Soil Biochemical Property Response to Drought Effects under Land-Use Change in the Context of Climate Change

ecological and socio-economic impacts ... Research framework ... Soil sampling locations at Phiem Ai Village, Dai Nghia Commune, Dai Loc District, Quang Nam Province ... Experiment setu[r]

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VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY

BUI HANH MAI

SOIL BIOCHEMICAL PROPERTY RESPONSE TO DROUGHT EFFECTS UNDER LAND-USE CHANGE IN THE

CONTEXT OF CLIMATE CHANGE

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VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY

BUI HANH MAI

SOIL BIOCHEMICAL PROPERTY RESPONSE TO DROUGHT EFFECTS UNDER LAND-USE CHANGE IN THE

CONTEXT OF CLIMATE CHANGE

MAJOR: CLIMATE CHANGE AND DEVELOPMENT CODE: 8900201.02QTD

RESEARCH SUPERVISOR: Dr HOANG THI THU DUYEN

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PLEDGE

I assure that this thesis is the result of my own research and has not been published The use of results of other research and other documents must comply with regulations The citations and references to documents, books, research papers, and websites must be in the list of references of the thesis

Author of the thesis

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TABLE OF CONTENT

LIST OF TABLES i

LIST OF FIGURES ii

LIST OF ABBREVIATIONS iii

ACKNOWLEDGMENT iv

CHAPTER 1: INTRODUCTION

1.1 Background and motivation of the study

1.2 Research framework

1.3 Drought in the world

1.4 Drought in Vietnam

1.5 Impact of drought and land use change on soil properties 10

1.5.1 Impacts of drought on soil microbial activities and biochemical properties 10

1.5.2 Impacts of land use change on soil microbial activities and biochemical properties 11

1.6 Objects and scope of the research 13

1.7 Research questions and hypothesis 17

1.7.1 Research questions 17

1.7.2 Hypothesis 17

CHAPTER METHODOLOGY 18

2.1 Data collection 18

2.1.1 Meteorological data 18

2.1.2 Remote sensing data 18

2.2 Methods of identifying and calculating drought indicators 20

2.3 Soil sampling and processing 21

2.4 Experiment setup 22

2.5 Determination of MBC and MBN 24

2.6 Identification of microbial basal respiration 24

2.7 Statistical analysis 25

CHAPTER 3: RESULTS AND DISCUSSION 26

3.1 Results 26

3.1.1 Land use and land cover maps 26

3.1.2 Drought progress characteristic 27

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3.1.4 Microbial activities 31

3.2 Discussion 37

3.2.1 Land use and land cover maps 37

3.2.2 Drought progress characteristics 37

3.2.3 Soil properties and microbial activities 37

CHAPTER CONCLUSIONS AND RECOMMENDATIONS 42

4.1 Conclusions 42

4.2 Recommendations for future research 43

REFERENCES 45

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i

LIST OF TABLES

Table 2.1 Land cover types description 189

Table 2.2 Classification used for K indices 21

Table 2.3 Methodologies to analyze soil physic-chemical properties 22

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ii

LIST OF FIGURES

Figure 1.1 Drought concept relevant to climate change Drought releases

ecological and socio-economic impacts

Figure 1.2 Research framework

Figure 1.3 Average monthly sunshine hours (2000 - 2019) in Quang Nam 14

Figure 1.4 Average monthly temperature (2000 - 2019) in Quang Nam 15

Figure 1.5 Average monthly precipitation (2000 - 2019) in Quang Nam 16

Figure 1.6 Average monthly evaporation (2000 - 2019) in Quang Nam 16

Figure 2.1 Soil sampling locations at Phiem Ai Village, Dai Nghia Commune, Dai Loc District, Quang Nam Province 231

Figure 2.2 Experiment setup for drought condition 23

Figure 2.3 Design experiment to analyze soil respiration 24

Figure 3.1 Land-use and land cover maps in Quang Nam (2003 – 2018) 26

Figure 3.2 The total area of each type of land use and land cover in Quang Nam 2003 – 2018 27

Figure 3.3 Drought frequency month during 2000 – 2019 28

Figure 3.4 K indices of mean drought months in dry season 28

Figure 3.5 K indices of drought months during dry season (2000 – 2019) 29

Figure 3.6 MBC of forest soil and pineapple soil 31

Figure 3.7 MBN of forest soil and pineapple soil 32

Figure 3.8 MBC:MBN ratio of two soil types and three treatments 33

Figure 3.9 The ratios of MBC to SOC and MBN to TN of both soils 33

Figure 3.10 The microbial basal respiration in the difference soil moistures of both soil 35

Figure 3.11 The amount of CO2 after three periods incubators at three treatments 35

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iii

LIST OF ABBREVIATIONS ANOVA One-way analysis of variance

C Carbon

ENSO El Nino Southern Oscillation

Gt Gigaton

IMHEN Institute of Meteorology, Hydrology and Climate Change IPCC International Panel on Climate Change

MBC Microbial biomass carbon MBN Microbial biomass nitrogen

MODIS Moderate Resolution Imaging Spectroradiometer

N Nitrogen

SOC Soil organic carbon SOM Soil organic matter

TC Total carbon

TN Total nitrogen

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iv

ACKNOWLEDGMENT

To complete this thesis, I would like to express my sincere thanks to the lecturers and staff of Program of Climate Change and Development, Vietnam Japan University, Vietnam National University, Hanoi, and other lecturers and students of Soil Sciences Department of Vietnam National University of who guided and facilitated me to complete my thesis on time

I would like to express my deepest and most sincere thanks to my supervisor - Dr Hoang Thi Thu Duyen, advisor - Dr Kotera Akihiko, Prof Phan Van Tan and Dr Nguyen Van Quang - Lecturers of Climate Change and Development program, Vietnam Japan University, VNU for their dedication and valuable comments on thesis

In addition, the research has also received support and help from leaders and staff of Quang Nam Crop Production and Plant Protection Subdepartment and Department of Agriculture and Rural Development Dai Loc District so that I could collect information related to the thesis

Last but not least, the author also appreciates financial support of VNU project (code QG.20.63, No 1086/QĐ-ĐHQGHN), without this support the implementation is impossible

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1

CHAPTER INTRODUCTION 1.1 Background and motivation of the study

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Figure 1.1 Drought concept relevant to climate change Drought releases ecological and socio-economic impacts (Wilhite, 2000)

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Land-use change, as well as drought, have an impact on the biochemical properties of the soil Increasing frequency, intensity and timing of drought is predicted to lead to reduce the functions of microorganism, which is essential of ecosystem sustainability (McHugh et al., 2017) Moreover, the structure of the soil microorganisms is greatly influenced by land use, land cover, and agricultural activities Those factors impact on SOM and lead to regulating the microbial structure appropriately (Moon et al., 2016; Bissett et al., 2011) Thus, it could impact on soil microbial biomass and the usage C efficiency of microorganism (Bauhus et al., 1998) Terrestrial plants are the main sources of soil organic matter (SOM) which retains moisture in different soil horizons However, during 20 years (1980 – 2000), more than 80% of newly cultivated land came from the intact and disturbed forests (Gibbs et al., 2010) Land conversion from forest to cultivated land reduces SOM content, leading to a decline in soil moisture content and lowering resistance and resilience capacity of the terrestrial ecosystem to drought impacts (de Vries et al., 2012) This land-use change also triggers potential drought events as the soil is over-exploited for intensive agricultural production, which causes exhaustion in soil nutrients, bio-balance and hence soil WHC

In tropical dry land ecosystems, studies in land-use change under drought are still restricted when compared with the total coverage of wet ecosystems around the world (Ramesh et al., 2019) Therefore, the study “Soil biochemical property response to drought effects under the land-use change in the context of climate change” is conducted in Quang Nam, Vietnam to elucidate the relationship between abiotic factor (soil moisture) and biotic factor (microbial biomass and activity)

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Risk Indices (CRI) (Eckstein et al., 2019), especially the increase of drought frequency causes negative impacts on the production activities of local people Quang Nam, where is located in the South Central region with diverse terrain conditions and harsh climate, severely impacted by drought Due to drought in the Southern sub-regions and South Central are highly sensitive to ENSO (Le et al., 2018) According to the People's Committee of Quang Nam province (2010) and IMHEN (2009), the prolonged drought damaged, 4,841/44,500 hectares of summer-autumn rice in plain districts; 660 hectares of rice were lost due to saline intrusion In addition, there are over 3,000 hectares of rice that cannot be sown due to aridity, along with 5,000 hectares of crops lacking irrigation water and nearly 5,000 people suffer from water shortages in the midland and mountainous districts of Quang Nam From the beginning of the Summer-Autumn season in 2019, the weather was abnormal and the hot and sunny situation happened continuously and lasted for many days The storage capacity in many irrigation and hydropower reservoirs is only about 20 – 60% of the designed capacity, lower than the average many years Many small reservoirs have dried up (EVN, 2019)

