IJERD – International Journal of Environmental and Rural Development (2021) 12-2

Research article
erd

Spatial Analysis of Yield Gaps Variability of Black Gram

Based on GIS and Geostatistics
HTAY HTAY OO *Yezin Agricultural University, Myanmar
Email: htay.agro96@gmail.com

KHIN MAR OO
Nyein Nyein Htwe and Hla Than, Yezin Agricultural University, Myanmar
Received 25 January 2020

Accepted 24 September 2021(*Corresponding Author)

Abstract Black gram, a pulse, is one of the major export crops in Myanmar. Because of adecline in the maximum possible yield of this crop in farmers’ fields, a yield gap exists
between potential and farmers’ actual yields. Reducing the gap between actual and potential
yields is critical for increasing crop production. This study was carried out to generate yield
gap mapping, and to investigate the reasons responsible for this yield gap in black gramproduction. An annual survey was carried out to determine farmers’ actual yield at Kyee Inn
Village, Pyinmana Township during October 2017 and 2018. Drone photos wereconsolidated and prepared for digitizing and analyzed with Pix4D software. ArcGIS 10.7
software was used to map the spatial distribution of the yield gap of black gram in the selected
area. Yield gaps of black gram with a range of 0.02 t ha-1 to 2.70 t ha-1 in the two consecutive
years were observed. The yield gap between potential and farmers’ actual yields (Yield GapI) was greater in 2018 compared to the 2017 season. The gap between the yield obtained
through an organized farm trials and the yield harvested by the farmers (Yield Gap II) wasalso larger in 2018 than that for the 2017 crop season. A technology gap for the variety Yezin6 (0.68 t ha-1) was observed in 2018 whereas there were no gaps for Yezin-2 and Yezin-5 in2017 and non for these varieties in the 2018 post-monsoon season. The reasons these large
yield gaps may exist, is probably due to differences in crop management practices used at theexperimental stations and those by farmers. The results of this study should provide useful
information for policymakers, researchers, extension agents, and other stakeholders toupgrade a location-specific package of practices and increase crop yield by supporting
technological solutions and training for the farmers.
Keywords black gram, yield gap, mapping, spatial, potential yield

INTRODUCTIONIn Myanmar, black gram (Vigna mungo L.) is one of the major exportable pulses and widely sown
as a second crop after monsoon rice. The total sown area of black gram was 0.98 million hectares,
with a production of 1.38 million tons from 2016 to 2018. Depending on the varieties, the averagepotential yield of black gram recorded by the Department of Agricultural Research (DAR) is 2.40 t
ha-1, whereas the average actual yield of black gram is 1.44 t ha -1(MOALI, 2018). Consequently, alarge yield gap exists between the farmers’ actual yield and the potential yield as demonstrated at theresearch station.
Expansion of cropland farmed is probably not a sustainable option to increase crop production
so there is a need to increase crop yield on existing cropland, providing sustainable intensification of
the current crop production (Forland, 2015). Presently, spatial and temporal variability of crop yields
exist across regions, even within the same climatic zones due to different levels of knowledge,
different farming practices, technologies, supply chains, and policies in that area (Licker et al., 2010).
This has resulted in a spatial variability of the yield gap in black gram production in Myanmar.
Additionally, farmers were still facing diverse technological gap in cultivation though there wereagricultural modernization in pulse crops.
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Analyzing the yield gap using Geographic Information System (GIS) can provide graphic
information demonstrating the variability of crop yields and possibly allow for the assessment of theunderlying causes of this yield gap, which would then assist in identifying strategies for narrowing
these yield gaps (Tran and Nguyen, 2006). Yield gap analysis can help to develop changes in methodsof crop production to bridge the gaps between the potential yield of crop variety obtained through anorganized farm demonstration during its development in research stations and the yield harvested by
the farmers (Jopir and Bera, 2017). It also provides valuable information for decision makers andresearchers allowing the development of strategies or action plans for minimizing yield gaps andimproving production sustainability.
OBJECTIVE
The objectives of this research were to develop mapping showing the spatial variation of yield gapsof black gram in selected areas to provide information that allows the generation of good production
practices for black gram production.
METHODOLOGY
Study Site
The research was conducted over two consecutive years of post-monsoon season, in 2017 and 2018,
at Kyee Inn Village, Pyinmana Township, Nay Pyi Taw Union Territory, which lies between
1970'66''- 1972'62'' N and 9622'43''- 9625'73'' E (Fig 1.). The total study area was 483 ha. Themajor pulse was black gram which is grown as a second crop after the harvest of monsoon rice. Thestudy area was characterized by low content of organic matter, it has (mostly) low to high levels oftotal nitrogen and low to medium ranges of total phosphorus, while the level of the CEC is very lowto low (Moe et al., 2019).

