Open Access

Effect of piospheres on physio-chemical soil properties in the Southern Rangelands of Kenya

Ecological Processes20176:14

DOI: 10.1186/s13717-017-0082-8

Received: 16 November 2016

Accepted: 11 April 2017

Published: 5 May 2017

Abstract

Introduction

Water-based interventions haphazardly introduced in the drylands of Kenya have led to the introduction of piospheres used as concentration mounts. Not much is known about the effect of these piospheres on soil physio-chemical properties, especially in the Kenyan rangelands where the government and other development agencies have created piospheres aimed at curbing water shortages and sustaining livestock production. The study assessed the effect of piospheres on soil physio-chemical characteristics in the southern rangelands of Kajiado, Kenya, in order to provide evidence-based insights that will be useful in guiding future water interventions.

Methods

Soil samples were collected within 0.25-m2 plots at 20-m intervals along 100-m transects from three piospheres (a dam, a trough, and a seasonal river). Two-way ANOVA was used to determine if there were significant differences in soil parameters between piospheric distances.

Results

Soil bulk density significantly different between piospheric distances (F = 22.25, P = 0.001) and piospheres (F = 13.10, P = 0.002), being highest at 20 m from the trough (1.1–1.21 gcm−3) relative to a similar distance from the dam (1.01–1.20 gcm−3) and the river (1.1–1.17 gcm−3). On the other hand, mean soil aggregate stability significantly increased (F = 66.89, P = 0.001) with piospheric distance, being lowest at 20 m from the trough (43.9–46.2%), the dam (43.1–48.9%), and the river (46.6–47.5%).

Conclusions

High soil bulk density and consequent low soil porosity, hydraulic conductivity, and moisture content demonstrated that grazing was high near the piospheres. It is recommended that livestock should be herded away from the piospheres after drinking water to ensure that grazing livestock spend less time near the piospheres if reduced soil compaction is to be realized. Piospheres should also be better planned and placed at landscape level to exploit landscape heterogeneity.

Keywords

Piospheres Grazing pressure Bulk density Hydraulic conductivity Rangelands

Introduction

Piospheres create an attenuating pattern form of grazing as a result of concentrated activity around them thereby developing a unique source of analysis of range trend and condition distinct from other environmental factors (Brooks and Matchett 2006; Todd 2006). Several studies have revealed that piospheres, created to curb water scarcity in most rangelands across the world, have adverse effects on both soil and vegetation (Brooks and Matchett 2006; Landman et al. 2012; Shahriary et al. 2012). Concentrated grazing around these piospheres leads to excessive trampling which causes soil compaction, increasing soil bulk density and reducing soil porosity in the process (Gomez et al. 2006; Stankovičová et al. 2008). Reduced soil pore volume impedes percolation of water through the soil hence low soil moisture levels (Chaichi et al. 2005). Compacted soils hamper air and water circulation, hinder root penetration into the soil, and limit seed germination and seedling establishment in the rangelands (Amiri et al. 2008; Azarnivand et al. 2010). Grazing animals also alter soil nutrient and chemical composition through deposition resulting from urination and defecation (Shahriary et al. 2012). Dung deposition has been known to influence soil organic carbon and total nitrogen concentrations (Han et al. 2008; Ingram et al. 2008) in addition to altering soil pH and soil microbial activity (Bell 2010; Alaoui et al. 2011).

Research has not conclusively established the effect of piospheres on physical and chemical properties of the soil. Therefore, there is need for conclusive insights on the piospheric effect on soil physio-chemical characteristics because such information is useful in developing sustainable water interventions for improved water availability in the rangelands. These findings will particularly be relevant in Kenyan rangelands where there is widespread introduction of piospheres to alleviate water scarcity (Wahome et al. 2014). Research on piospheric effect on soils has been done in parts of East Africa and Asia (Shahriary et al. 2012; Anthony et al. 2015). A study conducted by Anthony et al. (2015) in the Karamoja sub-region of Uganda showed low levels of nitrogen near the piospheres. On the contrary, Shahriary et al. (2012) reported a high concentration of nitrogen near the piospheres of Iran. The disparity observed in these findings could be because of the varying residence time spent by grazing animal around the piospheres as a result of, among other factors, different grazing regimes (Sternberg 2012), differences in piosphere types and location which determine the patterns of landscape use by grazing animals (Anthony et al. 2015) and differential response of various soil types upon exposure to grazing (Sun et al. 2011; Schrama et al. 2013). This study therefore sought to assess the effect of piospheres on soil physical and chemical characteristics in the Southern rangelands of Kenya and their predisposing factors.

