Shrestha et al.
Progress in Earth and Planetary Science(2022) 9:48
https://doi.org/10.1186/s40645-022-00506-2

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Exploration of spatial and temporalvariability of rainfall and their impact
on rice production in Burma in 1901–1939
during the colonial period
Badri Bhakta Shrestha1*, Akiyuki Kawasaki1, Tomoshige Inoue2, Jun Matsumoto3,4 and Tomoko Shiroyama5

Abstract
Climate is one of the main factors for rice crop growth. Understanding the relationship between climate variability
and rice production during the period from 1901 to 1939 in Burma can give a clear picture of the impact of climate
variability on rice yield since there were fewer human interventions on the catchment and almost no use of chemical fertilizer or high-yielding rice varieties at that time. However, the quantitative analysis of climate variability andits impact on rice production has not yet been paid sufficient scientific attention for the historic period. First, thechanging trends of rainfall and rice yield between 1901 and 1939 were analyzed, including the effect of rainfall
variability on rice production from multiple perspectives regarding rainfall characteristics, such as seasonal rainfall,various rainfall indices, rainfall anomalies, and monthly rainfall variability. Then, the relationship between rice yield andrainfall was investigated using multiple regression analysis to show how rainfall spatial and temporal variabilities haveinfluenced rice yield and production, including essential factors that affected rice yield in each Burma district. The
historical development of rice production in Burma during the period was also explored. Our findings indicate that
not only the annual variability of rainfall, but also its monthly variability within a particular year likely influenced riceproduction. Excessive rainfall in the early or middle stage of crop growth or less during the early-middle or latter halfof crop growth possibly caused the rice yield reduction in Burma during the colonial period. Furthermore, the results
indicated that although rainfall anomalies widely differed from period to period, rice yield anomalies clearly showed
the distinction of periods with higher or lower rice yields than average rice yield. Mostly higher than average rice yield
was observed before 1910 in the Coastal Zone and before 1918 in the Delta, Dry, and Hilly Zones. The results of thisstudy imply that selected rainfall indices could affect rice yield, positively or negatively, including the varied magnitude of their effects from one district to another, depending on climatic zones and agricultural ecosystems.
Keywords: Rice development, Rice production, Climate variability, Burma, Colonial period, Rainfall
1 Introduction
Rice crop is a major agricultural produce in terms of production and food consumption in many countries (Matsuda 2009; Auffhammer et al. 2012), particularly in Asia
and Africa, and it is also a major source of income of*Correspondence: badish10@gmail.com
1

Department of Civil Engineering, The University of Tokyo, Tokyo, Japan
Full list of author information is available at the end of the article

people living in many developing countries. Rice production substantially influences global food security as it isa crucial food for the daily life of the people (Ara et al.
2017). It is currently the staple food for over 4.7 billion
people globally, and rice demand is expected to continue
more in the future (Asada and Matsumoto 2009; Homma
et al. 2014; Akinbile et al. 2020). Alternatively, climate
variables, such as rainfall and temperature, may largely
influence rice production and food security worldwide© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

(Homma et al. 2014; Ara et al. 2017; Rokonuzzaman et al.
2018). Moreover, temporal changes in rice crop yield andproduction are driven by a several environmental variables, such as climate and agricultural practices. Therefore, it is necessary to understand variability of suchvariables and their impact on rice production that enables us to understand the capacity of agriculture to adapt
to climatic changes (Stuecker et al. 2018).
The climate is one of the main factors for rice crop
growth and yield. Climatic factors, mainly rainfall andtemperature, directly affect rice crop cultivation (Alamet al. 2011; Panda et al. 2019; Abbas and Mayo 2021),
and any changes in such climate factors may have a significant impact on rice crop yield and production andeconomic, social, and environmental sustainability of acountry (Alam et al. 2011; Panda et al. 2019). To reveal
relationships between climate factors and rice cultivation, numerous studies have been conducted at country
or regional level in various locations (e.g., Saseendran
et al. 2000; Sarker et al. 2012; Mainuddin et al. 2013;
Zakaria et al. 2014; Chung et al. 2015; Le 2016; Pheakdey
et al. 2017; Rene et al. 2016; Ara et al. 2017; Rahman et al.
2017; Ali 2018; Baliarsingh et al. 2018; Nath and Mandal
2018; Kingra et al. 2018; Stuecker et al. 2018; Panda et al.
2019; Abbas and Mayo 2021). Some used temperatureand rainfall factors in their analyses (Lansigan et al. 2000;
Alam et al. 2011; Rowhani et al. 2011; Masud et al. 2014;
Bhatt et al. 2019; Ratnasiri et al. 2019; Rayamajhee et al.
2021), and some only used rainfall as a primary climate
factor that affects the rice yield (Asada and Matsumoto
2009; Tiamiyu et al. 2015; Kunimitsu and Kudo 2015;
Riad and Peter 2017; Ferreira et al. 2018; Rokonuzzaman et al. 2018; Singh et al. 2018; Chandrasiri et al. 2020;
Molla et al. 2020). Most of the previous studies found thatrainfall is one of the most essential climatic variables that
affect the rice production in the tropics, because of its
two-sided effects: (i) a lacking resource such as droughtsand (ii) a catastrophic driver such as floods (Alam et al.
2011; Koudahe et al. 2017; Rokonuzzaman et al. 2018;
Singh et al. 2018; Panda et al. 2019; Chandrasiri et al.
2020; Molla et al. 2020). Particularly, the rainfed rice crop
has a direct relationship with seasonal and inter-seasonalrainfall, which makes it the most vulnerable to seasonal
climate variability (Molla et al. 2020). The excess rainfall may cause flooding in paddy fields, which can damage the rice crops. Alternatively, little or no rainfall canalso lead to dying rice crops. Walishinge et al. (2017) also
highlighted that the water availability for crops essentially depends upon the rainfall distribution, and excessive rainfall can damage early crop growth until the grain
harvest. Therefore, understanding the features of rainfall
variable in a particular area has significant importance inrainfed rice crops (Molla et al. 2020).

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Rainfall is the most variable climate parameter in space
and time (Mathanraj and Kaleel 2016). The impact ofrainfall variables on rice production can be positive or
negative in an area of interest depending on the characteristics of rainfall and rice production. For example,
a recent study by Chung et al. (2015) found that rainfall
positively affected rice yield in Vietnam, while Abbas
and Mayo (2021) observed the adverse effects of rainfall
on rice production in Pakistan. Chandrasiri et al. (2020)
reported that continuous rainfall during the harvestingtime caused complete damage on rice crop in Sri Lanka.
The study by Singh et al. (2018) shows evidence of significant influences of fewer rain days on crop production andsignificant positive correlation of moderate rainfall withcrop production in India. Molla et al. (2020) reported
that the annual rainfall amount and number of rainy days
had a strong significant positive correlation with rice
yield, while the number of dry spells had a strong negative correlation with rice yield in Ethiopia. In some areas,such as Bangladesh, Nigeria, Suriname, and Sri Lanka,
the evidence of a lesser rainfall effect on rice yield or alesser significant relationship between rainfall and rice
yield has also been reported (Zakaria et al. 2014; Tiamiyu
et al. 2015; Riad and Peter 2017; Walishinge et al. 2017).
This evidence found in previous researches clearly showsthat the features of rainfall variable and their impact onrice yield vary from place to place and region to region.
The effect of climate variables on agricultural production
is related to the variability in the local climate rather thanglobal climate patterns (Kingra et al. 2018). Hence, analyzing climate variability and its impact on agricultural
production for each region, and considering local characteristics of climate and crops, is crucial for the agricultural sector to adapt to climate changes.
Even though the impact of rainfall variability on rice
yield has been investigated in previous researches, thecomplete answers are still not clearly understood. Additionally, previous researches mainly focused on theanalysis of the effect of climate factors on recent rice production or periods in the latter half of the twentieth century (Lansigan et al. 2000; Chung et al. 2015; Kunimitsu
and Kudo 2015; Tiamiyu et al. 2015; Adedeji et al. 2017;
Rahman et al. 2017; Kingra et al. 2018; Singh et al. 2018;
Stuecker et al. 2018; Bhatt et al. 2019; Chandrasiri et al.
2020; Abbas and Mayo 2021). Few researchers attempted
to analyze the changing trend in rainfall and/or temperature for the first half of the twentieth century (Jones andHulme 1996; Vincent and Mekis 2006; Riad and Peter2017; Koudahe et al. 2017; Ferreira et al. 2018). Furthermore, the quantitative analysis of climate variability andits impact on rice production has not yet been paid sufficient scientific attention for the early twentieth century
or its first half. Moreover, since observed climate data

