Environmental Stewardship via Landscape Conservation Planning,
Yields Beneficial Results for Water Quality in an Agricultural
Watershed
by
Stephanie Shantz
A Thesis
presented to
The University of Guelph
In partial fulfilment of requirements
for the degree of
Master of Landscape Architecture
In
Landscape Architecture
Guelph, Ontario, Canada
© Stephanie Shantz, May, 2016
ABSTRACT
ENVIRONMENTAL STEWARDSHIP VIA LANDSCAPE CONSERVATION PLANNING,
YIELDS BENEFICIAL RESULTS FOR WATER QUALITY IN AN AGRICULTURAL
WATERSHED
Stephanie Shantz
University of Guelph, 2016
Advisor:
Professor R. Corry
Agriculture is one of the largest non-point source polluters contributing to the degradation,
toxicity, and damage of the Great Lakes. Trends in agricultural management practice have
shifted over the decades to a more holistic system of conservation agriculture that functions on
the tenets of landscape conservation planning, protecting the environment for future
generations, while also protecting revenues and lifestyles of the people that work in agriculture.
This study uses geophysical characteristics to reconfigure land cover in an agricultural
watershed known to have water quality issues, while maintaining field boundaries and
management, composition proportions, and it takes no land out of production. Using
AnnAGNPS a base and designed scenario were created, run, compared, and analysed. The
design resulted in a decrease of estimated parenthetical values of pollutants. Empirical results
show that this design approach reduces the impact of agriculture on the environment while
maintaining profitable production land. Working at this scale while using and producing
empirical data, suggests water quality improvements are possible through evidence-based
landscape architecture that maintains agricultural land covers changing only some of their
locations.
Acknowledgements
I would like to thank my advisor Robert Corry for his guidance, advice, patience and
encouragement throughout my thesis progress. To my friends and family, thank you for the
support through this academic endeavour and always encouraging me to persevere and find
inspiration when experimental components were delayed.
iii
Table of Contents
ABSTRACT .................................................................................................................... ii
Acknowledgements...................................................................................................... iii
Table of Contents ......................................................................................................... iv
List of Tables ............................................................................................................... vii
List of Figures ............................................................................................................ viii
1.0
Introduction ......................................................................................................... 1
1.1.
Background ................................................................................................................................... 1
1.2.
Scope of Study .............................................................................................................................. 3
1.3.
Research Significance .................................................................................................................. 3
1.4.
Thesis Structure ............................................................................................................................ 4
2.0
Literature Review ................................................................................................ 5
2.1.
Basic Environmental Processes.................................................................................................... 5
2.1.1.
Hydrologic Cycle ................................................................................................................... 5
2.1.2.
Soil erosion and transport ..................................................................................................... 7
2.1.3.
Pollution Transport .............................................................................................................. 10
2.2.
Conservation Practices ............................................................................................................... 14
2.2.1.
Landscape Conservation Planning ..................................................................................... 15
2.2.2.
Current Practices to Mitigate Resource Problems .............................................................. 16
iv
2.2.3.
Current Large Scale Reconfiguration .................................................................................. 25
2.2.4.
Summary ............................................................................................................................. 32
2.3.
Watershed models ...................................................................................................................... 33
2.3.1.
3.0
Distributed Models............................................................................................................... 34
Methodology ...................................................................................................... 38
3.1.
Research Question and Hypothesis ........................................................................................... 38
3.2.
Study Area Selection ................................................................................................................... 38
3.3.
Description of the study area ...................................................................................................... 39
3.4.
AnnAGNPS Data Requirements ................................................................................................. 41
3.4.1.
Climate data ........................................................................................................................ 41
3.4.2.
Geophysical data................................................................................................................. 42
3.4.3.
Land Use Data .................................................................................................................... 43
3.4.4.
Model Simulation Time Step ............................................................................................... 49
3.4.5.
AnnAGNPS Cells ................................................................................................................ 49
3.5.
Site Analysis ................................................................................................................................ 50
3.5.1.
Adjacency ............................................................................................................................ 50
3.5.2.
Geophysical characteristics ................................................................................................ 51
3.5.1.
Land Use ............................................................................................................................. 57
3.6.
Alternative Design ....................................................................................................................... 57
3.6.1.
Design Method .................................................................................................................... 58
v
3.6.2.
4.0
Configuration Results .......................................................................................................... 61
Results ............................................................................................................... 63
4.1.
Nitrogen ....................................................................................................................................... 63
4.2.
Phosphorus ................................................................................................................................. 65
4.3.
Sediment ..................................................................................................................................... 67
4.4.
Water Yield .................................................................................................................................. 68
5.0
Discussion ......................................................................................................... 70
5.1.
Nitrogen ....................................................................................................................................... 70
5.2.
Phosphorus ................................................................................................................................. 71
5.3.
Sediment ..................................................................................................................................... 75
5.4.
Water Yield .................................................................................................................................. 78
5.5.
Summary of Landscape Reconfiguration Results ....................................................................... 81
6.0
Conclusions ....................................................................................................... 83
6.1.
Research Objective ..................................................................................................................... 83
6.2.
Implications ................................................................................................................................. 83
6.3.
Study limitations .......................................................................................................................... 86
6.4.
Future Study ................................................................................................................................ 88
6.5.
Conclusion................................................................................................................................... 90
7.0
Bibliography ...................................................................................................... 91
vi
List of Tables
Table 2.1 Example of LESA scoring ........................................................................................................... 32
Table 3.1 K ranges by soil texture .............................................................................................................. 43
Table 3.2 Interpreted ARI crop percentages ............................................................................................... 45
Table 3.3 Crop rotations by cropping system ............................................................................................. 45
Table 3.4 Randomization of the first year of rotations .................................................................................. 2
Table 3.5 K-factor reclassification ............................................................................................................... 52
Table 3.6 Slope classification ..................................................................................................................... 54
Table 3.7 Slope gradient reclassification .................................................................................................... 54
Table 3.9 Accumulation count ..................................................................................................................... 56
Table 3.8 Rotation ranking .......................................................................................................................... 57
Table 3.10 Field parameter scoring process............................................................................................... 58
Table 3.11 Area difference by rotation ........................................................................................................ 59
Table 3.12 Rotation changing process ....................................................................................................... 60
Table 4.1 Base to design nitrogen yield comparison .................................................................................. 63
Table 4.2 Percentage difference over 100% for N by AnnAGNPS cell ...................................................... 64
Table 4.3 Base to design phosphorus yield comparison ............................................................................ 65
Table 4.4 Percentage difference over 100% for P by AnnAGNPS cell ...................................................... 66
Table 4.5 Base to design sediment yield comparison ................................................................................ 67
Table 4.6 Percentage difference over100% for sediment particles by AnnAGNPS cell ............................. 68
Table 4.7 Base to design water yield comparison ...................................................................................... 69
Table 4.8 Percentage change over 100% for water by AnnAGNPS cell .................................................... 69
Table 5.1 Crop residue weight at 30% coverage of land area .................................................................... 79
vii
List of Figures
Figure 2.1 The Hydrological Cycle ................................................................................................................ 6
Figure 2.2 Spring melt erosion ...................................................................................................................... 8
Figure 2.3 Erosion Processes ....................................................................... Error! Bookmark not defined.
Figure 2.4 The Nitrogen Cycle .................................................................................................................... 12
Figure 2.5 Example of LESA scoring ............................................................ Error! Bookmark not defined.
Figure 3.1 Grand River Watershed ............................................................................................................. 39
Figure 3.2 Canagagigue Creek and study site............................................................................................ 40
Figure 3.3 Rotational sequence creation ...................................................... Error! Bookmark not defined.
Figure 3.4 AnnAGNPS generated cells ...................................................................................................... 50
Figure 3.5 Reclassified K-factor .................................................................................................................. 53
Figure 3.6 Reclassification of slope ............................................................................................................ 55
Figure 3.7 Slope accumulation ................................................................................................................... 56
Figure 3.8 Fields changed by the design .................................................................................................... 61
Figure 3.9 AnnAGNPS cells changed by the design .................................................................................. 62
viii
1.0
Introduction
Chapter one gives a background on water in southern Ontario and the current problems that the
Great Lakes are having. It outlines current government actions to take control of water pollution
problems in the Great Lakes and places this study within that context. The scope of study is
outlined while also providing the significance of this research. A brief overview of the thesis
structure is also provided.
1.1. Background
Water is vital to all life on our planet. Approximately 71% of the earth’s surface is covered in
water, however only less than 1% of that water is fresh and drinkable (Mullen, 2012).
Unfortunately, water has been used for centuries as a conduit to remove waste from our land,
which contributes to the current and projected clean water shortages. Canada holds 7% of the
world’s available fresh water reserves (Environment Canada, 2013a). However, most of that
water flows north to the Arctic Ocean and is not useable by the large population in the south
(Environment Canada, 2013b). Much of the surface water in the southern Canada is shared with
the United States. This leads to many issues around water rights, water quality, and water
source protection.
Due to centuries of neglect and misuse, the Great Lakes have water quality issues. In some
cases leading to the death of the entire lake (such as Lake Erie) or toxicity levels so high they are
unfit for human use and deadly to most other creatures as well. High levels of chemicals and
nutrient loading leading to hypoxia, toxic algae blooms, and nuisance/invasive species are
serious problems in the Great Lakes (Environment Canada, 2013c). In 1972, the Canadian and
United States governments collaborated to improve the health of the Great Lakes so they can
again be used for the benefit of the public, to improve ecosystem health and water quality in a binational referendum called the Great Lakes Water Quality Agreement (GLWQA) (Environment
Canada, 2013d). However, severe water quality issues continue, even with the efforts of both
countries enacting the GLWQA. One example is a continued increase of toxic algae bloom
1
occurrences in Lake Erie, caused by high levels of phosphorus, some of which comes from
agricultural sources. The Canadian government has taken further actions to clean up and try to
prevent the pollution of our fresh water resource. In fact, prevention is the spearhead of the
Canadian efforts to protect the environment (Environment Canada, 2013a).
One area where the Ontario government, in conjunction with many other governments, has
identified a place where pollution prevention could have a significant impact is in the agricultural
landscape. Preventing pollution from agricultural land is identified by the Ontario government, as
being critically important for water quality, stream water quality, nutrient over loading, and soil
loss; all issues that have been raised to be of special concern (Harker, et al., 2013; Sustainable
Development Office, 2010). Harker et al (2013) directly links decreased water quality and
environmental degradation to agricultural practices that create large scale landscape alterations
and require a high consumption of water. Almost all aspects of agriculture impact water quality:
“Agricultural activities that cause [non-point source] NPS pollution include confined
animal facilities, grazing, plowing, pesticide spraying, irrigation, fertilizing, planting,
and harvesting. The major agricultural NPS pollutants result from these activities
are sediment, nutrients, pathogens, pesticides, and salts. Agricultural activities also
can damage habitat and stream channels. Agricultural impacts on surface water
and ground water can be minimized by properly managing activities that can cause
NPS pollution” (US Environmental Protection Agency, 2012, Para. 3).
As rain collects and moves through the highly altered agricultural landscape, chemicals and
nutrients from the soil are transported via overland flow. Runoff carrying pollutants drains off the
land, into drainage networks and streams which flow together into rivers and eventually (in
southern Ontario) into the Great Lakes before making their way to the ocean. As water volume
increases so too does the pollutant load.
While the amount of pollution coming from each individual farm may be small, when all the
agricultural lands are combined (as naturally occurs via the water system) it becomes a huge
2
problem. Furthermore some agricultural practices such as row crop agriculture increase erosion
and the run off of sediments laced with nutrients and toxins because of additional nutrient inputs.
These practices continue and are increasing in popularity because they produce predictable farm
revenues.
1.2. Scope of Study
This thesis investigates a way for farmers to continue to farm with current practices and takes no
land out of production but still reduce pollution inputs into stream waters. It fills an under
developed niche in the body of literature, which has many studies of the positive, and dramatic,
effect of inserting buffer strategies into the landscape and returning farmland to forest. And while
this is great for water quality, its shine is diminished by the fact that most farmers cannot afford to,
nor would be willing to, give up so much farmable land for the environment at their own expense.
It investigates how designing at a sub-watershed scale, reconfiguring crop rotations to better suit
geophysical characteristics, while keeping all of the farmed land in production, an idea that should
be more amenable to the realities of farming in Ontario, may lead to decreases in erosion, keep
more nutrients on the fields, which may reduce the amount of agri-pollutants entering the stream
network. If calculated over many watersheds it could have a significant cumulative effect on
water quality. It seems like a small part of such a large-scale problem, but as Ikerd (1993, p.153)
pointed out, “our landscape is a system interconnected and a measure of the cumulative effect of
each individual component”.
1.3. Research Significance
The appropriate configurations of landscape elements based on features in the watershed may
decrease the amount of pollutants leaving the land and entering into the water system. This
approach to watershed design could dampen the cumulative effect of pollution and improve water
quality all along the stream network. More research needs to be done and landscape architects,
with their knowledge of land sustainability, could be leaders in promoting research and re-
3
designing the highly altered, agricultural landscapes for sustainability, food production and
(eventually) providing clean drinkable water for future generations while continuing to produce
high quality foods.
1.4. Thesis Structure
This thesis consists of six chapters. Chapter one introduces the current state of our water, the
problem statement, purpose of the study and how this research will impact the current body of
knowledge. Chapter two reviews literature about the environmental processes that affect the
agricultural landscape, how nutrients are transported through the landscape, and different models
that simulate and estimate nutrient loading from agricultural watersheds.
Chapter three outlines the research question and possible outcomes to the study, while also
identifying and describing the attributes of the study area. This chapter also details the data
needed for the modeling software, and outlines the process for creating the data sets. Analysis of
the site for capabilities and restraints are outlined and the design rules presented. In chapter four
the simulated designed scenario results are documented for all of the nutrients, pollutants, and
water that are yielded from the watershed. A presentation on how they differ from the baseline
scenario is also provided.
Chapter five discusses and analyses the results from the baseline to designed scenario. Possible
reasons why the estimated yields values resulted are presented and placed within other studies
of a similar nature.
Chapter six presents a summary of the thesis, discusses study limitations, implications of this
thesis, and presents the thesis conclusions derived from the designed scenario results and the
current state of agricultural needs and practices. It also identifies future study opportunities that
have become illuminated through this research.
4
2.0
Literature Review
Basic environmental processes are presented to give a brief and focused description of the
natural processes that affect water in the landscape. Hydrologic cycle, erosion and transport and
nutrient movement in an agricultural setting are all described, illuminating how agricultural
practices affect our water system. Current overarching concepts of conservation, conservation in
agriculture, and different scales of design in the agricultural landscape are presented. Two
simulation models considered for this study are described and compared.
2.1. Basic Environmental Processes
This thesis focuses on water quality in an agricultural watershed. Key to this thesis is the
understanding of how water moves across the landscape and its kinetic force and capability of
transporting soil. In the agricultural landscape additives are used to amend the soil and produce
higher yields. Comprehending the fate of these nutrients and how they affect water quality are
the secondary conceptual fundamentals of this thesis.
2.1.1.Hydrologic Cycle
The hydrologic cycle is the complete system of water within the biosphere. Evaporation and
transpiration converts water near the earth’s surface. Evaporation is the process of water
molecules changing from liquid to vapour. Transpiration is the process of vegetation releasing
water from their leaves back into the atmosphere. Condensation occurs, which is water vapour
cooling as it rises in the atmosphere and converts back to a liquid state. It then collects to forms
clouds, which travel and collect more water vapour until saturated. Once saturated, clouds
produce precipitation that falls back to the earth’s surface, replenishing surface water and water
bodies. When a precipitation event occurs water hits the earth’s surface and becomes runoff or
infiltrates into the ground. Runoff occurs when there is enough excess water to begin to flow
across the soil surface. The severity of runoff depends on how fast and how much precipitation is
converted into overland flow (runoff). Surfaces with higher slopes and less porous soil generate
5
more runoff, while surfaces with smaller slopes and more porous soil increase the chance of
percolation. Infiltration and percolation of water into the soil profile will be used by plants, moving
horizontally as sub surface flow or vertically up as capillary action of roots or through evapotranspiration, or down to recharge ground water. Ground water is subterranean water that is
stored in the gaps between rock layers, underground streams, and soil.
In Canada, part of the hydrologic cycle is frozen during our winter season. Precipitation during
winter falls as snow or ice and is stored on the ground until spring. When temperatures increase
in spring snow and ice begin to melt and mark the beginning of the “spring melt”. This event
produces large amounts of water which can result in runoff and flooding. This occurs because
frozen soils are like impervious pavements; they do not accept any water through infiltration.
Spring runoff causes erosion as the surface of the soil warms and thin layers of the soil profile are
thawed while the under layers are still a solid frozen unit. Soil particles, and the nutrients bound
to them, are transported to streams, rivers, lakes and eventually the ocean.
Figure 2.1 The Hydrological Cycle
A visualization to demonstrates how precipitation falls, is used, moved and returned to the atmosphere
(Amin, 2015)
6
2.1.2.Soil erosion and transport
Soil erosion is a naturally occurring process moving soil from one location to another via wind,
water, and ice. Three distinct processes encompass erosion: soil detachment and entrainment,
transport, and deposition. Soil detachment is when a soil particle is dislodged from its
surrounding soil aggregate (Queensland Government, 2015). Detachment can occur by rain drop
splashing, moved by snow, and lifted by wind. Rain drops have enough force to separate soil
particles as it hits the soil. Snow has potential energy to cause erosion. When the snow melts
there are high volumes of water that flow over the soil surface picking up soil particles as it flows
(Ritter, 2012). If wind gusts are fast enough, wind can also lift dry soil particles into the air and
move them over great distances (Ritter, 2012). Entrainment is the process of particles being
surrounded by water molecules, and is more of a co-process than its own distinct class within
erosion (Pidwirny, 2013). Once particles have been detached they are lifted, entrained, and are
considered to be ready for transport.
There are four types of transport: suspension, saltation, traction, and solution. Suspension is
when soil particles are lifted from their location and suspended above their place of origin, but do
not touch the surface of origin (Pidwirny, 2013). Saltation is when particles move from their
surface of origin to water or air quickly and repeatedly in short cycles (Wind Erosion, 2016). This
generally produces more detachment and entrainment of new particles. Traction is the
movement of particles, essentially shuffling, rolling, or sliding across the soil surface (Wind
Erosion, 2016). This can happen in all types of erosion mediums: air, water, snow, and ice.
Solution is the dissolving of particles into an aqueous solution as individual ions.
Deposition is the final stage of erosion when particles being transported drop out of their erosion
medium and are deposited onto the land, effectively ending the movement of particles (Pidwirny,
2013). This occurs when there is a loss of velocity of the medium or something disrupts the flow.
Slowing of the erosive medium can occur when the slope changes to a gentler one that does not
promote fast flowing water and allows particles to drop out of solution. Disruption can occur when
the flow hits something such as rocks, vegetation, or other obstructions such as streams. In the
7
case of wind, slope changes, barriers and other factors effecting wind speed act in concert to
reduce velocity (Pidwirny, 2013).
In Ontario, soil erosion by water is a major issue in agricultural landscapes. Determining factors
of the rate and scale of soil erosion by water are: rainfall, runoff, soil erodibility, slope gradient
and length, cropping, vegetation type, and tillage practices. Rain fall intensity and duration affect
erosion potential. Short intense rainstorms, such as thundershowers, usually produce noticeable
soil erosion. Long and less intense rainstorms are less noticeable, however can cause high rates
of soil loss, especially when viewed over time. Frozen winter conditions store large amounts of
potential water as snow. As temperatures increase soil surface thaws as the soil below remains
frozen and impervious. This exposes this thin layer to erosion when water from melting snow
follows the flow path, picking up soil particles, and transporting it downhill.
