Reducing Greenhouse Gas Emissions through the use of Urease Inhibitors; A Farm Level Analysis

Domna Tzemia, James Breenb

aPostdoctoral Researcher, University of Lincoln, corresponding authorEmail: dtzemi@lincoln.ac.ukPhone: +447526052365Address: Lincoln International Business School, Brayford Pool, Lincoln, LN67TS, UK bAssistant Professor, University College DublinEmail: james.breen@ucd.iePhone: +35317167764Address: School of Agriculture & Food Science, Belfield, Dublin 4, Ireland

Highlights

A linear programming optimisation model for a representative dairy farm was

developed with the objective function of maximising farm gross margin under a

number of GHG emissions restriction scenarios.

This paper revealed that CAN, which is widely used by Irish farmers, is the least

profitable and environmentally efficient source of fertiliser.

The results indicate that in the absence of abatement strategies, farmers would have

to reduce livestock numbers in order to achieve potential GHG emission constraints.

The application of urea + NBPT allows for more livestock to be kept on the farm

while still meeting the GHG emissions constraint.

AbstractIreland’s ambitious plan to expand primary agricultural production and its commitment to reduce its non-ETS GHG emissions by 20% compared with 2005 emission levels constitute a considerable challenge for Irish farmers. Nitrous oxide emissions produced as a result of the application of artificial fertiliser accounts for 16% of Ireland’s agricultural GHG emissions (Teagasc, 2017a). The use of urea combined with NBPT has the potential to reduce GHG emissions from agriculture when compared with conventional fertilisers. This paper presents a farm level model which maximizes farm gross margin subject to constraints on production factors (labour, land etc.) as well as agronomic constraints (stocking rate, fertilisers, feed, etc.). The aim of this paper is to compare the farm gross margin of two dairy farm types under a base scenario with the results of five other scenarios which consider varying levels of emissions reduction targets and the potential of urea combined with NBPT as an abatement technology. Results inferred that there is a potential for urea combined with NBPT to offset nitrous oxide emissions from fertiliser application and hence contribute to Ireland’s GHG reduction target. Keywords: LP model, urea + NBPT, GHG mitigation, dairy production

1. Introduction

1

Agriculture is the single largest contributor to Ireland’s greenhouse gas (GHG)

emissions accounting for 33% of national emissions (EPA, 2016). Emissions from the

agricultural sector in Ireland are predominantly from ruminant animals, beef and dairy cattle

and to a lesser extent sheep (EPA, 2015). The dominant emission is Methane (CH4), which is

mainly produced by the ruminant’s digestion process, or from stored manure and Nitrous

Oxide (N2O) arising from natural processes of the nitrogen (N) cycle, when animal manure

and the application of N synthetic fertiliser to the soil (DAFM, 2017). In 2015, livestock

enteric fermentation was the cause of 55.3% of total agricultural emissions, while the

application of chemical and organic fertilisers’ as well as manure management were

responsible for 41% of total agricultural emissions (EPA, 2016).

Within the Irish agricultural sector, dairy farms have on average the highest GHG

emissions per farm, due to a higher intensity of production, as well as a larger farm size than

other livestock farms and typically a higher rate of application of synthetic fertiliser. Irish

dairy farming systems are primarily based on grazed grassland with spring calving of cows, in

order to maximise the utilisation of grass as a cheap feed source for the production of milk

(O'Donovan et al., 2011).

For the post-Kyoto protocol period 2013-2020, Ireland has agreed to reduce its

national GHG emissions by 20% compared with 2005 emission levels, while more recently

Ireland along with 194 other countries committed to limiting increases in global

temperatures to less than 2°C as part of the Paris Climate Agreement which was signed in

December 2015. Furthermore, the European Communities Regulations 2014 (Good

Agricultural Practice for the Protection of Waters), include the implementation of the

Nitrates Directive in Ireland, which limits the application of organic N to 170 kg of N from

livestock manure per hectare per year1 (DAFM, 2016). Therefore, the prospect of increasing

environmental regulation places a substantial burden on the agricultural sector and may

compromise the ambitious growth targets identified for the sector in the FoodWise 2025

plan (DAFM, 2017).

Achieving reductions in the volume of GHG emissions from Irish agriculture without

compromising food production represents a considerable challenge to the sector and to

achieve even moderate reductions in emissions would be likely to require a suite of 1 Farmers may apply for a derogation to permit an average organic N application rate up to 250 kg/ha, if they have more than 80% grassland.

2

mitigation measures involving changes in farm management practices and the adoption of

abatement technologies. Schulte and Donnellan (2012) and Lanigan and Donnellan (2018)

provide a detailed discussion of the range of mitigation measures available including

changes in farmers choice of synthetic fertiliser and fertiliser application methods. The

application of N fertilisers in order to achieve higher crop yields and sustain higher animal

stocking rates is common practice on the majority of European farms. N fertiliser application

is estimated to contribute significantly to food production (€20-80 billion of profit annually

for EU farmers) (Sutton et al., 2011). The global consumption of straight N fertilisers is 137.7

Mt N/year of which 63% is urea and 10% is ammonium nitrate/calcium ammonium nitrate

(AN/CAN). However, in Western Europe AN/CAN is the principal N fertiliser (Harty et al.,

2016). In Ireland, CAN and urea application on grassland is in the ratio of 84:16 (EPA, 2015).

The limited use of urea in Ireland is possibly due to past experiments which showed that

urea was less effective than other straight forms of N (Smil, 2001).

