Development of a method for estimating the ... - NRC Research Press

Development of a method for estimating the likelihood of crack flow in Canadian agricultural soils at the landscape scale Humaira Dadfar1, Suzanne E. Allaire2,5, Reinder De Jong3, Eric van Bochove1, Jean-Thomas Denault1, George The´riault1, and Farida Dechmi4 1

Soils and Crops Research and Development Centre, Agriculture and Agri-Food Canada, 2560 Hochelaga Blvd., Quebec, Quebec, Canada G1V 2J3; 2Horticultural Research Centre, Pavillon de l’Envirotron, Universite´ Laval, Quebec, Quebec, Canada G1V 0A6; 3Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, Ontario, Canada K1A 0C6; and 4Centro de Investigacioń y Tecnologıá Agroalimentaria (CITA-DGA), Avenida Montanãna 930, 50059 Zaragoza, Spain. Received 31 July 2009, accepted 28 October 2010. Dadfar, H., Allaire, S. E., De Jong, R., van Bochove, E., Denault, J.-T., The´riault, G. and Dechmi, F. 2010. Development of a method for estimating the likelihood of crack flow in Canadian agricultural soils at the landscape scale. Can. J. Soil Sci. 90: 129149. Indicators of risk of water contamination by agricultural pollutants are developed in Canada to assess sustainability of agriculture. Crack flow (CF), a key pathway for sub-surface contaminant transport, is part of the transport-hydrology algorithm used in two of these risk indicators. The objective was to develop a methodology for predicting the likelihood of CF in Canadian agricultural soils at the landscape scale. The algorithm considers soil clay content, crack development followed by a runoff event based on water budget, tile drainage, and crops. More than 40% of Canadian farmlands had moderate to very high likelihood of CF, mainly in Manitoba, Ontario and Quebec, due to frequent runoffs on cracked clay soils potentially contributing to groundwater contamination. In Ontario and Quebec, farmlands with high CF likelihood correspond to regions under intensive tile drainage, which increases the risk of lateral translocation of contaminants to surface water bodies. Besides being a component of risk indicators of water contamination by phosphorus and coliforms, the CF algorithm and maps can be used to identify areas at risk of subsurface water contamination. Best management practices, adapted to reduce CF can then be targeted to these areas. Key words: Agrichemicals, contaminant transport, macropore flow, preferential flow, risk assessment, risk indicators Dadfar, H., Allaire, S. E., De Jong, R., van Bochove, E., Denault, J.-T., The´riault, G. et Dechmi, F. 2010. Me´thode d’e´valuation de la probabilite´ d’ećoulement par les fissures des sols agricoles canadiens a` l’ećhelle du bassin versant. Can. J. Soil Sci. 90: 129149. Des indicateurs de risque de contamination de l’eau par les polluants agricoles ont e´te´ de´veloppe´s en support a` la durabilite´ de l’agriculture canadienne. Le mouvement des contaminants par les fentes de retrait du sol (MC), appeleés aussi crevasses, est une voie de transport pre´fe´rentielle vers les eaux souterraines. MC fait partie inte´grante d’algorithmes du transport hydrologique de ces indicateurs de risques. L’algorithme de MF conside`re la teneur en argile, le de´veloppement des fentes de retrait en lien avec le bilan hydrique, le ruissellement, le drainage artificiel et les cultures. Plus de 40% des terres agricoles canadiennes courent un risque moyen a` tre`s e´leve´ de MC, principalement au Manitoba, en Ontario et au Que´bec en raison des e´pisodes fre´quentes de ruissellement et de la teneur en argile des sols. Les terres agricoles du Que´bec et de l’Ontario a` risque relativement e´leve´ de MC correspondent a` celles draineés artificiellement, ce qui accroıˆ t les risques de transport late´ral des contaminants vers les eaux de surface. L’algorithme et les cartes de MC peuvent aussi servir a` identifier les zones ou` les eaux souterraines sont susceptibles d’eˆtre contamineés et modifier les pratiques culturales pour re´duire ce type de transport.

Abbreviations: ACFE, likely number of annual crack flow events; AWC, available water content; BF, burrow flow; CF, crack flow; CFE, frequency of crack flow event; CLAYSLC, average clay content in a SLC; FF, finger flow; IROWC, indicator of risk of water contamination; LF, lateral flow; NR, net rainfall; PF, preferential flow; RFC, factor for crops with roots favoring crack formation; SLC, Soil Landscape of Canada; TD, tile drainage; TEXT, normalized average clay content; T_H, transporthydrology; WDD, water deficit day

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To whom correspondence should be addressed (e-mail: [email protected]) 129

130 CANADIAN JOURNAL OF SOIL SCIENCE

Mots cle´s: Produits agrochimiques, transport des polluants, ećoulement par les macropores, mouvement pre´fe´rentiel, e´valuation des risques, indicateurs de risque

Contamination of water resources by undesirable chemicals, nutrients and waste materials applied on agricultural lands has raised environmental concerns. Identification of critical transport pathways is essential in designing strategies that can reduce the magnitude of the risk of groundwater and surface water contamination. The contribution of preferential flow (PF) has been recognized for over 100 yr (Lawes et al. 1882). During PF process, water with dissolved and/or suspended material flows through certain pathways in the soil, such that only a small portion of the porous media participates in most of the mass flow (Hendrickx and Flury 2001). The most recognized PF mechanisms in the unsaturated zone are: crack flow, burrow flow, finger flow, and lateral flow (Bosch et al. 2001; Roulier and Schulin 2006). Crack flow (CF) refers to PF along continuous cracks in soils (Blake et al. 1973); burrow flow (BF) to flow through channels created by soil fauna such as earthworms (Domı´ nguez et al. 2004); finger flow (FF) to flow through fingers starting at a fine-over-coarse soil interface (Starr et al. 1978); and lateral flow (LF) to lateral movement over an inclined hydraulically restrictive layer (Weyman 1973; Weiler and McDonnell 2007). The rapid transport of water through PF pathways may be important for water recharge and for decreasing surface runoff, especially in clayed soils. However from an environmental point of view, it is not desirable because it prevents the contaminants to mingle with matrix solution and minimizes retention time, which is necessary for surface exchange, catalysis reaction and biodegradation; resulting in acceleration of contaminant arrival to groundwater (Bergstro¨m et al. 2001), and surface water if transported to a drainage system (Brown et al. 1995; Jamieson et al. 2002). Another important and unwanted consequence of PF on agricultural soils is that it may lead to water (Thomas and Phillips 1979) and nutrient (Smalling and Bouma 1992) shortage to crops. The amplitude of PF for contaminant transport under various climate, soil and agricultural conditions is summarized in review papers (Flury 1996; Hendrickx and Flury 2001; Nieber 2001; Jarvis 2007; Clothier et al. 2008). Briefly, the amount of contaminant transported by PF is affected by its mass and initial position in the soil relative to water, and by the timing, the duration and the intensity of rainfall after the chemical has been applied (Edwards et al. 1993; Saini et al. 2003; McGrath et al. 2008). Preferential flow appears to be one of the most important pathways for the transport of strongly adsorbed and fast degrading agricultural chemicals (Villholth et al. 2000; Vereecken 2005), phosphorus (Ga¨chter et al. 1998; Jarvis 2007), and pathogens (Jamieson et al. 2002; Tallon et al. 2007) to tile drains, surface waters and groundwaters.

