystem. In L. S. Donald (Ed.), Advances in Agronomy (Vol. Volume 49, pp. 119-199): Academic Press.

Heard, J. W., Leddin, C. M., Armstrong, D. P., Ho, C. K. M., Tarrant, K. A., Malcolm, B., & Wales, W. J. (2012). The impact of system changes to a dairy farm in south-west Victoria: risk and increasing profitability. Animal Production Science, 52(7), 557-565. doi: http://dx.doi.org/10.1071/AN11291

Hendy, J., Kerr, S., & Baisden, T. (2007). The land use in rural new zealand model version 1 (LURNZv1): Model description. Available at SSRN 994697.

Herrero, M., Fawcett, R. H., Silveira, V., Busqué, J., Bernués, A., & Dent, J. B. (2000). Modelling the growth and utilisation of kikuyu grass (Pennisetum clandestinum) under grazing. 1. Model definition and parameterisation. Agricultural Systems, 65(2), 73-97. doi: http://dx.doi.org/10.1016/S0308-521X(00)00028-7

Ho, C. K. M., Malcolm, B., & Doyle, P. T. (2013). Potential impacts of negative associative effects between concentrate supplements, pasture and conserved forage for milk production and dairy farm profit. Animal Production Science, 53(5), 437-452. doi: http://dx.doi.org/10.1071/AN12140

Horizon. (2005). State of the Environment of the Manawatu Wanganui Region - Technical Report - Freshwater Quality (pp. 34). Manawatu.

Horizon. (2013). Proposed One Plan 2013, from http://www.horizons.govt.nz/about-us/publications/about-us-publications/one-plan/proposed-one-plan/

97

Horizon. (2014). Targeted catchments. from http://www.horizons.govt.nz/about-us/one-plan/targeted-catchments/

Jackson, T. M., & Dixon, J. (2007). The New Zealand Resource Management Act: an exercise in delivering sustainable development through an ecological modernisation agenda. ENVIRONMENT AND PLANNING B PLANNING AND DESIGN, 34(1), 107.

Jackson, T. M., Hanjra, M. A., Khan, S., & Hafeez, M. M. (2011). Building a climate resilient farm: A risk based approach for understanding water, energy and emissions in irrigated agriculture. Agricultural Systems, 104(9), 729-745. doi: http://dx.doi.org/10.1016/j.agsy.2011.08.003

Janssen, S., & van Ittersum, M. K. (2007). Assessing farm innovations and responses to policies: A review of bio-economic farm models. Agricultural Systems, 94(3), 622-636. doi: http://dx.doi.org/10.1016/j.agsy.2007.03.001

Jarvis, S. (1992). Grazed grassland management and nitrogen losses: an overview. Aspects of

Applied Biology, 30, 207-214. Jay, M. (2007). The political economy of a productivist agriculture: New Zealand dairy discourses.

Food Policy, 32(2), 266-279. doi: http://dx.doi.org/10.1016/j.foodpol.2006.09.002 Jorgensen, S. E. (2006). Environmental models and simulations, UNESCO: Encyclopedia of Life

Support Systems (EOLSS). Keith, L. H., Crummett, W., Deegan, J., Libby, R. A., Taylor, J. K., & Wentler, G. (1983). Principles

of environmental analysis. Analytical Chemistry, 55(14), 2210-2218. doi: 10.1021/ac00264a003

Kloeten, N. (2014). Eyes wide open. http://agrihq.co.nz/dairy-exporter/ Kowalenko, C. G., & Bittman, S. (2000). Within-season grass yield and nitrogen uptake, and soil

nitrogen as affected by nitrogen applied at various rates and distributions in a high rainfall environment. Canadian Journal of Plant Science, 80(2), 287-301. doi: 10.4141/P98-139

L'Huillier, P., Parr, C., & Bryant, A. (1988). Comparative performance and energy metabolism of

Jerseys and Friesians in early-mid lactation. Paper presented at the Proceedings of the New Zealand Society of Animal Production.

Landcare-Research. (2014). About S-map and S-map Online. from http://smap.landcareresearch.co.nz/about#whats-changed

Laws, J. A., Pain, B. F., Jarvis, S. C., & Scholefield, D. (2000). Comparison of grassland management systems for beef cattle using self-contained farmlets: effects of contrasting nitrogen inputs and management strategies on nitrogen budgets, and herbage and animal production. Agriculture, ecosystems & environment, 80(3), 243-254. doi: http://dx.doi.org/10.1016/S0167-8809(00)00150-X

Ledgard, S. (1991). Transfer of fixed nitrogen from white clover to associated grasses in swards grazed by dairy cows, estimated using 15N methods. Plant and Soil, 131(2), 215-223. doi: 10.1007/BF00009451

Ledgard, S., Ghani, A., Redding, M., Sprosen, M., Balvert, S., & Smeaton, D. (2008). Farmers taking control of their future: Research into minimising nitrogen and phosphorus from pasture land into Rotorua lakes. Carbon and nutrient management in agriculture, 500-510.

