PROCEEDINGS OF
INTERNATIONAL SYMPOSIUM
ON A RECENT PROGRESS
IN FOREST ECOLOGY AND MANAGEMENT

2021

INTERNATIONAL SYMPOSIUM
ON A RECENT PROGRESS IN FOREST ECOLOGY AND
MANAGEMENT 2021&
THE 9TH IC-GU12 ROUNDTABLE 2021
ORGANIZER:INTERNATIONAL CONSORTIUM OF UNIVERSITIES IN SOUTH AND SOUTHEAST ASIA FOR THE
DOCTORAL EDUCATION IN AGRICULTURAL SCIENCE AND BIOTECHNOLOGY (ICGU12)UGSAS-GU & BWEL JOINT POSTER SESSIONON AGRICULTURAL
AND BASIN WATER ENVIRONMENTAL SCIENCES
2021
ORGANIZERS:
THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCE, GIFU UNIVERSITY
GIFU UNIVERSITY REARING PROGRAM
FOR BASIN WATER ENVIRONMENTAL LEADERS

DATE: NOVEMBER 10-11, 2021

9th UGSAS-GU & 9th IC-GU12International Symposium
on a Recent Progressin Forest Ecology and Management
2021&
The 9th IC-GU12
Roundtable 2021

The United Graduate School of Agricultural Science, Gifu University
Tokai National Higher Education and Research System

Greetings
from the Roundtable and Symposium Chair

It is a great pleasure and an honor for me to welcome distinguished guests, colleagues, friends,
and our alumnae/alumni of the UGSAS to the 9th IC-GU12 (International Consortium of Universities
in South and Southeast Asia Region for the Doctoral Education on the Agricultural Science andBiotechnology; IC of Great Universities established in 2012) Roundtable and Symposium 2021.
Though meeting face to face is a shortcut to exchange knowledge and promote friendship, theroundtable and symposium of this year are carried on a video conference unfortunately like last year.
I am sorry for the inconvenience. We, however, have succeeded to invite many excellent researchers
to the symposium on forest management and ecology, which must contribute to the progress of ourconsortium.
The symposium of this year is a first case to discuss forestry at IC-GU12. Forestry is,however, one of most important fields of expertise for Gifu University, because Gifu prefecture has
the second largest ratio of forest area/total area among all the prefectures in Japan and therefore its
forest industry is active. Therefore, we have been hoping to have this symposium for long years. And
not a few member Universities considers the importance of forestry and agrees our proposal. I would
like to appreciate the cooperation. In addition, I believe expansion of the fields of expertise tocollaborate would strengthen the connection of each University in IC-GU12 like a line to a network.
I hope it will bring fruitful achievements and be a priming of research promotion of forestry
and ecology.
The following events are scheduled for the period:
1) Roundtable for "IC-GU12" at 18:00 JST(Japanese Standard Time) on 10th November
2) Symposium on forest management and ecology at 12:00 JST on 10-11th November
3) Joint Poster Session on Agriculture and Basin Water Environmental Science 2021
co-organized by BWEL at 15:00 JST on 11th November.
In this roundtable meeting, we will discuss about the sharing of international lectures using
a video conference system. It would give students a great chance to touch international education andencourage them to fly to the international society.
In the symposium of this year, “Forestry management and ecology” is the main theme asabove mentioned and it is expected to have interesting academic reports and intensive discussions.

9th UGSAS-GU & 9th IC-GU12

We also have the research activity reports by UGSAS (United Graduate School of
Agricultural Science) students and BWEL (Rearing Program for Basin Water Environmental Leaders)
students as we do every year. I eagerly ask you to join the session actively, encourage the students
and share their research results and accomplishments.
We would like to express our deep gratitude for your participation to our Roundtable andSymposium of IC-GU12 in November 2021.

Ken HIRAMATSU, Dr. Agr.
Dean of the United Graduate School of
Agricultural Science, Gifu University

The United Graduate School of Agricultural Science, Gifu University
Tokai National Higher Education and Research System

Message from the Editor in Chief of
REVIEWS IN AGRICULTURAL SCIENCE(RAS) e-journal

Greetings to all the participants of the UGSAS-GU International Symposium of 2021.
I would like to introduce myself. My name is Masateru Senge, chief-editor of theinternational journal "Reviews in Agricultural Science (RAS)" from April, 2020. And emeritus
professor Fumiaki Suzuki has also been kindly supporting to edit the submitted papers of the fields
related to the chemical and biotechnology as an associate editor.
There are 74 review papers published in this journal from the 1st (2014) volume to the 8thvolumes (2020), and which had been cited 817 times by scientific journal listed in "Google Scholar"
as of June 15th, 2021. The average citations per document during 2014 to 2020 exceeded 11times.
Furthermore, SCOPUS estimated the rank of RAS as Q3 in the field of “Agricultural and biologicalsciences”.
This year, there has been 44 papers submitted to our journal from the academia of 9 countries
including of 6 papers from UGSAS-GU until August 15th, 2021, and now 11 papers were acceptedfor publication and 8 papers are under review.
We welcome your review contributions to RAS, on any aspects that relate to agricultural
themes. Your submissions will certainly receive utmost attention and refinement by our editorial board
before acceptance.
Please have a look at our website https://www.jstage.jst.go.jp/browse/ras/-char/en in the JStage platform.

Masateru Senge, Ph.D.
Editor in Chief, Reviews in Agricultural Science
August 15, 2021

9th UGSAS-GU & 9th IC-GU12International Symposium on a Recent Progress in Forest
Ecology and Management 2021
10th November, 2021
12:00-12:05

OPENING REMARKS

12:00-12:05

Toshiyuki Ohtsuka, Professor
Head Organizer of the Symposium
Gifu University

12:05-13:25

KEYNOTE SPEECH 1

12:05-12:45

Michiko Nakagawa, Associate Professor
Nagoya University
Long-term Field Data Unveil Characteristics of Phenology and Forest Dynamics in a
Bornean Tropical Rain Forest

12:45-13:25

Sasitorn Poungparn, Associate Professor
Chulalongkorn University
Unfolding a Mystery of Tropical Mangroves from a Two-decade Journey in
Mangrove Study

13:25-13:45

Break

13:45-15:00
FOREST CARBON CYCLE AND CLIMATE CHANGE (Session 1)
Chair: Professor Toshiyuki Ohtsuka (Gifu University)
13:45-14:00

Suthathip Umnouysin, PhD (Silpakorn University)
Net Primary Productivity Estimation in the Chronosequence Coastal-mangrove
Plantations in Central Thailand

14:00-14:15

Christine Wulandari, PhD (The University of Lampung)
Relationships Between Carbon Stocks and Trees Biodiversity in Production Forestof Register 24, Lampung Province

14:15-14:30

Komariah, PhD (Sebelas Maret University)
The Effects of Climate Change on Indonesian Forests

14:30-14:45

Sutheera Hermhuk, PhD (Maejo University)
Tropical Montane Forest Ecotone Dynamics in the Context of Climate Change:
Case Study in the Doi Suthep-Pui National Park, Thailand

The United Graduate School of Agricultural Science, Gifu University
Tokai National Higher Education and Research System
14:45-15:00

Wongsatorn Phumphuang, Master (Kasetsart University)
Effect of Climate Changes on Dry Evergreen Forest Dynamics in Sakaerat
Biosphere Reserve, Northeastern Thailand

15:00-15:20

Break

15:20-16:05
ECOLOGY OF TROPICAL TO TEMPERATE FORESTS (Session 2)
Chair: Associate Professor Atsuhiro Iio (Shizuoka University)
15:20-15:35

Qingmin Han, PhD (Forestry and Forest Products ResearchInstitute (Japan))
Mast Seeding and Associated Resource Dynamics in Fagus Species

15:35-15:50

Nguyen Trong Minh, PhD (Vietnam National University of Forestry)
Anchorage and Stability Characteristic of Forest Tree Species in Tropical Region
and a Suitable Wind Damage Assessment Mode for Planted Forest in Vietnam

15:50-16:05

Momi Tsuruta, PhD (Forestry and Forest Products ResearchInstitute (Japan))Hybrid Breakdown as a Result of Up-regulated Expression of Defense-related
Genes in the Hybrid Seedlings of Japanese Flowering Cherry

16:05-16:25

Break

16:25-17:10
AGROFORESTRY OF TROPICAL REGION (Session 3)
Chair: Assistant Professor Isao Hirota (Gifu University)
16:25-16:40

Maria Theresia Sri Budiastuti, PhD (Universitas Sebelas Maret)
Agroforestry System as the Best Vegetation Management to Face Forest
Degradation

16:40-16:55

Cahyo Wisnu Rubiyanto, Master (Gifu University)
Contribution of Forest Resources in Shifting Cultivation System to Livelihood
Diversification in Mountain Villages of Northern Laos

16:55-17:10

Zuhud Rozaki, PhD (Universitas Muhammadiyah Yogyakarta)
Agroforestry Practice in Volcano Prone Area: Findings from Mount MerapiIndonesia

17:10-18:00

Break

18:00-19:00

The 9th IC-GU12 ROUNDTABLE 2021

9th UGSAS-GU & 9th IC-GU12

11th November, 2021
12:00-13:20

KEYNOTE SPEECH 2

12:00-12:40

Atsuhiro Iio, Associate Professor
Shizuoka University
Masting Impact on Canopy Structure and Photosynthesis in Beech Forests
Does Masting Perform as Gap?

12:40-13:20

So Hanaoka, Chief of Breeding Laboratory
Forestry and Forest Products Research Institute (Japan)
Forest Tree Breeding in Hokkaido Japan

13:20-13:40

Break

13:40-14:40
FOREST HYDROLOGY AND BIOGEOCHEMISTRY (Session 4)
Chair: Associate Professor Takeo Onishi (Gifu University)
13:40-13:55

Siyu Chen, PhD (Nanning Normal University)
Seasonal Variations of Dissolved Organic Carbon Concentrations and Fluxes in an
Evergreen Broadleaved Subtropical Forest in Central Japan

13:55-14:10

Taketo Uemura (Gifu University)
Differences of Water and Material Dynamics between Coniferous Plantation and
Deciduous Secondary Forest Watershed -Focusing on Dissolved Organic andInorganic Carbon

14:10-14:25

Dwi Priyo Ariyanto, PhD (Universitas Sebelas Maret)
Estimating of Run off Using SCS-CM Model in Gunung Bromo Education Forest
Discharging to Kalijirak River

14:25-14:40

Edwina Zainal, PhD (Bung Hatta University)
The Runoff and Sediment Response Due to Land Use Change in the Lubuk Paraku
Sub-Watershed

14:40-14:45

CLOSING REMARKS

14:40-14:45

Ken Hiramatsu, Professor
Dean of the UGSAS-GU
Gifu University

14:45-15:00

Break

15:00-17:15

UGSAS, GU & BWEL Joint Poster Session on Agricultural and Basin Water
Environmental Sciences 2021 (Poster Session)

The United Graduate School of Agricultural Science, Gifu University
Tokai National Higher Education and Research System

The 9th IC-GU12 Roundtable 2021
Date: 10th November
18:00~19:00JSTWeb Conference

Participants: Liaison of member universities in IC-GU12
Bangladesh Agricultural University (Bangladesh)
University of Dhaka (Bangladesh)
Guangxi University (China)
Assam University (India)
Indian Institute of Technology Guwahati (India)
Andalas University (Indonesia)
Bandung Institute of Technology (Indonesia)
IPB University (Indonesia)
Gadjah Mada University (Indonesia)
Sebelas Maret University (Indonesia)
University of Lampung (Indonesia)
National University of Laos (Laos)
Mariano Marcos State University (Philippines)
Chulalongkorn University (Thailand)
Kasetsart University (Thailand)
King Mongkut’s University of Technology Thonburi (Thailand)Hanoi University of Science and Technology (Vietnam)
Thuyloi University (Vietnam)
Shizuoka University (Japan)
Gifu University (Japan)
Chair: Ken HIRAMATSU, Dean of UGSAS
Moderator: Tomio YABE, Vice Dean of UGSAS (International Affairs)
Topic: Building a Framework for Collaborative Education among Member Universities of IC-GU12 Usinga Remote Lecture System

Contents
Discussion and information exchange

9th UGSAS-GU & 9th IC-GU12UGSAS-GU & BWEL Joint Poster Session
on Agricultural and Basin Water Environmental Sciences
2021
11th November
15:00-17:15 JST
Organized by The United Graduate School of Agricultural Science, Gifu University (UGSAS-GU)
Gifu University Rearing Program for Basin Water Environmental Leaders (BWEL)

Online Poster Presentation
15:00-15:05 Opening remarks
Prof. Ken Hiramatsu (Dean of UGSAS-GU)
Poster Core Time
15:10-15:50 Odd numbered posters
15:50-16:30 Even numbered posters
16:30-16:45 Free discussion time
16:45-16:50 Announcement of the winners of the Best Presentation Award
17:00-17:10 Awarding ceremony
17:10-17:15 Closing remarks
Prof. Fusheng Li (Head of the Promotion Office of BWEL)

Presentations
P01: Effects of curcumin nanoemulsion on the water vapor permeability and antioxidant activities ofchitosan-based films
Fakfan Luangapai (UGSAS-GU)
P02: LAMP-FLP for detection of thiophanate-methyl resistant strains of Fusarium fujikuroi in Japan
FangJing Li (UGSAS-GU; BWEL)
P03: Changes in chlorophyll and carotenoid profiles and gene expression during fruit regreening in Valencia
orangeNichapat Keawmanee (UGSAS-GU)
P04: Modified newmark approach for evaluation of earthquake-induced displacement of earthdams
Phuong Le-Hong (GUSAS-GU)
P05: Development of a HS-SPME/GC-MS method for the extraction and identification of the volatilecompounds in coffee
Fawzan Sigma Aurum (UGSAS-GU)
P06: Development of a program for constructing ortholog data set using taxonomic information
Tomoaki Watanabe (UGSAS-GU)The United Graduate School of Agricultural Science, Gifu University
Tokai National Higher Education and Research System
P07: Visualization of low molecule phenol (LMP) and copper naphthenate in oil palm veneer using X-ray
microtomogrpahy
Ayuni Nur Apsari (UGSAS-GU)
P08: Generalized pollination and floral scent chemistry in the invasive Coreopsis lanceolata
Muhammad Arifin (UGSAS-GU)
P09: Inhibition effect of different sorbent materials on transfer of cesium and arsenic from contaminatedsoil to vegetation
Shiamita Kusuma Dewi (UGSAS-GU; BWEL)
P10: Ensemble prediction of solar irradiance in tropical regions
Daiki Harada (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P11: Short-term prediction of cloud movements with meteorological satellite images processed in spectrum
space
Yuto Hara (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P12: Application of Kalman filter to solar irradiance forecasting with a meteorological model in tropicalregionNaoki Moriai (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P13: Bacterial activity and EPS affect the performance of household wastewater treatment facility
Haoning Su (Graduate School of Engineering, Gifu University; BWEL)
P14: Steam gasification of woody biomass with renewable hydrogen
Sheng-Wen Xiao (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P15: Adsorbability of organic matter released from drinking water treatment sludge onto activated carbon
Sri Anggreini (Graduate School of Engineering, Gifu University; BWEL)
P16: Coexisting and competing algal species with moldy odor-causing blue algae in Nagara River
Wenqing Li (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P17: Fate and effect of microplastic and heavy metal in agricultural soil added with biochar
Rahman Fariha (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P18: MODIS-based estimation of carbon sequestration of different vegetation in Qilian Mountains, China
Zhou Jieli (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P19: Association of bacterial community with heavy metals in soils of different coal mining areas
Yajie Wang (Graduate School of Engineering, Gifu University)
P20: Changes of 16S rDNA and antibiotic resistance genes in the drinking water treatment process
Nadya Diva Sagita (Graduate School of Engineering, Gifu University)
P21: Changes of dissolved organic matter from source to treated water in drinking water treatment plant:
Evaluation based on fluorescence excitation-emission matrix analysis
Maulana Yusup Rosadi (Graduate School of Engineering, Gifu University)

CONTENTS
KEYNOTE SPEECH 1
Long-term Field Data Unveil Characteristics of Phenology and Forest Dynamics in a Bornean
Tropical Rain Forest
Michiko Nakagawa ・・・・・・・・・・・・・・・・・・・・・・・・・・p. 2
Unfolding a Mystery of Tropical Mangroves from a Two-decade Journey in Mangrove Study
Sasitorn Poungparn ・・・・・・・・・・・・・・・・・・・・・・・・・・・p. 3

SESSION 1 -FOREST CARBON CYCLE AND CLIMATE CHANGENet Primary Productivity Estimation in the Chronosequence Coastal-mangrove Plantations inCentral Thailand
Suthathip Umnouysin, Sasitorn Poungparn, Vilanee Suchewaboripont,Chadtip
Rodtassana and Tanapon Pongsuvarod ・・・・・・・・・・・・・・・・・・p. 6
Relationships between Carbon Stocks and Trees Biodiversity in Production Forest of Register
24, Lampung Province
Christine Wulandari, Samsul Bakri, Yanfa Ghiyats Ghifari, Destia Novasari,
Lia Mulyana, Azhary Taufiq, Nindya Tria Puspita and Imawan Abdul Qohar
・・・・・・・・・・・・・・・・p. 11
The Effects of Climate Change on Indonesian Forests
Komariah and Ummi Marfu’ah ・・・・・・・・・・・・・・・・・・・・・p. 20
Tropical Montane Forest Ecotone Dynamics in the Context of Climate Change: Case Study in
the Doi Suthep-Pui National Park, Thailand
Sutheera Hermhuk and Dokrak Marod ・・・・・・・・・・・・・・・・・・p. 25
Effect of Climate Changes on Dry Evergreen Forest Dynamics in Sakaerat Biosphere Reserve,
Northeastern Thailand
Wongsatorn Phumphuang and Dokrak Marod ・・・・・・・・・・・・・・・p.29

SESSION 2 - ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Mast Seeding and Associated Resource Dynamics in Fagus Species
Qingmin Han, Daisuke Kabeya, Yoshiyuki Inagaki, Akira Kagawa, Günter Hoch andAtsuhiro Iio
・・・・・・・・・・・・・・・p. 36
Anchorage and Stability Characteristic of Forest Tree Species in Tropical Region and a
Suitable Wind Damage Assessment Mode for Planted Forest in Vietnam
Nguyen Trong Minh, Fukui Sho, Iwama Shintaro, Dang Thinh Trieu, Koyano Kaitaand Hizomi Mizunaga
・・・・・・・・・・・・・・・p. 40
Hybrid Breakdown as a Result of Up-regulated Expression of Defense-related Genes in theHybrid Seedlings of Japanese Flowering Cherry
Momi Tsuruta, Chunlan Lian and Yuzuru Mukai ・・・・・・・・・・・・・・p.47

SESSION 3 - AGROFORESTRY OF TROPICAL REGION –Agroforestry System as the Best Vegetation Management to Face Forest Degradation
Maria Theresia Sri Budiastuti, Djoko Purnomo and Desy Setyaningrum ・・・・p.54
Contribution of Forest Resources in Shifting Cultivation System to Livelihood Diversificationin Mountain Villages of Northern Laos
Cahyo Wisnu Rubiyanto and Isao Hirota ・・・・・・・・・・・・・・・・・p. 61
Agroforestry Practice in Volcano Prone Area: Findings from Mount Merapi Indonesia
Zuhud Rozaki, Nur Rahmawati, Oki Wijaya, Atikah Fajriyah Mubarok,
Masateru Senge and Mohd Fauzi Kamarudin ・・・・・・・・・・・・・・・p. 65

KEYNOTE SPEECH 2
Masting Impact on Canopy Structure and Photosynthesis in Beech Forests
Does Masting Perform as Gap?
Atsuhiro Iio
・・・・・・・・・・・・・・・p. 72
Forest Tree Breeding in Hokkaido, JapanSo Hanaoka

・・・・・・・・・・・・・・・p. 73

SESSION 4 - AGROFORESTRY OF TROPICAL REGION –Seasonal Variations of Dissolved Organic Carbon Concentrations and Fluxes in an Evergreen
Broadleaved Subtropical Forest in Central Japan
Siyu Chen, Ruoming Cao, Shinpei Yoshitake and Toshiyuki Ohtsuka ・・・・・・p. 76
Difference of Water and Material Dynamics between Coniferous Plantation and Deciduous
Secondary Forest Watershed - Focusing on Dissolved Organic and Inorganic Carbon
Taketo Uemura, Takeo Onishi, Toshiyuki Otsuka and Ken Hiramatsu ・・・・・p. 81
Estimating of Run off Using SCS-CM Model in Gunung Bromo Education Forest Discharging
to Kalijirak River
Dwi Priyo Ariyanto, Tio Egar Nurhutomo, Mujiyo, Komariah ・・・・・・・・p. 90
The Runoff and Sediment Response Due to Land Use Change in the Lubuk Paraku
Sub-Watershed
Edwina Zainal, Zufrimar, Zahrul Umar and Irfan Andika S ・・・・・・・・・p. 94

UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water
Environmental Sciences 2021
P01: Effects of curcumin nanoemulsion on the water vapor permeability and antioxidant
activities of chitosan-based films
Fakfan Luangapai 1, Methavee Peanparkdee and Satoshi Iwamoto ・・・・・・p. 100
P02: LAMP-FLP for detection of thiophanate-methyl resistant strains of Fusarium fujikuroi in
Japan
FangJing Li, Ryoji Komura, Chiharu Nakashima, Masafumi Shimizu,
Koji Kageyama and Haruhisa Suga・・・・・・・・・・・・・・・p. 102
P03: Changes in chlorophyll and carotenoid profiles and gene expression during fruit
regreening in Valencia orange
Nichapat Keawmanee, Gang Ma, Lancui Zhang, Masaki Yahata, Masaya Kato
・・・・・・・・・・・・・・・p. 104
P04: Modified newmark approach for evaluation of earthquake-induced displacement ofearthdams
Phuong Le-Hong, Shin-ichi Nishimura and Tatsuro Nishiyama ・・・・・・・・p. 106
P05: Development of a HS-SPME/GC-MS method for the extraction and identification of thevolatile compounds in coffee
Fawzan Sigma Aurum, Teppei Imaizumi, Manasikan Thammawong, Kohei Nakano
・・・・・・・・・・・・・・・p. 108
P06: Development of a program for constructing ortholog data set using taxonomic
information
Tomoaki Watanabe and Tokumasa Horiike
・・・・・・・・・・・・・・・p. 110
P07: Visualization of low molecule phenol (LMP) and copper naphthenate in oil palm veneer
using X-ray microtomogrpahy
Ayuni Nur Apsari1, Eko Sudoyo, Eka Mulya Alamsyah, Kenji Kobayashi and
Takashi Tanaka
・・・・・・・・・・・・・・・p. 112
P08: Generalized pollination and floral scent chemistry in the invasive Coreopsis lanceolata
Muhammad Arifin and Tomoko Okamoto
・・・・・・・・・・・・・・・p.114
P09: Inhibition effect of different sorbent materials on transfer of cesium and arsenic fromcontaminated soil to vegetation
Shiamita Kusuma Dewi and Yongfen Wei
・・・・・・・・・・・・・・・p. 116
P10: Ensemble prediction of solar irradiance in tropical regions
Daiki Harada, Naoki Moriai and Tomonao Kobayashi ・・・・・・・・・・・・p. 118
P11: Short-term prediction of cloud movements with meteorological satellite images processedin spectrum space
Yuto Hara, Rina Takada, Tomonao Kobayashi and Jun Yoshino ・・・・・・・・p. 120