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

cannot be easily obtained due to the lack of observation
stations or systematic recording of climate data, agricultural data on rice production and yield for this period are
unreadily available in many countries. In the first half ofthe twentieth century, there were fewer human interventions on the catchment and almost no use of chemical fertilizer or high-yielding rice variety, particularly
in developing countries (Win 1991; Young et al. 1998).
Therefore, studies on impact of climate variability on
agricultural production for this period can provide useful
information to better understand the impact of climate
variability on agricultural production. Moreover, theprevious studies only used local rainfall over the area toanalyze the effect of rainfall on rice production, and theuse of local rainfall is mainly appropriate to explore theimpact of rainfall shortages on crop reductions. However,in general, excess rainfall in the upstream runoff contributing to catchment areas can damage rice crops in thedownstream areas of the river basin. Only the occurrenceof rainfall in the upstream catchment of the river basin
can cause severe flooding in the downstream areas andcan have significant effects on rice production in downstream areas. To analyze crop yield loss due to flooding
or excess rainfall, rainfall in the upstream catchment
area is also an essential factor that needs to be includedin the analysis. Particularly in the case of large upstream
contributing catchment areas, the effect of rainfall in the
upstream catchment area on crop production cannot beignored. It is thus also necessary to analyze the relationship between rainfall in the upstream catchment areas
and rice yield to clarify the effects of rainfall variability onrice production.
Burma was the main rice exporter in the world during the colonial period. Rice production was mainly
dependent on rainfall in most of the districts in Burmaduring the colonial period. Only some areas in the Dry
Zone had irrigation systems, and the irrigation systems
in other areas were not well-developed during the study
period (Win 1991). No systematic studies have yet been
conducted about the trend of rainfall variability and its
impact on rice production in Burma during the colonial
period. In this context, the main objectives of this study
were to demonstrate how the spatial and temporal variability of rainfall impacted rice yield and rice production
and to identify the most critical factors that affected rice
yield in Burma during the colonial period (1901–1939),
by analyzing the spatiotemporal variability of rainfall
and rice yield, and exploring the relationship between
rainfall and rice yield. The historical development of riceproduction in Burma during the colonial period was also
explored. The novelties of this study were (1) to focus on
the effect of rainfall on rice yield for the early twentiethcentury (1901–1939) and (2) to investigate area-averaged

Page 3 of 26

rainfall over the districts, including the whole upstream
contributing catchment areas. The nonparametricMann–Kendall test for detecting the trend and Sen’s
method for estimating the magnitude of the trend were
employed to analyze rainfall and rice yield variability. The
relationship between rice yield and rainfall with different rainfall indices was analyzed on the basis of multiple
regression analysis. To explore the effects of rainfall onrice yield from the aspects of water shortage and floods,
local rainfall at each district and area-averaged rainfall
over the district, including the whole upstream contributing catchment areas (average rainfall over the catchment), were used.

2 Background of Burma (Myanmar)
Burma (currently Myanmar) is situated in Southeast Asia, lying between 10°N and 29°N in latitude andbetween 92°E and 101°E in longitude (Fig. 1). The elevation is highest in the northern part of the country exceeding 5,000 m, and lowest in the Delta areas in the southernpart of the country (Fig. 1). The country can be divided
into four main agroecological zones: (1) Coastal Zone,(2) Delta Zone, (3) Dry Zone, and (4) Hilly or Mountainous Zone (Fig. 2a). The climate of the country can bedivided into three seasons: (1) Summer (mid-February to
mid-May), (2) Monsoon (Rainy) season (mid-May to late
October), and (3) Winter (late October to mid-February)(DMH and UNEP 2012; Sein et al. 2018). The annual
average rainfall in the coastal area is about 5080 mm,
while it is about 762 mm in the central Dry Zone of thecountry (Aung et al. 2017). The mean maximum temperature is about 37.8 °C in the central area during Marchand April, while the mean minimum temperature isabout 4.4 °C–10.0 °C in the northern part of the country
during January and February (Aung et al. 2017).
Figure 2b shows the spatial distribution of soil types
in Burma based on the FAO/UNESCO soil map. We
observed that Acrisols, which are the most widespreadsoil type in Burma (covering 51.44% of the total landarea), were widely distributed in the Hilly Zone or higher
elevated lands. However, Gleysols (also known as paddy
soils) were widely distributed in the river plains, Delta
Zone, lowland areas of the Coastal Zone, and valleys ofthe Hilly or Mountainous Zone (covering approximately
14.82% of the total land area). Interestingly, the Gleysols
were saturated with groundwater for long periods during the year, making them suitable for paddy cultivation.
Alternatively, while Cambisols were only dominant in
districts located in the western part of Burma (covering 13.85% of the total land area), Fluvisols were foundin the Delta and Coastal Zones (covering 0.94% of thetotal land area). In contrast, Luvisols were mainly foundin central areas of the Dry Zone (covering 6.44% of theShrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 4 of 26

Fig. 1 The geographical location of Burma (Myanmar), topographical distribution in Burma based on HydroSHEDS digital elevation model, andlocation of rainfall stations (name and latitude and longitude coordinates of the stations are indicated in Additional file 1: Table S1)

total land area). They are the most important agriculturalsoils in the central areas of the Dry Zone. Furthermore,
Vertisols, which are rich in clay, and the best soils for irrigated agricultural cultivation, were also identified. They
are mainly distributed within the districts located in the
Dry Zone (covering 1% of the total land area). Furthermore, they are the second most important soil for agriculture in the central areas of the Dry Zone (IFDC 2018).
However, Nitosols cover 8.12% of the total land area andare mainly distributed in the inland valley of the Dry
and Delta Zones. They are found in some parts of the
Coastal and Hilly Zones. Other soil types discovered in
Burma are Lithosols (covering 2.57%), Planosols (covering only 0.06%), and Solonchaks (covering 0.76%). Soils
that are heavy and almost impervious in nature, such as
heavy clay, through which water cannot seep easily, aresuitable for rice cultivation (Siok-Hwa 2012). Thus, while
the Gleysols, Vertisols, Luvisols, Cambisols, and Fluvisols
are naturally good fertile, the Acrisols and Nitosols are
considered naturally low fertile soils (Win 1991; ICEM
2016; Htwe 2016; Elstner 2017; IFDC 2018). Nevertheless, although the Fluvisols, Gleysols, Luvisols, and Vertisols are the primary rice cultivation soils in these areas(Htwe 2016), upland rice is still grown in large areas withAcrisols in hilly or mountainous areas.
The British colonized the country in three stages.
The colonization of Burma began in 1826 withparts of Lower Burma by occupying the Arakan andTenasserim coastal strips. Pegu and Martaban werecolonized in 1852 and the rest of the country in1885 (Hlaining 1964; Win 1991). Burma politicallyremained a province of India until 1937 when BurmaShrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 5 of 26

Fig. 2 a Agroecological zones and district boundary during the Colonial Burma period (1925/26–1946/47) (Bennison 1933; Zaw and Than 2017;
Okamoto 1998) and b soil types in Burma based on digital soil map of FAO/UNESCO (https://​data.​apps.​fao.​org/). The district names are indicatedin Additional file 1: Table S5. The states with hatched lines (Northern Shan, Southern Shan, Chin Hill, and Karenni) were excluded from the study
because the rice crop data were unavailable for these states

was separated from India. Then, the Japanese occupied Burma in early 1942, and it was re-occupied by
the British in 1945. Finally, it was granted independence in 1948. The regions of Burma, which were under
the direct control of the British, were also commonly
called Burma Proper (Okamoto 1998). The administrative divisional boundaries of Colonial Burma werechanged many times. However, there were seven divisions with 37 districts from 1925/26 to 1946/47 thatwere directly under British rule. The administrative
district boundaries of Colonial Burma are indicated inFig. 2a. The Chin Hills, Northern Shan, Southern Shan,and Karenni states indicated in Fig. 2a were not under
the direct control of British rule (Okamoto 1998). The
population in the country was about 10.49, 14.53, and16.82 million people in 1901, 1931, and 1941, respectively (Bennison 1933; Aye 2019). The increase in population from 1901 to 1931 was about 38.5%.

3 Data
Table 1 indicates the list of obtained climate and crop
data for the study. The available climate data for Burmaduring the colonial period were only precipitation, andother climate data, such as temperature, were readily
unavailable. In this study, only precipitation data werethus used to analyze climate variability and its impact
on rice production. The daily precipitation data wereobtained for the period 1891–1939 at 32 stations. The
locations of precipitation stations are indicated in Fig. 1(also see Additional file 1: Table S1 for the latitude andlongitude coordinates of each station). During WorldWar II, the precipitation data were completely unavailable for December 1940 and from August 1941 to December 1946 at all 32 stations. There are no available data at
some stations before 1900. The data from 1891 to 1939
were digitized by some authors from the data books
listed in Table 1.