LIC, & DairyNZ. (2013). New Zealand Dairy Statistics 2012-13. Retrieved 01/06, 2014, from http://www.dairynz.co.nz/file/fileid/45159

Lilburne, L. R., Hewitt, A. E., & Webb, T. W. (2012). Soil and informatics science combine to develop S-map: A new generation soil information system for New Zealand. Geoderma,

170(0), 232-238. doi: http://dx.doi.org/10.1016/j.geoderma.2011.11.012 Lipper, L. (2001). Dirt poor: poverty, farmers and soil resource investment. Two essays on socio-

economic aspects of soil degradation, 1-48.

98

Longhurst, B., Houlbrooke, D., & Laurenson, S. (2013). On-farm farm dairy effluent risk assessment.

Loucks, D. P., Van Beek, E., Stedinger, J. R., Dijkman, J. P., & Villars, M. T. (2005). Water resources

systems planning and management: an introduction to methods, models and

applications: Paris: UNESCO. Lynn, I., Manderson, A., Page, M., Harmsworth, G., Eyles, G., Duglas, G., . . . Newsome, P. (2009).

Land Use Capability Survey Hand book a New Zealand handbook for the classification of

land Retrieved from http://www.landcareresearch.co.nz Ma, S., & Swinton, S. M. (2011). Valuation of ecosystem services from rural landscapes using

agricultural land prices. Ecological Economics, 70(9), 1649-1659. doi: http://dx.doi.org/10.1016/j.ecolecon.2011.04.004

Maddison, D. (2000). A hedonic analysis of agricultural land prices in England and Wales. European Review of Agricultural Economics, 27(4), 519-532. doi: 10.1093/erae/27.4.519

Malcolm, B., Ho, C. K. M., Armstrong, D. P., Doyle, P. T., Tarrant, K. A., Heard, J. W., . . . Wales, W. J. (2005a). Dairy Directions: a decade of whole farm analysis of dairy systems. Australasian Agribusiness Review, 20.

Malcolm, B., Malcolm, L. R., Makeham, J., & Wright, V. (2005b). The farming game: agricultural

management and marketing: Cambridge University Press. Manderson, A., & Palmer, A. (2006). Soil information for agricultural decision making: a New

Zealand perspective. Soil Use and Management, 22(4), 393-400. doi: 10.1111/j.1475-2743.2006.00048.x

Marini, J. C., & Van Amburgh, M. E. (2005). Partition of Nitrogen Excretion in Urine and the Feces of Holstein Replacement Heifers. Journal of Dairy Science, 88(5), 1778-1784. doi: http://dx.doi.org/10.3168/jds.S0022-0302(05)72852-6

Marshall, P. R., McCall, D. G., & Johns, K. L. (1991). Stockpol: a decision support model for

livestock farms. Paper presented at the Proceedings of the New Zealand Grassland Association.

McArthur, K. J. (2011). Integrating policy and science to improve the management of freshwater

[ie fresh water] in New Zealand: a thesis prepared in partial fulfilment of a Masters

Applied Science in Natural Resource Management. McArthur, K. J., Roygard, J., & Clark, M. (2010). Understanding variations in the limiting nitrogen

and phosphorus status of rivers in the Manawatu-Wanganui Region, New Zealand. Journal of Hydrology(New Zealand), 49(1), 15-33.

Submissions on the Proposed Hurunui and Waiau River Regional Plan, (2012). McCarl, B. A., & Spreen, T. H. (1997). Applied mathematical programming using algebraic

systems. McCarthy, S., Hutchinson, K., & Bowler, L. (2014). IDENTIFYING OPPORTUNITIES TO REDUCE N

LEACHED WHILE MAINTAINING FARM PROFITABILITY AND MILKSOLIDS PRODUCTION–A CASE STUDY ANALYSIS.

McCown, R. L. (2002). Changing systems for supporting farmers' decisions: problems, paradigms, and prospects. Agricultural Systems, 74(1), 179-220. doi: http://dx.doi.org/10.1016/S0308-521X(02)00026-4

McDowell, R. W., Larned, S. T., & Houlbrooke, D. J. (2009). Nitrogen and phosphorus in New Zealand streams and rivers: Control and impact of eutrophication and the influence of land management. New Zealand Journal of Marine and Freshwater Research, 43(4), 985-995. doi: 10.1080/00288330909510055

Melsen, M. G., Armstrong, D. P., Ho, C. K., Malcolm, B., & Doyle, P. T. (2006). Case-study forty-year historical analysis of production and resource use on northern Victoria dairy farming. AFBM Journal, 3(1).

Miller, L. A., Moorby, J. M., Davies, D. R., Humphreys, M. O., Scollan, N. D., MacRae, J. C., & Theodorou, M. K. (2001). Increased concentration of water-soluble carbohydrate in

99

perennial ryegrass (Lolium perenne L.): milk production from late-lactation dairy cows. Grass and Forage Science, 56(4), 383-394. doi: 10.1046/j.1365-2494.2001.00288.x

Ministry for the Environment. (2005). Reflections: A summary of your views on the sustainable

water programme of action. New Zealand Government: Retrieved from www.mfe.govt.nz.

Ministry for the Environment. (2013). Environment New Zealand Retrieved from http://www.mfe.govt.nz/publications/land/.

