IJERD – International Journal of Environmental and Rural Development (2020) 11-1

Research article
erdStudy of the Relationship between different Soil Properties
in Agricultural Fields, Kyee Inn Village, Myanmar
SWE SWE MAR*
Department of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: swesmooth@gmail.com

NAING NAING MOE
Department of Soil and Water Science, Yezin Agricultural University, Myanmar

KYAW NGWE
Department of Soil and Water Science, Yezin Agricultural University, Myanmar
Received 15 January 2020

Accepted 23 April 2020(*Corresponding Author)

Abstract An understanding of the physical and chemical properties of soils is necessary due
their relationship with productivity in agricultural fields. A study was conducted in 480
hectares of agricultural fields at Kyee Inn, Pyinmana Township, Naypyitaw, Myanmar todetermine the relationship between different soil properties (bulk density, soil moisture, soil
pH, cation exchange capacity, organic matter, total nitrogen, total phosphorus, and total
potassium) using Geographic Information Systems (GIS). Soil samples were collected on agrid method (300 m × 300 m) from three places in each grid at a depth of 0-15 cm. Global
Positioning System (GPS) was used to determine the coordinates of sampling points. The
collected samples were composited to 80 soil samples and analyzed. To compute therelationship between soil properties, simple linear correlation was performed using statistix
8th version. Finally, the relationship among the soil properties was shown by overlay mappingin Arc Map 10.5 with the spatial analytical function of ArcGIS software. The results revealed
soil pH was significantly and negatively correlated with total nitrogen (r = -0.412), andsignificantly and positively correlated with total phosphorus (r = 0.248). Total potassium
content was highly significant and positively correlated with soil moisture (r = 0.782). The
relationships that are commonly found among soil properties are evident in this study with apositive correlation of soil organic matter and total nitrogen (r = 0.058), a negative correlationof bulk density with soil organic matter(r = -0.191) and soil moisture (r = -0.066), a positive
correlation of cation exchange capacity with total potassium (r = 0.204), and a negative
correlation of cation exchange capacity with bulk density (r = 0.018), but these were notstatistically significant. The observed information from this study can provide a clear
understanding of the relationship among soil properties, which positively or negatively affectnutrient availability.
Keywords GIS, GPS, relationship among soil properties

INTRODUCTION
The overall productivity and sustainability of a given agricultural sector are functions of fertile soils
and productive lands (Buol et al., 2003). Food productivity and environmental quality is dependent
on the physical and chemical properties of soil, so it is very important to have a detailed knowledgeof properties of soil (Tale and Ingole, 2015).
The productive capacity of Myanmar soils is an increasing concern. Many years of poor
agriculture and land management practices has led to serious land degradation. As elsewhere, theagricultural sector’s performance in Myanmar is highly dependent upon soil quality (IFDC, 2018).
Soil quality is controlled by the physical, chemical and biological properties of soil and theirinteraction (Papendick and Parr, 1992). Therefore, it is important to maintain soil health for foodsecurity and to allow for increasing agricultural production. An understanding of physical andⒸ ISERD

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chemical conditions of any soil is essential for the proper implementation of other management
practices (Tale and Ingole, 2015). Most physical and some chemical characteristics of soils allow aranking, indicating whether an area has a high potential for agricultural production (Lelago andBuraka, 2019).Therefore a physico-chemical study of soil is very important because both theseproperties affect soil productivity (Tale and Ingole, 2015).
OBJECTIVE
The main objective of this research is to determine the relationship between different soil properties
in the agricultural fields of the study area.
METHODOLOGY
Site Description and Soil Sampling
The study was conducted in agricultural fields of Kyee Inn Village, Pyinmana Township, situated in
mid-Myanmar. The study covers a total area of 480 hectares, situated between 1942'30''-1943'40''
N and 9613'30''-1915'30'' E (Fig. 1). This area receives a mean annual rainfall of about 1420 mm
and an average temperature of 26.8°C and monsoon rice and pulses are the main crops. Soil sampling
was on a grid method (300 m × 300 m). There was a total 80 grid plots, and soil samples werecollected from three places for each grid at 0-15cm depth using GPS to determine the coordinates ofthe sampling points. All samples were taken after the harvest of the previous crops and before theland preparation for the next season to ensure base line conditions for the analysis.