P12: Application of Kalman filter to solar irradiance forecasting with a meteorological modelin tropical region
Naoki Moriai, Daiki Harada and Tomonao Kobayashi ・・・・・・・・・・・p. 122
P13: Bacterial activity and EPS affect the performance of household wastewater treatment
facility
Haoning Su, Miya Ogata, Yuuki Okochi, Shinya Okumura, Yasushi Ishiguro andFusheng Li・・・・・・・・・・・・・・・p. 124
P14: Steam gasification of woody biomass with renewable hydrogen・・・・・・・・・・・・・・・p. 126
Sheng-Wen Xiao and Kazuhiro Kumabe
P15: Adsorbability of organic matter released from drinking water treatment sludge onto
activated carbon
Sri Anggreini, Maulana Yusup Rosadi, Miya Ogata and Fusheng Li ・・・・・・p. 128
P16: Coexisting and competing algal species with moldy odor-causingblue algae in Nagara
River
Wenqing Li, Haoning Su, Shoma Wakamiya and Fusheng Li ・・・・・・・・p. 130
P17: Fate and effect of microplastic and heavy metal in agricultural soil added with biochar
Rahman Fariha, Shiamita Kasuma Dewi, Zaw Min Han and Wei Yongfen
・・・・・・・・・・・・・・p. 132
P18: MODIS-based estimation of carbon sequestration of different vegetation in Qilian
Mountains, China
Zhou Jieli, Zhang Fuping and Wei Yongfen
・・・・・・・・・・・・・・p. 134
P19: Association of bacterial community with heavy metals in soils of different coal mining
areas
Yajie Wang, Zãw Min Han, Yongfen Wei and Fusheng Li ・・・・・・・・・・・p. 136
P20: Changes of 16S rDNA and antibiotic resistance genes in the drinking water treatment
process
Nadya Diva Sagita, Sutra Maysaroh, Maulana Yusup Rosadi and Fusheng Li・・・・・・・・・・・・・・・・・・p. 138
P21: Changes of dissolved organic matter from source to treated water in drinking water
treatment plant: Evaluation based on fluorescence excitation-emission matrix analysis
Maulana Yusup Rosadi, Nadya Diva Sagita, Sutra Maysaroh and Li Fusheng・・・・・・・・・・・・・・・・・p. 140

INTERNATIONAL SYMPOSIUM ON A RECENT PROGRESS
IN FOREST ECOLOGY AND MANAGEMENT
2021

KEYNOTE SPEECH 1

Wednesday, 10th November 2021
12:05 - 13:25

ORGANIZER:
THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCE, GIFU UNIVERSITY

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Keynote Speech 1

Long-term Field Data Unveil Characteristics of Phenology andForest Dynamics in a Bornean Tropical Rain Forest
Michiko Nakagawa

Graduate School of Bioagricultural Sciences, Nagoya University
Corresponding Author: miko@agr.nagoya-u.ac.jp
SUMMARY
Tropical rainforests in Borneo, which are known to possess extremely species-rich tree communities,
are characterized by abundant rainfall throughout the year, and are thought to be among the most humid biomes
in the world. In such a forest, tree phenology and forest dynamics would be expected to be stable.
Meteorological conditions, flowering and fruiting phenology, litterfall production, tree growth, and treemortality/recruitment have been monitored for long term in Lambir Hills National Park, Sarawak, Malaysia,
with the cooperation of a lot of researchers and local assistants. Contrary to our expectations, long-term fielddata indicate that temporal fluctuations in climatic conditions drive community-wide phenomena such as
intermittent mass flowering and mass fruiting, dynamic changes in tree mortality, recruitment rate, and litterfall
production.
Keywords: General flowering and fruiting; Lambir Hills National Park; Tree mortality; Litterfall production; Drought2

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Keynote Speech 1

Unfolding a Mystery of Tropical Mangroves from a Two-decade
Journey in Mangrove Study
Sasitorn Poungparn

Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
Corresponding Author: Sasitorn.P@chula.ac.th
SUMMARY
Mangrove study has been attracting scientists for several decades as a prominent global carbon sinkecosystem. Relating to this topic, I have been spending two decades studying a mangrove forest under atropical monsoon climate in eastern Thailand. A long-term study plot was firstly established at this estuarine
mangrove forest in Trat province in 2001, led by Prof. A. Komiyama of Gifu University. My journey inmangrove study began with root biomass study because I was astonished by the peculiar shapes and large
accumulation of roots in the mangrove forest. However, the estimation of root biomass was a hard work at thatmoment. We took a big step closer in 2005 that we successfully established the common allometric
relationships for biomass estimation in mangrove forests by using an ecological theory of tree form “PipeModel”, which enables biomass estimation of major mangrove tree species.
By using the powerful tool “common allometry”, a study on net primary production (NPP) and CO2
efflux from the soil respiration was consecutively conducted in this study plot. The results revealed the high
NPP and low CO2 efflux by heterotrophic respiration under submerged soils of the mangrove forest andconsequently led to high net ecosystem production (NEP). The high NPP of this mangrove forest was partly
contributed by fine roots that we uncovered the high production of fine roots by using the ingrowth core
method. Moreover, we tackled a frequently-neglected problem “coarse woody debris” in the NPP estimation.
A long-term NPP estimation of this forest (2008-2018) signified that the interannual variation of the rainfall
and the maximum air temperature in the dry season caused increasing tree mortality leading to lower NPP.
However, another perplexing mystery of mangrove forests having high NPP and NEP will be
profoundly related to adaptation of mangroves in fluctuated salinity environment of the estuary. Congruently,
global climate change has intensified the extreme events which may alter the salinity environment in the
coastal areas and affect several organisms in coastal ecosystems. Therefore, our most recent studies focus on
how the mangroves can survive and provide high productivity in the fluctuating, both seasonally and daily,
saline environment under a tropical monsoon climate. At our study site, we found the seasonal variation ofsoil-water salinity that negatively correlated with monthly rainfall showing the lower salinity during the wet
season and vice versa in the dry season. The mysterious salinity fluctuation was clarified that the seasonalvariation was induced by the daily inundation and the pattern of soil water movement across the soil profiles
as we defined them as thin ‘tide-sensitive’ and ‘aquifer’ layers of the soils.
Interestingly, we disclosed that the Avicennia alba tree, a fast-growing species, has a seasonal trunkgrowth pattern, which has a higher growth rate in the wet season under low salinity of soil water close to the
freshwater. There was very small trunk growth during the dry season with high soil-water salinity close to theseawater level. Not only trunk growth but leaf emergence was found responding to the seasonal pattern ofsalinity that we found a synchronization between the rates of trunk growth and leaf emergence in A. alba trees.
These seasonal growth patterns are considered a growth strategy of A. alba to cope with the salinity fluctuation.
Our latest ongoing research exhibits the seasonal growth pattern of other key mangrove species. The researchseries from this long-term study plot disclose the secret of the growth strategy of mangroves that wecharacterized them as an ‘opportunist’ showing high growth rates when the freshwater is abundant in the wet
season. This character benefits as an adaptive plasticity to survive and maintain high NPP of the mangrove
forest under fluctuation of salinity environment in the tropical coastal areas. A propounded problem of“mangrove zonation” was addressed in that the heterogeneous salinity environments influenced the occurrenceof zonation. There is always a mystery of mangrove forests waiting to be solved through our long journey inmangrove study.
Keywords: Forest Productivity; Mangrove; Monsoon climate; Salinity fluctuation; Seasonal growth3

INTERNATIONAL SYMPOSIUM ON A RECENT PROGRESS
IN FOREST ECOLOGY AND MANAGEMENT
2021

SESSION 1
FOREST CARBON CYCLE AND CLIMATE CHANGE

Wednesday, 10th November 2021
13:45 - 15:00

ORGANIZER:
THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCE, GIFU UNIVERSITY

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Net Primary Productivity Estimation in the Chronosequence
Coastal-mangrove Plantations in Central Thailand

Suthathip Umnouysin 1*, Sasitorn Poungparn 2, Vilanee Suchewaboripont 3,
Chadtip Rodtassana 2, Tanapon Pongsuvarod 2
Department of Biology, Faculty of Science, Silpakorn University, Nakhon Pathom 73000,
Thailand2
Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok 10330,
Thailand3
The Institute for the Promotion of Teaching Science and Technology, Bangkok 10110,
Thailand*Corresponding Author: umnouysin_s@su.ac.th
1

SUMMARY
We present net primary productivity (NPP) estimation in chronosequence coastal-mangrove
plantation at Bang Pu, Samut Prakarn Province in central Thailand. The chronosequence plantations consistof four Avicennia alba plantations with the age of 15, 12, 10, and 8 years old. The NPP estimation was
calculated by a summation product of growth increment (∆Y) and litter production (L). The ∆Y included thebiomass increment of living trees, biomass increment of newly recruited trees, and fine root production from
June 2020 to May 2021. We also investigated the coarse woody debris (CWD) flux including necromass andcoarse litter production during this period. The results demonstrated that the ∆Y and L showed a positive trendwith the ages of plantations. Therefore, the NPP decreased with increasing plantation ages, they were 20.33,
22.84, 23.92, 34.00 t/ha/yr for 15-, 12-, 10-, and 8-year plantations, respectively. The ∆Y/ fine litter ratio was
calculated at 0.72, 0.71, 1.11, 1.63 for 15-, 12-, 10-, and 8-year plantations, respectively. The high ∆Y/finelitter ratio in the young A. alba plantation indicated that the young trees maximized the stand growth with theavailable leaf budget, and therefore resulted in the relatively high NPP of the young plantation. The proportionof CWD flux to NPP was averaged as 57.9% across the four plantations. It was extremely high in the 10-year
plantation (85.0%). The proportions of these chronosequence coastal-mangrove plantations were higher thanthe mature A. alba forest due to a process of self-thinning in the high-density stand of the young plantation.
Our study suggests that forest development could occur in the mangrove plantations with the high NPP of
young plantations. More accurate NPP estimation is required to assess the potential of mangrove plantations
that can provide ecological functions sustaining the carbon dynamics in coastal areas under climate change.
Keywords: Net primary production; Carbon dynamics; Mangrove plantations

mangrove forests were reported for the greater tree
diameter-size while the young stands usually had
higher stem density (Azman, M. S. et al., 2021).
In the last decades, mangrove plantation
has been publicly considered as an approach torestoring the coastal mangrove ecosystems,
especially the new mudflat areas, with an
engagement with several sectors including
government, private sectors, and the public.
However, the planting campaign usually ran for ashort period and frequently lacked post-planting
assessment. For successful mangrove rehabilitation,rather than an instant planting in the large area, longterm monitoring is required to protect losing of
established plantations (Lee, S. Y. et al., 2019).

Introduction
Mangrove forests significantly contributeto global carbon stock as well-known for the high net
primary productivity (NPP) (Alongi, D. M., 2014)
and the ‘vegetated blue carbon’ playing an importantrole in coastal carbon fluxes (Macreadie, P. I. et al.,2019). The NPP indicates forest productivity andcarbon capture in mangrove ecosystems, especiallyin the vegetative parts (both biomass and necromass),
which can be estimated by using the summation
method generally including the growth increment
and litter production (Komiyama, A. et al., 2008).
Forest productivity related to forest dynamics
according to forest structure and age that the mature

6

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –litter production (L) of each stand was calculated bysumming monthly litterfall from June 2020 to May 2021.
Net primary production (NPP) was
calculated by a summation of ∆Y and L, as shown inequation (2).

Therefore,
thechronosequencemangroveplantations are suitable to assess the potential ofmangrove plantations in terms of carbon
sequestration that will assist the carbon mitigation.
We aim to estimate the NPP of thechronosequence
coastal-mangrove
plantation(ranging from 8 to 15 years) at Bang Pu, central
Thailand by using the summation method including
the growth increment and litter production.
Moreover, the CWD fluxes were incorporated in the
carbon estimation. A more accurate NPP estimation
is important to improve an assessment of mangrove
plantations in an aspect of the carbon budget that will
benefit the planning of mangrove management andconservation under climate change.

Dead trees were recorded in May 2021 (t2).
The mortality rate was estimated using a logarithmic
model by Miura, M. et al. (2001). The necromass ofaboveground, leaf, and root components wasestimated using the common allometric equations(Komiyama, A. et al., 2005). An annual flux ofcoarse woody debris (CWD) was obtained bysumming necromass excluding leaf and coarse litter
production.

Materials and Methods

Results and Discussion

The study site is the chronosequence
coastal-mangrove plantations at Bang Pu Recreation
Center (13°31´N 100°39´E), Samut Prakarn
Province in central Thailand. The chronosequenceplantations are dominated by Avicennia alba withthe stand ages of 15, 12, 10, and 8 years old since
first planted in 2006.
At each stand, the four study plots (10 m 10 m) were established. We measured stem
diameter at breast height (DBH) and height (H) in
June 2020 (t1) and May 2021 (t2). Newly recruitedtrees (DBH reached 4.5 cm) were also recorded at t2.
The aboveground biomass (WTop) and root biomass(WR) were calculated using the common allometricequations for mangrove species (Komiyama, A. etal., 2005). Fine root production (FRP) was estimatedfrom the fine root production rates in the 15- and 10year plantations by using the ingrowth core method(0-30 cm depth) during July 2019 – July 2020
(unpublished data). The FRP rate was calculated as
0.03 t ha-1 yr-1 for all stand ages. Growth increment(∆Y) was calculated based on approach 1 of Clark,
D.A. et al. (2001); ∆Y included the biomass
increment of living trees present at t2 (∆B), biomass
increment of newly recruited trees (N), and FRP, as
shown in equation (1).

The
tree
densityacrossthechronosequence plantations ranged from 975 to1813 stems ha-1, which were purely A. alba trees.
The density tended to increase with decreasing standages (Table 1). However, the tree size showed an
opposite trend that the largest tree sizes were foundin 15-year plantation followed by the younger stands.

∆Y = ∆B + N + FRPNPP = ∆Y + L(2)

Table 1: Forest structure of living trees in June 2020and dead trees in May 2021

The mortality rate increased withdecreasing stand ages, which the highest number ofdead trees were found in the 8-year plantation withthe highest mortality rate (28.8%) (Table 1). During
forest development, the self-thinning process occurs
in a high-density stand due to the resourcecompetition supporting tree growth underconstrained environments (Kamara, M. and M.
Kamruzzman, 2020; Kamruzzman, M. et al., 2018).

(1)

Litterfall was collected monthly from 12
litter traps each stand then oven-dried at 80°C to a
constant mass. The litterfall was weighed separately
into fine litter (leaves and reproductive organs) andcoarse litter (branches and woody parts). The annual

7Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –The ∆B and ∆Y showed a positive trendwith increasing stand ages (Table 2). The ∆Y ranged
from 8.02 to 20.26 t ha-1 yr-1. The newly recruitedtrees were only found in the youngest plantation
where the highest mortality rate was observed. Fine
litter production ranged from 10.66–12.40 t ha-1 yr-1
(Table 2), which shared the high proportion of 86–
91% of total litter production. The NPP was
subsequently calculated as 20.33, 22.84, 23.92,
34.00 t ha-1 yr-1 for 15-, 12-, 10-, and 8-year
plantations, respectively. It is remarkable that thelower NPP was found in the older plantation ages
consistent with other studies (Azman, M. S., et al.,2021; Sahu, S. K. and K. Kathiresan, 2019).
The ∆Y/fine litter ratio varied across standages, which were 0.72, 0.71, 1.11, 1.63 for 15-, 12-,10-, and 8-year plantations, respectively. It wassuggested as 1:1 in mangrove ecosystems (Day, Jr.,J. W. et al., 1987). The ratios obtained from our study
were higher compared to the study in a subtropicalmangrove forest in Japan reported as 0.41 (Ohtsuka,
T. et al., 2019). However, our results showed that the
young stand ages had higher ratios which implies thepotential of young mangrove plantations to enhance
the stand increment.

Table 3: Annual coarse woody debris (CWD) flux

Figure 1: Proportion of the CWD flux to net primary
production (NPP)

Table 2: Biomass increment, fine root production,
growth increment, litter production, and net primary
production during June 2020 to May 2021

The young plantation (10-year) had thehighest necromass (excepting leaf mass),
particularly the aboveground part as shown in Table
3. The average DBH of dead trees in this stand waslarger than that of the 8-year plantation; however, theyoungest plantation had the highest number of deadtrees (Table 1). The CWD fluxes were high in theyounger plantations (10- and 8-year) withapproximately 1.5 to 2 times compared to the older
plantations (Table 3). While the proportions of
necromass production to the CWD flux were high inall stands (83–93%). Our findings confirmed thehigh CWD flux in the younger stands. Although, theyounger stands could produce relatively high standincrement. The residence time of abovegroundwoody components was relatively short due to highmortality rates of the dense stand, which werereported in tropical forests under disturbed
conditions (Muller-Landau, H. C. et al., 2021).
The proportion of CWD flux to NPP wasaveraged as 57.9% across all stand ages; theproportion of the 10-year plantation was8Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –forest: concepts and field methods. EcologicalApplications, 11(2):356–370.
Day, Jr. J. W., Conner, W. H., Ley-Lou, F., Day,
H., Navarro, A. M. (1987) The productivity andcomposition of mangrove forests, laguna deTérminos, Mexico. Aquatic Botany, 27:267–
284.
Kamara, M., Kamruzzaman, M. (2020) Selfthinning process, dynamics of abovegroundbiomass, and stand structure in overcrowded
mangrove Kandelia obovata stand. Regional
Studies in Marine Science, 38:101375.
Kamruzzaman, M., Ahmed, S., Paul, S., Rahman,
M. M., Osawa, A. (2018) Stand structure andcarbon storage in the oligohaline zone of the
Sundarbans mangrove forest, Bangladesh.
Forest science and technology, 14(1):23–28.
Komiyama, A., Ong, J.E., Poungparn, S. (2008)
Allometry, biomass, and productivity ofmangrove forests: a review. Aquatic Botany,
89:128–137.
Komiyama, A., Poungparn, S., Kato, S. (2005)
Common allometric equations for estimating thetree weight of mangrove. Journal of Tropical
Ecology, 21:471–477.
Lee, S. Y., Hamilton, S., Barbier, E. B., Primavera,
J., Lewis, R. R. (2019) Better restoration
policies are needed to conserve mangrove
ecosystems. Nature Ecology & Evolution,
3:870–872.
Macreadie, P. I. et al. (2019) The future of Blue
Carbon science. Nature Communications,
10:3998. https://doi.org/10. 1038/ s41467-01911693-w.
Miura, M., Manabe, T., Nishimura, N., Yamamoto,S. (2001) Forest canopy and community
dynamics in a temperate old-growth evergreen
broad-leaved forest, south-western Japan: a 7year study of a 4-ha plot. Journal of Ecology,
89:841–849.
Muller-Landau, H. C., Cushman, K. C., Arroya, E.
E., Cano, I. M., Anderson-Teixeira, K. J.,
Backiel, B. (2021) Patterns and mechanisms ofspatial variation in tropical forest productivity,
woody residence time, and biomass. New
Phytologist, 229:3065–3087.
Ohtsuka, T., Tomotsune, M., Suchewaboripont, V.,
Iimura, Y., Kida, M., Yoshitake, S., Kondo, M.,
Kinjo, K. (2019) Stand dynamics andaboveground net primary productivity of a

exceptionally high as 85.0% (Figure 1). In asecondary mangrove forest under natural
rehabilitation (on an estuary of the Trat River ineastern Thailand), Umnouysin, S. et al., (2017)
reported an average proportion of CWD flux to NPP
as 24.8%. It is apparently lower than that of thepresent study suggesting the significance of CWD
fluxes in the chronosequence plantation along withthe coastal areas. The high CWD fluxes assume theaccelerated stand dynamics under the developmentof mangrove plantations.
Conclusions
We demonstrated that the chronosequencemangrove plantations had different vegetation
structures leading to the different magnitudes of NPP.
The highest NPP was found in the young plantations
in consistence with the trends of mortality rate and
CWD flux. We confirmed that the CWD flux issubstantial for NPP estimation in mangrove
plantations during forest development. Our findings
will be beneficial for management planning for
mangrove restoration to enhance the capacity ofcarbon storage on the coastal areas.
Acknowledgment
This study was supported by the Toyota
Motor Thailand Co., Ltd. We are grateful to the staffof Bang Pu Recreation Center, which is under thesupervision of the Quartermaster Department of theRoyal Thai Army, and the staff of the Foundation forEnvironmental
Educationfor
Sustainable
Development (FEED), Thailand.
References
Alongi, D. M. (2014) Carbon cycling and storage inmangrove forests. Annual Review of Marine
Science, 6:195–219.
Azman, M. S., Sharma, S., Shaharudin, M. A. M.,
Hamzah, M. L., Adibah, S. N., Zakaria, R. M.,
MacKenzie, R. A. (2021) Stand structure,
biomass and dynamics of naturally regenerated
and restored mangroves in Malaysia. Forest
Ecology and Management, 482: 118852.
Clark, D. A., Brown, S., Kicklighter, D. W.,
Chambers, J. Q., Thomlinson, J. R., Ni, J.
(2001) Measuring net primary production in9

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Umnouysin, S., Sangtiean, T., Poungparn, S. (2017)
Zonal distribution of coarse woody debris andits contribution to net primary production in asecondary mangrove forest. EcologicalResearch, 32:51–60.

mature subtropical mangrove forest on Ishigaki
Island, south-western Japan. Regional Studies inMarine Science, 27:100516.
Sahu, S. K., Kathiresan, K. (2019) The age andspecies composition of mangrove forest directly
influence the net primary productivity andcarbon sequestration potential. Biocatalysis andAgricultural Biotechnology, 20:101235.

10

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Relationships between Carbon Stocks and Trees Biodiversity
in Production Forest of Register 24, Lampung Province
Christine Wulandari 1*, Samsul Bakri 2, Yanfa Ghiyats Ghifari 3,
Destia Novasari 1, Lia Mulyana 1, Azhary Taufiq 1,
Nindya Tria Puspita 1 and Imawan Abdul Qohar 1

1 Magister Program of Forestry, Faculty of Forestry, The University of Lampung

Magister Program of Environment, The University of Lampung
Forestry Department, Faculty of Agriculture, The University of Lampung*Corresponding Author: christine.wulandari@fp.unila.ac.id

2
3

SUMMARY
Many studies stated declining of forest biodiversity will have an impact on decreasing carbon stocks.
Based on this condition, it will be necessary to adapt the climate change for maintaining the carbon stock. The
availability of sufficient carbon stock will affect the environment condition surrounded the forest. Generally,
research that has been done is the correlation between trees biodiversity based on number of species and trees
growth phase. Specific study in West Nusa Tenggara, Indonesia mentioned that carbon stock is influenced bywood of trees, not by number of trees species. Based on this research results, the important question is how tomaintain carbon stock sufficiency in such production forests in Indonesia because community allow for logging?
Research conducted in October 2018 at Register 24 forest area in Lampung Province. This study objectives areto be examined whether trees in production forest of Register 24 are correlated with carbon stocks, and how
recommendations can be made to maintain this carbon stock adequacy because trees cutting is permitted in thistype of forest.
Keywords: Carbon stock; Trees biodiversity; Production forest

absorb carbon more optimally (Supriadi &Adiansyah, 2013). This will indirectly influence thecondition of Production Forests because inIndonesia's production forest policy it is permissible
to cut down trees. One of the existing production
forests in the Lampung area is the forest area ofregister 24 which is included in the management areaof the Bukit Punggur Forest Management Unit
(FMU), Lampung.
The FMU of the Bukit Punggur Model,
Way Kanan Regency is determined by the Decree ofthe Minister of Forestry Number SK. 439/MenhutlI/2012 covering an area of ±41,126 ha. The
dominant plants in the Bukit Punggur FMU are
marawan (Hopea mangarawan), medang (Litsea
firmaho), tenam (Shorea platicloda V.Sb.) cempaka(Beilschilda sp.), Benda (Arthocaprus sp.), Bungur(Lagerstroemia spp.), puspa (Schima spp.), rasamala(Altingia excelsa), rattan (Calamus sp.), forest
orchids and ferns. Animals in the Bukit Punggur
FMU forest area are gibbons (Symphalangus
syndactylus), monkeys (Presbytis melalopus),snakes (Phyton sp.), eagles (Heliastur sp.), parrots(Gracula), sambar deer (Cervus unicolor), deer

Introduction

Deforestation and forest burning are thecauses of damage to resources and the environment,
which will indirectly result in a decrease in the forest
area. Damage to resources and the environment will
have an impact on increasing the earth's temperature,
this is related to the carbon absorption processcarried out by the vegetation on the land (Erly et al.,2019). Vegetation has the ability in the process ofphotosynthesis (Rizki et al., 2016). The process ofphotosynthesis in plants can convert carbon dioxide
into glucose and oxygen through the absorption of
CO2 and water with the help of sunlight (Prihatmaji
et al., 2016; Hatulesila et al., 2018). The more typesof plants, the higher the value of carbon absorbed byplants on the land. So that the diversity of plant
species will also affect the amount of carbon in anarea.
The vegetation of a forest area generally
has several phases, including the phases of trees,
poles, saplings, and undergrowth. The tree phase is
the largest carbon store. This is in line with theresults of research conducted, which states thatwoody trees aged 6-9 years will have the ability to11Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –The object in this study is a tree. The tools
used in this study were a tape meter to measure
diameter, a Christian hypsometer to measure treeheight, an observation sheet (tally sheet), scissors to
cut undergrowth, an oven to remove the moisture
content of undergrowth and litter, a digital scale withgram units for weighing. undergrowth and litter,cameras for documentation, ropes for plotmeasurements, GPS for downloading plot
coordinates, and a laptop for data processing andmaking research reports.