Figure 2.2 Spring melt erosion
Soil characteristics determine soil erodibility. Erodibility, the capacity of a soil to resist erosion, is
determined by soil texture, organic matter, and infiltration capability (Wall G. , Coote, Pringle, &
Shelton, 2002). Light particles such as fine sand, clay, silt, and organic matter are easily
8
detached by rain drops and transported as excess water runs over the surface of the soil and
moves downhill. Tillage and management practices in agricultural landscapes affect the amount
and type of organic matter left on the surface of the land or incorporated into the top layer of soil.
Organic matter affects soil texture, structure, moisture, and increases soil resistance to erosion.
Field management practices can cause compaction of the soil which decreases infiltration
capacity. This combined with slope gradient and length set soils to even higher risk categories of
erodibility. The risk of water erosion increases with steeper and longer slopes. Large volumes of
water accumulate on long slopes increasing velocity as runoff flows downhill. Faster flowing
water entrains more particles, causing a scouring of the flow path and further erosion.
This can be disrupted though. Vegetation and crops can change the impact and flow of water.
Perennial vegetation provides a level of protection from erosion compared to annual vegetation
because perennial species remain in the fields for more than just one year (as with annual
vegetation). This reduces soil disturbance and the amount of time that the soil is bare. Crop
systems that have little soil coverage do not protect the soil from raindrop impact.
On the other
hand, fields that cover the soil with cover crops or crop residues have the capability of shielding
the soil and reducing raindrop impact, disrupting the erosion process. Field management
systems used on a farm determine the use of cover crops and crop residues. The amount of crop
residues left is governed by the harvesting and tillage practice. Conventional tillage systems
leave little to no crop residue on the surface of the field, which is not enough to mitigate erosion.
Conservation tillage systems leave a minimum of 30% or more crop residues on fields. Thirty
percent residue retention is enough to protect the soil from overland flow, rain impact, and snow
melt events, decreasing erosion potential (Ritter, 2012).
9
Figure 2.3 Erosion Processes
2.1.3.Pollution Transport
Nutrients travel from one place to the next via soil and water. Soil transports nutrients when they
attach to soil particles through chemical processes. This bonding is different from one nutrient to
the next and the capability of each nutrient to attach to soil varies. Nutrients dissolved in solution
are carried in water that moves as overland flow. Each nutrient varies in how readily it dissolves
and can be transported via water. The transport of nutrients is directly linked to erosion and the
hydrologic cycle. Nitrogen and phosphorus are the two key nutrients to understand the
detrimental effects of agricultural runoff.
Nitrogen (N)
Plant available nitrogen is a deterministic nutrient for crop growth and productivity. This nutrient
is applied to almost all crops as it is usually a limiting factor for crop development and yield
(Nitrogen cycle, 2008). There are not enough natural sources of available nitrogen to meet crop
needs, making the application of inorganic fertilizers necessary for proper plant growth and high
yields. Ammonium nitrogen and nitrate N are the two types of nitrogen that are available to plants
(Lamb, Fernandez, & Kaiser, 2014). Ammonium N is a solid form and is spread via broadcast
10
application or side dressing that can be left on the surface or incorporated into the soil.
Ammonium N adsorbs to soil particles because of its positive charge, creating attached nitrogen,
making it less mobile and unlikely to leach through the soil profile immediately after application
(Lamb, Fernandez, & Kaiser, 2014). Bacterial biological processes in the soil convert ammonium
nitrate into nitrate nitrogen starting two to three days after application, with full conversion to
nitrate nitrogen approximately 30 days after application (PennState Extention, 2015).
Nitrate N is generally in an aqueous solution. Liquid form nitrogen is spread in two ways, surface
and subsurface. Nitrate nitrogen does not readily attach to soil particles because of its molecular
charge, and readily dissolves into water, making dissolved nitrogen (Lamb, Fernandez, & Kaiser,
2014). This allows applied nitrate nitrogen to move freely through the soil profile and be
susceptible to leaching. Liquid nitrogen fertilizer moves into the soil with precipitation as water
percolates into the soil. Surface applied liquid nitrogen that has just been sprayed can be carried
away by runoff before it moves into the soil. When dissolved nitrogen percolates into the soil
profile some is immobilized by organisms, while a majority continues through the soil profile to the
root zone of crops. Nitrogen continues to move past the root zone to tile drains, and in some
cases where there are no tile drains, into the water table causing ground water contamination
(leaching). This is because it is not easily immobilized by minerals in the soil. Nitrogen is not as
readily adsorbed to soil particles compared to other nutrients such as phosphorus, however
certain forms of nitrogen fertilizer, such as ammonium nitrate, readily attach to soil particles
(Retallack, 2001; Lamb, Fernandez, & Kaiser, 2014). When erosion occurs during precipitation
events attached nitrogen moves with eroded sediment. Plants readily uptake nitrogen and use it
in their development stages, however due to its highly mobile nature nitrogen that is not used by
crops before precipitation events has a tendency to be moved in the landscape causing high
quantities in streams. This results in an eutrophic state within water shifting the natural rate and
balance of nutrients and plant growth. Fast growing plants, such as invasive and toxic algae, out
compete other native species, causing massive algae blooms that deplete oxygen levels within
water and degrade overall water quality for aquatic species and human use.
11
Figure 2.4 The Nitrogen Cycle
Phosphorus
Phosphorus is added to the soil naturally through weathering of phosphorus-containing rocks. In
agricultural settings phosphorus is added to the system by mineral fertilizers, plant residues, and
manures. Soluble phosphorus is what is used by plants. Phosphorus is less mobile in the
landscape compared to nitrogen because it is highly reactive to calcium, iron, and aluminum, all
of which bind phosphorus and make it immobile (Retallack, 2001).
The movement of phosphorus through the landscape affects water quality. Application of
phosphorus to meet crop needs is used by plants, moves through the soil, and leaches into tile
drain water. Crops only require certain amount of phosphorus, making excess a problem in
runoff and tile drain discharge. Tile drain water is then discharged into drainage ditches and ends
up in stream water. When a rain event occurs right after the application of phosphorus, the
12
nutrient has not had enough time to infiltrate into the soil, be used by plants, or be immobilized by
soil minerals and is moved across the landscape in runoff ending directly in streams. Plant
residues that are not incorporated into the soil release phosphorus to the soil surface when they
decay in the spring. Dues to the high volume of runoff in the spring, and soils being frozen, little
of the phosphorus from plant residue moves into the soil and travels in runoff (Retallack, 2001).
Other nutrients
Potassium and sulphur are two other common nutrients that are added to crops. Potassium (K) is
one of the three main nutrients (NPK) for plant growth. This nutrient is added to some crop types
in the form of fertilizers such as: potash, manmade fertilizers, properly composted and stored
manure, compost, and green manures (Mikkelsen, 2007). Agricultural regions that are mostly in
mixed farming, with live stock and crop production, use manure from their live stock and are
generally an adequate supply for crops, if stored properly and tested to ensure adequate
concentrations of K, reducing the need for potash fertilizer applications. Application of external
potassium to fields also stimulates the release of it from exchangeable sources into the soil
solution (Rehm & Schmitt, 2002). Potassium is less of a concern when examining agricultural
pollutants because much like phosphorus, potassium is not very mobile in soil (Ball, 2001).
Sulphur in southern Ontario is not usually applied to fields as a micronutrient. There is enough
sulphur in acidic precipitation, animal manure, and organic matter decomposition making it very
available to crops, especially those in areas that are predominately mixed farming operations
(Brown, 2013). Sulfur is highly mobile in soil, much like nitrogen and is likely to move beyond the
root zone of plants and leach into ground water (Soils, 2016). Because there is enough sulfur
provided by acidic precipitation in Ontario, extra applications are not common, making the
problematic qualities of sulfur, such as leaching, an unlikely concern when it is not a regularly
added nutrient. Summary
All environmental processes are interconnected and contribute to each other. Precipitation that
hits the soil can cause dislodgement of soil particles. Water that is not absorbed by soil puddles
13
and turns into overland flow carrying water soluble nutrients directly to streams unless otherwise
provoked to slow down and infiltrate into soils by landscape features. Runoff also causes erosion
which carries sediment and soil bound nutrients into streams. Certain regional factors, such as
frozen winters, contribute to higher levels of erosion and nutrient run off. Farmers apply extra
human made and natural fertilizers to achieve increased crop growth and yields as there is not
enough of each nutrient existing naturally in the landscape. The amount of nutrient loss and
erosion vary due to famers’ crop choices and management. However, in general any cultivated
land produces more erosion than naturally grassed or forested landscapes. Agriculture escalates
these natural processes by changing cover types, adding extra nutrients, and exposing soils
through cultivation practices. These landscape modifications increase nutrients, sediment, and
other contaminates in streams contributing to overall degradation of water quality.
2.2. Conservation Practices
There has been an increase in the demand for higher production of food to meet the growing
population worldwide and could as much as double between now and 2050 (Tilman, Blazer, Hill,
& Befort, 2011). Crop yield and profitability are at the forefront of agriculture, as farmers make
their money off the yields they produce and often financially struggle from one year to the next.
Their livelihood depends on the weather and other environmental changes as well as being
subject to the industrialization of farming. To be profitable and improve the lives of farmer and
farm hands and to meet growing demands on the industry, an increase in production must be
sustainable by minimizing negative effects on the environment so farming is functional in the
future (Hobbs, 2007). At the forefront of many government wide discussions focusing on
environmental sustainability is the environmental burden of agriculture (Sustainable Development
Office, 2010).
Agriculture, as practiced now and in the past, damages the natural environment (Balmford,
Green, & Phalan, 2012). High rates of nitrogen in fresh water are directly linked to inorganic
inputs, from agricultural practices that apply nutrients to fields in order to increase yields (Morrice,
et al., 2008). A projected increase in demand for agricultural products benefits the
14
socioeconomic component of conservation landscape planning. If not enacted with land and soil
conservation as major considerations in the planning and implementation the increase in
production will be detrimental to the environment.
Environmental concerns of agriculture’s detrimental effects on the environment have led to the
development of what is now termed “conservation agriculture”, which is a conservation strategy
for agricultural production, agricultural specialists, and environmentalist. The progression
towards a greener form of agriculture began in the 1930s when the dust bowl - where an
estimated 480 tons per acre [about 1076 tonnes per hectare] of topsoil were lost to severe soil
erosion - affected the Great Plains of the United States (Hansen & Libecap, 2004). This began
the era of conservation tillage. Conservation tillage is defined by the Food and Agriculture
Organization (FAO) of the United Nations (2006) as: “a set of practices that leave crop residues
after harvest on the soil surface which increases water infiltration and reduces erosion. It is a
practice still used in conventional agriculture to reduce the effects of tillage on soil erosion”.
Conservation practices such as zero tillage can be transition steps towards Conservation
Agriculture (FAO, 2006). Conservation Agriculture is a relatively new term used to describe an
approach to agriculture as a holistic operation, which include: sustainable productivity, increased
profits, increase food security, and enhancing the resource base. It is characterized by three
inter-related methods of: minimum soil disturbance, soil cover year round and diverse crop
rotations (FAO, 2015). Above and below ground biological diversity are increased when there is
a permanent layer of soil cover and zero or minimal soil disturbance (Hobbs, 2007). According to
the 2011 Census of Agriculture, of the reporting 51 182 farms across Canada, 16.69 million
hectares are seeded with a no-till or zero-till seeding method.
2.2.1. Landscape Conservation Planning
Steiner (2008) defines landscape conservation planning in his book, The Living Landscape as: an
Ecological Approach to Landscape Planning. Landscape: “the sum of the parts that can be seen,
the layers and intersections of time and culture that comprise a place—a natural and cultural
palimpsest.” He goes on to describe Planning as “the use of scientific, technical, and other
15
organized knowledge to provide options for decision making as well as a process for considering
and reaching consensus on a range of choices.” (p.4). Soesilo and Pijawka (1998) define
environmental planning as “the initiation and operation of activities to manage the acquisition,
transformation, distribution, and disposal of resources in a manner capable of sustaining human
activities, with a minimum distribution of physical, ecological, and social processes” (p. 2072).
Landscape conservation planning, then, is the dynamic incorporation of biological needs of the
environment and its interactions with humans. Effective landscape planning must account for the
needs of wildlife and people (Sanderson, Redford, Vedder, Coppolillo, & Ward, 2002).
Conservation must incorporate techniques to protect the environment, but also incorporate
stakeholder needs to ensure the integration of conservation effort exist across the landscape
(McShane, 1990). At the landscape scale, conservation has to be socially and ecologically
sustainable to succeed. Success is a mixture of different land uses that preserve biodiversity yet
also allow people to sustain themselves financially.
2.2.2.Current Practices to Mitigate Resource Problems
While conservation tillage practices reduce run off pollutants, there are still leakages from the
agricultural landscape going into the water system so other practices are employed as secondary
pollution interceptors. Current practices used and tested to improve runoff water are many and
have years of scientific study and experimentation behind them. There are a variety of best
management practices (BMPs) promoted to the agricultural community to mitigate non-point
source pollution that originates in the agricultural landscape. Some of them are vegetative
barriers, riparian forests, two-stage ditches, and constructed wetlands. These methods are
interjected onto productive crop land putting the financial and social burden upon the farmer.
Vegetative barriers
Vegetative barriers are a BMP that incorporate vegetation strategically within the agricultural
landscape. Vegetative barriers offer erosion control, sediment trapping, and improve the
functioning of other conservation efforts and are widely used in North America (USDA-NRCS)
16
(USDA-NRCS, 2000). There are several different types of vegetative barriers including: grass
barrier, grass filter strips, buffer strips, and field borders. Grass barriers are not considered
permanent, and can be removed easily at any time. They are narrow strips of tall erect perennial
grasses that are planted in hedges and have stiff stems planted on the field contour (Kemper,
Dabney, Kramer, & Keep, 1992). Stiff, erect grasses with deep roots encourage sediment
deposition and berm formation (Gilley, Eghball, Kramer, & Moorman, 2000). They also
encourage sedimentation and infiltration to withstand flooding and still be effective (Bentrup,
2008; Lee, Isenhart, & Schultz, 2003). Pan et al (2011) documented grass barrier did increase
infiltration, filter surface runoff, and caused deposition of suspended soil particles.
Grass filter strips use low growing grass species as trapping mats for runoff and the suspended
solids within it. The width of the strip determines the effectiveness of sediment retention. Filter
strips widths as small as 0.7 m saw reductions of 78% with the highest performance reached at
8 m with a reduction of 96% on a silt loam (Blanco - Canqui, Gantzer, Anderson, Alberts, &
Thompson, 2004). Studies such as Hernandez-Santana, et al., 2014, Ghadiri et al (2001), and
Borin, et al (2005) have all found filter strips encouraged infiltration by decreasing velocity and
flow volumes increasing trapping capacity which encourages sedimentation and filtration.
Contour buffer strips are similar to grass barriers in their plant material, location and function,
however, are planted as permanent herbaceous materials. Plant material can range from sod
forming grasses, stiff grass-legume mixture, and small shrubs. The choice of vegetation is
dependent on the overall purpose of the filter strip.
Field borders are permanent cover buffers that encircle the entire field (Dabney, Moore, & Locke,
2006). Plant material in this type of vegetative barrier differs from the plant material used in filter
strips. The vegetation choice is not as stiff and is generally shorter because farm machinery can
use this space for a turnaround while tending to the fields and therefore need to withstand
compaction and equipment traffic. These are particularly beneficial in preventing pesticide and
fertilizer drift to adjacent fields and sensitive areas. This type of buffering can also provide
minimal trapping of soil and organic sediments suspended in water traveling over the field and
17
connecting grassed waterways and other vegetative areas providing ease of access for buffer
maintenance (USDA-NRCS, 2000).
Not only do vegetation barriers function better when multiple types of vegetative barriers are
combined, they improve the function of other conservation field management efforts (USDANRCS, 2000). Studies substantiate this with the combined use of vegetative barriers with field
management techniques reduce runoff and soil loss rates such as no-till, low-till, and leaving crop
residue on fields. Grass barriers account for 7% of the receiving areas proven to have great
effects when used in conjunction with no-till, low-till, and even tilled practices compared with not
using barriers in those situations (Gilley, Eghball, Kramer, & Moorman, 2000). This study also
concluded leaving crop residues on the fields after harvest in conservation management
scenarios, allowed the grass barriers to function better and decreased overland pollution
accumulation.
Other studies have paired grass barriers with filter strips which show a decrease in pollutant
transportation through increased detention time and increased infiltration (Blanco - Canqui,
Gantzer, Anderson, Alberts, & Thompson, 2004). Sediment reduction of a 0.7 m grass barrier/
filter strip combination was equivalent to a 4 m fescue grass filter strip with peak effectiveness
being reached at 4 m within combination barriers. This indicates that the combination of filter
strips and narrow barriers enhances the function of filter strips (Blanco - Canqui, Gantzer,
Anderson, Alberts, & Thompson, 2004).
Riparian Forests
Riparian buffering zones vary in standards from one place to the next however generally are
comprised of three areas: one being the land directly adjacent to the water body that is usually
not disturbed, second the area directly adjacent to that is a managed forest area and third is a
filter strip adjacent to production lands to capture sediment and is an optional area within the
riparian buffer zone (Lee, Isenhart, & Locke, 2003). Riparian buffer strips are significant
contributors to water quality within the agricultural landscape as they remove excess sediment,
18
organic material, nutrients, and pesticides in surface runoff and shallow ground waters. They do
however take a significant amount of land out of production; however tend to use land that usually
should not be cropped due to location and soil type.
Grassed waterways
To protect an area from further degradation grassed waterways can be used to facilitate the
movement of water as well as stabilize the soils. The use of grassed waterways results in
retained top soil, decreased sediment transports and better water quality (Fiener & Auerswald,
2003). According to the USDA-NRCS CORE4 training guide (1999) grassed waterways are not a
buffering strategy on their own, as has been suggested by Dabney et al (2006), but act as a
transport network that is vegetated to prevent further soil loss and decreases velocity of traveling
water within the network. According to CORE4 (1999) water that enters the grassed waterway
will generally leave with the same amount of pollutants it started with unless paired with another
BMP to become a buffer.
Drainage Ditches
Traditional Ditches
Ditches are a unique feature in the agricultural landscape necessary for surface drainage to
promote higher crop productivity. Ditches possess the characteristics of both a stream and a
wetland, similar to how natural streams form floodplains (Needelman, Kleinman, Strock, & Allen,
2007). These landscape features also function as water table controls that can affect the
landscapes biological, chemical, and hydrologic processes while providing a place for the
transformation, storing and emitting of agri-pollutants (Needelman, Kleinman, Strock, & Allen,
2007). Traditional ditch design uses a trapezoidal form with a wider bottom than what would be
produced from a naturally forming fluvial channel making the channel wide and shallow which
results in it being oversized for small flows and provides no flood plain for larger flow events
(Ward, Meckledburg, Powell, Brown, & Jayakaran, 2004). Trapezoidal ditches often equalize
themselves and form benches and bars by fluvial processes within the channel which improve
19
stability during high flow events while concentrating and sustaining low flow events (Powell,
Ward, Mecklenburg, & Jayakaran, Two-stage channel systems: Part 1, a practical approach for
sizing agricultural ditches.(SPECIAL SECTION: DRAINAGE DITCHES)(Report), 2007). This
beneficial natural formation however is regularly removed along with the vegetation that takes
root there due to concern of vegetation and debris will reduce the outlet depth or conveyance
capacity of the ditch reach (Powell, et al., 2007). This form of ditch has been used for many
years with little to no changes in form. Recently, innovations in ditch design have shifted the
thinking of what a ditch can be and how it can function to better reduce transportation of agripollutants from crop lands.
Agricultural ditches are often straightened and deepened to accommodate storm discharge,
maximize conveyance, and facilitate flow from subsurface drainage tiles, resulting in incised
trapezoidal channels with no connection to active floodplains (Powell, et al., 2007). These
practices reduce contact time of water with sediments and soils which reduce nitrogen retention
in fields and can promote instability causing bank slumping which requires frequent maintenance
to retain ditch shape (Roley, et al., 2012). Frequent maintenance for the removal of excess
sediment and debris within the channel is costly and has adverse water quality impacts (Powell,
et al., 2007).