Yearly (37 districts)

Daily (32 stations)Temporal resolution

Yearly (37 districts)

District-level data
District-level data

Rice yield

Rice production–

Okamoto (1998)

Okamoto (1998) http://​hdl.​handle.​net/​10086/​
14672

Win (1991): 1830–1985
Okamoto (1998): 1901–1946

Okamoto (1998) http://​hdl.​handle.​net/​10086/​
14672

–

–

1860–1985

1830–1985

FAO/UNESCO https://​data.​apps.​fao.​org/​map/​
catal​og/​srv/​eng/​catal​og.​search?​id=​14116#/​
metad​ata/​446ed​430-​8383-​11db-​b9b2-​000d9​
39bc5​d8

Okamoto (1998), Myanmar Information Management Unit (http://​themi​mu.​info/)

HydroSHEDS (provided by the United States
Geological Survey) https://​www.​hydro​sheds.​
org/​downl​oads

Win (1991): 1860–1985

Win (1991): 1830–1985
Okamoto (1998): 1901–1946

1901/02–1946/47 (not avail- Okamoto (1998)
able for 1941/42–1944/45) **1901/02–1946/47**

1912/13–1946/47

1830–1985“Rainfall of India (1891–1914) and Daily Rainfall of India (1915–1937)” available on the web
page of the NOAA Central Library, National
Oceanic and Atmospheric Administration,
USAhttps://​libra​r y.​noaa.​gov/​Colle​ctions/​Digit​
al-​Docs/​Forei​gn-​Clima​te-​Data/​India-​Clima​
te-​Data
Data were checked by the original books at
the Library of the Indian Institute of TropicalMeteorology (IITM)

Sources

1938–1939 “Daily Rainfall Recorded in Burma” stored in
the Library of The Royal Netherlands Meteorological Institute (KNMI; 1938–1939)
1901/02–1946/47**

1891–1937

Period**Rice crop, yield, and production data for Maubin and Paypon district are available from 1903/04, and for Insein district from 1912/13(2022) 9:48

*The precipitation data from December 1940 and August 1941 to December 1946 were unavailable (missing or unreadable) at all 32 stations. The precipitation data are unavailable at some stations before 1900

Soil Types (Digital Soil Map)

Administrative boundary map and data District, country

30-arcsecond

Digital elevation data (DEM, flow direction, flow accumulation)–

Country level data (whole Burma) 10-Yearly (from 1860 to 1900), Yearly (from1900 to 1985)

Rice export

Country level data (whole Burma) 5-Yearly (from 1830 to 1900), Yearly (from1900 to 1985)

Yearly (37 districts)

Yearly (37 districts)

Country level data (whole Burma) 5-Yearly (from 1830 to 1900), Yearly (from1900 to 1985)

District-level data

Point data

Spatial resolution

District-level data

Rice-harvested area

Rice-cultivated area

Rice crop data

Precipitation* (rainfall)

Climate data

Data

Table 1 List of data available for study and their sources

Shrestha et al. Progress in Earth and Planetary Science
Page 6 of 26

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Rice crop data such as rice-cultivated areas, harvested
areas, rice yield, and rice production were obtained fromthe report published in Okamoto (1998). The rice crop
data at the district level were available for the period
1901/02–1946/47. However, the district-level data werecompletely unavailable at all districts during World War
II for 1941/42–1944/45. Since the crop data are available
from 1901 to 1939/40, this study focused on the analysis of rainfall impact on rice production over the period
from 1901 to 1939. Additionally, the crop data were unavailable for the Northern Shan, Southern Shan, Chin Hill,and Karenni states because these areas were under theindirect control of the British government rather thanthe direct British control (Okamoto 1998). Therefore,
these areas were also excluded from the study. The rice
crop area and production data for the whole Burma over
1830–1985 and rice export data over 1860–1985 werealso obtained for the study from previous reports (Win1991; Okamoto 1998).
The digital elevation model (DEM), flow direction
data, and flow accumulation data were obtained from
HydroSHEDS (https://​www.​hydro​sheds.​org/) (provided
by the United States Geological Survey) to extract watershed boundaries to calculate the basin average rainfall
for the districts. The administrative boundary maps anddata were obtained from Okamoto (1998) and Myanmar
Information Management Unit (http://​themi​mu.​info/) todigitize the district map for the Colonial Burma period.
Additionally, while the digital soil map was obtained fromthe FAO/UNESCO (https://​data.​apps.​fao.​org/), othersecondary information (paddy land areas for Upper andLower Burma, use of rice varieties, fertilizers, rice policy,
and rice development for the Colonial Burma period)
was obtained from previous studies.
The annual and seasonal rainfall data at each station forthe study period (1901–1939) are presented in Additional
file 1: Tables S2 and S3, respectively. The average monthly
rainfall during 1901–1939 is presented in Additional
file 1: Table S4. The average rice yield during 1901–1939
at each district in Burma is also presented in Additional
file 1: Table S5.

4 Overview of rice ecosystems and historicaldevelopment of rice production in Burma
4.1 Rice cultivation and ecosystems

The traditional rice cultivation methods in Burma can bedivided into lowland (wetland, i.e., practiced in lowland
or plain areas) and upland (dryland, i.e., practiced in hilly
areas or wooded hillsides) methods (Kyaw et al. 1977;
Mochizuki et al. 1993; Mackill et al. 1996; Young et al.
1998; Siok-Hwa 2012). The upland cultivation methods
practiced on wooded hillsides were typical slash-andburn methods, also called shifting cultivations (taungya)

Page 7 of 26

or fire farming (Siok-Hwa 2012; Denning et al. 2013).
The main differences between these two rice cultivation methods are that (i) in lowland rice cultivation, riceseeds are first sown in the nursery beds, and transplanting of seedlings is done in the paddy field, except in someareas where the soil is poor, labor is scarce, or in situation
when seedlings cannot be uprooted due to deep watering (where broadcasting or direct seeding was practiced),
while direct seeding of rice in the paddy field is practiced
in upland rice cultivation. (ii) The lowland rice is grown
on wet paddy fields or fields flooded either following
rainfall or irrigation, while upland rice is grown on dry
paddy fields without flooding. Additionally, most lowland
rice fields have a bund, except in some areas that are subject to flooding. The rice plant is kept partially submerged
from transplanting to harvesting in these areas. In comparison, upland rice cultivation is practiced withoutbunding (Win 1991; Young et al. 1998; Siok-Hwa 2012).
Finally, since upland rice depends on rainfall greatly, therice yield of upland rice is lower than lowland rice.
Rice ecosystems of Burma during the twentieth century were referred to as rainfed-lowland, deep-water,
irrigated-lowland, and upland rice (Kyaw et al. 1977;
Win 1991; Young et al. 1998; Denning et al. 2013). The
rainfed-lowland rice was the primary dominant rice production system in Burma during the colonial period, concentrated mainly in the Delta and Coastal Zones because
the rainfall amount in these areas during monsoon was
sufficient for rice cultivation without irrigation. However,the deep-water rice production was primarily concentrated in lower delta areas and coastal strips, usually cultivated under deep submerged water. Contrastively, theirrigated-lowland and upland rice productions were concentrated mainly in the Dry and Hilly Zones, respectively(Win 1991; Young et al. 1998; Denning et al. 2013). The
major production constraint in poorly drained rainfedlowland and deep-water areas is the excessive floodwater
during the monsoon (Young et al. 1998; Denning et al.
2013). Furthermore, while annual rainfall is inadequate
to produce a rainfed rice crop in the Dry Zone except in
low-lying areas with a high-water table, rice cultivation in
the Dry Zone can only be productive when rice is grownunder irrigation conditions because of the increased
number of sunshine hours (Denning et al. 2013).
4.2 Historical development of rice production

During the colonial period, Burma was the largest riceproducer and major rice exporter country in the world.
Figure 3 indicates the expansion trend of planted paddy
areas, rice production, and rice exports from 1830 to1985. The rice crop area in 1830 was only about 27,000 ha,
and it was increased to 3.46 million ha in 1900 and 5.0
million ha in 1930. Such rapid expansion of rice crop area

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 8 of 26

Fig. 3 The historical development of rice production and planted paddy area and rice export in Burma (Data Source: Win 1991)

was attributed to several reasons: (1) availability of vast
areas of deltaic land favorable for rice cultivation, (2) rising demand for rice in Europe and Asian countries such
as Malaya, India, and Sri Lanka, (3) provision of different
incentives and rice development policy, (4) improvementin transport and shipping facilities, and (5) the removal ofprevious prohibitions on rice exports by the British government (Jayasuriya 1984; Win 1991; Young et al. 1998).
The expansion of the rice crop area was then relatively
stable from 1930 to 1940 (Win 1991; Young et al. 1998).
The annual rice production increased from 44,000 tons
in 1830 to 5.5 million tons in 1900 and 7.2 million tons
in 1930, and it ranged from 5.39 to 7.9 million tons for
1900–1939. The rice production in Burma has continued
to increase, except for a considerable decline in the 1940s
during World War II period (Young et al. 1998; Matsuda2009).
British administration mainly focused on the development of rice crops in the Ayeyarwady Delta in Lower
Burma with rapid expansion of rice crop areas between
1885 and 1910 (see Additional file 1: Figure S1) because
the delta area was a favorable rice environment in termsof soil and climatic conditions. Figure 4 indicates thespatial development of rice crop area and rice production for the studied period (for the beginning: 5-year
average from 1901 to 1905, and for the end: 5-year
average from 1935 to 1939) and their variations. The
5-year average rice crop area for 1901–1905 was about
0.74, 2.16, 0.75, and 0.14 million ha, respectively, in the
Coastal, Delta, Dry, and Hilly Zones. For 1935–1939,
it was increased to 0.97, 2.86, 0.94, and 0.20 million
ha, respectively, in the Coastal, Delta, Dry, and Hilly
Zones. Similarly, the 5-year average rice production