Methods

Study area

The study was done in Kiserian, Kajiado County, Kenya. The County is located between 36° 5’ and 37° 5’ East and 1° and 3° South (Fig. 1). The altitude ranges from 1580 to 2460 masl. Kiserian is found in agro-ecological zone IV and is therefore a semi-arid region. Rainfall is generally low, bimodal and highly varying across the county. The average annual rainfall ranges between 327 and 1576 mm.yr. The short rains are received during November-December (30.97 ± 27.85% of the annual total) whereas the long rains fall from March to May (47.5 ± 15.06% of the annual total). The dry season spans from June to September. Droughts are long and frequent, and are majorly associated with failure of the short rains. Temperatures range between 12° C and 34° C, are coolest from July to August and hottest from November to April. Both the minimum (by 1.81 ± 0.46 °C between 1961 and 2013) and maximum (0.275 °C annual difference between 1976 and 2001) temperatures are rising in the county (Ogutu et al. 2013). The ratio of rainfall to evapotranspiration is <0.65 (Middleton and Thomas 1997). The main soil type in Kiserian is vertisol which is sticky when wet and forms large cracks when dry (Ombogo 2013). Acacia mellifera, Acacia tortilis, Acacia nubica, Acacia ancistroclada, Acacia nilotica, Commiphora riparia, Commiphora africana, and Balanites aegyptiaca are the most common species (Bekure 1991).
Fig. 1

Map of the study area

Research design

A randomized block design was used for this research with four watering points forming blocks while plots (5) were the distances (treatments) from water points. Quadrats (0.5 m by 0.5 m) were the main sampling points, placed at intervals of 20 m, 40, 60, 80, and 100 m within a 100-m transect from the watering point. Each treatment was replicated four times in the East, West, North, and South directions from the watering points. Two troughs, a dam and a river, were selected for study. The troughs were smaller in size compared to the dam and could have an impact by increasing grazing pressure around it due to greater animal concentration. The dam was large in size and thus could reduce grazing impact around it because of its large surface area that enabled animals even distribution. The river, being a natural water source, was used as a control, providing the basis for comparison between introduced systems and natural systems.

Soil sampling and laboratory analysis

Both disturbed and undisturbed soil samples were collected for analysis. Disturbed soil samples were collected using a 600 cm3 soil auger at a depth of 20 cm. Four samples were taken from the corners and centre of each quadrat and then mixed in a bucket to form a composite sample for each replication. These composites were divided into four segments where one segment was picked to form a representative sub-sample of 125 g. This procedure was repeated for all replications until a representative sample of 500 g was obtained. These representative samples were air-dried at room temperature for 72 h, ground and sieved through 2 mm mesh to remove plant roots, stones and organic residues. These samples were used for texture and pH determination. For texture and pH determination a 2 mm sieve was used because soil samples >2 g were required for analysis. Further sieving was done using a 0.5-mm sieve. This was to enhance soil sample homogeneity since <2 g of the soil sample was required for analysis (Brenner and Mulvaney 1982; Buresh et al. 1982). The samples obtained were used for organic carbon and total nitrogen determination. Undisturbed soil samples were obtained at the same depth using steel core rings for bulk density, porosity and saturated hydraulic conductivity determination.