Two stage ditches
Alternative to the traditional trapezoidal ditch is a two-stage ditch design that incorporates
features of naturally formed streams such as the floodplain. Two-stage design has a lower stage
and a higher stage within the ditch. The lower stage is an inset channel formed by fluvial
processes, while the second stage or higher stage is designed to provide stability using minimal
productive land to accommodate the drainage outlets from sub-surface tile drains while achieving
the desired capacity to prevent flooding of surrounding areas (Powell, et al., 2007). Vegetation
for two-stage ditches are recommended to be primarily grasses with an avoidance of woody
vegetation could shade the needed stabilizing grasses on the benches and bank side slopes
(Powell, et al., 2007). To date, there are not a lot of empirical studies on two stage ditch design,
20
however potential benefits are greater stability, reduced maintenance, increased conveyance
capacity of the ditch and pollution assimilation (Powell, et al., 2007).
Two stage ditches function very differently than traditional ditches and almost eliminate the issues
traditional trapezoidal ditch formations have. Two-stage channel design increases bank stability
because low flow events are below benches, keeping bank material dryer and less exposed to
water, which reduces sheer stress on banks (Powell & Bouchard, 2010). This was also observed
in a study by Roley et al (2012) where side slopes of ditches show higher stability because the
toe of the bank meets at the bench and not the bed bottom, only exposing the toe to water and
sediments when there is high flow not continuously as in low flow trapezoidal configurations,
reducing the potential for erosion and bank slumping.
Erosion is further decreased because the design of a two stage ditch allows water to overtake the
bench and spread out increasing surface area slowing the velocity of water and allowing
sedimentation in the channel and on the benches which decreased the potential for erosion
caused by scouring (Powell, et al., 2007). Stability within these ditch design is highly dependent
on vegetation quality and quantity on the benches within the ditch (Powell, et al., 2007).
Pollution assimilation within two-stage ditches occurs in the concentrated flow channel and on the
benches. The deposition of finer particle and the presence of vegetation litter on the benches
creates the conditions for denitrification during high flow events because water spreads out
across the floodplains (benches) in a two-stage system which slows water down allowing for the
deposition of fine sediments on the bench (Roley, et al., 2012). Aquatic denitrification rate
increases when water has longer contact time with benthic sediment, where large quantities of
denitrifying bacteria thrive (Galloway, et al., 2003). This is further Powell and Bouchard (2010)
who found organic matter in two-stage systems is higher on the slopes and benches of the ditch
network with higher denitrification rates in a two-stage ditch than a one stage/ trapezoidal form.
The form of two-stage ditch design has less maintenance and an increase in conveyance
capacity. The formation of the ditch decrease maintenance because within the fluvial channel
21
there is sorting of sediments with finer particles being deposited on the benches, not in the
concentrated flow channel while coarser materials are deposited in the bed of the channel
(Powell & Bouchard, 2010). With this separation of particles there is no need for dredging out the
channel. Conveyance capacity is increased in two-stage design because the flooded width of
two- stage channel design is three to five times the inset channel width (Powell, et al., 2007).
In the study by Roley et al (2012) nitrates in two-stage channels do not travel as far before
denitrification occurs compared with trapezoidal configurations, indicating that denitrification takes
place closer to the point of discharge, therefore decreasing the amount of nitrate in lower
reaches. Storm flow removal was greater in two-stage channels than in trapezoidal ones being
influenced by the denitrification rates on the floodplains, which is when agricultural land exports
the most amount of nitrates into the water system.
Two-stage ditch form removes the highest amount of nitrogen when low concentrations enter the
ditch network. This indicates that upland land management practices to reduce initial nitrogen
loads are necessary for performance. Cover crops and precision fertilizer application that reduce
nitrogen exports from production fields are recommended for this. Wetlands can also be a
complementary method to two-stage ditches as the wetland intercepts base tile drain flow,
decreasing nitrogen concentration in ditch water allowing the ditch preforming denitrification
during storm flow events and increase rates of removal during bench inundation (Roley, et al.,
2012).
Wetlands
Constructed wetlands have the potential to remove nitrogen and phosphorus. There are two
types of constructed wetlands that are used to mitigate agricultural nutrient loading to water
networks, Free Water Surface wetlands (FWS) and Subsurface Flow Wetlands (SSF). FWS are
shallow ponded water (<0.4m) with a low velocity and is controlled by the density of vegetation
present as well as the overall design of the wetland (Naja & Volesky, 2011). Sub-surface
systems are filled with porous substrates with water passing through them and always
22
maintaining the water level below the surface of the substrate (Naja & Volesky, 2011). There are
two types of sub-surface wetland, horizontal and vertical. Hybrid systems have been developed
to try and capture the benefit of both type of sub-surface wetland.
One type of hybrid system is a Vertical Flow Wetland to the Horizontal Flow Wetland (VF-HF) the
first stage provides aerobic conditions for nitrification and the second stage provides the
anoxic/anaerobic conditions necessary for denitrification with at third step to remove total nitrogen
(Vymazal, 2007).
The other type of hybrid system combines Horizontal Flow Wetlands and Vertical Flow Wetlands
(HF-VF), which place the large horizontal bed first in the sequence to remove organics and
suspended solids while also providing denitrification while the vertical flow bed is second in the
sequence which further removes organics and suspended solids and nitrifies ammonia to nitrate
(Vymazal, 2007).
The HF-VF system may function better than the VF-HF hybrid system because of the removal of
large particles and suspended solids, as cited by Hammer (1992), to show clogging in real life
applications. Subsurface systems cannot maintain dissolved oxygen levels necessary for
ammonia removal so a combination of SSF and FWS increase the range of nitrogen
transformation and removal (Hammer, 1992). Within wetlands there is an opportunity for the
interconversion of phosphorus with soluble reactive phosphorus being taken up by plants and
converted to tissue phosphorus; it can also be absorbed to sediments and soils in wetlands
(Vymazal, 2007, p. 59). With proper design and large enough treatment areas removal of
phosphorus can be readily accomplished in SSF component of the hybrid system (Hammer,
1992).
Plant species play an integral role in phosphorus removal and have greater efficiencies in lower
concentrations of nutrients (Gottschall, Boutin, Crolla, Kinsley, & Champagne, 2007). This,
however, is a short term storage of phosphorus and requires a regular harvest schedule to keep
plants at optimal uptake which occurs before maximum growth has been reached (Vymazal,
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2007). The reduction of phosphorus within wetlands studied in Vymazal’s (2007) were
considered low and the presence of a substrate in subsurface flow wetlands to remove
phosphorus would be needed to see any benefits. This however is contradictory to the research
by Yang et al (2007) that found high levels of phosphorus uptake by numerous species within
wetlands and suggest a multi tear design in plant selection that chooses plants based on their
highest nutrient intake so that throughout the entire growing season there are plants that are in
optimal phosphorus uptake to ensure reduction in phosphorus in constructed wetlands.
Wetlands cannot produce fully nitrified effluent because of limiting factors of each type of wetland
(Vymazal, 2007). Combinations of wetland types have been developed to achieve satisfactory
results in nutrient and sediment removal. Using wetlands in series increases the land making
constructed wetlands an expensive venture in terms of productive land.
Summary
All of these practices have been proven to reduce agricultural nutrients with varying degree. Most
studies indicate that a combination of conservation techniques that reduce the initial loading
would increase the functioning and subsequent uptake and removal of nutrients from the system.
Multiple studies indicate the use of best management practices in combination results in run off
and/or pollutant reduction. The use of vegetative barriers, riparian forests, two-stage ditches, and
constructed wetlands all require land to be taken out of production and converted to interception
areas for agri-chemicals. This reduces the amount of cultivated land, which in turn reduces the
economic gains and total production of goods. This is unfavourable to agriculture as an industry
because with growing demands for higher yields and more production of goods the decrease in
land will reduce total production and gains. One of the primary tenets of landscape conservation
planning is to try to mitigate these losses. The above proposed solutions favour the environment
and do not consider the farmer, therefore not fitting within this holistic concept.
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2.2.3.Current Large Scale Reconfiguration
Other practices of environmental conservation target pollutants at a broader scale. Programs
have been developed to aid in evaluating land for their suitability of land use. The Conservation
Reserve Program (CRP) in the USA uses geophysical characteristics of the land to determine if it
is suitable for agricultural use, or if it qualifies for a financial stipend to retire the land for a
negotiated period of time. Tools, such as Land Evaluation and Site Assessment (LESA), have
also been developed to holistically view agricultural land within the broader context of the
environment and human settlement. These tools for environmental conservation enable informed
decision making about the location (configuration) of land cover, and the composition of
agricultural land and other land uses such as conservation reserves and residential development.
Strategic Perennial Cover
Programs have been developed to utilize strategic design of a landscape to inform the placement
of continuous perennial cover. This type of strategy targets areas that meet landscape
characteristic criteria set forth by the conservation program. The Conservation Reserve Program
(CRP) is such a program that was started in the United States under President Ronald Ragan.
This program is a voluntary enrollment program that takes environmentally sensitive land out of
agricultural production for at least ten years, while paying the farmers a rental fee for the land.
Appropriate perennial plant species are planted to reduce erosion, increase water quality, and to
increase wildlife habitat (USDA Farm Service Agency, 2015a). There are many initiatives within
the CRP that focus on different aspects of environmental health. Those of interest in to this
thesis are the initiatives for Highly Erodible Lands and Flood Plain Wetland Initiatives. Areas that
qualify for the Highly Erodible Lands CRP are those that are evaluated as highly erodible under
annual cover with an erosion index of 20 or greater (USDA Farm Service Agency, 2015b). The
erosion index (EI) is an index of the potential for a soil to erode, which is based on climatic factors
and the physical properties of soil and terrain. The higher the number, the more likely it is to
erode. EI of eight or greater is considered highly erodible (United States Department of
Agriculture, 2007), so an EI of 20 is substantially vulnerable.
25
Flood plain wetlands initiative takes agricultural land that is often marginal and regularly floods,
and restores it back to a wetland. This tactic helps reduce the damages of flooding downstream
and adjacent areas of cropland by acting as a holding cell for overland flow. The return of
marginal farmland to a wetland also improves water quality because it provides a place for
dissolved nitrogen and phosphorus in overland flow to be removed. Not only do they reduce
nutrient pollutants, they also allow sedimentation to occur (USDA Farm Service Agency, 2015c).
All of these functions increased water quality overall. Marton et al (2014) found that CRP and
Wetlands Reserve Program (WRP) lands increased water quality by reducing nitrogen, sediment,
and phosphorus loads to water. The study evaluated environmental impacts of converting
agricultural land that is highly erodible and the restoration of historic wetlands. It was found that
re-establishing perennial cover in riparian zones reduced nitrogen and phosphorus outputs to
streams in the area. The restoration of ephemeral wetlands reduced phosphorus loading more
than restored riparian zones however each working in tandem made the largest impact to water
quality. Even though there were no wetlands used in the designed scenario proposed by this
thesis, the Flood Plain Initiative demonstrates how geophysical features of the landscape can be
used to inform design and placement of landscape features to decrease the effects of agriculture
on water quality.
Since the inception of the CRP many studies have shown the programs benefits to the
environment and wildlife habitat. A 14 year study by Lizotte et al (2014) evaluated the use of
multiple BMPs in Beasley Lake Watershed. The long duration of the study allowed for the
evaluation of BMPs added to the surrounding area of the lake over time. Mid way through the
study, CRP was initiated at the north end of the lake. CRP land improved water clarity,
decreased total suspended solids, and reduced total dissolved solids more than other BMPs in
the study. This indicates that there was a reduction of sediment delivery to the lake. The
combination of CRP with other BMP techniques showed greater decreases still. Another study
that investigated land use practices on water contaminants saw 2.7-6 times greater soil accretion
in soil cores from wetlands surrounded by row crops compared to CRP and native prairie grassed
surrounded wetlands. Phosphorus and nitrate concentrations were the lowest in CRP
26
surrounded wetlands, with ammonium nitrate, carbon, and nitrogen concentrations between
values recorded for native prairie grass and crop surrounded wetlands (Preston, Sojda, &
Gleason, 2013). A study by Randall et al (1997) investigated the conversion of CRP lands to
three different cropping sequences; with a base line plot kept as CRP cover. They determined
that continuous corn and corn soy rotation sequencing produced more residual soil nitrates and
had higher nutrient loads in tile drainage water compared to alfalfa (a perennial crop species) and
CRP plots, which are in perennial cover. This indicates a reconfiguration of perennial dominant
crop rotations to areas with erosion and nutrient loss as a concern benefit water quality by
reducing the buildup of nutrients in the soil and in overland flow.
Not only has the CRP decreased sediment and nutrients in water, but they also provide habitat.
Reynolds et al (2001) found that the conversion of 1.9 million ha of crop land to CRP perennial
cover, across three states, increased the recruitment rate of five duck species, compared to pre
CRP conversion. Using recruitment models the study took daily survival rates counts from the
pre CRP cover and post CRP conversion. Habitat increases through the CRP program increase
population counts for these duck species, but also for other fowl species. A study by Swanson et
al (1999) found that forty-three breeding bird species used the CRP fields with twenty-six species
using three or more of the CRP fields in the study. It was found that CRP lands that are
connected to existing grass lands had higher occurrences of monitored species. The increase in
habitat has been positively increasing species population that have been on the decline and even
some that are endangered.
The enrollment of row crop fields on erodible and water adjacent lands into the CRP has had
marked environmental benefits. CRP conversion creates wildlife habitat for many species. Of
particular study are the different kinds of breeding fowl that use the CRP as nesting and dwelling
grounds. Several studies conclude that avian species use the CRP regularly and that they are a
beneficial conservation strategy because they are increasing declining grassland fowl
populations. Not only do CRP lands provide wildlife habitat and benefits, they increase water
quality. Sedimentation and nutrient load in water bodies decreased in areas that have CRP in
27
place. Comparison of row crop to CRP fields indicate that there are significant environmental
benefits to the enrollment to CRP because the amount of nutrients and sediment delivered to
water bodies is significantly lower than row crops and even singular BMPs like vegetative buffers.
Evaluating geophysical characteristics of the landscape for high erosion risk and altering the
composition and configuration of landscape features to protect those areas year round has
decreased the yield of agricultural pollutants that enter into adjacent streams and subsequent
water bodies. This indicates that the evaluation of the geophysical properties of the landscape
and the reconfiguration of cropping sequences that have sequential years in perennial cover
would alter the amount of nutrients lost from the agricultural landscape.
Land Evaluation and Site Assessment (LESA)
Land Evaluation and Site Assessment (LESA) is a committee based analytical tool that assesses
agricultural target areas aiding in land use and planning on a variety of scales. It was developed
to be used as a systematic evaluation tool that is adjustable to the site/region it is being used in.
LESA is typically used when deciding which land should be designated as agricultural land in the
planning phase of a district, zoning land parcels, land-use permits and rezoning, and impact
analysis on the “creeping effect” (p. 111. Para.1). A guidebook was created to assist those
endeavouring into a LESA site evaluation. The LESA guidebook outlines the basic concepts of
the LESA system, the assessment process, developing a committee to construct the LESA
system in the local area, setting and selecting the Land Evaluation, selecting and scaling Site
Assessment factors, combining and weighing the Land Evaluation and Site Assessment factors,
Testing the draft LESA system, and interpreting the LESA score to aid in decision making. LESA
incorporates both physical and site features in tandem which allows for an analysis of
environmental and social impacts.
LESA is comprised of two categories which include Land Evaluation (land characteristics) and
Site Assessment (site characteristics). Land Evaluation (LE) consists of soil based factors in
relation to agriculture, while Site Assessment (SA) factors are not soil based, rather more
28
environmental and anthropological. The main task, and the most labourious, for the committee
are determining which factors to include in LE and the SA factor categories and the subsequent
unit scoring process. The main focus when choosing factors to include when using LESA for a
site evaluation is to always keep in mind, “what are we trying to learn from a LESA score?” (p.16
para.5). This determines what to include in the LE and SA factors. A relative score is assigned
to individual LE and SA factor categories with total scores of LE and SA being assigned to each
map unit. The results are then analysed by the committee and used in the decision making
processes.
Land Evaluation (LE)
The LE component of LESA is one or more land classification system chosen to analyze the soil
based qualities of the study site. There are four different classification systems that are
recommended: land capability classes, important farmlands classification, and soil productivity
ratings and/or soil potential ratings. Each land classification system evaluates different amounts
of information to form map units. Soil potential rating presents the most information about the soil
qualities including yield potential and the cost of overcoming soil limitations. Next, soil
productivity rates soil units by yield alone. Land capability classifies soils based on risk of
damage by cropping, where soil potential and soil productivity can be within the same land
capability class. The broadest soil classification system is important farmland classes which
distinguish map units based on federal definitions of agricultural land (p. 44).
Site Assessment (SA)
SA factors consist of things that effect agriculture but are not soil based. It consists of three
broad factor categories: “SA1 non-soil factors that limit agricultural productivity or farming
practices, SA2 factors related to development pressures, SA3 public value of the site (ex. historic
or scenic value)” (p. 13 para. 5). The committee will develop SA characteristics for one or more
categories. Categories chosen and populated are significant to the local agricultural community
and coincide with the committees’ objective for using LESA.
29
Examples of non-soil factors (SA1) that limit productivity are: size of site, compatibility with
adjacent uses, compatibility with surrounding non adjacent uses, shape of site, percent of site in
agricultural use, level of on farm investment, availability of agricultural support services,
stewardship of the site, environmental limitations on agricultural practices, and availability and
reliability of irrigation water (p. 65). Factors that relate to development pressures (SA2)
surrounding the land can include: land-use policy designation, percent of surrounding land in
urban or rural development use, distance to public utilities, length and/or road class of adjacent
roadways, and proximity to protected farmland (p. 76).
Public value of a site (SA3) is an optional component of LESA that evaluates the broader
landscape containing farmland. An ecosystem approach incorporates other land-use objectives
with farmland becoming more prevalent in land-use planning and policy. Identifying scenic view,
habitat for wild animals, open spaces, educational opportunities, historical architecture or
archaeological significance, wetland and riparian value, environmentally sensitive areas, and
floodplain protection sites all can be incorporated into the LESA system for analysis. SA3 factors
may be incorporated into other parts of the LESA system. The committee will determine how and
if this information is relevant to their study and they must determine how to combine these factors
with the LE components that look at soil based characteristics of the site (p. 80).
Not all SA factors discussed above are relevant to each study, and in some cases redundant with
other LE and SA factors. A recommendation is to have between three to seven SA factors, which
will adequately represent each unit of the site. The chosen SA factors must be relevant to the
site and fit with the goal of the LESA assessment. SA factors can be taken from the provided lists
from the LESA guidebook or other factors can be conceptualized based on the characteristics of
the study area and the nature of the LESA assessment.
Once the committee has determined the categories and components of both the LE and SA
factors that are important to the site each factor is scored, assigned a weighting value, and a
weighted score. This information is then used to create a site score for evaluation and decision
making. In the scoring process the committee determines which factors are more important than
30
others and assigns arbitrary factor values of 0-100 to all of the LE and SA factors. The factors
are then weighted to determine the score of the site. The weighting scheme used in the LESA
system totals 1.0 making each factor a fraction of the whole. A weighting value between 0.0-1.0
is assigned to all of the factors associated with the site. The weighting factors are then multiplied
by the factor score to produce the weighted score of each attribute. These scores are then tallied
up to produce a site total score. Certain LE and SA factors are scored and weighted differently.
The guidebook developed to aid the committee members in the LESA process details each factor
that requires a specific type of scoring protocol (p. 14).
Once the scores have been calculated interpretation of the results follows. The typical ways that
the scores are interpreted are to set threshold values for each of the applications of LESA (if
there is more than one). The threshold allows for a range of scores to be grouped into categories
indicating the quality of parcels for agricultural production. There are a number of ways to
develop the threshold values, one being a hard value, another a “fuzzy” threshold that sets a
range (p. 112). The choice of how to determine thresholds of LESA parcels is based on the
application of the study. It may be more appropriate to have hard set values, while other times it
may be more beneficial for the committee to have more lax classifications so that they can seek
outside expertise.