for 1901–1905 was about 1.50, 4.18, 1.05, and 0.24 million tons, respectively, in the Coastal, Delta, Dry, andHilly Zones, while it was about 1.34, 4.60, 1.16, and 0.27
million tons, respectively, in the Coastal, Delta, Dry,and Hilly Zones for 1935–1939. The rice crop areas
were vastly expanded in the districts where Gleysols
or Gleysols and Vertisols or Gleysols and Luvisols, or
Gleysols and Cambisols, or Gleysols and Fluvisols were
widely distributed. However, in some districts of thecentral Dry Zone, the rice crop areas were decreased,even with the wide distribution of naturally good andfertile soil types, such as Luvisols and Cambisols in theLower Chindwin District; Gleysols, Vertisols, and Luvisols in the Meiktila and Sagaing Districts; Luvisols in
the Myingyan District; and Gleysols and Vertisols in
the Magwe District. This contrasting observation was
possibly due to the insufficient water supply during ricecultivation in the central Dry Zone. Furthermore, we
observed that the widely distributed soil types in the
districts with an increased tendency of rice production (red/orange colored in Fig. 4) were Acrisols and
Gleysols in the Myitkyina District; Gleysols, Vertisols,and Luvisols in the Shwebo District; Gleysols, Cambisols, and Luvisols in the Minbu and Yamethin Districts;
Gleysols and Nitosols in the Prome and Pegu Districts;
Gleysols in the Maubin District; Gleysols and Fluvisolsin the Pyapon and Myaungmya Districts; and Gleysols
and Cambisols in the Bassein District. In addition, it
was observed that the rice production was primarily reduced in the districts where Acrisols (Katha andUpper Chindwin Districts) or Acrisols and Nitosols(Mergui District) or Cambisols (Sandoway and Kyaukpyu Districts) were widely distributed.

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 9 of 26

Fig. 4 a 5-year average planted rice crop areas in the beginning (average of 1901–1905) and end (average of 1935–1939) of the study period andtheir differences and b 5-year rice production in the beginning (average of 1901–1905) and end (average of 1935–1939) of the study period andtheir differences. The states with white colored (Northern Shan, Southern Shan, Chin Hill, and Karenni) were excluded from the study because therice crop data were unavailable for these states. (Note: Since data on crop area and rice production for Insein district are available only from 1912,
average data for years 1912–1916 were used for the 5-year average for the beginning of study areas in the case of Insein district)Burma rapidly became the major world rice exporter
during the colonial period, contributing 47% of world
exports in 1938/39, and it exported 2–3 million tons ofrice annually (up to 70% of national production) between
1900 and 1940 (Win 1991). Additionally, while Lower
Burma produced the quality of rice for external trade, theupland rice produced in Upper Burma was mainly used

for internal consumption because it was barely sufficientfor internal consumption in that region (Siok-Hwa 2012).
The main classes of rice produced in Burma during the
British Colonial period were Emata, Letywezin, Ngasein, Meedon, and Byat rice varieties (Win 1991; Younget al. 1998). No modern rice varieties were used during
this period in Burma as the high-yielding variety of rice

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 10 of 26

Fig. 5 Overview of the research methodology(IR-8) was first introduced in Burma in 1967 (Win 1991;
Young et al. 1998) (see Additional file 1: Figure S2). During the colonial period, fertilizer use was only limited toan experimental stage, and farmers mainly used domestic fertilizer such as cattle dung, fish waste, bat guano,
rice bran, cotton cakes, and bone meal (Win 1991). The
rice yield mainly depended on the fertility of the land
and the distribution of rainfall amount in a particulararea. The delta areas in Lower Burma had these favorable conditions for agriculture cultivation, and the highest
rice yield could be obtained. The rice yield in the Upper
Burma highly varied because some areas had irrigation,
while others received insufficient rainfall (Win 1991).
According to Siok-Hwa (2012), approximately half of thecrops grown in the Upper Burma were cultivated underirrigated systems from canals, storage tanks, and lakes.
For example, most crops were cultivated under canalirrigation in Mandalay, Kyaukse, Minbu, and Meiktila
Districts. However, in Shwebo and Yamethin Districts,
about half of the crops were cultivated under irrigation.
Finally, while about two-thirds of the crops were rainfed
in Pakokku, Sagaing, Bhamo, Katha, and Upper Chindwin Districts, crop cultivation was almost entirely rainfed
in Thayetmyo, Lower Chindwin, and Myitkyina Districts(Siok-Hwa 2012).

5 Method
The rainfall variability and their impact on rice yield
were analyzed for 1901–1939 based on the methodology (Fig. 5). First, spatial and temporal variability of rainfall and rice yield and variation in rice production wereanalyzed. Then, the relationship between different rainfall indices and rice yield was analyzed on the basis ofmultiple regression analysis. Finally, the effects of rainfall
on rice yield and factors that affected the rice production in Burma during the colonial period were explored.
In this study, the analyses were conducted at the districtlevel. To explore the effects of rainfall on rice yield fromthe aspects of water shortage and floods, local rainfall ateach district and area-averaged rainfall over the district,including the whole upstream contributing catchment
areas (basin average rainfall over the catchment), wereused. The details of the methodology are described in
Sects. 5.1, 5.2 and 5.3.
5.1 Rainfall characteristics and variability analysis

Daily rainfall data were used to calculate the annual,seasonal, and monthly rainfall. First, annual rainfall andmonthly average rainfall features in each zone (Coastal,
Delta, Dry, and Hilly Zones) were calculated using rainfall data at stations located in each zone. Then, the changing trend in local seasonal rainfall (local point rainfall)and basin average rainfall during the crop growing season
for each district was analyzed.
The rice crop could be affected by excessively low
or high rainfall at any particular time within the crop
growing season. To analyze the effect of rainfall shortage (low rainfall) on the rice crop production, daily
rainfall at the station located in each district was used
to calculate the local seasonal rainfall for each district.
If there were more than one station in the district, rainfall at the station with no missing data or less missing

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

data during monsoon was used. If there is no station in
the district, rainfall data at the nearest station locatedin a neighboring district in the same zone were used.
In Burma, the monsoon is from mid-May to late October (Matsumoto 1997), during which rice crop wascultivated, and rainfall amount during this period isessential for rice crop growing. Furthermore, the soil
moisture in cropland areas before rice transplanting
is an essential factor for rice crop cultivation. Therefore, local seasonal rainfall was calculated from May to
October.
To analyze the effect of extremely high rainfall or
flooding on rice crop yield, the rainfall distribution in
the upstream drainage basin is necessary to be includedin the analysis. Therefore, the basin average rainfall was
calculated by delineating the upstream drainage basinfor each district case. By using DEM, flow direction, andflow accumulation (30-arcsec spatial resolution) obtained
from HydroSHEDS, the upstream drainage basin of theriver, which passes through the district, was delineated in
ArcGIS for each district. Then, daily basin average rainfall for each district was calculated using observed daily
rainfall at the stations based on the Thiessen Polygon
method. In Burma, rice crop was planted in July and harvested in October, and rice crop was affected by flooding during this period. Therefore, seasonal basin average
rainfall was calculated from July to October.
The trend of local seasonal rainfall and seasonal basin
average rainfall for each district case was calculatedusing MAKESENS 1.0 (Mann–Kendall test for trend
and Sen’s slope estimates) excel-based tool (Salmi et al.
2002) (https://​en.​ilmat​ietee​nlait​os.​fi/​makes​ens). The
MAKESENS tool calculates the changing trend basedon the nonparametric Mann–Kendall test and the nonparametric Sen’s method for the magnitude of the trend.
The Mann–Kendall test (Mann 1945; Kendall 1975)
is a nonparametric test and does not need the data to
be normally distributed, and the test has low sensitivity to sudden breaks due to inhomogeneous time series
and outliers (Koudahe et al. 2017; Ali and Abubaker2019). The Mann–Kendall test is applicable to detecting
a monotonic trend of a time series with no seasonal orother cycles (Salmi et al. 2002). The Sen’s method (Sen
1968) uses a linear model for the trend and estimates themagnitude of trends in the time series. The spatial variability of rainfall was analyzed using a geographical information system (GIS) tool (ArcGIS). The variability inseasonal rainfall was also analyzed using the standardized
rainfall anomaly which was calculated as follows.

