IJERD – International Journal of Environmental and Rural Development (2021) 12-1

Research article
erd

Assessment of Soil Properties using GIS Technologies in a
Selected Area in Myanmar
HLA MOE KHAING*
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar
Email: hlamoekhaing45@gmail.com

KYAW NGWE
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar

SWE SWE MAR
Dept. of Soil and Water Science, Yezin Agricultural University, Myanmar

HTAY HTAY OO
Dept. of Agronomy, Yezin Agricultural University, Myanmar
Received 24 December 2020

Accepted 12 May 2021(*Corresponding Author)Abstract This study evaluates selected soil properties and maps these under different cropping
patterns of Sipintharyar village, Zeyarthiri Township, Myanmar. A total of 130 soil samples
were collected at a depth of 0-20 cm, with sample points selected using a Global Positioning
System. Soil fertility maps were created using Kriging interpolation in ArcGIS software 10.5.
Soil textures in the study area were loam, loamy sand, clay loam and sandy loam. The soils
were strongly acidic to moderately alkaline and contained a very low status of soil organicmatter (84%), available potassium (89%) while total nitrogen was at a medium (56.92%) level.
The coefficient of variation (CV) showed that soil pH was the least variable (9.91%) parameter
examined, with mean values ranging from 4.95 to 8.47, while available potassium was highlyvariable (86.95%) with content values ranging from 1 to 578 ppm. Other selected properties
such as bulk density, total nitrogen, CN ratio, electrical conductivity and soil organic matter
were found, respectively, to have the following variabilities; 11.68%, 33.84%, 34.86%, 72.23%
and 52.07%. Levels of soil organic matter were highly significant, and positively correlatedwith total nitrogen and available potassium. These variations in soil properties are probably
related to the different cropping patterns and fertility management practices employed in thestudy area.
Keywords soil properties, global positioning system, Arc GIS

INTRODUCTION
The agriculture sector in Myanmar accounted for 30% of gross domestic product, 60% of employment,
29% of value addition, and 23% of exports in 2016 (Agriculture Guide, Myanmar, 2018). Therefore, anevaluation of the fertility status of soils of an area is an important aspect in promoting sustainableagricultural production (Singh and Mishra, 2012). In Myanmar, the major soil fertility issues areunderstood only at the higher level with limited information at local levels. Soil fertility andproductivity have a direct relationship in ensuring food security for the increasing world population.
The optimum plant growth and crop yield might be influenced by not only the total amount of nutrients
present in the soil at a particular time, but also on the availability of these, a process controlled byphysicochemical properties (Bell and Dell, 2008). In addition, nutrient properties vary over time andspace. Wakene and Heluf (2003) stated that the periodic evaluation of important soil properties andⒸ ISERD1

IJERD – International Journal of Environmental and Rural Development (2021) 12-1

their responses to changes in land management is required to apply proper soil fertility management
techniques, and to improve and conserve the fertility and productivity of soils. Additionally, the spatial
variability of soil fertility and its classification can be mapped by applying GIS, to clearly show thespecific locations where attention is required with respect to management of plant nutrients (Jatav et al.,2013). Currently, there is little information on the spatial variability of these soil fertility parametersand very few efforts in generating soil fertility maps for the agricultural soils in Myanmar. Furthermore,
without detailed soil related information at a specific local level, sustainable crop production cannot beachieved. From this, understanding the spatial variability of soil fertility in specific agricultural fields isessential in optimizing the application of agricultural inputs and ensuring maximum crop yields inMyanmar.
OBJECTIVES
The main objective of this research is to evaluate the spatial variability of selected soil properties andto map soil fertility status under different cropping patterns in the study area.
METHODOLOGY
Study Area, Soil Sampling and Data Collection
Total study area is 0.6 km2 and it is located at Sipintharyar village (19˚44'43" N - 19˚45'22" N and96˚17'42" E - 96˚18'02" E), Kyidaung village Tract, Zeyarthiri Township, in central Myanmar (Fig. 1).
It receives a mean annual rainfall of about 1265 mm and has an average temperature of 26.8°C.
The grid map preparation was created using ArcGIS (Ver.10.5) software for the collection of soil
samples, in March 2020. The soil samples were collected after harvesting of crops and during thecultivation of horticultural crops, with samples from 300 m × 300 m grid points for determining soil
texture, and 75 m × 75 m for the other soil parameters, and at 0-20 cm depth by using auger and handhoe from the selected points (Fig. 2). Ten soil samples were taken from each grid to form a composite
a representative sample, with a 130 soil samples in total. After labelling and packing, samples weretested at the laboratory of the department of Soil and Water Sciences, Yezin Agricultural University,Myanmar. After air drying, the samples were gently crushed, sieved (2 mm) and properly stored foranalysis. Furthermore, interviews were conducted with farmers using structured questionnaires toidentify variations in management practices. Sixty farmers were selected as sample respondents andinterviewed. The data collected related to method of land preparation, fertilizer management and thecropping patterns practiced by the respondents in the study area.