The knowledge relative to the importance of agricultural contaminants transported by PF has led to the development of numerous complex models of solute transport (Sˇimu˚nek et al. 2003; Gerke 2006). Heathwaite (2003) stated that the process-based models are not suitable for simple decision support frameworks required by end-users such as government agencies, consultants, land managers and developers because these models are too complex and generally require data that are not available at large scale and across large areas. The author suggested the need for developing simple generic transport algorithms of diffuse sources that are based on expert knowledge and are easy to use. In response to these needs, environmental risk indicators are being developed in different countries (Reus et al. 2002; Djodjic and Bergstro¨m 2005; Jorgensen et al. 2005; Buczko and Kuchenbuch 2007). Although PF is widely recognized to be one of the most important processes in transport-hydrology, it has rarely been included in risk indicators, except in a few of them. A phosphorus index (Djodjic and Bergstro¨m 2005) developed in Sweden relates PF to the hydraulic conductivity and structural properties of the soil. The MACRO model (Jarvis and Dubus 2006) considers cracks for risk of pesticide transport. However, it only covers field scale. Other phosphorus index models (Beaulieu et al. 2006; Goulet et al. 2006) included preferential flow in a simple way, where risk of PF was only related to soil texture. Bolinder et al. (2000) proposed an early version of a Canadian indicator of risk of water contamination by phosphorus (IROWC-P) using an indexing approach. The authors stated the importance of PF for P transport, but did not integrate it in their proposed methodology. Recognizing the importance of various PF mechanisms on contaminant transport, IROWC-P (van Bochove et al. 2006) and IROWC for pathogens (IROWC-Coliform, van Bochove et al. 2009) added four PF mechanisms (CF, BF, FF, and LF) into their algorithms. This paper focuses on the CF subcomponent of the IROWC-P and IROWCColiform. These IROWCs are designed to classify croplands within five risk classes (very low, low, moderate, high and very high) of water contamination by agricultural contaminants at the scale of soil landscape polygons (SLC) [Agriculture and Agri-Food Canada (AAFC) 2008]. The objectives of this study were: (1) to develop a methodology for predicting the likelihood of CF, (2) to map CF across Canada using data from the latest Census of Agriculture (2006), and (3) to study variation in spatial distribution of CF over a 25-yr period (1981 2006). The CF component is designed to be part of the transport-hydrology algorithm of IROWCs, developed to assist national level policymaker, and end-users in

DADFAR ET AL. * CRACK FLOW IN CANADIAN SOILS

identifying areas where there is likelihood of surface water pollution by agricultural contaminants. The CF component is not intended to estimate concentration, flux or load of contaminants transported by CF mechanisms. It is rather developed to give a likelihood risk of contaminant transport through cracking soils. This paper put emphasis on the methodology for calculating CF. MATERIALS AND METHODS Transport-Hydrology Component of Risk Indicators The transport-hydrology (T_H) component describes the movement of chemicals and particles to surface water bodies. It is an essential part of IROWC-P (T_H, van Bochove et al. 2006) and IROWC-Coliform (van Bochove et al. 2009). It integrates three major transport processes, i.e., surface runoff (R), natural drainage (D), and soil water erosion (E), to the connectivity factors, i.e., the topographic index (TI), the surface drainage density (SD), tile drainage factor (TD), the proportion of sediments from agricultural land delivered to water bodies (DR), and four preferential flow sub-components (CF, BF, FF, and LF) as: T_H (R)[TI SDCF BF ]=4(D) [TDFF LF ]=3(E)[DR]

(1)

A soil water balance approach is used to partition evapotranspiration, R, and D using the Versatile Soil Moisture Budget model (VSMB, Akinremi et al. 1996). The SD is the proximity of surface water bodies, estimated using the drainage density of the hydrological features, such as brooks, rivers, lakes, and wetlands, extracted from the National Topographic Data Base of Canada (NTBD, 1:50 000) within the agricultural lands of Canada. TI (Kirby 1975) accounts for the propensity of watershed area for saturation excess runoff. TD estimates the proportion of agricultural lands that is drained with tiles. All of these components need to be rated at the SLC scale with data sets available at a broad national level. The likelihood of CF occurrence was calculated for 2780 agricultural SLC composing essentially all of the agricultural land in Canada, varying from 1.0 103 to 3.1 106 ha in size, having from 1.0 102 and 0.6 106 ha of crop and pasture lands (Sheppard et al. 2009). The units have been divided so that they are mutually exclusive polygons grouped by similarity in climatic and soil properties. Each unit typically includes soils of several series but the series are not exclusive to each unit. Rationale of Crack Flow Desiccation cracking is an important phenomenon in soil, and its significance in facilitating the movement of water and contaminants through the soil, especially in clayed soils, has long been acknowledged (Jamieson

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et al. 2002; Jarvis 2007). It is therefore imperative to include crack flow into the transport-hydrology component of water quality models and risk indicators. For example, Clay and Stott (1973) found that the pesticide simazine moved deeper (76 cm) than expected into the soil, in part associated with transport through cracks after rainfall. Flury et al. (1995) found that herbicides were transported below 0.9 cm in a loamy soil associated with transport with or without adsorption along cracks and fissures. Bacteria have been observed in depths greater than 1.5 m (Natsch et al. 1996) during a single precipitation event, probably due to CF and BF. Cracking due to desiccation is affected by several factors, such as the amount and type of clay (Zein El Abedine and Robinson 1971; Dasog et al. 1988; Baer and Anderson 1997), the type and density of vegetative cover (Zein El Abedine and Robinson 1971; Sharma and Verma 1977; Dasog et al. 1988; Dasog and Shashidhara 1993), drainage (Nuutinen et al. 2001), management practices (Vervoort et al. 2001), and environmental factors such as the amount and frequency of rainfall or irrigation events (Zein El Abedine and Robinson 1971). These factors are considered in the CF algorithm as discussed in detail in the following sections. Factors Included in the Crack Flow Algorithm

Clay Content Soils with significant amount of clay, especially those with expanding lattice (i.e., montmorillonite), swell upon wetting and shrink when desiccated, resulting in crack formation (Greco 2002; Wells et al. 2003). As the clay content of the soil increases, the cracking increases (Dasog et al. 1988; Baer and Anderson 1997). The average clay content was calculated at the soil series level for each agricultural SLC polygon across Canada. The depths of the three uppermost soil layers, representing 5, 7.5, and 12.5% of the rooting depth, and total clay content (TCLAY,% by weight) in each soil layer were used to obtain the depth-weighted average clay content of each soil series. The clay content of all soil series was then averaged for the SLC polygon (CLAYSLC) based on the proportion of the area of a SLC covered by a soil series (ASS,% by area):

p

S

i1

CLAYSLC

l

S (TCLAYj Lj )

j1

l

ASS

S Lj

j1 p

i

(2)

S ASSi

i1

where p is the number of soil series in a SLC polygon, l is the number of soil layers and L is the layer depth. An exact equation or a specific threshold values for minimal clay content for cracking is not known from the literature. However, it is well recognized that large


cracks form in soils with high clay content, with more than 40% clay, whenever the weather conditions favor soil drying (Dasog et al. 1988; Bronswijk et al. 1995). Comparatively, soils with lower clay content also crack, but to a lower extent, depending upon the clay content (Preston et al. 1997; Kim et al. 2004) as we observed in our fields with loamy sands. Based on the above information, the soil texture factor used in the CF algorithm (TEXT, dimensionless) was assigned a value of 1 if CLAYSLC ]40% (very high likelihood of crack if development), and TEXT 0.025(CLAYSLC) CLAYSLC B40%. The threshold value of CLAYSLC ] 40% for TEXT 1 encompasses clay, clay, silty clay, and sandy clay soil textural classes (Soil Classification Working Group 1998).