Urea is sensitive to ammonia volatilisation due to soil conditions and climatic factors

post-fertiliser application (Watson, 2000). However, it has been proven that the use of

urease inhibitors reduces ammonia (NH3) emissions from urea applied in arable soil (Abalos

et al., 2012, San Francisco et al., 2011). The way inhibitors act is by preventing the hydrolytic

action of soil urease, which will result in a delay of the rate of urea hydrolysis to ammonium

(NH 4+¿¿-N). Hence, NBPT moderates NH 4

+¿¿-N concentrations which result from urea

hydrolysis and are conductive to NH3 volatilisation (Harty, et al., 2016). Therefore, if Irish

farmers were to switch from the application of CAN to utilising urea with a urease inhibitor

it would have the potential to contribute to a reduction in Irish GHG emissions by nearly

60% in terms of fertiliser emissions, as reported in Harty et al. 2016.

The aim of this paper is to examine the potential for urea + NBPT to contribute to the

challenge of reducing GHG emissions on Irish dairy farms. With this research aim in mind, a

linear programming (LP) optimisation model for two typical dairy farms was developed with

the objective function of maximising farm gross margin under a number of GHG emissions

restriction scenarios.

2. Background

3

A large volume of literature has studied the challenges farmers are facing when

trying to improve efficiency, reduce costs and/or increase productivity under the constraints

of environmental and agricultural policies (Breen, 2008, Hennessy et al., 2005, Shalloo et al.,

2004), as well as, the impact on greenhouse gas emissions from changing mitigation

strategies (Hawkins et al., 2015). Farm level models have the advantage of comparing the

potential impact of alternative policy scenarios on the level of production activity and on

farm financial performance. LP models have been widely used in farm level analysis, to

examine the impact of policy on farm performance (Hennessy et al., 2005; Petersen et al.,

2003) or the assessment of the economic and environmental performance of livestock

production systems (Janssen and Van Ittersum, 2007).

Crosson et al. (2006), used LP to examine the impact on farm gross margin of beef

concentrate prices and technical development through the integration of an alternative

forage (maize) and the participation in agri-environmental schemes that limit N application.

Results demonstrated that improved grassland management has the potential to increase

grass utilisation rates. Participation in agrienvironmental schemes that limit N application

resulted in increased gross margin, while there was little change in enterprise output.

Hawkins et al. (2015), used a bio-economic model to identify the formulation of the

ration and crop production decisions that maximize farm profit while reaching specific GHG

emission targets. This study showed that farmers’ feeding decisions had important

implications for GHG emissions from intensive dairy production due to variations in

emissions from alternative crops that can be used in the ration. Petersen et al. (2003), used

a LP model of a steady-state single period to identify the impact of three GHG emission

abatement policies (tax on GHG emissions, tax on CH4 emissions and restrictions on the

amount of emissions allowed) on mixed cropping farms in Western Australia. Comparing the

three policies, restrictions on GHG emissions were found to be the most cost-effective

policy.

Schmit and Knoblauch (1995) used LP to estimate the economically optimal dairy

stocking rates (herd intensities), manure application rates, and crop mix for both

unrestricted and restricted N loss scenarios on New York dairy farms. They found that

restrictions on N loss reduced mean losses of N per hectare and had a substantial impact on

production decisions and profitability of dairy farms. Moraes et al. (2012) developed a LP

model to formulate diets for dairy cattle to reduce enteric CH4 emissions and found that an

4

increase in diet costs by 5% led to a 5% reduction in CH4, while a 13.5% reduction in GHG

emissions required more expensive (48.5%) ration than the base formulation.

3. Materials and Methods

3.1. Cluster analysis

The Teagasc National Farm Survey (NFS) surveys approximately 900 farms nationally

that are weighted to represent the total population of over 100,000 farms. The dataset

comprises of 296 specialist dairy farms which were grouped in 4 clusters. Utilising data from

the 2015 Teagasc NFS, a cluster analysis was used to identify groups of farms with similar

characteristics. Cluster analysis is a multivariate technique, accomplished mathematically,

and is used to gather similar cases into the same cluster (Romesburg, 2004). Commonly

used clustering techniques include hierarchical, non-hierarchical, iterative partitioning and

factor analytic techniques. The method of cluster analysis used in this study is the

agglomerative hierarchical technique which was carried out using Stata software. In the

agglomerative hierarchical technique, each object represents an individual cluster which is

then sequentially merged according to its similarity with another cluster (the smallest

distance between them). Previous farm level analyses that have used hierarchical cluster

analysis to make groups include (Rey and Das, 1997, Shrestha et al., 2007). The variables

used in the formation of clusters were soil type, total pasture area in hectares, milk yield

and stocking rate. The selection of the most representative cluster was followed by the

estimation of the mean values of this cluster which were then used in the construction of

the farm-level model.

A cluster with 137 farms was chosen as the most representative of a mid-sized dairy

farm (Table 1). The basic characteristics of this cluster were a predominant soil type on the

farm with a wide use-range, stocking rate of 2.09 livestock units per hectare, average total

Utilisable Agricultural Area (UAA) of 49.41 ha and an average milk yield per cow of 5367.51

litres per cow annually. In order to represent farm heterogeneity, a cluster with 42 farms

was also chosen to be compared with cluster 3. Both clusters have the same soil type, while

cluster 4 represents farms larger in size. In this study, it is assumed that the representative

farm types constructed from the cluster analysis, maximize farm market-based gross margin

subject to a number of constraints including land, labour, stocking density, policy related

5

(e.g. regulations stemming from the Nitrates Directive) and environmental (e.g. GHG

emissions).

Table 1. Results of cluster analysis

Variables N Soil type Tot pasture(ha)

Yield (litres) Stocking rate (LU/ha)

Cluster 1 90 2 53.09 5289.52 1.8Cluster 2 27 2.3 104.33 4799.52 1.81Cluster 3 137 0.99 49.41 5367.51 2.09Cluster 4 42 1 99.73 6395.14 1.94

3.1.1. Main characteristics of representative farms

The production activities available within the model include dairy, beef cattle and

tillage. The model consists of thirteen livestock activities; dairy cows, rearing of male and

female dairy calves, suckler beef cow, male and female beef calves, male and female beef

cattle younger than one year old, male and female beef cattle between one to two years

old, female and male cattle older than two years old and replacement heifers. Mixed dairy

beef enterprises were commonplace in Irish agriculture prior to the removal of the EU milk

quota regime in 2015. Furthermore, where labour availability is a binding constraint on a

farm the farmer may choose to keep fewer dairy cows and keep some beef cattle due to the

lower labour requirement for these animals. The average milk yield per cow annually is

5,367.51 litres for cluster 3 and 6395.14 litres for cluster 4, sold at a price of €0.3 per litre.