Nay Pyi Taw

Kyee InnVillage

Sample grid(300 m  300)m)
Sample plot

Pyinmana
TownshipFig. 1 The study area at Kyee Inn Village,
Pyinmana TownshipFig. 2 Sample grids and sample plots
taken in Kyee Inn VillageSource: https://myanmar.unfpa.org/…/union-report- volume-3o-nay-pyi-taw-union-territory-re
www.dop.gov.mm/sites/dop.gov.mm/files/publication _docs/pyinmana_0.pdf

Preparation of Sample Plots and Data Collection
The base-map preparation enabling use of ArcGIS 10.7 software was accomplished by data obtained
from a DJI Phantom 4 drone, with this processed using Litchi software. The photos were consolidated
and processed for digitizing using Pix4D software and incorporated onto the digital base map. Thestudy area was divided into 300 m × 300 m geographic grids. The 92 sample grids were encompassed
and 78 of these sample grids were under black gram cultivation (Fig 2).
The 70 farmers cultivating the 78 sample plots were selected as respondents and interviews wereconducted to identify variation in average yield and crop management practices in the sample area.
The potential yield (YP- irrigated conditions) of black gram was provided by the Oilseeds and Pulses
Crops Division, Department of Agricultural Research (DAR), Yezin, Nay Pyi Taw. The potentialyield (YW- rainfed conditions) of black gram was obtained from on farm trials conducted byⒸ ISERD78

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Department of Agronomy, at Kyee Inn Village, Pyinmana Township in the 2017 and 2018 (Oo et.
al., 2019). The farmers’ actual yield (YA) was recorded from the respondents in the study area for the2017 and 2018 crop seasons.
Preparation for Mapping
The polygon boundary shapefiles and merged polygon shapefiles were created using the ArcGIS
10.7 software. According to yield gap calculation, point shapefiles of Yield gap I and Yield gap II
were prepared for the study area for the two consecutive years. Because of limited number of sample
points, the spatial interpolation was conducted by Kriging, it is a geostatistical interpolation method(Karydas et al., 2009). Maps of the spatial distribution of Yield gap I and Yield gap II was generate
through ArcToolbox (Spatial Analyst > Interpolation > Kriging).
Calculations
Yield gap I is the difference between potential yield (YP- irrigated conditions) and farmers’ actual
yield (YA) and Yield gap II is the difference between water-limited potential yield (YW- rainfedconditions) and farmers’ actual yield (YA) (Equations 1 and 2).
Yield gap I = Potential yield (YP) – Farmers’ actual yield (YA)

(1)

Yield gap II = Water-limited potential yield (YW) – Farmers’ actual yield (YA)

(2)

The technology gap (YTG) was determined as the difference between the water-limited potential
yield (YW) and the highest farmers’ yield (YHF) (Equation 3). The highest farmers’ yield (YHF) is anempirical concept intended to define the maximum YA achieved. YHF was estimated by calculating
the mean of actual yields above the 90th percentile (Silva et al., 2017).
Technology gap (YTG) = Water-limited Potential yield (YW) – Highest farmers’ yield (YHF)(3)