(Muntiacus muntjak), tiger (Panthera tigris
sumatrae), root tiger (Felis bengalensis), sun bear
(Helarctos malayanus), tapir (Tapiorus indicus), andpangolin (Manis javanica) (KPH Bukit Punggur,2015). Production forest in FMU of Re-gister 24 ismanaged under the Social Forestry program, namely
the Community Forest scheme. Thus this forest is
managed on a community basis. This means that thisforest must provide ecological benefits for forest
sustainability and also have economic benefits forcommunity income and the FMU.
One way to maintain ecologicalsustainability through the conservation of
biodiversity is to preserve the function of carbon
sequestration and storage. Research conducted by
Susilo, A (2019) that monoculture forests such asteak forests or industrial plantation forests have low
biodiversity but high carbon stocks. The diversity ofplant species in a forest will also support thesustainability of the income of the community
managing it (Wulandari et al., 2021). Thus, thediversity of species in a forest location will support
ecological and economic functions towards
sustainability.
Several studies have found that tree
diversity also affects the amount of tree carbon
stored in forest areas. In addition, tree density andspecies are positively correlated with changes incarbon storage (Chairul et al., 2016; Banjarnahor etal., 2018; Erly, et al., 2019). Supriadi & Adiansyah(2013) and Markum (2013) added that the thicknessof the wood in a tree also affects the amount ofcarbon in the area. Therefore, this study aims to
examine whether trees in Register 24 production
forest are correlated with a carbon stock and todevelop recommendations to maintain adequate
carbon stock in Register 24 production forest
because tree cutting is allowed in this type of forest.

3. Research methods
a. Vegetation data
Accordingto Ambarwati(2018)
vegetation data in the form of undergrowth, saplings,
poles, and trees, namely the number of each species
found in the measuring plot with a size of 20 m x 20
m. Vegetation data is used to determine the INP in
the Forest Area Register 24, Way Kanan Regency,
Lampung.
b. Plot measuring
Determination of measuring plots as manyas 16 plots were carried out using the Stratified
Sampling method which is a carefully selected
sampling method so that it is relevant to sampling
data that has been grouped by altitude class(Bhaskara, 2018). Altitude ranges from 550-950 m
above sea level.
Table 1: Number of plots in research area
Height Class

HeightTotal Plot

1
2
3
4

550 – 650 m asl
651 – 750 m asl
751 – 850 m asl
851 – 950 m asl

4
4
4
4

Total

Research Methods

16c. Sample plot
The shape of the sample plot used in thisstudy is a square with a size of 20 m x 20 m which isused for biomass data collection (Bhaskara, 2018).
The size of the plot to measure each phase of plant
growth can be seen in Figure 1.

1. Research time and place
This research was conducted in October
2018. Data collection was carried out in the Register
24 forest area which is the Bukit Punggur FMU, Way
Kanan Regency, Lampung Province. The study was
conducted using the Stratified Sampling method in
the Register 24 Forest Area.
2. Research Objects and Tools

12Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –K=

Number of species-n

KR =

F=

DR =

Figure 1: Plot for biomass data collection.
A: It is a plot measuring 2 m x 2 m, used for sampling
litter and undergrowth with a height of <1.5 m.
B: This is a 5 m x 5 m sample plot, used for saplings
with a diameter of <10 cm and a plant height of >1.5m.
C: This is a sample plot measuring 10 m x 10 m, used
for pole level with a diameter of 10-20 cm.
D: This is a sample plot measuring 20 m x 20 m, used
for tree-level with a diameter of > 20 cm.

Species density-n

Whole species density

x 100%The number of sample plots found for a species-n

FR =

D=

(1)

The total area of the sample plot

Total Plot

Frequency of a species-n

Frequency of whole species

The total area of the base area

x100%

The total area of the whole plot

The dominance of a species

The dominance of whole species

x 100%

(2)
(3)
(4)
(5)
(6)

Based on this equation, to calculate theImportant Value Index (INP) in a species comes up
with the equation:
INP = KR + FR + DR

(7)

Description :INP: Determination of the dominance of one species
over other species from the total sample plots
KR: Percentage of the number of plant species inquestion from the total unit area
FR: Percentage of the number of plots where a species
is found from the total unit area
DR: Percentage of the main plant species that influence
the community in terms of the number of species,
size, and dominant growth

4. Data collection
a. Tree biomass
Biomass data were collected using a nondestructive method (not cutting down trees) on eachtree in the 20 m x 20 m plot. Trees taken for biomass
calculation must be accompanied by data on tree
species, diameter, and height of the tree. Biomass
measurements were carried out at the tree phase in
the 20 m x 20 m plot, the sapling phase in the 5 m x
5 m plot, and the pole phase in the 10 m x 10 m plot.

5. Biomass estimationa. Tree biomass

The results of measurements of tree
diameter and tree height were analyzed using theexisting allometric equations to estimate tree
biomass as in Table 2.

b. Importance value index
According to Winardi (2014), it isnecessary to carry out a vegetation analysis todetermine the dominance of a vegetation type overother species in a stand by using the Important Value
Index (INP). The INP calculation provides data on
the dominant tree species in research areas.
The amount of the Important Value Index(INP) in vegetation can be calculated based on
Indiyanto (2016) with the following equation.

b. Stored carbon

Carbon stored in forest vegetation can be
estimated using the biomass value obtained from theallometric equation or the BSN equation (2011)
which states that the percentage value of carbon
content is 47% so that the calculation of stored
carbon can be converted into carbon (tons/ha).
Calculation of carbon can be done using a
conversion factor of 0.47 which is expressed in thefollowing formula.
Carbon (C) = Total Biomass x 0,47

13

(8)

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Table 2: Allometric equation model

BKDρ= Dry weight (kg/tree),H= Total plant height (m)= Diameter breast height (cm),BA= Basal area (cm2)= Wood Specific Gravity (0,7 g/m3) and the density of dead wood (0,4 g/m3)

government must be obtained through the Bukit
Punggur FMU. Thus, the regulation of sustainability
must be a FMU policy that is supported by thecommunity who are members of the CF group, forexample in the selection of trees and species that
may be cut and will not interfere with the function of
the forest ecosystem. If logging is required, a permit
from the government must be obtained through the
Bukit Punggur FMU. Thus, the regulation ofsustainability must be a FMU policy that is
supported by the community who are members of
the CF group, for example in the selection of trees
and species that may be cut and will not interferewith the function of the forest ecosystem.

Results and Discussion
1. Importance Value Index (INP)The Important Value Index (INP)
according to Ambarwati et al (2019) is an indicatorused to determine the dominant species in a plantcommunity. The Important Value Index leads to theanalysis of vegetation, the emphasis of the analysis
lies on the species composition. According to Putri
and Indriyanto. (2021) species composition in aforest habitat is influenced by the habitat and plant
species in the area. The results obtained fromresearch on production forests which are the Bukit
Punggur FMU are 14 types of tree phase plants, 8
types of pole phase plants, 10 types of sapling phase
plants, and 26 types of seedlings and understorey
plants. As a production forest, the management
mechanism is different from that of a protected
forest. In production forests, logging is permitted aslong as it is following the permit granted. In Forest
Register 24, the production forest is managed by thecommunity through the Social Forestry Scheme,
namely Community Forest (CF). This means that the
management of production forests in Register 24 iscarried out on a community basis. If logging isrequired, a permit from the government must be
obtained through the Bukit Punggur FMU. Thus, theregulation of sustainability must be a FMU policythat is supported by the community who aremembers of the CF group, for example in theselection of trees and species that may be cut andwill not interfere with the function of the forest
ecosystem. If logging is required, a permit from the

1.1. Trees
Trees are cambium plants that have adiameter of more than 20 cm and a height of morethan 5 m. Tree phase measurements were carried out
on plots measuring 20 m x 20 m. The data obtainedin the tree phase contained 14 plant species which
can be seen in Table 3.
The results obtained from the study were
found 14 plant species in the tree phase. The
dominant tree species for the tree phase in all altitudeclasses were rubber (Hevea brasiliensis). This
species dominates the area with 81 trees. The species
with the lowest INP was the jackfruit (Artocarpus
heterophyllus) with 1 tree. The dominating species
are Multi Purpose Tress Species (MPTS) with 10
species, while for forestry there are only 4 species.

14Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –No

Local nameScientific name

1

Karet

Hevea brasiliensis

2

Pulai

Alstonia scholaris

3

MedangPhoebe hunanensis

4

JengkolArchidendron pauciflorum

5

DurianDurio zibethinus

6

AkasiaAcacia auriculiformis

7

Nangka

Artocarpus heterophylus

8

ManggaMangifera indica

has a diameter of 10-19 cm. Phase INP calculation
INP (%) results poles obtained 8 plant species (Table 4).
The INP results obtained by the pole phase
128.03amounted
to 8 species consisting of 6 species of
23.66
MPTS plant species and 2 types of forestry plants.
4.42
The dominant species in the four altitude classes
31.81
were rubber (Hevea brasiliensis) with a total of 91
15.60
trees. Species that have the lowest INP values are
16.84
medang (Phoeba hunanensis) and pulai (Alstonia
3.31
scholaris) with only 1 tree each.
4.84

9

Kapuk

Ceiba pentandra

4.59

10

Cengkeh

Syzygium aromaticum

19.36

11

PinangAreca catechu

6.78

12

Petai

Parkia speciosa

18.73

13

Tangkil

Gnetum gnemon

5.15

14

Mangium

Acacia mangium

16.86

Table 3: INP tree phase at the research site

Total

The species that dominates the pole phase is the sameas the dominant species in the tree phase, namely
rubber plants (Hevea brasiliensis). In the pole phase,rubber plants have an INP of 156.31% which tends thatrubber is grown in monoculture. According to Supriadi
et al. (2018) that forestry crops and other types ofrubber MPTS found in the field are generally planted
as land boundaries.

300

Source: Primary data (2019).

1.3. Stake
The stake phase was measured on a 5m x
5m plot. Saplings can be defined as plants that are
more than 1.5 meters tall and <10 in diameter cm.
The results of the INP calculation at the stake phasecan be seen in Table 5.
Table 4: INP of the poles phase at the research site

Figure 2: The tree phase significance value index.

The percentage of dominant plant species
in the tree phase can be seen in Figure 2. The species
with the largest INP is rubber (Hevea brasiliensis)
which has an INP percentage of 128.03%. Zhafira etal. (2019) in his research explains that rubber plants
in the research location are the dominating species
while forestry plant types include acacia (Acacia sp.),mahogany(Sweitenia
mahagoni),
jackfruit(Arthocarpus heterophyllus), durian (Durio zibethinus),
avocado (Persea americana) and pulai (Alstonia
scholaris). The dominant rubber plant (Hevea
brasiliensis) in this location was caused by the National
Movement for Land and Forest Rehabilitation(GNRHL) program in 2003-2004.

No

Local nameScientific name

INP (%)

1
2

Karet

Hevea brasiliensis

156.31

MedangPhoeba hunanensis

7.91

3

Jengkol

14.89

4

Pulai

Archidendron
pauciflorum
Alstonia scholaris

5

Cengkeh58.83

6

Jambu bol

7

DurianSyzygiumaromaticum
Syzygiummalaccense
Durio zibethinus

8

Petai

Parkia speciosa

17.49

Total
Source: Primary data (2019).

1.2. PoleThe pile phase was measured on a 10m x
10m plot. The pole phase in question is a plant that

15

6.33

6.91
31.33
300

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Table 5: INP of the stake phase in research areas

Table 6: INP of seedling and understorey phases
NIP
(%)

No

Local
name

Scientific name

INP
(%)

Coffee canephora

97.12

Syzygium aromaticum

95.21

Karet

Hevea brasiliensis

51.58

DurianDurio zibethinus

8.09

1
2
3
4
5
6
7
8
9
10
11
12
13

Karet
Pakis
Kopi
Kentangan
PaitanJambu bol
AkasiaHaredong
Waru
Sapat
Jambu biji
RayutaRumputkasapanSenggani
Kirinyuh
Tekelan
Alang
alang
Putri malu
CengkehSaga

Hevea brasiliensis
Cyclosorus aridus
Coffee canephora
Borreria latifolia
Axonopus compressus
Syzygium aqueum
Acacia auriculiformis
Clidemia hirta
Hibiscus tiliaceus
Macaranga tribola
Psidium guajava
Euphorbia milii
Hypolytrum nemorum

11.80
8.39
10.65
11.88
24.03
5.19
1.89
24.26
4.72
1.89
4.82
10,50
12.71

Melastoma candidum
Chromolaena odorata
Eupatorium riparium
Imperata cylindrica

2.39
7.79
8.92
8.87

Mimosa pudica
Syzygium aromaticum
Adenantherapavonina
Penisetum purpureum

2.33
3.30
4.34

Mangifera indica
Archidendron
pauciflorum

3.07
2.43

No

Local nameScientific name

1

Kopi

2

Cengkeh3
4
5Jambu biji

Psidium guajava

10.82

6

Jengkol

9.26

7

Sawo

Archidendron
pauciflorum
Manilkara zapota

8

Petai

Parkia speciosa

5.47

9

Waru

Hibiscus tiliaceus

5.42

10

AkasiaAcacia auriculiformis

6.68

Total

10.34

300

Source: Primary data (2019).

14
15
16
17

The species that dominated the saplingphase was the coffee species (Coffea canephora)with a total of 42 plants, followed by the clove
species (Syzygium aromaticum) with a total of 29
plants. Species that have a low INP are forestry
plants, namely waru (Hibiscus tiliaceus) with 1 plant.
The community manages the forest by planting
various kinds of MPTS plants. These activities are
the main source of income for the farmers managing
the CF. According to Amelia et al. (2019), currently
cloves are a potential commodity to increase
farmers' income in Lampung Province because theprice of cloves is high enough to reach Rp. 80.000/kg.
If the price of cloves continues to increase, theincome of clove farmers will be higher.
The species that dominates the saplingphase is coffee (Coffea canephora) with apercentage of 92.74%, followed by cloves (Syzygium
aromaticum) with a percentage of 72.59%. Types ofcoffee and cloves are intercropping plants that aredeliberately planted by farmers because the price of
rubber has continued to decline since 2011 until now
and greatly affects the income of farmers in meeting
daily household needs (Syarifa et al., 2016).

18
19
20
21
22
23

Rumputgajah
ManggaJengkol

Total

3.07

200

Source: Primary data (2019)

The dominant understorey species was
Paitan grass (Axonopus compressus) with apercentage of 24.03% while the one with the lowest
INP was Sapat (Macaranga tribola) with apercentage (1.89%) and shy daughter (Mimosa
pudica) with a percentage (2.33%). Seedling phase
plants also found on managed land were rubber(Hevea brasiliensis), coffee (Coffea canephora),acacia (Acacia auriculiformis), jambu bol (Syzygium
aqueum), waru (Hibiscus tiliaceus), jambu biji
(Psidium guajava), cloves (Syzygium aromaticum),saga (Adenanthera pavonina), mango (Mangifera
indica) and jengkol (Archidendron pauciflorum).
The seedling phase was dominated by rubber tillers(Hevea brasiliensis) with an INP percentage of
11.80%. As for forestry plant seedlings, waru

1.4. Underplants and seedlings
The understorey and seedling phases were
measured on plots measuring 2m x 2m. INP dataacquisition shows that there are 11 species ofseedlings and 15 understorey species (Table 6).

16

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –(Hibiscus tiliaceus) and saga (Adenantherapavonina) have an INP percentage low, namely
4.72% and 4.39%, respectively.
The existence of MPTS and forestry plants
is the intervention of farmers in choosing the desired
plant species. According to Bhaskara et al (2018)
farmers tend to throw away unwanted plants. In linewith the research of Wijayanto and Hartoyo (2015)that the consideration of choosing the type of plantis due to considering the technical aspects ofcultivation and economic aspects. The cultivation
aspect in question is a plant that is easy to plant, easy
to maintain, and resistant to pests and diseases.
Meanwhile, what is meant by the economic aspect isthat farmers are more likely to plant the MPTS typeas the superior crop.
2. Biomass and Stored Carbon
Biomass can be defined as the total weightor volume of an organization in a certain area orvolume (Bhaskara et al, 2018). Tampubolon (2011)
defines forest biomass as the total amount of livingorganic matter above the soil in trees and all parts ofthe population or community body expressed in dry
weight of the kiln per unit area (tonnes/ha). The
National Standardization Agency (2011) classifies
biomass into 2 types of biomass, the first type isaboveground biomass which includes all parts oftrees and understorey plants, and the second type issubsurface biomass which includes plant roots andsoil organic carbon. Research on carbon stocks is
included in the type of measurement of aboveground biomass which includes the type, height anddiameter of trees, forest litter and forest undergrowth.
Carbon (C) is a chemical element withatomic number 6 which includes metal elements andif it is released into the air and bound with oxygen(O2) it becomes carbon dioxide (CO2) (Arupa Team,
2014). Carbon is the basic building block of allorganics. Agus et al. (2011) explained that carbon
stock is the total weight of carbon stored in an
ecosystem in the form of plant biomass andnecromass. Carbon stored in the form of biomass
and necromass in a vegetation can be an indicator of
the success of forest management. The averagecarbon stored in each plot is 96.45 tons/ha (Table 7).

Table 7: Value of biomass, stored carbon, and CO2
uptake.
No Plot

Trees

Pole

Stake

Bottom

Litte

Total

StoredCarbon(tons)(tons)(tons)

Plantr(tons)

CarbonSequestration(tons)(tons(tonnes)(CO2(tons)

)
1

8.4

49.0

107.7

0.03

0.23

165.4

77.7

285.36

2

69.1

63.4

140.1

0.04

0.21

272.8

128.2

470.71

3

80.3

38.3

19.2

0.04

0.20

138.2

64.98

238.4

4

186.6

58,4

23.7

0.03

0.19

269.0

126.4

464.1

5

482.1

62.3

12.2

0.05

0.21

556.8

261.7

960.5

6

30.3

78,2

10.1

0.02

0.19

118.8

55.87

205.1

7

76.2

80.2

20.1

0.03

0.22

176.8

83.1

304.9

8

24.6

78.5

36.1

0.02

0.23

139.5

65.6

240.7

9

39.4

67.0

31.1

0.03

0.20

137.8

64.8

237.8

10

31.5

53.5

20.6

0.04

0.21

105,8

49.7

182.6

11

29.3

43.4

26.0

0.02

0.20

99.0

46.5

170.8

12

34.9

129.7

95.0

0.03

0.22

259.9

122.2

448.3

13

34.0

65.9

69.7

0.05

0.22

170.0

79.9

293.2

14

268.6

54.7

52.9

0.04

0.21

375.8

176.6

648.2

15

65.0

47.6

35.7

0.03

0.20

148.6

69.8

256.4

16

24.9

73.4

49.6

0.02

0.22

148.2

69.6

255.7

1543.

Quantity(tons)

1485.61044.0

749,7

0.5

3.36

3283.315663.4

92.8

65.2

46.8

0.03

0.21

112.35

96.4

353.9

45.2

31.8

22.8

0.02

0.1

100

Average(tonnes/ha)
Percentage (%)

Source: Primary data (2019).