The assumed replacement rate for the dairy herd is 15% and the replacement heifers are

assumed to calve at 24 months.

A calf to cow rate of 0.9 is assumed for both the dairy herd and the suckler herd to

allow for calf mortality and cows who are not in calf. The potential activities that can be

chosen within the beef herd include keeping suckler cows on the farm with their progeny

sold as weanlings. The rearing of beef calves to be sold as store cattle. The rearing of both

male and female store beef animals purchased at approximately one year old and sold at

two years old to be finished or used as replacement beef heifers. The finishing of beef cattle

purchased at approximately two years of age is also included as an activity. The model is a

single period model and therefore transition of livestock units between different age groups

is not considered, however, a lower bound constraint is placed on the number of female

6

dairy calves and replacement heifers to ensure that the farm does carry adequate

replacement stock.

Returns per animal include the annual average market prices for each animal category in

2015. The prices for male and female dairy calves sold are assumed to be the same since the

NFS does not make any distinction between male and female calves (€191.29 per head). The

price for male and female cattle less than a year is €553.84 per head. Female and male

cattle between one and two years of age are sold at €885.24 and €899.5 per head

respectively, while male and female cattle older than 2 years are sold at €1280.48 and €1129

per head respectively. In the case of the cattle 1-2 year and cattle finishing enterprises it is

assumed that these animals are purchased at the start of the year and sold again within 12

months with the purchase price of these animals deducted from the sales price in the

calculation of gross output.

The cropping options considered within the model are to grow pasture for grazing,

as well as, grass silage, spring maize for silage, hay, spring arable silage all of which are to be

consumed on the farm as a winter feed for livestock or to grow cereal crops for sale off farm

including spring barley, winter wheat, spring malting barley, spring wheat, winter barley,

winter oats, and spring oats on 49.41 ha of land area in the third cluster and 99.73 ha in the

fourth cluster of farms. Crop requirements in terms of the nutrients N, phosphorus (P) and

potassium (K) were taken from (Teagasc, 2017).

3.2. The Linear Programming models

The LP models for two representative dairy farms were constructed using data from

the 2015 Teagasc National Farm Survey (NFS).

The general structure of the mathematical model typically consists of linear equations and

inequalities.

Maximise Z = c’x

Subject to Ax ≤ b and x ≥ 0.

Where z, the expression being optimized is the objective function and x is a vector of

activities; c the vector of gross margins per unit of activity; A is the matrix of technical

coefficient; and b is the vector of right-hand side values2.

2 For more detailed description of LP models see Berentsen and Giesen, (1990).

7

LP models make four main implicit assumptions; proportionality, additivity,

divisibility and certainty. In relation to proportionality, linear programs assume that the

contribution of individual variables in the objective function is proportional to their value.

Additivity, refers to adding up the individual contributions from each variable to obtain the

total value of the objective function and each constraint function. Divisibility refers to

variables’ option to take any numerical values within a range specified by the constraints.

Lastly, the assumption of certainty means that the parameter values in the model are

known with certainty to ensure that the optimal solution of the model is of great value

(Hillier, 2012). This assumption means that all the exogenous factors are assumed to be

known and fixed. However, the exogenous parameters of LP models are usually estimated

by statistical techniques, thus, LP is usually followed by sensitivity analysis. Sensitivity

analysis is performed by varying one of the exogenous parameters and observing the

sensitivity of the optimal solution to that variation (Hillier, 2012).

In this model a deterministic LP model is used, hence it is assumed that there is no

uncertainty in the model. One of the advantages of static LP models is that they are short

term and the level of uncertainty tends to be minimal. Hence, the results of this model can

be successfully used in decision making under the assumption of complete uncertainty.

3.3. General description of the model

The model is a single year steady-state LP model, developed to maximize farm gross

margin under different scenarios using the CPLEX solver of the general algebraic modelling

system (GAMS) software. The comprehensiveness of the model is reflected in the detailed

specification of the activities and constraints. The heterogeneity of dairy farms is captured

by comparing the results of two clusters representing a mid-sized Irish dairy farm and a

large scale dairy farm. The model was initially run without any restriction on GHG emissions

(base model). Results of the unrestricted base model are compared with the model results

under five different scenarios and are explained in section 3.6.

3.3.1. Fertilisers

The model considers the full range of artificial fertilisers applied to grassland by the

farmers within the NFS. These fertilisers were grouped into sixteen categories based on

their content in nutrients and frequency of use. Widely used fertilisers were considered on

8

their own, while those products that were used by only a couple of farms were merged with

a category with similar content in nutrients. Table 2 presents the sixteen categories of

fertilisers and their nutrient content for a 50-kg bag of fertiliser. The fertilisers with the

highest content in N are UREA (46%) and CAN (27%). Urea combined with the urease

inhibitor NBPT has the same nutrient content as untreated urea. Prices for the fertiliser

products are taken from the NFS and were calculated as the ratio of fertiliser cost (euro)

divided by the number of 50kg- bags allocated.

