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Mohamed AW, SF J Global Warming, 2017, 1:2

SciFed Journal of Global Warming Open Access

Physical and Chemical Indicators Effects of Soil Quality on French Beans Productivity in Egypt Mohamed SZ, Ahmed HR, Ali MAM, Mohamed AW

*

Soil and water department, Faculty of Agriculture, Al Azhar University, Cairo, Egypt

*

Abstract This study was carried out in El-Sadat center, Al-Monufyia Governorate during 2013/2015 seasons, to study the relationship between physical and chemical soil quality indicators on French bean productivity. Eight soil samples of six locations have been sampling to a depth of 30 cm. All samples collected for each region separately, and analyzed for fourteen physical indicators viz. CS, FS, sand clay, BD, real density, hydraulic conductivity, field capacity, wilting coefficient, slowly drainable porosity, quickly drainable porosity, mean Wight diameter, water holding porosity and total porosity; as well as, seven chemical indicators viz. pH, electric conductivity, organic matter, cation exchange capacity, calcium carbonate, available potassium and total nitrogen. Wheat productivity is the basic factor in determining soil quality (SQ) using parameters or soil quality indicators. Results showed that, the main soil indicators which limitate soil quality for french bean productivity crop were silt (r = -0.770 and rw= 19.6 %); fine sand (r = 0.647 and rw= 16.3 %); CaCO3 (r = -0.605 and rw= 10 %); slowly drainable pores (r = 0.53 and rw= 6.7 %); clay content (r = 0.52 and rw= 6.2 %); water holding capacity (r = 0.50 and rw= 6 %) and hydraulic conductivity (r = 0.50 and rw= 3.4 %).

Keywords

Soil Quality; Soil Quality Indicators; French Bean

1. Introduction

The soil is one of the most important environmental factors, it's considered as the main source in providing essential plant nutrients, water reserves and a medium for plant growth. Soil quality is defined as the capacity of a soil function within an ecosystem and land use boundaries, to sustain biological activity, maintain environmental quality, and promote plant, animal, and human health [1]. Soil quality (SQ) depends partially on the natural composition of the soil, and on changes related to human use and management. Soil quality indices are considered the most common methods for soil quality evaluation due to ease of use, flexibility and quantification. These indices represent the cumulative effects of different soil properties (physical, chemical and ecological) as an index from the role of each parameter in soil quality [2-4] SF J Global Warming ISSN:XXXX-XXXX SFJGW, an open access journal

outlined five soil functions that may be used as the criteria for judging the soil quality: to hold and release water to plants, streams, and subsoil; to hold and release nutrients and other chemicals; to promote and sustain root growth; to maintain suitable soil biotic habitats; and to respond to management and resist degradation. *Corresponding author: Mohamed AW, Agronomy department, Faculty of Agriculture, Al Azhar University, Cairo, Egypt. E-mail: wanas. [email protected] Received July 15, 2017; Accepted October 10, 2017; Published October 23, 2017 Citation: Mohamed AW (2017) Physical and Chemical Indicators Effects of Soil Quality on French Beans Productivity in Egypt. SF J Global Warming 1:2. Copyright: © 2017 Mohamed AW. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Volume 1 · Issue 2 · 1000008 page 1 of 7

Citation: Mohamed AW (2017) Physical and Chemical Indicators Effects of Soil Quality on French Beans Productivity in Egypt. SF J Global Warming 1:2.

