IJERD – International Journal of Environmental and Rural Development (2019) 10-2

Research article
erd

An Assessment of Soil Chemical Properties and Yields ofBlack Gram, Using GIS, in a Selected Area of Myanmar
HTAY HTAY OO*
Yezin Agricultural University, Myanmar
Email: htay.agro96@gmail.com

KHIN MAR OO
Yezin Agricultural University, Myanmar

SWE SWE MAR
Yezin Agricultural University, Myanmar

NAING NAING MOE
Yezin Agricultural University, Myanmar
Received 14 December 2018

Accepted 10 July 2019(*Corresponding Author)

Abstract In Myanmar, pulses are a very important crop with great potential for export.
Black gram (Vigna mungo L.) is one of the predominant pulse species grown, with planting
after the harvest of monsoon rice in the winter season. Understanding the spatial variabilityof soil chemical properties is critical for improving the productivity of pulses and ensuring
sustainable farming practices. However, a systematic assessment on the spatial variabilityof pulses yield has not been conducted. Therefore, this study was carried out to examine
the variability of black gram yields in relation to soil chemical properties, at Kyee Inn
Village, Pyinmana Township, central Myanmar from May 2017 to March 2018. Eighty
composited soil samples were collected across the study area and analyzed for common
soil chemical properties. Sixty seven farmers were surveyed as sample respondents todetermine yields and management practices. Interpolation allowing overlay mapping ofvariations in black gram yields on soil chemical properties was accomplished using ArcGIS
software. It was observed that these soil chemical properties and black gram yield varied
considerably throughout the study area. Slight variability was observed in total nitrogencontent. The pH value of the soils was moderately acidic to moderately alkaline with pH
values of 5.48 to 7.58. The fields were characterized by low levels of soil organic matterand total phosphorus. The variation in black gram yields, ranging from 0.16 to 1.97 ton perhectare, is probably a result of the high variation of total soil nitrogen and phosphorus
content. The results suggest that to improve soil fertility and sustain and improve black
gram yields, it is very important to apply organic matter, nitrogen and phosphorus in partsof the study area.
Keywords black gram, GIS, overlay mapping, organic matter, total nitrogen andphosphorus, yield, sustainable farming

INTRODUCTIONIn Myanmar, pulses are an important crop with great potential for export, and the rapid growth inproduction has been driven by a huge demand from India. Black gram (Vigna mungo L.) is one of
the most favored species farmed in Myanmar, in terms of acreage and production output. Pulses areimportant for crop intensification, diversification and conservation of natural resources as well as
the sustainability of farm production systems, because of their short growth duration, dense crop
canopy and versatility (Katiyar and Dixit, 2010). However, black gram is generally produced infields that have a high degree of variability in soil type, topography, soil moisture and other major
factors that can affect crop yield.
Ⓒ ISERD

14

IJERD – International Journal of Environmental and Rural Development (2019) 10-2

GPS based soil fertility maps have been prepared by a number of different government andprivate sector organisations for different areas of Myanmar. Remote sensing (RS), geographicalinformation systems (GISs) and geo-statistics are tools becoming progressively more central tofields of research like agriculture (Bocchi et al., 2000; Basso et al., 2001). Scientific information
relating spatial variability to distribution of soil properties is critical for farmers attempting toincrease effectiveness of fertilizer use and maximize crop productivity. Fertilization of a crop basedon specific parameters of soil fertility, should also result in minimalizing fertilizer inputs without
reducing yield (Jalali, 2007). Geo-statistics have been used to analyse the spatial variability of soil
properties and crop yield (Stevenson et al., 2001). However, a systematic assessment on the spatial
distribution and variability of pulse yields and soil chemical properties has not been conducted in
Myanmar. Evaluating the spatial variability of pulse yields in relation to the chemical properties ofsoils allows micro-adjustments that are essential for regional planning purposes, promoting
sustainable pulse production and increasing levels of productivity.
OBJECTIVES
The present study was carried out to examine the current status and spatial variability of soil
chemical properties and the relationship of this to black gram yield. Maps of the study area weregenerated to illustrate this relationship.
METHODOLOGY
Study Site and Data Collection
The research was carried out in central Myanmar. The study site is Kyee Inn Village, Pyinmana
Township, which lies between 1970'66'' - 1972'62'' N and 9622'43'' - 9625'73'' E and examineda total study area of 486 ha, from May 2017 to March 2018 (Fig. 1). Farmers in the study area
usually practice what can be described as a monsoon rice-pulse cropping pattern. The mean annual
rainfall is approximately 1420 mm and daily mean temperature, 26.8°C. The study area wassuperimposed with 300 m × 300 m geographic grids using geographical information system (GIS)
tools, and comprised 80 sample grid points (Fig. 2). Among these 80, only those grids where
farmers had cultivated black gram were included in the survey. Accordingly, 67 farmers wereselected as sample respondents and information about average yield per acre and crop management
practices was collected through interview and structured questionnaire. For soil analysis, each grid
had three soil samples taken, these at a depth of 0-15 cm. The 3 samples from each grid werecombined to give a composite sample. To avoid undesirable effects due to fertilization in thegrowing period, samples were taken after harvest.
Sensors were on a DJI Phantom 4 drone and Litchi software was used to interpret information.
Flight altitude was 120 meters and the drone images were combined and processed using pix 4D
software for digitizing and relating these to the digital base map of the study area. ArcGIS softwarefrom Environmental Systems Research Institute (ESRI) was used to produce maps of the spatial
distribution of yield and soil chemical properties. Interpolation and overlay mapping weregenerated using Inverse Distance Weighting (IDW) and the Radial Basis Function (RBF) which
are deterministic interpolation methods in the Arc GIS 10.5 software according to Meng et al.
(2013).
Statistical Analysis
All relevant data were subjected to analysis using the Statistix 8 program. Mean, standard deviation,and standard errors were determined for each of the soil properties considered as well as soil
moisture content and yield of black gram, using descriptive statistics. The variability of each
property was measured by coefficient of variation (CV) expressed as a percentage.
Ⓒ ISERD