Table 2 Fertiliser nutrient units and costs per 50 kg-bag

Fertilisers Nutrients Cost (€ 50kg bag-1)N P K

UREA + NBPT 46 0 0 22.57a

UREA 46 0 0 20.34CAN 27 0 0 16Compound 15-3-20 15 3 20 21.68Compound 0-0-50 0 0 50 21.19Compound 22.7-2.5-5 22.7 2.5 5 21.18Compound 24-2.2-4.5 24 2.2 4.5 19.15Compound 24-2.5-10 24 2.5 10 21.4Compound 18-6-12 18 6 12 20.94Compound 23-0-0 23 0 0 17.37Compound 13-6-20 0 10 20 19.58Compound 10-10-20 10 10 20 21.78Compound 16-0-0 16 0 0 18.74Compound 38-0-0 38 0 0 21.26Compound 20-0-10 20 0 10 20.21Compound 25-2.2-20 25 2.2 20 18.41Compound 15-12-5 15 12 5 24.75

Source: NFS, 2015a Price was estimated based on current retail prices reported by expert knowledge

3.4. The objective function

The objective function of the model was to maximize the farm’s market based gross

margin within a set of constraints by choosing the optimal number of animals and hectares

of various crops to be grown, as well as, the mix of fertiliser products to be applied. Gross

margin was estimated as the difference between animal, milk and crop sales less the

purchase price of animals and the direct costs of production (see eq.1). Farm payments such

as the Single Farm Payment or Basic Payment Scheme or any form of agri-environmental

9

scheme payment are not included. The farm could generate gross output through the sales

of milk, livestock from the dairy and beef enterprises and crops. Market prices of each

fertiliser included in the model are given in Table 2. The equation of the objective function is

given below:

Maximize ∑i=1

9

Pricei∗X i + Pricemilk∗milk∗Xdair + ∑j=1

2

Price j∗Yield j∗Y j - ∑j=1

2

VCost j∗¿Y j ¿ -

∑i=1

13

VCost i∗¿X i ¿- ∑fr=1

16

Pricef∗FER f (1)

Where X iis the number of livestock units for each i animal category, and Y j is the hectares

of each j crop sold, and FERf the number of 50kg bags of each f commercial fertiliser that

maximize the objective function.Pricei, Pricemilk, Pricef and Price j are the prices of animals,

milk, fertilisers and crops respectively, while VCost i, VCost j are the variable costs for each

animal category and each crop category respectively.

3.5. Constraint equations

Farmers are expected to make decisions under a number of constraints. Some of the

constraints are associated with the scarcity of production factors. For example, the

constraints for available land and labour use are incorporated into the model. Agronomic

constraints such as stocking rate, nitrates regulation, fertilisers, and replacement cows are

also determined, and they are explained below.

Labour. The labour required on the farm is constrained by the available farm and

hired labour, totlab , measured in standard man days (SMD)3 per year. Animal labour

requirements data was based on the NFS and labour required by crops is based on a study

carried out by Shalloo et al., 2004. The labour equation constraint is represented by

∑i=1

13

X i∗l abreqi+∑j=1

11

Y j∗l aboreq j≤ totlab (2)

Where labreqi is the labour required for each animal category i, laboreq j is the labour

required for each crop j and totlab is the total available labour on the farm.

Land. Maximum available land must be also identified by a constraint in a LP

framework. The amount of land required to feed each livestock unit, which was determined

3 A standard man day equals eight hours of work supplied by a person over 18 years old.

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as animals’ requirement in forage area (grassland and silage in ha) and the area required by

crops to grow, is constrained by the available farm UAA (eq.3).

∑i=1

13

X i∗¿ foragereqi+∑bw

2

Y bw≤UAA ¿ (3)

Where foragereqi is the amount of forage area required for each animal category i and Y bw

is the hectares of land used for winter wheat and spring barley.

Stocking rate. Livestock units were constrained under a lower and upper fixed

stocking rate over grazing land. According to Teagasc very few farms have sufficient grass

growth to justify an overall farm stocking rate greater than 2.5 to 2.7 livestock units per

hectare. Therefore, stocking rate constraint was set to a range between 0 and 2.7 (Teagasc,

2015).

0≤∑i=1

13

stock ratei∗X i≤2.7 (4)

Where stock ratei is the estimated stocking rate for each i animal category based on their

grazing livestock unit equivalents.

Animal number constraints. In order to represent herd dynamics in the static

framework of this model, several restrictions on cow numbers have been applied.

Therefore,

X dfcalf ≤0.45∗Xdair (5)

X dmcalf ≤0.45∗X dair (6)

Where X dfcalf , the number of female dairy calves, and X dmcalf , the number of male dairy

calves, should not exceed 45% of the number of dairy cows, considering that the calf to cow

rate is assumed to be 0.9.

Similarly, the beef calves have been restricted as follows:

X bfcalf ≤0.45∗X suckl (7)

X bmcalf ≤0.45∗X suckl (8)

Where X bfcalf and X bmcalfthe female and male beef calves respectively and X suckl the number

of suckler cows.

Nitrate constraints. In 2005 Ireland designed the first Action Programme under the

Nitrates Directive in order to prevent pollution of surface waters and ground water from

agricultural sources and to improve the quality of water. Ireland’s recent Nitrate action

programme came into operation in 2014 and was revised in 2018. Under the Nitrates

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regulations, the total amount of N from organic manure applied to the farm (including that

deposited by animals themselves) should not exceed 170 kg of N per hectare (or 250 kg for

derogation farmers) (DAFM, 2016). Nitrogen excretion for each animal category has been

estimated e.g. dairy cows excrete 85 kg of N annually.

∑i=1

13

X i∗Nitr i≤170∗UAA (9)

Where Nitriis the N excretion for each animal category i.

Feed requirements. The feed requirements for animals were defined in terms of the

total energy required of each animal category, where energy produced from grass must

meet the livestock energy demand less feed energy purchased, which is the energy from

purchased concentrates and purchased bulky feeds. The energy from concentrates is

typically 0.94 Unité Fourragère Lait (Forage Unit for lactation) (UFL)4 per kg (O’Mara, 1996).