The average value of biomass in eachplant phase is different, namely in the tree phase 92.8
tons/ha, pole phase 65.2 tons/ha, sapling phase 46.8
tons/ha, and undergrowth phase 0.03 tons/ha. Ha.
The difference in the value of biomass in the plant
phase is influenced by the number, type of plant, andor plant density (Wulandari et al, 2018). The plant
phase that contributed the highest biomass value was
the tree phase. The biomass value is correlated withthe stored carbon value produced, which is anaverage of 96.45 tons/ha. The high value of biomassin the tree phase is influenced by the diversity of
MPTS and forestry tree species. This is in line witha study conducted by Wulandari et al. (2021), thepotential for carbon stored in complex agroforestry
cropping patterns is two times greater than in simple
agroforestry cropping patterns. From the point ofview of the function of the forest area, which is a17eq)

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Regency. Journal of Tropical Forests 7(2):112119.
Amelia, SM, Hasyim, AI, Situmorang, S. 2019.
Efficiency of the Clove (Syzygium aromaticum)
Marketing System in Pesisir Barat Regency.
JIIA 7(2):187-194.
Banjarnahor, K. G., Setiawan, A., and Darmawan,
A. 2018. Estimasi Perubahan Karbon Tersimpan
di Atas Tanah di Arboretum Universitas
Lampung. Jurnal Sylva Lestari 6(2): 51–59.
Bhaskara, DR, Qurniati, R. Duryat, Banuwa, IR
2018. Carbon Stored in Repong Damar Pekon
Pahmungan, Pesisir Tengah District, Pesisir
Barat Regency Sylva Lestari Journal. 6(2): 3240.
Chairul, Muchktar, E., Mansyurdin, Maideliza, T.,
and Indra, G. 2016. Struktur Kerapatan Vegetasi
dan Estimasi Kandungan Karbon pada Beberapa
Kondisi Hutan di Pulau Siberut Sumatera Barat.
Metamorfosa: Journal of Biological Sciences3(1): 15–22.
Erly, H., Wulandari, C., Safe'i, R., Kaskoyo, H.,
Winarno, G, D. 2019. Species diversity and treecarbon storage at Pemerihan Resort, Bukit
Barisan Selatan National Park. Sylva Lestari
Journal. 7(2): 139-149.
Hatulesila, J.W., Wattimena, C.M.A., Siahaya, l.
2018. Study on measurement and determination
carbon pool in traditional agroforestry system
for handling climate change. International
Journal of Forestry and Horticulture (IJFH).
4(2): 14-24.
Indriyanto. 2006. Ekologi Hutan.Buku. PT Bumi
Aksara.Jakarta. 210 p.
KPH Bukit Punggur. 2015. Rencana Pengelolaan
Hutan Jangka Panjang (RPHJP). Lampung.
Markum., Arisoesilaningsih, E., Suprayogo, D., andHairiah, K. 2013. Plant species diversity inrelation to carbon stocks at Jangkok Watershed,
Lombok Island. J. Agrivita 35(3): 207–216.
National Standardization Agency (BSN). 2011.
Measurement and Calculation of Carbon Stock.
Field Measurement for the Assessment of Forest
Carbon Stocks (Ground Based Forest Carbon
Accounting). Book. National Standardization
Body. Jakarta. 16p.
Prihatmaji, Y.P., Fauzy, A., Rais, S., Firdaus, F.
2016. Analisis carbon footprint gedung
perpustakaan pusat, rektorat, dan lab. MIPA UII
berbasis vegetasi eksisting sebagai pereduksi

production forest area, it is very vulnerable to tree
cutting. On the other hand, from a regulatory pointof view, the licensing scheme in the form of CF isvery possible to regulate the felling of trees, so thatthe intensity and quantity of trees cut can be limited
and regulated in a sustainable manner.
From a regulatory point of view, thelicensing scheme in the form of CF is very possible
to regulate the felling of trees, so that the intensityand quantity of trees cut can be limited and regulated
in a sustainable manner. The role of these regulations
can be carried out by FMUs to manage their areas.
The FMU has the ability with the CF
scheme to encourage farmers to use MPTS crops
wisely while maintaining plant survival andcultivating other plants according to land conditions,so that the ecological function of the forest area ismaintained.
Conclusions and Suggestions
The INP value in the tree phase wasdominated by rubber (Hevea brasiliensis) which was
128.03%. In the pole phase, rubber (Hevea
brasiliensis) was still the same with an INP of
156.31%. The sapling phase was dominated bycoffee plants (Coffea canephora) with an INP of
92.74%. The understorey phase was dominated by
Paitan grass (Axonopus compressus) with a large
INP of 24.03%. The average value of carbon storedin each plot is 96.45 tons/ha. This value shows that
there is a linear correlation between tree biomass andcarbon stock, the more tree vegetation, the morecarbon stock in the forest. Several efforts are needed
to balance the rate of carbon release with the rate ofcarbon sequestration, including the application of
CF rules in tree felling by FMUs, enrichment of
MPTS plant species and maintaining the existence ofexisting MPTS plants.
References
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dan kemelimpahan Donax faba
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Ambarwati, A., Duryat., Hidayat, W. 2019. INP
Vegetation and Carbon Stored in HKm Bina
Wana, Kebun Tebu Subdistrict, West Lampung
18

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Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Regency. Essay. University of Northern
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Wijayanto, N. and Hartoyo, APP 2015.
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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –The Effects of Climate Change on Indonesian Forests
Komariah 1* and Ummi Marfu’ah 2

Dept. of Soil Science, Faculty of Agriculture, Sebelas Maret University, Indonesia
Research Group of Climate Change and Land Resource Management, Sebelas Maret
University, Indonesia*Corresponding Author: komariah@staff.uns.ac.id

1
2

SUMMARY
Climate change phenomenon has led to the warmer temperature of air, hence resulted in higher
evapotranspiration from the ecosystem, including forest ecosystem. Especially the forests in Indonesia, which
lays in the tropical monsoonal area, the impact of climate change attributes to the increase intensity of drought,
as well as higher air temperature. Warmer air will increase air capacity to hold moisture rapidly hence lowering
the water table position. The high air temperature is the most important variable and increasing the forest fire,
especially in Indonesia. The risk even higher with the logging activity since it opens the canopy, produces drier
microclimate and debris thus increases inflammability. Besides increasing the forest fire incidences, higher
temperature also decreased the geographic suitability of some vegetation and fruit tree in the forest hence
threatening the habitat of predator such as orangutan and some insect species. Many insect species are very
sensitive to changes in temperature and humidity, thus the particular insect species shift to the pole ward or
higher elevation. However, at the same time, many phytophagous insects are quickly adapting to the new plant
host and environment, which frequently lead to insect outbreak to the surroundings. The early warning systems
based on fire danger is urgent to mitigate the fire disaster, as well as forcing the forest management agency toreassess policy and strategy. Since forest fire indirectly affects the loss off agricultural yield due to water
resources decline, modification of planting and harvest as well as crop selection can help to mitigate the impact
of climate change on forest and surroundings ecosystems.
Keywords: Climate Change; Forest Fire; Fire Disaster; Forest Ecosystem; Early Warning System
experienced in fires since at least the 17th century(Bowen et al. 2001). Fires in tropical rain forests are
considered endemic, but the frequency of occurrence
is rare and in intervals of hundreds or even thousandsof years, because historical records and soil profiles
show that fires have unprecedented, even in wetter
forests (Cochrane 2003). In 1982-83 Indonesia
experienced tropical rainforest fires on a large scale,
about 3.6 million hectares of land was affected(Tacconi 2003). Although tropical rainforests arenotoriously humid, always wet and are green forests,
the fires have repeated in 1987, 1991, 1994, and1997-98 (Bowen et al. 2001) since then the extent,
intensity and frequency of fires increased in eachyear to the year and showed yearly hotspot
distribution (Syaufina 2018). During the incident,
the most severe fires occurred in the period 1997-98
affecting 11.7 million hectares in Sumatra,
Kalimantan, Papua (Goldammer 2002) and hot spots
increased periodically every year. These large fires
correlate with El Nino events (Slik 2004) and climatechange (Flannigan et al. 2013) that are causing risingtemperatures and a long drought in Indonesia. In the

Introduction
Forest disturbance is a problem of both
local and global concern. The characteristic offorests in Indonesia are tropical rainforests that havehumid characteristics, high annual rainfall, and highevapotranspiration and are green forests. However,with climate change and human intervention inrecent decades, forests have been damaged by
wildfires, droughts and insect outbreaks thatstrongly impacts to the ecosystem of the forest to
provide ecosystem services to society. Weather andclimate including temperature, precipitation, wind
and atmospheric moisture are critical aspect of thisphenomenon (Flannigan et al. 2013). The
disturbance of forest have direct effects inbiodiversity, loss of property and the carbon
emission. This paper reviews the effects of climatechange in forest in Indonesia and the mitigation.
The Effects of Climate Change in Forest Fire
Indonesian forest in Kalimantan have20Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –length of the fire season, as expressed by measuresuch as temperature, drought, degree days, and fire
weather indices likely longer. Projection of theeffects of climatic change on fire regimes have beenof two types: mechanistic models that explicitly
represent fire weather, fire behavior and fire effects
at sub regional scales and statistical models atregional to continental scale often linked to out fromglobal circulation models (Seidl et al. 2017).

period 1979-2003 the annual average fire duration
on a global scale increased to 18% and experienced
longer fires more than doubled (Jolly et al. 2015).
Factors that increase the risk of forest fires
in Indonesia are logging, distance from rivers thatare too far away, the development of road andsettlement projects and the occurrence of increased
temperatures due to climate change (Syaufina 2018).
Land clearing by logging increases the fire potential
because it opens the canopy, resulting in a dry
microclimate so that climate change and increased
temperatures can cause dry conditions to increase
the occurrence of burning due to biomass (Herawati
and Santoso 2011). Rising temperatures due toclimate change drastically and rapidly can increase
the potential for forest fire activity (Pechony andShindell 2010). Temperature is the most importantvariable affecting annual wild land fire activity, withwarmer temperatures leading to increased fire
activity. Almost the entire world is expected to warm
during this century and wildfires may be a reflectionof this rise in temperature (Flannigan et al. 2013).
GISS (Goddard Institute for Space Studies) climate
simulation predict a significant warming over theforthcoming century. Rapidly rising temperaturesand regional drying reverse the recent fire activity
decline, driving a rapid increase after ∼ 2050.
Population growth, and to a lesser extent, land-coverchange, reduces the increase in fire activity, but does
not reverse the long-term trend.
The reasons for the positive relationshipbetween temperature and wildfires are three. First,
warmer temperatures will increase evapotranspiration,
as the atmosphere's ability to withstand moistureincreases rapidly with higher temperatures, thereby
lowering the position of the water table and reducing
fuel moisture unless there is a significant increase in
rainfall. Second, warmer temperatures translate into
more lightning activity which generally leads toincreased ignition. Third, warmer temperatures canlead to an extension of the fire season (Flannigan etal. 2013). Weather conditions are also critical driversof fire activity, and it is generally the co-occurrenceof optimal fire weather (hot, low humidity, andwindy) with already dry fuels due to anomalously
dry climate that promotes fire spread (Williams andAbatzoglou 2016). The combination of a warmer
climate with carbon dioxide fertilization may cause
more frequent and more severe fires in the westernarea. Global circulation models suggest that the

The Effects of Climate Change in Forest
Drought
Tropical rainforests experience an annual
dry period, but in the last 10 years tropicalrainforests in Indonesia experienced periods ofabnormal dry weather over a long period of time andcaused unbalanced hydrological conditions (Stocker2014). Tropical droughts are multi-year climate
cycles that are inter-annual, making long-term trends
difficult to predict (García-garcía and Ummenhofer2015), but there have been many detection models
that show the frequency and intensity of droughts invarious tropical forest regions increasing in thiscentury including in Indonesia (Zhang et al. 2015).
Based on predictions using The Climate Change
Risk and Adaptation Assessment (CCRAA)
approach, rainfall intensity on the island of Java in
2011-2040 is rarely greater than 300 mm so thepotential increase in drought hazard index (DHI) isvery possible because the increase in drought that
occurs can reach 45% (Fitriyanto et al.). Droughts
occur from shifts in climate patterns that are affectedby rising temperatures over a long period of time sothat transpiration and evaporation rates can increase,this can limit periods of rainfall. El-nino is one of themain causes of drought in tropical rainforests. In thesevere El-nino drought in 1997-1998, forests in East
Kalimantan experienced drought-related tree deaths
between 7.3 and 22.1%, this occurred because
understory light conditions changed thus affecting
the competition of tree species and the specificcomposition of canopy trees (Slik 2004).
Drought on tropical rainforest ecosysteminfluence tree mortality in Indonesia. In Kalimantan
forest Drought contributes to stem deaths of about
30% but the estimated contribution to basalt areas
die and biomass is higher at 52% and 63%
respectively (Nieuwstadt and Sheil 2005). Mortalityof tropical trees has been widely observed in both21

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –or effects of temperature on host chemistry. At thesame time, many phytophagous insects are able toquickly adapt to new environments, provided their
host plants are present. Switching to new hosts may
occur among non-specialist herbivores, and can be
the first consequence of the strong selection oncolonizers (Battisti, A., Stastny, M., Netherer, S.,
Robinet, C., Schopf, A., Roques, A., & Larsson2009). Long periods of low precipitation deplete soil
moisture in turn causing water stress and low xylem
pressure in trees which allow beetles (especiallyDendroctonus spp.) to colonize cambial tissue. An
outbreak of beetles in stressed trees can spread tohealthy trees and causing significant mortality toaccumulate woody fuels, which greatly increases thehazard of a stand replacing fire and subsequent
beetle attack on dead and damaged trees. Such acascade of spatial and temporal patterns ofdisturbance makes it difficult to achieveconservation goals for plant and animal species,
especially those associated with late successional
forest structure.

natural droughts and throughfall exclusion
experiments. Some mortality lags one or more years
after a drought. Some mortality lags one or more
years after a drought. Vulnerability varies among
species, sizes, ages, growth rates, and locations, withtree vigor the best predictor of individual mortality.
They also have keystone ecological roles, providing
substrates for epiphytes, cavities for hole-nesting
species, and favorable microclimates for understory
plants (Corlett 2016).
Droughts also cause decreased diameter
growth in tropical forests especially on large trees,
based on Bonal et al. 2016 This cannot be explainedin diameter measurements and photosynthesis alone,
but changes in carbon allocation in trees show that
there is a large decrease in carbon sequestration bytropical forests in years of drought. Diameter is an
important factor in tree mortality during drought,
mortality is much higher in trees with large
diameters in East Kalimantan, trees with dbh greaterthan 20 cm experience death up to 26.5% and trees
with dbh less than 20 cm have a lower mortality rateof 17.2% (van Nieuwstadt 2002). On observations incentral Kalimantan, tree deaths in peat-swamp
forests were higher (6.13% yr-1) than healthy forests(4.26% yr-1) during observations in the 2 years after
drought, in the dry season showed a higher mortality
rate than the rainy season (Nishimua et al. 2007).
Wood density could be another explanation of thehigher mortality rates among these pioneer trees.
Strong negative relation between wood density andtree drought mortality. This relationship can be
explained by the fact that wood density is an
important determinant of xylem water transport
properties and whole plant water relations (Slik2004).

Climate Change Mitigation for Forestry
The most needed and urgent treatment isfor early detection of drought development in aregion (Flannigan et al. 2016) and followed by acontinuous monitoring of its effects. A variety ofpassive and active satellite sensors, including thosemeasuring
opticalreflectance,
microwave
backscatter, thermal signals, gravitational field,
near-surface relative humidity, and atmospheric
CO2 concentrations, can be used to detect drought
and monitor impacts (Campoy et al. 2020).
Yield losses can be at least partiallymitigated through management practices, such as
modification of planting and harvest dates. However,recent research suggests that climate change over thelast 20 years is already having a deleterious impact
on agricultural production, and the ability of existingwater resources to keep pace with future climate
impacts is in question. Even with proactive
management, our results suggest increased drying,
driven primarily by increases, will likely havesignificant ramifications for globally importantregions of agricultural production in the Northern
Hemisphere mid-latitudes (Cook et al. 2014).
Many species, and insects in particular,
have specific temperature requirements that may

The Effects of Climate Change in Insect
OutbreakDistributions of organisms are often
defined by climatic factors, such as temperature andprecipitation.
Many
insectspecies,andphytophagous insects in particular, show relatively
rapid responses to the rising temperatures by shifting
their geographic range boundaries poleward or tohigher elevations. Most insects are very sensitive tochanges in temperature, which may affect theiractivity, development, phenology, and survival
directly, or indirectly through host phenology shifts

22Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Indonesian Case Study : Climate Projections andDrought Hazard Assessments in East Java
Province.
Flannigan M, Cantin AS, De Groot WJ, Wotton M,
Newbery A, Gowman LM. 2013. Global
wildland fire season severity in the 21st century.
For Ecol Manage. 294:54–61.
doi:10.1016/j.foreco.2012.10.022.
http://dx.doi.org/10.1016/j.foreco.2012.10.022.
Flannigan MD, Wotton BM, Marshall GA, de Groot
WJ, Johnston J, Jurko N, Cantin AS. 2016. Fuelmoisture sensitivity to temperature andprecipitation: climate change implications. Clim
Change. 134(1–2):59–71. doi:10.1007/s10584015-1521-0.
García-garcía D, Ummenhofer CC. 2015.
Multidecadal variability of the continental
precipitation annual amplitude driven by AMO
and ENSO. :526–535.
doi:10.1002/2014GL062451.Received.
Goldammer GJ. 2002. Fire Situation in Indonesia.
(26):37–45.
Herawati H, Santoso H. 2011. Tropical forest
susceptibility to and risk of fire under changing
climate: A review of fire nature, policy andinstitutions in Indonesia. For Policy Econ.
13(4):227–233.
doi:10.1016/j.forpol.2011.02.006.
http://dx.doi.org/10.1016/j.forpol.2011.02.006.
Jolly WM, Cochrane MA, Freeborn PH, Holden
ZA, Brown TJ, Williamson GJ, Bowman DMJS.
2015. Climate-induced variations in global
wildfire danger from 1979 to 2013. Nat
Commun. 6(May):1–11.
doi:10.1038/ncomms8537.
http://dx.doi.org/10.1038/ncomms8537.
van Nieuwstadt MGL. 2002. Trial by fire : Postfire
development of a tropical dipterocarp
forest. :142.
http://www.library.uu.nl/digiarchief/dip/diss/200
2-0927-111146/inhoud.htm.
Nieuwstadt MGLVAN, Sheil D. 2005. Drought ,fire and tree survival in a Borneo rain forest ,East Kalimantan , Indonesia. :191–201.
doi:10.1111/j.1365-2745.2005.00954.x.
Nishimua TB, Suzuki E, Kohyama T, Tsuyuzaki S.
2007. Mortality and growth of trees in peatswamp and heath forests in Central Kalimantan
after severe drought. Plant Ecol. 188(2):165–
177. doi:10.1007/s11258-006-9154-z.

render them sensitive to new temperature regimes.
Therefore, our ability to predict changes in species
distributions and their potential community impacts,
as well as to evaluate suitable management strategies,
would benefit from focusing on the mechanisms thatdrive correlations between climatic factors andsurvival of organisms (Battisti, A., Stastny, M.,
Netherer, S., Robinet, C., Schopf, A., Roques, A., &Larsson 2009).
Conclusion
Climate change has led to more intensive
forest fire, increased drought hazard index hence
higher trees mortality, as well as increased insect
outbreak in Indonesian forest. Among the mitigation
measures are by usage of futuristic technology, suchas satellite monitoring and management practicestrategies.
References
Battisti, A., Stastny, M., Netherer, S., Robinet, C.,
Schopf, A., Roques, A., & Larsson S. 2009.
Expansion Of Geographic Range In The Pine
Processionary Moth Caused By Increased
Winter Temperatures. Appl Sci. 2009(6):2008–2009.
Bonal D, Burban B, Stahl C, Wagner F, Hérault B,
Wagner F. 2016. The response of tropicalrainforests to drought — lessons from recent
research and future prospects. :27–44.
doi:10.1007/s13595-015-0522-5.
Bowen MR, Bompard JM, Anderson IP, Guizol P,
Gouyon A. 2001. ANTHROPOGENIC FIRESIN INDONESIA : A VIEW FROM SUMATRA
FOREST FIRES AND REGIONAL HAZE.
Campoy J, Campos I, Plaza C, Calera M, Bodas V,
Calera A. 2020. Estimation of Harvest Index in
Wheat Crops Using A Remote Sensing-BasedApproach. F Crop Res. 256(November2019):107910. doi:10.1016/j.fcr.2020.107910.
Cochrane MA. 2003. Fire science for rainforests.
421(February):913–919.
Corlett RT. 2016. The Impacts of Droughts in
Tropical Forests. Trends Plant Sci. 21(7):584–
593. doi:10.1016/j.tplants.2016.02.003.
http://dx.doi.org/10.1016/j.tplants.2016.02.003.
Fitriyanto MS, Supangat A, Ramadhan MR.
Developing and Applying Climate Informationfor Supporting Adaptation in South East Asia
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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Syaufina L. 2018. Forest and land fires inIndonesia: Assessment and mitigation. In:
Integrating Disaster Science and Management:
Global Case Studies in Mitigation andRecovery. Elsevier Inc. p. 109–121.
http://dx.doi.org/10.1016/B978-0-12-8120569/00008-7.
Tacconi luca. 2003. Fires in Indonesia: causes,
costs and policy implications. Fires Indones
causes, costs policy Implic.(38).
doi:10.17528/cifor/001552.
Williams AP, Abatzoglou JT. 2016. Recent
Advances and Remaining Uncertainties in
Resolving Past and Future Climate Effects on
Global Fire Activity. Curr Clim Chang Reports.
2(1):1–14. doi:10.1007/s40641-016-0031-0.
Zhang KE, Castanho ADDEA, Galbraith DR, Costa
MH, Malhi Y, Longo M, Knox RG. 2015. The
fate of Amazonian ecosystems over the coming
century arising from changes in climate,atmospheric CO2, and land use. :1–19.
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Pechony O, Shindell DT. 2010. Driving forces ofglobal wildfires over the past millennium andthe forthcoming century. Proc Natl Acad Sci U S
A. 107(45):19167–19170.
doi:10.1073/pnas.1003669107.
Seidl R, Thom D, Kautz M, Martin-Benito D,
Peltoniemi M, Vacchiano G, Wild J, Ascoli D,
Petr M, Honkaniemi J, et al. 2017. Forest
disturbances under climate change. Nat Clim
Chang. 7(6):395–402.
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Slik JWF. 2004. El Niño droughts and their effects
on tree species composition and diversity intropical rain forests. Oecologia. 141(1):114–
120. doi:10.1007/s00442-004-1635-y.
Stocker T. 2014. Climate change 2013: the physicalscience basis: Working Group I contribution to
the Fifth assessment report of theIntergovernmental Panel on Climate Change.
Cambridge university press.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Tropical Montane Forest Ecotone Dynamics in the Context of
Climate Change: Case Study in the Doi Suthep-Pui National Park,
Thailand
Sutheera Hermhuk 1*, Dokrak Marod 2

Faculty of Agricultural Production, Maejo University, Thailand
Faculty of Forestry, Kasetsart University, Thailand*Corresponding Author: h.sutheera@gmail.com

1
2

SUMMARY
Recently, climate changes are the most argument topics, particular relating to adaptation andmigration of plants in the mountain ecosystem. This study aimed to clarify the forest dynamics; particularsapling and tree stages related to micro-climate changes in a permanent plot along altitudinal gradient (ranged
from 900 to 1,100 m above sea level). A permanent transect plot (30 m × 600 m) was established at forest
ecotone (FE) between deciduous dipterocarp forest (DDF) and lower montane forest (LMF). All trees withdiameter at breast height (DBH > 4.5 cm) were tagged, measured and identified. The monitoring was done
every two years since 2012 to 2016. The micro-climatic factors were automatic recorded, and the amount of
rainfall was received from meteorological station. All tree (DBH > 4.5 cm) were analyzed to detect the
dynamics relating to the micro-climate changes. The results showed environmental changes strongly impact
on species coexisting, particular the in FE between DDF and LMF, where the intermediate environmental
factors were found. High mortality rate was mostly found in the dominant species of LMF (evergreen species),and positively significant related to decreased rainfall and increased temperature. It indicated that drought
conditions had strongly influenced on tree mortality, particular evergreen species. However, the deciduous
species of DDF had increased their density, particular at the FE zone, indicating they had high adapted todrought resistance and may be occupied the forest ecotone, instead of LMF species. Thus, the knowledge on
coexisting species based on their niches along the altitudinal gradient is useful for the restoration program,particular, in the mountain ecosystem.
Keywords: Trees diversity; Ecological niche; Environmental gradient
For instant, in Doi Inthanon National Park (the topof mountain in Thailand) set up the permanent plot
and focused on forest diversity, ecological niche ofdominance species, biomass and carbon storage,
diversity of plant species structure along altitudinal
gradient (Teejuntuk, S. et al, 2002; Khamyong, S. etal., 2003). In addition, at Doi Suthep-Pui National
Park which mainly covered by LMF, also found thereported on species composition and distribution(Marod, D. et al., 2014), seedling dynamics in forest
gap and plant functional traits of seedling fromdominant species for restoration of this forest type.
The results of this research should support natural
resources management, especially the selection oftrees for restoration programs in mountainecosystems.

Introduction
Long-term ecological research (LTER)
based on permanent plots may provide theknowledge on forest dynamics, especially tree
regeneration, relating to their environmental changes(tree density, basal area, recruitment and mortality intree density) (Sherman, R. E. et al., 2012). Manysites of LTER plots in Thailand displayed theimportant outputs such as change of forest cover
during successional stage, dynamics of forest
structure and spatial distribution related toenvironmental factors (Bunyavejehwin, S. et al.,2001). Although studies have covered almost allforest types, few have documented the forest ecotone
between lowland and upland forest along altitudinal
gradients. At present time, many research topics
such as; forest biodiversity, forest biomass, carbon
storage and forest succession from shifting
cultivation etc. were done in both UMF and LMF.