RESULTS AND DISCUSSION
Survey Result of Yield and Crop Management Practices
In this study area, the farmers’ actual yield (YA) was in the range of 0.08 to 1.97 t ha-1, for the 2017and 2018 post-monsoon seasons (Table 1.). Most of the respondents (85%) do not retain residues of
black gram after harvesting. About 32% of respondents use optimum seeding rate recommended byDOA. Only 18% of respondents applied urea and compound fertilizers as basally at planting time.
There was no evidence of the application of phosphorus and potash fertilizers for black gramproduction. The most common type of chemical sprayings used by respondents for black gramcultivation were foliar and fungicides and about 12% of respondents follow optimum spraying
frequency (4 times for whole season) recommended by DOA. Seed sowing method was broadcastingso that weeding was not done for black gram cultivation in the study area.
Spatial Analysis on the Variation in Yield Gap I
The potential yield (YP) obtained from the DAR for black gram varieties used in the study area ranged
from 1.75 to 2.78 t ha-1 while the farmers’ actual yield (YA) was in the range of 0.08 to 1.97 t ha-1,
for the 2017 and 2018 post-monsoon seasons (Table 1). The Yield gap I of black gram was recordedwith the range of 0.73 to 2.70 t ha-1 in 2017 and 0.28 to 2.62 t ha-1 in the 2018 post-monsoon season(Table 2). The results indicate that variations of Yield gap I occurred during these two consecutiveyears.
A wider Yield gap I of 0.73 to 1.99 t ha-1 and 0.81 to 2.70 t ha-1 was recorded for the varieties
Yezin-2 and Yezin-6, respectively in the 2017 and 2018 post-monsoon seasons (Table 2).

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Table 1 Potential yield and farmers’ actual yield of black gram varieties in Kyee Inn Village,
Pyinmana Township during 2017 and 2018 (post-monsoon seasons)Variety

Potential yield (YP)(t ha-1)

Potential yield (YW)(t ha-1)

Yezin-2
Yezin-5
Yezin-6

2.15
1.75
2.78

0.88
0.73
0.93Farmers’ actual yield (YA)(t ha-1)
2017
2018
0.43-1.42
0.16-1.47
0.16-1.82
0.16-1.47
0.08-1.97
0.16-0.25

Table 2 Yield gap I and Yield gap II of black gram varieties in Kyee Inn Village, Pyinmana
Township during 2017 and 2018 (post-monsoon seasons)
Yield gap I (t ha-1)
Variety
Yezin-2
Yezin-5
Yezin-6

Yield gap II (t ha-1)

2017

2018

2017

2018

0.73-1.72
0.80-1.59
0.81-2.70

0.68-1.99
0.28-1.59
2.53-2.62

0.21-0.45
0.02-0.57
0.10-0.85

0.23-0.47
0.08-0.32
0.68-0.77

The spatial variations of Yield gap I appears to be higher and range from 1.26 to 1.60 t ha-1 in
2017 whereas most fields displayed a Yield gap I of 0.91 to1.25 t ha-1in 2018 (Fig. 3). The highest
Yield gap I (1.61-1.89 t ha-1) happened in both years, but showed a greater and more scattered patternin 2017 (Fig. 3). These differences may possibly be due to differences in crop and nutrientsmanagement practices between the trial station and the farmer’s fields.
2017