15

IJERD – International Journal of Environmental and Rural Development (2019) 10-2

Laboratory Analysis
Laboratory analysis was conducted for the composite soil samples. This involved air-drying,pounding, and sieving (2.0 mm sieve) and storage at 4°C in a cool room. Soil pH was measured
using a digital pH meter in soil to water solutions, consisting of a 1:5 suspension, after 30 minutesof mixing (Hesse, 1971). Total soil nitrogen was determined using the Kjeldahl digestion,
distillation and titration method (Bremmer, 1982) whereas total soil phosphorus was analyzed
using spectrophotometer measurement of blue molybdatephosphate complexes under partial
reduction with ascorbic acid (Jackson, 1958). Soil organic matter was determined using the wet
digestion method (Walkley, 1934).
Pyinmana TownshipFig. 1 Location of the study area

Fig. 2 Grid sampling points

RESULTS AND DISCUSSION
Spatial Variation in Soil Chemical Properties
The coefficients of variation (CV) were grouped into three categories; these being, least variable(<15%), moderately variable (15-35%) and extremely variable (>35%). That is, the higher the CV
the more variable the soil property measured. Data were subjected to analysis by descriptive
statistics to determine significant differences between the black gram yield, soil moisture content(MC) and soil chemical properties. A large variation in soil chemical properties was observed
across the study area. In terms of variation in soil properties, total nitrogen shows the highest
variability with a CV of 68.52%, followed by soil organic matter with a CV value of 34.56%,
whereas variations for soil pH and total phosphorus show least variability with CVs of 5.13% and6.25% respectively (Table 1). The results indicate that there is natural variation, which is often
compounded by nutrient management practices used by farmers. This has implications for black
gram production since soil chemical and soil water content variability can be expected to lead tovariability in crop yield.
Soil pH
Table 1 Descriptive statistics of yield, soil water content and soil chemical properties
Variables

