CANB v4.0: A Model for Simulating Residual Soil Nitrogen and

Nitrogen Leaching in Canadian Regional Scale

J. Y. Yang

, C. F. Drury

, R., De Jong

, E. C. (Ted) Huffman

and X. M. Yang

Greenhouse & Processing Crops Research Centre, Agriculture and Agri-Food Canada, 2585 County Road 20,

Harrow, Ontario, N0R1G0, Canada

Eastern Cereal & Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 Carling Ave,

Ottawa, Ontario, K1A 0C6, Canada

Keywords: Canadian Agricultural Nitrogen Budget (CANB) Model, Agri-Environmental Indicators, Residual Soil

Nitrogen, Water Contamination by Nitrogen, Environmental Modeling.

Abstract: A Canadian Agricultural Nitrogen Budget model (CANB v4.0) was developed to calculate two Agri-

environmental Indicators; Residual Soil Nitrogen (RSN) and the Indicator of Risk of Water Contamination

by Nitrogen (IROWC-N) at 1:1M Soil Landscape of Canada scale for all Canadian farmland. The RSN (kg

N ha

-1

) is the amount of inorganic N which remains in the soil at the end of the growing season and it is

calculated as the difference between the total inputs of N and removal of N by the crop and atmospheric

losses. The IROWC-N provides an estimate of the concentration and amount of the RSN which can be lost

due to surface and groundwater via leaching. Both the growing season and non-growing season N leaching

losses were simulated by a daily N leaching model. The outputs are displayed using EasyGrapher software

and mapped using Arc-GIS software. The Ecoregion maps and graphs of the RSN, N lost and IROWCN

from the CANB v4.0 model were displayed and the results were interpreted. The results indicate that there

is an increasing risk of water contamination over time in Canadian farmland. The model can also be used for

policy scenario analysis or integrated into a GIS framework at watershed scales.

1 INTRODUCTION

Nitrogen losses to air and water from agricultural

practices are important issues affecting global

environmental health. For example, N

O emissions

from agricultural systems account for 50% of global

greenhouse gases (Rochette et al., 2008) and NH

emissions from the soil are involved in the formation

of the PM2.5 (Sheppard et al., 2010). NO

, on the

other hand, is easily removed with run off and

leaching water to surface and groundwater bodies,

affecting human and animal health (Drury et al.,

2007). To reduce NOx emissions and leaching losses

in agricultural production systems, we need to

understand and manage the annual nitrogen cycle as

shown in Figure 1. N input to farmland is mainly

from inorganic N fertilizers, manures, legume N

fixation and atmosphere N deposition. N output

from farmland is mainly by N removal in harvested

crops, from ammonia volatilization, from

denitrificaiton (N

, N

O, NO), as well as from NO

N leaching (Figure 1). Field research or field

modeling on these processes has been conducted for

decades, but regional and national estimates of the

nitrogen cycle in agricultural systems is still a

challenge due to incomplete data sets and the

requirement for developing a larger scale model.

Models of the nitrogen cycle in a larger

agricultural system have been made during last two

decades using survey or census database. For

example, the Organization of Economic Cooperation

and Development (OECD) developed a series of

environmental indicators (OECD 2008), including

the residual soil N indicator. These larger scale

indicators/models show increasing importance in

assessing environmental health as affected by the

agricultural N cycle.

In Canada, a nationwide Agro-environmental

indicator program has been carried out over the last

15 years to develop regional and national water

quality policies (Eilers et al., 2010). For this reason,

a Canadian Agricultural Nitrogen Budget (CANB)

529

Yang J., Drury C., DeJong R., Huffman E. and Yang X..

CANB v4.0: A Model for Simulating Residual Soil Nitrogen and Nitrogen Leaching in Canadian Regional Scale.

DOI: 10.5220/0005005005290536

In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),

pages 529-536

ISBN: 978-989-758-038-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

model has been developed to estimate two nitrogen

indicators; (1) the residual soil nitrogen (RSN) and

(2) the indicator of risk of water contamination by

nitrogen (IROWC-N), at the Soil and Landscapes of

Canada (SLC) 1:1M scale.

During the last 10 years, the CANB model has

been updated on a regular basis as new information

and data have become available. The CANB v1.0 to

v3.0 models were integrated with a Canadian

Regional Agricultural Model for policy scenario

analysis (Yang et al., 2007ab; 2011; 2013). A

graphic interface program, EasyGrapher, was also

developed to support visualization of the CANB

outputs (Yang et al., 2014).