Fig. 1 Loation of study area

Fig. 2 Soil sampling grid points

Laboratory AnalysisⒸ ISERD

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The composited soil samples were analyzed to determine soil texture, adopting a pipetted method(Ryan et al., 2001), with bulk density determined by gravimetric method. Soil pH and electricalconductivity were measured in an extract, with a soil: water (1:5) suspension, using a digital pH-meterand EC meter (Hesse, 1971). Total nitrogen was determined by the Modified Kjeldahl Digestion
method (Ohyma et al., 1991). Heanes (Rayment and Lyons, 2011) wet digestion method was used todetermine soil organic matter and available potassium was determined by Atomic Absorption
Spectrophotometer.
Statistical Analysis and Soil Fertility Mapping
The soil results were analyzed by using statistix (8th version). The coefficient of variation (CV) was
also determined to measure nutrient variability according to Ogunkunle (1993), where, soil properties
having a CV between 0 and 15% are considered least variable, those with 15 and 35%, moderatelyvariable, and larger than 35%, highly variable. These results were used to create soil fertility maps. Thesoils were classified into different categories of fertility i.e., very low, low, medium, high, and very
high on the basis of the measured soil parameters. Nutrient index was also calculated by Ramamoorthy
and Bajaj (1969).
RESULTS AND DISCUSSION
Spatial Distribution in Soil Properties
The results of the descriptive statistics of soil samples are presented in Table 1. There is a large
variation in soil properties in the study area. Electrical conductivity shows the highest variability with
72.23% CV, followed by soil organic matter, with a CV value of 52.07%. However, least variability
across sample areas was found for bulk density and soil pH, with CV values of 11.68%and9.91%respectively. Moderate variability occurred for the CN ratio and total nitrogen, which have CV
values of 34.86% and 33.84%, respectively.
Table 1 Descriptive statistics of soil physicochemical parameters
Unit*
Minimum
Maximum
Sand%
24.44
78.80
Silt%7.72
33.36
Clay%
13.06
42.20
Bulk Density
g cm-3
0.80
1.51
pH
4.95
8.47
Electrical ConductivitydS m-1
0.03
0.72
Organic Matter%
0.31
3.55
Total Nitrogen
%
0.11
0.39
Available Potassium
ppm
1.00
578.00Carbon : Nitrogen (CN ratio)
1.19
7.85
SD: Standard Deviation, CV: Coefficient of Variation;* Units represent for the columns of minimum, maximum and mean in the table
Variables

Mean
56.38
17.04
26.58
1.21
6.94
0.17
1.41
0.23
87.08
3.43

SD
18.04
8.23
11.04
0.14
0.69
0.12
0.73
0.08
75.721.20

CV%
31.99
48.29
41.53
11.68
9.91
72.23
52.07
33.84
86.95
34.86

Soil Texture and Bulk Density (BD)
Sandy loam was the dominant soil textural class throughout the study area (Fig. 3), indicating asimilarity in soil forming processes and parent materials. According to USDA soil texture classification
system, six points in a 12 point of soils describes a sandy loam (58.33%) textural class; whereas twoⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-1points describe clay loam (16.67%) and loam (16.67%) and one point represents loamy sand (8.33%),
respectively. The spread of BD values ranged from 0.8 to 1.51 g cm-3, with a mean value of 1.21 g cm-3.
From the survey, all farmers use machine, animal and manpower from the land preparation to theharvesting process, for crop production and grow the different cropping patterns in every year.
Soil pH and Electrical Conductivity (EC)The pH values of the soil samples ranged from strongly acidic to moderately alkaline, (4.95 to 8.47)
with a mean value of 6.94. The spatial distribution map of soil pH is presented in Fig. 5 and the pH canbe seen as mostly neutral (47.69%), strongly alkaline (1.54%), moderately alkaline (26.92%), slightlyacidic (13.85%), moderately acidic (9.23%) with only 0.77 % strongly acidic. Gazey and Davies(2009) indicated that the pH values between 5.5 and 8.0 are considered as ideal for plant growth.