RAi = (Ri − Rmean )/RSD(1)

where RAi is the standardized rainfall anomaly for year
i, Ri is the annual or seasonal rainfall for year i, Rmean isPage 11 of 26

the mean rainfall of time series, and RSD is the standard
deviation of time series.
5.2 Rice yield and production variation analysis

To understand variations in rice yield, first, average rice
yield and its change trend in each zone were calculated
for the period 1901–1939. Then, variations in rice croparea, rice-harvested area, rice yield, and rice productionin each district were analyzed for the period 1901–1939
using district-level data. Based on this, the extremely
low- and high-yield years were identified, and the rainfall variability in those years was analyzed to explore
the effect of rainfall on rice yield. The spatial variationsof rice yield/production were analyzed using GIS tools(ArcGIS). Further, the standardized rice yield anomaly
was calculated in the same way as for rainfall (Eq. 1), andthe correlation between the rice yield and rainfall anomalies was estimated for each district.
5.3 Analysis of relationship between rainfall and rice yield

The relationship between rainfall and rice yield for each
district was explored using seasonal rainfall and othervarious rainfall indices (Table 2). To analyze the effectof rainfall shortage on rice crop, rainfall-related indices,such as seasonal local rainfall (SLR), the number of dry
days (NDD), the maximum number of consecutive drydays (CDD), and maximum number of consecutive wetdays (CWD) during monsoon, which was calculatedusing daily local rainfall, were used. Similarly, to analyze
the effect of flooding on rice crops, seasonal basin average rainfall (SBR), number of heavy rainfall days (HRD),
and 5-day maximum rainfall (5DR), which were calculated using basin average daily rainfall data, were used.
The definition of each rainfall index is also indicated inTable 2. The seasonal rainfall provides information on
the variability of interannual rainfall during the monsoon. The NDD provides data on total dry days during
the monsoon. The maximum number of consecutive dryor wet days was used to characterize the length of dry or
wet periods (Vincent and Mekis 2006; Wu et al. 2016). By
referring to a previous research (Vinnarasi and Dhanya2016), the dry day was defined as a day with rainfall lessthan 2.5 mm, while the wet day was defined as a day with
rainfall of 2.5 mm or more rainfall. The number of HRD
and 5-day maximum rainfall describe the features ofextreme rainfall. The threshold for heavy rainfall day wasused as the day with rainfall ≥ 20 mm (Wu et al. 2016;Pińskwar et al. 2019).
Regression analysis is a useful approach to analyze therelationship between dependent and independent variables. In this study, multiple regression analyses were conducted to determine the relationship between rice yieldand features of the rainfall indices indicated in Table 2,

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 12 of 26

Table 2 Various rainfall indices and their definition
Rainfall Index

Definition

Unit Remarks

For rainfall shortage effect: extracted using local rainfall
Seasonal local rainfall (SLR)

Total amount of rainfall during monsoon season (May to October)mm

Number of dry days (NDD)

Number of days with daily rainfall < 2.5 mm during monsoon season (May to October)day

Maximum consecutive dry days (CDD)

Maximum number of consecutive dry days during monsoon season (May to October).
The dry day is defined as the day with rainfall < 2.5 mm

dayMaximum consecutive wet days (CWD) Maximum number of consecutive wet days during monsoon season (May to October).
The wet day is defined as the day with rainfall ≥ 2.5 mm

day

Vinnarasi
and Dhanya(2016)For flooding effect: extracted using basin average rainfall
Seasonal Basin average rainfall (SBR)

Total amount of basin average rainfall during crop growing period (July to October)mm

Number of heavy rainfall days (HRD)

Number of days with daily basin average rainfall ≥ 20 mm during crop growing period(July to October)day

5-day maximum rainfall (5DR)

Maximum 5-day basin average rainfall (during July to October)mm

which is widely used in previous researches for analyzing
the relationship between crop yield and climate variables(e.g., Jayakumar et al. 2016; Pheakdey et al. 2017; Sitienei et al. 2017; Panda et al. 2019). Multiple regression
analysis is useful to determine the relationship between
the dependent variable and many independent variables
and the importance of each independent variable to therelationship.

6 Results
6.1 Characteristics and variability of rainfall

Figure 6a indicates the variability of annual rainfall at
the stations located in each zone of Burma from 1901 to
1939. The annual rainfall in the Coastal Zone was comparatively higher (more than 4000 mm at most of thestations) than in other areas. The annual rainfall amount
mostly ranges from 2000 to 3000 mm in the Delta Zone,
from 700 to 1300 mm in the Dry Zone, and from 1500 to
2200 mm in the Hilly Zone. Figure 6b indicates the average monthly rainfall from 1901 to 1939 for each zone.
The monthly rainfall amount was also comparatively highin the Coastal Zone than in other areas, and less in the
Dry Zone. The monthly average rainfall in Dry Zone was
below 200 mm in the months. Figure 6b indicates thatthe rainfall mainly occurred from May to October in allzones.
Figure 7 indicates spatial pattern of change trend of
seasonal local and basin average rainfall. The changing
trends in seasonal local and basin average rainfall for theselected districts in the period from 1901 to 1939 are presented in Additional file 1: Figs. S3 and S4, respectively.
The detailed statistics summary of change trend analysisof rainfall for all districts is presented in Additional file 1:

Wu et al.
(2016),
Pińskwar
et al. (2019)

Table S6. There was a clear interannual variation in therainfall in each district. SLR positively varies over time at
25 districts out of 37 districts, while the SBR positively
varies over time at 32 districts out of 37 districts, i.e.,most of the districts experienced an increase in seasonalrainfall for the period 1901–1939. However, the increasing trend of local rainfall over time was statistically significant with a significant level of 5% (two-tailed test)
only in six districts, while the increasing trend of basin
average rainfall was statistically significant with a significance level of 5% or 10% (two-tailed test) in 14 districts(Additional file 1: Table S6), which were located in the
Delta and Dry Zones.
6.2 Variations in rice yield and production

Figure 8a indicates the spatial pattern of the averagevalue of rice yield from 1901 to 1939. The rice yield washighest in the Delta Zone than in other zones because
this zone had favorable soil and rainfall conditions forrice crop cultivation than in other areas. As expected,
this zone had an average rice yield that was > 1500 kg/
ha in all the districts where Gleysols were widely distributed, except in Insein, and > 1800 kg/ha rice yields
in Myaungmya (2000 kg/ha), Henzada (1869 kg/ha), andTharrawaddy (1889 kg/ha) Districts. The lowest rice yield
was obtained in Insein District (1424 kg/ha). Furthermore, while the average value of district-level rice yieldin the Coastal Zone varied from 1360 kg/ha in the Arakan District to 1710 kg/ha in the Sandoway District, therice yield was > 1500 kg/ha in the Sandoway (areas with
Cambisols and Fluvisols), Akyab (areas with Gleysols andCambisols), and Mergui (areas with Acrisols and Nitosols) Districts. It was also observed that although theShrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 13 of 26

Fig. 6 Rainfall characteristics in the Coastal, Delta, Dry, and Hilly Zones of Burma from 1901 to 1939: a annual rainfall and b average monthly rainfall

average rice yield was much higher in the Mergui District, low fertile soils (Acrisols and Nitosols) are widelydistributed in the district. Along the seacoast in the
Amherst and Thaton Districts of the Coastal Zone werehigh-yielding areas; however, crop damage from flooding of the Salween River led to low yields in many of theinland areas, reducing the average value of rice yield forthese districts (Siok-Hwa 2012). Alternatively, rice yield
was lowest in the Dry Zone. A study observed that while
there were high variations in rice yield of the Dry Zone
because some areas in this zone had irrigation systems(large-scale irrigation systems developed by the Burmese
King and small-scale irrigation systems constructed by
local people, such as small diversions, wells, and ponds),

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 14 of 26

Fig. 7 Spatial pattern of the change trend of seasonal a local rainfall and b basin average rainfall from 1901 to 1939 (with significance level of
two-tailed test)other areas received little rainfall (Win 1991). The district-level rice yield in the Dry Zone varied from 702 kg/
ha in the Myingyan District to 1828 kg/ha in the Minbu
District. Additionally, the highest yield in the Dry Zone
was obtained in the Minbu District, where the soil types
were Cambisols, Gleysols, and Luvisols, and most of thecrops were cultivated under an irrigation system. Moreover, the average value of rice yield in the Dry Zone washigher in Mandalay (1608 kg/ha) and Kyaukse (1726 kg/ha) Districts, where Vertisols, Luvisols, and Acrisols
were distributed, and most of the crops were cultivatedunder an irrigation system. In contrast, the average valueof rice yield was 950 kg/ha in the Meiktila District, eventhough the crops were cultivated under irrigation and in
good-fertile soils (Gleysols, Luvisols, and Vertisols). One
possible reason for such lower rice yield in the Meiktila
District was crop damage due to flooding in the areas.
We also observed that the average values of rice yield in
Shwebo (areas with mostly Vertisols, Gleysols, and Luvisols) and Yamethin (areas with Gleysols, Luvisols, andNitosols), where half of the crops were cultivated underirrigation, were 1258 and 1313 kg/ha, respectively. In
contrast, the average value of rice yield in the Hilly Zone

varied from 1243 kg/ha in the Salween District (areas
with Acrisols) to 1758 kg/ha in the Myitkyina District(areas with Gleysols and Acrisols). Nevertheless, the rice
yields in Upper Burma were considerably lower than thatin Lower Burma, mainly due to insufficient water supply
in Upper Burma (Siok-Hwa 2012).
Figure 8b indicates the changing trend of average rice
yield over the period from 1901 to 1939 for each zone,
and rice yields in all zones show the long-term reducing
trend during the study period. The long-term reducing
trend of rice yield in all areas was statistically significant
at 0.1% significance level of two-tailed test.
Figure 9 indicates the variation of the rice crop plantedarea, rice-harvested area, and rice yield for selected districts from each zone for the study period. The crop areas
were rapidly expanded in the Delta and Coastal Zones inthe Lower Burma than in other areas where the annual
or seasonal rainfall amount was sufficiently higher than
the water requirement for the rice crop cultivation. Rice
production and yield fluctuated depending on the behavior of monsoon rainfall and its distribution pattern. Particularly in the years 1919 and 1923, there was a large
reduction in rice yield in some districts located in theShrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 15 of 26

