IJERD – International Journal of Environmental and Rural Development (2011) 2–1

Research article
erd

Effect of Land Use Agglomeration on Nitrogen
Concentrations in River Water in the Tokachi River
HIROMU OKAZAWA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Email:h1okazaw@nodai.ac.jp

TOSHIMI MUNEOKA
Obihiro University of Agriculture and Veterinary Medicine, Hokkaido,JapanYUDAI KUDO
Docon Company Limited, Hokkaido, Japan

YASUSHI TAKEUCHI
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, Japan
Received 10 December 2010

Accepted 9 January 2011

Abstract Since in the nineties, contamination of river water by nitrogen from cultivatedland has been an issue in major agricultural region of Hokkaido. This study examines theeffects of the ratios of cropland or forestland to the watershed area on the nitrogenconcentration in the river water, taking as its survey area the Tokachi River basin ineastern Hokkaido, Japan. The study also investigates the impact of cropland and forestland
agglomeration on the nitrogen concentration in river water. Survey samples were taken at
37 locations on the Tokachi River. Nitrogen concentrations in the river water weremeasured at all locations in June, August, and October of 2007, when water levels werenormal. In addition, a GIS land use analysis was conducted using a land use map of theregion with a grid resolution of 100 m. This analysis determined that cropland andforestland accounts 90% of the total area in the study watershed. Moreover, spatialcontinuity (SC) was calculated as an index of cropland and forestland agglomeration. SC
represents the average area of a contiguous patch of cropland or forestland in this study.
Based on the accumulated data, the followings were found; 1) nitrogen concentration inriver water are positively correlated with the proportion of cropland, but are negatively
correlated with the proportion of forestland. It is clear that the proportion of each land use
is a factor affecting nitrogen concentrations in the river water. 2) The correlation between
river nitrogen concentrations and SC values is positive for cropland, but negative forforestland. This indicates that nitrogen concentrations in river water likely decreases if theland use pattern is changed: scattering cropland to reduce its patch size, or connecting
forestland together to make the forest patch size larger.
Keywords cropland, forestland, land use, agglomeration, nitrogen, river, watershed

INTRODUCTION
It is known that in agricultural watersheds where cropland is abundant, there is a high correlation
between the proportion of cropland area and the nitrogen concentration in river water (Jordan et al.,
1997; Ahearna et al., 2005; Zampella et al., 2007). This correlation is reported particularly in many
agricultural watersheds in Hokkaido, the prefecture with the largest area of agricultural land inJapan (Tabuchi et al., 1995; Woli et al., 2002 and 2004; Okazawa et al., 2003 and 2010). As theresults, this high correlation between the nitrogen concentration in river water and agricultural land
use in watersheds in upland farming regions highlights the importance of managing land use toprotect the quality of river water (Okazawa et al., 2010).
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IJERD – International Journal of Environmental and Rural Development (2011) 2–1

Table 1 Summary of data collected from different locations in the surveyed watershed*PointRiver nameLand use (%)

SC

T-N (mg/L)

NO3 -N (mg/L)