Ministry for the Environment. (2014). Freshwater environments of New Zealand. 2014, from https://www.mfe.govt.nz/publications/ser/enz07-dec07/html/chapter10-freshwater/page1.html

Monaghan, R. M., Carey, P. L., Wilcock, R. J., Drewry, J. J., Houlbrooke, D. J., Quinn, J. M., & Thorrold, B. S. (2009). Linkages between land management activities and stream water quality in a border dyke-irrigated pastoral catchment. Agriculture, ecosystems &

environment, 129(1–3), 201-211. doi: http://dx.doi.org/10.1016/j.agee.2008.08.017 Monaghan, R. M., Smeaton, D., Hyslop, M. G., Stevens, D. R., De Klein, C. A. M., Smith, L. C., . . .

Thorrold, B. S. (2004). A desktop evaluation of the environmental and economic

performance of model dairy farming systems within four New Zealand catchments. Paper presented at the Proceedings of the New Zealand Grassland Association.

Monaghan, R. M., Wilcock, R. J., Smith, L. C., Tikkisetty, B., Thorrold, B. S., & Costall, D. (2007a). Linkages between land management activities and water quality in an intensively farmed catchment in southern New Zealand. Agriculture, ecosystems & environment, 118(1–4), 211-222. doi: http://dx.doi.org/10.1016/j.agee.2006.05.016

Monaghan, R. M., Wilcock, R. J., Smith, L. C., Tikkisetty, B., Thorrold, B. S., & Costall, D. (2007b). Linkages between land management activities and water quality in the Big Burn Stream,Southland. Agriculture, ecosystems & environment, 118(1–4), 211-222.

Moorby, J. M., Evans, R. T., Scollan, N. D., MacRae, J. C., & Theodorou, M. K. (2006). Increased concentration of water-soluble carbohydrate in perennial ryegrass (Lolium perenne L.). Evaluation in dairy cows in early lactation. Grass and Forage Science, 61(1), 52-59. doi: 10.1111/j.1365-2494.2006.00507.x

Moreau, P., Ruiz, L., Mabon, F., Raimbault, T., Durand, P., Delaby, L., . . . Vertès, F. (2012). Reconciling technical, economic and environmental efficiency of farming systems in vulnerable areas. Agriculture, ecosystems & environment, 147(0), 89-99. doi: http://dx.doi.org/10.1016/j.agee.2011.06.005

MPI. (2013a). Livestock statistics. Retrieved 01/06, 2013, from http://www.mpi.govt.nz/news-resources/statistics-forecasting/livestock-statistics.aspx

MPI. ( 2013b). Projected emissions for the agriculture sector for the Kyoto Commitment Period

2008–2012 New Zealand Government Retrieved from http://www.mpi.govt.nz/news-resources/publications.aspx?title=Situation+and+Outlook+for+Primary+Industries&keywords=SOPI&2012.

MPI. ( 2013c). Situation and Outlook for Primary Industries (SOPI) 2013. Nelder, J. A., & Mead, R. (1965). A Simplex Method for Function Minimization. The Computer

Journal, 7(4), 308-313. doi: 10.1093/comjnl/7.4.308 Nousiainen, J., Shingfield, K. J., & Huhtanen, P. (2004). Evaluation of Milk Urea Nitrogen as a

Diagnostic of Protein Feeding. Journal of Dairy Science, 87(2), 386-398. doi: http://dx.doi.org/10.3168/jds.S0022-0302(04)73178-1

O'Leary, Z. (2005). Researching real-world problems: A guide to methods of inquiry: Sage. Olmos, C. J. J., & Broderick, G. A. (2006b). Effect of Dietary Crude Protein Concentration on Milk

Production and Nitrogen Utilization in Lactating Dairy Cows. Journal of Dairy Science,

89(5), 1704-1712. doi: http://dx.doi.org/10.3168/jds.S0022-0302(06)72238-X OMSL. (2014). Overseer® Best practice data input standards. Wellington: Overseer Management

Services Limited.

100

Pacheco, D., & Waghorn, G. (2008). Dietary nitrogen-definitions, digestion, excretion and

consequences of excess for grazing ruminants. Paper presented at the Proceedings of the New Zealand Grassland Association.

Parfitt, R. L., Frelat, M., Dymond, J. R., Clark, M., & Roygard, J. (2013). Sources of phosphorus in two subcatchments of the Manawatu River, and discussion of mitigation measures to reduce the phosphorus load. New Zealand Journal of Agricultural Research, 56(3), 187-202. doi: 10.1080/00288233.2013.799497

Park, S. (2014). Using Overseer within Rules for the Lake Rotorua Catchment Bay of Plenty: Bay of Plenty Regional Council

Paterson, J., Brocksopp, A., & van Reenen, E. (2014). A JOINT INDUSTRY APPROACH TO MONITOR AND REPORT ON FARM PROGRESS TOWARDS CATCHMENT ENVIRONMENTAL TARGETS.

PCE. (2004). Growing for Good, Intensive Farming, Sustainability and New Zealand’s

Environment. Wellington: New Zealand Government Retrieved from http://www.pce.parliament.nz/publications/all-publications/growing-for-good-intensive-farming-sustainability-and-new-zealand-s-environment-3.