Fig. 8 a Spatial pattern of the average value of rice yield from 1901 to 1939 and b change trend of average rice yield over the time during theperiod for 1901–1939 for the Coastal, Delta, Dry, and Hilly Zones (Nx = Year—First Data Year) (with significance level of two-tailed test)Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 16 of 26

Fig. 9 Variation of planted and harvested cropland areas and rice yield for selected districts (blue vertical dashed line: years (1919 and 1923) inwhich there was a large reduction in rice yield at some districts located in Coastal, Delta, and Dry Zones)

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Coastal, Delta, and Dry Zones. The rice-harvested areas
were also lower than the rice crop planted areas in theyears 1919 and 1923 in the zones. The difference between
planted and harvested rice areas likely depends on thezone. In the districts located in the Dry Zone except for
Prome District and some districts of the Hilly Zone, therice-harvested area was usually lower than the rice crop
planted area, and a reason for such lowering in rice-harvested area was insufficient amount of rainfall in the zone
for rice cultivation. The occurrence of continuous rainfall during the early growth period of the rice plant often

Page 17 of 26

caused floods in the areas, which reduced rice yield andproduction (Win 1991). In some years, the reduction inrice yield was caused by the occurrence of less rainfall ina particular area.
Figure 10 indicates the interannual variations andtrends of rice yield over the period from 1901 to 1939
for the selected districts, and summary statistics ofthe trend analysis are indicated in Additional file 1:
Table S7. Figure 11 indicates the spatial pattern ofchange trend of rice yield with a statistically significant
level (0.1% or 1% or 5% or 10%) (two-tailed test). The

Fig. 10 Interannual changes and change trend of rice yield over the period for 1901–1939 for selected districts (Nx = Year – First Data Year) (withsignificance level of two-tailed test)Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Fig. 11 Spatial pattern of change trend of rice yield during theperiod for 1901–1939 (with significance level of two-tailed test)changing trend was statistically significant with a significance level of 0.1% or 1% or 5% or 10% at 31 districts.
The results of trend analysis indicate that 33 districts
out of 37 districts experienced a decrease in rice yieldfor 1901–1939. The rice yield increased in trend only infour districts (Arakan, Hanthawaddy, Pyapon, and Salween). The rice yield mainly depended on the fertility of
the land and the amount and distribution of rainfall in aparticular area. The reducing trend of rice yield over theyears in Burma during the colonial period was attributed to several reasons, such as variability of seasonaland monthly rainfall and other environmental factors
during the crop growth period, the estimation of rice
yield covering wider areas representing various types ofsoil and climate with the years, and reaching the minimum fertility rate of the land after several years of ricecultivation. During this period, cropland areas for ricecultivation were expanded by clearing swamps, grasslands, and forest areas, and the fertility of such lands
possibly became the minimum level after many years ofcultivation. Even though rice yield tended to decreaseduring the study period, rice production remarkably

Page 18 of 26

increased due to the rapid expansion of cropland areas
for rice cultivation.
Figure 12 shows the cross-plot between rainfall andrice yield anomalies with their linear tendency. The linear regression analysis statistics are also indicated in
Additional file 1: Table S8. The results showed that rice
yield anomaly positively varied with rainfall anomaly inall dry and hilly zone districts, except Salween District inthe Hilly Zone. We also observed that rice yield anomaly
negatively varied with rainfall anomaly in four (Amherst,
Thaton, Sandoway, and Akyab) of eight districts in the
Coastal Zone and seven (Rangoon, Pyapon, Myaungmya,
Bassein, Maubin, Insein, and Pegu) of 11 districts in the
Delta Zone. There were also some rice yield variation tendencies due to rainfall deviation, such as a reduction inrice yield when rainfall is lower than the average rainfall,particularly in most dry and hilly zone districts or higher
than the average rainfall in more than half of the districts
located in the Delta and Coastal Zones. However, the linear relationship between rice yield and rainfall anomalies was statistically significant, with a significance level(p-value using two-tailed test) of 5% or 10% only in the
Sandoway District (areas with mostly Cambisols) in the
Coastal Zone; Insein District (areas with Gleysols andNitosols) in the Delta Zone; Prome (areas with Gleysols
and Nitosols), Thayetmyo (areas with mostly Luvisols andCambisols), Meiktila (areas with Gleysols, Luvisols, andVertisols), and Myingyan (areas with Luvisols) Districtsin the Dry Zone; and Bhamo District (areas with Gleysols
and Acrisols) in the Hilly Zone. The annual variation in
rainfall and rice yield anomalies with their 5-year moving average values is presented in Additional file 1: Figure
S5. The characteristics of rainfall and rice yield anomalies
were completely different. The rainfall anomaly widelydiffers from period to period; however, results of rice
yield anomaly clearly show the distinction of the periodwith higher or lower rice yield than average rice yield.
Rice yield tended to decline after 1910 in most districts(Win 1991; Okamoto 1998). Mostly higher than average
rice yield was observed before 1910 in the Coastal Zone
and before 1918 in other zones, while mostly lower than
average rice yield was observed after 1910 in the Coastal
Zone and after 1918 in other zones. Moreover, during thelatter part of the study period, the reduction in rice yield
was possibly due to the variability of various environmental factors and the expansion of cropland areas withless favorable environments.
6.3 Exploration of relationship between rainfall and riceyield

The summary results of multiple regression analysis withslope coefficient, constant, significance level, and t-statvalues of each rainfall index are indicated in Additional

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 19 of 26

Fig. 12 Cross-plots between rice yield and seasonal local rainfall anomalies with their linear regression trend for selected districts (with significancelevel of two-tailed test)Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

file 1: Table S9 for all districts. The t-stat values withbold in Additional file 1: Table S9 are statistically significant variables (at 10% or 5% or 1% or 0.1% significancelevel of two-tailed test). The results of multiple regression analysis indicate that the rice yield varied negatively
with SLR, NDD, CDD, CWD, and SBR at most of the districts located in the Coastal Zone. In contrast, rice yield
varied positively with HRD and 5DR. In Delta and Hilly
Zones, rice yield varied negatively with all rainfall indices at most of the districts. In the Dry Zone, rice yieldcorrelated positively with SLR and SBR and negatively
with NDD, CDD, CWD, HRD, and 5DR at most of the
districts, respectively. The results show that the floodrelated indices, such as SBR, HRD, and 5DR, had both
positive and negative effects on rice production, i.e., rice
yield varied negatively with those indices at some districts and positively varied in some other districts. The
rice yield negatively or positively varied with rainfall indices. We observed that the relationship between rice yieldand each rainfall index was statistically significant, with asignificance level of 0.1% or 1% or 5% or 10% (two-tailedtest) in the districts presented in Table 3. The SLR was
statistically significant, with a significance level of 10%
only in the Bhamo District in the Hilly Zone. Similarly,
the NDD was statistically significant, with a significancelevel of 5% or 10% in the Tavoy District of the Coastal
Zone; Hanthawaddy, Tharrawaddy, and Toungoo Districts of the Delta Zone; Meiktila and Mandalay Districtsof the Dry Zone; and the Katha District of the Hilly Zone.
Furthermore, while CDD was statistically significant,with a significance level of 1% or 10% in the Pegu andToungoo Districts of the Delta Zone and the Bhamo District of the Hilly Zone, the CWD was statistically significant, with a significance level of 10% only in the Rangoon
District in the Delta Zone. Results also showed that the
HRD was statistically significant, with a significance levelof 5% or 10% in Sandoway and Kyaukpyu Districts of the
Coastal Zone; Magwe District of the Dry Zone; and thenPage 20 of 26