Frequency of Crack Flow Event In soils with a sufficient amount of clay, especially those with shrink-swell properties, cracks are formed due to soil desiccation caused by factors such as root water extraction, evaporation, and drainage (Nuutinen et al. 2001). Crack formation is also strongly influenced by the precipitation (or irrigation), soil and crop management. Precipitation frequency must be sufficiently low to allow crack formation between events. The depth and width of cracks increase as long as soil desiccation continues from the surface to lower depths (Sharma and Verma 1977; Wells et al. 2003) unless the soil has reached it minimal volume. After cracks are formed, precipitation (with sufficient depth, duration, and intensity) must occur to cause surface runoff so that water can enter the cracks. When precipitation intensity exceeds the infiltration capacity of the soil matrix, water accumulates at the soil surface and moves through runoff toward regions with higher hydraulic conductivity such as cracks open to the soil surface (McLeod et al. 1998), creating PF. During rainfall, cracks do not immediately close. It requires time and a sufficient amount of water in the soil (Nova´k and Sˇolte´sz 1984). Even after closure, cracks will have high hydraulic conductivity, water flowing at faster rates than in the soil matrix (Bauma and Loveday 1988). Tillage operations such as conventional and secondary tillage destroy surface connectivity of cracks and macropores (Vervoort et al. 2001), loosening the soil structure resulting in higher total porosity (Gantzer and Blake 1978), but decreasing macroporosity (Shipitalo and Protz 1987). Tillage for seedbed preparation may also result in uniform water flow in the tilled layer (Jarvis et al. 2007), reducing likelihood of CF. Thus, for predicting crack formation in a field, it is important to account for soil water budget (Bronswijk 1989), since cracking is negatively correlated with water content in soil (Bandyopadhyay et al. 2003), and climatic conditions in addition to soil and crop management. The above mentioned factors were integrated for predicting

the frequency of crack flow events using a water budget model. A modified version of the versatile soil moisture budget model (VSMB, Akinremi et al. 1996) was used to estimate the likely number of annual crack flow events (ACFE) in each soil series and for each crop groups in all SLC polygons from 1976 to 2006. It estimates daily soil water content and components of the soil water budget resulting from precipitation, R, evepotranspiration, and D under annual and perennial crops. Water is withdrawn at different rates from different soil depths depending on the potential evapotranspiration, stage of crop development, soil water retention, and available soil water content. The soil profile was divided into six layers, representing from the surface downward 5, 7.5, 12.5, and 25% of the rooting depth (1 m). Each layer was characterized by three parameters: water content at saturation, field capacity, and permanent wilting point. Concave, linear, and convex soil water retention curves (Dyer and Baier 1979; De Jong and Bootsma 1988) were used for coarse (40 and 65% sand), fine (65 and 80% clay) and medium (all others) textured soils. Water from precipitation cascaded from upper to lower layers when the upper ones reached field capacity. The upward and downward redistribution of soil water was simulated using an algorithm adapted from the Ceres-Wheat model (Ritchie and Otter 1985). The snow budgeting procedure (Baier et al. 1979) was used during the winter period to provide an estimate of soil water contents in early spring. Soil surface temperature was calculated using the algorithm adapted from the EPIC model (Williams 1995; Akinremi et al. 1996). When the soil surface was frozen, surface runoff was calculated using the procedure outlined by Ash et al. (1992). For non-frozen soils, surface runoff was calculated with the Curve Number technique (USDA Soil Conservation Service 1972). Simulations started with an assumed soil water content of 75% of its saturation value on 1975 Apr. 15. Eight major crops: (1) cereals (spring wheat, other spring cereals, buckwheat); (2) canola; (3) grass (seed forages, tame hay, improved pasture, unimproved pasture in eastern Canada); (4) rangeland (unimproved pasture in western Canada); (5) alfalfa; (6) corn (grain corn, silage corn); (7) soybean; and (8) potato and summerfallow were used in the model. These groupings are based on similarities of evapotranspiration characteristics of the crops. The 4 yr prior to a given census year were assigned the same crop data as the given census year. Daily potential evapotranspiration was calculated using the Baier and Robertson (1965) technique. Water uptake by the roots was simulated using depth-dependent crop coefficients (De Jong and MacDonald 1975; Baier et al. 1979). Following Gallichand et al. (1991), the water content at which the actual evapotranspiration rate fell below the potential one was crop dependent, ranging from 0.65 for potato to 0.40 for


alfalfa (Van Keulen and Wolf 1986; Allen et al. 1998). Planting dates of annual crops were estimated according to Bootsma and De Jong (1988) and De Jong et al. (2001). Growing season start and end dates of perennial crops were determined using the procedures described by Sly (1982). The duration of each crop growth stage was defined by a biometeorological time scale model (Robertson 1968) for cereals, and growing degree days for corn, soybeans and potato (Shaykewich et al. 1998; De Jong and MacDonald 1999). Rainfall interception by plants was calculated according to Feddes et al. (1978). Cracks were assumed to open at the soil surface when the soil available water content (AWC) in the upper layer was less than 80% of its holding capacity. This event was characterized as the first water deficit day (WDD). Further drying of the soil (i.e., when the AWC of the three upper layers was less than that of the previous day) caused further cracking and an increase of WDD each day. After the accumulation of at least 10 WDD, a CF event could occur, provided that the net rainfall (NR daily precipitation daily interception) was at least 10 mm. Cracks were set to close and cumulative WDD were set to zero when the soil AWC of the upper three layers reached field capacity, or after a tillage operation. Tillage operation corresponded to the planting and harvesting of potato, and planting of annual crops. The likely number of crack flow events (ACFE, dimensionless) was recorded on an annual basis, with cracks present at the end of one year carried over to the following one. These values were averaged based on the area of each crop group and soil series in each SLC, which was then averaged over 5 yr to calculate the frequency of crack flow event (CFE, dimensionless) as:

n

S

i1

m

S

j1

S ACFEk ASSk

k1

p

AC

m

S ACj

1

j1

S ASSk

k1

CFE

p

j

i

n (3)

where n is the number of years (5 yr, a census year plus 4 yr preceding that year), m is the number of crop groups (8) based on their evapotranspiration similarities, p is the number of soil series in a SLC polygon, ASS is the proportion of the area of a SLC covered by a soil series (% by area), and AC is the sum of crop areas (ha) in each crop group used in the VSMB model. The ACFE data over 5 yr were used in calculation of CFE to account for natural variability in weather data.

Tile Drainage In the vicinity of tile drains, the soil is drier due to lower water table, creating favorable condition for soil cracking (Nuutinen et al. 2001). In addition, surface

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connected cracks may provide a direct pathway for surface applied contaminant transport to subsurface drains (Akay and Fox 2007; Sims et al. 1998; Jamieson et al. 2002). This direct impact of TD on contaminant transport is already accounted in the transport-hydrology equation, but its indirect effect on cracking is considered in the CF algorithm. Tile drainage factor (TD) was estimated as the ratio of total area (ha) of tile drained land (ATD) to the total farmland area (ha) covered by all census of Agriculture land classes (ATF, ha) in each SLC. Data on tile drain areas were collected from local experts, resulting in data heterogeneity and imprecision. It was assumed that annual crops were grown on moderately to very welldrained soils (naturally or artificially). TD was then calculated as follows:

N

S TACi

i1

N TD

(100 IMP) ATF 100 ATF

(4)

where N is the number of years (5 census years, 1981 2001), TAC is the total area (ha) of annual crops in a given census year in each SLC. IMP is the summed area (ha) of imperfectly, poor and very poor drained soils. All TAC grown in excess to the total area of moderately to very well drained soils are considered to be drained. This algorithm was used only in areas known by local expert to have tile drains. The data collected in Quebec and Ontario, the two provinces where most of the subsurface drains are installed, were used to validate the results of the TD algorithm.