2018

0.84-0.90 t ha
1.26-1.60 t ha-1

0.91-1.25 t ha-1-1

1.61-1.89 t ha-1

Fig. 3 Spatial variations of Yield gap I of black gram in Kyee Inn Village, Pyinmana Township
Spatial Analysis on Variation of Yield Gap II
The Yield gap II varied from 0.02 to 0.85 t ha-1 in 2017, and 0.08-0.77 t ha-1 in 2018 (Table 2). Thevariation in Yield gap II across the fields appears to indicate varied crop management practices
among black gram farmers in the study area. The trial plantings, used as a base comparison, usuallyfollow recommended crop management practices, especially regarding basal and foliar fertilizers
application, weeding, and plant protection measures. However, the potential yield (YW) obtained was
lower than that of some farmers’ actual yield. The Yield gap II ranged from 0.20 to 0.55 t ha-1 in thetwo consecutive years (Fig. 4). The larger proportion of the fields had a Yield gap II of 0.26-0.35 t
ha-1 in 2017 and this gap appear to be larger 0.36-0.45 t ha-1in 2018. The greatest Yield gap II of0.46-0.55 t ha-1 was observed in the 2018 crop season, and such a gap was not present in the 2017crop. The slight increase of Yield gap II in the 2018 season may have been due to a steady decreasein farmers’ actual yield during the study period. The study area was characterized by low content oforganic matter, it has (mostly) low to high levels of total nitrogen and low to medium ranges of totalⒸ ISERD

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phosphorus, while the level of the CEC is very low to low (Moe et al., 2019). According to the resultsof interviews, farmers in the study area did not apply of phosphorus and potash fertilizers for black
gram production and thus, the Yield gap II was resulted for two consecutive years.
2017

2018

0.20-0.25 t ha

0.36-0.45 t ha-1

0.26-0.35 t ha-1

-10.46-0.55 t ha-1

Fig. 4 Spatial variations of Yield gap II of black gram in Kyee Inn Village, Pyinmana TownshipTechnology Gap of Black Gram in Kyee Inn Village, Pyinmana Township
The technology gap represents the difference between the potential yield (YW) and the highest
farmers’ yield (YHF). The technology gap was not found in the 2017 post-monsoon seasons (Table3). It was observed only by the variety of Yezin-6 (0.68 t ha-1) in 2018. From 2017 to 2018 postmonsoon seasons, the technology gap was not recorded by the variety of Yezin-2 and Yezin-5. The
results indicated that the recommended crop production practices of a black gram would need toupgrade for increasing crop yield in the study area.
Table 3 Technology gap of black gram varieties in Kyee Inn village, Pyinmana Townshipduring 2017 and 2018 (post-monsoon seasons)Variety
Yezin-2
Yezin-5
Yezin-6

Potential yield(YW)(t ha-1)
0.88
0.73
0.93

Highest farmers’ yield (YHF)(t ha-1)
2017
2018
1.42
1.19
0.91
1.23
1.32
0.25

Technology gap (t ha-1)
2017-2018
0.68

CONCLUSION
A crop yield gap was found in most farmers’ fields due to their differences in crop management
practices including residue management, basal and foliar fertilizers applications, weeding and plant
protection measures. Therefore, it is necessary to introduce an appropriate package of production
practices for the study area. Farmers should be encouraged to follow the systematic crop management
practices as much as possible to increase crop production. As expected, the spatial variation of Yield
gaps exists largely due to the differences in the crop management practices used at the experimental
stations and those by farmers. The wide Yield gaps were observed in both years. The reason theselarge yield gaps may exist, is probably due to not applying phosphorus and potash fertilizers to theblack gram. Therefore, the results of the study provide useful information for policy makers,
researchers, extension agents, and other stakeholders to upgrade location-specific information
packages outlining best practice. This study has shown that the use of new technologies such as GISand Geostatistics can provide important information for evaluating the status of Yield gap of black
gram in the study area.

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ACKNOWLEDGEMENTS
We would like to gratefully acknowledge the Japan International Cooperation Agency (JICA), who
supervise the Project for Capacity Development of Yezin Agricultural University, and who haveprovided technical and financial support for this study. We are also appreciative for the information
provided by the authorized persons from Department of Agricultura (DOA), Department ofAgricultural Research (DAR) and Department of Agricultural Land Management and Statistics.
Finally, we are very grateful to the farmers in the study area for their keen participation throughout
the study.
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