Area(km2 )①Cropland②Forest①+②Other

Cropland

Forest

③Mean

SD

④Mean

SD

④/③

1

Tokachi

632

18889

1133

3690

0.32

0.06

0.12

0.06

0.36

2

Tokachi

658

18889

11314125

0.36

0.15

0.21

0.07

0.59

3

Tokachi

801

288

90

10

412199

0.68

0.27

0.35

0.18

0.51

4

Tokachi

806

387

90

1049

2136

0.42

0.14

0.20

0.04

0.47

5

Tokachi

840

58590

1075

1131

0.44

0.02

0.21

0.04

0.47

6

Tokachi

858

78490

10851009

0.63

0.17

0.28

0.02

0.44

7

Tokachi

1,289

157791

8151

352

0.82

0.120.56

0.12

0.68

8

Tokachi

1,531

197292

8195

242

1.15

0.18

0.65

0.15

0.56

9

Tokachi

1,777

21

70

91

9225

223

1.07

0.14

0.73

0.13

0.69

10

Tokachi

1,798

21

70

91

9225

214

1.47

0.32

0.82

0.16

0.56

11

Tokachi

2,669

31

60

91

9393

123

1.49

0.14

0.97

0.25

0.65

12

Tokachi

2,683

32

59

91

9386

122

1.69

0.25

1.10

0.10

0.65

13

Tokachi

2,686

32

59

91

9377

122

1.92

0.37

1.20

0.00

0.63

14

Tokachi

4,479

32

58

90

10367

117

1.85

0.51

1.23

0.25

0.67

15Tokachi

5,098

36

54

90

10

418

99

2.94

0.52

2.33

0.21

0.79

16

Tokachi

8,224

30

61

92

8219

142

1.90

0.24

1.43

0.23

0.76

17

Tokachi

8,982

30

62

92

8

207

148

1.85

0.21

1.57

0.21

0.85

A

Penkenai

23

098

98

2

1

2250

0.37

0.10

0.20

0.08

0.53

B

Penkenikorobetsu

48

198

99

19

2366

0.30

0.09

0.15

0.03

0.51

C

Penkenikorochin

72

198

99

110

7059

0.34

0.08

0.17

0.07

0.52

D

Sahoro

337

26

68

95

2161

163

1.36

0.13

0.98

0.11

0.72

E

Memuro2103856946248

80

1.28

0.08

0.98

0.11

0.77

FPiuka

2671

1283

17262

12

6.54

0.77

5.63

0.95

0.86

G

Bibaushi

35

79

1997

3301

31

1.69

0.79

1.30

0.82

0.77

HBisei

180

1674

91

8225

211

1.17

0.30

0.89

0.20

0.76

I

Shinobihiro

164

74

1690

10630

17

3.97

1.49

3.27

1.25

0.82

J

Shibusarabibaushi

33

53

43

96

3

156

1.17

0.28

0.83

0.18

0.71

K

Shikaribetsu

667

48

4592

8867

54

2.44

0.28

2.23

0.25

0.92

L

Otofuke

693

1969

88

12254

190

1.94

0.08

1.530.21

0.79

MSatsunai

704

306191

9262

115

1.85

0.21

1.53

0.32

0.83

N

Obihiro

197

66

1480

20

442

16

5.64

1.56

4.07

0.23

0.72

O

Shihoro

316

64

2993

7443

32

5.14

0.81

3.43

0.67

0.67

PTobetsu

127

79

1392

8494

13

4.65

1.27

4.13

0.85

0.89

Q

Sarubetsu

449

69

2695

5558

27

7.00

1.04

5.57

0.64

0.80

R

Toshibetsu

2,850

2075

95

584

306

0.77

0.05

0.32

0.04

0.42

S

Ushisyubetsu

173

24

61

85

396

224

0.36

0.02

0.16

0.13

0.44

T

Rebunnai

66435193

69782

1.35

0.41

0.93

0.15

0.69

*1 to 17 : Mainstream, A-T : Tributary

Since in the eighties, the water purification mechanism of vegetation found nearby rivers
called riparian buffers zone which are composed of forests and marshes has been drawn in attention.
Many studies have demonstrated that preserving forest areas near rivers helps keep the nitrogenconcentration in river water to a low level. Excess nitrogen compounds discharged fromagricultural land are captured and absorbed by riparian buffers, reducing the amount of nitrogenreleased into rivers (Johnston et al., 1984; Lowrence et al., 1984; Hill et al., 2000). However, there
are only few studies with the same topic conducted in Asia which is distinguished by Asia
monsoon climate area (Okazawa et al., 2010).
It is believed that nitrogen concentrations in river water vary even among watersheds withidentical ratios of forestland and cropland, depending on whether the forests are concentrated nearrivers or distributed across the watershed. Such spatial distributions of land use are called land use
agglomeration. Not many studies have examined the relationship between land use agglomerationand nitrogen concentration in river water (Okazawa et al., 2009).
This study examines the relationship of nitrogen concentration in river water in agriculturalareas. In particular, the study demonstrates the relationship between the proportions of croplandand forestland in agricultural watersheds and the concentration of nitrogen, and the effects of theagglomeration of cropland and forestland on the nitrogen concentration.
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IJERD – International Journal of Environmental and Rural Development (2011) 2–1

METHODOLOGY
Overview of watershedsThe Tokachi River basin located in eastern Hokkaido has a total area of 9,010 km2 (Fig.1), the sixth
largest river in the country and second in Hokkaido. More than 2,500 km2 of this area is mainly
consists of potatoes, beans, wheat, corn feedstuff, and sugar beets cropland.

Legend—— RiverPacific Ocean

Cropland
Forestland
OthersSampling point on mainstream (1-17)
Sampling point on tributaries (A-T)

Fig. 1 Map of the Tokachi River basin
The summary of the surveyed watershed areas is given in Table 1. There were thirty-sevenwatersheds chosen for this study. The proportions of cropland and forestland in each watershedarea varied significantly ranging from 0% to 79% and 12% to 98%, respectively. In all watersheds,
however, cropland and forestland areas accounting at least 80% of the total land area. Meanwhile,
the proportion of other land use patterns (such as residential or industrial) was small.
Water quality survey
Water quality was surveyed on sunny days to avoid the effects of flood. The field survey wasconducted three times in June, August, and October of 2007. Samples were taken from river waterat the downstream end of each watershed. Seventeen survey locations were chosen between theupstream end (Sampling Point 1) and the downstream end (Sampling point 17) of Tokachi River’s
main stream, and 20 locations (A–T) along its primary tributaries. The samples were taken back to
the laboratory and their nitrogen concentration was analyzed using the Japanese Industrial
Standards (JIS) method.
The nitrogen concentrations of river water in respective watersheds (mean values obtained
from three survey results) are shown in Table 1. T-N ranges from 0.30 to 7.00 mg/L (mean: 1.81mg/L), and NO3-N from 0.12 to 5.63 mg/L (mean: 1.32 mg/L). The percentage of NO3-N
constituting T-N ranged from 33% to 92% (mean: 64%), showing that the majority of T-N iscomposed of NO3-N.
Land use analysis
Rivers and their respective watershed boundaries were determined by using 1:25,000 topographic
map. In addition, a land use map with a 100 m grid resolution, published by the Ministry of Land,
Infrastructure, Transport and Tourism was used to determine the land use system such as cropland© ISERD39IJERD – International Journal of Environmental and Rural Development (2011) 2–1and forestland. Land use ratios and agglomeration were analyzed using GIS software (ArcGIS 9.1,ESRI).
Two types of land use index were used in this study; the percentage of forestland or croplandin a watershed and the agglomeration of land use. Indices for the land use agglomeration include
those that showed how ‘joined’ or ‘clumped’ land use areas are, as well as the ‘spatial continuity’(SC) index of identical land use. In this study, SC which can be simply derived from GIS wasemployed as the index for land agglomeration. SC was proposed by Tsunekawa et al. (1991). Aland use map with a grid resolution of 100 m was used to calculate SC.