Crops Crop root system influences the cracking pattern by influencing water distribution. Deep-rooted crops have a tendency to favour larger and deeper cracks (Bronswijk 1989; Dasog and Shashidhara 1993), while shallow and fibrous roots render soil mechanical binding stronger, reducing crack spacing and depth (Zein El Abedine and Robinson 1971). Based on the literature and expert knowledge, crops were classified for their rooting system as crops promoting or not promoting crack development. Small grain crops, vegetable crops, potatoes, tame hay and fodder crops, pasture and summerfallow were considered as not promoting crack development because they favor only fine shallow cracks compared with the other crops that favor deeper cracks. All other crops were considered to favor crack development. When more than one crop was reported in the same area (i.e., spring and winter wheat, oats, barley, mixed grains, fall and spring rye, corn for grain, buckwheat and triticale) without indication of their individual area and their location on the SLC, it was assumed that 50% of the area was covered with crops favoring cracks. For nursery products, it was


assumed that 39% of the area was covered with crops favoring cracks, based on Quebec’s data, as obtained from the horticultural industry (http://www.fihoq.qc.ca/ html/la_production.html). The factor in the CF algorithm considering roots favoring cracks (RFC, dimensionless) was estimated as the ratio of the sum of areas (ha) covered by crops with roots promoting cracks (ARFC, ha) to ATF (ha): RFC

ARFC ATF

(5)

Crack Flow Algorithm In developing CF algorithm, several assumptions and simplifications were required to make it manageable for the whole of Canada due to limitations in data availability. Despite this challenge, it was attempted to include the main factors contributing to CF in the algorithm: CF d(TEXT )(CFE TDRFC)

(6)

where d (0.1) is a scaling factor used to set maximum CF (dimensionless, ranging from 0 to 1) to 1, which facilitates its incorporation into the T_H algorithm. TEXT (dimensionless, ranging from 0 to 1) is the normalized average clay content, CFE (dimensionless, ranging from 0 to 15) is the factor for the frequency of crack flow event in a year, TD (dimensionless, ranging from 0 to 1) is the factor for tile drainage, RFC (dimensionless, ranging from 0 to 1) is the factor for crops with roots favoring crack formation. In the above algorithm, TEXT is a multiplicative factor because clay content is required for crack development in soil. The CFE had a weight 15 times higher than TD and RFC because these two factors are not necessary for CF to occur, they only enhance the process when present. During CF calculation, a value of 0.0 was used for missing TD and RFC data, while CF was reported as missing for missing TEXT and CFE data. Data Sources Soil data, including texture, water content at saturation, 10, 33 and 1500 kPa of matric potential were obtained from the Canada Soil Information System (CanSIS) of the National Soil Layer File (AAFC 2008). The areal extent of soil series, drainage class and rooting depth were obtained from the CanSIS National Soil Component Table for each soil series within each SLC polygons. The area covered by each crop (27 agricultural crops) was collected once every 5 yr (1981, 1986, 1991, 1996, 2001 and 2006) by Statistics Canada through the Census of Agriculture, and these data were allocated to each SLC polygons (Huffman et al. 2006). Weather data, including daily maximum and minimum air temperatures (8C) and daily precipitation (mm) for the

period 1975 to 2006 were obtained from AAFC Ecodistrict climate data base (scale 1:7 500 000). Because meteorological data were not available at the SLC scale, it was assumed that all SLC polygons falling within an ecodistrict have the same climate. Sensitivity Analysis Sensitivity analyses were carried out on parameters that were considered important for CF and for which no exact values were found in the literature. Three parameters (CLAYSLC, WDD and NR) were tested (Table 1) within known possible ranges (high and conservative values) observed in Canadian lands. (1) It is well-known that large cracks form in soils with high clay content whenever the weather conditions favor soil desiccation (Dasog et al. 1988; Bronswijk et al. 1995). This was the rationale for using CLAYSLC ]40% as an upper threshold for calculating TEXT. Soils with lower clay content also crack, but to a lower extent depending upon the clay content (Preston et al. 1997; Kim et al. 2004). Based on expert knowledge, CLAYSLC was modified to 35%, the boundary between sandy clay and sandy clay loam soils, and to 28%, the boundary between clay loam and loamy soils (Soil Classification Working Group 1998). Important cracking was observed in our loamy sand soils in the field and thus this lower limit is known to be reasonable. Yet, not all loamy sands crack even under favorable climate conditions. This test was completed in order to examine the sensitivity of the CF algorithm to the threshold value used for TEXT. (2) It is recognized that the number of days required for crack formation varies depending upon soil, weather conditions, crops, and drainage. All of these parameters are considered in calculation of CF. However, the precise extent of desiccation for crack formation is not well-known, and a threshold value was necessary for the algorithm. Based on expert knowledge, a number of 10 WDD is believed to be sufficiently long for the development of large cracks under Canadian weather conditions. The standard value for WDD was set to 10 d and was modified to 5 d (representing rapid cracking conditions) and to 15 d (corresponding to a conservative threshold whenever when the soil only slowly dries in cool conditions with low evapotranspiration). Table 1. Combination of thresholds for net rainfall (NR) depth, water deficit days (WDD) and average clay content (CLAYSLC) in various tests used in the sensitivity analysis of crack flow (CF) Test 1 2 3 4 5 6 Standard

NR (mm)

WDD (days)

CLAYSLC (%)

10 10 10 10 5 15 10

10 10 5 15 10 10 10

35 28 40 40 40 40 40


(3) The amount of rainfall that generates surface runoff depends on the initial soil water content, slope, and soil hydraulic properties; data that are not available in sufficient detail in the CanSIS data base. Based on expert knowledge, the minimal NR amount used in the calculation of ACFE was set to 10 mm and was changed to 5 and 15 mm, which might be the possible high and very conservative values, respectively. Mapping Mapping of CF and its subcomponents was carried out at the SLC scale using ArcGIS (version 9.2) software. Only the agricultural farmlands are discussed and mapped. CF values were categorized into the following classes: (1) no-likelihood (CF 0, class value 0); (2) very low (0 BCF 50.2, class value 1); (3) low (0.2 B CF 50.4, class value 2); (4) moderate (0.4 BCF 50.6, class value 3); (5) high (0.6 BCF 50.8, class value 4); and (6) very high (0.8 BCF 51.0, class value 5). The change in CF likelihood class over time or associated with the sensitivity analysis was calculated from the difference in the numerical value of the associated classes. For mapping CFE, the following six classes were used: (1) CFE 0; (2) 0BCFE 53; (3) 3 B CFE 56; (4) 6 BCFE 59; (5) 9BCFE 512; and (6) 12 BCFE 515. The values of CF, TEXT, CFE, TD, and RFC were scaled up from the SLC polygon level to provincial and national averages using the methodology described by Yang et al. (2007). RESULTS AND DISCUSSION Crack Flow

Canada The average CF for Canada was 0.43 in 2006, which corresponds to the moderate risk class (Table 2). About 6% of Canadian lands fell into the very high CF class, mainly located in Manitoba, Ontario, and Quebec (Table 3, Fig. 1). Similarly, farmlands with high CF likelihood (11% in 2006) were mostly located in Manitoba, Ontario, and in parts of Saskatchewan and Quebec. In these farmlands, transport along desiccation cracks can be the dominant mechanism for transport of contaminants to groundwater. Most lands with very high and high likelihood of CF in Manitoba, Ontario, and Quebec are located in proximity of large water bodies such as Lake Manitoba and Lake Winnipeg (the 20th largest freshwater lake in the world) in Manitoba; Lake Ontario, Lake Erie, and Lake Huron in Ontario; and southwest of the Saint Lawrence River in Quebec. Thus, these water bodies may be at risk of contamination when cracks are connected to lateral flow pathways such as tile drains (Scott et al. 1998). In southern Ontario and southwest of the Saint Lawrence River in Quebec, much of the agricultural lands are tile-drained and are covered with soils and crops that favor crack formation (Table 2, Fig. 2). A combination of CF and subsurface drainage can result in chemical and biologi-

135

cal contamination of surface water bodies in these parts of the country. Strategies to reduce CF need to be considered, such as tillage operations (Jarvis 2007), increasing soil organic matter content, switching to crops that require less water or by selecting another crop type with roots promoting good soil structure rather than cracking. Normally, less than 0.5% of Canadian farmlands fell in the no-risk class, while about 12% was estimated in the very low class, most of located in British Columbia, Prince Edward Island and Newfoundland (Table 3, Fig. 1). In general, 71% of the 61 Mha area remained in the same CF class from 1981 to 2006, while about 7% decreased by at least one class, and 22% increased by at least one class (Table 4, Fig. 1). Shifts to higher CF class over time occurred mainly in Alberta, Saskatchewan, Quebec, Nova Scotia, Prince Edward Island and Newfoundland. The variations in CF were mostly explained by temporal variations in CFE (Table 4). For instance during a 25-yr period, 11% of the area decreased, while 21% increased by at least one CFE class. Changes in CF due to variation in CFE between 1981 and 2006 are presented as temporal variation rather than temporal trend in weather, since the CFE data calculated between 1977 and 2006 did not show any temporal trend (data not presented). The interpretation of CF results for Canada should be done carefully since 84% of the Canadian farmland area is located in the Prairie Provinces of Alberta, Saskatchewan and Manitoba.