Materials and methods
1. Data collection
All studies were conducted at forest
25

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –(17.34 %), Litsea martabanica (17.05 %), Schima
wallichii (15.86 %), Lithocarpus garrettianu(12.20 %), Castanopsis acuminatissima (11.40 %),Castanopsis tribuloides (8.90 %), Shorea obtuse(7.96 %), Dipterocarpus tuberculatus (6.96 %).
The forest dynamics of trees in the 1.8 hapermanent plot, fluctuated among periods during
2012-2016. The basal area tended to increase during
the first period, 2012–2014, (29.97 m2/yr to 32.34
m2/yr), contrasting with the second period, 2014–2016 (Table 1). It mainly due to sized dependentmortality, particular in the small size class (DBH <10 cm) (Figure 5). However, the highest of species
number was found in 2016 (102 species). However,it varied among periods. The mortality andrecruitment rate had high fluctuated among study
period. Trees mortality rate increased about threetimes for trees DBH ≥ 4.5 cm. In addition, therecruitment rate also trended to decrease about seventimes in second period.

ecotone between DDF and LMF (latitude 18° 47'
4"N and longitude 98° 54' 58"E) at Doi Suthep-Pui
National Park, Chiang Mai province. A Transect
permanent plot, 50 m × 600 m (3 ha), wasestablished in 2012 which ranged from 900 to 1,100m above sea level. Subplots of 10 m × 10 m weredivided (total 300 subplots). All tree species withdiameter at breast height (DBH) larger than 1 cm
were tagged, DBH measured, identified andanalyzed only tree stage (DBH > 4.5 cm).
Monitoring was done every two year since 2012 to2018. In addition, the micro-climatic factors,
temperature and light intensity, were automaticallymeasured since 2012 by Hobo Data Loggers. The
data loggers were set up in three lines, and 50 m
adjacent distant for each logger in each line (total 36
loggers). In addition, the amount of rainfall was also
analyzed based on the data from Doi Suthep-Pui
National Park Meteorological station.
2. Data analysis
To evaluate the dominant of sapling
species in the area, the importance value index (IVI)(Whittaker, R. H. 1975). The forest dynamics werecharacterized using annual mortality rate (M) andrecruitment rate (R) (Sherman, R. E. et al., 2012) ofselected species which had more than 100
individuals, were used for Generalized linear model(GLM) analysis to detect the relationship between
micro-climatic changes based on R program version
3.2 (Team, 2013).

Table 1 Summary data of forest dynamics in theforest ecotone permanent plot

Results and Discussions

The results from GLM analysis for
detecting the relationship between forest dynamics,
mortality and recruitment rate, showed that only,
mortality rate of both trees and saplings had
significant highly correlated to decreasing of rainfall
and increasing of temperature. In particular, longdrought period was found in the second period, it
showed negatively affected by low decreasing
rainfall (p < 0.05); the rain-free days less than 52
days (2014 = 158 days and 2016 = 106 days) and had
positively related to the increasing of temperature (p< 0.01).
Considering to species dynamics based on
their characteristics, evergreen and deciduous
species, net trees mortality and recruitment rate of
dominant deciduous species group of DDF wererelatively balanced (less than 10%/yr) such as

Within the 1.8 ha permanent plot, there
were 165 species, 118 genera and 59 families. In
addition, tree density and basal area were 1,190
individual/ha and 29.56 m2/ha, respectively. The
dominant families, based on species number, were
Fagaceae, Phyllanthaceae, and Fabaceae withspecies numbers of 15, 12, and 10, respectively.
These dominant families also had a high total BA(m2), particularly, the family of Fagaceae andDipterocarpaceae and family of Fagaceae,
Rubiaceae and Lauraceae, which had high total stem
density, which was the highest tree density found in
FE zone. The species of Quercus brandisiana(32.34 %) was ranked first of dominant species(based on IVI) followed by Dipterocarpus
obtusifolius (17.73 %), Wendlandia paniculata

26

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Conclusions

Aporosa villosa, Aporosa nigricans, Dipterocarpus
obtusifolius, Quercus branisiana, Rothmannia
sootepensis, and Wendlandia paniculata (Figure 1).
In contrast, the dominant evergreen species group of
LMF had high fluctuated on the net mortality andrecruitment rate. The mortality had highly
negatively relationship to decreased rainfall (p <0.05), particular high increased in the second period
such as Castanopsis tribuloides, Castanopsis
acuminatissima, Litsea martabarnica, Tarennoidea
wallichii, Schima wallichii, Myrsine seguinii, andPersea gamblei (Figure 1). Indicating that the
drought conditions had strong influenced on
evergreen species mortality, and may be induced
more deciduous species which high adapted todrought resistance to replace at the forest ecotone.
Thus, the climatic conditions are the main
environmental factors for plant regeneration,
especially in sapling stage that is sensitive to thechanges (Frelich, L. E. 2002). This result study also
clarified that the changed of rainfall and temperature
were the key factors in the survival of saplings forcoexisted species of LMF and DDF along thealtitudinal gradients similar to Martin, H. P. et al.
(2007). Deciduous species well adapting to drought
condition by showed high recruitment rate and canbe explant up to the high altitude, in contrast withevergreen species, most of them die due to microclimate changes.

The environmental changes strongly
impact on species coexisting, particular the in FE
between DDF and LMF, where the intermediate
environmental factors were found. High mortality
rate was mostly found in the dominant species of
LMF (evergreen species), and positively significant
related to decreased rainfall and increased
temperature. Indicating that drought conditions hadstrongly influenced on tree mortality, particular
evergreen species. However, the deciduous speciesof DDF had increased their density, particular at theforest ecotone zone, indicating they had highadapted to drought resistance and may be occupied
the forest ecotone, instead of LMF species.
Acknowledgment
This research was supported by the Center
for Advanced Studies in Tropical National
Resources (CASTNaR), Kasetsart University,
Bangkok, Thailand. We would like to thank the headof the Doi Suthep-Pui National Park, Chiang Mai
Province and his assistants for their supports during
field works. Special thanks to all of my team,students from the Thai Forest Ecological Research
Network (T-FERN).

Figure 1 Relationship between recruitment and mortality rates of dominant tree species during study period27Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Reference

Martin, H. P., Sherman R. E. and Fahey, T. J.
(2007). Tropical montane forest ecotone:
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Sherman, R. E., Fahey T. J., Martin P. H. andBattles, J. J. (2012). Patterns of growth,
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forest, Dominican Republic. Journal of Tropical
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Bunyavejchewin, S., Baker, P. J. Lafrankie J. V. andAshton, P. S. (2001). Stand structure of aseasonal dry evergreen forest at Huai Kha
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Frelich, L. E. (2002). Forest dynamics anddisturbance regimes: studies from temperate
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Khamyong, S., Lykke, A. M., Seramethakun, D.,
Barfod A. S. (2003). Species composition andvegetation structure of an upper montane forestat the summit of Mt. Doi Inthanon, Thailand.
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Marod, D., Sangkaew, S., Panmongkal, A., Jingjai,
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Mai Province. Thai Journal of Forestry, 33 (3):23-33.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Effect of Climate Changes on Dry Evergreen Forest Dynamics in
Sakaerat Biosphere Reserve, Northeastern Thailand
Wongsatorn Phumphuang 1*, Dokrak Marod 1

Department of Forest Biology, Faculty of Forestry, Kasetsart University, Bangkok 10900,
Thailand*Corresponding Author: fns_pon@hotmail.com
1

SUMMARY
Long-term ecological research based on large permanent plot can explore the forest dynamics inrelation to environmental changes. This study aimed to detect the tree distribution related to environments aswell as clarifying tree demography, mortality and recruitment, in response to drought conditions in the dry
evergreen forest (DEF) at Sakaerat Biosphere Reserve (SBR), northeastern Thailand. In 2010, a 16 ha
permanent plot was established and all trees with diameter at breast height (DBH) ≥ 4.5 cm were tagged,
measured, recorded their coordinates and identified to species level. Tree monitoring was in 2015 and 2020
which included new trees with a DBH ≥ 2 and 1 cm, respectively. Within the permanent plot, topographic
factors and soil properties were collected to investigate tree distribution using canonical correspondence
analyses (CCAs). The climate change based on the Standardized Precipitation-Evapotranspiration Index(SPEI), a drought index, was constructed using meteorological data from SBR since 2010–2020. In 2020, a
total of 81,728 individuals (≥ 1 cm DBH) comprising 204 species 156 genera and 62 families were found. The
stem density and basal area were 5,108 individual/ha and 30.16 m2/ha, respectively. According to the importantvalue index, Hydnocarpus ilicifolia, Hopea ferrea and Walsura pinnata were the dominant species in thecommunity. Topographic factors (elevation and slope), soil textures and soil nutrients based on CCAs were
determined tree distribution (p < 0.05). In addition, tree species characteristics can be divided into two groups,
drought and moist preferred groups which were distributed under sandy and clay texture, respectively. The
mortality and recruitment rates of trees DBH ≥ 4.5 cm during 2010–2020 were mostly balanced (1.96 and1.93 %/year, respectively); nevertheless, the rates varied among periods. According to SPEI, humid conditions
were widespread, particularly in extended humid conditions during the second period. Higher mortality offavored dry species such as Arytera littoralis and Nephelium hypoleucum was influenced by thesecircumstances, demonstrating that tree species in the DEF had distinct adaptations to climate change. Hence,
our knowledge supports the species ecological niche and can be applied to forest conservation and reforestation
program based on suitable species selection.
Keywords: IC-GU12; Format and layout; International symposium; Soluble protein; Hoover Dam

In Thailand, there are not many LTER
with large scale permanent plots regarding dry
evergreen forest (DEF). This forest type cover
approximately twenty percent of protected area
(Pongpattananurak, N., 2014) and usually locatedbetween deciduous and evergreen forest, which they
appeared in mixed plant species. Thus, the LTER in
the DEF using the large permanent plot should beestablished, to detect how plant species respond toenvironmental changes. This study aimed to detect
the tree distribution related to environments as well
as clarifying tree demography, mortality andrecruitment, in response to drought conditions in thedry evergreen forest

Introduction
The long-term ecological research (LTER)
with large scale permanent plot is imperious tounderstand the impacts of past disturbances andpredicting the effects of future changes on tropicalforests (Feeley, K. J. et al., 2011). The generalcharacteristics of the forest communities cannot be
detected by small scale plot because heterogeneity inenvironments is disregarded (Miura, M. et al., 2001).
Demographic responses to natural disturbances,such as drought condition, will differ among tree
species and especially for tree establishment will bediverse based on the heterogeneity of the environments(Körner, C., 2004; Feeley, K. J. et al., 2011).

29

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Materials and Methodm) (Delgado-Baquerizo, M. et al., 2020). Soilsamples were conducted using disturbed soil
samples from the surface (0–15 cm depth) with totalof 64 points. The soil samples were analyzed for soil
physical and chemical properties in laboratory.

1. Study site
The study was conducted in the DEF at the
Sakaerat Biophere Reserve (SBR) Wang Nam Khiao
district (14°30´N, 101°56´E), Nakhon Ratchasima
province, northeastern Thailand. The altitudinalrange is from 250 to 762 m asl. The climate is
tropical with rainfall mainly occurring from July to
October, while less rain falls in the remaining 8
months. The mean annual rainfall is ca. 1,100
mm/year and the mean monthly temperature is
27.3°C. The geology and soils are characterized by
Triassic and Cretaceous sandstone formations fromthe Phra Wihan formation of the Korat group,
overlain by shallow to moderately deep red-yellow
podzol (Bunyavejchewin, S., 1999). The total area of
SBR is approximately 81 km2. The SBR is borderedby extensive agricultural areas and humanresidences (Trisurat, Y., 2010). In the past, thenatural forest was diminished due to encroachment
and illegal logging. Forest types comprise DEF,
DDF and mixed deciduous forest, MDF, which DEF
mostly occupied in the area. However, many
restoration areas were implemented both inside andoutside the SERS since 1994, then, the forest cover
was increased. Additionally, man-made fires
generally find in the DDF and MDF during the dry
season.

4. Data analysis
Canonical Correspondence Analysis(CCA) was performed to correlate tree communityand environments. The CCA ordination were done at
the scale of 50 m × 50 m (John, R. et al., 2007). The
species abundance matrix was total basal area foreach species. Only species with < 10individuals/quadrat in all quadrats were excluded
from the analysis (De Souza, J. P. et al., 2007). The
Monte Carlo permutation test was analyzed, toevaluate the statistical significance of theenvironmental variables (5% confidence level with999 random permutations). Only the significant
variables were then selected. All analyses were doneusing the vegan package by R software version 3.6.1.
To examine drought events, thestandardized precipitation-evapotranspiration index(SPEI) (Vicente-Serrano, S. M. et al., 2010) was
purposed through the SPEI package (Beguería, S. etal., 2017) in R software version 3.6.1, using themeteorological data from SBR since 2010–2020,
including precipitation data, mean temperature andthe station’s latitude. In this study, the Hargreaves
method for computing PET was chosen. Thisapproach decreases the effort required to obtainmeteorological variables such as air humidity,
radiation, and wind speed (Rascón, J. et al., 2021).
Finally, the 12-month SPEI was chosen because it
can distinguish between long-term dry and wet
periods, making it more reflective of drought's
effects on hydrologic regimes (Moreira, E. et al.,2018).
The parameters of forest dynamics were
described followed standard methods as purposed by(Condit, R. et al., 1999), for calculating mortality(M) and recruitment (R) following the equations:

2. Plot establishment and investigation
In 2010, a 16 ha permanent plot wasestablished and was divided into subplots of 10 m ×
10 m, total of 1,600 subplots. All trees with DBH
greater than 4.5 cm were tagged, measured andidentified. Tree monitoring was in 2015 and 2020.
Especially in 2020, all trees with DBH over than 1
cm were included. In this study, tree distribution was
focused only 2020 data with tree over than 1 cm
DBH, but the forest dynamics was investigated on
all three-census data with tree greater than 4.5 cm
DBH.
3. Environmental collection
The elevation of every corner of all
subplots (10 m × 10 m) were collected based on
digital elevation model. The elevation of SBR plot
ranged from 420-475 m asl.
Sampling plots were established within
the permanent plot using a grid structure (50 m × 50

and

𝑀𝑀 =
𝑅𝑅 =

ln 𝑛𝑛0 − ln 𝑆𝑆𝑡𝑡𝑡𝑡

ln 𝑛𝑛𝑡𝑡 − ln 𝑆𝑆𝑡𝑡𝑡𝑡

where the census interval is t, and the population
sizes at time 0 and time t are n0 and nt. The numberof survivors at time t is St, with the number of30

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –recruits is nt – St. Furthermore, the analysis ofvariance and the Tukey test was used to determine
differences among censuses for tree density.

2. Climate change
On a 12-month time scale, the SPEI values oscillate
between -1.3 and 2.0 (Figure 2). In the SBR, the mostmoderately wet years were observed in 2012, 2014 and2019, as well as an extremely wet year in 2017–2018.
A moderate drought, on the other hand, occurred only
in 2016. As a result, the SBR was largely impacted by
wet events during the period of the 10 years, withoccasional drought.

Results and Discussion
1. Ordination of tree community

Tree species fluctuate between environments,
according to the CCA ordination plot (Figure 1).
Hopea ferrea, Walsura pinnata and Memecylon
ovatum, the most abundant species with the higher
IVI of the plot, were unaffected by any of theenvironments. However, the other species had adifferent relationship with their environments. For
example, higher levels of sandy soil were found in
Nephelium
hypoleucum,
Aryteralittoralis,
Melodorum fruticosum and Afzelia xylocarpa. Sandy
soils are characterized as good drainage soil, whichcharacterizes as the drier condition of the plot. In
contrast, species of Syzygium siamense and Vatica
harmandiana were linked to high silty soil,
magnesium, and a lower elevation. Moreover,
Murraya paniculata was found in an area with ahigher proportion of clayey soil. Clayey soil andsilty soil can hold more total water than other soil
types (Finch, H. J. S. et al., 2014), which describes
the moist condition of the area. Therefore, this
explained the two groups of species that distribution
in the different of environments.

Figure 2: SPEI 12-month for the 2010–2020 period in
Sakaerat Biosphere Reserve.
3. Forest dynamics

The summary of forest dynamics for tenyear period was shown in Table 1. The number of
species tend to decline, which is most likely to theresult of random fluctuation. It found that almost
100% of the species that became locally disappear
had only one individual, and most of them are called
pioneer species, such as Micromelum minutum. The
density and basal area were quite balanced, which
peak in 2015. In the first period, around 80% of thegain in basal area was belonged to growth ofsurviving trees, and the rest is from new recruit. But
in the second period, loss of basal area was found
due to a high number of dead trees, causing a
reduction of basal area in 2020. The net mortality
and recruitment were quite similar (1.96 and1.93 %/year, respectively). In the 1st period,
recruitment showed higher than mortality (2.56 and2.00 %/year, respectively). And then, both rates werereduced, which mortality was greater than
recruitment (1.88 and 1.27 %/year, respectively).

Figure 1: Ordination plot by CCA of tree species of thedry evergreen forest in Sakaerat Biosphere Reserve.
Only top 60 % of species were shown.

31

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Table 1: Summary table of forest dynamics (≥ 4.5 cm
DBH) in the permanent plot in Sakaerat Biosphere
Reserve

recruitment. The recruitment rate was higher thanthe mortality rate in the first period, indicating thatthe wet in 2012 and 2014 may have allowed the trees
to establish rapidly. This corresponds to aconsiderable increase in tree density between 2010
and 2015. The mortality rate was more equitably
spread in the second period, although the recruitment
rate was lower than in the first. According to the
SPEI, there is a drought, which will be followed byhighly wet conditions. Because of the drought, thedeath rate should be higher during this time. Thissuggests that the first period's extremely wet
conditions may have played a role since they canbuffer the negative effects of short-term severe
drought, reducing mortality (Zhang, Q. et al., 2017).
This could be the cause for the relatively stablemortality in the second period. Nevertheless, limited
storage reserves for tree regeneration, low vacant
space for growing, and competition among trees(Maza-Villalobos, S. et al., 2013) may have led to
the reduction in recruitment in the second period.
This is consistent with the fact that tree density
declined statistically significantly in 2020.

The difference in tree density across threecensuses, 2010, 2015, and 2020, was evaluated using
a one-way ANOVA. The density of trees differedstatistically significantly among census years (F(2,4797) = 4.52, p < 0.05). In 2015 (14.98±4.77
individual/subplot), the density was higher than in2010(14.58±4.74
individual/subplot).
In
comparison to 2015, it declined in 2020 (14.53±4.47
individual/subplot). However, there was no
statistically significant difference in tree density
between 2010 and 2020. Tukey post-hoc analyses
revealed that the increase from 2010 to 2015 was
statistically significant (p < 0.05), as was thedifference between 2015 and 2020 (p < 0.05) (Figure
3).

Conclusions
Climate changes had influenced on forest
dynamics. Net recruitment and mortality rates were
almost similar. However, the recruitment of thesecond period was decreased doubly when comparedto the first period. Our knowledge supports the
species ecological niche and can be applied to forest
conservation and reforestation program based onsuitable species selection.
Acknowledgment
This research was financially supportedby the Royal Golden Jubilee Ph.D. Programme(Grant No. PHD/0144/2561), through the Thailand
Research Fund and the National Research Council ofThailand.
Reference

Figure 3: Tree density among three censuses in thepermanent plot in Sakaerat Biosphere Reserve.

Beguería, S., Vicente-Serrano, S. M., and Beguería,
M. S. (2017). Package ‘spei’. Calculation of theStandardised Precipitation-Evapotranspiration
Index.
Bunyavejchewin, S. (1999). Structure anddynamics in seasonal dry evergreen forest inDuring the 10 years, the study site wasprimarily subjected to humid events. These factors
largely influenced the forest's dynamics, particularly
tree density, which has a direct impact on

32

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 1 – FOREST CARBON CYCLE AND CLIMATE CHANGE –Maza-Villalobos, S., Poorter, L., and MartínezRamos, M. (2013). Effects of ENSO andtemporal rainfall variation on the dynamics of
successional communities in old-field
succession of a tropical dry forest. PloS one,
8(12): e82040.
Miura, M., Manabe, T., Nishimura, N., andYamamoto, S. i. (2001). Forest canopy andcommunity dynamics in a temperate old–growthevergreen broad–leaved forest, south–westernJapan: a 7–year study of a 4–ha plot. Journal of
Ecology, 89(5): 841-849.
Moreira, E., Russo, A., and Trigo, R. M. (2018).
Monthly prediction of drought classes using loglinear models under the influence of NAO forearly-warning of drought and water
management. Water, 10(1): 65.
Pongpattananurak, N. (2014). Prioritizing forest
complexes of Thailand using landscape metrics.
Thai Journal of Forestry, 33(2): 61-76.
Rascón, J., Gosgot Angeles, W., Quiñones
Huatangari, L., Oliva, M., and Barrena
Gurbillón, M. Á. (2021). Dry and Wet Events in
Andean Populations of Northern Peru: A Case
Study of Chachapoyas, Peru. Frontiers inEnvironmental Science, 9: 54.
Trisurat, Y. (2010). Land use and forested landscapechanges at Sakaerat environmental researchstation in Nakhon Ratchasima province,
Thailand. Ekológia (Bratislava), 29(1): 99-109.
Vicente-Serrano, S. M., Beguería, S., and LópezMoreno, J. I. (2010). A multiscalar drought
index sensitive to global warming: thestandardized precipitation evapotranspirationindex. Journal of climate, 23(7): 1696-1718.
Zhang, Q., Shao, M. a., Jia, X., and Wei, X. (2017).
Relationship of climatic and forest factors to
drought-and heat-induced tree mortality. PloS
one, 12(1): e0169770.

northeastern Thailand. Journal of Vegetation
Science, 10(6): 787-792.
Condit, R., Ashton, P. S., Manokaran, N.,
LaFrankie, J. V., Hubbell, S. P., and Foster, R.
B. (1999). Dynamics of the forest communities
at Pasoh and Barro Colorado: comparing two
50–ha plots. Philosophical Transactions of theRoyal Society of London. Series B: Biological
Sciences, 354(1391): 1739-1748.
De Souza, J. P., Araujo, G. M., and Haridasan, M.
(2007). Influence of soil fertility on the
distribution of tree species in a deciduous forest
in the Triângulo Mineiro region of Brazil. Plant
ecology, 191(2): 253-263.
Delgado-Baquerizo, M., Reich, P. B., Bardgett, R.
D., Eldridge, D. J., Lambers, H., Wardle, D. A.,
Reed, S. C., Plaza, C., Png, G. K., andNeuhauser, S. (2020). The influence of soil age
on ecosystem structure and function acrossbiomes. Nature communications, 11(1): 1-14.
Feeley, K. J., Davies, S. J., Perez, R., Hubbell, S. P.,
and Foster, R. B. (2011). Directional changes inthe species composition of a tropical forest.
Ecology, 92(4): 871-882.
Finch, H. J. S., Samuel, A. M., and Lane, G. P. F.
(2014). Soils and soil management. Lockhart &Wiseman’s Crop Husbandry Including
Grassland, 9th ed.; Elsevier: Amsterdam, The
Netherlands: 37-62.
John, R., Dalling, J. W., Harms, K. E., Yavitt, J. B.,
Stallard, R. F., Mirabello, M., Hubbell, S. P.,
Valencia, R., Navarrete, H., and Vallejo, M.
(2007). Soil nutrients influence spatial
distributions of tropical tree species.
Proceedings of the National Academy of
Sciences, 104(3): 864-869.
Körner, C. (2004). Through enhanced tree
dynamics carbon dioxide enrichment may cause
tropical forests to lose carbon. PhilosophicalTransactions of the Royal Society of London B:
Biological Sciences, 359(1443): 493-498.