Salween and Katha Districts of the Hilly Zone. Likewise,
5DR was also statistically significant, with a significancelevel of 5% or 10% in Amherst and Kyaukpyu Districts of
the Coastal Zone; Bassein District in the Delta Zone; andBhamo District in the Hilly Zone (see Table 3 and Additional file 1: Table S9).
The relationship between rice yield and annual rainfall variability with various rainfall indices was analyzed using multiple regression analysis, and the effectof annual rainfall variability on rice production was
explored. However, the monthly variability of rainfall
during the crop growth period in the years may significantly impact on rice yield because the amount and distribution of rainfall in a particular year determine thesuccess or failure of rice crop production. Rice crop
yields will boost significantly if the precipitation occurs at
the right times and in the right amounts. To explore themonthly variability of rainfall in the years, the monthly
rainfall during monsoon for rice yield failure or successyears was analyzed. Figure 13 indicates the variation ofmonthly rainfall for crop failure years for the selected districts. (Blue bar diagram in the figure is the average of theperiod 1901–1939.) From the results, we observed thatthe monthly rainfall that resulted in years of crop failure
was usually either comparatively higher during the crop
growth period (particularly in July and August) than the
average monthly rainfall of period 1901–1939 or less during the early or latter half of crop growth (Fig. 13). For
example, there was a higher rainfall between July andAugust in most districts of the Coastal and Delta Zones
in 1923 (e.g., Bassein, Maubin, Henzada, Pegu, Sandoway,
Akyab, and Prome (near delta area) Districts in Fig. 13).
Contrastively, in 1919, less rainfall was observed during
the latter growth stage of rice crops in most districts.
Investigations also showed that some districts experienced rice crop failure due to less rainfall during theearly or middle stage of crop growth in some years (e.g.,
Maubin, Pegu, Meiktila, Shwebo, and Katha Districts inTable 3 District with a statistically significant relationship between rice yield and rainfall indices
Rainfall indicesDistrict with statistically significant relationship between rice yield and rainfall indices (significancelevel of two-tailed test)

Seasonal local rainfall (SLR)

Bhamo (10%)

Number of dry days (NDD)

Tavoy (10%), Hanthawaddy (10%). Tharrawaddy (5%), Toungoo (10%), Meiktila (10%), Mandalay (10%), Katha(5%)

Maximum consecutive dry days (CDD)

Pegu (1%), Toungoo (10%), Bhamo (10%)

Maximum consecutive wet days (CWD)

Rangoon (10%)

Seasonal Basin average rainfall (SBR)–

Number of heavy rainfall days (HRD)

Sandoway (5%), Kyaukpyu (10%), Magwe (5%), Salween (5%), Katha (10%)

5-day maximum rainfall (5DR)

Amherst (5%), Kyaukpyu (10%), Bassein (5%), Bhamo (5%)

Details of summary results of multiple regression analysis are presented in Additional file 1: Table S9

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Page 21 of 26

Fig. 13 Monthly variability of rainfall in crop failure years

1918 or 1920). However, other districts had higher rainfall during the early growth stage and less rainfall during
the latter growth stage of the rice crop in 1919 (e.g., Bassein, Maubin, Sandoway, Akyab, and Shwebo Districts in
Fig. 13). Additionally, while higher rainfall during crop
growth often triggered flooding, which reduced production as a result, lesser rainfall during crop growth also
reduced rice production. The monthly distribution of
rainfall for crop success years (years with higher yields
than the average value of rice yield from 1901 to 1939)
is indicated in Additional file 1: Figure S6. Furthermore,
we observed that the amount of monthly rainfall during
the crop growth period for crop success years was usually closer to the average value of 1901–1939 in most districts located in the Delta and Coastal Zones and some
districts in the Hilly Zone. These results confirmed thatthe monthly distribution of rainfall within a particular
year determines the influence of rainfall on rice production. Thus, it is vital to analyze the impact of monthly
rainfall on rice yield and production. Previous researches
reported that continuous rain during the early growth

period of the rice plant often caused floods, reducing riceproduction in Burma (Win 1991). The reduction in rainfall amount could cause negative consequences on riceproduction, particularly in the Dry and Hilly Zones of
Burma.

7 Discussion and conclusions
This study evaluated the spatial and temporal variabilities
of rainfall and their effects on rice production in Burmaduring the colonial period, considering the differencesin soil types and fertility depending on region, the differences in cultivation methods such as planting and irrigation, and the differences in rice varieties. Furthermore, it
analyzed the effects of rainfall variability on rice production from multiple rainfall characteristics perspectives,
such as seasonal rainfall, various rainfall indices, rainfall
anomalies, and monthly rainfall variability. Our findings showed that the expansion of crop land areas or riceproduction in Burma was largely dependent on climatic
zones and soil-type distribution (e.g., districts with Gleysols or Gleysols and Fluvisols in the Delta Zone; Gleysols

Shrestha et al. Progress in Earth and Planetary Science(2022) 9:48

and Cambisols in the Coastal Zone; and Gleysols, Vertisols, and Luvisols in the Dry Zone) (Figs. 2b and 4).
Additionally, we observed that while annual andmonthly rainfall in the Coastal Zone was comparatively
higher than in other zones where rainfed-lowland anddeep-water rice were cultivated (Fig. 6), the Dry Zone
received less rainfall where rainfed-lowland, irrigatedlowland, and upland rice productions were concentrated(Fig. 6). Moreover, during the study period (1901–1939),
increased seasonal rainfall trends in most Burma districts
were observed (Fig. 7 and Additional file 1: Table S6); precisely, strong increasing trend of seasonal local and basin
average rainfall with a significance level of 10% or less(using the two-tailed test) was observed in six and 14 districts, respectively (Fig. 7 and Additional file 1: Table S6),
mostly located in the Dry and Delta Zones. Furthermore,
we observed that rice yield was decreasing in trend during the study period in most districts (Figs. 10 and 11; andAdditional file 1: Table S7), which started to decline after
1910 in most districts (Fig. 9 and Additional file 1: Fig.
S5). The decreasing trend in rice yield was statisticallysignificant, with a significance level of 0.1% or 1% or 5%
or 10% (with a two-tailed test) in most districts (Fig. 11
and Additional file 1: Table S7). Nevertheless, higher
rice yield was usually observed in most districts when
the rice crop was widely cultivated in recently or newly
cleared lands, particularly before 1910 (Fig. 9 and Additional file 1: S5). Therefore, the higher yield was possibly
due to higher land fertility on recently or newly cleared
lands. However, the decrease in rice yield may be due tothe variability of monthly rainfall during crop growing
period such as excessively low or high rainfall during thecrop growing period and the variability of other environmental factors. Based on these findings, we establish that
although rice production increased rapidly because of the
expansion of rice crop areas, there was a declining trend
in rice yield.
Investigations also revealed that although the annual
or seasonal rainfall in the Coastal and Delta Zones wascomparatively higher than the water requirement forrice crop cultivation, excessive continuous rainfall during the rice growth period may cause frequent flooding
in paddy areas and damage to rice crops. The reduction
in rice yield was observed in most districts located in the
Coastal and Delta Zones when monthly rainfall between
July and August was comparatively higher than the average rainfall (e.g., in 1923) (Fig. 13). However, less rainfall during the early-middle or latter half periods of crop
growth reduced the rice yield (Fig. 13). Specifically, while
the lack of rainfall during the latter stage of crop growth
possibly caused the rice yield reduction of most districts
in 1919, the rice yield reduction of some other years (e.g.,
in 1918, 1920) in some districts was possibly due to lessPage 22 of 26

rainfall during the early or middle stage of crop growth(Fig. 13). Furthermore, we observed variability in spatial
rainfall distribution patterns over the affected rice production areas. Accordingly, recent studies highlighted
that rice production depended on rainfall patterns in theGanges–Brahmaputra–Meghna River basin (Asada andMatsumoto 2009; Matsumoto and Asada 2020) and the
Odisha region, India (Panda et al. 2019).
Our investigation showed that the rice yield anomaly
varied positively with rainfall anomaly in all the dry andhilly zone districts, except the Salween District in the
Hilly Zone (Fig. 12 and Additional file 1: Table S8). Thisfinding indicated higher yield when rainfall amount was
higher in the Dry and Hilly Zones. Contrastively, while
rice yield varied negatively with rainfall anomalies inmost districts of the Delta and Coastal Zones (Fig. 12
and Additional file 1: Table S8), the linear relationshipbetween rice yield and rainfall anomalies was statisticallysignificant, with a significance level of 5% or 10% (with a
two-tailed test) in only seven districts (Additional file 1:
Table S8), primarily located in the Dry Zone.
Multiple regression analysis results showed that while
the effect of selected rainfall indices affected the rice
yield positively or negatively, depending on climatic
zones, the magnitude of their effect varied immenselyfrom one district to another. These results indicate that
relationship between rice yield and rainfall index usedin this study was statistically significant, with a 1% or
5% or 10% significance level of two-tailed test in 16 of
37 districts (at least one or more significant rainfall
indices) (Tables 3 and Additional file 1: S9). We also
observed that while the SLR affecting the rice yield
was statistically significant, with a significance level of
10% only in the Bhamo District of the Hilly Zone, theNDD affecting the rice yield was statistically significant,with a significance level of 5% or 10% in the Tavoy District of the Coastal Zone; Hanthawaddy, Tharrawaddy,
and Toungoo Districts of the Delta Zone; Meiktila andMandalay Districts of the Dry Zone; and the Katha
District of the Hilly Zone. Furthermore, although the
CDD affecting the rice yield was statistically significant,with a significance level of 1% or 10% in the Pegu andToungoo Districts of the Delta Zone and the Bhamo
District of the Hilly Zone, the CWD affecting rice yield
was statistically significant, with a significance level of
10% only in the Rangoon District of the Delta Zone.
Moreover, while the HRD affecting rice yield was statistically significant, with a significance level of 5% or
10% in the Sandoway and Kyaukpyu Districts of the
Coastal Zone; the Magwe District of the Dry Zone; andthe Salween and Katha Districts of the Hilly Zone, 5DR
that affects rice yield was statistically significant, witha significance level of 5% or 10% in the Amherst andShrestha et al. Progress in Earth and Planetary Science(2022) 9:48