British Columbia The average CF (0.25) for British Columbia was lower than the Canadian mean (0.42), mainly due to lower CFE and to a lesser extent to soils with relatively low clay content (Table 2). In 2006, about 3% of the approximately 1.9 Mha farmlands fell into high risk class, while about 17% had moderate CF likelihood (Table 3). The farmlands with high and moderate likelihood of CF were mainly located in northern British Columbia (Fig. 1), in the vicinity of the Peace River. These farmlands compose about 40% of the province’s agricultural lands and are characterized by rolling hills with grain and cattle farms. In these farmlands CF may be the dominant mechanism for transport of contaminants to groundwater. In addition, a combination of CF and soils on rolling hills may create risks of surface water contamination due to lateral flow. Although the proportion of farmland in the very high CF likelihood was generally zero, in some years, depending on weather, it can be significant, as indicated in 1996 (Table 3). Compared with 1981, in 2006, about 15% of the British Columbia area upgraded or downgraded in CF class as a result of temporal variability in CFE associated to weather, and to a lower extend, due to change in crops (Tables 2 and 4).

Canada British Columbia Alberta Saskatchewan Manitoba Ontario Quebec New Brunswick Nova Scotia Prince Edward Island Newfoundland

Farmland area

Ratio of the Canadian farmlands

CF

106 ha

(%)

1981 1986 1991 1996 2001 2006

60.90 1.91

3.1

0.41 0.39 0.43 0.45 0.42 0.43 0.24 0.23 0.27 0.29 0.24 0.23

0.62 0.49

6.26 6.01 6.61 6.74 6.32 6.42 4.19 4.08 4.59 4.78 4.23 4.01

19.2 25.1 7.0 4.6 2.4 0.17 0.15 0.2

31.7 41.3 11.5 7.6 4.0 0.28 0.25 0.33

0.36 0.37 0.62 0.52 0.40 0.38 0.28 0.18

0.39 0.42 0.56 0.53 0.43 0.38 0.32 0.21

0.63 0.63 0.68 0.53 0.52 0.46 0.37 0.25

5.52 5.70 8.54 8.71 6.90 7.91 7.28 7.21

0.01

0.02

0.16 0.20 0.25 0.21 0.24 0.26

0.47

4.18 5.43 5.89 5.66 6.11 5.84

0.35 0.37 0.53 0.53 0.43 0.41 0.30 0.17

0.40 0.40 0.62 0.52 0.46 0.36 0.30 0.17

0.44 0.42 0.60 0.50 0.39 0.38 0.32 0.16

TEXT

0.37 0.40 0.64 0.50 0.43 0.37 0.28 0.19

CFE

TD

RFC

1981 1986 1991 1996 2001 2006 (10 2) 1981 1986 1991 1996 2001 2006

5.38 5.63 7.22 8.75 7.36 8.50 7.84 6.48

6.03 6.13 8.75 8.67 7.80 7.38 7.73 6.73

6.51 6.38 8.29 8.13 6.55 7.99 8.13 6.18

5.61 5.91 8.90 8.09 6.98 7.67 7.23 7.41

4.20 1.40

5.90 0.03 6.25 0.001 7.52 0.007 8.59 39.7 7.20 29.9 7.98 2.30 8.01 3.30 8.16 1.60 0.00

0.11 0.15 0.16 0.19 0.25 0.27 0.09 0.12 0.12 0.12 0.14 0.13 0.08 0.05 0.18 0.43 0.21 0.06 0.12 0.06

0.12 0.09 0.21 0.45 0.26 0.07 0.15 0.07

0.13 0.10 0.23 0.50 0.30 0.10 0.17 0.07

0.14 0.15 0.24 0.53 0.36 0.13 0.23 0.07

0.16 0.24 0.28 0.59 0.46 0.14 0.25 0.11

0.19 0.27 0.33 0.56 0.49 0.18 0.29 0.14

0.02 0.04 0.04 0.12 0.09 0.14


Table 2. Mean (dimensionless) crack flow (CF), normalized average clay content (TEXT), factor for the frequency of crack flow event (CFE), factor for tile drainage (TD), and factor for crops favoring crack formation (RFC) values for farmland area in Canada and in Canadian provinces between 1981 and 2006

Table 3. Proportionz (%) of farmland area in different crack flow (CF) likelihood classes in Canada and Canadian provinces in different years Class

Canada

British Columbia

Saskatchewan

Manitoba

Ontario

Quebec

New Brunswick

Nova Scotia

Prince Edward Island

Newfoundland

0.0 0.0 0.0 0.0 0.0 0.0

0.1 0.1 0.1 0.1 0.2 0.2

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

2.7 2.4 2.4 2.3 2.1 2.0

5.1 4.9 4.9 4.9 4.9 4.9

0.5 0.5 0.7 1.4 1.3 1.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

51.1 51.8 50.8 53.2 52.9 59.4

8.4 9.0 8.2 7.1 9.6 7.9

11.3 11.3 10.4 10.5 10.8 10.0

10.4 11.9 11.7 11.9 11.6 11.7

10.6 10.5 10.7 10.1 10.2 10.4

15.8 15.6 15.4 18.8 17.2 15.6

17.2 10.9 17.2 10.1 13.2 16.9

21.6 11.7 19.9 11.6 17.4 9.5

54.7 81.1 81.2 81.2 44.2 30.0

53.3 38.7 31.0 36.6 41.1 28.0

18.9 34.9 18.2 16.4 21.4 18.4

59.2 59.1 51.7 41.0 54.4 43.6

52.5 53.1 37.0 26.9 40.4 34.3

7.7 14.0 5.1 4.3 6.4 13.7

22.1 22.0 21.7 25.3 25.2 21.0

37.2 34.9 31.2 35.8 34.8 33.4

25.6 24.1 35.0 27.9 23.9 33.1

63.5 67.4 59.7 57.6 74.7 64.4

45.3 18.9 18.9 18.8 55.8 70.0

41.6 53.5 58.9 63.4 50.7 57.9

Moderate (0.4BCF 50.6) 1981 28.1 28.2 1986 26.7 10.2 1991 33.0 21.0 1996 38.8 14.9 2001 29.9 19.2 2006 37.8 17.4

27.8 28.5 29.2 37.4 26.4 41.3

30.6 26.1 42.8 49.7 38.5 44.6

29.8 35.7 25.7 29.6 23.7 28.3

22.8 21.9 21.0 23.1 22.1 23.6

12.7 14.7 14.6 14.2 12.7 16.4

52.8 60.0 44.1 57.2 58.0 44.5

14.0 19.7 18.4 29.7 7.9 25.0

0.0 0.0 0.0 0.0 0.0 0.0

5.1 7.9 10.1 0.0 8.2 6.9

No-likelihood (CF0) 1981 0.5 1986 0.4 1991 0.4 1996 0.4 2001 0.4 2006 0.4 Very low (0BCF 50.2) 1981 11.9 1986 12.2 1991 11.5 1996 11.4 2001 12.2 2006 11.7 Low (0.2BCF50.4) 1981 44.9 1986 46.7 1991 36.0 1996 28.7 2001 39.2 2006 33.5