Fig. 3 Spatial variability of soil texture

Fig. 4 Spatial variability of soil bulk density

Fig. 5 Spatial variability of Soil pH

Fig. 7 Spatial variability of total N

Fig. 6 Spatial variability of ECFig. 8 Spatial variability of available KⒸ ISERD

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Therefore, the observed pH values are suitable for different crops and the availability of most ofplant nutrients should not be subject to restrictions within the observed pH range. The values of EC
range from 0.03 to 0.72 dS m-1 with an average of 0.011 dS m-1 (Fig. 6). According to the Soil Guide(Moore, 2001), these EC values are situated between a low EC value (0.03 – 0.47 dS m-1; 96.92%) anda medium level (0.52 – 0.72 dS m-1; 3.77%), which would have no effect on plant growth.
Total Nitrogen (TN) and Available Potassium (AK)
Variation in the TN content of the soil samples ranged from 0.11% to 0.39%, with a mean value of
0.23%. The study confirms that about 20.77% of the sampled area has low levels of TN, while 56.92%
has medium levels and 22.31% of the sampled area has high levels (Fig. 7). The areas of medium tohigh (79.23%) levels of TN would probably be as a result of the addition of nitrogen fertilizer and theintercropping with legumes. The AK levels appears to be low, with 89% of the sampled area being in
the lower range, whereas 9% and 2% of the samples showed medium and high levels (Fig. 8),
respectively. The low levels of AK may be due to infrequent application of potassium fertilizers intheir cultivation, and this only through the use of compound fertilizers.

Fig. 9 Spatial variability of SOM

Fig. 10 Spatial variability of CN ratio

Soil Organic Matter (SOM) and Carbon : Nitrogen (CN Ratio)
The spatial distribution of SOM is displayed in Fig. 9. The SOM content of soil ranges from 0.31% to3.55% with a mean value of 1.41%. Most (84%) of the study area has very low levels of SOM. Basedon the survey data, this may be as a result of the complete removal of crop residues, the failure to add
organic manures and the burning of crop residues after harvesting. This finding is in line with
Alemayehu, K. and Sheleme, B. (2013) who report that SOM recorded in cultivated fields as being
lower than for other land uses because of the effect of continuous cultivation and SOM oxidation.
There is a very low spatial variation of CN ratio throughout the study area, with ratios ranging from1.19 to 7.85, and with a mean value of 3.43 (Fig. 10).
Nutrient Index Value (NIV) and the Relationship among Soil Parameters
NIV of the SOM, CN ratio and AK content for the studied area clearly reveals these parameters are atlow levels, while soil TN was at a medium level. The ranking of nutrients according to NIV is total N >available K > Soil organic matter > CN ratio. According to the results, TN content was highlysignificant and positively correlated with SOM and AK at a 1% confidence interval level, and CN ratio
at a 5% level, and SOM also shows a highly significant positive correlation with AK and the CN ratio(Table 2).

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Table 2 Correlation among the different soil parameters
BDpHEC
TNSOM
C:N
pH
0.255 **
EC
0.225**-0.029 ns**TN-0.437-0.490**0.018 ns
SOM
-0.333**-0.399**-0.081 ns0.791**
nsnsns
C:N-0.034-0.095-0.0930.212*
0.730**
K-0.119 ns
0.003 ns
0.053 ns
0.431**
0.302**
0.059 ns
*Correlation is significant at the 0.05 level; **Correlation is significant at the 0.01 level(EC: electrical conductivity, BD: bulk density, SOM: soil organic matter, TN: total nitrogen, C:N: carbon nitrogen ratio,
K: Available potassium)

Crop Patterns and Soil Fertility Management Practices
The most common cropping patterns in the study area are rice-black gram, rice-fallow, maize-tomato,maize- tomato and lablab bean intercropping, okra-tomato and okra-Japanese mustard-onion. Moreover,some farmers grow mango, guava, banana and ambarella. Almost all farmers, except rice growers,
practice mixed cropping, crop rotation and intercropping systems for fertility management. According
to the survey data, 78% of farmers have no knowledge about the soil fertility testing or analysis,
whereas the remaining 22% know about this but cannot afford the cost of analysis. The most common
types of inorganic fertilizers used by the respondents were urea, foliar and NPK compound fertilizers.
CONCLUSION
The ranges of soil pH and EC measured should not be detrimental to crop cultivation. However, thedistribution of soil TN percentage are 50% of the study area at medium, 25% low and 25% at highlevels, whereas most of the levels for SOM, CN ratio and AK are at low levels. The study also showsthat NIV of TN and SOM are only at medium levels, while that of CN ratio and AK are at low levels.
Based on the survey results, farmers in the study area, in their crop management practice, use nitrogen(urea) fertilizer regularly, but without applying potassium fertilizer, apart from manure and NPKcompound. The spatial variability in soil properties appear to be largely due to the differences in crop
management practices, diverse cropping patterns and the variety of chemical fertilizers available.
Therefore, these soil fertility maps may greatly assist farmers in identifying nutrient levels for thespecific areas they crop and so help them to improve their crop fertility management programs, leading
to an increase in productivity and higher incomes.
ACKNOWLEDGEMENTS
I would like to gratefully acknowledge the JICA-TCP, Japan International Cooperation Agency,
through its Capacity Development program at Yezin Agricultural University, for providing theresearch grant. My thanks also go to the supervisory committee, Staff at DOA, and to the farmers inthe study area who have provided technical and manual support through their participation in the study.
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