33

INTERNATIONAL SYMPOSIUM ON A RECENT PROGRESS
IN FOREST ECOLOGY AND MANAGEMENT
2021

SESSION 2
ECOLOGY OF TROPICAL TO TEMPERATE FORESTS

Wednesday, 10th November 2021
15:20 - 16:05

ORGANIZER:
THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCE, GIFU UNIVERSITY

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Mast Seeding and Associated Resource Dynamics in Fagus Species
Qingmin Han 1*, Daisuke Kabeya 1, Yoshiyuki Inagaki 2,
Akira Kagawa 3, Günter Hoch 4, Atsuhiro Iio 5

Department of Plant Ecology, Forestry and Forest Products Research Institiute (FFPRI)
Shikoku Research Center, FFPRI
3
Department of Wood Properties, FFPRI
4
Department of Environmental Sciences - Botany, University of Basel
5
Faculty of Agriculture, University of Shizuoka*Corresponding Author: qhan@ffpri.affrc.go.jp
1
2

SUMMARY
Mast seeding or masting is a well-known phenomenon in temperate and tropical forests and a
reproductive trait that generally exists in afforestation tree species. Although its evolutionary significances
have been well demonstrated, its physiological mechanism remains poorly understood. Using long-term field
investigation and stable isotopic technology, we demonstrated that nitrogen is the key resource that limits
masting events in Fagus crenata, whereas stored starch plays a role as a “pinch-hitter” when asynchrony ofcarbon supply and demand occurs.
Keywords: Allocation; Beech; Carbohydrate; Masting; Nitrogen; Reproduction; Stable isotope; Starch; Storage

substantial N resources in mature forests.
In the same stands, a significant reduction
in individual N storage pools following wasobserved after the full masting, which was followedby years of replenishment of this stored N pool.
These results provide direct evidence of a decline instored nutrients after a mast event in mature forests.
In addition, significant decreases in leaf litter N were
found in the years following masting events,
indicating that the N storage pool was reduced as aresult of fruiting and this prevented allocation of N
to leaves in subsequent years. Together with ourprevious finding that floral buds have two-foldhigher N content than leaf buds (Han et al. 2008),
preferential allocation of resources to seeds in the

Introduction
Natural forests occupy one third of theearth’s land surface and account for two third of dry
matter production among terrestrial plants. However,its natural regeneration is often impeded by low seedavailability, especially in mast seeding species thatrefers to the synchronous intermittent production oflarge seed crops in a population of perennial plants(Kelly 1994). Moreover, reproduction is a key lifehistory event in respect of maintaining biodiversity
by providing energy to pollinators and predators.
However, the physiological mechanism of mastingremains poorly understood (Isagi et al. 1997, Satakeand Iwasa 2000). Here, we represent related results
achieved from resource dynamics of non-structuralcarbohydrate (NSC) and nitrogen (N) in Fagus
species using both natural and pulse-labellingisotopic approach.
Results
1. Nitrogen dynamics in relation to masting

Nitrogen demand for masting events wasestimated from litter traps monitored in two Fagus
crenata stands located at 900 m and 1500 m
elevation (Han et al. 2014). Average N amount infruit litter (cupules and nuts) was about equivalent to
the N quantity in leaf litters in a full masting year
(Fig. 1), indicating that masting events consumes

Figure 1: Inter-annual variations in the amount of
nitrogen (N) in fruit and leaf litters in a Fagus
crenata stand located at 1500 m elevation.

36

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –of masting (Abe et al. 2016), these results
demonstrate that N is the key limiting resource for
masting events in F. crenata.

mast year reduced the availability of resources forflower primordium formation, and this may haveaccounted for the poor seed production in thefollowing year that is a typical character of masting
event (Hilton and Packham 2003). These results
indicate that masting affects N cycle dynamics inmature F. crenata forests.
To further understanding how mature trees
acquiring N resources from soil and the influence ofmasting on N allocation, pulse 15N labeling wasapplied to fruiting and non-fruiting trees of F.
crenata in the 900 m stand (Han et al. 2017, Han etal. 2020). When labelling was conducted in latespring after the leaves have completely unfurled,
fruiting leads to greater 15Nexcess uptake from the soil
during the first month following application (Fig. 2).
Moreover, cupules absorbed more 15Nexcess even thanleaves in fruiting trees, whereas 15Nexcess in leaves
was about the same between fruiting and nonfruiting trees. On the other hand, half of 15N wasrecovered in nuts at the end of the growing season.
These results indicate that 15N uptake was driven by2.

NSC dynamics in relation to masting

Mature trees store substantial amounts of
NSC, mainly starch and soluble sugar, in woodorgans (Hoch et al. 2003). At the whole-tree level,
NSC buffers the asynchrony of carbon supply anddemand, and thus plays important functions in plant
growth and survival (Chapin et al. 1990, Kozlowski
1992, Hartmann and Trumbore 2016). Its role in
reproduction remains poorly understood because it
is difficult to discriminate NSC storage from new
photo-assimilate in mature tall trees. We validated
that bulk leaf δ13C measured with high temporal
resolution could be used as a proxy for new photoassimilate (Han et al. 2016). Using this proxy, andparallel measurements of seasonal δ13C pattern infruits of F. crenata, we found that fruit production
relies on NSC storage until the middle of thegrowing season then shifts to new photo-assimilates(Han et al. 2016), at which time growth of branchand stem have finished (Kabeya et al. 2017). These
results complemented our previous finding using alarge-scale, continuous 13C labeling technique in amature forest (Hoch et al. 2013), from which seedsof three deciduous tree species were demonstrated to
be produced by new photo-assimilate but not by
NSC storage.
In order to elucidate whether masting
depletes the individual NSC storage pool, seasonaland inter-annual variations in NSC concentrations inbranchlets, stems and coarse roots of Fagus crenata
were measured in the aforementioned stands over
five years after the full masting event (Kabeya et al.
2021). Individual NSC storage pools decreased after
leaf fall in the year of the full masting andreplenishment was then observed in the next 2-3
years in both stands. In addition, temporary
reduction in starch concentration in summer due tomoderate fruiting was found in coarse roots andstems (Fig. 3), but could not be detected after leaf
fall, indicating refilling of NSC storage pool during
the growing season. These results indicate thatwhether a decrease in NSC storage caused bymasting occurs or not depends on the quantity offruit production. This also highlights the importanceof timing in detecting masting-related NSC
variations.

Figure 2: Pulse labelling-derived isotopic nitrogen(15Nexcess) for leaves (Lf), current-year branches(Br), cupules (Cp) and nuts (Nt) represented atcurrent-year shoot level averaged from three-yearold branches sampled from upper crowns of fruiting
trees (F) and (b) non-fruiting trees (NF) of 89-90year-old Fagus crenata. Values shown are mean ±SE (n = 3). The date of 15N labelling was 31 May
2011.
sink strength. In addition, translocation of N fromcupules and senescing leaves contributed also to nut
ripening. Together with the effect of N level on
flowering gene expression (Miyazaki et al. 2014)
and validation of both N and carbon in the resource
budget model to explore the proximate mechanism
37

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –-1

Stem starch (mg g )60

Reference

3cm
4cm

1cm
2cm

Abe T, Tachiki Y, Kon H, Nagasaka A, Onodera K,
Minamino K, Han Q, Satake A (2016)
Parameterisation and validation of a resource
budget model for masting using spatiotemporal
flowering data of individual trees. Ecology
Letters 19:1129-1139.
Chapin FS, Schulze E-D, Mooney HA (1990) The
ecology and economics of storage in plants.
Annual Review of Ecology and Systematics
21:423-447.
Furze ME, Trumbore S, Hartmann H (2018)
Detours on the phloem sugar highway: stem
carbon storage and remobilization. Current
Opinion in Plant Biology 43:89-95.
Han Q, Kabeya D, Iio A, Inagaki Y, Kakubari Y(2014) Nitrogen storage dynamics are affectedby masting events in Fagus crenata. Oecologia
174:679-687.
Han Q, Kabeya D, Iio A, Kakubari Y (2008)
Masting in Fagus crenata and its influence on
the nitrogen content and dry mass of winterbuds. Tree Physiology 28:1269-1276.
Han Q, Kabeya D, Inagaki Y (2017) Influence ofreproduction on nitrogen uptake and allocation
to new organs in Fagus crenata. Tree
Physiology 37:1436-1443.
Han Q, Kabeya D, Inagaki Y (2020) Reproduction
affects partitioning between new organs of apulse of 15N applied during seed ripening inFagus crenata. New Forests 51:739-752.
Han Q, Kagawa A, Kabeya D, Inagaki Y (2016)
Reproduction-related variation in carbon
allocation to woody tissues in Fagus crenata
using a natural 13C approach. Tree Physiology
36:1343-1352.
Hartmann H, Trumbore S (2016) Understanding theroles of nonstructural carbohydrates in forest
trees – from what we can measure to what wewant to know. New Phytologist 211:386-403.
Hilton GM, Packham JR (2003) Variation in themasting of common beech (Fagus sylvatica L.)
in northern Europe over two centuries (18002001). Forestry 76:319-328.
Hoch G, Richter A, Körner C (2003) Non-structuralcarbon compounds in temperate forest trees.
Plant, Cell & Environonment 26:1067-1081.
Hoch G, Siegwolf RTW, Keel S, Körner C, Han Q(2013) Fruit production in three masting tree

40
20

Ma
y
Jul
Se
Nop
v

Jun
J
Seupl
No
v

0
900 m

1500 mFigure 3: Seasonal variations in starchconcentrations for stem cores of different depths
starting from the cambium at the 900m and 1500m
sites in 2007, a year with moderate fruiting at the1500 m site only. Values represent the mean ± SE
for three to five individuals.
This fruiting related reduction in starchconcentration in summer was found in all sampled
stem cores, representing tree rings formed 20 years
ago (Fig. 3). Starch is stored in ray and axial
parenchyma, and new photo-assimilates are mixed
with old NSC in older rings (Richardson et al. 2013,
Richardson et al. 2015, Furze et al. 2018). Our
results provide the first evidence that starch in deep
sapwoods is available for masting events, which isconsistent with it being accessible for tree growthand metabolism. Together with a higher starchstorage pool in roots than in branchlets, these results
indicate that starch stored in roots and stems isavailable and supports life-history traits such as
masting events, as a “pinch-hitter” when asynchronyof carbon supply and demand occurs.
Acknowledgment
The authors would like to thank Professor
Emeriti of Y. Kakubari and Ch. Körner for various
supports, members of FFPRI and University of
Shizuoka who has helped field investigation. This
work was supported by JSPS KAKENHI(C18580155, B21380103, B25292094, A26251042,
17H03837, 20H00436), OECD Co-operative
Research Program, and Grant for Environmental
Research Projects from The Sumitomo Foundation(113250).

38

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Miyazaki Y, Maruyama Y, Chiba Y, Kobayashi MJ,
Joseph B, Shimizu KK, Mochida K, Hiura T,
Kon H, Satake A (2014) Nitrogen as a key
regulator of flowering in Fagus crenata:
understanding the physiological mechanism of
masting by gene expression analysis. Ecology
Letters 17:1299-1309.
Richardson AD, Carbone MS, Huggett BA, Furze
ME, Czimczik CI, Walker JC, Xu X, Schaberg
PG, Murakami P (2015) Distribution and mixingof old and new nonstructural carbon in two
temperate trees. New Phytologist 206:590-597.
Richardson AD, Carbone MS, Keenan TF,
Czimczik CI, Hollinger DY, Murakami P,
Schaberg PG, Xu X (2013) Seasonal dynamics
and age of stemwood nonstructuralcarbohydrates in temperate forest trees. New
Phytologist 197:850-861.
Satake A, Iwasa Y (2000) Pollen coupling of forest
trees: forming synchronized and periodic
reproduction out of chaos. Journal of
Theoretical Biology 203:63-84.

species does not rely on stored carbon reserves.
Oecologia 171:653-662.
Isagi Y, Sugimura K, Sumida A, Ito H (1997) How
does masting happen and synchronize? Journalof Theoretical Biology 187:231-239.
Kabeya D, Iio A, Kakubari Y, Han Q (2021)
Dynamics of non-structural carbohydrates
following a full masting event reveal a role forstored starch in relation to reproduction in
Fagus crenata. Forestry Research
1:doi:10.48130/fr-2021-0018.
Kabeya D, Inagaki Y, Noguchi K, Han Q (2017)
Growth rate reduction causes a decline in theannual incremental trunk growth in masting
Fagus crenata trees. Tree Physiology 37:14441452.
Kelly D (1994) The evolutionary ecology of mast
seeding. Trends in Ecology and Evolution
9:465-470.
Kozlowski TT (1992) Carbohydrate sources andsinks in woody plants. The Botanical Review
58:107-222.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Anchorage and Stability Characteristic of Forest Tree Species inTropical Region and a Suitable Wind Damage Assessment Mode for
Planted Forest in Vietnam
Nguyen Trong Minh 1,2,3,*, Fukui Sho 1, Iwama Shintaro 1,
Dang Thinh Trieu 4, Koyano Kaita 1, Hizomi Mizunaga 1

1 Faculty of Agriculture, Shizuoka University, JapanThe United Graduate School of Agriculture, Gifu University, Japan
Faculty of Silviculture, Vietnam National University of Forestry, Vietnam
4Silviculture Research Institute, Vietnam*Corresponding Author: minhnguyen1408@gmail.com
2
3

SUMMARY
Wind is considered as a major disturbance agent in forests and plays an important role in the dynamics ofmany forest ecosystems. This study focused on two purposes, including i) to investigate the anchorage and stabilityof forest tree species related to wind damage by conducting pulling experiments to determine the mechanical tree
resistance to over-turning for 17 tree species in planted and natural forest in Vietnam; ii) to examine the efficiency
of using a fluid model in complex topography to evaluate the wind hazard model by identifying the most important
predictor variables (e.g. stand attributes, topographic and wind speed) in this model, in the tropical zone. This study
found that the tree stability was strongly affected by tree characteristics and site conditions, in which the hazard
ratio increased as tree size in-creased, and was driven by tree species, site, and buttress. We applied an empiricalmethod combining airflow simula-tions, and historical records of wind disturbance within a GIS in middle Vietnam.
The wind damage probability of planted forest (Acacia hybrid) was associated with stand characteristics, windvelocity, and topographic characteristics found in the test model. Higher damage probabilities were found in stands
with taller tree height, on higher slopes, and with higher wind speed. Our results, therefore, provide evidence forusing fluid models in complex topography to estab-lish the wind hazard model.

Keywords: Anchorage and stability; Tropical zone; Wind hazard model; Fluid model; Complex topography

were to examine the efficiency of using a fluid modelin complex topography to evaluate the wind hazard
model by identifying the most important predictor
variables for statistical modeling of the probability ofwind risk. Our research on integrating GIS,
aerodynamics, and satellite image analysis to establish
a logistic model to clarify affected factors to determine
wind hazard for A. hybrid plantations is the first suchapproach in Vietnam. The results will contributetowards knowledge relating to mechanistic models oftree failure, and the further development of mechanistic
wind damage risk models.

Introduction
Strong tropical storms that occur in Asia
cause serious natural disturbances to forest ecosystems
because wind-induced damage can substantially change
forest ecosystem functions, owing to changes in forest
structure (Gardiner 2007). Many researches wereconducted to discuss the impacts of wind damage indetail for many different regions, forest types, and thefactors affecting the levels and types of damage.
However, there are limited researches related to winddamage on forest in the sub- and tropical zone
(Gardiner 2021). Therefore, we processed this researchwith the main focus on increasing the understanding ofprocesses behind the occurrence of wind-induced
damage in the tropical region.
In this research, the anchorage and stabilityof trees were studied by applying the Cox regression
model, and based on relationships between maximum
turning moment (TMmax) and site characteristics, tree
species, tree weight (TW), and tree size (multiplied treeheight (H) and square of diameter at breast height(DBH2) for 17 forest tree species in planted and naturalforest, in North and Mid of Vietnam. The impact ofthese factors on the stiffness index, with a GLMM, wasalso investigated. Besides that, we conducted a study in2017 to present an analysis of wind damage data at aregional scale in the A. hybrid plantation forest, using
an empirical method. The main objectives of this study

Material and methods
1. Study on anchorage and stability

Four research sites were selected to conducttree-pulling experiments in Vietnam located in thenorth of Vietnam (Dai Lai, Cuc Phuong) and central
Vietnam (Thanh Hoa, Quang Tri). Pulling experiments
using wire and robe were conducted according to
Nguyen et al. 2021 to detect the TMmax of trees. Table
1 indicated the pulled tree information for a total of 107
pulling trees.
The TMmax, stiffness index (SI), and buttressindex (BHI) were shown in Eq.1, Eq.2, and Eq.3 asfollows:

40

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –77 plots with a size of 1000m2) and an evaluation
dataset (30% of total sample plots, 33 plots), were used
for estimated the tree height growth curve in R software
applied with the ‘rstan’ package. After validating
participated equations, the most suitable equation wasused for calculating Hdom for A. hybrid plantation,then to build the Digital Surface Model (DSM).
Prediction of wind properties: Windspeed
and wind turbulence were estimated using RIAMCOMPACT (Uchida 2008) at an approximate height of
10 and 15 m above the canopy. The research used
30×30m grid data from DSM topography. Power law of
1/7 of wind profile, an initial wind speed of 10 ms-1,
and a buffer zone with a distance of 2.5km were usedas parameters for the simulation.
Estimation of wind damage locations: The‘randomForest’ package in R was used for building theclassification model, in which damage condition(damage or no damage) was the response variable,
while the predictor variables were shown in Table 3.
Predicting the wind damage in A. hybrid
plantations: A logistic regression (Collett 2003) wasused to assess the probability of wind disturbance, inwhich, predictor variables were calculated using
ArcGIS and then input to R to predict damage
probability (Table 3).

TMmax = F × Hcable (cosαsinβ + sinαcosβ) + TW x G (1)SI = TMmax / β(2)

BHI = BH / BL

(3)

Where: F (kgf) is the maximum load, α(degree) is the angle between the wire and thehorizontal line from the anchor tree’s stem base, β(degree) is the mean stem angle from the original
position of the stem at the maximum force, and Hcable(m) is the attached wire height; BH is buttress height;
BL is buttress length.
The “coxme”, “glmm” and “lme4” packages
were selected to investigate factors impacted on the riskof tree failure and SI in R. All model formulation were
presented in Table 2.
2. Study on wind hazard for A. hybrid plantation
The area of interest, located in Quang Tri,
Vietnam, measures an area of 10×10 km2 with anelevation ranging 37–550m and is located in yellow soil
(Ferralsols). There were four main data processing: i)tree height estimation to predict the dominant height(Hdom) of A. hybrid stands, ii) prediction of windproperties via RIAM-COMPACT, iii) estimation ofwind damage by classifying LANDSAT-8 images, andiv) wind risk model for A. hybrid plantations (Figure 1).
Tree height estimation: Two data groups,
included a training dataset (70% of total sample plots,

Table 1. Description of sample trees in the tree pulling experiments
Site

SpeciesH (Std)(Unit: m)
19.9-26.2
6.6-19.7
14.6-27.3

Number of trees(Unit: trees)
12
12
12

Forest typeA. hybrid
P. caribaea
E. urophylla

Dbh (Std)(Unit: cm)
11.2-23.1
8.6-22.7
9.8-25.9

Dai Lai

Thanh Hoa

A. hybrid
12 species

13.2 - 29.9
13.2 - 29.9

11.8 - 23.5
11.8 - 23.5

5
17Plantation
NaturalCuc Phuong

Parashorea & Pometia

7.5-24.4

9.2-25.0

19

NaturalQuang TriA. hybrid

8.9-22.4

8.6-24.6

30

Plantation

Plantation
Plantation
Plantation

Table 2. The model formulation for predicting the hazard ratio of tree failure and the stiffness index (SI)Model code
(1.1)
(1.2)
(1.3)
(1.4)
(1.5)
(1.6)
(1.7)

Model formulation
h(t,x) = h0(t)exp(a*Xi)h(t,x) = h0(t)exp(a*Xi + csite)
h(t,x) = h0(t)exp(a*Xi + b*Species)h(t,x) = h0(t)exp(Xi ※ Species)lgSI = a*Xi + b
lgSI = a*Xi + csite
lgSI = a*Xi + b*Species

Regression model types
The Cox regression
The Cox regression
The Cox regression
The Cox regression
GLMMs
GLMMs
GLMMs

In which: Xi is an explanation factor; csite is the random effect; ※ represents the interaction between explanatory
variables and species; h(t,x) is the hazard function; t is considered as the moment of tree failure.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Figure 1. The flowchart describes data processing in this study
Table 3. The model form used for estimating the wind damage probability
Mode code
1.7

Model form
logit (p) ~ a + b×Age + c× Hdom + d× WS(WS3)

1.8

logit (p) ~ a + b×Age + c× Hdom + d× Turblence intensity

1.9

logit (p) ~ a + b×Age + c× Hdom + d×Elv + e×Slo

1.10

logit (p) ~ a + b×Age + c×Hdom + d×WS (WS3) +e×Turblence intensity + f×Elv + g×Slo

Note: p is the probability of a stand being disturbed; Age is the stand age; Hdom is the dominant height of a stand; Elv iselevation; Slo is the slope; and a, b, c, d, e, f, g are the parameters of model.

differences related to soil type and thickness andstrongly affected the estimated TMmax. The main soil
type in Dai Lai was Ferralic Acrisols with a thicknessof around 70 cm, whereas in Thanh Hoa the one was
Ferralsols derived from basalt with thickness from 30
to 50 cm. This study also provided a graph of TMmax forA. hybrid by groups of soil type (Figure 2).
Effects of species on the risk of tree failure: It
was found that species have a significant impact on therisk of failure. A difference was clearly seen between E.
urophylla and A. hybrid, with a probability of tree
resistance being lower in E. urophylla (p < 0.05)(Figure 3a). The survival probability dramaticallyreduced around 30 kNm for all species in the selected
mode. However, there was no significant difference
between P. chinesis and P. pinata when the pulling
experiment was conducted in the natural forest (Figure3b). The findings were following Ribeiro (2016) inwhich TMmax varied among species groups in the
Amazon forest based on tree size (e.g. DBH).

Results and discussion
1. Anchorage of forest tree species in the tropical region

Effects of tree characteristic on the risk oftree failure: Significant correlations were foundbetween the risk of tree failure and explanatory factors(e.g. tree size, TW, H and DBH with a highly
statistically significant coefficient (p < 0.05)), in which,the risk of failure can reduce with an increase of thesefactors. The best model was selected in which tree size
was the most suitable indicator for estimating the riskof tree failure. This pulling test confirmed the effect oftree size on TMmax, as suggested by previous in whichtree stability increased substantially with an increase intree size (Ribeiro et al. 2016).
Effects of site on the risk of tree failure:
Results on the tested model showed that the difference
between sites was significant, in which, the hazard ratio
was higher in Thanh Hoa than in Dai Lai. The

42

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Figure 2. The hazard ratio related to sites (a) and TMmax by groups of soil type for A.hybrid (b).

a)b)

Figure 3. Species effects on the probability of tree resistance in Dai Lai in plantation forest (a) and in Cuc Phuong in
natural forest (b).

root anchorage. These results, therefore, were clear
evidence for the impacts of the buttress and were
contributed to predicting the TMmax and SI. Thesefindings also confirm the discussion of Crook (1996),
in which the author found that the buttress height was
one main factor contributing to the root anchorage of the tree.