Soil organic carbon concentration was determined using Walkley-Black wet oxidation method as described by Nelson and Sommers (1982), while total nitrogen was determined using Kjeldahl digestion method (Brenner and Mulvaney 1982). Bulk density was estimated using the core method after oven drying the soil at 1050 c for 48 h (McKenzie et al. 2004), and was calculated by dividing the mass of dry weight of soil (g) by the soil volume (cm3). From the bulk density values obtained, porosity, f, was calculated in accordance with Flint and Flint (2002) using the formula \( 1-\frac{\rho_b}{\rho_S} \) where, ρ b is bulk density and ρ s the particle density taken as 2.65 g cm−3. Particle size distribution was analyzed using the hydrometer method after dispersing soil and eliminating organic matter (Day 1965), and pH-H2O (ratio 1:2.5) by a pH meter (Mclean 1982). Aggregate stability was determined by the wet sieving method while soil moisture content was determined by gravimetric method. Saturated soil hydraulic conductivity was determined by the constant head permeameter described by Reynolds and Elrick (2002) based on application of Darcy equation. A hydraulic head difference was imposed on the soil column and the resulting flux of water measured.
$$ \mathrm{Conductivity}=\frac{V. L}{A. T. H} $$
, whereV = volume of water(Q) that flows through the sample of cross sectional area (A) in time T and H is the hydraulic head difference imposed across a sample length (L).

Statistical analysis

Statistical analyses for soil parameters were performed using GenStat 15th edition. Two-way ANOVA was used to determine if there were significant differences between means of various treatments and seasons. Tukey’s HSD test was used to compare the means. Significance was obtained at P ≤ 0.05.

Results and discussion

Soil bulk density

Table 1 shows soil bulk density, porosity, saturated hydraulic conductivity, aggregate stability, and soil moisture content at various distances from the dam, the trough, and the seasonal river. Soil bulk density significantly decreased (F = 25.07, P = 0.001) with piospheric distance and was significantly different (F = 13.10, P = 0.002) between piospheres. Troughs were smaller in size compared to the dam. Therefore, the surface area available for grazing animals was reduced leading to greater compaction. Due to the fact that the main soil type was vertisols, bulk density was significantly different between seasons (F = 5.92, P = 0.035), being higher during the wet season as a result of greater compaction. During the dry, season vertisols become hard and crack, making it difficult to compact even under heavy grazing. The interactions between treatments (distance × season × piosphere) were, however, not significant (F = 0.52, P = 0.818).
Table 1

Soil physical properties at various piospheric distances (in meters)

 

Wet season

Dry season

Piosphere

Distance (m)

BD (gcm−3)

%Porosity

K-Sat

% SA

%MC

BD (gcm3)

Porosity

K-sat

SA

%MC

Dam

20

1.20b

55.84a

0.04a

43.05a

20.90a

1.09c

58.86a

0.05a

48.86a

18.90a

40

1.07ab

59.24ab

0.07a

46.52ab

25.40ab

1.06bc

60.00ab

0.07a

50.00a

21.10a

60

1.05ab

60.00b

0.13b

50.30bc

25.80b

1.06ab

60.00ab

0.11ab

46.17a

19.60a

80

1.02a

60.37bc

0.13b

50.81c

26.20bc

1.05ab

60.37b

0.12b

50.37a

20.40a

100

1.01a

61.13c

0.33c

51.80d

30.70c

1.03a

61.13c

0.29c

51.13a

20.60a

Trough

20

1.23c

53.66a

0.11a

43.92a

18.40a

1.19c

56.23a

0.11a

46.23a

11.60a

40

1.19bc

55.09ab

0.13a

44.53a

21.90ab

1.16bc

56.43ab

0.12a

46.42a

11.90a

60

1.16ab

56.41b

0.19b

50.31b

24.20b

1.14bc

57.17b

0.20a

47.17ab

12.60ab

80

1.09ab

58.87bc

0.94c

50.31b

26.10bc

1.11b

58.11bc

0.89b

48.11ab

12.80ab

100

1.07a

59.81c

5.15d

56.98c

31.80c

0.96a

63.96c

4.68c

53.96b

16.50b

River

20

1.17c

54.72a

0.03a

47.54a

20.10a

1.2d

56.61a

0.02a

46.61a

11.50a

40

1.08ab

59.62ab

0.11ab

48.89ab

21.20a

1.07c

59.64b

0.11a

49.64b

11.60a

60

1.06b

60.37b

0.39b

49.25bc

22.60ab

1.02b

61.52bc

0.37a

51.52bc

11.90a

80

1.05b

61.51bc

4.64c

50.87c

23.10b

1.01b

61.89bc

4.68b

51.89bc

12.60ab

100

1.00a

61.89c

5.81d

55.85d

25.10c

0.89a

66.41c

5.92c

56.41c

13.70b

 