This study was conducted to provide a general overview of the biochemical and microbiological activity of two different land-use types, namely forest and pineapple land in Quang Nam, under drought conditions The findings will provide stakeholders in Quang Nam with scientific background for adaptation strategy to climate change while maintaining soil health Moreover, in order to mitigate the effect of climate change, the identification of management practices and appropriate land-use in each location is one of the necessary methods Thus, this study performs with three main objectives:

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2 To demonstrate the effect of drought on microbial activities including microbial biomass C and N (MBC and MBN) and microbial community composition in different land-use

3 To evaluate nutrient mineralization under drought impacts in different land-use

1.2 Research framework

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Figure 1.2 Research framework 1.3 Drought in the world

As mention above, drought directly affects agriculture Droughts often cause loss of agricultural land, crop structure changes and crop yields decline That impacts the lives of people and national food security Besides, drought also affects forest resources Increased temperature and evaporation cause prolonged drought, which will affect the growth ability of forest plants and animals Some regions in the world have occurred a trend to more longer and

Climate change

- Precipitation - Temperature - Wind

Drought

Land-use change

Physical properties Bulk density

Soil texture

Chemical properties pH

Total Carbon Total Nitrogen

Biological properties MBN and MBC Soil respiration

Solutions Impact

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severe droughts since the 1950s, especially in West Africa and southern Europe (IPCC, 2012)

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In addition to using observational meteorological data to study drought, drought estimates by simulation results of climate factors from dynamic models have also been strongly developed in many countries In warmer future climates, most atmospheric circulation models anticipate increased summer drought and winter wetness in most of the medium latitudes and high latitudes in the north It is the summer drought that will lead to a greater drought disaster, especially in areas where rainfall decreases (IPCC, 2007) Kim and Byun (2009) estimated the effects of global warming on drought conditions in Asia in the late 21st century under the A1B scenario The results indicate that rainfall rates decrease the highest in North Asia in all seasons, in West Asia average rainfall plummets from winter to summer, leading to future droughts in these two areas have more frequency, stronger intensity, longer drought cycle than in the past, especially in summer The severity of drought in India is projected to increase under wetter and warmer future climate (Aadhar and Mishra, 2018) Due to there is an increase in precipitation, and more than degree rise in temperature leads to more atmospheric water demand and an increase in drought severity by the end of the 21st century Under the RCP 8.5, almost all of India shows high-frequency of severe drought events in the end period, more than three severe events per decades The area affected by severe drought is predicted to increase by 150% with warming by the end of the 21st century

1.4 Drought in Vietnam

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the Central sub-regions often occurred drought more severe than in other areas The periods of drought events were typically longer So, the frequency of drought was also larger Moreover, the severity drought showed events, which have a very high drought intensity, in these sub-regions The variability of drought in the South Central and Southern has been highly sensitive to ENSO Besides the increasing temperature, decrease precipitation and soil moisture deficit in the summer, climate seasonality, large-scale drivers, and topography conditions also impacted on drought in Vietnam

In addition to studies on Vietnam's drought history, there have been studies on drought prediction in Vietnam based on scenarios A1B and A2 According to Ngo Thi Thanh Huong (2011), the results of drought estimates under the A1B scenario for climatic regions in Vietnam showed that droughts are more likely to occur in the future, especially in the period 2011 – 2030 in the Northwest climatic region and the period 2031 – 2050 in the three climatic regions of the South Central, Central Highlands and Southern regions Future lighter drought occurs in the Northeast and North Central regions

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1.5 Impact of drought and land use change on soil properties

1.5.1 Impacts of drought on soil microbial activities and biochemical properties

IPCC (2019) emphasized that "Climate change, including increases in frequency and intensity of extremes, has adversely impacted food security and terrestrial ecosystems as well as contributed to desertification and land degradation in many regions" In the fact, extremes of arid conditions reduce the growth of most plants and microbial decomposition Moreover, microbial functions are important for ecosystem sustainability McHugh et al (2017) stated that increasing drought prediction lead to decline in microbial functions When soil drier, less SOC in the soil is decomposed and respired to CO2, due to in soil pores have less water, thus resources in the soil cannot link together (Schimmel, 2018) In addition, these factors interact with the reduction loss of C through suppressed respiration (Heimann and Reichstein, 2008) In grassland ecosystems, the soil micro-biome can be impacted long-lasting by drought, due to the dominance of drought-tolerant plant species cause the changes in vegetation and root microorganisms also change (de Vries et al., 2018)

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Conditions that favor microorganism growth will favor fast decomposition rates The product of complete decomposition are CO2, NH4

+

, NO3

-, SO4

2-, H2PO4

-, H2O resistant residues, and multiple other necessary nutrient elements for plants in smaller quantities Chemical soil degradation is likely nutrient decreased because of the imbalance of nutrient extraction resulting from harvested products and fertilization Excessive N fertilization and export in harvested biomass increase acidification in croplands because of the depletion of cation like calcium, magnesium or potassium in the soil (Guo et al., 2010) In the context of climate change, the depletion of organic matter pool causes soil chemical degradation processes Tillage and the belowground plant biomass inputs reduction cause the increase of respiration rates, which reduced organic matter in agricultural soils The warming directly impacts on the decline of SOM pools in both under natural vegetation and cultivated land (Bond-Lamberty et al., 2018) Creating energy from harvesting residues also could lead to reducing organic matter in the forest (Achat et al., 2015) A “hub” of degradation processes could be SOM, which also is an important connection with the climate system (Minasny et al., 2017) Zhao et al (2017) stated that interaction between temperature and precipitation influences not only terrestrial ecosystem productivity but also the decomposition rate of SOC That is the reason why those environmental factors are the most affecting soil CO2 efflux rates

1.5.2 Impacts of land use change on soil microbial activities and biochemical properties

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The processes, namely the oxidation of superficial soil C stocks, enhancing gas emissions (COz and other gases) to the atmosphere, are markedly impacted by the changes from forest to agriculture and grassland (IPCC, 1992) That conversion also causes soil organic C loss (Kasel and Bennett, 2007; Guo and Gifford, 2002) According to IPCC (1992), land-use change and deforestation emitted about 55±30 Gt CO2

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forest land converted to croplands may sequester less C than when converted to grasslands

He also proved that improving agroecosystems sustainability and increasing SOC storage based on conservation management practices, namely integrated nutrient management practices, manure application, residue incorporation, use of cover crops, and no-tillage However, the organic manure application and residues enhance CO2 emission to the atmosphere Thus, the utmost crucial factor to mitigate the changing of climate is C sequestration in soil from identification appropriate management practices and land-use

1.6 Objects and scope of the research - Study site: Quang Nam province

Quang Nam is located in the central region of Vietnam, is a region with relatively complex topography, lower from the West to the East, forming three ecological regions: high mountains, midlands, and coastal plains The province is divided by the Vu Gia and Thu Bon river basins

Quang Nam is located in the typical tropical climate region, with only two seasons: the dry season (from January to August) and the rainy season (from September to December) However, there still influence by the cold winter in the North

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meteorology data was collected in Tra My station is used to calculate the relevant meteorological factors for the western mountainous region of the province

In general, the number of sunshine hours in Quang Nam was quite high (Figure 1.3) The mean sunshine hours (2000 – 2019) of Quang Nam province were 1850 hours in mountainous area and 2000 hours in coastal plain In May, the highest number of sunshine hours was from 227 to 242 hours December was the least sunshine hours in a year, from 55 – 68 hours

Figure 1.3 Average monthly sunshine hours (2000 – 2019) in Quang Nam Temperature: The annual mean temperature in Quang Nam area was quite high, about 25.4oC in mountainous and 26.6oC in coastal plain The mean temperature of the months in winter has not exceeded the 20oC The coldest month was January with a mean temperature of 21.3oC (mountainous) and 22oC (coastal plain) The hottest month was June, with a mean temperature of about 29.5 – 31.5oC (Figure 1.4) The minimum temperature in Quang Nam was 12oC (mountainous) – 13.6oC (coastal plain) and the highest can be over 40.1oC (mountainous) – 41oC (coastal plain)

0 50 100 150 200 250 300

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Su

n

sh

in

e

h

o

u

r

(h

o

u

rs

)