As a complex function state, soil quality cannot be measured directly, but may be inferred from soil quality parameters. Soil quality parameters are measurable properties of soil or plants that provide clues about how well the soil can function. Soil quality parameters must provide a sensitive and timely measure of the soil’s ability to function and be able to identify whether the change in soil quality is induced by natural processes or it occurs because of management [1]. Soil quality parameters can be divided into physical, chemical, and biological parameters such as available water holding capacity, relative field capacity to water saturation, macroporosity, bulk density, cation exchange capacity, contaminant presence, electrical conductivity of soil: water extracts, exchangeable sodium, pH, available potassium, and available phosphorus etc [5]. Several authors have proposed various soil quality parameters that can be easily measured and they are sensitive to change of soil condition and therefore, they must be able to identify appropriated sustainable soil conditions [6-10] established a soil quality index based on twenty-six soil physical, chemical and microbiological properties in a paddy soil of China by using both Traditional Dimension System (TDS) and Multidimensional System (MDS) methods. In general, most researchers used a set of predefined soil parameters indicators suggested by [7,11] to assess soil quality and sustainability of the agricultural land. The process of degradation in arid and semiarid regions such as Egypt has intensified due to lack of farmers’ knowledge of agricultural soil conditions, and lack of proper equipment's. Under these conditions, the soil quality is often influenced by limiting factors such as high temperature, poor soil fertility, low available water holding capacity (AWHC), soil organic carbon (SOC) and high concentrations of salt and pH. A soil’s physical properties affect crop performance in many ways. Plant health and growth are heavily influenced by the soil’s texture, bulk density (a measure of compaction), porosity, water-holding capacity, and the presence or absence of hard pans. These properties are all improved through additions of organic matter to soils. Soil physical properties also influence soil-water and plant-water relationships. The partitioning of water at the soil surface is important because it determines both the quantity and the quality of surface and groundwater, as well as the amount of water that will be available for plant growth. When soil quality parameters are in the optimum range, crop yield response would be optimal (maximum SF J Global Warming ISSN:XXXX-XXXX SFJGW, an open access journal

obtainable yield) [5]. Therefore, the objective of this research is to estimate soil quality indicators in some soils of Monufyia Governorate and study relationship with alfalfa productivity during 2013/2015 seasons.

2. Materials and Methods

The current study was carried out to estimate soil quality indicators (physical and chemical) in El-Sadat area, Monufyia Governorate during winter seasons of 2013 to 2015 and their relationships with French beans productivity. The present materials and methods are introduced under the follows topics; Map of locations; Data collection; laboratory analysis; and statistical analyses.

2.1. Maps of Locations

The studied seven locations located within ElSadat area, Monufyia Governorate between 30°40'13" and 31°50'12" eastern longitudes, and 30°22'50" and 31°31'10" northern latitudes, shown in Figure 1. Figure1: Map of the Studied Locations

2.2 Laboratory Analysis

The soil functions are difficult to measure directly, so they are usually assessed by measuring soil quality indicators. There are two main categories of soil indicators: physical and chemical. Soil physical parameters: Particle size distribution, particle density, bulk density, total porosity, and hydraulic Volume 1 · Issue 2 · 1000008 page 2 of 7


conductivity coefficient were determined according to [12]. Field capacity, wilting coefficient, available water or water holding capacity, quickly drainable pores and slowly drainable pores were determined from moisture characteristic curve (pF curve) according to [13]. Aggregates stability was estimated aggregate size distribution by dry sieving to calculate the mean weight diameter (MWD) according to [19] as follows: MWD = ∑ Xi Wi where: I = 1, X = mean diameter of the considered fraction mm, W = weight of the dry sieving fraction g. Soil chemical parameters: pH, EC, organic matter, calcium carbonate, cation exchange capacity, available potassium and total nitrogen were determined according to [14].

Data in Table1 showed that, the texture of the studied soil samples were as follows: clay loam, sandy clay loam, loam and sandy loam. These locations showed variations in soil texture class compared the previous locations of wheat and alfalfa crops. The values of soil bulk density ranged between 1.22 to 1.32Mg m-3; real density ranged between 2.50 to 2.75 Mg m-3; total porosity ranged between 50.2 to 55.22%; and hydraulic conductivity between 4.2 to 12.5cm/h. For the soil moisture constants, the values of the studied samples ranged from 20.90, 7.8 and 10.3 to 39.79, 24.89 and 15.2 for field capacity, available water and wilting coefficient, respectively.

2.3Statistical Analyses

The values of soil chemical indicators were shown in Table 2. It noticed that, the electric conductivity values ranged between 0.22 to 0.88 dS m-1; pH values ranged between 7.14 to 7.84. Also, the result in Table 3 appeared that, the cation exchange capacity lied between 9.7 to 40 C mol/kg; calcium carbonate content ranged from 1.33 to 3.65 %; organic matter ranged between 0.16 to 2.23%; total nitrogen ranged from 21 to 49 mg /kg; and available potassium lied between 17.55 to 195 mg/kg. The variation in values of soil chemical properties may be due to the management processes difference of these locations such as organic manure and crop rotation.

SYSTAT Statistical software [15] was used for all Statistical analyses. Soil properties were plotted with each other and with crop productivity variables to determine the nature of these relationships. Linear equation was used to determine the relationship among soil indicators and alfalfa productivity. All values are presented as means standard deviations of eight fields or laboratory measurements. Significant differences between treatments were analyzed using correlation matrix test in SPSS version 21 [15]. Treatment differences were deemed significant at p>0.05. The principal component analysis (PCA) was performed in SPSS version 21. Descriptive statistics and linear regressions were computed in [16] and all the figures were obtained using [17].