Therefore, the equation that describes feed demand and feed availability is as follows:

Total energy required – purchased energy ≤ ∑c=1

2

yieldc∗energyc∗Y c (10)

Where yieldc refers to the yield of c crops (pasture and silage) in Dry Matter (DM) kg ha-1 yr-1

and energyc refers to the amount of energy contained in a DM kg of pasture and silage (1

and 0.77 UFL respectively). Y c, is the area in hectares of crop c cultivated.

The total energy required was calculated based on five equations: maintenance

requirements, milk yield, requirements for foetal growth and body weight change. Each

component was estimated through the following equations:

Maintenance =

∑i=1

13 [((1.4+0.6∗(CLW i

100 ))∗(GSL365 )∗1.2+(1.4+0.6∗(CLW i

100 ))∗((1−(GSL365 ))∗1.1))∗365∗X i] (11)

Where CLW i stands for the cow live weight for each animal category i, and GSL represents

grazing season length, which is assumed to be 280 days (Shalloo et al., 2011).

Milk production =

( (0.054∗Fat% )+(0.031∗Prot% )+ (0.028∗Lact%)−0.015 )∗Totalmilk yield∗Xdair

(12)

4 UFL measures the units of feed for lactation and is a measurement of energy.

12

This equation is associated only with dairy cows and the proportion of fat and protein were

taken from the NFS in 2015 and are 4.03% and 3.5% respectively. The proportion of lactose

was assumed to be 4.3% (O'Brien, 2014).

Pregnancy = 153*X replac*0.95. This equation shows the energy required for pregnant

replacement heifers, where 0.95 is the assumed proportion of cows pregnant multiplied

with the estimated energy required for pregnant cows (153 UFL). The same equation is

applied for pregnant dairy cows but with an 85% proportion of pregnant cows.

Live weight change refers only to dairy cows as their live weight may fluctuate with

calving and milk production during the lactation. It is assumed that live weight change is

50UFL per cow per year (Shalloo et al., 2004).

Growth is estimated as the energy demanded for each animal category based on the

energy demanded for each animal category to grow.

Fertiliser constraint. In addition to organic fertilisers (eq.13), chemical fertilisers were

also constrained. Crop nutrient requirements were taken from Teagasc and Plunkett et al.

(2016). The following equation describes that nutrient allocation in kg from fertilisers should

at least meet crop nutrient demand in kg.

∑f=1

16

nitfert f∗F ERf ≥∑j=1

11

nitcrop j∗Y j (13)

Where nitfert f is the amount of N included in each fertiliser in kg, and F ER f is the amount

of 50kg bags for each commercial fertiliser bought. The amount of N requirements for each

crop c is given by nitcropcand the area occupied by each crop j in ha is given by Y j . In order

to meet the crop requirements in all three nutrients we created two more fertiliser

constraints for K and P nutrients separately.

CAN, urea ratio. In an attempt to capture the actual use of fertilisers in Irish farms,

the application of CAN and urea was constrained in the ratio of 84:16 according to Harty et

al. (2016).

16* F ERCAN = 84* F ERUREA (14)

Where F ERCAN and F ERUREA is the amount of 50kg bags of CAN and urea respectively.

Forage production. In order to capture the relationship that exists between

alternative levels of N application and associated DM response the following relationship

was used (eq.15). This relationship is based on a non-orthogonal hyperbola identified and

applied to describe the reaction of plants to nutrient supply (Berentsen and Giesen, 1995,

13

Thornley, 1976). Based on this hyperbola, Teagasc Moorepark research Centre using grass

growth data based on four alternative levels of N application, generated the following

function for annual grass DM production:

Yield = 16136.2(x )

(85.49091+x ) (Butler, 2005) (15)

3.6. Scenarios

3.6.1. Scenarios 1,2,3: Emission constraint by 15%, 25% and 35%

The emission accounting method used in this study follows the Intergovernmental

Panel on Climate Change approach (IPCC, 2001). In general, the IPCC approach of computing

GHG emissions relies on the linear relationship between emissions and the farm activities

through the use of emission factors (De Cara et al., 2005). Nitrous oxide emissions caused by

N inputs occur both directly and indirectly. Direct N2O emissions from managed soils

resulting from N fertilisers (inorganic and organic).

N2Odir –N = (FSN + FON) *EF1 + (FPRP,CPP*EF3PRP,CPP) (16)

where FSN is the amount of annual synthetic fertiliser N allocated, FON is the N of annual

manure applied and EF1 is the emission factor for N2O emissions coming from N inputs.

FPRP,CPP is the annual amount of urine and dung N deposited by grazing animals and EF3PRP,CPP is

the emissions factor for N2O emissions from urine and dung N deposited on pasture, range

and paddock by grazing animas. The general equation used for estimating Direct N2O

emissions from managed soils is equation 11.1 in volume 4, chapter 11 of the 2006 IPCC

guidelines.

Indirect emissions from managed soils come from atmospheric deposition (N2OATD), N

leaching and run-off (N2OL) (EPA, 2015). Equation 11.9 in Volume 4, Chapter 11 of the 2006

IPCC Guidelines is used to estimate the emissions.

N2OATD –N = [(FSN*FracGASF) + ((FON + FPRP) *FracGASM1)] *EF4 (17)

N2OL –N = (FSN + FON + FPRP) *FracLEACH * EF5 (18)

Where FracGASF is the fraction of synthetic fertiliser N that volatilises as NH3 and NOx, FracGASM1

is fraction of applied organic N fertiliser materials (FON) and of urine and dung N deposited

by grazing animals (FPRP) that volatilises as NH3 and NOx. FracLEACH is the fraction of all N

14

added to soils in regions where leaching/runoff occurs that is lost through leaching and

runoff.