High (0.6BCF50.8) 1981 8.8 1986 9.4 1991 13.0 1996 14.4 2001 12.3 2006 10.6

0.0 1.2 8.5 9.3 4.6 2.6

4.5 3.2 8.6 9.1 8.8 6.0

5.5 9.5 9.5 12.4 10.0 10.2

26.8 23.1 34.5 33.5 26.2 23.5

16.3 17.2 21.4 24.2 25.1 18.9

13.1 9.0 11.7 10.4 10.8 9.3

3.9 4.0 3.0 3.4 3.6 4.5

1.0 1.2 2.0 1.1 0.0 1.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 4.9

Very high (0.8BCF B1.0) 1981 5.6 1986 4.4 1991 5.9 1996 6.0 2001 5.7 2006 5.7

0.0 0.2 0.0 4.8 0.0 0.0

0.0 0.0 2.3 5.2 0.7 0.9

0.1 0.1 0.3 0.5 0.4 1.0

25.4 15.3 23.1 20.7 32.2 22.7

23.6 24.3 21.3 13.4 13.5 22.5

13.4 18.4 19.8 13.6 17.4 17.9

0.0 0.6 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0

z

The proportion of farmland area in different classes does not add up to 100% because some polygons were excluded from calculations due to missing data.


Alberta

137


Fig. 1. Spatial distribution of crack flow (CF) likelihood (a) in 2006, (b) in 1981, and (c) change in CF class between 1981 and 2006 for agricultural SLC polygons across Canada.

Fig. 2 (right). Spatial distribution of factors used in the calculation of CF in 2006, (a) normalized average clay content (TEXT), (b) factor for the frequency of crack flow event (CFE), (c) factor for tile drainage (TD), and (d) factor for crops with roots favoring crack formation (RFC) in agricultural SLC polygons across Canada.


Fig. 2 (Continued)

139


Prairie Provinces Throughout the prairie provinces, Vertisolic soils are found on parent materials high in clay (i.e., greater than 60% clay). These soils swell when wet and shrink when they dry out, which causes large cracks at the soil surface (Wells et al. 2003). The mean CF values in Alberta were slightly lower than the Canadian average, and ranged from 0.35 to 0.44, which corresponds to low and moderate CF classes, respectively (Table 2). Over 25 yr, much of the 19 Mha farmlands fell into low CF class (Fig. 1). The proportions of farmlands in high and very high CF likelihood, respectively, varied from 3 to 9% and from 0 to 5% (Table 3), mainly due to temporal variability in weather. Farmlands with very high CF likelihood (0.9%) were located in central part of Alberta at the Athabasca River Basin, while farmlands with high CF likelihood were mainly located on the floodplains in the vicinity of the Peace River in northwestern Alberta, and in the central parts of the province. In these lands, contaminant transport through cracks may contribute to a decline in groundwater quality. In the southwestern part of Alberta is the Oldman River Basin, with high concentrations of cattle and confined livestock operations and dryland farming. In the southern part of Alberta, surrounding Lethbridge, are irrigated lands and a vast network of irrigation canals, which pass through the region and eventually drain into the Oldman River. Much of these areas are characterized by moderate to high CF likelihood, where agrichemicals and feces in rich in bacteria have the potential to move through cracks and contaminate groundwaters and surface waters. Detectable herbicide and pesticide concentrations are reported in farm dugouts, several small lakes, and farm wells (Chambers et al. 2002). During 25 yr, in 69% of Alberta farmland the likelihood of CF remained in the same class, while in about 25% of farmland CF risk was increased by at least one class, mainly due to increase in production of crops that favor cracking (RFC), and to a lower extent, due to temporal variability in CFE (Table 4). The farmlands where the likelihood of CF changed were scattered in different parts of the province (Fig. 1c). In Saskatchewan all the soils have a high potential to swell and shrink, and are characterized by seasonal moisture deficits sufficient to induce cracking (Dasog et al. 1987). This province has almost half of Canada’s total cultivated farmland (Table 1). Mean CF was similar to that in Alberta due to similar values of TEXT and CFE (Table 2). Generally, about 10% of farmlands had high CF likelihood and less than 1% had very high likelihood of CF, scattered in different parts of the province (Fig. 1, Table 3). Preferential flow through cracks may be the dominant mechanism contributing to groundwater contamination in this province. In a 1996 survey, pesticides were detected in 26% of 184 farm wells (Chambers et al. 2002). Much of approximately 25 Mha agricultural lands fell into the low and moderate

CF likelihood classes (Table 3, Fig. 1). In Saskatchewan, 68% of the lands did not change CF class over 25-yr (Table 4). The average CF values in Manitoba (0.530.64) were consistently higher than the other prairies provinces and the Canadian average due to high clay content (TEXT 0.68), and higher CFE (7.28.9) (Table 2, Fig. 2). Much of the approximately 7 Mha farmland areas fell into moderate (2436%), high (2335%) and very high (1532%) CF likelihood classes (Table 3). In these lands, desiccation cracks are potentially important pathways for the transport of contaminants to groundwater. Many rural communities and most farms in Manitoba rely on groundwater. There is evidence of water contamination by agricultural activities in this province (Betcher et al. 1995). Lands with high and very high likelihood of CF are mainly located in the southeast of the province surrounding Winnipeg, in the vicinity of Lake Winnipeg and Lake Manitoba. In the presence of lateral water movement, the cracks may act as a pathway connecting the surface applied contaminants to surface waters. These water bodies are important for human activities drinking, fisheries, and recreation and their possible contamination is a great concern. Between 1981 and 2006, 73% of the area in Manitoba remained in the same CF class, 23% decreased by at least one class, and only 4% increased by at least one class, as a consequence of changes in CFE and RFC (Table 4, Fig. 1). Generally, the prairie provinces have the right conditions for crack development because most soils have high clay content (Fig. 2, Table 2) and sufficiently long periods of dryness, but the amount of rainfall is usually too low to have frequent CF events. Snowmelt is the critical event for surface and groundwater recharge in the prairies (Chambers et al. 2002). CF is at high risk when snowmelt occurs when cracks are formed before snowmelt.

Central Canada In Ontario the number of CFE was generally higher than other parts of the country, and much of the agricultural lands were tile-drained. Mean CF ranged from 0.50 to 0.53, which is always higher than the Canadian average, but lower than Manitoba (Table 2). In 2006, in more than 41% of farmlands, the likelihood of CF was in high and very high classes (Table 3). Most of these lands were located in southeastern part of the province, surrounded by Lake Huron, Lake Erie and Lake Ontario (Fig. 1), and close to important rivers such as Grand River. These lands are characterized by intensive crop production and high concentration of cattle operations. Preferential flow of agrichemicals and pathogens through desiccation cracks may contribute to contamination of groundwater in these parts of the province. A survey conducted in 1992 reported the detection of herbicides in 11.5% of 1204 farm wells,

Table 4. Proportionz (%) of farmland area where crack flow (CF), factor for the frequency of crack flow event (CFE), and factor for crops favoring crack formation (RFC) remained in the same class, increased or decreased by at least one class, in Canada and Canadian provinces between 1981 and 2006 Factors CF

RFC

z

No change Decreased Increased No change Decreased Increased No change Decreased Increased

71.4 6.5 21.7 67.1 11.3 21.2 38.3 0.3 61.3

82.6 8.7 6.1 64.5 20.1 12.8 84.5 2.3 12.7

69.0 5.4 25.4 74.2 6.4 19.2 54.0 0.2 45.6

68.0 2.8 29.1 64.9 4.6 30.4 24.5 0.0 75.5

73.2 22.5 4.3 53.6 41.7 4.9 29.3 0.3 70.4

86.5 7.2 4.7 68.9 21.2 8.1 47.4 1.7 50.9

80.6 3.9 13.0 70.3 6.2 21.1 11.7 0.1 88.2

81.5 11.9 6.6 64.4 17.5 18.2 76.5 0.0 23.5

Nova Scotia

Prince Edward Island

Newfoundland

77.1 0.9 22.0 65.3 6.0 28.7 29.8 0.0 70.2

75.4 0.0 24.7 67.0 0.0 33.0 85.0 0.0 15.0

62.2 0.0 35.5 8.8 0.0 88.9 51.1 0.0 48.9

The proportion of farmland area in different classes does not add up to 100% because some SLC polygons were excluded from calculations due to missing data.