3. Results and Discussion 3.1. Soil Physical Indicators of the Studied Locations

3.2. Soil Chemical Indicators of the Studied Locations

3.3. The Correlation Matrix between Indicators for the Studied Locations

Soil

Correlation in Table 4, show a positive significant correlation between coarse sand and HC (r = 0.76*); fine sand and both of SDP (r = 0.60*), HC (r = 0.79*) and pH (r = 0.66*); silt and both of OM (r = 0.84**), CEC (r =

Table 1: Soil Physical Indicators of the Studied Locations Location

1

Particle size distribution (%) C.S

F.S

Silt

Clay

5.90

25.10

34.00

35.00

TC

B.D (Mg.m-3)

RD (Mg.m-3)

T.P (%)

CL

1.32

2.71

51.29

2

5.85

42.15

25.00

27.00

SCL

1.25

2.68

55.22

3

6.80

44.20

20.00

29.00

SCL

1.32

2.69

50.92

H.C (cm/h)

4.5

MWD (mm)

Soil moisture constants

QDP

SDP

8.0

3.5

15.0

11.0

4.18

13.0

28.40

15.40

19.3

4.1

1.43

12.10

27.52

15.40

2.25

W.C % 14.90

F.C % 39.79

A.W % 24.89

4

4.70

36.30

44.00

15.00

L

1.25

2.50

52.00

6.6

22.0

5.2

2.18

10.50

24.80

14.30

5

10.60

69.40

9.00

11.00

SL

1.22

2.62

53.43

12.5

21.6

10.6

1.14

10.30

20.90

10.90

6

6.60

65.40

6.00

22.00

SCL

1.31

2.75

52.36

6.5

22.0

7.6

0.92

15.20

23.00

7.80

7

8.00

64.95

12.68

14.37

SL

1.27

2.55

50.20

8.5

13.2

7.6

0.92

11.41

29.40

17.66

8

13.60

65.40

6.00

15.00

SL

1.30

2.64

50.76

9.8

12.4.0

7.8

0.82

10.90

29.60

18.70

Where: C.S = cores sand, F.S = fine sand, F.C = field capacity, WC = wilting coefficient, AW = available water, TC = textural class, B.D = soil bulk density, RD= real density particles, H.C = hydraulic conductivity, T.P = total porosity, MWD= mean wight diameter, QDP= quickly drainable pores, SDP= slowly drainable pores, L= loam, SCL= sandy clay loam, CL = clay loam, SL = sandy loam

SF J Global Warming ISSN:XXXX-XXXX SFJGW, an open access journal



Table 2: Some Soil Chemical Indicators of the Studied Locations Soluble ions

pH (soil suspension) (1:2.5)

Location

EC dSm

Cations (meq/L)

-1

Ca+2

Mg+2

Anions (meq/L)

Na+

K+

CO3-

HCO3-

Cl-

SO4-2

1

7.14

0.85

2.90

1.80

2.90 0.90

----

2.90

2.95

2.65

2

7.45

0.33

1.10

0.50

1.45 0.40

----

1.20

0.60

1.65

3

7.60

0.88

2.85

1.95

2.80 1.10

----

2.95

2.97

2.78

4

7.50

0.25

0.80

0.30

1.10 0.44

----

0.90

0.65

1.09

5

7.65

0.27

0.64

0.20

1.40 0.40

----

1.25

0.70

0.69

6

7.84

0.22

0.60

0.30

1.10 0.37

----

1.25

0.60

0.52

7

7.40

0.26

0.70

0.30

1.30 0.34

----

1.30

0.66

0.68

8

7.53

0.23

0.50

0.20

1.20 0.40

----

0.80

0.45

1.05

Table 3: Some Soil Chemical Indicators of the Studied Locations location

CEC (cmol/kg)

Ca CO3 (%)

O.M (%)

Av-k (mg/kg)

T.N (mg/kg)