LSD

0.05

2.04

3.17

3.74

5.54

0.82

2.93

2.17

3.87

6.95

Means with the same letters within a column are not significantly different (P ≤ 0.05)

Key: BD = Soil bulk density; K-sat = saturated hydraulic conductivity; %SA = Soil aggregate stability; %MC = Soil moisture content

The results demonstrate greater compaction around zones of high intensity grazing. Arnhold et al. (2015) observed increased soil bulk densities in areas where high intensity grazing was applied in the Lambwe Valley of Kenya. Similarly, Shahriary et al. (2012) and Anthony et al. (2015) reported increased trampling and soil compaction around the piospheres of Iran and Uganda, respectively. The findings of this study also corroborate with those of Smet and Ward (2006) who reported high soil compaction levels around South African piospheres.

Saturated hydraulic conductivity and soil moisture content

Saturated hydraulic conductivity significantly increased (F = 1084.51, P < 0.001) with piospheric distance, being higher away from the piospheres. This could be attributed to high compaction levels that reduced soil porosity inhibiting percolation of water into the soil, further exacerbated by high animal trampling which reduced plant cover and exposed the soil to solar radiation triggering moisture loss through evaporation. No significant difference was observed in saturated hydraulic conductivity between the piospheres (F = 2.53, P = 0.294) and seasons (F = 1.07, P = 0.326). The interactions between treatments (distance × season × piosphere) were also not significant (F = 0.60, P = 0.762). Due to the low infiltration near the piospheres, soil moisture content was significantly lower (F = 16.94, P < 0.001) near the piospheres. No significant difference (F = 0.26, P = 0.618) was observed in soil moisture content between piospheres. Higher rainfall during the wet season increased moisture input into the soil as compared to the dry season. Consequently, soil moisture content was significantly higher (F = 256.76, P < 0.001) during the wet season. The interactions between treatments were not significant (F = 1.57, P = 0.247).

These findings corroborate with those of Zhang et al. (2006) and Azarnivand et al. (2010) that high soil compaction reduced water infiltration in the loess soils of China and the rangelands of Hosainabad, respectively. Amiri et al. (2008) also observed higher soil moisture content in light and moderately grazed lands compared to areas under heavy grazing intensity in the rangelands of Isfahan.

Soil aggregate stability

Soil aggregate stability significantly increased (F = 66.89, P < 0.001) with piospheric distance, though there was no significant difference (F = 3.43, P = 0.073) between piospheres. Because the soil class type was mainly sandy clay loam, soil was highly disintegrated during the wet season when sticky as opposed to dry season when it cracked. The aggregate stability of the soils was thus therefore significantly lower during the wet season (F = 698.41, P < 0.001). The interactions between treatments (distance × season × piosphere) were not significant (F = 1.55, P = 0.254).

Heavy grazing reduces soil aggregate stability due to high compaction. Animal trampling reduces plant cover thereby exposing the soil to direct raindrops which disintegrate soil particles (Wasonga 2009; Mugerwa and Emmanuel 2014). This could be the probable reason for the low aggregate stability observed near the piospheres. Alphayo (2015) also observed low soil aggregate stability under high intensity grazing in Laikipia County, Kenya. Similarly, Azarnivand et al. (2010) and Cournane et al. (2010) reported low soil aggregate stability under heavy grazing compared Hosainabad and Otago rangelands, respectively.