Phillips, C. (2008). Cattle behaviour and welfare: Wiley. com. Powell, J. M., Jackson-Smith, D. B., McCrory, D. F., Saam, H., & Mariola, M. (2006). Validation of

Feed and Manure Data Collected on Wisconsin Dairy Farms. Journal of Dairy Science,

89(6), 2268-2278. doi: http://dx.doi.org/10.3168/jds.S0022-0302(06)72298-6 Probert, M., Dimes, J., Keating, B., Dalal, R., & Strong, W. (1998). APSIM's water and nitrogen

modules and simulation of the dynamics of water and nitrogen in fallow systems. Agricultural Systems, 56(1), 1-28.

Quinn, J. M., Cooper, A. B., Stroud, M. J., & Burrell, G. P. (1997). Shade effects on stream periphyton and invertebrates: An experiment in streamside channels. New Zealand

Journal of Marine and Freshwater Research, 31(5), 665-683. doi: 10.1080/00288330.1997.9516797

Rawnsley, R. P., Chapman, D. F., Jacobs, J. L., Garcia, S. C., Callow, M. N., Edwards, G. R., & Pembleton, K. P. (2013). Complementary forages – integration at a whole-farm level. Animal Production Science, 53(9), 976-987. doi: http://dx.doi.org/10.1071/AN12282

Refsgaard, J. C. (2001). Towards a formal approach to calibration and validation of models using spatial data. Spatial Patterns in Catchment Hydrology: Observations and Modelling, 329-354.

Riden, C. (2009). NZ Dairy Farms - Optimising Resource Allocation. http://www.agprodecon.org/node/99#Management%20Changes

Ridler, B., Anderson, W., & Fraser, P. (2010). Milk, money, muck and metrics: inefficient resource

allocation by New Zealand dairy farmers. Paper presented at the 2010 Conference, August 26-27, 2010, Nelson, New Zealand.

Ridler, B., Rendel, J., & Baker, A. (2001). Driving innovation: Application of Linear Programming

to improving farm systems. Paper presented at the PROCEEDINGS OF THE CONFERENCE-NEW ZEALAND GRASSLAND ASSOCIATION.

Roberts, A., & Watkins, N. (2014). ONE NUTRIENT BUDGET TO RULE THEM ALL–THE OVERSEER® BEST PRACTICE DATA INPUT STANDARDS.

Robson, M., & Edmeades, D. (2010). IS DAIRYING SUSTAINABILE? A CASE STUDY. Rotz, C. A., Taube, F., Russelle, M. P., Oenema, J., Sanderson, M. A., & Wachendorf, M. (2005).

Whole-Farm Perspectives of Nutrient Flows in Grassland Agriculture This paper resulted from the symposium “Whole-Farm Perspectives of Nutrient Flows in Grassland Agriculture” sponsored by Division C6 at the Annual Meetings of the Crop Science Society of America, Denver, CO, 3 Nov. 2003. Crop Sci., 45(6), 2139-2159. doi: 10.2135/cropsci2004.0523

101

Roygard, J., & McArthur, K. (2008). A Framework for Managing Non-Point Source and Point Source Nutrient Contributions to Water Quality (H. R. Council, Trans.). In J. Watson & A. Beveridge (Eds.), (Vol. Technical Report to Support Policy Development).

Russelle, M. (1997). Nutrient management of humid, temperate region forages: Recommendations and practices. Proc. 18th Int. Grassland Congr., Winnipeg and

Saskatoon, Canada, 8-19. Rutledge, D. T., Cameron, M., Elliott, S., Fenton, T., Huser, B., McBride, G., . . . Woods, R. A.

(2008). Choosing Regional Futures: Challenges and choices in building integrated models to support long-term regional planning in New Zealand*. Regional Science Policy &

Practice, 1(1), 85-108. doi: 10.1111/j.1757-7802.2008.00006.x Rutledge, D. T., Cameron, M., McBride, G., McDonald, G., Phyn, D., Poot, J., . . . Delden, H.

(2007). Creating Futures (Choosing Regional Futures - FRST Project) Spatial Decision Support System Regional Science Policy & Practice (Vol. 1, pp. 85-108): Landcare Research.

Ryan, W., Hennessy, D., Boland, T. M., & Shalloo, L. (2012). The effect of grazing season length on nitrogen utilization efficiency and nitrogen balance in spring-calving dairy production systems. The Journal of Agricultural Science, 150(05), 630-643. doi: doi:10.1017/S002185961200010X

Ryan, W., Hennessy, D., Murphy, J. J., Boland, T. M., & Shalloo, L. (2011). A model of nitrogen efficiency in contrasting grass-based dairy systems. Journal of Dairy Science, 94(2), 1032-1044. doi: http://dx.doi.org/10.3168/jds.2010-3294

Samarasinghe, O., & Greenhalgh, S. (2013). Valuing the soil natural capital: a New Zealand case study. Soil Research, 51(4), 278-287.