Pyinmana Township

Kyee Inn Village fields(480 hectares)

Fig. 1 Location of the study site
Laboratory Analysis
Table 1 Soil parameters and analytical methods adopted for laboratory analysis

PhysicalSoil Parameters

Unit

Analytical Methods

Bulk Density

g cm-3

Core sampler method (Black, 1965)

Soil Moisture%

Gravimetric method (Black, 1965)+

ChemicalSoil pH-log [H ]1:5 (soil: water) pH meter (Hesse, 1971)-1
Cation Exchange Capacity meq100 g soil Bascomb’s method (Bascomb, 1964)
Organic Matter
Total Nitrogen
Total Phosphorus%%%

Total Potassium

mg kg-1

Walkley and Black method (Walkley and Black, 1934)
Modified Kjeldahl Digestion method(Ohyma et al., 1991)
Molybdivanado phosphoric acid method(Spectrophotometer)
Atomic Absorption Spectrophotometer (AAS) (Flame)Ⓒ ISERD

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The collected soil samples were composited for each grid. The samples were air-dried, ground, andsieved. The soils’ pH levels, organic matter content, cation exchange capacity, bulk density, soil
moisture, total content of nitrogen, phosphorus, and potassium were measured. Analysis was done at
the laboratory of the Department of Soil and Water Science, Yezin Agricultural University. The
analytical methods used for conducting this analysis are described in Table 1.
Statistical Analysis and Overlay Mapping
The laboratory results of all soil properties were subjected to correlation analyses to detect functional
relationship among soil parameters using statistix (8th version). Firstly, the interpolation maps weregenerated using an inverse distance weighting (IDW) method in ArcGIS software version 10.5 andthen some of the more significant relationships among soil properties were able to be shown by
overlay mapping.
RESULTS AND DISCUSSION
Correlation Matrix of Soil Properties
The correlation coefficient values of soil parameters viz; soil pH, cation exchange capacity, bulk
density, soil moisture, soil organic matter, total content of nitrogen, phosphorus and potassium were
determined for the surface soils (0-15 cm) and are presented in Table 2.
Table 2 Correlation between the different soil parameters under study
pH
CEC
OM
TN
TP
TK
BD
SM

pH
1
0.014
-0.025-0.416**
0.242*
0.047
0.052-0.047

CEC

OM

TN

TP

TK

BD

1
0.035
0.145
-0.0360.204-0.0180.352**

1
0.058-0.1070.238*-0.191
0.162

1
0.216
0.045
0.092
0.182

1
-0.217
0.096-0.1171-0.161
0.782**

1-0.066SM

1*Correlation is significant at the 0.05 level; **Correlation is significant at the 0.01 level
CEC: cation exchange capacity, OM: organic matter, TN: total nitrogen, TP: total phosphorus, TK: total potassium,
BD: bulk density, SM: soil moisture

Relationships between Soil Parameters
In Fig. 2 (a), the correlation coefficient reveals soil pH shows a highly significant but negative
correlation with total nitrogen content (r = -0.416). This suggests that pH accounts for about 16.96%
of the total variability in total nitrogen. Similarly, with an increase in pH, total nitrogen decreases
progressively and vice-versa. In addition to this, with an increase on soil pH by one unit, total
nitrogen decreases by 0.098 unit and vice-versa. According to major crops growing in this area,pulses may take much cations and thus may cause lowering soil pH, but growing of pulses on every
year may also lead to encourage total nitrogen content by fixing nitrogen of pulses.
Similar results were obtained by Singh and Mishra (2012). Similarly, Khadka et al. (2016) statedthat total nitrogen content was significantly and negatively correlated with soil pH. Xue et al. (2013)
also reported that the correlation between the soil nitrogen forms and soil pH was negative. However,a significant and positive correlation between soil pH and total nitrogen was obtained by Athokpam etal. (2013), and a non-significant relationship was obtained by Dhamak et al. (2014).
Figure 2 (b) illustrated that soil pH is significantly and positively correlated with total phosphorus(r = 0.242). For these parameters, the results are in harmony with the findings of Athokpam et al.
(2013). But non-significant correlation between them was observed by Ogaard (1994).
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According to the literature, soils with inherent pH values between 6 and 7.5 provide ideal
conditions for phosphorus availability, while pH values below 5.5 and between 7.5 and 8.5 may limit
phosphorus availability to plants due to fixation by aluminum, iron, or calcium. The observed pH
values of this study ranged from 5.48 to 7.58, and therefore, the soil test phosphorus content would
be expected to be significantly and positively correlated with this observed pH range.