CFE

Class change Canada British Columbia Alberta Saskatchewan Manitoba Ontario Quebec New Brunswick

141


the majority of which were situated in areas of intense agriculture (Chambers et al. 2002). In Ontario, farmlands with high and very high CF likelihood (Fig. 1) correspond to regions under intensive tile drainage (Fig. 2) and, thus, to direct lateral translocation of contaminants to surface water bodies. There is considerable evidence that agricultural activities have resulted in some degree of impact on surface water quality in many sensitive areas. For example, contamination of the Grand, Saugeen and Thames Rivers (1981 and 1985) and the Payne River (1991 to 1992) by pesticides have been reported (Chambers et al. 2002). Frank et al. (1991) reported detection of atrazine and desethylatrazine in the tile drainage water samples collected from the 1.0-m and groundwater samples beneath the field between a depth of 1.2 and 4.6 m, following application on a clay loam soil in Ottawa, Canada. Between 1981 and 2006, the area of land in no-likelihood, very low, low and very high CF likelihood classes decreased by 3%, which was compensated with an increase in moderate and high CF classes (Table 3, Fig. 1). Most of these changes were associated with changes in climate (CFE), rather than to crops. Comparing 1981 and 2006, more than 85% of the 4.6 Mha of farmland areas remained in the same CF class (Table 4). Although mean TEXT in Ontario (0.53) and Quebec (0.52) were similar, mean CFs in Quebec (0.400.46) were relatively smaller than in Ontario (0.500.53) because it rained more in Quebec resulting in less time for crack development, as can be observed by looking at the CFE values (Table 2, Fig. 2). In addition, TD and RFC in Quebec were lower than in Ontario (Table 2, Fig. 2). Only about 5% of 2.4 Mha land in Quebec had no-likelihood of CF. The proportion of land in high and very high CF likelihood classes was 913% and 13 20%, respectively (Table 3). Most of farmlands with high and very high likelihood of CF were found southwest of the Saint Lawrence River (Fig. 1), due to a combination of soils with high clay content, CFE, and the production of crops that favor cracking, and tile drainage (Fig. 2). This region is under intensive agricultural production and supports concentrated pig and dairy cattle production. Livestock agriculture is considered one of the main causes of bacterial contamination of surface and groundwaters (Jamieson et al. 2002). Thus, transport of nutrients, agrichemicals and waste material through cracks may cause groundwater contamination in this region. It has been reported that, 55 78% of private wells in potato-growing areas monitored between 1999 and 2001 had detectable concentration of pesticides (Chambers et al. 2002). A large portion of agricultural lands with high and very high CF risk (Fig. 1) corresponds to areas under tile drainage (Fig. 2), which may also increase the risk of surface water contamination. Total P losses from poorly drained clay soils through tile drainage in Quebec can range from 0.01 to 1.17 mg P L1 or higher (Carefoot and Whalen 2003). During 25-yr, more than 81% of farm-

land remained in the same CF class, and 13% increased by at least one class (Table 4, Fig. 1). This change was mainly due to temporal variability, rather than temporal trend in CFE. In southern Ontario and Quebec, where most tile drained lands are located, applications of contaminants (i.e., pesticides and nutrients) near the tiles should be avoided and beneficial management practices should be considered to minimize the impacts on water resources.

Atlantic Provinces All Atlantic provinces have mean CF values much lower than the Canadian average. In New Brunswick, CF values were considerably higher than in Newfoundland, although the average value of TEXT (0.46) was similar to that in Newfoundland (0.47), due to weather conditions that favored crack development, and due to higher values of TD and RFC. In New Brunswick, mean CF ranged from 0.36 to 0.41 (Table 2), mainly due to temporal variability in CFE. More than 44% of 0.2 Mha lands fell into moderate CF class, while less than 5% fell into high and very high CF classes (Table 3). High CF likelihood was mainly found in the south of the province (Fig. 1), close to the Bay of Fundy, due to sufficient number of CFE, and soils with high clay content (Fig. 2). While, farmlands with moderate CF likelihood were scattered in different parts of the province (Fig. 1), lands with CF were mainly along the Saint John River, which drains production farming regions. In these regions CF may cause groundwater contamination, and when cracks are connected to lateral flow paths it will also contribute to surface water contamination. Between 1981 and 2006, more than 80% of the area remained in the same CF class, while 12% decreased by at least one class, mainly due to decrease in CFE values caused by temporal variability in weather (Table 4, Fig. 1). The majority (58%) of lands in Nova Scotia fell into the low CF class (Table 3), because of soils with low clay content. Depending on weather condition, 8 to 30% of farmlands had moderate, and less than 2% had high CF likelihood (Table 2). Most of these lands were located in central Nova Scotia in the vicinity of the Bay of Fundy. Over a 25-yr period, in 70% of farmlands the production of crops favoring cracking (RFC) increased by at least one class (Table 4). In Prince Edward Island, all farmland area fell into low and very low CF classes (Table 3), mainly because the soils are very sandy (TEXT 0.25) (Table 2, Fig. 2). Between 1981 and 2006, 75% of 0.2 Mha lands stayed in the same CF class, and none decreased class, whereas 25% increased by one CF class changing from very low to low class. The increase in likelihood of CF was associated with weather conditions and the production of crops favoring crack formation. In Newfoundland, no lands fell into the very high CF class, while in 2006 about 5% of farmlands had high CF likelihood. Mean CF ranged from 0.16 to 0.26 (Table 2).


Between 1981 and 2006, more than 62% of farmlands remained in the same CF class. The area of lands in the very low CF class decreased significantly from 53 to 28%, which was compensated by an increase in low, moderate and high CF classes (Table 3). The increase in CF likelihood was mainly due to more frequent dry periods followed by a runoff event and increase in production of crops favoring cracking (more annual crops). Sensitivity Analysis Sensitivity analyses were performed for 2006 census year. When the threshold value for CLAYSLC was modified from 40 to 35% (Test 1), the likelihood of CF increased in 26% of farmlands by at least one class (Fig. 3a). The proportion of area in the very low and low CF classes decreased by 14%, which resulted in an increase in the area of land in moderate (by 6%), high (by 6%) and very high (by 2%) CF classes. When the threshold value was further decreased from 40 to 28% (Test 2), in 63% of farmlands the likelihood of CF increased by at least one class, of which 26% corre-

143

sponded to high and very high CF classes (Fig. 3b). The changes mainly occurred in the prairie provinces and in central Canada, where the soils contain significant levels of clay. Some of these changes occurred in farmlands located in the proximity of large surface water bodies, such as Lake Manitoba and Lake Winnipeg in Manitoba, the Great Lakes in Ontario, and the Saint Lawrence River in Quebec. In general, British Columbia, Prince Edward Island, and Nova Scotia seemed least sensitive to this threshold value. The threshold value of CLAYSLC used in this study is a conservative one, and may underestimate the risk in some sensitive areas. Therefore, further research is required to better define this threshold value, and CF algorithm will be updated as soon as a better value is defined. When the threshold value for WDD was decreased from 10 to 5 (Test 3), about 74% of the farmlands remained in the same CF class, while 25% increased by at least one class (Fig. 4a). The increase in CF mainly occurred in the moderate (by 7%), high (by 5%) and very high (by 2%) classes. When WDD increased from 10 to 15 (Test 4), a similar scenario occurred, with 76%

Fig. 3. Spatial distribution of the changes in likelihood of crack flow (CF) classes [difference between the value of CF class calculated with the sensitivity analysis test and the values of the class calculated with the standard method (10 mm NR, 10 WDD, 40% CLAYSLC)] showing the impact of the threshold in clay content based on 2006 data, (a) Test 1 (10 mm NR, 10 WDD, 35% CLAYSLC), and (b) Test 2 (10 mm NR, 10 WDD, 28% CLAYSLC).