Recently, the CANB v4.0 model have just been

updated (methods, parameters) with new databases

of 2011 agricultural census data, N deposition, N

emission rates and 10 by 10 grid daily weather data

at various scales, representing the state-of-the-art of

larger scale soil N model in Canada. Although the

CANB (v1.0-v3.0) model’s results have been

reported at the SLC, province and Canada scales (De

Jong et al., 2009; Yang et al., 2010, 2013), they were

never reported in ecoregional scales (ecodistrict,

ecoregion and ecozone). The objective of this paper

focuses on reporting the CANB v4.0 model’s design,

I/O structure, scaling up and data visualization

methods. The temporal changes of RSN and N

leaching during 1985-2010 will be illustrated at the

Ecoregion- an inter-mediate scale in Canada’s

ecological classification framework.

2 PROGRAM DESIGN

The CANB model is written using Intel Fortran

compiler. The graphic support software,

EasyGrapher, is written using MS Visual Studio

NET which is linked with MS Excel (Yang et al.,

2014). Arc GIS v10 is used for mapping the model

outputs. The CANB program can be run under

various computer Windows systems. The following

sections describe the CANB input/output data and

structure.

2.1 Soil Landscapes of Canada (SLC)

The SLCs are a series of GIS coverage at a 1:1

million scale organized by a uniform national soil

and landscape criteria based on permanent natural

attributes for the whole SLC polygons

(http://sis.agr.gc.ca/cansis) (Soil Landscapes of

Canada Working Group, 2005). In Canadian

ecosystem framework, the SLC v3.x polygons are

the most detailed spatial entities within the

ecological framework

(

http://sis.agr.gc.ca/cansis/nsdb/ecostrat/index.html),

including a nested hierarchy of 15 Ecozones (i.e.,

each color represent an ecozone in Figure 2), 194

Ecoregions (i.e., curved lines are boundaries of

ecoregions within ecozones in Figure 2), 1027

Ecodistricts (nested within Ecoregions) and 12353

SLC polygons (nested within Ecordistrict) (Table 1).

Figure 1: Conceptual view of the nitrogen cycle in

agricultural soils. Residual soil nitrogen (RSN) is the

amount of inorganic N remaining in the soil after harvest.

IROWC-N is the estimate of the amount of RSN which

leaches from the agricultural soils.

Figure 2: Canada Ecoregions - subdivisions of the

ecozones characterized by distinctive regional ecological

factors, including climate, physiography, vegetation, soil.

Water, fauna and land use.

The names of Canada province (Figure 2) in this

paper were coded as follows: BC: British Columbia,

AB: Alberta, SK: Saskatchewan, MB: Manitoba,

ON: Ontario, QC: Quebec, NB: New Brunswick,

NS: Nova Scotia, PE: Prince Edward Island, NL:

Newfoundland and Labrador. NT: Northwest

Territories, YT: Yukon Territory and NU: Nunavut.

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Over the last 15 years, the SLC v3.x has also

compiled with agricultural census database (every

five year) and national climate data (every year) for

use by the National Agri-Environmental Health

Analysis and Reporting Program (Eilers et al.,

2010), the National Carbon and Greenhouse Gas

Emission Accounting and Verification System and

other related national programs.

2.2 Input Data and Collection

The CANB program requires input data from

various scales. (1) The SLC scale datasets that are

geographically located in 3247-3345 Soil

Landscapes of Canada 1:1M polygons (Soil

Landscapes of Canada Working Group, 2005).

These include crop area, animal numbers and crop

managements that come from the census of

agriculture every five years (Table 1). Canadian Soil

Information Service (http://sis.agr.gc.ca/cansis)

provides the SLC v3.2 mapping framework, soil

classification, and profile data. (2) Daily weather

data (1980-2012) were obtained from Canadian

weather framework, and was allocated to each SLC

to drive the water balance model to estimate soil N

loss over both growing and non-growing seasons

(De Jong et al., 2009). (3) Data that are collected at

the provincial scale include industrial fertilizer sales

(CFI, 2006), annual crop yields from Statistics

Canada (2003), fertilizer N application rates from

provincial agronomic recommendations and manure

storage and management methods from Farm survey

(Yang et al., 2011). (4) Data from the Canada scale

include average N fixation rates and animal N

excretion rates (Yang et al., 2007a).