LESA is a general model to be adapted to local conditions and needs. The developed guidebook
aids the committee in adapting LESA to what is needed for that study. This is a very flexible tool
allowing the user to choose only relevant factors for their site, weighting of factors, and thresholds
used to interpret the data generated from LESA. It is an objective numerical analytical tool that is
intended to be a part of a decision making process, not a stand-alone tool. Combination of land
characteristics and site characteristics allows the site to be analysed based on a well-rounded
representation of the site (Pease & Coughlin, n.d.).
31
Table 2.1 Example of LESA scoring
1
2
Factor name
Factor rating
3
X
(0-100)
Weighting
4
=
(Total = 1.00)
Weighted
factor rating
Land evaluation (site with one soil):
1. Land capability
68
X
0.3
=
20.4
2. Soil productivity
62
X
0.2
=
12.54
subtotals
Site assessment-1 (agricultural use
factors):
0.5
32.8
3. Acreage of farm
100
X
0.15
=
15
4. Farm investment
80
X
0.05
=
4
5. Surrounding uses
60
X
0.1
=
6
subtotals
Site assessment-2 (development
pressure):
0.3
25
6.Protection by plan or zoning
90
X
0.06
=
5.4
7. Distance to sewer
70
X
0.05
=
3.5
subtotals
0.11
8.9
Site assessment-3 (other factors):
8. Scenic quality
50
subtotals
Total of factor weights
X
0.09
=
0.09
4.5
4.5
1
(must equal 1.00)
Total LESA score
71.2
(sum of weighted factor ratings)
2.2.4.Summary
Conservation agriculture began after the 1930 Dust Bowl in the Great Plains of the US that led to
financial devastation, starvation, and economic distress. Practices such as conservation tillage
are now integrated into many farms across North America and is widely promoted by
governments here and around the world. Moving forward from conservation agriculture,
Landscape Conservation Planning is a logical forward step in the development of agriculture as a
conscious conserving industry. It accounts for the needs of farmers, stakeholders, and working
personnel yet also has the environment at the forefront of development. The integration of
environmental health and sustainability has spurred many practices and techniques to help
32
reduce the impact of agriculture on the environment, while maintaining profitability for those that
run and work the farms.
All of these practices have been proven to reduce agricultural nutrients to some degree. Most
studies indicate that there is a need for multiple buffers in sequence because the first buffer in the
series reduces the initial loading of sediment, nutrients, and water and allows the subsequent
buffers to function properly (i.e. not overloading the system), resulting in a more consistent overall
decrease of pollutants at the end of the sequence. The use of vegetative barriers, riparian
forests, two-stage ditches, and constructed wetlands all require land to be taken out of production
and converted to interception areas for agri-chemicals. This reduces the amount of cultivated
land, which in turn reduces the economic gains and total production of goods, which is
unfavourable to agriculture as an industry. One of the primary tenets of landscape conservation
planning is to try to mitigate these losses while caretaking the environment. Current programs
and tools have been developed that align with the tenets of landscape conservation planning.
CRP incentives remove land from production based on the erodibility of the soil. There is a rental
fee given to the farmers to take that land out of production. This system considers the
environment, while also understanding that land is the revenue generator for farmers. Tools such
as LESA provide a different method of determining the vulnerability of an agricultural parcel. It
evaluates farm land in the context of the environment, productivity, and external pressures that
may affect agricultural land. This too takes into account the environmental concerns and the
concerns of farmers.
2.3. Watershed models
There are many models developed for simulation of watersheds to determine pollution loading
rates. How a model spatially represents the study watershed varies from lumped models to
distributed models. Time scale in models also varies greatly, from minutes, hours, days, and
months. This section will discuss two recognized models and key feature in simulating water
quality and quantity. Original model documents will be reviewed and differences between these
models will be explored. Only process based distributed models will be discussed, since lumped
33
models impose a homogenous average of watershed features to the entire study watershed.
This study was looking to maintain as much heterogeneity in the study area during simulation to
have a better representation of real life processes.
2.3.1.Distributed Models
“A distributed approach to modeling a watershed consists of a grid representation of topography,
precipitation, soils, and land use/cover that accounts for the variability of all these parameters”
(Vieux, 2004 p. 3). Distributed models best represent the real landscape as they do not lump
whole catchments together and apply an average of the parameter to the entire area, which
would not capture topographical differences of the landscape (Vieux, 2004). There are two subcategories of distributed models, Semi and fully distributed. Semi-distributed models consider a
catchment as a series of lumped models within a catchment or Hydrologic Response Units
(HRUs). According to Schumann (1993), HRUs blanket several spatial units with similar land
use, soil type, and topography. This method simplifies the watershed but still captures spatial
differences, crop, soil, and hydrologic conditions (Sanzana, et al., 2013). Each lumped division
has the average of the parameter applied to it. This type of model tends to need less input data
and take less time to compute results from simulation. Fully-distributed models calculate
parameters for specific grid locations within the catchment. These hydrological models are time
consuming and take a long time to calculate and return results, however all parameters produce
data to be analysed (Harun, Jajarmizadeh, & Salarpour, 2012).
SWAT Model
Soil and Water Assessment Tool (SWAT) is a river basin or watershed model that predicts the
long term impact of agricultural practices in watersheds. The SWAT model is a physically based,
semi-distributed model that is continuous time based (Arnold, et al., 2011). Physically based
models allow the measurement of any parameter because the physical attributes and processes
of the watershed are the basis for determining the way the model simulates. It can simulate the
impact of land management on water, sediment, and agricultural yields. Soil composition, landuse and management can vary through-out the simulation time period and area. The simulation
34
area is sub-divided into smaller sub-basins or sub-watersheds. These subdivisions are grouped
based on climate, hydrologic response units (HRUs), ponds, ground water, and main channels.
SWAT model components include weather, soil, topography, vegetation, and land management
practices as input data to run a simulation (Arnold, et al., 2011). Input data is required at different
scale: watershed, sub-basin, and HRU scale. This model is not designed to be used for
simulation of single events, and must simulate over long periods of time (Arnold, et al., 2011).
Most users of this model do so to study the long-term effects of land management strategies.
Users typically use SWAT to estimate nutrient, sediment, and chemical build up within the basin
or stream network (Arnold, et al., 2011).
AnnAGNPS Model
AnnAGNPS is a distributed model that is physically based. It is a watershed-scale model that
allows the analysis of non-point source pollutant loading in agricultural watersheds . Input data
required for this software are: weather, soil, topography, vegetation, and land management
practices, and land use (Bingner, Theurer, & Yuan, 2015). The software is equipped with
additional modules to aid in the construction of input data to be used in simulation. These
modules include: TOPAGNPS, a batch program for input file formats, GIS input editor, a visual
interface program, and an output editor to analyse results. TOPAGNPS allows for the processing
of topographical information from a digital elevation model (DEM). TOPAGNPS is a composite of
many individual computational processes that have been combined into one component for
simplification. Once all sequences are complete within TOPAGNPS the watershed is divided into
smaller drainage catchments known as cells. Individual output files such as the cell file and the
sub watershed file are then used in later components to build input data files. Input Editor uses
GIS data to intersect the created watershed cell file with soil, climate, and land use. None of the
cells and, therefore, geophysical information is grouped as with HRUs in semi-distributed models,
making the digital representation of the watershed as close to real life conditions as possible
(Bingner, Theurer, & Yuan, 2015).
35
Summary
There are many watershed models that have been developed to estimate nutrients, sediment,
and water volumes that leave a given study area. However, two that suited the current study
have been presented. The semi-distributed model SWAT uses a grouping approach to
overlaying geophysical information on the watershed. This model produces a less than realistic
representation of the watershed and loses information in doing so. AnnAGNPS, a fully distributed
model uses geophysical information at a finer scale and does not group areas of the watershed
together like SWAT. Both models require almost the exact same data to produce a simulation of
the study watershed. SWAT differs in outputs with the ability to estimate snowmelt runoff, where
AnnAGNPS cannot. AnnAGNPS does not measure snow melt runoff but does compute
snowmelt.
Conclusion
This chapter reviewed the environmental processes that move water, nutrients, and sediment
through the landscape, ending in streams, degrading water quality in the watershed and in
receiving waters. A discussion of the current trends in conservation and techniques currently
used to mitigate agricultural pollutants illustrates where current research is focused and the niche
that this thesis fulfills.
The unaltered natural environment has a finite amount of nutrients available to plants. To meet
the demand of farm goods, intensive farming requires the use of fertilizers, manmade and natural,
to increase the presence of naturally occurring nutrients. Nutrients added to the soil are carried
on soil particles and in water during erosion and runoff events, exacerbated by the absence of
naturally occurring vegetation, end up in our streams, causing water quality issues such as
hypoxia and algae blooms. These water quality issues have spurred the development of
conservation agriculture that aims to reduce the environmental impacts of the industry. To
reduce pollutants in runoff, water buffering strategies and best management practices that trap
soil and intercept runoff water have been developed. These strategies require productive land to
36
be converted into buffers, which is problematic when earnings are derived from yields and food
demands continue to increase. Landscape conservation planning is a modern approach that
balances environment protection and human needs. This approach also balances economic
gains to sustain the people that work and own the farms, which in turn warrant the correct
mentality of those people towards the environment, and the implementation of conservation
efforts across the landscape. Programs like the CRP have balanced this concept by offering
farmers financial incentive and payment for land to be removed from production and converted
into conservation lands. Tools, such as LESA, were developed to aid in the evaluation of
farmland and to understand the economic, social, and environmental aspects that surround
agricultural land in a study area.
While literature shows the value and approaches to landscape conservation planning,
appreciations are few. Re-configuring land covers within agricultural watersheds- without taking
land out of production- should be studied to determine how it affects agricultural nutrients and
sediments. If re-configuration of land cover is effective it offers insights for the application of
agricultural landscape conservation planning.
37
3.0
Methodology
This section identifies the research question and discusses possible outcomes from testing this
question on an agricultural sub watershed through model simulation. A description of how the
study area was chosen and the physical and cultural properties of the sub watershed are given to
place the study in its environment. Data sets and sub sets for the model are outlined and
described. The procedures for obtaining data to create and build the digital geophysical
representation of the watershed are documented. Site analysis of the study area lead to the
creation of a set of design rules to locate crop rotations in the designed scenario.
3.1. Research Question and Hypothesis
This project aimed to learn if an agriculturally dominated watershed can be reconfigured, while
keeping the existing composition the same, to decrease agricultural pollution delivered to
streams? Expected results from the redesign of the study area are decreases in total pollution in
the forms of nutrients (nitrogen and phosphorus) and sediment. Particularly, nitrogen is expected
to decrease the most because of its use to rank crop rotations by nutrient input. Phosphorus is
expected to decrease, however not as much as nitrogen, as it wasn’t directly included in the
design reconfiguration protocol. Erosion rates will decrease with the reconfiguration due to the
relocation of crop sequences that have more ground covering capabilities to more vulnerable
landscape positions. However, erosion was not directly included in the reconfiguration protocol
so decrease is not an expected outcome.
3.2. Study Area Selection
The study area was selected in an agriculturally dominated watershed. Application of landscape
design principles improving water quality and soil stability can be achieved across many
Canadian landscapes. Southern Ontario has most of Canada’s high-quality farmland. Criteria for
the selection of the study area were that the agricultural capability should be high (Canada Land
Inventory agricultural capability class 1-3 land should dominate), farming should be the dominant
38
land use, soil erosion and water quality should be documented concerns, and geophysical
characteristics and vegetation should be diverse so that there are opportunities for moreresponsive design. Finally, spatial data must be available for the landscape and ideally the
watershed is already well-documented for land management concerns.
3.3. Description of the study area
The Grand River watershed (Figure 3.1) is one of the largest in Southern Ontario. The Grand
River flows from its headwaters in Dundalk, Ontario and terminates at Lake Erie (Grand River
Conservation Authority , 2015). This watershed is dominated by agricultural lands making it a
strong candidate for testing a hypothesis that investigates changing water quality in an
agricultural watershed. There are many tributaries that join the Grand River, some of which are
rivers in their own rite. The size of the Grand River watershed makes it too unwieldly to study at
a fine level of detail. A sub watershed dominated by agricultural practice was chosen to study the
change in water quality by changing the location of field rotations, while keeping the overall
composition of the watershed the same.
Figure 3.1 Grand River Watershed, Ontario
39
The Canagagigue creek is a sub-watershed (figure 3.2) of the Grand River located in the Upper
middle Grand basin. This minor basin has five sub-catchments and is located in Woolwich
Township in the Regional Municipality of Waterloo. One of the five sub catchments was chosen
based on agricultural land use that had similar ratios of each type of agricultural production as the
entire Canagagigue Creek watershed. This watershed has been the subject of other research
projects that investigate agricultural pollution (for example: Oogathoo, 2006; Environment
Canada, 1983; Gharabaghi et al, 2006).
Figure 3.2 Canagagigue Creek and study site
The study area is located in the north eastern quadrant of the Canagagigue Creek watershed. It
is 2888 ha with predominantly level to very gentle slopes with a mean gradient of just over three
percent slope. Loam is the predominant soil texture. Agriculture dominates the study area
covering 84% of the sub catchment. Agricultural activities consist of mixed farming of livestock
40
(i.e. pasture) and cropping of corn, small grains, soybeans and hay. The remainder of the sub
catchment consists of forests, urban development, farmsteads, streams, and small water bodies.
3.4. AnnAGNPS Data Requirements
The model used for this study is the AnnAGNPS model. The need for an as accurate as possible
representation of the watershed outweighed the heftiness of the data sets and procedures
needed to run a simulation. In addition, AnnAGNPS produced all of the required outputs to
compare the baseline and designed scenario with minimal extra data sets for analysis.
AnnAGNPS requires three minimum data sets to run a pollutant loading simulation, climate data,
geophysical data, and land use data. Some of these component data sets require multiple subcomponent files to be constructed to run the simulation.
3.4.1.Climate data
Climate data required for running an AnnAGNPS simulation include precipitation (timing and
amount), solar radiation (intensity), air temperature, wind speed and direction, dew point and sky
cover (proportion of cloud). All climatic data were required as daily values. AnnAGNPS required
Climate data to be in specific formats to run the simulation. Daily totals were required for
precipitation and solar radiation. Daily air temperature was required as maximum and minimum
values. Twenty-four hour averages were required for wind speed and direction, dew point, and
sky cover.
Elora Research Station (ERS) provided hourly data for all climate data. If data were missing or
unavailable from ERS, Government of Canada hourly climate reports were used to fill in gaps
where available. Solar radiation had the highest amount of incomplete records in the ERS data
sets. Solar radiation data are only measured at a few climate stations in Southern Ontario.
Proximity to the study area and different formats of measured solar radiation made other sources
non- viable to complete the missing radiation data. To complete the missing records, equal
amount of years before and after the missing records were used to average existing data and
transpose the average to the missing daily records. Sky cover was calculated internally within the
41
AnnAGNPS software by using solar radiation data. Sky cover specifically was the most
incomplete data set; therefore, this parameter was left to be calculated internally by AnnAGNPS
software. Dew point is not included in historical weather data. Calculation of dew point was
preformed using the following excel adapted formula (Lawrence, 2005):
TD=243.04*(ln(RH/100)+((17.625*T)/(243.04+T)))/(17.625-ln(RH/100)-((17.625*T)/(243.04+T)))
TD is the total dew point. RH is the relative humidity. T refers to air temperature.
3.4.2.Geophysical data
The topography data for the study area were obtained from a 10x10 DEM from the Ontario
Ministry of Natural Resources 2006 Provincial tiled dataset. The DEM was clipped, converted
into ASCII format to be used in the AGNPS GIS tool. Soil data and broad scale land use data
were acquired from Agriculture and Agri-Food Canada’s National Soil Data Base (NSDB).
Soil data necessary to run the AnnAGNPS software included the soil type and the soil layers
within that soil type. Soil type requirements include hydrologic soil group, erodibility k-factor,
depth of soil layers, and depth to bedrock, soil name, and texture and the number of soil layers
present. Hydrologic soil group is an American soil data component and not directly present in
Canadian soil data. Hydrologic soil groups describe saturated hydraulic conductivity and are
classified into four categories (from best to worse drained): A, B, C, D. Hydrologic groups were
determined using the saturated conductivity values from NSDB data sets and translated into
categories A through D. K-factor was estimated using Table 3.1 K ranges by soil texture. Soil
layer information included: soil layer depth, bulk density, clay, silt, sand, rock and very fine sand
ratios, calcium carbonate content, saturated conductivity, field capacity, wilting point and pH. All
of these values were obtained from the NSDB Detailed Soil Survey (DSS). The DSS data had to
be manipulated to reflect the correct units of measure required by AnnAGNPS software.
42
Table 3.1 K ranges by soil texture
Surface Soil Texture
Very fine sand
Loamy very fine sand
Very fine sandy loam
Silt loam
Silty clay loam
Clay loam
Clay
Loam
Sandy clay loam
Heavy clay
Fine sand
Sandy loam
Coarse sandy loam
Loamy sand
Sand
Silty clay
Relative Susceptibility
to Water Erosion
K ranges
Very highly susceptible
>0.05
Highly susceptible
0.04 - 0.05
Moderately susceptible
0.03 - 0.04
Slightly susceptible
0.007 - 0.03
Very slightly susceptible
<0.007
Loamy fine sand
Note. Reprinted from (Wall G. , Coote, Pringle, & Shelton, 2002, p. 15)
3.4.3. Land Use Data
AnnAGNPS required land use data including field shape polygons, land use type, and
management. Land use shape file consists of the landscape fabric of the watershed
distinguishing field boundaries from other landscape components such as forests, residential and
farmstead, transportation, and water. The shape file was created using the 2006 Southwestern
Ontario Orthophotography (SWOOP 2006) (First Base Solutions, 2010) to digitize the watershed.
This process outlined fields, roads, farmsteads, urban development, extraction sites, riparian
zones, and forest.
Land use types consist of the field rotation, a broad agricultural land use descriptor, management
schedule (for tillage and fertilizer applications), and the erosion code. Field rotations were
compiled from a number of sources. The use of existing land use data from the Agricultural
Resource Inventory (ARI), Agronomy Guide for Field Crops - Publication 811, and rotational
practices common to conservation management practices were used to inform the creation of
crop rotations. Assigning field rotations was completed through the lottery process (see the
section below). Broad land use type categories of cropland, pasture, forest, urban, and
residential were given by the AnnAGNPS software. Assignment of land use type was straight
forward. The use of SWOOP 2006 allowed a visual identification of each type of broad land use.
43
Lottery
Field rotations were assigned through the use of a lottery system that determined the rotation and
the start year for each field, so AnnAGNPS would have a representative mix of land management
for the start year of the simulation. The ARI data set was used to determine the broad scale
cropping system of each field. The ARI shape file was clipped to create ARI blocks that
contained individual field boundaries. These blocks were then assigned the broad scale cropping
system. Cropping systems defined in the ARI are: corn system, grain system, mixed system,
continuous row crop system, hay system, and pasture system. There are four individual crop
types identified in the ARI: hay, grain, corn, soy.
Percentages for individual crops within each cropping system were available in the ARI guide
book (see Table 3.2). The percentages were worked out to determine how many fields would to
be assigned each of the four crop types. Twelve crop rotations were created and divided into the
six cropping systems to fulfill the field rotation component for the AnnAGNPS software. Once the
ARI block were determined the fields that fell within each block were recorded and randomized
using a true randomizing software, from RANDOMIZE.ORG.