The values of the emission factors for N2O emissions from all N fertilisers (E F1 ¿ and

from urea with NBPT (E FurNBPT) are given in Table 2. E F4 , E F5 are the emission factors for

N2O emissions from atmospheric deposition of N on soils and water surfaces and from N

leaching and runoff (EPA, 2015a). Table 3, displays the values of the emission factors and

fractions used in the model. Methane emissions arise from both enteric fermentation and

manure management and are calculated using the emission factors from the Irish EPA

emission inventory in 2015. Global warming potential for N2O (GWPN2O=298) and for CH4

(GWPCH4=25) are provided by the IPCC in its Fourth Assessment Report5.

The first scenario (S1) requires farmers to reduce GHG emissions by 15% compared

with the level of GHG emissions estimated in the base model. In the second (S2) and third

(S3) scenario the farm gross margin and farm activities were further examined under GHG

emissions constraints of 25% and 35% respectively. These scenarios are assumed to be

binding regulations of an environmental policy to reduce GHG emissions. Therefore, these

scenarios examine the potential impact of national regulations imposing a reduction in GHG

emissions at the farm-level without making any changes in the technology they are already

using.

The purpose of the first three scenarios is to evaluate the effect on farm gross

margin and production levels of a potential implementation of a 15%, 25% and 35%

reduction target in farms’ GHG emissions. As livestock account for the bulk of emissions on

the two farms modelled, under all three scenarios, the number of animals is expected to be

reduced resulting in a decline in farm gross margin. A reduction in the quantity of synthetic

fertilisers applied is also expected as a result of the reduction in total feed demand due to

reduced animal numbers.

Table 3 Direct and Indirect N2O emissions from managed soils

Parameter Emissions factor Sources of emission factorsDirect N2O emissionsEF1 0.01 kg N2O-N/N kg Table 11.1, volume 4, Ch 11 of the

IPCC GuidelinesEF3PRP,CPP 0.02 kg N2O-N/N kg Table 11.1, volume 4, Ch 11 of the

5 IPCC in its fifth assessment has estimated new values for GWPCH4=28, GWPN2O=265

15

IPCC GuidelinesEFurNBPT 0.004 Harty et al., 2016EFCAN 0.0149 Harty et al., 2016Indirect N2O emissionsEF4 0.01 kg N2O-N/N kg EPA, 2015a

EF5 0.0075 EPA, 2015a

FracGASF 0.019 EPA, 2015a

FracLeach 0.1 OSPAR Convention (NEUT, 1999, Prado et al., 2006, Richards et al., 2009, Ryan et al., 2006)

3.6.2 Scenario 4: Profit maximization scenario

In the fourth scenario (S4), the model is run again without the fertiliser constraint in

equation 14 and giving farmers the option to adopt urease inhibitors under no constraint in

GHG emissions. The most common commercially available urease inhibitor is Agrotain®,

which has been available in the US market since the mid 1990’s. This product contains the

active ingredient N-(n-butyl) thiophosphoric triamide (NBPT), a structural analog of urea

(Harty et al., 2016).The objective of this scenario is to allow the model to choose the set of

fertilisers that maximize farm gross margin and cover crops’ nutrient requirements without

considering fertilisers’ N efficiency.

3.6.3. Scenario 5: Urea combined with urease inhibitors (NBPT) plus 15% reduction in GHG

emissions

The fifth and last scenario is a combination of the first and fourth scenarios. This

scenario examines the potential impact of adopting Urea + NBPT as a GHG mitigating

strategy and its impact on farm gross margin under an emissions reduction scenario. The

comparison of S1 with S5 demonstrates the potential benefits of the adoption of the urease

inhibitor as an abatement practice under a potential GHG emission restriction policy. In

order to quantify the potential benefits of the urease adoption no other abatement

practices were considered.

4. Results

4.1. Base model

The objective function in the base scenario is to maximize annual farm gross margin

without any constraint on GHG emissions. The maximum annual gross margin for the

16

representative mid-sizes dairy farms was found to be €72,352.18 (see Table 4) which is

slightly higher than the average national gross margin of €70,410 for dairying system

recorded by the NFS in 2015 (Hennessy and Moran, 2017).The maximum gross margin of

the large scale farm is twice the gross margin of the average mid-sized farm. This is not

surprising considering that the farm size of the large scale farm is approximately two times

the size of the mid-sized dairy farm. In the baseline solution, the farmer keeps 93.32 dairy

cows, 13.99 female dairy calves and 13.99 replacement heifers, which is 121.3 LU, compared

with 107.17 LU recorded by the NFS in 2015 (Hennessy and Moran, 2017), while these

numbers are almost double on the larger scale farm.

In the baseline scenario the farm purchases 98.53 bags of urea, 517.28 bags of CAN,

257.76 bags of the fertiliser compound “25, 2.2, 20” and 125.89 bags of the compound “15,

12,5” and grows 37.8 and 15.85 ha of pasture and silage respectively (Table 4). The large

size farm uses the same types of fertilisers but at a larger scale (Appendix). In both farm

types there is no land allocated to the production of other crops for sale and all animal feed

requirements are met from the production of pasture and silage on the farm and purchased

concentrate feed. The total GHG emissions in the base model (466.58 CO2 tn eq) are

consistent with the GHG emissions emitted by the average Irish dairy farm (456 CO2 tn eq) in

2015 (Lynch et al., 2016).

4.2. GHG restricted models (S1, S2, S3)

Reducing emissions by 15%, 25% and 35% (S1, S2, S3), resulted in a decline in farm

gross margin of 11.78%, 18.87% and 25.95% respectively for the average dairy farm.

Similarly, the farm gross margin decline in the large size farm was of 14.91%, 21.56% and

28.21% respectively. The numbers of dairy cows, dairy calves and replacement heifers are

reduced in order to comply with GHG emissions targets; however, the loss in gross margin

from the reduction in animal numbers is partly offset by the reallocation of land no longer

needed for pasture or silage production to winter wheat.