Effects of tree characteristic on SI: Tree size,
tree species were also strongly associated with SI. The
SI was increased with the increase of tree size (positive
estimated parameters; not shown here). The change ofthese factors led to the change in SI, in which SI
increased with an increase in tree size and showed clear
differences among species groups. The current results
agree with the findings of Kamimura’s research (2012).
However, together with site effects, the current study
proposed scientific evidence for the impact of species.
The SI was also strongly dependent on species groups,
for example, A. hybrid had a high SI value comparedwith P. caribaea (Figure 4a). This signal was clearlyseen in recorded videos while pulling these tree species.
The P. caribaea’ stem still continued to curve, whilethat of A. hybrid was uprooted or broken. Figure 4b
presents images of P. caribaea stem and tap-root of E.
urophylla under maximum load in the pulling test.
Effects of buttress on TMmax and SI: The
buttress was associated with TMmax and SI, and strongly
affected tree stability along with effects of tree size, inwhich TMmax and SI increase with the increase of BHI.
This suggests that the existence of buttress affected thetree resistance to uprooting or breaking the stem andsupported the tree stability (positive Coef., Table 4),
whereas this one decreases the angle of the stem at theTMmax in the pulling experiment. The model including
BH and tree size was the best model for SI and thenshows the buttress role as one of the critical factors fora)b)
Figure 4. Stiffness index varied among tree species (a)and the flexible stem of P. caribaea (upper image) and
tap-root broken of E. urophylla (lower image) (b).

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Table 4. The effect of buttress characteristic on TMmax and stiffness index
Variables
Intercept
Tree size
BH
Intercept
Tree size
BHI

Turning moment
Coef.
Sig.
11.24
0.02(*)
32.42
0.00(*)
0.84
0.83
8.80
0.04(*)
29.83
0.00(*)
4.81
0.06(.)

AIC
126.32121.69

Stiffness index (SI)
Coef.
Sig.
-0.82
0.03(*)
2.60
0.00(*)
0.69
0.05(*)-0.81
0.04(*)
2.81
0.00(*)
0.45
0.06(.)

AIC
40.06

41.04

BH is buttress height; BHI is buttress index; Coef.is the regression coefficient; Confidence level:‘*’ 0.05; ‘.’ 0.1.

whereas stand age and elevation seemed to be lesssusceptible (Table 5). Stands growing on soil type Fs
were more likely to be stable against wind damage
than those on soil type Fd (there was a significant
difference, between the two soil types, p < 0.5).
Since it had the lowest AIC value, the south direction
was considered to be the wind flow direction, which
was used for predicting wind speed when validating
the final model.

2. Wind hazard model for A. hybrid plantation forest

By examining five components extracted
from the LANDSAT 8 images at the times before andafter typhoon Doksuri 2017 hitting Vietnam, thestudy provided a good qualitative classification for
estimating damaged or undamaged areas, with highoverall accuracy (95.83%) and kappa index (0.88)(Figure 5). The results from image classification,
therefore, meet the aim of detecting the disturbanceareas caused by strong wind of the typhoon and thenwere used to provide a dataset for building a windhazard model.

Table 5. Estimated model coefficients of thegeneralized linear model for the probability ofoccurrence of wind damage.

a)b)

Explanatory
variable

Height above canopy at 15m (AIC =309.66)
Coef.
SE
Sig.

(Intercept)
Sty Fs§
A
Hdom
WS3
Slo
Ele

8.869
-0.809-0.919
3.009
0.252
0.043
-0.007

2.946
0.334
0.329
1.050
0.152
0.021
0.002

0.003**
0.015*
0.005**
0.004**
0.097.
0.044*
0.003*: Soil type Fd was used as the basis for comparison.

§

Confidence level: ‘***’ 0.001; ‘**’ 0.01; ‘*’ 0.05; ‘.’Figure 5. The importance of variables (a) and ROC

0.1.

curves (b) of the model created using the RandomClassification performance tests showedthat the wind damage probability model performed
moderately. A higher probability of wind disturbancefor taller trees/stands, higher wind speeds, andforests on higher slopes was found. The inclusion ofstand age had a negative effect on damage
probability (p < 0.05). Increasing stand age,
therefore, may lead to a decrease in wind damage
because of the decline in H/DBH (Gardiner 2013).
The taller stands (tree height) were more

Forest algorithm.

The final model for predicting winddamage risk selected using the lowest AIC value,
included Sty, A, Hdom, WS3, Elv, and Slo. In thismodel, WS3 was calculated as the wind direction
from south to north, and the height at 15 m above thecanopy. The model showed that Hdom, WS3, and Slo
were associated with high damage probabilities,

44

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –images, and a wind condition prediction model
enabled an analysis of wind damage prediction
concerning stand and topography.

likely sensitive to wind damage (p < 0.05). If a tree
is taller, it is more likely to fall over because it is lessstable (Dhubhain et al. 2001). In the current study,
Hdom was selected since it was better than otherfactors as a measure of wind vulnerability (Gardiner2013).
The current study found a significant windspeed predictor (WS3 at 15 m high above the canopy),
with increasing WS3 leading to a greater probabilityof wind damage. Higher wind speed causes moredamage in a stand (Schindler et al. 2012). The results
concurred with the findings of Uchida (2008), in
which the forest stands were damaged not only bywind speed enhanced by terrain conditions but also
by the fluctuations in wind velocity.
Concerning soil type, the results showedthat the stands on Fs were more likely to be stable ina tropical storm than those on Fd (p < 0.05). To
validate the model result on the effect of soil type,the physical stability TMmax of A. hybrid wascompared on both soil types. The data analysis
revealed that TMmax in the model of tree failure wasgoverned by tree size and soil type, and the stabilityof trees in Fd was smaller than those in Fs with thesame tree size (Figure 2b). Therefore, the effect ofsoil type on modeling forest wind damage was
supported by this mechanistic measurement. The
coincidence of the statistical model from satellite
image with mechanistic measurement suggested theeffectiveness of the model as the evaluating factor
related to the wind damage.

Acknowledgments
The authors would like to thank thecommunity of Dai Lai, Lang Chang District, Cuc
Phuong, and Quang Tri for their cooperation, and the
local government officers for their kind support. The
study was financially supported by the Laboratory ofSilviculture, Faculty of Agriculture, Shizuoka
University. The authors would like to thank thestudents of the Silviculture Laboratory for their help
in the statistical approach and their support of theexperiment.
Reference
Crook, M. J., Ennos, A. R., & Banks, J. R. (1997).
The function of buttress roots: a comparative
study of the anchorage systems of buttressed(Aglaia and Nephelium ramboutan species) andnon-buttressed (Mallotus wrayi) tropical trees.
Journal of Experimental Botany, 48(314),
1703–1716.
Dhubháin, A., et al. (2001). The initial developmentof a windthrow risk model for Sitka spruce in
Ireland. Forestry 74: 161–170.
Gardiner, B. (2021). Wind damage to forests andtrees: a review with an emphasis on planted andmanaged forests. Journal of Forest Research,
26:4, 248-266.
Gardiner, B. et al. (2013). Living with Storm
Damage to Forests. European Forest Institute,
Joensuu.
Kamimura, K., et al. (2012). Root anchorage ofhinoki (Chamaecyparis obtuse Sieb. Et Zucc.
Endl.) under the combined loading of wind andrapidly supplied water on soil: analyses basedon tree-pulling experiments. European Journalof Forest Research 131: 219 – 227.
Nguyen, et al. (2021). Anchorage and stability of
three major plantation forest species in Vietnam.
Journal of Tropical Forest Science 33(1), 30–40
Quine, C & Gardiner B. (2007). Understanding
how the interaction of wind and trees results inwindthrow, stem breakage and canopy gap
formation. Pp 103–155 in Johnson E &Miyanishi K (eds) Plant Disturbance Ecology:

Conclusion
The results provided the Cox hazard
proportion that can be used to predict the risk of tree
failure and also showed a comparison of anchorage
between forest tree species in a tropical zone. The
data analysis revealed that the hazard ratio in themodel of tree failure and SI were governed by tree
size attributes, and varied among tree species andsite properties. In addition, the current study
analyzed the wind damage probability in complex
terrain for A. hybrid plantation forests in Vietnam byintegrating an aerodynamics model for simulating
wind speed, wind disturbance records, and GIS data
at a landscape level. The wind hazard ratio of A.
hybrid plantations was associated with standcharacteristics, wind velocity, and topographic
characteristics. The combination of GIS, satellite

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Uchida, T. & Ohya, Y. (2008). Micro-siting
technique for wind Turbine generators by usinglarge-eddy simulation. Journal of Wind
Engineering and Industrial Aerodynamics 96:
2121–2138.

The Process and the Response. Elsevier,
Amsterdam.
Ribeiro, G.H., et al. (2016). Mechanicalvulnerability and resistance to snapping anduprooting for Central Amazon tree species.
Forest Ecology and Management 380: 1–10.
Schindler, D., et al. (2012). Wind effects on trees.
European Journal of Forest Research 131: 159–
163.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Hybrid Breakdown as a Result of Up-regulated Expression of Defenserelated Genes in the Hybrid Seedlings of Japanese Flowering Cherry
1

Momi Tsuruta 1*, Chunlan Lian 2, Yuzuru Mukai 3

Department of Forest Molecular Genetics and Biotechnology, Forestry and Forest Products
Research Institute2

Asian Research Center for Bioresource and Environmental Sciences, Graduate School of
Agriculture and Life Sciences, The University of Tokyo

3

Faculty of Applied Biological Sciences, Gifu University*Corresponding Author: m.tsuruta.lfme@gmail.com
SUMMARY
Knowledge of post-zygotic hybrid incompatibility is essential to understanding speciation. Although
the genes and molecular mechanisms involved in hybrid incompatibility are being elucidated in model plants
and crops, the information on woody non-model plants is lacking. In the seedlings of a cross between the most
famous ornamental cherry cultivar Cerasus × yedoensis ‘Somei-yoshino’ and its closely related wild species
Cerasus itosakura, we discovered a hybrid incompatibility characterized by a phenotype in which growth stops
after the expansion of the first true leaves and eventually dies. To elucidate the molecular mechanisms related
to this seedling necrosis, we performed a comprehensive expressed genes analysis on normal-growth andnecrotic weak-growth (SW) hybrid seedlings. The RNA-seq results showed over 1,500 differentially expressed
genes (DEGs) specified for the SW. Numerous genes associated with plant defense response, such as
pathogenesis-related genes, and several receptor-like protein kinases were included in SW-specific up-regulated
DEGs. The Gene Ontology (GO) enrichment analysis also showed the significant association of “defense
response” in SW seedlings. On the contrary, the reduction of genes expression related to photosynthesis was
also observed in the SW. Our results suggest that an excessed defense-related gene expression suppresses theother physiological activities, resulting in growth failure as an autoimmune response in hybrid cherry seedlings.
Keywords: Autoimmunity; Defense response; Hybrid necrosis; Somei-yoshino; Transcriptome

is maintained despite the frequent interspecific geneflow requires elucidating the mechanism of postzygotic reproductive isolation, such as hybrid
sterility, weakness, necrosis, and hybrid breakdown(hybrid incompatibility that occurs in later
generation after F1).
In the model plants Arabidopsis and rice,genes underlying the post-zygotic reproductive
isolation have been identified (Bomblies, K. et al.,2007; Yamamoto, E. et al., 2010). Here, the genes
derived from other lineages interacted with anotherlocus located in a cluster of receptor-like kinase(RLK) genes with leucine-rich repeat (LRR) domain.
The RLKs are associated with the recognition of
external stimuli, such as various pathogens, andprecipitate downstream defense responses. These
excessive defense reactions resulted in the hybrid
seedling growth failure as an autoimmune response.
Currently, the involvement of defense-related genes

Introduction
Flowering cherry (Rosaceae, Cerasus) is
one of the most famous ornamental trees in Japan.
More than 10 wild species are distributed in smallisland Japan and their habitats often overlap. Since
the 18th century, various cultivars have been
produced, many of which are originated frominterspecific hybridization (Ohba, H. et al., 2007;
Kato, S. et al., 2014). Because no physiologicalinterspecific incompatibility has been observed
before fertilization, frequent pollen transfer has been
observed among cherry species. Thus, floweringcherry is considered to be a taxon with low barriers
to interspecific hybridization. Nevertheless, distinct
morphological and genetic differentiation of eachspecies has been observed (Ohba, H. et al., 2007;
Kato, S. et al., 2014). Understanding how speciation47

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –and autoimmunity in hybrid incompatibility has
been observed in several plant taxa (Wan, W.L. et al.,2021). However, these studies are limited to herbal
model plants and crops, and knowledge about hybrid
incompatibility in woody species remains lacking.
In our previous study, we observed that thelethal growth failure (necrotic) phenotype appearedin about half of the seedlings from crosses between
cherry cultivar Cerasus × yedoensis (Matsum.)
Masam. et S.Suzuki ‘Somei-yoshino’ and wild
cherry species C. itosakura (Siebold) Masam. et
S.Suzuki (Tsuruta, M. and Y. Mukai, 2015). ‘Someiyoshino’ is thought to have originated from a crossbetween two wild species, C. speciosa (Koidz.)
H.Ohba and C. itosakura (Ohba, H. et al., 2007;
Kato, S. et al., 2014; Shirasawa, K. et al., 2019).
Given that the seedlings from the crossing of‘Somei-yoshino’ and C. itosakura can be considered
as a backcross progeny at the species level, theseedling growth failure may therefore be due tohybrid breakdown. In the present study, toaccumulate knowledge on the physiologicalmechanisms of hybrid necrosis in tree species, we
conducted a comprehensive gene expression
analysis for the normal-growth (SN) and necrotic
weak-growth (SW) seedlings. We clarified thephysiological mechanism responsible for the lethal
hybrid seedlings of flowering cherries. Here, we also
attempted to show that the autoimmune response due
to high expressions of defense-related genes is acommoncausalphenomenon
inhybridincompatibility even in forest woody plants.

pollination procedures and seed handling protocols
followed the methods described in the work of
Tsuruta and Mukai (2015). After three months of wet
and chilling incubation, seeds were planted on avermiculite bed. Of the 31 germinated seedlings, 12(38.7%) grew normally, and 13 (41.9%) showed thelethal phenotype. The phenotype was characterized
as the plants that grew until the emergence of firsttrue leaves but failed to further develop (Fig. 1).
Finally, the SW seedlings died after several weeks ora month. Additionally, we collected naturally
pollinated seeds from a wild cherry tree, C.
jamasakura (Siebold ex Koidz.) H.Ohba, andseedlings were obtained after treating the seeds as
described above. The necrotic phenotype was neverobserved in these wild seedlings. Approximately two
weeks after germination, SN, SW, and control wild
cherry (CJ) seedlings were collected with three
replicates each, immediately frozen by liquid N2,
and stored at –80 °C until RNA extraction.
2. RNA extraction and sequencing

From the seedling samples, total RNA wasextracted using the modified CTAB method. The
qualified RNAs were used for further analysis.
RNA-seq was performed following Macrogen’s
regular workflow (Macrogen Inc, Seoul, South
Korea). Briefly, high-throughput sequencing
libraries were constructed using the TruSeq strandedmRNA Library Kit (Illumina, San Diego, CA, USA)
following the quality control of RNA. Each library
was sequenced by the Illumina platform(NovaSeq6000) at 100 or 150 bp pair-end reads.

Materials and Methods
3. RNA-seq data processing and identification of DEG

1. Normal and necrotic plant materials

The sequenced reads were first analyzed
with fastp (Chen, S. et al., 2018) for quality checking
and trimming of low-quality base and read. The
genome sequence of ‘Somei-yoshino’ (Shirasawa, K.
et al., 2019) was used as a reference. The genome
consists of two phasing contigs derived fromancestral C. itosakura (CYE_SPA) and C. speciosa(CYE_ SPE). The clean reads were mapped to thereference genomes using bowtie2 (Langmead, B.
and S.L. Salzberg, 2012). Uniquely mapped reads
were used for gene expression quantification withfeatureCounts (Liao, Y. et al., 2014). The
differentially expressed genes (DEGs) between the
two treatments of CJ, SN, and SW were identified
using a TCC-GUI pipeline (Su, W. et al., 2019).

We artificially pollinated C. itosakura(pollen donor) to ‘Somei-yoshino’ (seed parent). The

Figure 1: The normal-growth (A) and necrotic
weak-growth phenotypes (B) of the hybrid
seedlings approximately two weeks after
germination.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Significant DEGs that were common in CJ vs. SW
and SN vs. SW but not present in CJ vs. SN were
determined as SW-specific DEGs.

CYE_r3.1SPA8_g020410 and CYE_r3.1SPE8_g024490, paralogs of the basic PR protein 1 ofArabidopsis (AT2G14580), were highly upregulated in SW seedlings. Several other PR genes,
such as thaumatin-like protein (TLP), a member ofPR5, and Bet v1 superfamily gene belonging to
PR10, were also found in the up-regulated DEGs.
Another remarkable defense-related gene was amember of RLK. Of the CYE_SPE genome genes,
CYE_r3.1SPE4_ g004570, which has RLK and LRR
domains, was the most up-regulated DEG. OtherLRK genes, including cysteine-rich LRK (CRK),
were also found in the DEG lists (Table 2).

4. Annotation of DEGs and Enrichment Analysis

Gene information, such as annotations,
BLAST results, InterPro, Gene Ontology (GO)terms, and GO accessions, were searched in the
Genome Database for Rosaceae website(https://www.rosaceae.org). The R package goseq
v1.38.0 (Young, M.D. et al., 2010) was used to
identify significantly enriched GO terms in the SWspecific up- and down-regulated DEGs.
Table 1: Gene expression number in eachreferenced genome for the normal (SN), necrotic
weak growth (SW), and wild cherry (CJ)
seedlings.
Reference
genomeTranscri
bable
gene

‘Someiyoshino’

95,07663,483 63,896 62,580

CYE_SPA48,28033,097 33,073 31,470 28,484

CYE_SPE

46,79630,386

Number of expressed gene

SN

SW

30823

CJ

All
55,749(78.8%)

31,110 27,265

Results
1. Gene expression profiling and detection ofcharacteristic DEGs in SW seedlings

From the RNA-seq, over 50 M sequencing
reads with 8.1–13.2 G bases per seedling sample
were obtained. Over 97.8% of the quality-filteredclean reads were mapped to the ‘Somei-yoshino’reference genomes. Of 95,076 transcribable genes in
the reference, 70,746 genes (CYE_ SPA: 36,392,
CYE_SPE: 34,354) were expressed in any seedling.
Among genes, 55,749 (86.2%) were commonly
expressed in all seedlings (Table 1). TCC analysis
determined that 20,005, 22,789, and 5,477 genes
were significant DEGs in the comparison of CJ vs.
SN, CJ vs. SW, and SN vs. SW, respectively. A totalof 1,535 genes (up-regulated: 871, down-regulated:
664) were identified as SW-specific DEGs (Fig. 2).

Figure 2: An example for the identification ofsignificantly up- and down-regulated DEGs in thecomparison of SN vs. SW (A). Several
characteristic DEGs listed in Table 2 are indicatedby arrows. Of DEGs, those that were common to
CJ vs. SW and SN vs. SW but not present in CJ
vs. SN (underlined) were determined as SWspecific DEGs (B).

2. Annotation of DEGs

From the results of the BLAST search,
many plant defense-related genes were found in the
up-regulated DEGs (Table 2). One is a member ofthe pathogenesis-related (PR) gene group.

49

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –terms, such as “oxidation-reduction process” in BP
and “oxidoreductase activity” and “protein kinase
activity” in MF was also detected (Fig. 3A).
On the other hand, 13, 14, and 7 GO terms
for BP, MF, and CC were significantly enriched in
down-regulated DEGs, respectively (Fig. 3B). The
photosynthesis-related GO terms, such as“photosynthesis, light harvesting” in BP and“photosystem I reaction center” in CC, weredetected as significantly enriched GO terms (Fig.
3B). The “oxidation-reduction process” was also
observed in the significant GO enrichment of downregulated DEGs.

Additionally, several genes with oxidoreductase
activity, such as cytochrome P450 (Cyt_P450) and
2-oxoglutarate and Fe(II)-dependent oxygenase(2OG-FeII), and WRKY transcription factors werelisted in the up-regulated DEGs (Table 2).
Table 2: Annotations of defense response-related
DEGs listed in the top 15 up-regulated DEGs in the
necrotic hybrid seedlings referencing the ‘Someiyoshino’ CYE_SPA and CYE_SPE genome.

3. Enrichment analysis

From the results of goseq, we determinedthat 8, 18, and 2 GO terms belonging to biologicalprocess (BP), cellular component (CC), andmolecular function (MF), respectively, weresignificantly enriched in the up-regulated DEGs (Fig.
3A). Of the BP terms, “abscisic acid-activatedsignaling pathway” was the most significantly
enriched GO term with a 3.96e-30 FDR level. In
addition, “defense response” was identified as the
2nd significantly enriched term in the up-regulated
DEG list. As shown in the annotation of up-regulated
DEGs, the significant enrichment of several GO

Figure 3: Significantly enriched GO terms
detected by goseq in the up- (A) and downregulated DEGs (B). Significant enrichment of
biological process (BP), cellular component(CC), and molecular function (MF) GO terms
was represented with a q value (FDR) and thenumber of DEGs (dot size). The GO term
“defense response” and photosynthesis related
terms are indicated by red arrows.

50

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –Discussion

activities (Tian, D. et al., 2003). Various gene
pathways were significantly suppressed in the SW
seedlings. Photosynthesis-related GO terms also
showed significant enrichment in the downregulated DEGs of SW seedlings. These results may
be related to growth arrest, but the detailed
mechanism is still unknown. Additional detailsampling design for the seedling parts, such as theshoot apex, hypocotyls, and leaves, measurement of
the concentrations plant hormones, acting as
signaling pathways through defense response,
peroxide accumulation, and morphologicalobservation of cell division in the shoot apex will be
required for elucidating the growth failure
mechanism.
The causative gene of seedling necrosis
may still need to be discussed. On the other hand, theup-regulation of defense-related gene expression
probably commonly leads to hybrid weakness andhybrid breakdown, which is a phenomenon observed
not only in herbal plants but also in woody species.