Santos, H. B. (2003). Effects of forage source and dietary protein content on milk production and

nitrogen utilization by lactating cows: University of Wisconsin--Madison. Schils, R. L. M., Olesen, J. E., del Prado, A., & Soussana, J. F. (2007). A review of farm level

modelling approaches for mitigating greenhouse gas emissions from ruminant livestock systems. Livestock Science, 112(3), 240-251. doi: http://dx.doi.org/10.1016/j.livsci.2007.09.005

Shadbolt, N., & Martin, S. (2005). Farm management in new zealand: Oxford University Press. Shadbolt, N., Newman, M., & Lines, I. (2007). DAIRY FARM BUSINESS ANALYSIS; CURRENT

APPROACHES AND A WAY FORWARD. Paper presented at the 16TH INTERNATIONAL FARM MANAGEMENT ASSOCIATION CONGRESS A VIBRANT RURAL ECONOMY–THE CHALLENGE FOR BALANCE.

Shalloo, L., Dillon, P., Rath, M., & Wallace, M. (2004). Description and Validation of the Moorepark Dairy System Model. Journal of Dairy Science, 87(6), 1945-1959. doi: http://dx.doi.org/10.3168/jds.S0022-0302(04)73353-6

Shepherd, M., & Wheeler, D. M. (2010). OVERSEER® Nutrient Budgets - When developing a decision support tool, is it possible to please all of the people all of the time? . Farming’s

Future: Minimising Footprints and maximising margins., Occasional Report No. 23, pp. 192-202.

Shepherd, M., & Wheeler, D. M. (2012). OVERSEER® Nutrient Budgets–the next generation. Advanced Nutrient Management: Gains from the Past-Goals for the Future.(Eds LD Currie

and C L. Christensen). http://flrc. massey. ac. nz/publications. html. Occasional

Report(25). Shepherd, M., Wheeler, D. M., Selbie, D., Buckthought, L., & Freeman, M. (2013). OVERSEER®:

ACCURACY, PRECISION, ERROR AND UNCERTAINTY. Sherwood, M. (1986). Nitrate leaching following application of slurry and urine to field plots. Smith, C. M., Wilcock, R. J., Vant, W. N., Smith, D. G., & Cooper, A. B. (1993). freshwater quality

in New Zealand and the influence of agriculture Wellington: New Zealand Government Retrieved from maxa.maf.govt.nz/mafnet/publications/9310a.pdf .

102

Spek, J. W., Dijkstra, J., van den Borne, J. J., & Bannink, A. (2012). Short communication: Assessing urea transport from milk to blood in dairy cows. Journal of Dairy Science,

95(11), 6536-6541. doi: http://dx.doi.org/10.3168/jds.2012-5395 Stafford, A., & Peyroux, G. (2013). CLEARVIEW (BALLANCE PGP)–A FIRST LOOK AT NEW

SOLUTIONS FOR IMPROVING NITROGEN AND PHOSPHORUS MANAGEMENT. Accurate

and efficient use of nutrients on farms. Eds. Currie, LD. Statistics New Zealand. (2012). Agricultural Production Statistics: June 2012. from

http://www.stats.govt.nz/browse_for_stats/industry_sectors/agriculture-horticulture-forestry/AgriculturalProduction_final_HOTPJun12final.aspx

Stout, W. L., & Jung, G. A. (1992). Influences of Soil Environment on Biomass and Nitrogen Accumulation Rates of Orchardgrass. Agron. J., 84(6), 1011-1019. doi: 10.2134/agronj1992.00021962008400060021x

Tamubula, I., & Sinden, J. A. (2000). Sustainability and economic efficiency of agroforestry systems in Embu District, Kenya: An application of environmental modelling. Environmental Modelling & Software, 15(1), 13-21. doi: http://dx.doi.org/10.1016/S1364-8152(99)00028-6

Teague, M. L., Bernardo, D. J., & Mapp, H. P. (1995). Farm-level economic analysis incorporating stochastic environmental risk assessment. American Journal of Agricultural Economics,

77(1), 8-19. Teitzel, J., Gilbert, M., & Cowan, R. (1991). Sustaining productive pastures in the tropics. 6.

Nitrogen fertilized grass pastures. Tropical Grasslands, 25, 111-118. Ten Berge, H. F. M., Van Ittersum, M. K., Rossing, W. A. H., Van de Ven, G. W. J., & Schans, J.

(2000). Farming options for The Netherlands explored by multi-objective modelling. European Journal of Agronomy, 13(2–3), 263-277. doi: http://dx.doi.org/10.1016/S1161-0301(00)00078-2

The Treasury. (2013). New Zealand Economic and Financial Overview 2013. New Zealand Government Retrieved from www.treasury.govt.nz/economy/overview/2013/nzefo-13.pdf .

Titchen, N., & Scholefield, D. (1992). Strategy of fertilizer nitrogen applications to grassland. Aspects of Applied Biology, 30, 223-229.

Totty, V. K., Greenwood, S. L., Bryant, R. H., & Edwards, G. R. (2013). Nitrogen partitioning and milk production of dairy cows grazing simple and diverse pastures. Journal of Dairy

Science, 96(1), 141-149. doi: http://dx.doi.org/10.3168/jds.2012-5504 Traoré, N., Landry, R., & Amara, N. (1998). On-farm adoption of conservation practices: the role

of farm and farmer characteristics, perceptions, and health hazards. Land Economics, 114-127.