Using the previously calculated field count, based on the ARI percentages, each field was
randomly assigned a number from one to four, while each crop type, hay, grain, corn, soy was
also denoted a number from one to four to determine the initial crop type present. Rotations were
then categorized into four cropping systems: continuous row crop, corn system, mixed system
and grain system (see Table 3.3). Hay and pasture system were not included because if hay or
pasture system were present all six years of rotation were the same and did not need further
development. Within each ARI block rotations were randomly assigned using
RANDOMIZE.ORG. Error! Reference source not found.Error! Reference source not found.
clarifies this process. Rotational sequencing varies within field. A field that has the same rotation
as another may be in a different year in the rotation sequence. To mimic this likely reality, the
first year of rotation was randomly assigned to each field. In cases where the present crop type
has only one occurrence within a rotation that became the first year of rotation. In cases where
44
there was more than one occurrence of the present crop type the year in which they occur was
denoted a number and randomly assigned using RANDOMIZE.ORG. Table 3.5 illustrates this
process.
Table 3.2 Interpreted ARI crop percentages
C= corn S= soy GR= grain H= hay P= pasture WH= weedy hay
Cropping System
Crop Type
Coverage
Continuous Row Crop
C/S
100% each
Corn System
C/S
40% each
Mixed System
GR/C/S/H
25% each
Grain System
GR
85%
Hay System
H
100%
Pasture System
sod crop
100%
Crop Type
Coverage
H or G
20%
H
15%
WH/P
Table 3.3 Crop rotations by cropping system
CONTINUOUS ROW
CROP
CORN
SYSTEM
MIXED
SYSTEM
GRAIN
SYSTEM
HAY
SYSTEM
CCSCCS
CSWCSW
CSWHHH
WBHHHC
HHHHHH
CSCSCS
CSCHHH
CSWBHH
OrcBHHHC
COrcHHCS
COrcHHCS
SWHHHC
45
Table 3.4 Rotational sequence creation
Coarse ARI
description of the
cropping system
Field count
within each
system
block
Block 1
Count 10
MIXED SYSTEM
MIXED SYSTEM
MIXED SYSTEM
MIXED SYSTEM
MIXED SYSTEM
MIXED SYSTEM
MIXED SYSTEM
MIXED SYSTEM
MIXED SYSTEM
MIXED SYSTEM
Block 5
1
1
1
1
1
1
1
1
1
1
GRAIN SYSTEM
GRAIN SYSTEM
GRAIN SYSTEM
GRAIN SYSTEM
Randomized
field IDs
Crop Type
Crop
type
Hay
5
5
5
5
5
174
175
176
177
178
179
180
221
226
530
178
176
226
530
221
179
175
177
174
180
2
2
3
2
4
4
1
1
3
1
240
241
242
243
365
241
243
240
365
242
4
2
4
3
3
Count 4
40
40
40
40
Grain
1
Count 5
CORN SYSTEM
CORN SYSTEM
CORN SYSTEM
CORN SYSTEM
CORN SYSTEM
Block 40
Field ID
number
20
60
71
75
71
60
20
75
1
2
2
2
Corn
2
Soy
3
4
0.3
0.3
0.2
0.2
3
3
3
3
2
2
2
2
0.1
0.1
0.4
0.4
0.5
0
0.5
1
2
2
2
2
0.15
0.85
0
0
0.6
1
3.4
3
0
0
0
0
< Interpreted ARI percentages by crop type
< Necessary number of fields per crop
< Assigned crop type amount
46
Table 3.5 Randomization of the first year of rotations
Start Year Key
Randomly Generated year #
Crop Start Year
Crop Type
1
2
3
1
3
5
corn
4
5
6
2
4
6
soy
Random Rotation
Assignment
Randomly Generated year
#
Start
Yr
Final Rotation
Sequence
CSCSCS
1
1
CSCSCS
2
CSCSCS
1
1
CSCSCS
SOY
2
CSCSCS
4
2
SCSCSC
4
SOY
2
CSCSCS
5
4
SCSCSC
355
4
SOY
2
CSCSCS
5
4
SCSCSC
CRC
26
3 CORN
2
CSCSCS
3
5
CSCSCS
CRC
199
4
2
CSCSCS
6
6
SCSCSC
CRC
160
3 CORN
2
CSCSCS
1
1
CSCSCS
CRC
196
3 CORN
2
CSCSCS
1
1
CSCSCS
CRC
198
4
2
CSCSCS
4
2
SCSCSC
ARI system
ID_num
Start Crop
CRC*
160
3 CORN
2
CRC
196
3 CORN
CRC
198
4
CRC
27
CRC
SOY
SOY
47
Management data
Management schedules identify the tillage and nutrient management approaches for the field. These
approaches identify the type of land use and denote a code to be used within the modeling software.
These codes connect with other files that give the Soil Conservation Service (SCS) runoff curve
numbers and the management schedules of crop rotations. Runoff curve numbers were determined
through the use of given files that came with the AnnAGNPS software.
Management schedule files consist of the runoff curve number code, field schedule, management
operations and fertilizer application. Field schedule refers to the operations that happen within each
field. This file outlines the planting, fertilizing, harvesting and tillage practice and timing. Planting and
fertilizer dates were gleaned from the OMAFRA publication 811 section for each crop type. The use of
best yields and their planting windows and crop growing days determined planting dates. Fertilizer
application dates were based on the optimal time to fertilize for best yield and plant development.
Types of fertilizer were determined by the most common fertilizers used according to the “Canadian
Farm Fuel and Fertilizer: Prices and Expenses” (Agriculture and Agri-Food Canada, 2012). Those
fertilizers were then sourced out at a fertilizer retailer local to the study area. Fertilizers that were
available and in current use by local farmers were used as the fertilizer types for the fertilizer data set
for the watershed.
Erosion codes could range from one to four (least to most-erodible). Correspondence with an
AnnAGNPS technician and the use of the AH-703 (pg. 34) software documentation resulted in the
choice of three for the erosion code. This code identifies rill erosion as the dominant erosive force for
bare soil. This code was used because of Canadian winter. In the spring during the thaw, frozen soils
have no capability to absorb water, as they are already saturated. The dominant erosive force during
this time is rill erosion. Rill erosion can be defined as small channels formed by overland flow of runoff
from a storm event.
48
3.4.4.Model Simulation Time Step
The AnnAGNPS model is capable of varying time steps of daily, monthly, and yearly. For this study the
time step used was one year to match cropping cycles. Simulation period for the watershed study was
six years. Six years is the duration of rotational sequencing. Modeling was done for the full six years to
produce average annual results across full rotational sequences for nutrients; sediment and water yield
because the evaluation was of how water quality changed after relocating rotational sequences to more
appropriate geophysical locations.
3.4.5.AnnAGNPS Cells
AnnAGNPS cells are the basis for simulation. They are layered with all of the climatic, geophysical, and
land use data that has been generated and collected. AnnAGNPS returns results based on the cell ID.
To create the cells a concentration source area (CSA) value is to be determined by the user. For this
study a value of 5ha was used. This creates small cell sizes and represents the watershed better than
a larger CSA size.
49
Figure 3.3 AnnAGNPS generated cells
3.5. Site Analysis
Land use was the only parameter that was altered in the designed scenario. Management practices
used in the baseline scenario were kept the same in the designed scenario. That is, fertility, tillage, and
timing practice data sets were not altered. The alternative design change rotation locations only. The
process to determine whether or not a field changed rotations was selected based on adjacency,
geophysical characteristics, and the rotation type.
3.5.1.Adjacency
Adjacencies were prioritized by type of neighbour. There are three classifications that were ranked in
order of design priority: stream, headwaters, and field. Adjacencies are key because identifying what a
field drains into or onto illuminates what possibility there is for runoff to deposit sediments or infiltrate
into the ground before it enters the water course. Fields that are directly adjacent to open water or
50
concentrated flow have no or limited opportunity of interruption before they hit the riparian zone and
subsequently the flow zone. Fields that drained directly into streams were ranked higher than fields that
drained into headwaters. The rationale behind this is water that enters into the stream has no
opportunity to infiltrate into the ground or to deposit any suspended particles on the landscape before
the end point of a watercourse. Headwaters however, have some, yet limited, opportunity for infiltration
and deposition before they concentrate into a stream. Fields were ranked last within the adjacency
parameter because there is greater possibility for infiltration and deposition as runoff flows from one
field to the next.
3.5.2.Geophysical characteristics
Geophysical characteristics were analyzed next because regardless of what is happening culturally in
the field these characteristics influence the occurrence of erosion. Four characteristic were identified to
determine susceptibility of soil loss within the watershed: land curvature, K factor, slope, and
accumulation. Characteristics were ordered in terms of their effect on erosion potential:
Field curvature
Fields that are predominantly convex along the dominant slope profile were identified and subdivided
into two categories, depositional and non-depositional. Depositional convex slopes have an area at the
bottom of the slope profile in the field that in concave and could potentially be an area of deposition for
runoff. Those that did not have an area of deposition were ranked higher than fields that did because
there is no likely opportunity for deposition before runoff leaves the field to continue its flow path.
Erodibility factor (K-factor)
K-factor (K) "is a quantitative measure of a soil's inherent susceptibility/resistance to erosion and the
soil's influence on runoff amount and rate" (Wall G. , Coote, Pringle, & Shelton, 2002). High K values
are more likely to transport in rain events, while low K values are less likely.
The use of k-factors determined where in the watershed likely occurrences of erosion would take place
based on the soil characteristics. K values were mapped across the watershed. Figure 3.4 depicts K
51
values across the watershed. K-factor was reclassified into three range classes based on the table from
RUSLEFAC (Table 3.1) (Wall G. J., Coote, Pringle, & Shelton, 2002). Fields that contained any HS Kfactors were considered for cover change. These fields were then ranked high to low into three classes
denoted by one, two and three respectively. Class one indicated fields that have 55% or more of its
area in the highly susceptible range of k factor. Class two fields contained a mix of HS and MS K range.
Class three fields are composed of a mixture of HS and SS and VSS K range. (See Table 3.6).
Table 3.6 K-factor reclassification
55%+ : 55% or more of the field area, HS: highly susceptible K= 0.04 - 0.05, MS: moderately susceptible K= 0.03 - 0.04, SS:
slightly susceptible K= 0.007 - 0.03, VSS very slightly susceptible K= ≤ 0.00
55%+ HS
1
142
144
155
156
237
239
240
241
279
282
283
326
327
330
331
332
333
334
HS &MS
2
337
346
347
350
354
365
395
417
420
422
28
138
139
140
270
274
275
277
278
280
321
322
323
324
329
355
368
396
HS &SS/VSS
3
397
399
414
415
416
418
419
201
202
238
345
349
52
Figure 3.4 K-factor
Slope gradient
Slope data were computed from a DEM. The study area has slopes that range from nearly level (0-2%)
to strong slopes (33%+). Slopes were classified into ten slope categories (see
Table 3.7). Of particular importance are slopes that are above 5% because these slopes move water at
a fast enough rate to potentially cause erosion. Fields that had slopes ranging from 5%-33% were
further analysed. Slope gradients were mixed within field boundaries, so a classification system was
developed. Slopes were reclassified based on how much of the field area was covered by the slope
range. Classes of 3/4+, 1/2, 1/3, 1/4 coverage were created and each class given a numerical value
from one to four respectively.
53
Table 3.7 Slope classification
Slope class
Percent slope
Approximate degrees
Terminology
1
2
3
4
5
6
7
8
9
10
0-0.5
>0.5-2
>2-5
>5-10
>10-15
>15-30
>30-45
>45-70
>70-100
>100
0
0.3-1.1
>1.1-3
>3-5
>5-8.5
>8.5-16.5
>16.5-24
>24-35
>35-45
>45
level
nearly level
very gentle slopes
gentle slopes
moderate slopes
strong slopes
very strong slopes
extreme slopes
steep slopes
very steep slopes
Note. Reprinted from (Soil Classification Working Group, 2013, pg. 149)
Table 3.8 Slope gradient reclassification
Proportion of
field in slope
class
Slope code
Field numbers in
gradient class
1
33
37
159
161
191
194
207
3/4+
1/2
1/3
1/4
1
2
3
4
219
222
224
225
226
228
231
258
259
261
446
453
60
2
30
34
69
70
154
158
162
166
233
235
236
310
337
422
433
444
448
450
458
463
3
19
38
65
74
163
195
211
218
223
229
240
241
256
308
334
412
443
445
454
4
21
22
67
112
148
175
189
237
244
252
260
292
307
316
350
54
420
423
743
Figure 3.5 Reclassification of slope
Slope accumulation
Slope length of fields was measured to determine how much water accumulation a field receives. As
the slope length increases without interruption there is more water that accumulates and is flowing down
the slope profile. Suspended soil particles increase as water continues down the slope, increasing
scouring, this increases erosion. Fields were analysed to determine how many fields were draining into
each one, and were categorized into three classes 5+, 3-4, 2, and given a number of one to three
respectively (see Table 3.9). The protocol for determining a continuous slope length (multiple fields
draining into another) was taken from the RUSLE handbook.
55
Table 3.9 Accumulation count
Number of fields draining
into another
Accumulation code
Field numbers in
accumulation class
1
17
26
46
64
65
5+
3-4
2
1
2
3
69
113
200
230
232
272
325
350
379
402
418
441
2
90
104
206
212
307
316
338
369
3
2
79
120
184
185
211
297
Figure 3.6 Slope accumulation
56
3.5.1.Land Use
Twelve rotational sequences were assigned to the fields in the watershed. The twelve rotations were
ranked by the amount of nutrient inputs. Rotations were ranked by nitrogen and phosphorus. The two
ordered data sets had different rotations in rank 1-7 but had the same rotations in rank 8-12. Nitrogen is
more mobile in the landscape than phosphorus. Because of this, nitrogen was the variable that
rotational sequences were ordered by to determine priority of field cover change. However, to include
the high ranking phosphorus rotations the top seven ranking rotations in the nitrogen data set were used
to elicit cover change.
Table 3.10 Rotation ranking
ROTATION
N
P
N RANK
CCSCCS
412
240
1
CSWCSW
350
180
2
CSCSCS
273
210
3
COrcHHCS
168
190
4
CSWBHH
157
160
5
WBHHHC
157
160
6
CSCHHH
133
190
7
OrcBHHHC
122
160
8
SWHHHC
112
160
9
CSWHHH
112
160
10
HHHHHH
15
130
11
Pasture
0
0
12
Rotation codes: C is corn; S is soybeans; W is wheat; Orc is oats under seeded with red clover;
H is hay, B is barley
3.6. Alternative Design
The purpose of the alternative design was to keep land use in fixed proportions to the base scenario
while reconfiguring the landscape based on design rules. It only changed land use, while keeping
management, climatic, and geophysical inputs the same as the base scenario. Design rules were
developed to determine which AnnAGNPS cells needed to be changed. Geophysical and rotation
characteristics were used as deciding factors for changing land use in a field.
57
3.6.1.Design Method
The design rules ordered fields to be considered for change. Ordering of fields was done based on field
adjacency, geophysical and rotational properties. Adjacency to headwaters, streams, and fields were
ordered from highest to lowest priority respectively. Geophysical characteristics were all ordered within
each component as seen in section 3.3.2. Fields that had rotational values from eight to twelve (see
Error! Reference source not found.) were left as is in the redesign process. Fields that had rotational
values of one to seven were considered for change because these were most likely to lead to soil
erosion.
The number of geophysical analysis parameters present in each field were then tallied and assigned as
the score for the field. Fields with more parameters fulfilled- a higher score value- were ranked highest
in the design priority. The score was ordered from highest to lowest, four to one. Those fields that had
high score numbers and fell in an area of headwaters or streams were considered first and second
priority for change (see Table 3.11). Fields that were adjacent to another field were considered third
priority.
Table 3.11 Field parameter scoring process
LU_ID
N
ran
k
Adjacenc
y
Curvatur
e
SCHHHC
WBHHHC
SCHHHC
WBHHHC
WCSWCS
LU_52
LU_28
LU_52
LU_28
LU_58
7
6
7
6
2
HW
HW
HW
HW
HW
X
V
X
X
X
156
130
376
370
62
67
HHCSCOrc
HHCSCOrc
CSWCSW
CSCOrcHH
SCOrcHHC
SWCSWC
LU_44
LU_44
LU_8
LU_37
LU_53
LU_56
4
4
2
4
4
2
HW
HW
HW
HW
HW
HW
V
40
SCOrcHHC
LU_53
4
ST
ID
#
Rotation
64
374
367
47
70
Kfactor
Slope
accumulatio
n
Slope
gradien
t
4
2
1
2
2
1
1
1
1
3
3
1
1
1
1
1
1
4
3
1
1
1
scor
e
HW: headwaters, ST: stream, X: convex curvature, V: concave curvature
Not all fields were classified as needing to change by the design rules. Fields that were next to
headwaters or streams and already had field rotations that ranked from eight to twelve for nitrogen
58
demands were left the same. Fields that were not located next to head waters or streams and had no
concerning geophysical characteristics were used to switch out fields that were considered for change.
The purpose of this experiment was to keep the composition of the watershed as similar as possible
when changing the configuration. Total area was calculated for each field rotation type as determined
by the lottery process. All fields were assigned an area in hectares. Area of each field was used to try
and keep the proportions of field rotations as close to equal as possible during the reconfiguration.
In most cases fields that being switched with each other did not directly match the other field area that
was available for change. Multiple field areas that had the same land use ID were combined to make
pairing and switching easier (see Table 3.13). Area values were kept relatively equal to the original
area values; however total areas were not exactly matched (see Table 3.12).