2.3 CANB Model Structure

The CANB v4.0 model’s data are structured into

data folders as illustrated in Figure 3. First the model

reads input data from their files and folders. Then

the CANB calculates the RSN and N lost to leaching

at the SLC polygon level from 1981 to 2011. The

outputs are either saved as text files or scaled-up

from the SLC level to the eco-regional scale (Figure

3). The output data are displayed either graphically

or as data sheets. The required maps for the CANB

output are mapped by the Are GIS software. The

detailed module links and I/O data flow in the

CANB model can be seen from Figure 3.

2.4 RSN Module

Residual Soil Nitrogen (kg N ha

-1

) is calculated as

the difference between the total N input into soil

input

) minus the total N output (N

output

) for each

hectare of farmland (Farmarea) at the end of the

growing season. Detailed descriptions on N input

and N output equations were given in our previous

publications (De Jong et al., 2009; Yang et al.,

2007a, 2010; 2011; 2013) In this paper, for easy

illustration purpose, we present following basic

equations 1-3 as below. For each of the 3247 to 3345

soil polygons from 1981 to 2011, the RSN is

calculated by:

RSN N

input

 N

output

/Farmarea

(1)

input

 N

fert

 N

man

 N

fix

 N

depo

(2)

out

 N

cro

 N

O  NH

(3)

where all N components in Eqs (2) and (3) are

expressed as kg N SLC

-1

. N

fert

is the total amount of

inorganic N from fertilizer applied; N

man

is the

amount of available inorganic N from manure

applied to crops and pasture after N losses, plus the

amount of N mineralized from the organic manure

that was applied in the previous 3 years; N

fix

is the

amount of N fixed by leguminous crops after

subtracting legume residue N being carried over to

the next year, plus the amount of N mineralized

from legume residue and roots remaining from the

previous 3 years; N

depo

is the amount of wet and dry

deposition of atmospheric N;. N

crop

is the amount of

N removed in the harvested portion of crops and

pasture, N

O is the amounts of greenhouse gas lost

to the atmosphere and NH

is the amounts of

ammonia N gas lost to the atmosphere.

2.5 IROWC-N Module

The IROWC-N module calculates N lost by leaching

and N concentration in the leached water based on

salt leaching concepts (De Jong et al., 2009). The

IROWC-N module first takes the RSN from the

CANB model as an input. The amount of N leaching

from the soil in the drainage water during the non-

growing season N

lostNGS

and growing season N

lostGS

(kg N ha

-1

) was simulated as shown in Figure 4.

The N concentration in the non-growing season

concNGS

) (mg N L

-1

) and growing season (N

concGS

)

(mg N L

-1

) were then calculated using the

cumulative drainage water volumes in the growing

and non-growing seasons. These were simulated

using a modified daily Versatile Soil Moisture

Budget model (Baier et al., 1979) which was

CANBv4.0:AModelforSimulatingResidualSoilNitrogenandNitrogenLeachinginCanadianRegionalScale

531

integrated into the IROWC-N module using daily

weather datasets across Canadian farmland (De Jong

et al., 2009).

Table 1: List of CANB v4.0 Inputs, output datasets,

variables and numbers of locations from 1981 to 2011.

Figure 3: Flow chart of the Canadian Agricultural

Nitrogen Budget (CANB) v4.0 model.

2.6 RSN and IROWC-N class

For mapping and result interpretation purposes, the

RSN result is grouped into 5 classes; 0-9.9 (very

low), 10-19.9 (low), 20-29.9 (medium), 30-39.9

(high) and ≥40 (very high) in kg N ha

-1

farmland.

The water contamination indicator, IROWC-N, is

classified to 5 risk classes based on combination of

N lost and N concentration (Table 2) (De Jong et al

2007).

Table 2: The IROWC-N classification.

2.7 Scaling up Module

The RSN, N lost (N

lostGS

and N

lostNGS

) and N

concentration (N

concGS

and N

concNGS

) values were

scaled up from the SLC 1:1M scale to the

Ecodistrict, Ecoregion, Ecozone, provincial and

national scales using the farmland area weighted

averages developed as Yang et al. (2007a).

Figure 4: Data flow for N lost and N concentration in the

leached water over the non-growing, growing seasons.

3 DATA VISUALIZATION

3.1 Maps at the Ecoregional Scales

Maps of the CANB outputs are produced using Arc

GIS Map software and can be made at any

ecological scales of SLC, Ecodistrict, Ecoregion and

Ecozone. As examples, the RSN class in 1985 and

2010 and the IROWC-N risk classes for the same

years are mapped in Figure 5 and Figure 6.