The results indicate that in the absence of abatement strategies to offset GHG

emissions, farmers would have to reduce livestock numbers in order to achieve potential

GHG emission constraints. A significant reduction in dairy cows was expected given that

dairy cows have the highest GHG emissions coefficient per animal and the bulk of the

livestock on the farm were dairy cows. Each dairy cow emits approximately 3.86 tn CO 2 eq

17

year-1 per head on average, with replacement heifers following with 2.02 tn CO2 eq year-1

per head and dairy calves with 1.29 tn CO2 eq year-1. Therefore, it was expected that placing

a restriction on GHG emissions would induce a decrease in the number of cows resulting in a

reduction in the quantity of pasture and silage area needed as well as a reduction in the

quantity of fertilisers to be applied to grassland due to less feed demand.

4.3 Profit maximization scenario (S4)

Under this scenario farmers’ potential response to the introduction of urea + NBPT is

examined. The constraint in equation 14 and the constraint in GHG emissions are removed

and the model is allowed to choose the most financially beneficial fertiliser source under no

emissions constraint. It is noteworthy that in both farm types the model switches from CAN

and urea to only plain urea and instead of the “15, 12, 5” compound it switches to “10, 10,

20”. Comparing this scenario with the baseline scenario farm’s profit is better off from

replacing CAN with plain urea. Urea + NBPT was not chosen by the model as the most

profitable because its price per 50kg bag is higher than the price of CAN and urea.

Therefore, in this scenario the model has chosen urea as the main profitable source of N,

without taking

Variable Baseline scenario

S1 S2 S3 Profit maximization (S4)

Urea + NBPT,15% GHG restriction (S5)

15% 25% 35%Gross margin € 72,352.1

863,826.09

58,702.66

53,579.2

74,670.47 67,953.86

Change % -11.78 -18.87 -25.95 3.2 -6.08Livestock numbersDairy cows 93.32 75.40 64.63 53.86 93.32 81.83Male dairy calves - - - - -Female dairy calves 13.99 11.31 9.69 8.08 13.99 12.27Suckler cows - - - - - -Cattle calves - - - - - -Male cattle < 1 year - - - - - -Female cattle <1 year

- - - - - -

Male cattle 1 – 2 years

- - - - - -

female cattle 1 – 2 years

- - - - - -

Male cattle > 2 years - - - - - -Female cattle > 2 - - - - - -

18

yearsReplacement heifers 13.99 11.31 9.69 8.08 13.99 12.27Crops in haPasture 37.80 30.54 26.18 21.82 37.80 33.15Silage 15.85 12.81 10.98 9.15 15.85 13.90Winter Wheat - 10.30 16.49 22.68 - 6.60Spring barley - - - - - -FertilisersUrea 98.53 76.36 63.04 49.7

2 485.51 -

CAN 517.28 400.92 331.00 261.07 - -Urea + NBPT n/a n/a n/a n/a - 440.12“25, 2.2, 20” 257.762 308.15 338.43 368.71 104.47 113.62“15, 12, 5” 125.859 155.50 173.31 191.13 - -“10, 10, 20” - - - - 184.75 212.64GHG emissions (CO2

tn eq)466.58 396.59 349.94 303.27 443.4 396.59

Table 4. Results of the linear programming model for mid-sized farms

into account, however, the N efficiency of urea + NBPT. This is examined in the following

scenario.

4.4. Urea + NBPT Scenario plus 15% GHG restriction (S5)

Under this scenario, urea with NBPT is included in the model and simultaneously a

15% constraint on a farmer’s GHG emissions is imposed. In this scenario the farmer can

maximise gross margin by using Urea + NBPT as the preferred source of N requiring 440.12

50kg bags for the average size farm and 851.37 bags for the large farm. Comparing the

present scenario with the S1 scenario, in both scenarios a 15% restriction on GHG emissions

is imposed, while S5 uses urea + NBPT and S1 uses plain urea and CAN. Because of its low

emission factor (EFurNBPT), urea + NBPT has the potential to reduce the emissions from

fertiliser production and therefore, to allow for more livestock to be kept when compared

with the S1 while still meeting the GHG emissions constraint. Comparing the S1 and S5

scenarios with the baseline scenario (Table 4), there is a 12% reduction in gross margin

under the S1 and only a 6% reduction under the S5, while the corresponding change in gross

margin as a result of a 15% reduction obligation in GHG emissions for the largescale farm is

19

15% and 9.5% respectively (Appendix). Therefore, under a 15% GHG restriction policy and

under the adoption of urea + NBPT the mid-sized dairy farmer would be able to keep almost

6.4 more dairy cows and 12.45 more dairy cows the largescale farmer and experience higher

gross margin than in the absence of adoption.

4.5. Sensitivity Analysis

The impact of alternative price levels of urea combined with NBPT was also

considered through a sensitivity analysis without a GHG restriction (Table 5). The price for

urea + NBPT which is used in the model is considered as the baseline. Starting with the

initial urea + NBPT price of (€22.57 per bag) the model choses to use urea + NBPT, due to its

low emission factor

and high N content. Increasing the initial price of urea + NBPT by €2, urea + NBPT is still the

best option. Increasing the price of urea + NBPT to €25.6, switching point, the model

chooses CAN as the main N source followed by urea + NBPT and plain urea. Increasing even

more the price of urea + NBPT at €27.56 the gross margin is slightly reduced, and plain urea

is the only beneficial source of N. The number of fertiliser bags remains constant because

this model has associated fertilisers with the number of animals, crops and GHG emissions

and not farmers’ response to market change. Therefore, change in prices does not capture

farmers’ rational to buy more or less fertiliser products.