Fig. 4. Spatial distribution of the changes in likelihood of crack flow (CF) classes [difference between the value of CF class calculated with the sensitivity analysis test and the values of the class calculated with the standard method (10 mm NR, 10 WDD, 40% CLAYSLC)] showing the impact of the threshold in the number of consecutive drying days based on 2006 data, (a) Test 3 (10 mm NR, 5 WDD, 40% CLAYSLC), and (b) Test 4 (10 mm NR, 15 WDD, 40% CLAYSLC).

of the lands remaining in the same CF class; however, in 24% of farmland, the likelihood of CF decreased by at least one class (Fig. 4b). No spatial trend was observed in the distribution of the farmlands that changed class with changes in WDD; they were scattered all over the country. The changes affect some farmlands near large surface water bodies especially in Manitoba and Quebec. This is very important, since most of these farmlands are under intensive agricultural production. When the threshold value for the depth of net rainfall (NR) amount was decreased from 10 to 5 mm (Test 5), only 13% of Canadian farmlands remained in the same CF class, while 87% increased by at least one class (Fig. 5a). The proportion of area in the high and very high CF classes, respectively, increased by 9 and 53%. Generally the significant class changes (up to three classes) were observed in the prairies and British Columbia. When the threshold value was increased from 10 to 15 mm, only 25% of Canadian farmlands remained in the same CF class, while 75% decreased by at least one class, mainly in prairies and southern Ontario (Fig. 5b). Thus, the

algorithm seems more sensitive to a decrease than to an increase in the NR threshold. Sensitivity analyses indicate the need for further refinement of thresholds used in the algorithm, in part, through additional research. Limitations and Future Work The threshold value of parameters used in CF calculation is based on expert knowledge and on possible intervals for each parameter known from the literature. It is not an assessment of crack depth, but only areas where deep cracks are likely to occur. Additional research is required to check these thresholds in order to fine tune the algorithm. In addition, the following factors influence the precision of CF likelihood. (1) There was a lack of accessible data on clay mineralogy at the SLC scale across Canada. Some clay types exhibit much higher shrinking-swelling behavior than others (i.e., montmorillonite vs. kaolinite). Many soils in the prairie provinces have clays with high shrink-swell properties such as smectite (Dasog et al. 1988), which would increase their CF risk. When


145

Fig. 5. Spatial distribution of the changes in likelihood of crack flow (CF) classes [difference between the value of CF class calculated with the sensitivity analysis test and the values of the class calculated with the standard method (10 mm NR, 10 WDD, 40% CLAYSLC)] showing the impact of the threshold in the net rainfall depth using 2006 data, (a) Test 5 (5 mm NR, 10 WDD, 40% CLAYSLC), and (b) Test 6 (15 mm NR, 10 WDD, 40% CLAYSLC).

the data become available, they will be incorporated into the CF algorithm, in order to enhance prediction accuracy. (2) A national data base was not available for TD. The available data are not updated and their precision is not homogeneous across Canada. (3) The exact location of crop area in a SLC was not known. Crop area in each SLC polygon was not associated with its exact soil series, making it impossible to know if crops with roots favoring crack formation were grown on soils vulnerable to cracking within each SLC. (4) In the Census of Agriculture, some crops were grouped together without indication of the area covered by each crop. In such cases, it was assumed that crops with roots favoring crack formation were covering 50% of the area. This may have resulted in the underestimation of CF in various parts of the country. It should be noted that the results of this modeling exercise provide only the risk of contaminant movement through cracks. The CF algorithm has not been validated yet. Water quality data from several water-

sheds across Canada have been compared with data from risk indicators, which include preferential flow components. The preliminary results indicate that, together, preferential flow components (CF, BF, FF, LF) were significant. Fine-tuning, field experiment and comparisons with water quality data (field validation) will be completed in the near future. CONCLUSIONS The distribution of CF likelihood across Canada was estimated based on soil characteristics, water budget related to weather, crop physiology, crop mangement, and tile drainage. Less than 6% of the Canadian farmlands fell into very high likelihood class, and 9 14% fell into the high CF likelihood class, mainly located in prairie provinces, southern Ontario and southern Quebec. Dryer weather conditions favor crack formation in Manitoba, while intense rainfall in summer with sufficient drying time, tile drainage and cultivation of crops favoring cracks tended to higher CF in Quebec


and Ontario. Although soil clay content in Alberta and Saskatchewan was favorable for crack formation, average CF was lower than in Ontario due to a smaller amount of rainfall and to low areas with crops favoring cracks and almost no TD. CF was low in Prince Edward Island because of its sandy soils. The prediction of CF was sensitive to different thresholds used in the calculations. The sentivity was different between threshold parameters and between provinces. The lack of data on clay mineralogy and tile drainage in the national data base, and a lack of universal relationship between clay content and crack development in the literature are perhaps the major factors influencing the accuracy of CF prediction. The CF data are intended to be used in the transporthydrology component of risk indicators of surface water contamination that are developed for assisting endusers, law makers and legislation in identifying SLC polygons where there is the likelihood of surface water pollution by agricultural contaminants. These maps are not intended to replace direct field investigations. The CF algorithm and maps can be used to identify areas at risk of ground water contamination, and may help in the development of best management practices to minimize the impact on water resources. They can also be used to study the impact of changes in crop, land management, and climate on CF. ACKNOWLEDGEMENTS The authors acknowledge Bahram Daneshfar and Ryan Ogston for providing technical information on interpolated Census of Agriculture data. The project was funded by the National Agri-environmental Health Analysis and Reporting Program of Agriculture and Agri-Food Canada. Agriculture and Agri-Food Canada. 2008. Soil landscape of Canada (SLC), v3.1.1 and Census of agriculture re-allocation. Unpublished data for research purposes, AAFC, Ottawa, ON. Akay, O. and Fox, G. A. 2007. Experimental investigation of direct connectivity between macropores and subsurface drains during infiltration. Soil Sci. Soc. Am. J. 71: 16001606. Akinremi, O. O., McGinn, S. M. and Barr, A. G. 1996. Simulation of soil moisture and other components of the hydrological cycle using a water budget approach. Can. J. Soil Sci. 76: 133142. Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, FAO, Rome, Italy. Ash, C. H. B., Shaykewich, C. F. and Raddatz, R. L. 1992. Moisture risk assessment for spring wheat on the eastern prairies: a water-use simulation model. Clim. Bull. 26: 6578. Baer, J. U. and Anderson, S. H. 1997. Landscape effects on desiccation cracking in an Aqualf. Soil Sci. Soc. Am. J. 61: 14971502. Baier, W., Dyer, J. A. and Sharp, W. R. 1979. The versatile soil moisture budget. Tech. Bull. No. 87, LRRC, Research Branch, Agriculture Canada, Ottawa, ON. pp. 52.

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