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Figure 1.4 Average monthly temperature (2000 – 2019) in Quang Nam Rainfall: Rain was not evenly distributed according to space, rainfall in mountainous areas was more than plain Mountainous was the center of heavy rainfall in Quang Nam, the total annual rainfall during 2000 – 2019 reaches 4311 mm, while the average annual rainfall measured at coastal plain station is 2840 mm (Figure 1.5) The rainy months were from September to the end of December, the peak was October to November with rainfall of about 899 – 1058mm (mountainous), 601 – 693mm (coastal plain), and accounting for 45.6 – 54.5% of the total mean rainfall of 19 years The lowest rainfall a year was from February to April, accounting for only about 5.7 – 7% of the total mean rainfall of 19 years

0 10 15 20 25 30 35

T

em

p

erat

u

re

(

o C

)

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Figure 1.5 Average monthly precipitation (2000 – 2019) in Quang Nam Evaporation: The average evaporation in many years in Quang Nam was 568 – 825 mm in both monitoring stations In the dry season, due to the high air temperature, low humidity, high winds, the evaporation in the dry months can be twice compared with the rainy months It can be seen from Figure 1.6, the amount of evaporation in April – August was the most, while the rainfall was low

Figure 1.6 Average monthly evaporation (2000 – 2019) in Quang Nam Humidity: The annual average humidity was 85.6% in the lowlands (coastal plain) and 88% in the mountains (mountainous) However, there was a

0 200 400 600 800 1000 1200

Preci

p

it

at

io

n

(m

m

)

Coastal plain Mountainous

0 20 40 60 80 100 120

E

v

ap

o

rat

io

n

(m

m

)

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fluctuation between the months of dry and rainy seasons, the month with the lowest humidity was June (78 – 84%), and the highest humidity was December from 91 – 93%

- Time performed the research: From January to end of May, 2020 - Objects of the research: Forest soil and pineapple soil have the same topographic characteristics and soil types

1.7 Research questions and hypothesis 1.7.1 Research questions

1 How has the drought situation been changing in Quang Nam in recent years?

2 How does drought situation impact on microbial activities and microbial community composition in different land use?

3 How is the nutrient mineralization in different land use under water limitation?

1.7.2 Hypothesis

1 Drought is getting more severe in Quang Nam province, depending on land-use types

2 Drought reduces microbial activities (MBC, MBN and microbial basal respiration) but the reduction is stronger in pineapple soil than forest soil

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CHAPTER METHODOLOGY 2.1 Data collection

2.1.1 Meteorological data

To assess the drought conditions of Quang Nam province, daily meteorological data of Mountainous and Coastal plain meteorological stations from 2000 to 2019 were collected from Vietnam Institute of Meteorology, Hydrology and Climate Change (IMHEN), including average air temperature, maximum air temperature, minimum air temperature, average air humidity, number of sunshine hours, amount of evaporation, rainfall, wind direction and wind speed

2.1.2 Remote sensing data

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Table 2.1 Land cover types description (Sulla-Menashe and Friedl, 2018)

Name Value Description

Evergreen

Needleleaf Forests

Dominated by evergreen conifer trees (canopy >2m) Tree cover >60% Evergreen Broadleaf

Forests

Dominated by evergreen broadleaf and palmate trees (canopy >2m) Tree cover >60%

Deciduous

Needleleaf Forests

Dominated by deciduous needleleaf (larch) trees (canopy >2m) Tree cover >60% Deciduous Broadleaf

Forests

Dominated by deciduous broadleaf trees (canopy >2m) Tree cover >60%

Mixed Forests

Dominated by neither deciduous nor evergreen (40-60% of each) tree type (canopy >2m) Tree cover >60% Closed Shrublands Dominated by woody perennials (1-2m

height) >60% cover

Open Shrublands Dominated by woody perennials (1-2m height) 10-60% cover

Woody Savannas Tree cover 30-60% (canopy >2m) Savannas Tree cover 10-30% (canopy >2m)

Grasslands 10 Dominated by herbaceous annuals (<2m) Permanent Wetlands 11 Permanently inundated lands with 30-60%

water cover and >10% vegetated cover Croplands 12 At least 60% of area is cultivated cropland Urban and Built-up

Lands 13

At least 30% impervious surface area including building materials, asphalt, and vehicles

Cropland/Natural

Vegetation Mosaics 14

Mosaics of small-scale cultivation 40-60% with natural tree, shrub, or herbaceous vegetation

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Name Value Description

Ice and ice for at least 10 months of the year

Barren 16

At least 60% of area is non-vegetated

barren (sand, rock, soil) areas with less than 10% vegetation

Water Bodies 17 At least 60% of area is covered by permanent water bodies

Unclassified 255 Has not received a map label because of missing inputs

2.2 Methods of identifying and calculating drought indicators

The drought extension over time is determined by rainfall as follows (Vietnam Meteorological and Hydrological Administration, 2014):

- Drought occurs when the amount of rainfall per month is less (equal) than 30mm

- Drought frequency month caculated by: P =

Where: m is drought frequency observation month n is frequency of rainfall observation month

- To describe the general situation of drought in the areas and their evolutions over time, the drought indices (Nguyen Trong Hieu, 1998) of months and years was used:

Km =

Where: Km: Drought indices month (year)

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21 Rm: Monthly (annual) rainfall

Table 2.2 Classification used for K indices

K value Class

K < 0.5 Very wet

0.5 K <1 Wet

1 K < Slightly dry

2 K < Dry

K > Too dry

2.3 Soil sampling and processing

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Figure 2.1 Soil sampling locations at Phiem Ai Village, Dai Nghia Commune, Dai Loc District, Quang Nam Province

The samples were preserved in laboratory under 5oC and sieved through 2mm mesh to remove plant litter, roots and gravels larger than 2mm A subsample was detached to measure basic soil properties Table 2.3

Table 2.3 Methodologies to analyze soil physic-chemical properties Criteria Methodologies

Soil bulk density TCVN 11399 : 2016

pHH2O TCVN 5979 : 2007

Soil texture TCVN 8567 : 2010

Total C TCVN 6642 : 2000

Total N TCVN 7373 : 2004

Total P TCVN 7374 : 2004

2.4 Experiment setup

Water holding capacity (WHC) was determined by modifying methods from methods of Naeth et al (1991) 30g soil was placed in a 100cm3 cylinder The cylinder was kept on 20cm sand layer within a big container, which was then saturated with water for at least 24 hours After that, water was drained out of the big container for 24 hours Finally, soil in the cylinder will be dried in an oven overnight at 105oC WHC was calculated as below:

WHC (%) = (Water saturated soil weight dry weight)

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Soil dry constant (k) = ( )

Water added (g) = x

x 0.6 The boxes were divided into sets (Figure 2.2): set containing soil at 60% WHC at the initial experiment stage, set containing soil at 10% WHC and set was control soil (60% WHC) at the time of drought All the soil containers are kept at 28oC for week to stabilize microbial growth conditions During the pre-incubation, soil weight was gravimetrically checked MBC, MBN, and basal respiration were measured for each set of soil container including:

 Set – harvested right after pre-incubation (60% WHC)  Set – harvested as soil moisture reduced to 10% WHC  Set – harvested as a control for set (60% WHC)

Figure 2.2 Experiment setup for drought condition Each treatment setup with replicates (Figure 2.3)

a) Forest soil

Control

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Figure 2.3 Design experiment to analyze soil respiration 2.5 Determination of MBC and MBN

Microbial biomass was defined using the chloroform fumigation extraction according to Brookes and Joergensen (2006) Accordingly, for each sample, 5g soil fumigated with CHCl3 for 24 hours (FT) and the dissolved organic C extracted with 20ml K2SO4 0.5M Another 5g soil subsample extracted immediately with 20ml K2SO4 0.5M, non-fumigation (NFT) MBC and MBN were calculated by differences between fumigated and non-fumigated samples with a conversion factor of 0.45 for MBC (Beck et al., 1997) and 0.54 for MBN (Brookes et al., 1985)

2.6 Identification of microbial basal respiration

50g soil subsample was incubated in Mason Jars for hours, 18 hours and 24 hours at a fixed temperature and atmosphere pressure (28oC) A small vial containing 10ml NaOH 1N was placed in the jar to trap CO2 The vial was measured every hours, 18 hours and 24 hours The trapped CO2 defined using titration with HCl 0.1N against the phenolphthalein endpoint (Zibilske, 1994) CO2 trapped was the net emissions of CO2 for soil, which was calculated as follows:

CO2 trapped(mg kg

-1

) = (VNaOH(ml)*1(mol.l -1

) – VHCl(ml)*0.1(mol.l -1

))*44 (g.mol-1)/2/dry weight of bulk sample (kg)

Control

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The microbial basal respiration was calculated by dividing sum of trapped CO2 by 48 hours (mg.kg−1) (Qiao et al., 2013)

2.7 Statistical analysis

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CHAPTER RESULTS AND DISCUSSION 3.1 Results

3.1.1 Land use and land cover maps

The interpretation of the MODIS satellite image (MCD12Q1) from 2003 to 2018 (Figure 3.1) shows that the forest area is largest, account for 60.27±1.77% total area The agriculture area just is 5.89±0.23% and concentrated on the coastal plain

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about 0.83% of the total agriculture area The forest area gradually decreased from 2003 to 2013, about 8.34% of the total forest area However, until 2018, the forest area increased by up to 5.86% compared with 2013 By contrast, non-vegetated land tended to be the complete opposite of forest land Non-vegetated areas increased by about 7.93% from 2003 to 2013 and decreased by 6.65% during 2013 – 2018

Figure 3.2 The total area of each land-use type and land cover in Quang Nam 2003 – 2018

3.1.2 Drought progress characteristic

The calculated drought frequency by observed rainfall at two monitoring stations showed a similar trend during 2000 – 2019 in two areas, namely mountainous and coastal plain The drought frequency month trended to increase (Figure 3.3) In particular, the drought frequency month in 2010 and 2013 of the coastal plain was highest In general, the drought frequency of mountainous was lower than the drought frequency of coastal plain However, the drought frequency was similar in both areas in 2005 and 2019

0 2000 4000 6000 8000

2003 2008 2013 2018

A

rea (k

m

2 )

Forest Non-vegetated Wetlands

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Figure 3.3 Drought frequency month during 2000 – 2019

The K indices of dry season months is used to assess the drought severity level The K indices calculated results showed that the drought severity level has decreased slightly during 2003 - 2019 Figure 3.4 revealed that the severity drought concentrated in the coastal plain The drought years appeared 11 times (out of 19 years of observation) and the highest K indices in 2003 showed that this was the most severe drought

Figure 3.4 K indices of mean drought months in dry season 0.05 0.1 0.15 0.2 0.25 0.3 0.35 200 200 200 200 200 200 200 200 200 200 201 201 201 201 201 201 201 201 201 D ro u g h t freq u en cy m o n th (P)

Linear (Coastal plain) Linear (Mountainous)

0 0.5 1.5 200 200 200 200 200 200 200 200 200 200 201 201 201 201 201 201 201 201 201 201 D ro u g h t in d ices (K m )

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In general, drought mainly occurs in the dry season from February to August (Figure 3.5) in the period 2000 – 2019 Severe drought concentrates mainly from February to April The highest K indices in February showed that this month was the most severe drought month of the year Moreover, the drought level in the coastal plain was severer than mountainous

a) Coastal plain

b) Mountainous

Figure 3.5 K indices of drought months during dry season (2000 – 2019) 50 100 150 200 200 200 200 200 200 200 200 200 200 200 201 201 201 201 201 201 201 201 201 201 D ro u g h t in d ices (K m )

Feb Mar Apr May Jun Jul

0 50 100 150 200 200 200 200 200 200 200 200 200 200 200 201 201 201 201 201 201 201 201 201 201 D ro u g h t in d ices (K m )

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30 3.1.3 Basic soil properties

The results in Table 3.1 indicated that the soil texture of the two types of land use is silt loam The bulk density result is similar between forest soil and pineapple soil

Table 3.1 The basic properties of forest soil and pineapple soil Forest soil Pineapple soil Soil bulk density (g/cm3) 1.3±0.015a 1.3±0.117a

Soil moisture 0.163±0.010a 0.117±0.034b

pHH2O 6.6±0.076

a

5.73±0.044b

Soil texture

Clay (%) 27.538±0.625 28.458±0.231 Silt (%) 51.768±1.173 49.55±1.270 Sand (%) 20.695±1.783 21.992±1.082 SOC (%) 1.041±0.023a 0.817±0.023b Total Nitrogen (%) 0.15±0.007a 0.12±0.004b Total Phosphorus (%) 0.8±0.390a 0.87±0.502a C:N ratio 6.99±0.35a 6.8±0.29a

Note: Values shown in Table 3.1 are means ± standard error (n = 4) a, b in a row indicated the significant difference at p = 0.05

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31 3.1.4 Microbial activities

3.1.4.1 MBC and MBN

The value of MBC in the initial period (60% WHC) was a significant difference between forest soil and pineapple soil (p < 0.05) (Figure 3.6) However, after three weeks of incubation, there was an insignificant difference in MBC value between both soils Regardless of soil types, MBC in the incubated soil (control, 60% WHC) was significantly higher than that of initial period (60% WHC) (p < 0.05) There was still a significant difference between the MBC of the initial period soil and incubated soil at 10% WHC in pineapple soil, while forest soil did not have a significant difference between them Moreover, in both soils, the MBC value of incubated soil at 10% WHC was significantly lower than the MBC value of incubated soil with 60% WHC, account for 1019.710 µgC.g-1soil in forest soil and 972.093 µgC.g-1soil in pineapple soil

Figure 3.6 MBC of forest soil and pineapple soil

MBN showed higher values in the forest soil compared with those in pineapple soil At three treatments, significantly lower MBN of pineapple soil

0 500 1000 1500 2000

Innitial (60% WHC)

Incubated (60%WHC)

Incubated (10% WHC)

MBC (µ

g

C.g

-1 so

il

)

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compared with MBN of forest soil (p < 0.05) through analysis independent sample t-test (Figure 3.7) For forest soils, the ANOVA showed the lower value of MBN at initial period soil (60% WHC) than MBN at incubated soil (60% WHC and 10% WHC), increased about 11.579 µgN.g-1soil and 17.035 µgN.g-1soil (p < 0.05), respectively Moreover, the MBN of soil incubated at 10%WHC and 60% WHC were 5.455 µgNg-1soil (p < 0.05) There was the same pattern in pineapple soil The MBN at initial period soil (60% WHC) was lower by 6.814 µgN.g-1soil than the MBN at incubated soil 60% WHC and lower by 7.331 µgN.g-1soil than the MBN at incubated soil 10% WHC (p < 0.05)

Figure 3.7 MBN of forest soil and pineapple soil

MBC:MBN ratios of forest soil and pineapple soil was significant different The MBC to MBN ratios at three treatments in forest soil was lower than that in pineapple soil The value of MBC:MBN ratios were higher than 20, without the value of MBC:MBN ratio at incubated soil with 10% WHC, about 18.151±3.072 (Figure 3.8)

0 10 15 20 25 30

Innitial (60% WHC) Incubated (60%WHC)

Incubated (10% WHC)

MBN

(µ

g

N

.g

-1 soil)

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Figure 3.8 MBC:MBN ratio of two soil types and three treatments There were significant effects of two land-use types on the ratios of MBN/TN The MBN to TN ratio, MBN/TN, was 0.0073% in the forest soil and 0.0014% in the pineapple soil (Figure 3.9) The MBC/SOC ratio was not a significant difference between both land-use types, 0.0522% in the forest soil and 0.0547% in the pineapple soil

Figure 3.9 The ratios of MBC to SOC and MBN to TN of both soils 20 40 60 80 100 120 140

Innitial (60% WHC) Incubated (60% WHC) Incubated (10% WHC) MBC: MBN rat io Treatment Forest Pineapple 00 05 2 00 05 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 MBN/TN MBC/SOC %

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34 3.1.4.2 Microbial basal respiration

The soil basal respiration was strongly impacted by moisture The maximum soil basal respiration was found in the incubated soil with 60% WHC, but in the incubated soil with 10% WHC, the soil basal respiration strongly decreased (Figure 3.10)

For forest soils, the ANOVA showed the higher value of soil basal respiration at incubated soil with 60% WHC than soil basal respiration at initial period and incubated soil with 10% WHC, about 10.011 mgCO2.kg

-1

soil.h-1 (p=1.4e-6 < 0.05) and 39.609 mgCO2.kg

-1

soil.h-1 (p=7.8e-12< 0.05), respectively Moreover, the difference of soil basal respiration between incubated soil with 10% WHC and 60% WHC was 29.37 mgCO2.kg

-1

soil.h-1 (p=1.1e-10 < 0.05) A similar pattern was found in the pineapple soil The difference between soil basal respiration at initial period soil and incubated soil with 60% WHC was about 5.907 mgCO2.kg