Trigg, T., & Parr, C. (1981). Aspects of energy metabolism of Jersey cows differing in breeding

index. Paper presented at the Proceedings of the New Zealand Society of Animal Production.

Trucano, T. G., Swiler, L. P., Igusa, T., Oberkampf, W. L., & Pilch, M. (2006). Calibration, validation, and sensitivity analysis: What's what. Reliability Engineering & System Safety,

91(10), 1331-1357. Tyrrell, H., Reynolds, C., & Baxter, H. (1990). Energy metabolism of Jersey and Holstein cows fed

total mixed diets with or without whole cottonseed. Journal of Dairy Science,

73(Supplement 1). van Duinkerken, G., Smits, M. C. J., André, G., Šebek, L. B. J., & Dijkstra, J. (2011). Milk urea

concentration as an indicator of ammonia emission from dairy cow barn under restricted grazing. Journal of Dairy Science, 94(1), 321-335. doi: http://dx.doi.org/10.3168/jds.2009-2263

Van Es, A. (1961). Between-animal variation in the amount of energy required for the

maintenance of cows. Wageningen: Pudoc.

103

Vellinga, T. V., Van Der Putten, A. H. J., & Mooij, M. (2001). Grassland management and nitrate leaching, a model approach. NJAS - Wageningen Journal of Life Sciences, 49(2–3), 229-253. doi: http://dx.doi.org/10.1016/S1573-5214(01)80009-9

Vetsch, J. A., Randall, G. W., & Russelle, M. P. (1999). Reed Canarygrass Yield, Crude Protein, and Nitrate N Response to Fertilizer N. jpa, 12(3), 465-471. doi: 10.2134/jpa1999.0465

Vogeler, I., Vibart, R., Mackay, A., Dennis, S., Burggraaf, V., & Beautrais, J. (2014). Modelling pastoral farm systems — Scaling from farm to region. Science of The Total Environment,

482–483(0), 305-317. doi: http://dx.doi.org/10.1016/j.scitotenv.2014.02.134 Waghorn, G., Burke, J., & Kolver, E. (2007). Principles of feeding value. Pasture and Supplements

for Grazing Animals. Occasional Publication(14), 35-59. Waghorn, G., Shelton, I., McNabb, W., & McCutcheon, S. (1994). Effects of condensed tannins

Lotus pedunculatuson its nutritive value for sheep. 2. Nitrogenous aspects. J Agricultural

Science, 123, 109-119. Walker, S., Price, R., & Rutledge, D. T. (2008). New Zealand's remaining indigenous cover: recent

changes and biodiversity protection needs: Science & Technical Pub., Department of Conservation.

Webb, J., Pain, B., Bittman, S., & Morgan, J. (2010). The impacts of manure application methods on emissions of ammonia, nitrous oxide and on crop response—A review. Agriculture,

ecosystems & environment, 137(1–2), 39-46. doi: http://dx.doi.org/10.1016/j.agee.2010.01.001

Webby, R. W., McCall, D. G., & Blanchard, V. J. (1995). An evaluation of the Stockpol model (Vol. 55): New Zealand Society of Animal Production.

Wedderburn, M. E., Kingi, T. T., Mackay, A. D., Brown, M., DE Oca, O. M., Maani, K., . . . Manhire, J. (2011). Exploring rural futures together.

West, C., & Mallarino, A. (1996). Nitrogen transfer from legumes to grasses. Paper presented at the Proceeding of Symposium “Nutrient cycling in forage systems”. Ed. Joost, RE: and Roberts, CA Columbia, Missouri.

Wheeler, D. M., Cichota, R., Snow, V., & Shepherd, M. (2011). A revised leaching model for OVERSEER® Nutrient Budgets. Adding to the knowledge base for the nutrient manager.

Eds Currie LD and Christensen C L. http://flrc. massey. ac. nz/publications. html.

Occasional Report(24). Wheeler, D. M., Ledgard, S. F., De Klein, C. A. M., Monaghan, R. M., Carey, P. L., McDowell, R. W.,

& Johns, K. L. (2003). OVERSEER® nutrient budgets–moving towards on-farm resource

accounting. Paper presented at the Proceedings of the New Zealand Grassland Association.

Wheeler, D. M., Ledgard, S. F., & DeKlein, C. A. M. (2008). Using the OVERSEER nutrient budget model to estimate on-farm greenhouse gas emissions. Australian Journal of

Experimental Agriculture, 48(2), 99-103. doi: http://dx.doi.org/10.1071/EA07250 Wheeler, D. M., Ledgard, S. F., & Monaghan, R. M. (2007). Role of the Overseer® nutrient budget

model in nutrient management plans. Designing sustainable farms: critical aspects of soil

and water management, 58-62. Wheeler, D. M., Ledgard, S. F., Monaghan, R. M., McDowell, R. W., & De Klein, C. A. M. (2006).