 Forest
 Cropland River River
Step 1 Prepare vector maps of the watershed

Step 2 Plotting raster maps onto vector maps12
 Cropland Others

Step 3 Extracting cropland grid cells

4
3

Step 4 Adding up the number of patches (4 patches).

Fig. 2 Method of calculating spatial continuity or SC
Vector maps of the investigated watersheds were prepared (Step 1). Then raster data maps ofeach investigated watersheds were plotted onto the vector maps (Step 2), and only cropland gridcells were extracted from each watershed (Step 3). To obtain an SC index, grids representing
cropland were grouped into ‘patch’ units (Step 4). Patch refers to a group of grid cells connected ina vertical, horizontal, or oblique direction. Referring to Fig.2, cropland of 100 grid cells evolve andare in four patches as in Step 4. The value of SC was obtained by using Eq. 1 as shown below.
SC = k / Cpatch(1)

Where k is the number of grid cells representing the cropland area in a watershed and Cpatch isthe number of patches represents the grouping of grid cells. In the example given in Fig.2, thenumber of grid cells representing cropland k is 100 (Step 3) and Cpatch is 4 (Step 4), so SC is equal
to 25. In other words, SC is equivalent to the average area of a patch in each watershed. The spatial
continuity for forestland was also calculated using the same method.
RESULTS AND DISCUSSION
The relationship between the proportion of cropland in each watershed and the concentration of TN in the river is shown in Fig.3. There was a significant positive correlation between the two© ISERD40IJERD – International Journal of Environmental and Rural Development (2011) 2–1

parameters. However, even in areas with a comparable cropland ratio, there were variations in theT-N concentration. For example, the watersheds G and P both had a cropland ratio of 79% but thenitrogen concentrations were 1.68 mg/L and 4.65 mg/L respectively.
Fig.4 shows the relationship between the proportion of forestland and the T-N concentration.
In contrast to Fig.3, the larger the forest area, the lower the nitrogen concentration was. The
correlation was stronger than that of cropland area ratio and nitrogen concentration.
The relationship between SC for cropland or the agglomeration of cropland use and thenitrogen concentration is shown in Fig.5. A rise in SC was often observed with a rise in T-Nconcentration and a significant correlation (r2 = 0.48; significance level at 0.01) was found between
the T-N concentration and SC of cropland. This demonstrates that the nitrogen concentration ishigher in a watershed where croplands are contiguous but lower where croplands are scattered. In
other words, since the SC index of cropland represents the average area of a patch of cropland ineach watershed, therefore the larger the cropland’s patch area in a watershed, the higher thenitrogen concentration of river water and vice versa.

Fig. 3 Relationship between cropland
and total nitrogen

Fig. 4 Relationship between forestland
and total nitrogen

Fig. 5 Relationship between SC ofcropland total nitrogen

Fig. 6 Relationship between SC offorestland and total nitrogen

CONCLUSION
The effect of land use agglomeration on the concentration of nitrogen in river water in the TokachiRiver basin in Hokkaido, Japan was examined. The results indicated that in addition to theproportion of cropland and forestland, their contiguity or SC had an impact on the quality of riverwater. This shows that decreasing the patch area of cropland or increasing the patch area offorestland could likely improve the quality of river water. However, the grid resolution of the land
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IJERD – International Journal of Environmental and Rural Development (2011) 2–1

use data in this study was set at 100 m; reassessment of the appropriateness of the grid size may berequired in future studies.
ACKNOWLEDGEMENTS
This research was supported by Grant-in-Aid for Young Scientists (B) from The Ministry ofEducation, Culture, Sports, Science and Technology (MEXT), Japan (No.22780223). We would
like to express our gratitude to Prof. Dr. Osamu Tsuji (Obihiro University of Agriculture andVeterinary Medicine). The authors are also grateful for the research assistance provided by thestudents of the Environmental and Rural Engineering unit, Obihiro University of Agriculture andVeterinary Medicine, and the laboratory of Hydro-structure engineering, Tokyo University ofAgriculture.
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