-1

soil.h-1 (p=1.9e-6< 0.05) The lower value of soil basal respiration at incubated soil with 10% WHC than soil basal respiration at incubated soil with 60% WHC and initial period, about 35.904 mgCO2.kg -1

soil.h-1 (p=2.1e-13< 0.05) and 29.997 mgCO2.kg -1

soil.h-1, respectively (p=1.0e-12< 0.05)

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Figure 3.10 The microbial basal respiration in the difference soil moistures of both soil

After 24 hours incubation, in general, the value of soil basal respiration decreased at initial period soil By contrast, the value of soil basal respiration slightly increased in soil incubated with 10% WHC In soil initial period, the value of soil basal respiration increased after 18 hours incubated, but in the next hours, the value of soil basal respiration slightly decrease (Figure 3.11)

a) Forest soil b) Pineapple soil

90 100 110 120 130 140 150

Innitial (60% WHC) Incubated (60% WHC) Incubated (10% WHC)

T o tal res p irat io n (m g CO kg -1 so il h -1 )

Forest soil Pineapple soil

1500 1700 1900 2100 2300

6h 18h 24h

m g C O2 k g -1soi l.h -1

Initial (60% WHC) Incubated (60% WHC) Incubated (10% WHC)

1500 1700 1900 2100 2300

6h 18h 24h

m g C O2 k g -1soi l.h -1

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36

Figure 3.11 The amount of CO2 after three periods incubator at three treatments

There was a negative correlation between soil basal respiration and MBN of forest soil in incubated soil with 10% WHC (r = -0.998*, n = 3, Figure 3.12) However, soil basal respiration was no correlation with MBN in pineapple soil at three treatments and forest soil at initial period and incubated soil with 60% WHC

a) Forest soil

b) Pineapple soil y = -0.3517x + 110.12

r = -0.998* 99.8 100 100.2 100.4 100.6 100.8 101

26 27 28 29

Soil respiration (m gCO kg -1 soil.h -1 )

MBN (µgN.g-1soil)

Soil respiration Linear (Soil respiration)

y = 0.4867x + 93.485 r = 0.283

99.8 100 100.2 100.4 100.6 100.8 101

13.5 14 14.5 15 15.5

Soil respiration (m gCO kg -1 soil.h -1 )

MBN (µgN.g-1soil)

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Figure 3.12 The correlation between MBN and soil respiration of both soil in incubated with 10% WHC

3.2 Discussion

3.2.1 Land use and land cover maps

The figures showed the result of the MODIS image interpretation of the land-use types distribution and land-land-use change over the years in Quang Nam, especially forest land and cropland Through interviews with local leaders, combined with field survey methodology, the results indicated that the area of paddy land converted because drought occurred usually in the riparian area This area had the characteristics as located at the foothills and foot of the mountains and far away from irrigation areas Therefore, rice cultivation there depended entirely on natural conditions, so the plants were prone to droughts in the dry season despite the use of drought-tolerant variety plants

3.2.2 Drought progress characteristics

The drought indices combined with climate factors, such as rainfall, evaporation, temperature, and sunshine hours, indicated that drought in Quang Nam province tends to be more complicated Severe drought concentrates in February and April, which was an important time of spring crop growth, thus greatly affecting the agricultural area in Quang Nam, especially rice land As a result, drought was the reason for rice land being converted from 2003 to 2018 with the purpose to adapt to the complicated drought in agriculture production

3.2.3 Soil properties and microbial activities

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to pineapples, especially N fertilizer Indeed, an NH4-N fertilizer, which added to the soil, always causes soil acidification, because the nitrification of NH4

+

increases H+ ions in the soil (White, 2015) Moreover, the leaching of bases from the soil profile due to pineapple cultivation on slope may contribute to the loss of base, lowering soil pH (Fageria and Nascente, 2014) The C:N ratio is often used to assess the decomposition rate and quality of SOM (Saljnikov et al., 2013) The C to N ratio less than 20 has sufficient N to supply the decomposing microorganisms with rapid rate and to release or mineralization N for plant use The C:N ratio higher than 20 occurs immobilization N (Gardiner and Miller, 2008) The C:N ratio in this study is nearly 7:1 (Table 3.1), indicating a potential SOM decomposition by microorganism at a high rate

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2007) Moreover, both soil types not have a significant difference in MBC values in three treatments Therefore, land-use types not have much effect on the change of MBC compared to drought factor

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changes over time Soil respiration tended to increase slightly at soil incubated 10% soil WHC in 24 hours The reason for those phenomena might be that in the soil have microbial species tolerate drought (Sheik et al., 2011), so soil respiration still maintained Besides, fungi hyphae could be linkage spatially discrete resources in the soil and maintain C and N cycling when drought occurs (Guhr et al., 2015; Treseder et al., 2018) Also, the same pattern with MBC, soil respiration was not influenced by land-use types The structure and the state of the microbial community are evaluated based on MBC/MBN ratio with a higher ratio shows the domination of fungi in the microbial biomass, while a low value indicates that bacteria are predominant in the microbial population (Campbell et al., 1991) The values of MBC/MBN in both soils are high, it showed the domination of fungi in the microbial biomass However, the fungal abundances microbial communities in forest soils are higher than that of microbial communities in arable land This result is similar to the study result of Bölscher et al (2016)

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use types were an insignificant difference, resulting possibly from soil conditions (soil bulk density, soil texture, temperature and other conditions) was not different

MBN/TN ratio usually reflects the soil N availability and mineralization (Thomas et al., 2007) A high MBN/TN ratio suggests a high-speed N transformation and high N supply ability (Yu et al., 2008) In this study, the higher MBN/TN ratio of forest soil indicated a higher potential in soil N mineralization, while the soil N supply ability in the pineapple soil was lower Furthermore, the average of MBN/TN ratio (0.0014%) of pineapple soil is lower than that of forest soil (0.0073%) because SOM in pineapple soil might be lower (Jones et al., 2008) The ratios of MBN/TN are generally much greater than those of MBC/SOC (Fauciand and Dick, 1994) However, in this research, the MBC/SOC ratio is higher than MBN/TN ratio

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CHAPTER CONCLUSIONS AND RECOMMENDATIONS 4.1 Conclusions

The study has identified that droughts in coastal plains occurred more frequently and more severely than mountainous areas In the period of 2003 – 2019, the drought situation in Quang Nam became more complicated While the drought frequency has tended to increase, the drought intensity has tended to decrease Moreover, drought is also one of the reasons of land-use change in Quang Nam Although the agricultural land area increased slightly (0.83%), the forest land area also increased significantly by 5.86% This change is aimed to adapt to unpredictable drought situations

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and MBN to change Pineapple cultivation has caused those soil indices to decrease

Furthermore, the calculation results of MBN/TN and MBC/SOC show that substrate availability is quite high and the difference is not significant in both land-use types due to soil conditions, namely soil texture, bulk density and temperature, are the similarly Besides, the mineralization potential of N is higher in forest soil, while the N ability supply on pineapple soil is lower Thus, the change of land-use and complex situation of drought under the context of climate change is possible to change the C and N cycles in the soil, thereby increasing the amount of greenhouse gases released into the atmosphere This can make the drought situation more and more severe in the future

4.2 Recommendations for future research

A number of areas requiring additional research have been identified as a direct result of this study For example, a large-scale interview with local people would yield an improved understanding of the impact of drought on agricultural production and why people have to change land use A more detailed analysis of drought maps stricken areas in Quang Nam is also advised

The present soil biochemical analysis was limited by time as well as financial constraints, but a larger sample size, as well as an investigation into other aspects of soil biology, would result in a better understanding of the differences in soil under drought between different land-use types

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53 APPENDIX Appendix A: In-depth interview questions

1 The newspaper and news regularly reported that Quang Nam was seriously affected by drought So, how has the drought caused damage to agriculture?

2 Which areas are regularly affected by drought? And what are the characteristics of agricultural production in that area?