Soil textural characteristics

Table 2 shows soil textural characteristics, soil organic carbon, total nitrogen, and pH at various distances from the dam, the trough, and the river. The soil textural class was sandy clay loam across all the piospheres. Sand content was higher near the piospheres although the difference between piospheric distances was not significant (F = 2.73, P = 0.090). Besides, the difference in sand content was not significantly different between piospheres (F = 1.79, P = 0.217) and seasons (F = 0.86, P = 0.574). The interactions between treatments (distance × season × piosphere) were also not significant (F = 0.01, P = 1.000).
Table 2

Soil textural properties, organic carbon, nitrogen and pH at various piospheric distances

 

Wet Season

Dry season

Piosphere

Distance(m)

% Sand

% Clay

% Silt

% OC

% N

pH

%Sand

% Clay

% Silt

% OC

% N

pH

Dam

20

65.40c

28.50a

6.10a

2.92d

0.35b

6.46b

64.60c

28.62a

6.78a

3.01d

0.36b

6.42b

40

62.80b

32.50ab

4.70a

2.74cd

0.33ab

6.03b

62.84bc

32.57ab

4.59a

2.69cd

0.31ab

6.04b

60

62.60b

34.50bc

2.90a

2.58bc

0.24ab

5.85ab

62.71bc

34.29bc

3.01a

2.54cd

0.21a

5.88ab

80

62.60b

34.50bc

2.90a

2.56ab

0.14ab

5.85ab

61.90b

34.51bc

3.59a

2.59bc

0.21a

5.72a

100

55.80a

36.50c

7.70a

2.43a

0.09a

5.75a

55.80a

36.50c

7.71a

2.33ab

0.21a

5.66a

Trough

20

61.50b

29.50a

9.01a

3.21d

0.43c

6.32b

61.50b

32.50a

6.01a

3.14d

0.37b

6.43b

40

60.40b

31.60ab

8.02a

3.21d

0.26b

6.07ab

61.21b

34.60ab

4.19a

3.07cd

0.33ab

6.08b

60

58.70ab

34.30bc

7.01a

3.15cd

0.23ab

5.92ab

58.75ab

35.32bc

5.93a

2.96bc

0.25ab

5.94ab

80

57.64ab

35.20bc

7.16a

2.98ab

0.22ab

5.88ab

57.37ab

38.50bc

4.13a

2.89ab

0.20a

5.89a

100

55.82a

36.90c

7.28a

2.92a

0.19a

5.71a

55.72a

40.50c

3.67a

2.85a

0.18a

5.76a

River

20

64.92c

28.60a

9.80a

3.42c

0.23ab

6.37b

64.92c

26.50a

8.58a

3.24d

0.25b

6.26c

40

63.21c

29.50a

7.19a

3.02bc

0.23ab

6.05b

61.21bc

29.50ab

9.28a

2.91cd

0.24b

6.03b

60

60.30bc

32.10b

9.29a

2.92ab

0.22ab

5.87ab

59.89bc

32.10bc

9.01a

2.88bc

0.21a

5.93ab

80

58.5b

32.50bc

9.10a

2.53ab

0.21a

5.81a

57.90b

32.50bc

9.60a

2.61ab

0.23a

5.85a

100

54.70a

35.50c

9.80a

2.28a

0.21a

5.69a

54.81a

35.46c

10.69a

2.41a

0.19a

5.82a

 

LSD

7.15

6.02

5.33

0.66

0.16

0.33

5.51

4.2

4.79

0.28

0.04

0.27

Means with the same letters within the same column are not significantly different (P ≤ 0.05; Soil Textural Class = Sandy Clay Loam

Key: OC = Soil organic carbon; N = Total Nitrogen

Clay content significantly increased (F = 14.43, P < 0.001) with distance from the piospheres. The difference observed in clay content was however neither significant between piospheres (F = 0.38, P = 0.557) nor seasons (F = 0.01, P = 1.000). The interactions between the various treatments (distance × season × piosphere) were also not significant (F = 0.01, P = 1.000).

The high sand content near the piospheres could be attributed to increased degradation around the piospheres that exposed the soil to erosion. Fine particles of clay and silt were thus carried off by either wind or water erosion, justifying the significantly higher clay content observed away from these piospheres. Similar observations were made by Al-Seekh et al. (2009) who reported higher percentage of sand in grazed areas compared to un-grazed and mildly grazed sites in the rangelands of Hebron in Palestine. Pei et al. (2008) also observed higher sand content in degraded rangelands relative to enclosures of Palestine. In addition, Mohammed (2000) reported that overgrazing in the southern West Bank resulted in severe soil erosion that caused the soil to lose its silt and clay and increase sand content.