CANBI/Odata Variable

No.of

agricultural

polygons

Inputs

Cropareafor27croptypes 27 3247‐3345

Animalnumbersfor21animaltypes 21 3247‐3345

FertilizerNfor27croptypes 27 3247‐3345

ManureNfor27croptypes 27 3247‐3345

Nfixationfor27croptypes 27 3247‐3345

AtmospherewetanddryNdeposition 3 3247‐3345

N

mineralizationfromlegumesand

manure 4 3247‐3345

Cropyieldfor27croptypes 27 3247‐3345

Nuptakefor27croptypes 27 3247‐3345

N2O,N2,NH3‐Nemissions 3 3247‐3345

Outputs

RSNandNcomponents 15 3247‐3345

Nlost(summer,winter&annual) 3 2780

Nconcentrationinleachedwater 3 2780

Drainage(summer,winter&annual) 3 2780

Scaleup

output

SLC(totalof12353) >20 3247‐3345

Eco‐district(totalof1027) >20 377

Eco‐region(totalof194) >20 68

Eco‐zone(totalof15) >20 8

Province >20 10

Canada >20 1

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Figure 5: Residual Soil N (RSN) levels on Canadian

Ecoregion in 1985 (a), and 2010 (b).

3.2 Data Handling

The CANBv4.0 input and output files (more than

500) are saved as text files at various scales (Figure

3). To analyze these data efficiently, we have

developed two supporting programs to handle and

visualize the CANB outputs: EasyFormatter and

EasyGrapher, using the MS Visual Basic NET. The

EasyFormatter was designed to transfer the CANB

output files to Microsoft Excel, then save as the

Excel data file. For example, there are 31 files

named 1981RSN.out to 2011RSN.out, dealing with

yearly data for the ~3400 SLC polygons. Each file

has a data matrix of >3000 records and up to 50

variables. The program can transfer these 31 files to

a single MS Excel file with different sheets within

seconds. This way, the data can be viewed and

analyzed with MS Excel and Access easily.

Figure 6: Indicator of Risk of Water Contamination by

Nitrogen (IROWC-N) risk class on Canadian Ecoregion in

1985 (a), and 2010 (b).

3.3 Graph

Similarly, the EasyGrapher software was designed to

automatically graph the CANB’s outputs (Yang et

al., 2014). It first transfers CANB’s output into MS

Excel sheets, and then automatically performs a

series of graphical tasks. The bar graphs are

generated for all output variables within 30 seconds

for each CANB output file. The trend and variation

of each variable (Y axis) against their ecoregions (X

axis) are displayed in the output graphs.

EasyGrapher can plot graphs on CANB output at all

scales listed in Table 1. Examples of graphs at the

Ecoregion are shown in Figures 7-9. Detailed

descriptions on EasyGrapher software can be read

from our previous publications (Yang and Huffman,

2004, Yang et al., 2014))

CANBv4.0:AModelforSimulatingResidualSoilNitrogenandNitrogenLeachinginCanadianRegionalScale

533

Figure 7: RSN at harvest (a), growing and non-growing

season N lost (b) and drainage (c) at the Canada Ecoregion

in 1985.

4 RESULTS INTERPRETATION

This section is to show how the map and figures are

interpreted but not a completed results and

discussion.

4.1 RSN and IROWC-N Risk Classes

at Ecoregion

The RSN distribution maps at the Ecoregion

(Figures 5) showed significant regional differences

in RSN. In 1985, the RSN level was lower in the

Western provinces (British Columbia, Alberta,

Saskatchewan and Manitoba) but it was high in

Central (Ontario, Quebec) and Eastern Canada

(Figure 5a). It was also observed that RSN risk

classes generally increased from low to high risk

classes over a 25 year time especially in Manitoba,

Ontario, Quebec and Eastern Canada (Figure 5b).

Similar regional differences were found for the

IROWC-N class distribution (Figure 6). The

IROWC-N classes were in the very low and low

classes in Western Canada except a moderate zone

in British Columbia. Most farmland in Central and

Eastern Canada showed moderate, high and very

high IROWC-N classes (Figure 6). In Central and

Eastern Canada, IROWC-N classes were shafted

from low, moderate in 1985 (Figure 6a) to moderate,

high and very high classes in 2010 during a 25 year

period (Figure 6b).