Fig. 2 Relationship between soil pH with (a) total nitrogen and (b) total phosphorus
The total potassium content is positively correlated with soil moisture, to a highly significant
level, expressed in the correlation matrix of r = 0.782 (Fig. 3 (a)). The results were in conformity
with those of Singh and Singh (2004), and Zeng and Brown (2000) who found a positive correlation
between moisture content and potassium content in soils. Total potassium also shows a significant
positive correlation with soil organic matter and a significant negative correlation with total
phosphorus. In addition, it shows a non-significant positive correlation with soil pH, total nitrogen,cation exchange capacity, and a negative correlation with soil bulk density. This means that the total
potassium content may increase with increases in pH, cation exchange capacity, soil organic matter,
total nitrogen, and may decrease with increases in bulk density and total phosphorus and vice versa.

Fig. 3 Relationship between soil moisture with (a) total K and (b) CEC
The commonly found relationships between soil properties is also observed in non-significant
correlations, such as the positive correlation of soil organic matter and total nitrogen (r = 0.058), thenegative correlation of soil bulk density with soil organic matter(r = -0.191) and soil moisture (r = 0.066), and the positive correlation of the soil’s cation exchange capacity with total potassium (r =0.204) and the negative correlation of cation exchange capacity with bulk density (r = -0.018). A
significant and positive correlation between cation exchange capacity and soil moisture content (r =0.352) is also observed (Table 2 and Fig. 3 (b)). According to topographic position of this area,
relatively higher soil moisture content was found on slightly lower elevation of southwest and also
northeast portion as located nearby a small stream on this side. The cation exchange capacity andtotal potassium were obviously increased with high moisture content portions of this area.
The negative correlation between bulk density and soil moisture content suggests two
possibilities that soil moisture content declines as bulk density increases due to less storage space orⒸ ISERD

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that soil moisture content is sufficient for the soil to resist compaction (Carter and Shaw, 2002). Thisis consistent with studies that have examined bulk density and soil moisture content (Hill and Sumner,1967). Many researchers (Askin and Ozdemir, 2003; Morisada et al., 2004) obtained the relationshipbetween organic matter and bulk density of soils all indicating a strong correlation between them.
Bulk density tends to decrease as a soil’s organic matter concentration increases (Curtis and Post,
1964).
Overlay Mapping of Significant Relationships of Soil Parameters
Figure 4 (a) and (b) display an overlay map showing significant relationships of soil pH with the total
contents of nitrogen and phosphorus in the study area. It can be seen clearly that the areas with higher
total nitrogen content correspond to areas largely occupied ‘Moderately acidic’ soil pHs in the study areaand vice versa. The total phosphorus content, however, is positively distributed in all classes of soil pHof the study area. The overlay map of the significant positive relationships of soil moisture content with
CEC and total potassium are shown in Fig. 5 (a) and (b). It is obvious that the variation of CEC and total
potassium is positively coincidental with areas with a higher amount of soil moisture content.

Fig. 4 Overlay maps of soil pH with (a) total N and (b) total P

Fig. 5 Overlay maps of soil moisture with (a) CEC and (b) total K
CONCLUSION
Results from correlation analysis show that many of the soil parameters are interrelated with eachother but often these relationships are non-significant. Since this study area occupies agricultural
fields and thus some soil properties can be reflected by many practices of crops growing such as
tillage systems, application of inputs, cropping patterns and so on. The results of this study havediscussed and suggested with farmers as field day for proper implementation of their soil
management practices. Finally, this study clearly illustrates that an understanding of soil properties
and their inter-relationship, would prove to be useful for the development of an effective soil
management plan allowing for efficient utilization of limited agricultural land resources.

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ACKNOWLEDGEMENTS
We would like to gratefully acknowledge the Japan International Cooperation Agency (JICA), who
supervised the Project for Capacity Development of Yezin Agricultural University, and who haveprovided technical and financial support for this study.
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