Soil organic carbon and total nitrogen

A significantly higher (F = 17.24, P < 0.001) soil organic carbon was recorded near the piospheres, being highest at a piospheric distance of 20 m. There was, however, no significant difference in soil organic carbon between piospheres (F = 0.77, P = 0.489) and seasons (F = 0.95, P = 0.520). Moreover, the interactions between treatments (distance × season × piosphere) were not significant (F = 1.38, P = 0.309).

Similarly, total nitrogen significantly decreased (F = 3.90, P = 0.037) with piospheric distance. No significant difference was however observed in total nitrogen between piospheres (F = 0.74, P = 0.503), seasons (F = 3.55, P = 0.089), and the interactions between the treatments of distance × season × piosphere (F = 0.73, P = 0.663).

It was observed that grazing livestock spent more time near the piospheres. As such, defecation and urination by these animals could have enhanced nutrient deposition and accumulation leading to soil organic carbon and nitrogen augmentation. Stumpp et al. (2005) reported high dung deposits around the Mongolian piospheres. Smet and Ward (2006) and Shahriary et al. (2012) also reported high soil organic carbon and total nitrogen around the piospheres of South Africa and Iran, respectively. Anthony et al. (2015) also observed high total nitrogen near the piospheres of Karamoja, Uganda.

Soil pH

Soil pH significantly decreased (F = 12.69, P = 0.001) with piospheric distance. No significant difference was however observed in soil pH between piospheres (F = 0.46, P = 0.874), seasons (F = 0.01, P = 1.000), and the interactions between the treatments of distance × season × piosphere (F = 0.01, P = 1.000).

High compaction near the piospheres reduced infiltration which might have hampered nutrient leaching to the lower horizons of the soil profile. According to Beukes and Ellis (2003), sodium and calcium ions accumulate at the soil surface when leaching is hindered, leading to increased soil pH. This could have been the possible reason for the significantly higher (P ≤ 0.05) soil pH observed near the piospheres. Al-Seekh et al. (2009) also observed high soil pH in high intensity grazing areas in the West Bank rangelands of Pakistan. Similarly, Smet and Ward (2006) and Anthony et al. (2015) reported high soil pH near the piospheres of South Africa and Karamoja, Uganda, respectively. Further, Shahriary et al. (2012) witnessed a decreasing trend in soil pH from Iranian piospheres.

Conclusions

The high soil compaction around the piospheres resulted in high soil bulk density and reduced soil porosity. Consequently, soil hydraulic conductivity was hampered, reducing soil moisture content. Bare grounds near the piospheres further exposed the soil to impact of raindrops, decreasing soil aggregate stability and making the soils vulnerable to erosion. This could have been the reason sand content was higher near the piospheres, because the fine clay and silt particles had been carried off by either water or wind. It is recommended that that grazing animals be placed under strict monitoring to reduce the amount of time spent around the piospheres and minimize soil compaction. Alternatively, the number of piospheres can be increased to reduce the number of animals drinking from each. Range reseeding should also be done to rehabilitate the areas that are already degraded.

Declarations

Acknowledgements

This study was funded by the International Canopy of Conservation (I-CAN) and the African Conservation Centre (ACC) through the African Drylands Institute for Sustainability (ADIS) at the University of Nairobi. We also acknowledge Anyika, F, Muliro BA, and P Kimotho for their help in soil analysis. Special thanks to the management of Mopel Ranch, Kiserian for allowing us to conduct the research within their premises.

Authors’ contributions

JS designed the study, collected the data, and participated in the data analysis; JS also drafted the manuscript write up and revised the manuscript. KO was part of the study design, data cleaning and analysis, and manuscript write up and revision. KG was part of the study design, data analysis and presentation, and manuscript editing. MJ was part of the research design and methodology, data analysis, and contributed to the results write up and manuscript editing. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests in this paper and the study as a whole.

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Authors’ Affiliations

(1)
Department of Land Resource Management and Agricultural Technology (LARMAT), University of Nairobi

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