1

37

2.40

2.10

195.00

21.0

2

40

2.40

2.00

74.10

21.0

3

40

2.40

2.20

195.00

49.0

4

38

3.57

2.23

91.65

21.0

5

29

2.43

1.20

54.60

21.0

6

10

1.33

0.16

35.10

24.5

7

28

1.75

1.20

50.70

21.0

8

9.7

3.65

0.20

17.55

25.2

0.75*) and Av-K (r = 0.58*); clay content and both of F.C (r = 0.67*), WC (r = 0.76*), EC (r = 0.82**) and Av-K (r = 0.77*); total porosity and both of SDP (r = 0.68*) and MWD (r = 0.71*); quickly drainable pores and pH (r = 0.83**); water holding capacity and FC (r = 0.94*); field capacity and both of EC (r = 0.58), Av-K (r = 0.55); MW and both of OM (r = 0.63*) and CEC (r = 0.65*); EC and Av-K (r = 0.96**), TN (r = 0.62*); organic matter and both

of CEC (r = 0.98**), Av-K (r = 0.76*),and cation exchange capacity and Av-K (r = 0.71*).

3.4. Descriptive Statistics of Soil Quality Indicators under the Studied Locations

The descriptive statistics data of 21 soil quality indicators have been presented in Table 4. It is revealed that weight and relative weight of soil indicators and

Table 4: Correlation matrix of soil quality indicators and French beans productivity (n = 21) CS

YIELD

FS

SILT

CLAY

R.D

B.D

T.P

Q.D.P

CS

1.00

YIELD

0.43

1.00

FS

0.69

0.647*

1.00

SILT

-.720

-0.77

-.898**

CLAY

-.526

0.52

-.729*

0.37

1.00

R.D

-.019

0.35

-.051

-.346

0.67*

1.00

B.D

-.070

-.152

-.254

-.075

0.64*

0.60*

1.00

T.P

-.230

0.14

-.074

0.10

0.06

0.21

-.574

1.00

Q.D.P

-.160

0.06

0.32

-.088

-.434

-.157

-.384

0.30

1.00

S.D.P

0.38

0.53

0.60

-.499-

-.491

-.025

-.694*

0.68

0.21

S.D.P

W.H.C

F.C

W.C

H.C

M.W.D

E.C

PH

OM

CACO3

CEC

AV-K

T.N

1.00


1.00



W.H.C

0.02

0.50

-.563

0.39

0.45

-.055

0.36

-.416

-.909**

-.481

1.00

F.C

-.145

-.274

-.624*

0.37

0.67

0.22

0.55

-.349

-.917**

-.529

0.94**

1.00

W.C

-.452

0.20

-.327

0.02

0.76

0.79

0.63*

0.10

-.270

-.257

0.10

0.43

1.00

H.C

0.76

0.50

0.79*

-.572

-.872**

-.385

-.562

-.037

0.25

0.55

-.325

-.523

-.646*

1.00

M.W.D

-.552

-.307

-.651*

0.60*

0.48

0.08

-.257

0.71

-.178

0.19

0.20

0.25

0.21

-.563

E.C

-.318

-.068

-.670*

0.37

0.82

0.46

0.59

-.199

-.311

-.675*

0.50

0.58

0.38

-.619*

0.18

1.00

PH

0.20

0.32

0.66*

-.571

-.428

0.18

-.113

0.20

0.83**

0.37

-.935**

-.878**

-.094

0.35

-.401

-.414

1.00

OM

-.668

-.361

-.818**

0.84**

0.47

-.232

-.132

0.17

-.072

-.382

0.37

0.31

-.065*

-.555

0.63*

0.59

-.545

1.00

CACO3

0.30

-.605

-.253

0.39

-.244

-.483

-.178

-.081

-.082

-.139

0.31

0.08

-.603*

0.16

0.12

-.026

-.236

0.19

CEC

-.631

-.214

-.727*

0.75*

0.42

-.230

.235

0.25

-.056

-.242

0.32

0.25

-.101

-.481

0.65*

0.53

-.514

0.98**

0.11

1.00

AV-K

-.519

-.205

-.786*

0.58

0.77

0.25

0.45

-.187

-.242

-.730**

0.50

0.55

0.31

-.671*

0.27

0.96**

-.495

0.76

-.013

0.71*

1.00

T.N

-.031

0.12

-.090

-.098

0.35

0.30

0.51

-.314

0.21

-.436

-.063

-.056

-.007

-.332

-.196

0.62

0.26

0.22

-.033

0.19

0.53

1.00

1.00

1.00

* Correlation is significant at P < 0.05 level. ** Correlation is significant at P < 0.01 level

the importance of each indicators contribution to soil quality are usually different, and can be indicated by a weighting coefficient. The weights and relative weights of eachparameter calculated according to [18]. The results in Table 5 and Figure 2, revealed that silt represent the important relative weight (19.6%) followed by fine sand,CaCO3, slowly drainable pores, clay, water holding capacity and hydraulic conductivity (16.3, 10.0, 6.7, 6.2, 6.0, 3.4% respectively). Then come, coarse sand (3.4), quickly drainable pores (3.2), wilting coefficient (2.2), cation exchange capacity (1.9), total porosity (1.9) and finally other soil indicators.