3 With those effects of drought, local people have any measures to adapt?

4 The local government, has any methods to adapt to drought?

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Appendix B: The results of drought indices analysis

Table B.1: The months occurred drought during 2000 – 2019 period Year Drought months (dry season) Drought months (wet season)

Coastal plain Mountainous Coastal plain Mountainous

2000 2,

2001

2002 3, 4,

2003 4,

2004 3,

2005 2, 3, 1, 2,

2006 3, 4,

2007 2,

2008

2009 2,

2010 2, 3, 4, 2,

2011 2,

2012 3,

2013 5, 8, 12

2014 2

2015 2, 5,

2016 3, 4

2018

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Table B.2: K indices of mean drought months in dry season

Year Mountainous Coastal plain

K indices Status K indices Status 2000 0.152637567 very wet 0.574546605 wet 2001 0.157422093 very wet 0.806494367 wet 2002 0.331861962 very wet 1.084043442 slightly dry 2003 0.231149672 very wet 1.793222854 slightly dry 2004 0.144939324 very wet 1.010926119 slightly dry 2005 0.28100079 very wet 1.146255061 slightly dry 2006 0.205074046 very wet 1.323472781 slightly dry 2007 0.202174232 very wet 0.86989011 wet 2008 0.178081003 very wet 0.670245796 wet 2009 0.155266816 very wet 1.072499129 slightly dry 2010 0.242828908 very wet 1.348265896 slightly dry 2011 0.149142309 very wet 0.999449945 wet 2012 0.198085528 very wet 1.18334794 slightly dry

2013 0.168570215 very wet 0.9401694 wet

(65)

56

Table B.3: K indices of drought months during dry season (2000 – 2019) in Coastal plain

Year

Feb Mar Apr May Jun Jul

K

indices Status

K

indices Status 2000 1.29 slightly

dry 14.44

very

wet 0.40

very

wet 0.62 wet 0.86 wet 0.80 wet

2001 0.50 very

wet 0.56 wet 76.42 too dry 0.45

very

wet 1.00 wet 1.36

slightly dry 2002 1.42 slightly

dry 3.41 dry 14.98 too dry 1.79

slightly

dry 1.08

slightly

dry 4.71 too dry 2003 0.59 wet 1.58 slightly

dry 6.58 too dry 2.74 dry 1.21

slightly

dry 5.04 too dry 2004 1.13 slightly

dry 3.08 dry 9.65 too dry 1.96

slightly

dry 0.40

very

wet 0.90 wet 2005 3.27 dry 0.81 wet 14.31 too dry 8.75 too dry 1.88 slightly

dry 0.44

very wet 2006 1.00 slightly

dry 9.46 too dry 50.61 too dry 3.65 dry 12.56 too dry 0.73 wet 2007 140.75 too dry 0.29 very

(66)

57 Year

K

indices Status 2008 0.66 wet 0.58 wet 1.15 slightly

dry 0.17

very

wet 0.90 wet 3.06 dry

2009 2.19 dry 7.59 too dry 0.19 very

wet 1.02

slightly

dry 1.74

slightly

dry 4.08 too dry 2010 180.25 too dry 3.14 dry 3.33 dry 3.60 dry 6.35 too dry 0.83 wet 2011 4.44 too dry 0.35 very

wet 1.07

slightly

dry 5.34 too dry 1.34

slightly

dry 1.03

slightly dry 2012 0.48 very

wet 13.50 too dry 4.87 too dry 3.17 dry 0.60 wet 1.75

wet 0.34

very

wet 0.58 wet 3.57 dry 3.51 dry 1.94

slightly dry 2014 too dry 1.82 slightly

dry 2.38 dry 2.37 dry 2.78 dry 0.44

very wet 2015 2.07 dry 0.36 very

wet 0.73 wet 8.96 too dry 5.28 too dry 1.64

wet 3.44 dry too dry 1.26

slightly

dry 0.72 wet 1.47

(67)

58 Year

K

indices Status 2017 0.22 very

wet 1.51

slightly

dry 2.06 dry 2.06 dry 0.87 wet 0.22

very wet 2018 3.88 dry 1.19 slightly

dry 0.53 wet 1.83

slightly

dry 0.51 wet 0.27

very wet 2019 5.13 too dry 1.53 slightly

dry 158.17 too dry 0.51 wet too dry 1.02

(68)

59

Table B.4: K indices of drought months during dry season (2000 – 2019) in Mountainous

Year

K

indices Status 2000 0.45 very

wet 1.27

slightly

dry 0.20

very

wet 0.10

very

wet 0.19

very

wet 0.47

very wet 2001 0.25 very

wet 0.23

very

wet 0.66 wet 0.12

very

wet 0.61 wet 1.08

slightly dry 2002 1.16 slightly

dry 1.34

slightly

dry 1.29

slightly

dry 0.46

very

wet 0.53 wet 3.35 dry

2003 0.87 wet 1.07 slightly

dry 1.02

slightly

dry 0.44

very

wet 0.37

very

wet 0.40

very wet 2004 2.00 dry 1.40 slightly

dry 0.45

very

wet 0.46

very

wet 0.29

very

wet 0.25

very wet 2005 2.03 dry 0.27 very

wet 8.87 too dry 0.41

very

wet 0.39

very

wet 0.57 wet 2006 0.15 very

wet 1.41

slightly

dry 1.31

slightly

dry 0.25

very

wet 1.30

slightly

dry 0.42

very wet 2007 46.08 too dry 0.92 wet 0.49 very

wet 0.18

very

wet 0.31

very

wet 0.67 wet 2008 0.23 very

wet 0.52 wet 0.65 wet 0.17

very

wet 0.31

very

(69)

60 Year

K

indices Status 2009 1.63 slightly

dry 1.64

slightly

dry 0.29

very

wet 0.10

very

wet 0.76 wet 0.20

very wet 2010 19.00 too dry 0.87 wet 3.42 dry 0.85 wet 0.45 very

wet 0.25

very wet 2011 1.54 slightly

dry 0.11

very

wet 1.52

slightly

dry 0.35

very

wet 0.43

very

wet 0.64 wet 2012 0.34 very

wet 1.42

slightly

dry 0.37

very

wet 0.28

very

wet 0.72 wet 0.43

very wet 2013 0.55 wet 0.13 very

wet 0.53 wet 0.27

very

wet 0.33

very

wet 0.28

very wet 2014 7.53 too dry 1.69 slightly

dry 0.34

very

wet 0.40

very

wet 0.53 wet 0.24

wet 0.13

very

wet 0.50

very

wet 0.24

very

wet 0.69 wet 0.39

wet 0.92 wet 3.42 dry 0.38

very

wet 0.15

very

wet 0.33

wet 0.20

very

wet 0.48

very

wet 0.09

very

wet 0.14

very

wet 0.09

(70)

61 Year

K

indices Status 2018 0.65 wet 0.52 wet 0.26 very

wet 0.46

very

wet 0.80 wet 0.47

very wet 2019 too dry 1.06 slightly

dry 3.45 dry 0.15

very

wet 1.67

slightly

dry 0.31

(71)

61

Appendix C: Results of the statistical analysis

Table C.1: Results of the biochemical properties of Group Statistics analysis in both land-use types

Type N Mean Std Deviation Std Error Mean

MBNi F 10.626890 2698181 1557795

P 7.188048 0353600 0204151

MBN60 F 22.206230 1280338 0739204

P 14.001939 5654602 3264686

MBN10 F 27.661618 1.2275448 7087233

P 14.519295 7173944 4141879

MBCi F 456.444090 12.4385770 7.1814158

P 389.796152 15.0604022 8.6951273

MBC60 F 1519.421233 214.8388033 124.0372409 P 1534.490312 158.0685411 91.2609147 MBC10 F 521.968599 117.1965145 58.5982573

P 547.266014 68.4329001 34.2164500

Moi F 163165 0009147 0004574

P 117201 0027967 0013983

pH F 6.6250 17078 08539

P 5.7500 12910 06455

BD F 2.191814 0522241 0261121

P 2.246210 2388795 1194398

BRi F 129.849404 1.1400311 5700156

P 131.180948 9062752 4531376

BR10 F 100.380322 3541120 1770560

P 100.493397 3366394 1683197

BR60 F 139.764042 1.6902551 8451275

P 136.397100 9377638 4688819

(72)

62

Type N Mean Std Deviation Std Error Mean

P 77.056609 66.8073858 33.4036929

Clay F 27.537500 1.2495966 6247983

P 28.457500 4614741 2307370

Silt F 51.767500 2.3454406 1.1727203

P 49.550000 2.5399081 1.2699541

Sand F 20.695000 3.5661978 1.7830989

P 21.992500 2.1641376 1.0820688

TN F 150000 0141421 0070711

P 120000 0081650 0040825

TC F 1.040909 0451505 0225752

P 817045 0462618 0231309

TP F 800000 7791876 3895938

P 870000 1.0049212 5024606

C:N F 6.985286 7017438 3508719

(73)

63

Table C.2: The Independent Samples Test analysis results of biochemical properties