Unit

Minimum

Maximum

Mean

SE

SD

CV%

Estimated Yield

ton ha-1

0.160

1.970

0.840

0.043

0.364

43.348pH-log [H+]
%

5.480

7.580

6.244

0.038

0.320

5.127

1.790

9.640

4.928

0.182

1.545

31.358Organic Matter

%

0.200

1.700

0.893

0.036

0.309

34.562

Total Nitrogen

%

0.010

0.330

0.114

0.008

0.078

68.520Total Phosphorus

%

0.017

0.024

0.019

0.001

0.001

6.249

Soil Water

SE: Standard error; SD: Standard deviation; CV: Coefficient of variationⒸ ISERD

16

IJERD – International Journal of Environmental and Rural Development (2019) 10-2

The soil pH of the study area was found to be acidic to slightly alkaline and ranged from 5.48 to7.58 (Table 1). Black gram prefers soil with a pH in the range 5.5 - 6.5, tolerating 4.5 - 7.5 (Katiyar
and Dixit, 2010). Thus, the variability of soil pH may be assumed to not represent a major
contributing factor to variation in black gram yield.
Soil Organic Matter and Soil Water
The estimated average yield for black gram in the study area was obtained through surveying
farmers, by both interview and structured questionnaire. From the sample plots, black gram yields
range from 0.16 to 1.97 ton ha-1 (Table 1). These data were overlain on to maps of soil organic
matter and soil moisture for the study area (Fig. 3a and 3b).
The black gram cultivars commonly used in Kyee Inn Village, Pyinmana Township are Yezin-2,
Yezin-5 and Yezin-6 (Fig. 2). In general, yield exhibited the following characteristics. The grid points
in the eastern part of the study area produced higher yields, ranging from 0.99 to 1.48 ton ha-1.
Moreover, the sample grid points where the black gram cultivars were Yezin-2 and Yezin-6 hadhigher yields than area where Yezin-5 was planted. According to the Department of Agricultural
Research (DAR), the potential yield of Yezin-5 (1.6 - 2.0 ton ha-1) is lower than that of Yezin-2 (2 2.4 ton ha-1) and Yezin-6 (2.4 - 3.2 ton ha-1) when grown under same conditions. Thus, the study
affirms that Yezin-2 and Yezin-6 should be grown instead of Yezin-5 in upcoming seasons toincrease productivity.
Soil organic matter and soil water have very important functions that correlate positively tonutrient availability and retention. It should also be taken into consideration that the nutrient input
from soil contributes considerably to crop nutrition (Fu et al., 2010). The amount of soil organic
matter is highly dependent on a range of ecological factors such as climate, soil type, vegetative
growth, topography in which it occurs as well as land use and management and tillage of the soil
and intensive cropping (Rawal et al., 2018). The spatial distribution of organic matter and soil
moisture (in percent) exhibits a gradual percentage increase from the northeast portion to thesouthwest portion of the study area, where the highest content for both are found. One possible
reason for this is the slightly higher elevation of the northern portion with accumulation of organic
matter and moisture often favored at lower levels. The average soil organic matter content of study
area varies from 0.2 to1.7% with a mean value of 0.893%, which is very low when soil organic
matter greater than 2.6% is considered to provide good nutrient storage (Purdie, 1998). In theoverlay mapping, all sampling grid plots had insufficient levels of soil organic matter and there ishigh variation in percentage content. This suggests the low yields for black gram, and spatial
variability of these yields may be attributed to the poor crop management practices, with a failure
to systematically apply organic and inorganic fertilizers.

Fig. 3 Overlay mapping of distribution of yield and cultivars for (a) soil water content and (b)soil organic matter in Kyee Inn Village, Pyinmana Township, 0.98: mean yield of black
gram in ton ha-1
Ⓒ ISERD

17

IJERD – International Journal of Environmental and Rural Development (2019) 10-2

Total Nitrogen and Total Phosphorus
Soil fertility is one of the major constraints to agricultural production in tropical countries,
including Myanmar. Examining the current soil fertility status is important because it could provide
valuable information relating to crop research (Khadka et al., 2017). Soil nitrogen is one of themost important plant nutrients, and of all nutrients, the most frequently deficient (Havlin et al.,2010). The total nitrogen content varies from 0.01% to 0.33% with the mean value of 0.114% inthis study (Fig. 4a). The critical value of total N in soil is 0.12% (Shah et al., 2008). The overlay
mapping, show most of the sampling grid plots have low levels of total nitrogen. This low nitrogencontent may relate back to the insufficient level of soil organic matter content in the study area.
The percentage value of total phosphorus ranges from 0.017 to 0.024% with a mean value of0.019%. In most soils, P content is very low in the surface layer and represents less than 1% of total
P. However the total P content of a soil may vary widely and depend on factors such as organic
matter content, climatic conditions, parent materials and application of fertilizers (Mansour et al.,2014). In the overlay mapping, about half of the sampling area is medium in range, whereas the restof the study area is in a lower range for phosphorus content (Fig. 4b).
The yield variations in black gram already mentioned also may be related to the variations insoil nitrogen and phosphorus content. Low levels of phosphorus can be ameliorated by replenishingwith fertilizers which contain high amounts of available phosphorus. In the overlay mapping of Fig.
4.a and Fig. 4.b, it can be seen that most sampling grid plots not only have low levels of soil
nutrients but are sown with different black gram cultivars. Therefore, results suggest the spatial
variability of black gram yield can be attributed to the poor crop management practices, such as theinsufficient application of nitrogen and phosphorus fertilizers, in combination with the selection ofpoor yielding cultivars for planting.
(a)

(b)

Fig. 4 Overlay mapping on distribution of yield and cultivars on to (a) total nitrogen and (b)total phosphorus in Kyee Inn Village, Pyinmana Township, 0.98: mean yield of black
gram in ton ha-1
CONCLUSION
Results clearly show that most of the area under black gram cultivation has low levels of organic
matter, with total soil nitrogen and phosphorus also below desired levels. Due to continuous tillage
and the failure to add organic matter, and with minimal nutrient supply from either natural or
artificial sources, low levels of these nutrients pertain in the study site. The pH range of soils in the
study area should not present a problem for the black gram cultivation, and cannot be assumed as amajor factor contributing to the spatial variation in yields of black gram. The results suggest that toimprove soil fertility and sustain black gram production, it is very important to apply organic
matter and phosphorus, particularly in certain parts of the study area. The overlay maps provide aⒸ ISERD