Table 3.12 Area difference by rotation
Rotation
Area Difference (ha)
Rotation
Area Difference (ha)
CCSCCS
COrcHHCS
CSCHHH
CSCSCS
CSWBHH
CSWCSW
CSWHHH
HHHHHH
OrcBHHHC
Pasture_G
SWHHHC
WBHHHC
BHHCSW
BHHHCOrc
BHHHCW
CHHHCS
CSCCSC
CSCOrcHH
HCSCHH
HCSCOrcH
-0.45
=
=
=
-0.11
-0.08
0.14
-0.45
=
0.57
0.08
0.32
=
-0.07
=
0.02
=
0.14
=
0.04
HCSWBH
HCSWHH
HHCOrcBH
HHCSCH
HHCSCOrc
HHCSWB
HHCSWH
HHHCSC
HHHCSW
HHHCWB
OrcHHCSC
SCCSCC
SCHHHC
SCOrcHHC
SCSCSC
SWBHHC
SWCSWC
WBHHCS
WCSWCS
WHHHCS
=
0.05
=
=
-0.08
-0.04
0.02
=
0.01
=
-0.02
=
-0.57
-0.34
=
0.01
0.08
=
0.19
0.57
Rotation codes: C: corn; S: soybeans; W: wheat; Orc: oats under seeded with red clover; H: hay, B: barley
59
Table 3.13 Rotation changing process
ID_num
64
374
367
47
70
156
130
376
370
62
67
40
346
123
248
91
110
212
313
33
141
117
120
438
81
105
257
146
462
113
279
34
267
38
108
226
78
744
463
122
133
394
132
315
51
349
350
57
165
184
354
43
124
145
292
Rotation
SCHHHC
WBHHHC
SCHHHC
WBHHHC
WCSWCS
HHCSCOrc
HHCSCOrc
CSWCSW
CSCOrcHH
SCOrcHHC
SWCSWC
SCOrcHHC
SWBHHC
HHCSWB
SWCSWC
HCSCOrcH
COrcHHCS
SCHHHC
SCHHHC
CSWBHH
SWBHHC
SCOrcHHC
OrcHHCSC
CHHHCS
SWCSWC
CSWCSW
SWCSWC
HCSCOrcH
WCSWCS
OrcHHCSC
OrcHHCSC
HCSCOrcH
SCHHHC
OrcHHCSC
COrcHHCS
COrcHHCS
HCSCOrcH
HHCSCOrc
CSWCSW
OrcHHCSC
HHCSCOrc
SCOrcHHC
OrcHHCSC
WCSWCS
OrcHHCSC
COrcHHCS
HHCSCOrc
CSWCSW
CSWCSW
CSWCSW
CCSCCS
SCOrcHHC
HHCSCOrc
COrcHHCS
HHCSCOrc
ROTATIONS TO BE CHANGED
LU_ID RANK ADJ CURVE K
ACC SL % SCORE AREA HA ASSIGNMENT TOTAL AREA
LU_52 7
HW
X
4
2
6.29
A
26.12
LU_28 6
HW
V
2
2
2.06
B
4.70
LU_52 7
HW
X
1
5.95
A
LU_28 6
HW
X
1
2.64
B
LU_58 2
HW
X
1
7.91
C
7.91
LU_44 4
HW
V
1
3.75
D
15.96
LU_44 4
HW
1
1
12.21
D
LU_8
2
HW
1
1
3.18
E
3.18
LU_37 4
HW
1
1
13.83
F
13.83
LU_53 4
HW
3
1
12.37
G
12.37
LU_56 2
HW
3
1
6.82
H
6.82
LU_53 4
ST
1
1
4
3
3.45
J
7.23
LU_55 5
ST
X
2
2
1.96
K
6.40
LU_45 5
ST
X
3
2
3.43
I
3.43
LU_56 2
ST
V
2
2
9.30
M
18.05
LU_39 4
ST
V
1
2
9.46
N
29.45
LU_4
4
ST
V
2
2
1.84
U
LU_52 7
ST
1
2
2
7.02
A
LU_52 7
ST
X
1
2.28
A
LU_7
5
ST
X
1
1.50
O
1.50
LU_55 5
ST
X
1
4.44
K
LU_53 4
ST
X
1
3.78
J
LU_50 4
ST
X
1
7.42
Q
23.09
LU_35 7
ST
V
1
10.64
L
10.64
LU_56 2
ST
V
1
3.37
M
LU_8
2
ST
V
1
9.23
R
14.63
LU_56 2
ST
V
1
5.38
M
LU_39 4
ST
1
1
1.95
N
LU_58 2
ST
1
1
5.18
S
5.57
LU_50 4
ST
2
1
4.03
Q
LU_50 4
ST
2
1
8.24
Q
LU_39 4
ST
3
1
6.23
N
LU_52 7
ST
3
1
4.58
A
LU_50 4
ST
3
1
3.41
Q
LU_4
4
ST
1
1
2.05
U
18.74
LU_4
4
ST
1
1
6.36
U
LU_39 4
ST
2
1
11.80
N
LU_44 4
ST
2
1
5.18
W
LU_8
2
ST
2
1
5.40
R
LU_50 4
ST
3
1
6.99
V
15.38149515
LU_44 4
ST
3
1
6.58
W
11.75747593
LU_53 4
ST
3
1
5.95
P
9.523289968
LU_50 4
ST
4
1
3.53
V
LU_58 2
ST
4
1
0.39
S
LU_50 4
HW
0
4.86
V
LU_4
4
HW
0
4.85
U
LU_44 4
HW
0
7.25
Y
10.78696375
LU_8
2
HW
0
6.92
T
23.64844325
LU_8
2
HW
0
12.47
T
LU_8
2
HW
0
4.26
T
LU_3
1
HW
6.46
X
6.457979631
LU_53 4
ST
0
3.57
P
LU_44 4
ST
0
2.24
Y
LU_4
4
ST
0
3.63
U
LU_44 4
ST
0
1.30
Y
REPLACEMENT ROTATIONS
avail. CELLS USED (ID #)
AREA
218 223 219
26.69
158
4.38
162
300
301
458
453
650
299
298
298
202
174
143
293
128
205
306
302
302
443
318
319
272
268
276
334
265
404
270
352
297
649
356
4
269
245
116
430
16
433
20
151
144
428
317
319
442
AREA LEFTOVER
0.57
-0.32
7.93
15.93
0.02
-0.03
3.18
13.70
12.34
6.90
7.07
6.39
3.47
17.90
29.40
0.00
-0.14
-0.03
0.07
-0.16
-0.01
0.04
-0.15
-0.04
1.62
0.11
23.10
10.62
0.01
-0.02
14.68
0.04
5.36
-0.20
18.735499
0.00
15.392884
11.734731
10.050468
0.01
-0.02
0.53
10.917887
23.694016
0.13
0.05
6.9128707
0.45
Adjacency priority: Red to yellow (high to low); rotation areas grouped for switch by colour
60
3.6.2. Configuration Results
The resulting design changed the fabric of the landscape to better suit its geophysical features and
characteristics. Most field changes based on the design rules occurred adjacent to streams and head
waters. There were not enough low input rotations in the watershed to change fields beyond key
adjacent areas. Approximately 615 ha were reconfigured. Of that, 315 ha of fields with ill suited
rotations were switched with 300 ha of low ranking rotations. All of the key adjacencies, identified in the
site analysis, were changed successfully to hopefully decrease agricultural pollutants leaving the
watershed and discharging into the stream.
Figure 3.7 Fields changed by the design
61
Figure 3.8 AnnAGNPS cells changed by the design
62
4.0
Results
This chapter describes the AnnAGNPS output for the baseline and the designed alternative landscape.
It focuses on nutrients (nitrogen and phosphorus), sediment, and water runoff for the study area. The
chapter concludes by comparing the relative change that resulted from re-designing the agricultural
landscape pattern.
4.1. Nitrogen
Total nitrogen estimated by AnnAGNPS decreased from the baseline to the designed scenario.
Nitrogen yielded 2.98 kg/ha/yr nitrogen in the baseline. The redesigned scenario had estimated
nitrogen yields of 2.84kg/ha/yr. Attached nitrogen in the baseline scenario produced 2.18 kg/ha/yr. The
design decreased by 6.42% from the baseline with a yield of 2.04 kg/ha/yr.
Dissolved nitrogen yielded from the baseline watershed configuration was 0.81 kg/ha/yr, while the
designed watershed configuration yielded 0.80 kg/ha/yr. Watershed totals for each nutrient state and
the overall total of nitrogen are presented in Table 4.1
Table 4.1 Base to design nitrogen yield comparison
Base
Scenario
Designed
Scenario
kg/ha/yr
Percentage Change
(from base)
%
Attached
2.18
2.04
-6.42
Dissolved
0.81
0.80
-1.23
Total
Nitrogen
2.98
2.84
-4.70
63
Pollution yield is estimated by AnnAGNPS. AnnAGNPS raster cell structure reports outputs at a fine
scale. There are over 1100 cells that the pollutant model simulated to estimate pollution outputs for the
study area. Increases and decreases of attached, dissolved, and total nitrogen within the watershed
were expected as rotational sequences were relocated to better suited landscape position. Some cells
were expected to increase more than others as rotations were taken from one part of the land and
relocated to another. Most AnnAGNPS cells are within a conceivable range of increase or decrease;
however, there are some particular cells to note. High unexpected increase values are outlined in Table
4.2.
Table 4.2 Percentage difference over 100% for N by AnnAGNPS cell
Attached Nitrogen
Base
Cell #
42
122
123
172
251
253
262
283
493
502
531
551
552
553
562
593
623
643
653
673
683
692
712
713
723
Design
kg/ha/yr
0.30
1.85
1.54
4.34
4.16
7.48
11.28
0.83
0.72
1.51
0.53
0.08
0.05
0.05
0.07
0.00
2.05
11.25
4.16
2.69
3.70
2.65
3.07
2.48
1.19
2.01
2.75
2.27
0.66
1.08
1.98
2.98
3.49
0.10
0.20
2.52
5.31
1.68
1.93
2.53
1.52
0.00
1.65
1.58
6.06
8.35
5.96
0.00
0.00
0.34
Percentage
Difference
%
582.03
48.70
48.05
-84.71
-73.98
-73.55
-73.55
318.59
-86.19
-86.51
377.99
6378.05
3556.52
3466.67
3459.15
+
-100.00
-85.34
-62.01
125.64
125.59
125.14
-100.00
-100.00
-71.36
Dissolved Nitrogen
Base
Design
kg/ha/yr
0.80
0.74
0.73
0.08
0.15
0.15
0.15
0.36
0.07
0.07
0.76
0.07
0.02
0.02
0.02
0.03
0.06
0.08
0.19
0.68
0.67
0.67
0.06
0.06
0.21
0.05
2.98
2.97
0.20
0.92
0.92
0.91
3.96
0.80
0.80
0.51
1.29
0.83
0.80
0.80
1.19
0.18
0.20
2.08
0.26
0.27
0.27
0.18
0.18
0.70
Percentage
Difference
%
-93.26
305.31
305.87
163.16
522.45
523.13
516.22
1012.08
1065.22
1059.42
-33.38
1884.62
4805.88
4316.67
4338.89
3490.91
200.00
161.84
1024.86
-61.62
-59.70
-60.12
195.08
196.72
234.93
Total Nitrogen
Base
Design
kg/ha/yr
1.10
2.58
2.27
4.42
4.31
7.63
11.42
1.19
0.79
1.57
1.29
0.15
0.06
0.07
0.09
0.03
2.11
11.33
4.34
3.37
4.37
3.31
3.13
2.54
1.40
2.07
5.72
5.25
0.86
2.00
2.90
3.89
7.45
0.90
1.00
3.03
6.60
2.52
2.72
3.33
2.71
0.18
1.85
3.66
6.32
8.62
6.22
0.18
0.18
1.04
Percentage
Difference
%
88.41
121.65
131.26
-80.47
-53.62
-62.05
-65.91
526.05
14.00
-36.21
134.55
4391.16
3829.69
3680.56
3595.56
8100.00
-91.34
-83.69
-15.71
87.79
97.32
87.83
-94.25
-92.88
-25.78
64
732
742
811
951
972
981
982
992
993
1052
1092
1093
0.00
0.00
2.60
0.02
0.02
0.02
0.08
0.55
0.41
1.58
0.13
0.12
7.80
7.24
7.72
0.88
1.47
1.13
1.95
0.69
2.60
0.24
3.57
4.61
+
+
196.54
4066.67
7255.00
7406.67
2494.67
26.61
527.54
-85.00
2668.99
3908.70
0.18
0.18
0.83
0.02
0.02
0.02
0.04
0.21
0.42
0.03
0.10
0.10
0.06
0.06
0.90
0.61
0.72
0.72
0.90
0.53
0.63
0.71
3.20
0.97
-66.67
-66.11
8.18
3470.59
4152.94
4129.41
2142.50
156.31
50.24
2428.57
3132.32
868.00
0.18
0.18
3.44
0.04
0.04
0.03
0.12
0.75
0.83
1.61
0.23
0.21
7.86
7.30
8.62
1.48
2.19
1.85
2.84
1.22
3.23
0.95
6.77
5.58
4196.72
3956.11
150.95
3797.37
5829.73
5665.63
2371.30
62.18
287.52
-41.23
2869.74
2506.07
Positive values indicate an increase while negative numbers indicate a decrease.
+ refers to an increase where the percentage difference could not be calculated because one value was
zero
4.2. Phosphorus
Phosphorus is estimated by AnnAGNPS in two forms, attached and dissolved. Overall total estimated
phosphorus yield increased by 0.09 kg/ha/yr. Attached phosphorus saw the only decrease in yield
quantity. Baseline yield was estimated at 0.64 kg/ha/yr, while the designed scenario returned an
estimate of 0.60 kg/ha/yr. Dissolved phosphorus increased by 4.28% in yield from the baseline
scenario to the designed scenario. Yields were estimated by AnnAGNPS to be 3.04 kg/ha/yr in the
baseline. The designed scenario estimation was a yield of 3.17 kg/ha/yr.
Table 4.3 Base to design phosphorus yield comparison
Baseline
Scenario
Designed
Scenario
kg/ha/yr
Percentage Change
(from base)
%
Attached
0.64
0.60
-6.25
Dissolved
3.04
3.17
+4.28
Total
Phosphorus
3.68
3.77
+2.45
65
Much like nitrogen, there were specific cells that increased above expected range. Individual
AnnAGNPS cell increases and decreases were expected to fluctuate through the reconfiguration
process. However, there are some cells that increased or decreased higher than 100%. These cells
are outlined in Table 4.4.
Table 4.4 Percentage difference over 100% for P by AnnAGNPS cell
Attached Phosphorus
Base
Cell #
42
251
253
262
283
371
531
551
552
553
562
653
673
683
692
712
732
742
811
951
972
981
982
992
993
1092
1093
Design
kg/ha/yr
0.09
1.23
2.22
3.34
0.25
1.24
0.16
0.03
0.01
0.02
0.02
1.23
0.78
1.07
0.77
0.93
0.00
0.00
0.76
0.01
0.01
0.01
0.02
0.17
0.13
0.04
0.04
0.62
0.33
0.60
0.90
1.01
0.04
0.76
1.55
0.50
0.56
0.74
0.48
1.81
2.49
1.78
0.00
2.36
2.19
2.24
0.26
0.44
0.34
0.58
0.21
0.77
1.04
1.39
Percentage
Change
%
601.14
-73.40
-72.98
-72.97
300.00
-96.77
379.11
6084.00
3471.43
3418.75
3414.29
-60.94
132.09
131.97
131.55
-100.00
+
+
196.04
4250.00
7250.00
6660.00
2536.36
23.21
514.40
2564.10
3865.71
Dissolved Phosphorus
Base
Design
kg/ha/yr
0.11
1.01
1.01
1.02
4.69
1.61
0.12
5.35
0.09
4.29
4.25
1.61
4.01
4.05
4.05
2.33
0.21
0.21
4.06
0.09
0.09
0.09
4.88
0.08
4.17
6.02
6.03
0.00
5.25
5.25
5.24
4.80
6.91
0.67
5.87
1.23
4.72
4.73
6.88
5.19
5.19
5.20
0.21
2.30
2.33
4.81
0.36
1.02
1.03
5.19
0.67
4.67
4.68
5.29
Percentage
Change
%
-100.00
417.46
418.48
412.12
2.32
329.91
486.09
9.66
1219.35
10.09
11.24
327.13
29.22
28.18
28.30
-90.94
989.57
1003.32
18.51
290.11
1025.27
1014.13
6.34
768.83
12.02
-22.36
-12.27
Total Phosphorus
Base
Design
kg/ha/yr
0.20
2.25
3.23
4.36
4.95
2.85
0.27
5.38
0.11
4.31
4.28
2.84
4.79
5.12
4.82
3.26
0.21
0.21
4.81
0.10
0.10
0.10
4.90
0.24
4.29
6.06
6.07
0.62
5.58
5.85
6.14
5.81
6.95
1.43
7.42
1.73
5.29
5.47
7.36
7.00
7.68
6.97
0.21
4.66
4.52
7.05
0.62
1.47
1.36
5.77
0.88
5.44
5.72
6.68
Percentage
Change
%
208.50
148.11
81.05
40.74
17.49
144.14
424.18
37.91
1514.02
22.75
27.93
159.15
45.94
49.92
44.70
-93.52
2106.64
2040.28
46.46
535.05
1394.90
1318.75
17.70
259.43
26.68
-5.74
10.10
+ refers to an increase where the percentage difference could not be calculated because one value was zero
= represents no change when a calculation couldn’t be completed because a zero value was present
66
4.3. Sediment
Sediment data is broken down by particulate types: clay, silt, sand, and small and large aggregates. All
particle types decreased or remained the same from the baseline scenario to the designed scenario.
Estimated clay yield in the baseline scenario was 0.09 tonnes/ha/yr. The designed scenario clay yield
was estimated at 0.08 tonnes/ha/yr. The baseline yielded 0.24 tonnes/ha/yr of silt. This decreased in
the designed scenario to 0.22 tonnes/ha/yr. Sand particle yield in the baseline scenario was 0.10
tonnes/ha/yr. The designed scenario yielded less sand with an estimate of 0.09 tonnes/ha/yr. Small
and large aggregates were not yielded from the landscape in either the baseline or the designed
scenarios. See Table 4.5 for comparison of sediment particles from baseline to designed scenario.
Percentage difference by cell number is presented in Table 4.6.
.
Table 4.5 Base to design sediment yield comparison
Baseline
Scenario
Designed
Scenario
Percentage
Change
(from base)
tonnes/ha/yr
Clay
0.09
0.08
-11.11
Silt
0.24
0.22
-8.33
Sand
0.10
0.09
-10.00
Sm. Agg.
0.00
0.00
0.00
Lg. Agg.
0.00
0.00
0.00
Total Sediment
0.42
0.39
-7.14
This is sediment yield for rill erosion only, based on the model erosion type selected
67
Table 4.6 Percentage difference over100% for sediment particles by AnnAGNPS cell
Clay
Cell
#
Base
Design
tonnes/ha/yr
42
223
243
283
531
551
552
553
562
623
673
683
692
712
713
811
951
972
981
982
993
1092
1093
Silt
Percentage
Change
%
Sand
Percentage
Base Design
Change
tonnes/ha/yr
%
Base
Total Sediment
Percentage
Design
Change
tonnes/ha/yr
%
Base
Design
Percentage
Change
tonnes/ha/yr
%
0.01
0.05
542.86
0.03
0.22
535.29
0.02
0.11
581.25
0.06
0.37
549.12
0.16
0.25
58.86
0.30
0.49
61.92
0.05
0.10
120.00
0.51
0.84
65.94
0.15
0.24
57.62
0.29
0.46
61.67
0.04
0.09
123.81
0.48
0.80
65.83
0.04
0.18
400.00
0.08
0.43
410.71
0.02
0.16
685.00
0.14
0.77
447.14
0.01
0.06
425.00
0.06
0.29
400.00
0.03
0.16
423.33
0.10
0.51
403.96
0.00
0.22
7100.00
0.01
1.00
7576.92
0.00
0.04
4100.00
0.02
1.26
7282.35
0.00
0.04
4200.00
0.01
0.20
3820.00
0.00
0.11
3600.00
0.01
0.35
4275.00
0.00
0.05
5200.00
0.00
0.15
3625.00
0.00
0.19
4675.00
0.01
0.39
4266.67
0.00
0.07
3400.00
0.01
0.20
3800.00
0.01
0.24
3916.67
0.01
0.51
4116.67
0.05
0.00
-100.00
0.19
0.00
-100.00
0.11
0.00
-100.00
0.34
0.00
-100.00
0.07
0.15
118.57
0.27
0.58
118.80
0.20
0.43
115.15
0.53
1.16
117.64
0.10
0.21
118.75
0.37
0.80
118.85
0.27
0.59
116.18
0.74
1.60
117.55
0.07
0.15
117.39
0.26
0.57
118.70
0.19
0.42
116.06
0.52
1.14
117.56
0.07
0.00
-100.00
0.27
0.00
-100.00
0.16
0.00
-100.00
0.51
0.00
-100.00
0.06
0.00
-100.00
0.22
0.00
-100.00
0.13
0.00
-100.00
0.41
0.00
-100.00
0.07
0.22
216.18
0.27
0.83
201.82
0.16
0.59
268.75
0.50
1.63
225.55
0.00
0.02
+
0.00
0.10
4750.00
0.00
0.06
5600.00
0.00
0.18
4275.00
0.00
0.04
+
0.00
0.17
8400.00
0.00
0.09
8800.00
0.00
0.30
7275.00
0.00
0.03
+
0.00
0.13
6550.00
0.00
0.07
6700.00
0.00
0.23
7533.33
0.00
0.06
2950.00
0.01
0.22
3042.86
0.00
0.10
2425.00
0.01
0.38
2628.57
0.01
0.08
583.33
0.05
0.30
547.83
0.01
0.13
807.14
0.07
0.51
604.17
0.01
0.21
3316.67
0.01
0.45
3323.08
0.00
0.11
3466.67
0.02
0.76
3191.30
0.01
0.24
4780.00
0.01
0.53
4291.67
0.00
0.09
3033.33
0.02
0.87
4230.00
+ An increase where the percentage difference could not be calculated because one value was zero
4.4. Water Yield
Direct runoff produced in the watershed was measured as a watershed total in two ways, subsurface
and surface. Water yield was estimated by AnnAGNPS for the entire watershed, not each individual
cell. The watershed saw an overall increase of runoff production in the designed scenario. Baseline
scenario total runoff amount was estimated at 113.22 mm/yr. The designed scenario yielded more
runoff with an estimated 117.01 mm/yr. Subsurface runoff amounted to 2.25 mm/yr in the baseline
scenario. The designed scenario produced an estimate of 1.91 mm/yr. Surface runoff increased from
baseline to designed scenario. Baseline estimates totaled 110.97 mm/yr. AnnAGNPS estimated
68
surface runoff for the designed scenario at 115.10 mm/yr. See Table 4.7 for percentage change of total
runoff, subsurface and surface runoff. Some cells within the simulation changed far more than expected
in the onset of the simulation. There was an expectation for some to change more than others, but
some of the percentage change values came as a bit of a surprise because they were so high. These
cells have been recorded in Table 4.8.