Kyaukpyu Districts of the Coastal Zone; Bassein District of the Delta Zone; and the Bhamo District of the
Hilly Zone. In comparison, researches during recent
periods reported that the relationship between rainfall
and rice production was not statistically significant in
Mali (Dembélé 2015), Assam in India (Nath and Mandal 2018), Nigeria (Tiamiyu et al. 2015), and Benin(Agnontcheme 2018). However, some other studies discovered a significant relationship between rainfall andrice crop production in the Punjab Province of Pakistan (Mohmood et al. 2012), India (Farook and Kannan
2015; Panda et al. 2019), Northwest Ethiopia (Molla
et al. 2020), and other parts of Nigeria (Adedeji et al.
2017).
Our findings also revealed that rainfed rice yield in
Burma depended greatly on rainfall amount and fertile soil distribution. Specifically, a higher rice yield wasobserved in the Delta Zone where Gleysols (good-fertile
soils) were widely distributed, and rainfall amount was
sufficiently higher than the water requirement for crop
cultivation. However, the rice yield in the Dry Zone varied greatly, depending on the amount of rainfall, availability of irrigation system, and soil-type distribution.
Some districts with Vertisols and partially or mostly irrigated areas also produced higher rice yields in the Dry
Zone. This study confirmed that it is vital to examine
not only the interannual variability of rainfall and theirrelated rainfall indices, but also the impact of variabilityof monthly rainfall on rice yield and production. Furthermore, we observed that monthly distribution of rainfall within a particular year determined the influence ofrainfall on rice production. Particularly, monthly rainfall
during crop failure years was either significantly higher(in the early-middle stage of crop growth) or less (in theearly-middle or latter part of crop growth), indicating
the variability of monthly rainfall during the crop growing period as one of the factors that significantly affectthe rice yield. Riad and Peter (2017) highlighted thatshort-term climate variability (extreme climate events)
was more likely to reduce rice production in Suriname.
Based on our findings, some of the possible reasons forthe decline in rice yield during the study period (1901–
1939) could be variability in seasonal rainfall, variability
in monthly rainfall during the crop growth period, andcrop cultivation under different soil and climate conditions in the same district.
This study focused on analyzing rainfall effects on riceproduction for the historical period from 1901 to 1939.
There were fewer human interventions on the catchment
and almost no use of chemical fertilizers or high-yielding
rice varieties in the study area. Therefore, the impact ofrainfall variation on rice production may be better understood from this historical period, enabling us to clearly

Page 23 of 26

understand the relationship between rainfall and rice
yield. Our findings can also be useful to understand thecapacity of agriculture to adapt to rainfall variability.
Other climatic factors, such as temperature andrelative humidity, which may also influence rice yield,
including effects of socioeconomic (e.g., land tenancy/
ownership, availability of labor, capital, accessibility tomarkets) and political (e.g., rice policies of the government, policies on marketing and international trade,
subsidies, loan policy, tax policy) factors were notconsidered in this study, which needs to be exploredin the future. Furthermore, although the average rice
yield for each district was used in the analysis becauseof limited data availability for the study period, we
observed that rice crop was cultivated under different rice ecosystems or rice varieties in some districts(e.g., rainfed-lowland and deep-water rice cultivation
in some delta and coastal zone districts, including
rainfed-lowland and irrigated-lowland rice cultivation in some dry zone districts). Therefore, rice yield
may vary depending on rice cultivation systems or rice
varieties, which needs to be considered, particularlyin the districts where rice crops were cultivated under
different rice cultivation ecosystems. Furthermore,because soil fertility is an important factor in enhancing crop productivity, more detail analyses considering
impact of soil fertility on rice productivity can further
enhance our understanding of the factors that affectrice productivity.
Abbreviations
DMH: Department of Meteorology and Hydrology ; UNEP: United Nations
Environment Programme; IFDC: International Fertilizer Development Center;
ICEM: International Centre for Environmental Management; NOAA: National
Oceanic and Atmospheric Administration, USA; IITM: Indian Institute of Tropical Meteorology; KNMI: Royal Netherlands Meteorological Institute; FAO/UNESCO: Food and Agriculture Organization/United Nations Educational, Scientificand Cultural Organization; DEM: Digital elevation model; GIS: Geographicalinformation system; SLR: Seasonal local rainfall; NDD: The number of dry days;
CDD: The maximum number of consecutive dry days; CWD: Maximum number of consecutive wet days during monsoon; SBR: Seasonal basin average
rainfall; HRD: Number of heavy rainfall days; 5DR: 5-Day maximum rainfall.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​022-​00506-2.
Additional file 1. Table S1. Coordinates of rainfall stations. Table S2.
Annual rainfall data at the stations (in mm). Table S3. Seasonal rainfall
data at the stations (from May to October) (in mm). Table S4. Averagemonthly rainfall during 1901–1939 at the stations (in mm). Table S5.
Average rice yield during 1901–1939 at each district in Burma. Figure S1.
Development of paddy areas in the Lower and Upper Burma from 1880 to1940 (Data Source: Siok-Hwa 2012). Figure S2. Use of high yield rice varieties and NPK fertilizer in Burma (Data Source: Young et al. 1998). Figure
S3. Interannual changes and change trend of seasonal local rainfall for theselected districts from 1901 to 1939 (Nx = Year – First Data Year) (with significance level of two-tailed test). Figure S4. Same as in Fig. S3 but for theShrestha et al. Progress in Earth and Planetary Science(2022) 9:48

basin average rainfall for selected districts (with significance level of twotailed test). Table S6. Statistics summary result of trend analysis of rainfall.
Table S7. Statistics summary result of trend analysis of rice yield. Table S8.
Statistics summary of linear regression analysis between rice yield andrainfall anomalies. Figure S5. Anomaly of seasonal local rainfall and rice
yield with a 5-year moving average value for selected districts from 1901
to 1939. Table S9. Statistics summary of multiple regression analysis. Figure S6. Monthly variability of rainfall in crop success years (the years withrice yield higher than average value of rice yield from 1901-1939).
Acknowledgements
The authors would also like to thank Prof. Ikuko Okamoto of Toyo University,
Japan, for providing district-level data on rice crop area, yield, production,and other data related to rice crop. The authors would also like to thank Dr.
Noriyuki Osada of Institute of Developing Economies, Japan External Trade
Organization, Japan, for providing valuable suggestions.
Author contributions
BBS and AK proposed the topic and conceived and designed the study. TI
and JM collected and digitized the rainfall data; BBS performed research andanalyzed the data. AK, TI, JM, and TS collaborated with the corresponding
author in the development of the manuscript. All authors read and approved
the final manuscript.
Funding
This research was partially supported by Japan Society for the Promotion of
Science (JSPS) KAKENHI Grant Number 26220202 and 17H06116 and UTokyo
Strategic Partnership Program for Yangon Technological University, Japan.
Availability of data and material“Rainfall of India (1891–1914)” and “Daily Rainfall of India (1915–1937)” areavailable on the web page of the NOAA Central Library, National Oceanic andAtmospheric Administration, USA ( https://​libra​ry.​noaa.​gov/​Colle​ctions/​Digit​
al-​Docs/​Forei​gn-​Clima​te-​Data/​India-​Clima​te-​Data). Rainfall data were also
checked by the original books at the Library of the Indian Institute of Tropical Meteorology (IITM). “Daily Rainfall Recorded in Burma” for 1938–1939 isavailable in the Library of The Royal Netherlands Meteorological Institute. The
annual and seasonal rainfall data at each station for 1901–1939 are presented
in Additional file 1: Tables S2 and S3, and the average monthly rainfall during
1901–1939 is also presented in Additional file 1: Table S4. Data on rice yield,
rice production, and area of rice crop cultivated and harvested are available in
Okamoto (1998) (http://​hdl.​handle.​net/​10086/​14672). Other secondary data
used in this study are available in the sources mentioned in the manuscript.

Declarations
Competing interests
The authors declare that they have no competing interest.
Author details
1
Department of Civil Engineering, The University of Tokyo, Tokyo, Japan. 2 Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan.
3
Department of Geography, Tokyo Metropolitan University, Tokyo, Japan.
4Center for Coupled Ocean‑Atmosphere Research, Japan Agency for Marine
Earth Science and Technology, Yokosuka, Japan. 5 Graduate School of Economics, The University of Tokyo, Tokyo, Japan.
Received: 26 February 2022 Accepted: 31 August 2022

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