Levene's Test for Equality of Variances

t-test for Equality of Means

F Sig t df

Sig (2-tailed)

Mean Difference

Std Error Difference

95% Confidence Interval of the Difference

Lower Upper

MBNi Equal

variances assumed

11.836 026 21.888 000 3.4388418 1571116 3.0026302 3.8750535

Equal variances not assumed

21.888 2.069 002 3.4388418 1571116 2.7839026 4.0937811

MBN 60

Equal variances assumed

3.824 122 24.510 000 8.2042908 3347327 7.2749239 9.1336577

24.510 2.205 001 8.2042908 3347327 6.8848204 9.5237612

MBN 10

.671 459 16.010 000 13.1423236 8208778 10.8632015 15.4214457

Equal variances not

(74)

64

F Sig t df

Lower Upper

assumed

MBCi Equal

variances assumed

.129 738 5.910 004 66.6479375 11.2773211 35.3370747 97.9588004

5.910 3.862 005 66.6479375 11.2773211 34.8911620 98.4047131

MBC 60

.136 731 -.098 927 -15.0690787 153.9928300 -442.6217177 412.4835604

-.098 3.675 927 -15.0690787 153.9928300 -457.9752573 427.8371000

MBC 10

1.130 329 -.373 722 -25.2974156 67.8566224 -191.3365892 140.7417580

Equal variances not

(75)

65

F Sig t df

Lower Upper

assumed

Moi Equal variances assumed

2.310 179 31.242 000 0459645 0014712 0423645 0495645

31.242 3.635 000 0459645 0014712 0417125 0502165

pH Equal variances assumed

.214 660 8.174 000 87500 10704 61307 1.13693

8.174 5.585 000 87500 10704 60828 1.14172

BD Equal variances assumed

3.376 116 -.445 672 -.0543956 1222608 -.3535569 2447658

Equal variances not

(76)

66

F Sig t df

Lower Upper

assumed

BRi Equal variances assumed

.004 950 -1.829 117 -1.3315434 7281836 -3.1133446 4502578

-1.829 5.710 120 -1.3315434 7281836 -3.1355443 4724575

BR10 Equal

variances assumed

.027 875 -.463 660 -.1130752 2442956 -.7108451 4846947

-.463 5.985 660 -.1130752 2442956 -.7112157 4850652

BR60 Equal

variances assumed

.856 391 3.484 013 3.3669415 9664837 1.0020410 5.7318419

Equal variances not

(77)

67

F Sig t df

Lower Upper

assumed

MBC :MB N

.472 518 -.495 638 -21.1852364 42.8140715 -125.9474953 83.5770226

-.495 5.729 639 -21.1852364 42.8140715 -127.1599012 84.7894285

Clay Equal

variances assumed

5.271 061 -1.381 216 -.9200000 6660424 -2.5497471 7097471

-1.381 3.803 243 -.9200000 6660424 -2.8075563 9675563

Silt Equal

variances assumed

.012 916 1.283 247 2.2175000 1.7285995 -2.0122306 6.4472306

Equal variances not

(78)

68

F Sig t df

Lower Upper

assumed

Sand Equal

variances assumed

1.025 350 -.622 557 -1.2975000 2.0857408 -6.4011238 3.8061238

-.622 4.946 561 -1.2975000 2.0857408 -6.6768185 4.0818185

TN Equal variances assumed

1.000 356 3.674 010 0300000 0081650 0100210 0499790

3.674 4.800 015 0300000 0081650 0087455 0512545

TC Equal variances assumed

.014 909 6.926 000 2238636 0323215 1447758 3029515

(79)

69

F Sig t df

Lower Upper

TP Equal variances assumed

.733 425 -.110 916 -.0700000 6358066 -1.6257626 1.4857626

-.110 5.650 916 -.0700000 6358066 -1.6494584 1.5094584

C:N Equal variances assumed

.285 613 335 749 1533958 4574239 -.9658801 1.2726718

(80)

70

Table C.3: The Independent Samples Test analysis results of biochemical properties in forest soil

Levene's Test for Equality of

Variances

F Sig t df

Sig (2-tailed) Mean Difference Std Error Difference

Lower Upper

MBN i – MBN 60 Equal variances assumed

3.578 132 -67.155

4 000 -11.5793402 1724282 -12.0580777 -11.1006027

Equal variance not assumed -67.155

2.857 000 -11.5793402 1724282 -12.1439286 -11.0147518

MBN i-MBN 10 Equal variances assumed

2.819 168 -23.475

4 000 -17.0347287 7256418 -19.0494333 -15.0200241

Equal variances not assumed -23.475

2.193 001 -17.0347287 7256418 -19.9080507 -14.1614066

MBN 60-MBN Equal variances assumed

(81)

71

Variances

F Sig t df

Lower Upper

10 Equal variances not assumed

-7.656 2.044 016 -5.4553885 7125679 -8.4596066 -2.4511704

MBC i-MBC 60 Equal variances assumed

3.598 131 -8.555 001 -1062.977143 124.2449591 -1407.936452 -718.0178352

-8.555 2.013 013 -1062.977143 124.2449591 -1594.162955 -531.7913322

3.514 134 -.562 604 -43.2669945 76.9953917 -257.0404729 170.5064838

-.562 2.035 630 -43.2669945 76.9953917 -369.1357084 282.6017194

MBC 60-MBC Equal variances assumed

(82)

72

Variances

F Sig t df

Lower Upper

(83)

73

Table C.4: The Independent Samples Test analysis results of biochemical properties in pineapple soil

Variances

F Sig t df

Lower Upper

6.002 070 -20.831

4 000 -6.8138912 3271063 -7.7220839 -5.9056985

2.016 002 -6.8138912 3271063 -8.2109015 -5.4168809

3.906 119 -17.679

4 000 -7.3312469 4146907 -8.4826128 -6.1798809

2.010 003 -7.3312469 4146907 -9.1072746 -5.5552192

MBN 60-MBN Equal variances assumed

(84)

74

Variances

F Sig t df

Lower Upper

-.981 3.793 385 -.5173557 5273835 -2.0136617 9789503

5.360 082 -12.487

4 000 -1144.6941599 91.6742046 -1399.222557 -890.1657632

2.036 006 -1144.6941599 91.6742046 -1532.472741 -756.9155789

3.039 156 -3.899 018 -172.6013759 44.2629725 -295.4950891 -49.7076627

-3.899 2.160 053 -172.6013759 44.2629725 -350.1283578 4.9256060

MBC 60-MBC Equal variances assumed

(85)

75

Variances

F Sig t df

Lower Upper

(86)

76

Table C.5: Results of the Correlation Analysis – Forest soil

MBNi MBCi Moi pH BRi MBC:MBN TC TN C:N

MBNi Pearson Correlation 692 164 030 -.927 -.308 980 218 181

Sig (2-tailed) 514 895 981 245 800 128 860 884

N 3 3 3 3

MBCi Pearson Correlation 692 826 -.702 -.912 474 822 -.554 836

Sig (2-tailed) 514 .381 505 269 686 386 626 370

N 3 3 3 3

Moi Pearson Correlation 164 826 -.981 -.522 888 358 -.927 578

Sig (2-tailed) 895 381 .124 651 304 767 245 422

N 3 3 3 3

pH Pearson Correlation 030 -.702 -.981 347 -.960 -.171 982 -.978

Sig (2-tailed) 981 505 124 .774 181 891 121 135

N 3 3 3 3

BRi Pearson Correlation -.927 -.912 -.522 347 -.071 -.983 164 -.537

Sig (2-tailed) 245 269 651 774 .955 117 895 639

N 3 3 3 3

(87)

77

MBNi MBCi Moi pH BRi MBC:MBN TC TN C:N

MBN Sig (2-tailed) .800 .686 .304 .181 .955 .928 .060 .316

N 3 3 3 3

TC Pearson Correlation 980 822 358 -.171 -.983 -.112 019 374

Sig (2-tailed) 128 386 767 891 117 928 .988 756

N 3 3 3 3

TN Pearson Correlation 218 -.554 -.927 982 164 -.996 019 -.920

Sig (2-tailed) 860 626 245 121 895 060 988 .256

N 3 3 3 3

C:N Pearson Correlation 181 836 578 -.978 -.537 880 374 -.920

Sig (2-tailed) 884 370 422 135 639 316 756 256

N 3 3 3 3

(88)

78

Table C.6: The Correlation Analysis results of MBC, MBN and soil respiration – Forest soil

MBNi MBCi MBN60 MBN10 MBC60 MBC10 BRi BR60 BR10 MBNi Pearson Correlation 692 501 910 -.876 -.003 -.927 026 -.880