Table 4.7 Base to design water yield comparison
Base
Line
Designed
Scenario
Percentage Change
mm/yr
%
Subsurface
2.25
1.91
-15.11
Surface
110.97
115.10
3.72
Total
Water
113.22
117.01
3.35
Table 4.8 Percentage change over 100% for water by AnnAGNPS cell
Cell
#
Base
Subsurface
Percentage
Design
change
mm/yr
42
112
162
163
223
243
392
463
732
742
811
851
862
863
951
5.90
10.31
2.36
2.26
8.23
8.13
2.26
6.14
3.23
3.47
6.36
2.30
2.28
2.28
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
2.20
2.21
0.00
12.88
11.88
11.88
0.00
%
-100.00
-100.00
-100.00
-100.00
-100.00
-100.00
-100.00
-100.00
-32.03
-36.42
-100.00
461.09
420.42
420.28
-100.00
Base
Surface
Percentage
Design
change
mm/yr
140.77
135.51
126.42
114.75
180.75
180.54
114.52
149.42
3.68
3.88
152.01
119.04
118.65
118.65
148.17
117.38
160.44
139.57
130.31
147.85
147.65
129.95
125.32
147.82
149.21
123.86
141.26
140.89
140.89
123.24
%
-16.61
18.40
10.41
13.56
-18.20
-18.21
13.47
-16.13
3912.38
3745.57
-18.52
18.66
18.75
18.75
-16.82
Total
Base
Design
mm/yr
146.66
145.82
128.77
117.01
188.97
188.66
116.78
155.56
6.92
7.35
158.37
121.34
120.93
120.93
148.17
117.38
160.44
139.57
130.31
147.85
147.65
129.95
125.32
150.01
151.42
123.86
154.14
152.77
152.77
123.25
Percentage
change
%
-19.97
10.03
8.39
11.37
-21.76
-21.74
11.28
-19.44
2068.13
1959.24
-21.79
27.03
26.33
26.33
-16.82
69
5.0
Discussion
This chapter discusses the results generated from creating the alternate design scenario. It discusses
the change in estimated values from AnnAGNPS software for nutrients, sediment and water yield. The
change in values that were not expected within each category will be analyzed and possible
explanations for percentage changes that are above 100% increase or decrease will be proposed.
5.1. Nitrogen
Total nitrogen saw an overall decrease from the baseline scenario to the designed scenario. The
designed scenario estimated nitrogen decreased by 4.70%, totaling 0.14 kg/ha/yr less than the baseline
scenario. This result was expected from the designed change because nitrogen was directly included in
the design process. Nitrogen inputs were used to rank rotational sequencing. Those values determined
if the rotation would stay in the existing location or move to a better suited landscape position. This
inclusion leads to the logical decrease in estimated total nitrogen yield. Other studies that investigate
more perennial based rotations and annual based rotations found that soil nitrogen levels were lower in
more perennial rotations (Randall, et al., 1997). This is also substantiated by another study that
recorded decreased levels of nitrate in tile drain water when perennial grasses and legumes were
included in crop rotations (Huggins, Randall, & Russelle, 2001).
Attached nitrogen decreased more than dissolved nitrogen. Attached nitrogen decreased by 6.42%,
while dissolved only decreased by 1.23%. Attached nitrogen levels found in output water from the
watershed would have a likely decrease if erosion was also reduced in the watershed. Erosion
estimates did decrease in the designed scenario making this a likely contributor to the decrease in
attached nitrogen yield during simulation.
Dissolved nitrogen is mobilized via water and not particles like attached nitrogen. This is more likely to
happen through subsurface tile drains than surface runoff as many reports have concluded that there is
more inorganic nitrogen in runoff from fields drained via subsurface drainage than undrained fields
(Bechann, 2014). Subsurface runoff estimates decreased in the designed scenario which is the most
70
likely method of conveyance of dissolved nitrogen (David, Gentry, Kovacic, & Smith, 1997). The
correlation between the decrease in subsurface flow and dissolved nitrogen are strong making a
proposed cause of the decrease of dissolved nitrogen likely.
Keeping the composition of the watershed as similar to the baseline scenario as possible did not
increase or decrease perennial cover at the watershed scale. Therefore, the capability of the watershed
to decrease dissolved nitrogen due to heavier perennial incorporation, like the study by Huggins et al
(2001), was limited. However, the relocation of heavier perennial rotations close to headwaters and
streams seems to have affected the levels of dissolved nitrogen. As shown in Table 4.8 there are a
number of cells that increase or decrease over 100%. Individual cells were expected to fluctuate as
rotations were reconfigured throughout the watershed. It was not expected that cells would change at
such high rates. Analysing the individual cells that had higher than expected calculated percentage
change confirmed that the change of rotational location had greatly affected the estimated cell nutrient
yield. Taking rotations that are higher in nitrogen inputs and moving them to other locations where they
would be further away from key adjacencies would locate high demanding nitrogen rotations with other
of a similar input level. This would explain the extremely high values seen in some of the cells. Also,
the setup of the simulation created many small cells to best represent the watershed and register the
changes made to them. This affected the area of the cells, which, when the higher nitrogen demanding
rotations were switched into locations away from key adjacencies, would make the percentage change
high. That is, a small area is now producing much more nutrients than previously, which is logical. All
of the cells that are shown in Table 4.8 have been determined to be attributed to cells changing from
low nitrogen input (rank 8-12) to high nitrogen input rotations (rank 1-7).
5.2. Phosphorus
Total phosphorus saw an overall increase in the designed scenario compared to the baseline scenario.
As seen in Table 4.3 phosphorus levels were estimated at 3.68 kg/ha/yr in the baseline scenario and
3.77 kg/ha/yr in the designed scenario. Overall, the designed scenario increased estimated phosphorus
yield by 2.45%. Phosphorus was not directly included in the design rules like nitrogen because it is
considered less mobile in the landscape dues to its ability to fix to multiple elemental components in the
71
soil (Retallack, 2001). There was some consideration for phosphorus levels in the design rules. When
the ranking of nitrogen was determined, rotations were split into two divisions of ranks 1-7 and 8-12.
The division of the rotations was done this way because top phosphorus input rotations matched the top
7 ranks of nitrogen. This is very indirect; however, trying to include the top phosphorus ranking within
the design change rules was attempted.
Overall, attached phosphorus was the only state that decreased from the baseline to the designed
scenario from 0.64 kg/ha/yr to 0.60 kg/ha/yr (see Table 4.3). Nutrients that are attached to soil particles
move through the landscape through erosion. Phosphorus readily adsorbs to soil particles through the
reactions with different soil minerals (Retallack, 2001). Decrease of attached phosphorus, as estimated
by AnnAGNPS, suggests that decreased levels of erosion would influence this yield estimation. Total
erosion estimated by AnnAGNPS decreased from the baseline to the designed scenario by 7.14%.
Causes of decreased erosion will be discussed later in this section.
Erosion control can be linked to conservation tillage. Conservation tillage decreases the amount of soil
disturbance and subsequently movement of soil particles through the landscape by increasing soil
coverage, with crop residues, during spring melt and crop establishment (periods of high erosion
potential). Conservation tillage was the main management system that was chosen for this study as it is
a common practise and widely promoted in Canada (Carter, 1994; Larney et al., 1994). Studies have
found that conservation tillage decreased erosion in turn leading to lower rates of sediment and
sediment bound nutrients in runoff water (Baker and Laflen, 1983; Fawcett et al, 1994; Sharpley et al,
1994).
A number of cells in the designed scenario increased in phosphorus estimates far greater than
expected. In Table 4.3 there are 21 cells that increase over 100%. As discussed above there are
several factors affecting the loss of phosphorus from the landscape to water. Attached phosphorus is
related to erosion. The calculation within the AnnAGNPS software works under the assumption that
attachment to soil particles is directly associated with clay content (Bingner, Theurer, & Yuan, 2011).
Attached phosphorus increases were seen in 21 cells after simulation, of these, the highest increases
are also the highest increases of clay erosion. Most cells that were over 100% increase decreased in
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phosphorus inputs, based on rotation, from baseline to designed scenario, making increase in
phosphorus inputs an unlikely cause of increased phosphorus in simulation. The cells that did increase
in estimated attached phosphorus but decreased in actual phosphorus inputs had an increase in the
amount of hay within each rotation. The cells that increased in phosphorus inputs in the designed
scenario also had rotations that switched from having more hay years in the rotation to having less.
There is also an increase in the amount of corn and soy years within rotations in these cells. This
suggests that there is a correlation between canopy cover and the estimated loss of attached
phosphorus and the amount of canopy cover played a role in the amount of erosion that occurred, which
directly affects the estimated amount of attached phosphorus in runoff.
Estimated dissolved phosphorus yield was higher in the designed scenario than the baseline scenario.
It increased from 3.04 kg/ha/yr to 3.17 kg/ha/yr amassing to an increase of 4.28%. Dissolved
phosphorus moves through the landscape via surface runoff and tile drains. AnnAGNPS does not
differentiate between subsurface and surface pollutants (Bingner, Yuan, Theurer, Rebich, & Moore,
2005) so it is not possible to determine if the increase in dissolved phosphorus is coming from surface
or subsurface sources. However, dissolved phosphorus moves differently through the landscape than
attached, which is dependent on clay particle attachment and dislodgement. Dissolved phosphorus is
unlikely to concentrate in subsurface drainage compared to surface runoff because of the molecular
structure of the nutrient, which is highly reactive to soil elements and is typically immobilized in the soil
(Retallack, 2001).
Subsurface runoff via tile drain was estimated as a decrease within the simulation
making the increase in dissolved phosphorus less likely to be originating from tile drains.
Estimated subsurface drainage water yield in the designed scenario decreased overall in the designed
simulation by 15.11%. This coupled with how phosphorus moves slowly through the soil profile
(Retallack, 2001) make subsurface drainage not likely to be responsible for all of the dissolved
phosphorus output. However, there was still water being exported through the drainage tiles. Physical
characteristics of the landscape and rotational management can affect the amount of dissolved
phosphorus moving through the soil profile and into tile drains. Soil texture characteristics have been
shown to have great influence on the amount of phosphorus lost through the soil (Sims, Simard, &
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Joern, 1998). Sims et al (1998) identified that coarser soil texture that has high sand, organic matter
and high amounts of macropores are more susceptible to loss of phosphorus. Analyzing the study
watershed’s soil texture, coarser soils were not the only soil texture that showed increases in dissolved
phosphorus. Loam soils had a high number of increases while coarse soils such as sandy loams,
gravely loams and fine sandy loams had less cells increase in phosphorus output. This indicates that
soil texture did not play an integral role in the output of dissolved phosphorus and indicates that other
factors are more likely to contribute to the higher rates of dissolved phosphorus outputs. When
reconfiguration occurred, rotations high in phosphorus inputs, yet low in nitrogen rank would have been
switched to these locations. This is a consequence of not directly including phosphorus within the
design rules. As you can see from figure 5.1 several of the fields that were relocated adjacent to
streams and headwaters would be at risk for phosphorus leaching due to the soil texture. Analysing the
designed scenario output values for cells that showed increases in dissolved phosphorus resulted in no
correlation to soil texture and higher transport of dissolved phosphorus. However, there was a
correlation between coarse soil and the type of rotation on them.
Surface runoff increased from the baseline to designed scenario by 3.72%. This increase could partially
explain the increase in dissolved phosphorus estimates by AnnAGNPS. As stated earlier, conservation
tillage was the selected management operation for the watershed. This means that a minimum of 30%
crop residue is left on the fields. Increase in crop residue has been linked to increase in dissolved
phosphorus in runoff water (Mathers & Nash, 2009; Michalak, et al., 2013). Studies by McDowell and
McGregor (1984), and Tiessen et al (2010) both found higher levels of dissolved phosphorus being
exported off of the conservation tillage plots compared to conventional tillage plots.
McDowell and
McGregor compared no till to conventional tillage management while Tiessen et al (2010) compared
reduced tillage with conventional tillage management, both studies maintaining conservation tillage
practices of 30% (or more) crop residue. The simulation of the current study used a reduced tillage
operation rather than no-till management system. Using past studies as a guide, the presence of crop
residues in this study that were not incorporated into the plow layer would have contributed to the
increase of dissolved phosphorus as estimated by AnnAGNPS.
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Conservation management practices leave fields minimally disturbed after fertilizer application as well.
Studies that have investigated conservation management practices in comparison with conventional
practices have found that with minimal soil mixing during planting and fertilizer applications increases
the amount of nutrients at the soil surface layer. Fertilizer applications such as broadcast and surface
applications have been documented as increasing the amount of nutrients in runoff water, especially
after a large precipitation event right after fertilizer application causing phosphorus to dissolve and
become mobile on the soil surface (Carpenter et al., 1998; Douglas et al., 1998; Hansen et al., 2002).
The management schedules created for the watershed did use surface application of phosphorus.
There was only one crop that received surface application of phosphorus: hay. Crop rotations that were
predominantly switched into locations that had rotations that did not work well with the geophysical
characteristics of that location and were lower in nitrogen input values, however the amount of hay in
them is at least half, or all hay. All of the switched fields were along the stream. The use of surface
applied phosphorus and crop residues remaining on the field would both contribute highly to the
increase in dissolved phosphorus estimated by AnnAGNPS.
5.3. Sediment
Sediment yield from the baseline scenario to the designed scenario decreased. Estimated erosion
totals decreased by 7.14% when comparing the baseline to the designed scenario. Individually, all
particle types also decreased. Clay decreased the most out of the differentiated particle types with a
decrease of 11.11 %. Sand had the next highest decrease of 10% and silt was the smallest decrease of
8.33%. Decreases in soil erosion were somewhat expected through land cover manipulation.However,
factors affecting soil erosion were considered with the inclusion of geophysical characteristics such as:
long uninterrupted slopes (slope accumulation), K-factors, and curvature. AnnAGNPS uses the revised
soil loss equation (RUSLE) to calculate erosion within each AnnAGNPS cell. AnnAGNPS calculates
erosion via slope factor (LS), crop/vegetation and management (C), support practice factor (P) for each
AnnAGNPS cell and erodibility factor (K) for each soil (Bingner, Theurer, & Yuan, 2011). In the design
rules some of these factors were considered. Geophysical features of the landscape were analysed for
long continuous slope profiles (LS factor), and soil erodibility (K factor) were mapped for the entire
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watershed. Crop and vegetation management (C factor) was considered through the analysis of crop
rotations and their nitrogen input ranks. P factor was not included because P factors include: contour
farming, cross slope farming, strip cropping and terracing. These support practices are not common in
the study watershed and were not a topic of this study. Fields within the watershed that had long
uninterrupted slopes and/or high erodibility were considered to be areas of concern when analyzing the
crop rotations on them and determining if that rotation was appropriate for that location. That was the
extent of the inclusion of the RUSLE parameters within the study. The design rules were more nutrient
driven than sediment driven, in the sense that high input crops were to be relocated off of these geophysically “vulnerable” locations because of the way that nitrogen and phosphorus are transported in
the landscape. Because there was not a direct design change that impacted erosion, there is indication
of other process at work that reduced erosion overall.
Factors such as crop residue, canopy cover, and root biomass play a role in the reduction of erosion.
Crop residue has been documented in the reduction of erosion (Wilson, Dabney, McGregor, & Barkoll,
2004). Conservation tillage, leaving residues of 30% or more on the field, have been promoted as a
conservation method for erosion control (Carter, 1994). Residues left on the field intercept the
raindrops and decrease soil particle detachment caused by the force of rain drop impact (Cassol & De
Lima, 2003). Crop residues have reduced erosion in many studies (Baker and Laflen, 1983; Fawcett et
al., 1994; Sharpley et al., 1994). Residues increase surface roughness and slow sheet flow by reducing
the force of runoff water and allowing it to spread out due to the roughness of the residue (Cassol & De
Lima, 2003).
Canopy cover plays a role in erosion reduction as well. The ability of a crop to intercept raindrops and
decrease impact force varies, but in general has been proven to reduce erosion (Ma, Yu, Ma, Li, & Wu,
2014). Ma et al (2014) have found that different crop type preform differently when it comes to the
reduction of soil erosion via splash detachment. When compared to bare soil, wheat was the most
effective at the reduction of splash erosion during normal and high intensities of simulated rain tests,
followed by corn and soybean next. Not only is the above ground biomass important to erosion control,
below ground root mass also plays a role in the resistance of soils to runoff.
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Below ground, roots increase soil aggregate stability, infiltration capacity, and bulk density, all factors
that are important to soil’s ability to resist transport (Amezketa, 1999; Gyssels, Poesen, Bochet, & Li,
2005). Root mass data taken from the supplied AnnAGNPS files were used for simulation. Analysing
the supplied data different crops had varying amounts of root mass within the top 100 mm of the soil
profile it is clear that not all crops provide the same amount of root mass. Crop root mass was greatest
in three years or more rotations of hay, a combination of alfalfa and brome, with a mass of 4900 kg/ha in
years two and later. Alfalfa and oats, present in rotations when a switch from hay production to oat
production occurs, was next with a mass of 1600 kg/ha. Barley was next in mass production with 980
kg/ha with corn following close behind at 950 kg/ha. Oats, wheat and soy are at the lower end of the
root biomass production with 800 kg/ha, 760 kg/ha, and 400 kg/ha respectively. Rotations fluctuate
through the six year rotational sequence. This would also change the root biomass being produced in
any given year. From the results presented by Gyssel et al (2005) as root biomass increase erosion
decreased. By this logic, rotations that have more years of soy, wheat, and oats than other higher root
mass crops within the rotation would be less likely to reduce soil movement than rotations that had more
perennial crops such as alfalfa brome mix (hay), barley, and corn. In most cases crop residue, canopy
cover and root biomass did play a role in the reduction of erosion. However, it should be noted that
cells that increased over 100% did not show correlation with these concepts.
Crop rotations that had high above and below ground biomass crops increased in erosion yet also
decreased. This may be explained by the development of roots and canopy cover from one period in
the growing year to another. The same rotations with the same unchanged parameters as another cell
still showed varied results in terms of increases and decreases in erosion. As stated above, crops vary
in their ability to intercept rain drops and prevent soil dislodgement and eventual transport. Depending
on the type of crop grown before another, crop residues may vary in their ability to shield the soil.
Further investigation into the correlation of rotation sequence, crop resides and root biomass would be
an interesting study, however the direct effects of crop properties are outside of this study.
High increases in some cells that have the same rotations as cells that decreased, indicates that there
is another factor. Analysing the erosion data of the cells that increased over 100% soil type and
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properties of that soil seem to have a correlation with increases. For instance, it is noted that particular
soil types always increased when rotations switched from a more annual rotation to a more perennial
rotation, indicating that the properties of the crops within the rotation did not inhibit erosion like in other
cells with different soil types. This is a point for further investigation outside of this study.
To be thorough, it should be noted that cells that increased over 100% have been analysed according to
the above points on erosion reduction. Cells that increased over expected values did not show any
clear reason as to why they increased so much compared to other cells. Cells with identical baseline
rotations and designed scenario rotations increased for one cell and decreased for another. There are
other factors that are affecting the amount of sediment being produced but are not directly evident with
the output data from AnnAGNPS and geo-physical data. Cells over 100% increase and decrease have
been noted as a finding to provide a full perspective of what happened when the baseline scenario was
changed to the designed scenario.