The results of the sensitivity analysis for the largescale farm (Table 6) showed that

the

switching price at which the model stops choosing only urea + NBPT is higher than the

average dairy farm. It is noteworthy that for the largescale farm when the price increases to

€27.56 the model chooses only plain urea as the most profitable source of N and zero bags

from CAN

or urea + NBPT. Therefore, the sensitivity analysis results show the switching price at which

the adoption of urea + NBPT would not be the best financially beneficial source of N and

that largescale farms are more resilient to price change.

20

Table 5 Gross margin sensitivity analysis with changing price of urea + NBPT in the mid-sized dairy farm

Urea + NBPT price change in €22.57* 24.57 25.6 27.7

Gross margin € 74,400.84 74,159.02 74,034.49 73787.99Urea (50kg-bags) - - 68.90 478.53CAN (50kg-bags) - - 361.75 -

Urea + NBPT (50kg-bags)

485.51 402.15 120.91 -

Table 6 Gross margin sensitivity analysis with changing price of urea + NBPT in the largescale farm

Urea + NBPT price change in €22.57* 24.59 25.57 27.56

Gross margin € 147.200 145,220 144,260 142,350Urea (50kg-bags) - - - 924.79

21

CAN (50kg-bags) - - - -

Urea + NBPT (50kg-bags)

981.49 851.37 851.37 -

5. Discussion and conclusions

Linear programming either by maximizing or minimizing an objective function has

been frequently used to assist in managing and planning production units in agricultural

systems, as well as to contribute to the understanding of the impact of potential policies on

production units (Lengers and Britz, 2012, Moraes et al., 2012). The aim of this paper is to

compare the farm gross margin of two types of dairy farms under a base scenario with the

results of five other scenarios which consider varying levels of emissions reduction targets

and in the case of S4, S5 the potential of urea combined with NBPT as an abatement

22

technology. Under all five scenarios, dairy cows, calves and replacement heifers are the

production activities that maximize farm gross margin for both farm types.

Ireland meeting its national GHG emission targets for reductions in non-ETS

emissions by 2020 will be a strenuous task. Applying a GHG restriction policy on farms

would contribute to a reduction in Ireland’s national GHG emissions. However, such a policy

may require a significant reduction in livestock units and consequently farmer’s profit. The

results of this paper indicate that the adoption of abatement technologies such as urease

inhibitors have the potential to mitigate farmer’s cost under a GHG restriction policy. The

results of S4 show that both dairy farm types would maximise farm gross margin if they

chose urea as the main source of N and not CAN as they currently do. The gains in farm

gross margin from replacing CAN with urea would be approximately 3.2% for both types of

dairy farm.

The results of S5 illustrate the potential for technologies, in this case the adoption of

Urea + NBPT, to offset some of the cost to the farmer from achieving an emissions reduction

target. Through the adoption of the NBPT product farmers can reduce emissions from

fertiliser and thus are required to make a smaller reduction in animal numbers.

It is proven that urea + NBPT tends to retain N in the soil longer, therefore it reduces

the amount of GHG emissions compared with plain urea or CAN. However, NBPT’s higher

price than CAN and urea will possibly be a barrier to volunteer adoption by the mid-sized

dairy farm if the price of urea + NBPT exceeds €25.6 and by the largescale farm if the price

exceeds €27.56, assuming that farmers are profit maximizers. Emissions reduction

regulations may provide the incentive needed to get farmers to adopt abatement

technologies such as urea with NBPT. In the absence of an emissions target there is an

important role for agricultural extension agents to play in educating and informing users

about the benefits of the product, but ultimately price signals such as a subsidy for urea +

NBPT may be the most effective means of encouraging its adoption.

Harty et al. (2016) indicated that by switching formulation from CAN to urea with

NBPT, N2O emissions can be reduced by 73%. However, the majority of GHG emissions on

dairy farms are produced by the animals themselves. Therefore, reducing emissions

produced only from fertilisers through the adoption of urea NBPT is not a “silver bullet” that

will lead to large scale emissions reduction, however, it could form part of a suite or

23

combination of mitigation practices that collectively can be adopted to reduce emissions

from agriculture.

Acknowledgements

We are grateful to all the farmers who took part in the survey. This study was funded by the

Environmental Protection Agency in Ireland as part of a Doctoral Research Scholarship

Scheme. We would also like to thank the staff of the Teagasc National Farm Survey for their

assistance in undertaking this research.

Appendix

Variable Baseline scenario

S1 S2 S3 Profit maximization (S4)

Urea + NBPT,15% GHG restriction (S5)

15% 25% 35%Gross margin € 145,160 123,520 113,870 104,210 149,870 131,340Change % -14.91 -21.56 -28.21 3.24 9.52Livestock numbersDairy cows 189.39 143.05 122.37 101.69 188.42 155.50Male dairy calves - - - - -Female dairy calves 28.40 21.45 18.35 15.25 28.26 23.32Suckler cows - - - - - -

24

Cattle calves - - - - - -Male cattle < 1 year - - - - - -Female cattle <1 year - - - - - -Male cattle 1 – 2 years - - - - - -female cattle 1 – 2 years

- - - - - -

Male cattle > 2 years - - - - - -Female cattle > 2 years - - - - - -Replacement heifers 28.40 21.45 18.35 15.25 28.26 23.32Crops in haPasture 76.71 57.94 49.57 41.19 76.32 62.98Silage 32.18 24.30 20.79 17.28 32.01 26.42Winter Wheat - 26.64 38.53 50.42 0.56 19.48Spring barley - - - - - -FertilisersUrea 199.96 142.64 117.07 91.49 - -CAN 1,049.79 748.9 614.62 480.33 981.49 -Urea + NBPT n/a n/a n/a n/a - 851.37“25, 2.2, 20” 523.11 653.41 711.56 769.72 212.02 239.03“15, 12, 5” 255.42 332.08 366.29 400.50 - -“10, 10, 20” - - - - 374.95 457.25GHG emissions (CO2 tn eq)

896.08 761.67 672.06 582.45 896.08 761.67

Table Results of the linear programming model largescale farms

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