18

IJERD – International Journal of Environmental and Rural Development (2019) 10-2

readymade source of information about the status soil fertility and can serve as a spatially accurate
decision making tool for successful black gram cultivation. It can be concluded from the present
study that GIS based soil fertility maps help farmers by providing spatially specificrecommendations regarding fertilizer application, improving crop production and ensuring greater
sustainability.
ACKNOWLEDGEMENTS
We would like to acknowledge the Japan International Cooperation Agency for TechnicalCooperation Project (JICA-TCP) at Yezin Agricultural University for providing financial andtechnical support. We wish to extend sincere gratitude to Pro-Rector Dr. Kyaw Kyaw Win, and Dr.
Kazuo Watanabe for their generous technical support and valuable advice in this study. Our
appreciation is also extended to the authorized staff at the Department of Agriculture (DoA) and
Department of Agricultural Land Management and Statistics (DoALMS) for data provision.
REFERENCES
Basso, B., Ritchie, J.T., Pierce, F.J., Braga, R.P. and Jones, J.W. 2001. Spatial validation of crop models for
precision agriculture. Agric. Syst., 68, 97-112.
Bocchi, S., Castrignano, A., Fornaro, F. and Maggiore, T. 2000. Application of factorial kriging for mapping
soil variation at field scale. Eur. J. Agron., 13, 295-308.
Bremmer, J.M. and Mulvaney, C.S. 1982. Nitrogen-total. In: Page, A.L., Miller, R.H. and Keeney, D.R.
(Eds.), Methods of Soil Analysis, Part 2-Chemical and Microbiological Properties, ASA-SSSA, Madison,
WI. 595-624.
Fu, W.J., Tunney, H. and Zhang C.S. 2010. Spatial variation of soil nutrients in a dairy farm and its
implications for site-specific fertilizer application. Soil Tillage Res., 106, 185-193.
Havlin, H.L., Beaton, J.D., Tisdale, S. L. and Nelson, W.L. 2010. Soil fertility and fertilizers, An introduction
to nutrient management. 7th Edition, PHI Learning Private Limited, New Delhi, India. 516.
Hesse P.R. 1971. A text book of soil chemical analysis. John Murray, London.
Jalali, M. 2007. Spatial variability in potassium release among calcareous soils of western Iran. Geoderma,
140, 42-51.
Jackson, M.L. 1958. Soil chemical analysis. Prentice-Hall. Engle-wood Cliffs, N.J. 498.
Katiyar, P.K. and Dixit, G.P. 2010. Genetic divergence in Indian black gram (Vigna mungo) cultivars. Indian
J. Agric. Sci., 80 (3), 242-243.
Khadka, D., Lamichhane, S., Shukra, R.S. and Pant, B.B. 2017. Evaluation of soil fertility status of regional
agricultural research station, Tarahara, Sunsari, Nepal. Eurasian J. Soil Sci., 6 (4), 295-306.
Mansour, A.S., Latif, A.A., Zaho, S.M.E. and Abdullah, I.N. 2014. A case study of the total and available
phosphorus concentration in libyan agricultural soils in different depths and seasons in long termchemical and animal manure fertilization. International Journal of Research Studies in Biosciences(IJRSB), 2, 1-9.
Meng, Q., Liu, Z. and Borders, B.E. 2013. Assessment of regression kriging for spatial interpolationcomparisons of seven GIS interpolation methods. Cartogr. Geogr. Inf. Sci., 40, 28-39.
Purdie, B. 1998. Understanding and interpreting soil chemical and physical data. In: Moore, G. (Ed.), Soil
Guide: A Handbook for Understanding and Managing Agricultural Soils, Agriculture Western Australia
Bulletin, No. 4343, Department of Agriculture, Perth, Australia.
Rawal, N., Acharya, K.K., Bam, C.R. and Acharya, K. 2018. Soil fertility mapping of different VDCs of
Sunsari district, Nepal using GIS. Int. J. Appl. Sci. Biotechnol., 6 (2), 142-151.
Shah, A.L., Islam, M.R., Haque, M.M., Ishaque, M. and Miah, M.A.M. 2008. Efficacy of major nutrients inrice production. Bangladesh J. Agril. Res., 33 (3), 639-645.
Stevenson, F.C., Knight, J.D., Wendroth, O., van Kessel, C. and Nielsen, D.R. 2001. A comparison of two
methods to predict the landscape-scale variation of crop yield. Soil Tillage Res., 58, 163-181.
Walkly, A. and Black, I.A. 1934. An examination of the degtjareff method for determining soil organic
matter and a proposed modification of the chromic acid titration method. Soil Sci., 37, 29-38.

Ⓒ ISERD

19