Sig (2-tailed) 514 666 272 320 998 245 983 314

N 3 3 3 3

MBCi Pearson Correlation 692 972 330 -.258 720 -.912 -.704 -.266

Sig (2-tailed) 514 .152 786 834 488 269 503 828

N 3 3 3 3

MBN60 Pearson Correlation 501 972 097 -.022 864 -.789 -.852 -.030

Sig (2-tailed) 666 152 .938 986 336 422 350 981

N 3 3 3 3

MBN10 Pearson Correlation 910 330 097 -.997* -.417 -.688 438 -.998*

Sig (2-tailed) 272 786 938 .048 726 517 711 042

N 3 3 3 3

MBC60 Pearson Correlation -.876 -.258 -.022 -.997* 484 632 -.504 1.000**

Sig (2-tailed) 320 834 986 048 .678 564 664 005

N 3 3 3 3

(89)

79

MBNi MBCi MBN60 MBN10 MBC60 MBC10 BRi BR60 BR10

Sig (2-tailed) 998 488 336 726 678 .757 008 684

N 3 3 3

BRi Pearson Correlation -.927 -.912 -.789 -.688 632 -.372 350 639

Sig (2-tailed) 245 269 422 517 564 757 .772 559

N 3 3 3 3

BR60 Pearson Correlation 026 -.704 -.852 438 -.504 -.992** 350 -.497

Sig (2-tailed) 983 503 350 711 664 008 772 .669

N 3 3 3

BR10 Pearson Correlation -.880 -.266 -.030 -.998* 1.000** 477 639 -.497 Sig (2-tailed) 314 828 981 042 005 684 559 669

N 3 3 3 3

(90)

80

Table C.7: Results of the Correlation Analysis – Pineapple soil

MBNi MBCi Moi pH BRi MBC:MBN TN TC C:N

MBNi Pearson Correlation -.726 -.593 995 -.989 -.778 996 -.046 -.837

Sig (2-tailed) 483 596 065 093 432 056 971 369

N 3 3 3 3

MBCi Pearson Correlation -.726 -.123 -.792 818 997 -.662 721 984

Sig (2-tailed) 483 .921 418 390 051 539 488 114

N 3 3 3 3

Moi Pearson Correlation -.593 -.123 -.508 470 -.044 -.662 -.777 055

Sig (2-tailed) 596 921 .661 689 972 540 434 965

N 3 3 3 3

pH Pearson Correlation 995 -.792 -.508 -.150 -.838 982 -.147 -.888

Sig (2-tailed) 065 418 661 .850 367 121 906 304

N 3 3 3 3

BRi Pearson Correlation -.989 818 470 -.150 861 -.973 191 907

Sig (2-tailed) 093 390 689 850 .339 149 878 276

N 3 3 3

(91)

81

MBNi MBCi Moi pH BRi MBC:MBN TN TC C:N

MBN Sig (2-tailed) .432 .051 .972 .367 .339 .488 .539 .063

N 3 3 3 3

TN Pearson Correlation 996 -.662 -.662 982 -.973 -.720 042 -.785

Sig (2-tailed) 056 539 540 121 149 488 .973 425

N 3 3 3 3

TC Pearson Correlation -.046 721 -.777 -.147 191 663 042 586

Sig (2-tailed) 971 488 434 906 878 539 973 .602

N 3 3 3 3

C:N Pearson Correlation -.837 984 055 -.888 907 995 -.785 586

Sig (2-tailed) 369 114 965 304 276 063 425 602

(92)

82

Table C.8: The Correlation Analysis results of MBC, MBN and soil respiration – Pineapple soil

MBNi MBCi MBN60 MBN10 MBC60 MBC10 BRi BR60 BR10 MBNi Pearson Correlation -.726 998* 400 549 361 -.989 -.237 -.034

Sig (2-tailed) 483 043 738 630 765 093 848 979

N 3 3 3 3

MBCi Pearson Correlation -.726 -.770 340 176 380 818 -.496 712

Sig (2-tailed) 483 .440 779 887 752 390 669 496

N 3 3 3 3

MBN60 Pearson Correlation 998* -.770 338 492 298 -.997 -.171 -.100

Sig (2-tailed) 043 440 .781 672 808 050 890 936

N 3 3 3 3

MBN10 Pearson Correlation 400 340 338 986 807 -.263 -.985 903

Sig (2-tailed) 738 779 781 .108 193 831 110 283

N 3 3 3

MBC60 Pearson Correlation 549 176 492 986 978 -.422 -.942 817

Sig (2-tailed) 630 887 672 108 .135 723 218 392

N 3 3 3 3

(93)

83

MBNi MBCi MBN60 MBN10 MBC60 MBC10 BRi BR60 BR10

Sig (2-tailed) 765 752 808 193 135 .858 083 256

N 3 3 3

BRi Pearson Correlation -.989 818 -.997 -.263 -.422 -.222 093 178

Sig (2-tailed) 093 390 050 831 723 858 .941 886

N 3 3 3 3

BR60 Pearson Correlation -.237 -.496 -.171 -.985 -.942 -.992 093 -.963

Sig (2-tailed) 848 669 890 110 218 083 941 .174

N 3 3 3 3

BR10 Pearson Correlation -.034 712 -.100 903 817 920 178 -.963 Sig (2-tailed) 979 496 936 283 392 256 886 174

N 3 3 3 3

(94)

78 Where:

F = forest soil P = pineapple soil

MBNi = microbial biomass nitrogen initial period

MBN60 = microbial biomass nitrogen incubated with 60% WHC MBN10 = microbial biomass nitrogen incubated with 10% WHC MBCi = microbial biomass carbon initial period

MBC60 = microbial biomass carbon incubated with 60% WHC MBC10 = microbial biomass carbon incubated with 10% WHC Moi (moi) = moisture

pH = pH (H2O) BD = bulk density

BRi = soil respiration initial period

BR10 = soil respiration incubated with 60% WHC BR60 = soil respiration incubated with 10% WHC

MBC:MBN = microbial biomass carbon to microbial biomass nitrogen ratio TN = total nitrogen

TC = total carbon TP = total phosphorus

(95)

79

Appendix D: The activities during the master thesis implementation

Interview local authorities

Soil sampling

(96)

80

(97)

81 Appendix E: Learning outcome

Program Learning Outcomes (PLOs) of MCCD

Results of the Master’s thesis

Other outcomes of the Master’s thesis

Maps Soil

analysis Laboratory skills

PLO1: Accumulating and mastering

the basic knowledge on principles of Marxism - Leninism, Political

Theory and Ideology of Ho Chi Minh; and general knowledge about administration and management

PLO2: Mastering the fundamental,

interdisciplinary knowledge and methodologies to assess and address actual problems (fate and features) related to CC mitigation, adaptation for sustainable development at global, national and local levels

x x

PLO3: Understanding and

developing systematic thinking; necessary knowledge on science, technology, innovation and

governance related to CC response for development; identifying,

analyzing, assessing and forecasting the issues related to CC and CCR; predicting the developing trend of CC science

x x x

PLO4: Applying knowledge to solve

the problems in CC and CCR: planning and approaching the works in field of CC; proposing the

initiatives as well as the researches on CC; implementing the solutions on science, technology, mechanism, policy and finance for CCR and development

x

PLO5: Having skills of cooperation

with personal, agencies,

organizations domestically and internationally to solve the CC

(98)

82

Maps Soil

issues, communication in works, projects on CC; and organizing, managing and administrating advanced career development

PLO6: Accumulating soft skills to

self-directed and adapt to

competitive working environment such as English proficiency (at level 4/6 according to English

competencies Framework for

Vietnam), Japanese communication skills; having skills on time

management; using the basic

computer skills proficiently; working and researching independently; having skills of research and

development; and using technologies creatively in academic and

professional fields

x x x

PLO7: Dynamic, confident,

persistent, enthusiastic, and risk-taking and management

x x x

PLO8: Having social/community’s

responsibility and professional morality, especially for the scientific research results; being able to adapt to multicultural environment, ensure the harmony between the

stakeholders, CCR and development; having good social morality, assist the vulnerable people to climate change; compliance with the law; discipline at work and positive lifestyle; having good attitude to their career in climate change response for sustainable development

x x

PLO9: Having responsibility for

(99)

83

Maps Soil

knowledge, and offering new ideas on climate change response in different complex situations; adapting and guiding other people and making expert decisions on climate change response; managing research, having high responsibility in learning in order to develop

professional knowledge, and creating new ideas in new process; and

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