OVERSEER® nutrient budget model-what it is, what it does. Implementing sustainable

nutrient management strategies in agriculture. Occasional Report(19), 231-236. Wheeler, D. M., & Shepard, M. (2013). Overseer® - answers to commonly asked questions. Wheeler, D. M., Shepherd, M., Freeman, M., & Selbie, D. (2014). OVERSEER® NUTRIENT

BUDGETS: SELECTING APPROPRIATE TIMESCALES FOR INPUTTING FARM MANAGEMENT AND CLIMATE INFORMATION.

Whitehead, D. C. (1995). Grassland nitrogen: CAB international.

104

Wilcock, B., Biggs, B. J. F., Death, R. G., Hickey, C., Larned, S. T., & Quinn, J. M. (2007). Limiting

nutrients for controlling undesirable periphyton growth. Prepared for Horizons Regional

Council. Hamilton: NIWA Retrieved from www.niwa.co.nz. Wilcock, R. J., Nagels, J. W., Rodda, H. J. E., O'Connor, M. B., Thorrold, B. S., & Barnett, J. W.

(1999). Water quality of a lowland stream in a New Zealand dairy farming catchment. New Zealand Journal of Marine and Freshwater Research, 33(4), 683-696. doi: 10.1080/00288330.1999.9516911

Williams, R., Brown, H., Dunbier, M., Edmeades, D., Hill, R., Metherell, A., . . . Thorburn, P. (2011). A critical examination of the role of OVERSEER® in modelling nitrate losses from arable crops.

Willock, J., Deary, I. J., Edwards-Jones, G., Gibson, G. J., McGregor, M. J., Sutherland, A., . . . Grieve, R. (1999). The Role of Attitudes and Objectives in Farmer Decision Making: Business and Environmentally-Oriented Behaviour in Scotland. Journal of Agricultural

Economics, 50(2), 286-303. doi: 10.1111/j.1477-9552.1999.tb00814.x Woodward, S. L., Waghorn, G. G., Watkins, K. A., & Bryant, M. A. (2009). Feeding birdsfoot trefoil

(Lotus corniculatus) reduces the environmental impacts of dairy farming. Paper presented at the Proceedings of the 69th Conference of the New Zealand Society of Animal Production, Canterbury, New Zealand, 24-26 June 2009.

Woodward, S. R., Romera, A. J., Beskow, W. B., & Lovatt, S. J. (2008). Better simulation modelling to support farming systems innovation: Review and synthesis. New Zealand Journal of

Agricultural Research, 51(3), 235-252. doi: 10.1080/00288230809510452 Yin, R. K. (2008). Case study research: Design and methods (Vol. 5): SAGE Publications,

Incorporated.

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APPENDICES

APPENDIX A

INFORMATION SHEET AND PARTICIPANT CONSENT FORM

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INFORMATION SHEET

Modelling a whole farm system within a sensitive catchment to determine if the imposed leaching limits affect the farms economic viability Purpose of the study The purpose of this study is to analyse a farm system in a sensitive catchment using a whole-farm modelling approach, scenarios will be first developed around mitigating N-leaching to within the allocated N-leaching limits on different LUC class types soils. A whole-farm modelling approach will be used to incorporate the different scenarios and compared against each other to evaluate which option best suits the stakeholder and is economically viable under the One Plan. Overall aims of the project

To answer the research question: Can a dairy farm in a sensitive catchment be

economically viable?

Information As part of modelling a whole farm system relating to mitigating N leaching and economic viability of a farm, I require the collection of data, physical and financial and some follow up questions regarding the current system. I endeavour to do this before the end of 2013. The data collection along with the interview is expected to take no longer than three hours. The interview is expected to be recorded and transcribed. You are under no obligation to accept this invitation. If you choose to participate, you have the right to:

• decline to answer any particular question;

• withdraw from the study at any time;

• ask any questions about the study at any time during participation;

• provide information on the understanding that your name will not be used unless you give permission to the researcher;

• be given access to a summary of the project findings when it is concluded, and;

• ask for the recorder to be turned off at any time.

A copy of the transcript will be provided so you can request any parts or comments to be excluded from the final dissertation. The findings will be verified with you before being written up. The verified findings will then be published in a dissertation. Confidentiality of identity will be preserved through not providing your name in any of the publications. Requested documentation

• A completed nutrient budget plan along with OVERSEER® .xml file.

• Three years of DairyBase level 1 and level 2 data or financial information for

three years.

• Pasture growth rates, feed budget.

Interview A suitable time for the farm owner will be selected and the farmer will be asked permission for the interview to be recorded. The data gathered is then analysed so any questions relating to gaps or clarification of the physical and financial information can be obtained. The interview will then proceed with questions relating to any processes undertaken relating to the research. On completion of the interview the farm owner will

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be thanked for their time and will be informed if further information will then be required or if any data is needed for the project. Sample research questions

• How do you handle stock in winter, off grazing, at home and why do you do it

this way?

• How do you feed the animals supplements and why do you do it this way?

• How is the effluent handled and why do you do it this way?

• How much fertiliser do you apply, timing, application and why do you do it this

way?

• Do you have crops and how is this method achieved and why is it done this way?

• Do you have any restriction on how a system change could take place and why

do you currently run the system like this?