5.4. Water Yield
Overall water yield increased from the baseline scenario to the designed scenario by 3.35%. Water
yield was subdivided into two states by AnnAGNPS, subsurface and surface. Subsurface flow saw a
decrease in the estimated yields by 15.11%. Opposite of this, surface runoff estimates increased from
the baseline to the designed scenario by 3.72%. It is interesting that the water balance of the
watershed changed from one scenario to the other because the amount of crop rotation was closely
maintained in the field lottery.
Decreases in subsurface drainage seemed to be variable with crop rotations, soil type, and gradient. In
general rotations with more crop variability within the sequence seemed to decrease subsurface flow.
The increase of perennial cover within rotations did not directly decrease the amount of water flowing
into tile drains, as was found in other cells. Cells that had the same switch from one rotation to another
that was identical for one cell and another had different results on subsurface flow. For example
switching from SWCSWC to HHHHHH produced an increase in one cell and a decrease in another.
Soil type among the cells that decreased varied. Soil characteristics, specifically ones that affect
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moisture movement, play a role in how water infiltrates (Woods, Sivapalan, & Robinson, 1997). This
indicates that the soil type plays a role in the amount of infiltration into the soil and infiltrates differently
when different crops are on that soil type. Slope ranged from 1.50% to 6.20% and the soil type varied
as well. This indicates that geophysical characteristics of the cells played a role in the decease of
subsurface runoff.
Surface runoff increased when comparing baseline to designed scenario. The increase on a cell to cell
basis was expected when changing rotations. Some factors that could contribute to this are crop
residue and canopy cover during different climatic events. Crop residue has been documented in the
decrease of surface runoff (Baker & Laflen, 1983). The amount of crop residue available for off season
soil protection depends on the amount of the above ground biomass (Cantero-Martinez, Peterson,
Sherrod, & Westfall, 2006). Available crop residue varies between crops at the conservation tillage
minimum of 30%. Residue levels are presented in Table 5.1. As you can see from Table 5.1 there is
great variability between crops and their residue that is produced and left. More annual rotations had
high producing residue crops such as corn, soy, and wheat, while the more perennial rotations had half
of the rotation as hay and the other half as corn, soy, and wheat/small grain would produce fewer
residues. Hay does not produce a lot of crop residue that directly cover the soil as it is mostly
harvested. Stubble is left on the field but does not cover the soil directly like corn would.
The
difference in available crop residue for each crop type could explain why cells that changed from a more
annual system to a more perennial system increased in surface runoff when comparing the baseline to
designed scenario.
Table 5.1 Crop residue weight at 30% coverage of land area
Crop Type
Crop Residue
(kg/ha)
Hay
Oats following Hay yr 1
Barley
Corn
Soybean
Wheat
716.8
1064
1064
1176
672
672
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Climatic events and rotations sequence would play a role in both subsurface and surface runoff
increases and decreases from baseline to designed scenario. Climatic data was kept the same from
baseline to designed scenario. Canopy cover varies throughout the growing season leaving the soil
uncovered in the spring and winter and covered after crop vegetation maturity. Rotational sequences
that were similar in crop components, but started on a different year resulted in varying amounts of
increase or decrease.
Different events happening a varying time through the year would produce
different amounts of runoff. This is due to the development of the crop canopy, the amount of residue
left from the previous year and the state of the soil, frozen or thawed, when a climatic event occurred.
The lack of cover on the soil in spring and winter would generate more surface runoff than when the
canopy if fully closed in, this has been discussed earlier. Rotations that have less residue left on the
surface from the previous crop would also decrease in the ability to slow surface runoff during spring
thaw and rain events. The frozen or thawed state of the soil also contributes to the type of runoff
generated. Frozen soils cannot infiltrate water, turning precipitation into surface runoff during frozen
periods. This coupled with the type and amount of residue left on the soil surface from the previous
year would vary the amount of water that turns into concentrated flow, and can partially explain why
there was an increase in surface runoff from baseline to designed scenario.
Certain cells changed more than expected. Table 4.8 outline the cells that increased or decreased over
100% for both types of direct runoff from baseline to designed scenario. The highest increased cells for
subsurface runoff changed from pasture to an annual perennial rotation (a mixture of annual and
perennial crops within the same rotation). The change of rotation in these cells would change the
amount of available crop residue and root mass left on the field and in the soil for the winter months and
subsequently change the way that runoff moves across the soil. The change of pasture land to a
rotation that has a mix of annuals and perennials changes the way that water fluctuates in the system.
A study of the long term changes within the USA investigated the changes of traditionally pasture lands
converted to annual systems. It found that the conversion of land from perennial/annual productio to
more intensively cultivated rotations increased the amount of runoff that the watershed produced
(Schilling, Jha, Zhang, Gassman, & Wolter, 2008). This is similar to the three cells that increased over
1000% for surface runoff (cells: 593, 732, 742). These cells changed from pasture to annual/perennial
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systems where there was half hay and the other half of the rotation was annual crops. However, this
does not explain the other increases above 100% for subsurface runoff.
The other increases over 100% for subsurface runoff went from annual systems to annual/perennial
systems. Possible explanation of this is that there are more years of perennial cover within the rotation.
Alfalfa had been documented in increasing infiltration rates into soil (Meek, Rechel, Carter, De Tar, &
Urie, 1992) which is a component in the hay mix used in this simulation. Decreases over 100% did not
follow this trend. The cells that decreased by 100% seemed to do so when there was more variability in
crop components within the rotation. Soil type and slope of these cells seemed to influence the amount
of subsurface runoff. Soil type could be dictating the amount of infiltration because of the architecture of
the soil and its natural ability to accept water. As the slope increases so does the speed at which water
travels over land. If water has a faster velocity at higher slopes then the reduction of velocity via
residues would also decrease in slowing capabilities. The decreases of 100% are not of real concern
since the overall goal of the designed scenario was to decrease agricultural pollutants being transported
to stream networks.
5.5. Summary of Landscape Reconfiguration Results
A landscape analysis was performed on part of the Canagagigue Creek sub watershed to understand
the constraints and capabilities of the existing geophysical features. Constraint areas were delineated
by identifying areas that were next to headwaters or directly adjacent to streams, had higher slope
gradients, long slope lengths, and soils that were considered highly erodible. Capabilities were
identified in areas that were away from headwaters and streams, had low slope gradients, interrupted
slope lengths, and soils that were less likely to erode.
Next, a suitability assessment was conducted
on the crop nutrient characteristics throughout the watershed. Crop rotations were graded based on the
quantity of nutrient inputs. The geophysical capabilities were overlapped with the suitability
assessment, which indicated areas that needed to be addressed in the design process. Rotations that
had high inputs and were in areas of constraint were moved to areas marked as capability zones. This
design process created the designed scenario that was modeled with AnnAGNPS software to compare
to the base scenario.
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The results from base scenario to designed scenario are higher water quality overall. Total nitrogen
decreased by a small amount of 0.14 kg/ha/yr which equates to a 4.70% decrease. Phosphorus saw a
slight overall increase of 0.09 kg/ha/yr. This is because dissolved phosphorus actually increased by
6.25% or 0.04 kg/ha/yr. Sediment loading was decreased by 0.03 tonnes/ha/yr, which is a 7.14%
decrease from baseline to designed scenario. Water yield had surprising results as the water balance
between subsurface and surface drainage occurred. Subsurface, or tile drain discharge decrease by
15.11%, a decrease of 0.34 mm/yr, while surface drainage increase in yield by 4.13 mm/yr, indicating
that more runoff is being produced in the designed scenario. The increases and decreases of estimated
nutrients, and sediment seem small in the grand scheme of things, however every fluctuation one way
or the other compounds as water collects in other sub watersheds and basins and creates large
problems or great benefits in rivers and lakes.
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6.0
Conclusions
6.1. Research Objective
The research question and hypothesis for this thesis was derived from examining the current literature
and identifying trends in agricultural pollution reduction. Current studies focus on inserting strategies
and techniques (e.g., best management practices) into the landscape, removing land from production.
There is little to no research that keeps all agricultural land in production while striving for better water
quality. This approach, known as “working lands” in agricultural policy uses techniques and strategies to
mitigate harmful effects of production. Working lands approaches have not had the same level of
success as land retirement programs that remove land from crop production. There are few examples of
re-designing the agricultural landscape according to capability and suitability for different agricultural
land covers. There is a dearth of studies at a similar scale to this thesis where a sub watershed is
redesigned and simulated in totality.
6.2. Implications
The design approach used in this thesis takes no land out of production, while designing for better
stream health and water quality.
This benefits farmers and society who want to take steps to increase
water quality, maintain soils, and protect the landscape for future generations. Because this approach
does not remove land from production but rather re-configures the existing agricultural land covers,
perhaps farmers would be more likely to adopt it, collaborate with each other, and reduce the effect of
agriculture on water quality.
The approach taken here is not one of land retirement (like the effective CRP in US farm conservation
policy), nor is it one of adopting site-specific BMPs to mitigate the negative consequences of cropping in
working lands (like adopting measures funded through Environmental Farm Plans in Ontario). Rather
this approach is rooted in fundamental landscape analysis of capability and suitability to re-arrange
agricultural enterprises while holding areas constant. In the sub-watershed area, the same landscape
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composition (within reason) can result in improvements in soil retention and water quality through the
practice of sound landscape architecture.
Within landscape architecture and landscape planning, composition and configuration are often
manipulated simultaneously. The results here have shown that modifying configuration can yield better
outcomes for soil and water. Compositional changes in any landscape can have marked effects but the
effects are hypothesized to be more intense when a design process strategizes the locations of
changes to composition (Asbjornsen et al., 2013).
For landscapes where the consequences of land cover types and locations are not optimum, it appears
that landscape architecture can improve environmental and societal outcomes through intentional,
strategic design. This is accomplished through the use of previous research that has investigated
outcomes of design components. This report fills a niche of research that was lacking in large scale
landscape design and provides a start point for others to develop.
Landscape architecture has occasionally focused on fine-scale sites (less than a hectare) but classic
landscape architecture and environmental planning engaged with landscapes at a scale similar to the
definition supplied by Forman (1995): an area with repeating patterns that is at least kilometres-wide.
Working at the sub-watershed scale has many implications for landscape architecture. Landscape
architects benefit from working at this scale because it produces better designs by retaining the
complexity of ecological processes, while keeping the data to manageable proportions. Reputable
quantitative data from other disciplines can be found and used for suitability and capability analysis.
Using a quantitative approach to design places landscape architecture among empirical sciences
because measurable values are used in the design process to inform, guide, and make decisions. The
sub-watershed scale allows empirical data acquired through quantitative analysis to be extrapolated to
smaller and larger sites with less error. This is because the complexities of the processes that affect the
site are not lost when the scale of the design changes, rather they are just shifted in physical scale.
Sub-watershed scale design makes large scale changes to a landscape which can affect many different
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factors on and off site. Because of this, landscape design needs rigorous scientific research to
understand the environmental and social processes relevant to the design task and to be held
accountable for their decisions.
Empirical data and accountability for design increases public perception of the profession. To increase
public perception, a decrease in error at a discipline scale is necessary. To accomplish this, landscape
architecture should be placed in the arena of other quantitative fields that have proven their legitimacy
and commitment to empirical research, design, and experimentation. The use of quantitative
research/design with qualitative links expected results to a hard number rather than to subjective
notions. Quantitative research and design can be verified in situ which increases the credibility and
therefore the perception of the profession.
Research based design and the combination of qualitative and quantitative data throughout the
research and design process create better designs. The use of tested design elements that have an
empirical value to decrease or increase a factor within the landscape will ensure that there is a positive
outcome towards the study goal. Implementation of designs like this may increase when quantitative
data and the tenets of Conservation Landscape Planning are used to inform design decisions. When all
aspects of a site are at an equal level, both environment and human needs are being addressed
equally, the design is more likely to be implemented because there is no priority towards one or the
other. Designing in this way is beneficial to the profession because quantitative comparison of
environmental and economic factors of the site provides an explanation of design decisions, increasing
stakeholder understanding of the design and builds trust in landscape architects.
Ongoing testing of design concepts and research, shifting public perception of landscape architecture
by using both qualitative and empirical data for evidence based design will help to shift concepts that
landscape architects are just designers or gardener, to one of scientific research that aims to increase
the contribution to scientific literature and knowledge. Shifting to evidence based landscape architecture
through incorporation of quantitative data legitimizes landscape architecture amongst the sciences
because it provides quantifiable deliverables. This justifies the knowledge being produced, which
increases trust in the legitimacy of the research/design and landscape architecture as a profession.
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Working at the sub-watershed scale changes the way that landscape architects have to approach
design. At a scale that is classic of landscape architecture and landscape planning rigorous research
and acquisition of empirical data to inform, support, and guide landscape design are necessary to
decreases error, improve design, change the perception of the profession, and legitimizes landscape
architecture amongst other recognized quantitative disciplines. Not only does this scale shift the way
that design is being approached, it also takes the landscape architect from, “behind the garden wall” (p.
91), as Steiner (2002) criticizes, and shifts public perception of the discipline from extravagant designer
back to one of landscape stewardship and environmental planning scale design.
6.3. Study limitations
Inherent in all studies, are limitations. Model comprehension and possible error, missed errors,
unconsidered variables in the initial design process, and time constraints are all limitations that affected
this thesis. This thesis used estimated land management information, which is an innate limitation in
the study also.
Using an agricultural pollutant model that is hefty in data file demands, is new to the user, and time
consuming make it hard to perfect the modeling software. Many files were needed to be created to run
the AnnAGNPS software. Three basic file sets were outlined, however each one of those sections had
many sub file sets that needed to be developed. AnnAGNPS was developed for American data sets
and already existing publically accessible data. Canadian data lacks certain components, and does not
structure data files in the same way. This required using multiple sources to compile one file, something
that would have been readily available if the study was applied to an American landscape. The
requirements of these files were sometimes hard to understand and required additional computations
for data acquired from Canadian sources to match unit requirements. Other files do not exist in
Canadian data and are solely American concepts such as the RUSLE EI value. It was obtained through
dialogue with AnnAGNPS model specialists Ron Bingner. The value was extrapolated from Flint
Michigan’s EI value because it and the study area are at similar latitudes and share similar climatic
seasons. Because the model was new to the user, there are inherent errors that could have been made
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such as certain values that determined the contributing source area size for computations. The model
itself is continually under revision, as any good modeling software should be, so errors within the
models’ script are always a possibility and can be a limitation.
During the design process there were errors that were not caught in time to be changed. For example,
there was a doubling of rotation CSWHHH within the rotation creation stage with the inclusion of
SWHHHC. The ordering of the rotation is different from CSWHHH; however, the overall sequence was
the same. This would have weighted the number of fields that received that rotation sequence in the
lottery process in the base scenario creation. This would have been carried over into the designed
scenario, because the amount of any one rotation was not changed, just moved around in the
landscape. Other errors may also have ensued in the design, as human error is unavoidable.
Once the study was underway, and the design process was in full swing, a solution to include
phosphorus directly in the design rules was conceived. Another variable, such as canopy cover, was
something that was not considered, but would have affected erosion in the landscape. Time constraints
made changing the design process and rules that were already arranged and being enacted not
feasible.
In all studies time constraint is a common limitation. If there were no deadlines the study could be
tweaked and rerun endlessly. However, within the timeframe allotted, results of the study prove
promising for increasing water quality.
This thesis uses projected data. It used estimated land management inputs that may be different from
actual conditions of the study area. Actual geophysical data were used to describe the watershed but
land use and management practices were contrived from combining external sources, not from sources
within the watershed itself. Land use and management practices were developed based on literature,
common practices in the study area, and randomized assignment of cropping sequences. Using real
data collected from farmers in the watershed would have created an authentic representation of the
processes and pollutant yields occurring in the study area. Knowing that learning new software and
87
creating the data sets for the simulation would be time consuming, collection of data from farmers on
crop rotations and management practices was unrealistic to complete the study in a timely manner.
6.4. Future Study
Future study opportunities from this thesis are many. The inclusion of unconsidered variables, changing
model parameters, using live data, applying this study to broader scales, and investigating the profit
shift of the designed scenario would all be interesting additions to this study. Variables that could be
directly included in the design process, for instance phosphorus, and variables that were unconsidered
at the beginning of the design process, for example canopy cover, may change the results of the study.
It would be interesting to compare the results of this study to one that included phosphorus and canopy
cover directly in the design to see if it had the same effect, increase or decrease water quality.
Tweaking model parameters, while keeping the variables the same, would also be interesting. Perhaps
the change in concentrated source area value would produce finer scale AnnAGNPS cells, which would
represent the individual fields in the watershed better. This may change yield amounts within the
watershed, which would identify that contributing source area values effect the simulation and that they
need to be considered closely when using the AnnAGNPS model.
Simulating the watershed using collected data from land owners within the study area would model
existing conditions and produce figures that could inform landscape conservation planning in the area.
This study shows that the reconfiguration of theoretical management data reduced agricultural
pollutants exported from the watershed. Using AnnAGNPS with collected data would influence
implementation and policy in the watershed.
Taking the physical scale of the study one step back and trying to apply the concept to a single farm
may be useful. If collaborating with other farmers is too farfetched, perhaps if it can be done within one
farms’ boundary it would be more implementable. It would also prove to be an interesting comparison
between a watershed scale design and a farm scale design to see how pollutants are reduced. Going
one step further in physical scale, taking this sub-watershed scale study and applying it to the entire sub
watershed of Canagagigue Creek, the Grand River Basin and even the entire Lake Erie Basin would
88
examine the compounded effects of every watershed within the boundary. This could have implications
of basin scale design to improve water quality.
Further, the use of the developed conceptual frame work to investigate profits of each farm in a
watershed scale plan and identify types of policy that could make this concept an implementable reality
would be interesting for planners and policy makers. If farms did not make the same amount of profits
from the designed scenario because different crop are sold at different rates, perhaps watershed
agreements of profit sharing could be developed to ensure that all the farmers are receiving a fair share
of the profits. This would also reduce the draw backs of shifting crop rotations to improve water quality
and perhaps make it more implementable by farmers.
Given that re-configuring land cover types within a landscape can lead to improvements in modeled
environmental outcomes, other landscape types might likewise be examined for opportunities for better
landscape planning. In many urban areas, for example, river floodplains are common sites for
recreational (sports) fields and surface parking. With close proximity to surface waters and known
contaminants associated with fertilizers, automotive fluids, and de-icing salts, re-configuration of urban
landscapes might usefully be examined for implications of designed patterns of land use.
Using the concepts within this study and applying them to other types of landscape would prove to be
interesting. For instance, reconfiguration of human settlement, keeping the composition the same,
using geophysical characteristics to inform their location within the watershed boundaries could have
beneficial water quality results. The use of iterative modelling, testing designed reconfigurations to
determine which meets the targeted reductions of pollutants. This could inform the planning of future
residential development, mitigating the impact of humans on the natural landscape.
The designed scenario presented for this thesis is only one iteration of many. Configuration and
composition could have been done differently. Due to time constraints, the possibility of other designs
and the process of iterative design and modeling were not possible. Future work could tweak different
components of the design framework, such as: parcels included in reconfiguration, changing the priority
of geophysical and management factors that indicate ill-suited land use, the use of continuous living
89
cover strategically throughout the watershed and the addition of other variables that were not
considered. The data produced could further the understanding of how certain factors affect the
transport pollutants and where strategic targeting could be most effective.
6.5. Conclusion
This study conceptualized a new approach to agricultural landscape planning and conservation to
decrease pollution-loading streams. This thesis identifies and shows that a designed re-arrangement of
agricultural land cover types within the Canagagigue Creek sub watershed can decrease sediment and
some nutrient loss to improve surface water quality and increase soil retention. This design stuck to the
major tenets of Conservation Landscape Planning by keeping human and environmental needs both at
the forefront of design. It proved that large scale design of an agricultural watershed could reduce the
effects of agriculture on our water system. Similarly, not designing too broadly or largely filled a niche in
this field of research. There are many takeoff points from this research that could be explored to further
the understanding of how this type and scale of design could further benefit water quality.
90
7.0
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