4.2 N Lost at the Ecoregion

In 1985 the RSN values were higher (> 20 kg N ha

-1

)

in Ecoregions 64-135 compared with the RSN

values (<20 kg N ha

-1

) in Ecoregions 137-213,

except in Ecoregion 196 where the RSN was

extremely high (48.5 kg N ha

-1

) (Figure 7a). The N

leaching loss was higher in Ecoregions 109-135 and

180-200 (Figure 7b) because the drainage water was

higher in these regions (data not shown).

Figure 8: RSN at harvest (a) and growing and non-

growing season N lost (b) at the Canada Ecoregion in

2010.

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In 2010, the RSN levels were more than 20 kg N

-1

in most of the Ecoregions (Figure 8a) and the N

lost levels increased (Figure 8b) compared with

1985, driven by both higher RSN and higher

drainage levels in 2010 (data not shown). It was

found that N lost in non-growing season was 2-5

times higher than in growing season in both 1985

(Figure 7b) and 2010 (Figure 8b).

Figure 9: Growing season (GS) and non-growing season

(NGS) N concentration at Ecoregion in 1985 (a) and in

2010 (b).

4.3 N Concentration at the Ecoregion

N concentration increased from 0.5-13.8 mg N L

-1

1985 to 0.8-22.4 mg N L

-1

in 2010 at Ecoregion

(Figure 9), showing significant regional difference

among Ecoregions. There was an obvious increasing

trend of the N concentration in most Ecoregions in

2010 compared with 1985. For example, 5-7

Ecoregions in 1985 showed that N concentrations

were above the drinking water guideline of 10 mg N

-1

(i.e., horizontal lines in Figure 9a), while 8-10

Ecoregions were shown that N concentration was

greater than 10 mg N L

-1

in 2010 .

4.4 N Input Changes in Canada

N input increased gradually with time, while N

output increased slowly, fluctuating with climate

conditions (Yang et al., 2013). This fluctuation

resulted in the increase of RSN values of 13.4 kg N

-1

from 1985 to 22.3 kg N ha

-1

in 2010.

The quick increase of N input was attributed to

the increased use of N fertilizer and legume N

fixation, while manure N and N deposition were

fairly constant over the last 25 year period (Yang et

al., 2013).

Figure 10: Percentage of fertilizer N (N

fert

), N fixation

fix

), manure N (N

man

) and N deposition (N

depo

) in N

input in 1985 and 2010.

In summary, fertilizer N contributed 37-39% of

total N input, and N fixation accounted for 31-35%.

Manure N (19-26%) and N deposition (6-7%)

contributed small percentages of total N input

compared with fertilizer N and N fixation (Figure

10).

5 CONCLUSSIONS

RSN and the risk of N loss through leaching were

successfully simulated by the Canadian Agricultural

Nitrogen Budget model from 1981 to 2011 for

Canadian farmland at the Soil Landscapes of Canada

1:1 million scale.

Increasing trends of both the RSN and IROWC-

N indicators were significant during a 25 year period

in Canadian farmland as shown by Ecoregion maps.

We concluded that the Ecoregion maps of N

indicators are at a suitable ecological scale for

presenting the results to public users, such as citizen

and students at school and universities because the

detailed SLC map of the RSN and IROWC-N

indicators are designed for specialists, consultant,

researchers and policy makers, and difficult to

interpret. Increases in RSN values were found from

Western to Eastern Canada. In Western region

except BC, the RSN increased from Southern to

CANBv4.0:AModelforSimulatingResidualSoilNitrogenandNitrogenLeachinginCanadianRegionalScale

535

Northern regions.

Graphic display of the RSN and N loss values at

Canada Ecoregion provided quantitative

visualization of which Ecoregions the RSN and N

lost levels were high and we concluded that high N

input resulted in high RSN, and high N lost was

driven by both high N input and high precipitation

and drainage in a given ecoregion. The increased

RSN values were mainly due to the continuous

increase of N input from fertilizer, manure and

biological N fixation compared to moderate

increases in N uptake by crop yields. Farm

management response options should be established

to reduce nitrate N leaching in higher risk regions,

such as reducing livestock numbers, matching

fertilizer N to crop requirements etc.

The principle of the CANB v4.0 program can be

applicable to other regional scales, such as

watershed, forestry, urban areas or other countries

for estimating the amount of surplus N entering into

ecosystem, environment or human food chain.

ACKNOWLEDGEMENTS

We appreciate the funding support provided by

Agriculture and Agri-Food Canada.

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