The researchers of the project are Trevor Sulzberger an honours year student at Massey

University. Mr Tom Phillips and Prof Nicola Shadbolt from the Institute of Agriculture &

Environment, Massey University will provide support and supervision.

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Researchers address in New Zealand 108 Gillespies Line Cloverlea Palmerston North trevor@sulzberger.net.nz Telephone Number: +64 6 3536680 Mobile: +64 27 243 5436 Supervisors’ address “This project has been evaluated by peer review and judged to be low risk.

Consequently, it has not been reviewed by one of the University’s Human Ethics

Committees. The researcher named above is responsible for the ethical conduct of this

research.”

“If you have any concerns about the conduct of this research that you wish to raise with

someone other than the researcher, please contact Professor John O’Neill, Director,

(Research Ethics), telephone 06 350 5249, email humanethics@massey.ac.nz.”

Prof Nicola Shadbolt

Institute of Agriculture & Environment, PN 433

College of Sciences, Massey University,

Private Bag 11-222, Palmerston North

Email: N.M.Shadbolt@massey.ac.nz

Mr Tom Phillips

Institute of Agriculture & Environment, PN

433

College of Sciences, Massey University,

Private Bag 11-222, Palmerston North

Email: t.i.phillips@massey.ac.nz

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PARTICIPANT CONSENT FORM

Project Title: A whole farm modelling approach to evaluate the economically viability of a dairy farm in a sensitive catchment.

Researcher: Trevor Sulzberger

I have been provided with information about this research project and have had the

opportunity to clarify any questions I may have.

YES/NO

I understand the information I provide is confidential and that my name will not be used

in project reports or publications.

YES/NO

I agree to participate in this study under the conditions set out in the Information Sheet.

YES/NO

I agree to abide by the above conditions.

Signature: Date:

Full Name - printed

108 Gillespies Line Cloverlea Palmerston North trevor@sulzberger.net.nz Telephone Number: +64 6 3536680 Mobile: +64 27 243 5436

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APPENDIX B: GSL DATA FROM CASE STUDY FARM

Production Scenarios

Case study farm analysis

Base Farm Nx - 7.5% Nx - 15% 4 Nx - 15% Nx -15% Nx - 20% Nx -25% Nx - 30% Nx -35% Nx -40%

115ha Non Irrg Opt Cows Opt Cows Opt Cows Opt Cows Opt Cows Opt cows Opt cows Opt cows Opt cows

98 ha Irrg No Oats No Oats No Oats, Chicory

No Oats, Chicory

No Oats, Chicory

No Oats, Chicory No Oats, Chicory

38 ha effluent

N appln100 kg/ha Opt N 1 - 100 Opt N 1 - 100 No N No N No N No N No N No N

89627 NX 82509 NxLmt 76183 NxLmt 76183 NxLmt 76183 NxLmt 70447 NxLmt 67220NxLmt 62739 NxLmt 58258 NxLmt 53776 NxLmt

Run 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

Total KG MilkSolids 268,695 237,895 223,579 218,356 218,477 194,654 185,423 172,888 160,359 145,625

Income $1,740,323 $1,535,622 $1,443,275 $1,409,546 $1,410,359 $1,256,564 $1,196,918 $1,116,037 $1,035,155 $940,021

Farm Working Exps $1,077,834 $771,651 $703,556 $668,123 $635,079 $498,343 $473,431 $454,792 $438,723 $409,395

$Surplus $662,489 $763,971 $739,719 $741,423 $775,280 $758,221 $723,487 $661,245 $596,432 $530,626

Number Milking Cows 620 521 490 478 479 426 406 379 351 319

Milking Cows per Ha 2.9 2.4 2.3 2.2 2.2 2.0 1.9 1.7 1.6 1.5

Kg/Milk Solids/Cow 433 457 456 457 456 457 457 456 457 457

Supplements Made KG/DM 0 0 0 30,960 0 59,676 10,766 0 0 0

Pasture discard (Kg/DM) 0 0 0 0 0 0 90,767 237,838 389,084 429,989

Purchased feeds

Maize Silages Purchased KG/DM

240,000 0 0 0 0 0 0 0 0 0

PKE Purchased KG DM 220,000 0 0 47,123 0 0 0 0 0 0

Crop Area Chicory (Ha) 25 25 25 25 25 0

Crop Area Oats (Ha) 15 15 15 0 0 0

Average Crop+pasture DM/Ha 12,000 12,000 12,000 12,000 12,000 0

Crop Total KG/DM 480,000 480,000 480,000 300,000 300,000 0 0 0 0 0

Total N Excreted (Urine) Nx 89,627 68,991 76,183 76,183 70,447 70,447 67,220 62,739 58,258 53,776

Total N Retained 19,070 13,630 15,842 15,472 15,481 13,789 13,134 12,247 11,359 10,235

KG N Leach/Ha (Overseer) 23 22 20 13 13 9 9 9 9 8

N Conversion Efficiency % 43 45 54 54 53 50 54 60 65 67

Total GHG Emissions (CO2) 15580 13917 10936 10511 10478 9372 8916 8520 8139 7540

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