OM

1.41

0.86

0.010

1.0

F.C

27.92

0.79

0.009

0.9

R.D

2.64.

083

0.009

0.9

B.D

1.28.

037

0.004

0.4

Av-k

0.45

0.35

0.004

0.4

E.C

0.39

0.29

0.003

0.3

pH

7.51

0.20

0.002

0.2

T.N

0.36

0.13

0.001

0.1

Figure 2: Contribution of Important Soil Quality Indicators in Bean Productivity

Table 5: Descriptive statistic of soil quality parameters under study locations (n=21) Descriptive Statistics Parameter

Mean

Std. Deviation

W= St D. of indicator/ sum. of St. D

Relative weight

Silt

51.61

16.71

0.196

19.6

F.S

19.58

13.94

0.163

16.3

CaCO3

21.04

8.56

0.100

10.0

S.D.P

2.49

5.72

0.067

6.7

Clay

7.17

5.29

0.062

6.2

W.H.C

15.67

5.15

0.060

6.0

H.C

7.16

2.92

0.034

3.4

CS

7.75

2.95

0.034

3.4

Q.D.P

16.68

2.77

0.032

3.2

W.C

12.28

1.91

0.022

2.2

CEC

28.96

1.65

0.019

1.9

T.P

52.02

1.64

0.019

1.9

M.W.D

1.73

1.13

0.013

1.3


These results and interpretation in harmony with[19] who stated that the soilquality is measured by soil indicatorssuch as air capacity, available water holdingcapacity, relative field capacity to watersaturation, macro porosity, bulk density, soil organic carbon, structuralstability index. Also, they reported that soil organic matter accumulation can improve soil quality. Based on the results of relative weight values, the properties that explained the greatest proportion of the total variance in the present study included silt, fine sand,CaCO3, slowly drainable pores, clay, water holding capacity and hydraulic conductivity. These soil characteristics seem to be the suitable parameters for assessing the effects of soil indicators on bean yield in the study region.



Figure 3: Eigen Values of the Correlation Matrix – The Cattell Test

3. Singh MJ, Khera KL (2009) Physical indicators of soil quality in relation to soil erodibility under different land uses. Arid Land Res Manag 23: 152-167. 4. Larson WE, Pierce FJ (1991) Conservation and enhancement of soil quality. In: Evaluation for Sustainable Land Management in the Developing World (Dumanski J Ed). Proceedings of the International Workshop, Chiang Rai, Thailand Technical papers Int. Board for Soil Res. and Management, Bangkok, Thailand 2: 175-203.

3.5: French Beans Productivity as Affected by Soil Quality Indicators

The important function of soils is crop productivity, which is one of the good ways to evaluate the soil quality. In the present investigation, high and significant correlations were observed between some soil indicators and French beans yield. The data presented in Tables 4, 5 showed a significant correlation between french beans yield and some soil indicators (P < 0.05) of the selected 21 soil indicators. The highest correlation and relative weight were observed with the following parameters: silt (r = - 0.77 and rw = 19.6 %), fine sand (r = 0.647 and rw = 16.3 %),CaCO3 (r = - 0.605 and rw = 10.0 %), SDP (r = 0.53 and rw = 6.7 %), clay content (r = 0.52 and rw = 6.2 %), water holding capacity (r = 0.50 and rw = 6.0 %), and hydraulic conductivity (r = 0.50 and rw = 3.4 %), compared with the other indicators. These results in agreement with those of [20], they observed that soil quality decreased due to long term tobacco production because of decreasing in soil aggregate stability, available water capacity and cation exchange capacity [21].

4. Conclusion

From the above-mentioned results, it can be concluded that there are main soil parameters more effective on the productivity such as silt content, fine sand content, CaCO3, SDP, clay content, WHC, and HC are responsible on most other soil properties and consequently soil productivity.

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