Through the comprehensive gene
expression analysis of hybrid seedlings of ‘Someiyoshino’ and C. itosakura, various genes associatedwith plant defense response were found in the upregulated DEG list specified in the growth failure
seedlings. In particular, PR1 homologous to
Arabidopsis basic PR1-like genes was detected as amajor up-regulated DEG of SW seedlings in bothreference genome analyses. PR1 is expressed bypathogen infection and associated with plant defense(van Loon, L.C. et al., 2006). Another group of
detected PR protein, PR5, also referred to as TLP,
and Bet v1 family gene, a member of PR10, are also
associated with plant defense (van Loon, L.C. et al.,2006).
The RLK gene CYE_r3.1SPE4_g004570
was identified as the most up-regulated DEG in the‘Somei-yoshino’ SPE genome. RLK which
promotes the participation of other defense-related
genes triggered an autoimmune response in
Arabidopsis and rice (Bomblies, K. et al., 2007;
Yamamoto, E. et al., 2010). Although the paralog
gene of CYE_r3.1SPE4_ g004570 did not become
DEG in CYE_SPA (data not shown), the RLK generemains the most likely candidate for triggering
hybrid incompatibility in flowering cherries.
Meanwhile, other RLKs, CRK, were also found in
SW up-regulated DEGs. These genes are also
associated with pathogen resistance and cell death inArabidopsis (Yadeta, K.A. et al., 2017). Highexpressions of genes associated with oxidationreduction, such as Cyt_P450 and 2OG-FeII, werealso found in the SW seedlings. These genes are
involved in the detoxification of peroxides and arehighly expressed during the immune response inplants. The DEG list included several transcription
factors called WRKYs. The WRKYs regulate
various biological processes, several of which arehighly expressed upon pathogen infection, and upor down-regulate the expression of themselves, othertranscription factors, and downstream defenserelated genes (Chen, X. et al., 2019).
The high expression of these series ofdefense-related genes without pathogen inoculationstrongly indicates that an autoimmune response also
occurs in the hybrid seedlings of flowering cherry.
The excess of these defense responses probably
resulted in the reduction of other physiologicalAcknowledgment
This work was supported by the JSPS
KAKENHI, Grant-in-Aid for Early-Career
Scientists (JP18K14489).
Reference
Bomblies, K., Lempe, J., Epple, P., Warthmann, N.,
Lanz, C., et al. (2007) Autoimmune response asa mechanism for a Dobzhansky-Muller-type
incompatibility syndrome in plants. PLoS Biol
5: e236.
Chen, S., Zhou, Y., Chen, Y., Gu, J. (2018) fastp: anultra-fast all-in-one FASTQ preprocessor.
Bioinformatics 34: i884-i890.
Chen, X., Li, C., Wang, H., Guo, Z. (2019) WRKY
transcription factors: evolution, binding, andaction. Phytopathol Res 1: 13.
Kato, S., Matsumoto, A., Yoshimura, K., Katsuki,T., Iwamoto, K., et al. (2014) Origins ofJapanese flowering cherry (Prunus subgenus
Cerasus) cultivars revealed using nuclear SSR
markers. Tree Genet Genome 10: 477-487.
Langmead, B., Salzberg, S.L. (2012) Fast gappedread alignment with Bowtie 2. Nat Methods 9:357-359.
Liao, Y., Smyth, G.K., Shi, W. (2014)
featureCounts: an efficient general purpose
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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 2 – ECOLOGY OF TROPICAL TO TEMPERATE FORESTS –van Loon, L.C., Rep, M., Pieterse, C.M. (2006)
Significance of inducible defense-related
proteins in infected plants. Annu Rev
Phytopathol 44: 135-162.
Wan, W.L., Kim, S.T., Castel, B., Charoennit, N.,
Chae, E. (2021) Genetics of autoimmunity inplants: an evolutionary genetics perspective.
New Phytol 229: 1215-1233.
Yadeta, K.A., Elmore, J.M., Creer, A.Y., Feng, B.,
Franco, J.Y., et al. (2017) A cysteine-richprotein kinase associates with a membrane
immune complex and the cysteine residues are
required for cell death. Plant Physiol 173: 771787.
Yamamoto, E., Takashi, T., Morinaka, Y., Lin, S.,
Wu, J., et al. (2010) Gain of deleterious function
causes an autoimmune response and BatesonDobzhansky-Muller incompatibility in rice. Mol
Genet Genomics 283: 305-315.
Young, M.D., Wakefield, M.J., Smyth, G.K.,
Oshlack, A. (2010) Gene ontology analysis forRNA-seq: accounting for selection bias.
Genome Biol 11: R14.

program for assigning sequence reads togenomic features. Bioinformatics 30: 923-930.
Ohba, H., Kawasaki, T., Tanaka, H., Kihara, H.
(2007) Flowering cherries of Japan, New edn,
Yama-kei Publishers, Tokyo (in Japanese).
Shirasawa, K., Esumi, T., Hirakawa, H., Tanaka, H.,
Itai, A., et al. (2019) Phased genome sequenceof an interspecific hybrid flowering cherry,‘Somei-Yoshino’ (Cerasus × yedoensis). DNA
Res 26: 379-389.
Su, W., Sun, J., Shimizu, K., Kadota, K. (2019)
TCC-GUI: a Shiny-based application fordifferential expression analysis of RNA-seq
count data. BMC Res Notes 12: 133.
Tian, D., Traw, M.B., Chen, J.Q., Kreitman, M.,
Bergelson, J. (2003) Fitness costs of R-genemediated resistance in Arabidopsis thaliana.
Nature 423: 74-77.
Tsuruta, M., Mukai, Y. (2015) Hybrid seedlinginviability locus (HIs1) mapped on linkage
group 4 of the Japanese flowering cherry,
Cerasus × yedoensis ‘Somei-yoshino’. Tree
Genet Genome 11: 88.

52

INTERNATIONAL SYMPOSIUM ON A RECENT PROGRESS
IN FOREST ECOLOGY AND MANAGEMENT
2021

SESSION 3
AGROFORESTRY OF TROPICAL REGION

Wednesday, 10th November 2021
16:25 - 17:10

ORGANIZER:
THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCE, GIFU UNIVERSITY

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –Agroforestry System as the Best Vegetation Management to Face
Forest Degradation
Maria Theresia Sri Budiastuti 1*, Djoko Purnomo 1,
Desy Setyaningrum2

Department of Agrotechnology, Faculty of Agriculture, Universitas Sebelas Maret, Indonesia
Doctoral Program of Agriculture Science, Faculty of Agriculture, Universitas Sebelas Maret,
Indonesia*Corresponding Author: mariatheresia@staff,uns.ac.id
1
2

SUMMARY
Indonesia, as a country with a tropical climate, has a forest area of 94.1 million ha, and in 2019
deforestation reached 3,500,637.7 ha as a result of large-scale illegal logging for many activities. In addition,
the area of primary forest has decreased over the last 15 years and is positively correlated with landdegradation, carbon sequestration, and plant production. Low carbon sequestration capacity triggers global
warming, which impacts the average temperature increase in Indonesia by 0.7°C from 1981 to 2020. Therefore,
the restoration of forest functions to support the stability of ecosystems is the first step in forest management
planning. Forest management can reduce greenhouse gas emissions, preserve and restore ecosystems thatfunction to support ecological responses. It is proven that carbon emissions from deforestation are the second
largest after the energy sector from anthropogenic activities, which is 12-15%. Various efforts to restore thefunction of the forest are carried out by empowering suboptimal lands as land for agricultural and forest crop
production. The suboptimal land area is very high (16,025,000 ha), however, the potential land that can beutilized for agriculture is only limited to 9,358,643 hectares or about 58.4%. One of the efforts to empower
suboptimal land is cultivating tree-based plants, known as the agroforestry system. Such systems can maintainwater and soil stability, support carbon sequestration, and create microclimate conditions (reduce temperatureand maintain soil moisture). In addition, the absorption of soil carbon in the agroforestry system (11.29 t C
ha-1 year-1) was higher than in the monoculture system (4.38 t C ha-1 year-1). Agroforestry systems provide
options to reduce the impact of global warming, increase crop yields, and support ecosystem stability. Thus,a well-managed and sustainable Agroforestry system is the best vegetation management to overcome theimpact of degraded forests.
Keywords: Agroforestry; Forest Degradation; Tree; Vegetation Management

sequestration capacity (Siarudin et al., 2021).
Carbon dioxide emissions in Indonesia increased by
4.7% in 2017 which amounted to 487 million tons
(MtCO2) (Ma et al., 2020). CO2 emissions triggered
an increase in the average temperature of Indonesia's
mainland by 0.7°C from 1981 to 2020 (Alfiandy etal., 2020), and increased evaporation from the soil
surface (Tahmasebi et al., 2020). CO2 andtemperature are the main factors that affect plant
growth, development, and function. Changes inthese two factors will affect the productivity,
structure, and composition of the ecosystem (Lee etal., 2021; Mosavi et al., 2020). An increase intemperature can cause damage to soil nutrients,
decrease microbial activity in the soil, increase therate of degradation of organic matter in the soil,
which ultimately causes soil erosion (Arévalo-

Introduction
Indonesia has a tropical climate and has aforest area of ninth after Australia and Argentina.
Indonesia's forests experienced a relatively rapid
decline in the area from 2014-2019. In 2019,
deforestation reached 3,500,637.7 ha (BPS, 2019).
This reflects that the use of forests for various
purposes (industry, plantations, agriculture) is
experiencing a relatively fast rate and often without
considering the function of the forest as a providerof ecosystem services. In addition, several studies
have shown that deforestation causes an increase inglobal warming (Luhas et al., 2021; Reppin et al.,2020; Widianingsih et al., 2019).
The decrease in forest area has a verysignificant positive correlation with carbon

54

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –organic matter, low soil moisture content, too low or
too high pH, and even accumulation of metalelements that are toxic to plants. (Keshavarz et al.,2017). Suboptimal land characteristics are improved
through the application of tree-based agriculture(Agroforestry).

Gardini et al., 2020; Sharma & Anandhi, 2021).
Appropriate management efforts areneeded to deal with forest deforestation, namely
through well-organized vegetation management. It
provides several functions, namely water, and soil
conservation, mitigating the impact of global
warming and crop production. Vegetation is criticalin compensating for the negative effects ofdeforestation. Therefore, concrete steps are based on
the role of trees in rainwater movement (soil andwater conservation function), carbon sinks (global
warming impact mitigation function), andmicroclimate support for successful crop production(plant production function).
The integration of trees and crops is an
option in vegetation management that benefits theecosystem. Efforts can be made to reduce the impact
of deforestation by increasing carbon sequestration.
The potential for increased carbon sequestration canoccur in agricultural land and forests through treebased agricultural cultivation technology (Legout etal., 2020; Trebilco et al., 2013). The agroforestry
system provides an option to reduce the impact of
deforestation because it is considered capable ofincreasing carbon sequestration in the soil. This
article thoroughly reviews the role of trees in theagroforestry system as a form of management of the
impact of deforestation.
1.

1.1 The role of agroforestry as a rainwater regulator(Soil and water conservation function)

Conversion of forest to agriculture
disrupts the ecological function of the forest inabsorbing rainwater and preventing erosion. Forest
conversion can impair the performance ofhydrological systems associated with infiltration.
Various efforts to restore the function of the forest
are carried out by empowering sub-optimal lands asland for agricultural and forest crop production(Tschora & Cherubini, 2020). An agroforestry
system supported by high tree diversity can maintainhigh infiltration rates and can have a positive impact
on hydrological function through (1) green canopy
cover at tree and understory level, (2) soil surface
roughness, (3) litter in the soil surface, and (4) water
absorption by trees and vegetation (Nöldeke et al.,2021; Rothé et al., 2019). Tree canopy cover > 80%
qualifies as an “infiltration friendly” land use, due tohigher rainfall but relatively low erosion rates(Suprayogo et al., 2020). In agroforestry systems
with a higher number of perennial crops have lower
erosion rates than annual crops (Chofyan & Andriani,2020).
Agroforestry has a function as landconservation so that it can be used to improve
suboptimal land quality (van Noordwijk, 2021).
Several studies show that agroforestry systems canbe used to increase soil fertility because it is
supported by an increase in the diversity of fungi andsoil microorganisms. The diversity of fungi and soil
microorganisms plays a role in improving thephysical and chemical properties of the soil
(Arévalo-Gardini et al., 2020).
The most extensive suboptimal land inIndonesia is a peatland. Indonesia's peatlands
contain 77% of the total tropical peat carbon stores.
Poor peatland management can lead to degraded
land, forest, and land fires. Land-use conversion of
about 10 million ha of peatland results in annual
emissions of 132-159 Mt C year-1 from peatoxidation and will increase greenhouse gas
emissions (Corradini et al., 2020). Agroforestry is

The role of agroforestry

Deforestation causes land degradation,and the approach to solving this problem is anactivity that considers the combination ofenvironmental services and human well-being.
Agroforestry is an important option to reverse theimpacts of deforestation and associated ecosystem
functions (Kurniatun Hairiah et al., 2020; Noordwijk
et al., 2019). In particular, agroforestry plays animportant ecological role, including landconservation (maintaining soil and water resources),
carbon sinks, and supporting crop production.
The availability of suboptimal land inIndonesia is very high and reaches 16,025,000
hectares with the categories of critical (9,454,000
hectares) and very critical (4,553,000 hectares) (BPS,2018). However, the potential of land that can beused for agriculture is only 58.4%, namely
91,904,643 hectares. The soil in suboptimal land has
low quality, which is caused by several limiting
factors such as sloping topography, low nutrients,
55

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –vegetation have the highest carbon stocks of 108.9
Mg ha−1 (Siarudin et al., 2021). Carbon stocks are
related to species density and diversity (Brancalion& Chazdon, 2017; Riwidiharso et al., 2020).

one alternative that can be proposed for peat
restoration activities. Research result (Binhar et al.,2020) that peatlands and marginal lands can be used
for the cultivation of lowland rice, corn, andsoybeans with an agroforestry system. This systemcauses the diversity of land to increase so that theecological conditions and land structure become
better. Thus, agroforestry is beneficial in reducing
water and wind erosion, increasing soil nutrition.
Agroforestry plays a role in soil and water
conservation; this is supported by an increase inbiodiversity. Such systems can contribute tobiodiversity
conservation
andminimizehomogenization (Marconi & Armengot, 2020).
Agroforestry is considered a distinctive “landsharing” strategy as it supports biodiversity
conservation and agricultural production (Betts et al.,2017). Vegetation diversity in agroforestry systems
increases beetle species (De Beenhouwer et al.,2016), bird (Clough et al., 2009) and natural enemies(Staton et al., 2021). These variations may affect thepotential for biodiversity conservation of different
taxa.
Soil microbial community in agroforestry
system is higher than monoculture system.
Mycorrhizal diversity in agroforestry systems is very
high because tree shade affects the efficiency of
mycorrhizal bioinoculants, phosphate solvents andrhizobacteria (Shukla et al., 2018). The agroforestry
system increases the abundance of heterotrophic
bacteria and fungi (Afolabi & Muoghalu, 2018).
Agroforestry can also maintain microbial diversity
important for soil health and productivity (Dobo etal., 2018).

1.3 The role of Agroforestry as a microclimate support(plant production function)

Agroforestry plays a role in maintaining
microclimate conditions such as light, wind stability,
temperature and humidity. This is caused by trees asclimate support (Budiastuti et al., 2020). Hightemperatures above 30◦C cause the photosynthesis
mechanism, leaves, and flowers to be damaged,
affecting plant physiology and yields (Tscharntke etal., 2011). Tree-soil-plant interactions inagroforestry systems have a function in agricultural
production. For example, a coffee plantation with theuse of shade trees to support less than optimal
environmental conditions (Sarmiento-Soler et al.,2020). The role of agroforestry in coffee plantations
can improve the microclimate, increase soil nutrientsand diversify income and increase food security(Phondani et al., 2020). The agroforestry systemincreases cocoa by 31% and this increase is due to adecrease in the environmental temperature in thesystem (Kaba, Out-Nyanteh, & Abunyewa, 2020).
2.

The best vegetation management

Agroforestry is a sustainable land use
approach that can combine three main roles insupporting agricultural activities (rainwater
management: soil and water conservation function,
carbon sink, microclimate support). Trees as asupporter of the agroforestry system and become
part of forest management. Trees in agroforestry
systems are assumed to provide ecosystem benefits
as forests (Asante et al., 2021) because it plays a rolein providing ecosystem services and functions, andis able to create a microclimate that reduces
temperature and heat stress, maintains soil moistureand produces nitrogen (Budiastuti et al., 2018).
Tree root systems stabilize hillsides andriverbanks, reducing the risk of landslides (K.
Hairiah et al., 2021). Agroforestry will control thereactivation of landslides if the arrangement of trees
and plants is based on morphological units formed
from previous landslides (Purwaningsih et al., 2020).
The role of trees also supports the function ofagroforestry to maintain biodiversity, namely

1.2 Role of agroforestry as carbon sequestration(Mitigation function of global warming)

Agroforestry systems are considered themost effective in carbon sequestration (Theresia &Budiastuti, 2020). Tree-based farming systems havethe capacity to absorb carbon dioxide from theatmosphere above the ground, such as stems,
branches, and leaves, and below ground, namely theroot system, (Percival et al., 2020; Santosa et al.,2021) and can reduce greenhouse gas emissions(Lipper et al., 2010). Soil carbon absorption in theagroforestry system was higher at 11.29 t C ha-1 year-1
than the monoculture system at 4.38 t C ha-1 year-1.
Agroforestry systems with various trees and56

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –Betts, M. G., Wolf, C., Ripple, W. J., Phalan, B.,
Millers, K. A., Duarte, A., Butchart, S. H. M., &Levi, T. (2017). Global forest loss
disproportionately erodes biodiversity in intact
landscapes. Nature, 547(7664), 441–444.
https://doi.org/10.1038/nature23285
Binhar, Sugianto, & Zainabun. (2020). Evaluasi
Tingkat Kesesuaian Lahan Gambut Sebagai
Alternatif Pengembangan Tanaman Pangan di
Kecamatan Kota Subulussalam. Jurnal Ilmiah
Mahasiswa Pertanian, 5, 551–563.
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Brancalion, P. H. S., & Chazdon, R. L. (2017).
Beyond hectares : four principles to guidereforestation in the context of tropical forest andlandscape restoration. Restoration Ecology, 1–6.
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Budiastuti, S., Purnomo, D., Supriyono,
Yunindanova, M. B., Mahardini, P. C. A., &Utami, R. R. (2018). Land management strategy
for cocoa cultivation at home gardens. IOP
Conference Series: Earth and Environmental
Science, 200(1). https://doi.org/10.1088/17551315/200/1/012005
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Ahmad, N., Adinda, E., Nijman, V., Budiadi, B.,
Imron, M. A., & Nekaris, K. A. I. (2021). Shadetrees and agrochemical use affect butterfly
assemblages in coffee home gardens.
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319(November 2020), 107547.
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Chofyan, I., & Andriani, D. I. (2020). Soil erosionin agroforestry development in South Bandung
Region, Indonesia. Journal of Physics:
Conference Series, 1469(1).
https://doi.org/10.1088/17426596/1469/1/012122
Clough, Y., Dwi Putra, D., Pitopang, R., &Tscharntke, T. (2009). Local and landscapefactors determine functional bird diversity inIndonesian cacao agroforestry. BiologicalConservation, 142(5), 1032–1041.
https://doi.org/10.1016/j.biocon.2008.12.027
Corradini, E., Dreibrodt, S., Erkul, E., Groß, D.,
Lübke, H., Panning, D., Pickartz, N., Thorwart,
M., Vött, A., Willershäuser, T., Wilken, D.,
Wunderlich, T., Zanon, M., & Rabbel, W.
(2020). Understanding wetlands stratigraphy:

increasing the abundance and diversity of
microorganisms and pollinator (Campera et al.,2021). Trees support ecosystem biodiversity(Rosenstock et al., 2019) and is a major component
in the water, nutrient and carbon cycle (Van Den
Berge et al., 2021).
Conclusions
Agroforestry has three main functions,
namely regulating rainwater (soil and water
conservation), absorbing carbon (mitigating theimpact of global warming), and supporting
microclimate (plant production). These threefunctions are supported by vegetation. Agroforestry
systems provide options to reduce the effects ofglobal warming, increase crop yields, and support
ecosystem stability. Thus, a well-managed andsustainable agroforestry system is the best
vegetation management that can overcome theimpact of deforestation.
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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –Contribution of Forest Resources in Shifting Cultivation System toLivelihood Diversification in Mountain Villages of Northern Laos
Cahyo Wisnu Rubiyanto 1*, Isao Hirota 2

United Graduate School of Agricultural Science, Gifu University
Faculty of Applied Biological Sciences, Gifu University*Corresponding Author: wr.cahyo@yahoo.com

1
2

SUMMARY
Drastic globalization is being serious in Southeast Asia especially for local people who still
depending on shifting cultivation in mountain villages. Shifting cultivation system provides a lot of activities,
not only for subsistence but also commercial purposes including collection forest products such as Non-Timber
Forest Products (NTFPs). In this study, to understand the contribution of forest resources to livelihood
diversification, we focused on historical dynamics of livelihood transition show the pattern of household
livelihood strategy in mountain villages of northern Laos. Field survey was carried out in Sone district,Houaphan province. We chose 4 villages in three different conditions of road accessibility; poor (Bong village),
middle (Houay Su and Houay Sanguan village), and good (Houay Lao) accessibility, to know the influence ofroad development on agriculture and forest resources and to compare how different livelihood strategies local
people chose. This study revealed that in first stage after village establishment local people were still
maintained subsistence activities combined with NTFPs collection. NTFPs collection plays an important rolefor cash in poor and middle accessibility villages and it was considered to be selected as a livelihood activity
to increase investment in local communities. The results showed that diversity of income positively correlated
to total income and suggested that the pattern of livelihood diversification in this area tended to be progressive.
Keywords: Road accessibility; Livelihood transition; Combination of livelihood activities; Cross-border trade;
Southeast Asia

explain this, we have to look at their situation from
two aspects, the historical aspect and the economic
aspect. The historical aspect can understand thechanging livelihood transition, while the economic
aspect help us to understand the contribution of theirlivelihood to livelihood diversification. From thisstudy, we aim to explored the livelihood activities andits history from the first stage before local people areallocated near the main road, and investigate thefactors of livelihood diversification in mountainous
villages of Northern Laos.

Introduction
Local people in mountain villages ofSoutheast Asia who still depend on shifting
cultivation are experiencing drastic changes from theimpact of globalization. In northern Laos, ourresearch focus, road development project was bigger
than central or southern Laos, contributes 60% ofnational road development project. The constructionof this road has rapidly reformed local communities
into market-oriented activities (Hirota et al, 2014). As
a result, diversification of forest products collection is
the mainstay of local people as income-generating
livelihoods.
Although infrastructure facilities such as
roads have encouraged the formation of and change inlivelihood structure from subsistence-based tomarket-oriented or farm to non-farm (Nguyen andYokoyama, 2019), its diversification is questionable.
In order to understand current situation of theirlivelihoods, it is better for us to elaborate thelivelihoods that have been changing in the past twenty
years and explore their contribution to livelihood
diversification (Rubiyanto and Hirota, 2021). To

Methods
Study site

We chose Sone district in Houphan
province, northern Laos as study site because there
was big road construction. Sone is also one of themost forested district which enclosed by aconservation area. Four sample villages were selectedin this study based on their road accessibility such as
Bong village (poor accessibility), Houay Su andHouay Sanguan village (middle accessibility), andHouay Lao village (good accessibility). Besides roadaccessibility, we also used other criteria including the61Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –represent basic household condition, subsistence andeconomic livelihood activities.

same ethnic groups and similar livelihood activities toknow the influence of road development on
agriculture and forest resources and to compare how
different livelihood strategies local people chose (seeTable 1). The four villages were also established in theearly 2000s. The data collection took place at two
points in time, specifically in April-June 2018, andJanuary to March 2019.

Results and Discussion
Change of rice cultivation

The area of upland rice cultivation
gradually decreased from establishment of village torecent year in all villages, as 84 % of the households
in Bong village, 71 % of the households in Houay
Sanguan villages and 95 % of the households in
Houay Lao village reported a decrease compared topast. Surprisingly, this decrease in villages withpoorer accessibility occurred with an increase in the
expansion of paddy rice, while in village with better
access, the households have still maintained upland
rice without paddy rice. Shifting from upland rice topaddy rice can be accomplished from richer
households because they were able to buy and reclaim
new paddy field. Ducourtieux and Castella (2006)
revealed that households who shift to paddy field fromupland field, they could become to allocate their laborfor other activities such as NTFPs collection,cultivation of cash crops or raising livestock.

Table 1. Characteristics of villages survey

Data collection and analysis

A total 125 semi-structured householdinterview were used to collect data of basic
information of household and their characteristics
including explore their history of land use andlivelihoods change. We identified their livelihood
activities that can generate income and can befollowed by an interview about past activities; upland
rice cultivation, paddy rice cultivation, cultivation ofmaize, cultivation of other cash crops, red mushroom
collection, and other NTFPs collection. We ask
starting and ending year of each livelihood activity,
and accumulate the household number to village level.
The percentage of the number of household in eachlivelihood activity to the number of total sample
households in each village in each year was calculated
to know the trend of livelihood transition in village
level along road improvement after establishment ofvillages (See table 2).
We asked the sources of household income
and the amount of income which each householdobtained within one year from each survey period.
Then, we calculated the income sources diversity byusing Simpson index and we explored the relationshipbetween its diversity and total income. Income
diversity and total income were selected as dependent
variables, and we carried out multiple linear
regression to identify direction of livelihood strategy
of each household. In this study we selected 13
variables into the analysis as independent variables toChange of cash crop cultivation

The ratio of household with maize
increased until the year of road pavement in allvillages, but showed decreased after the year of roadpavement, except in Houay Lao village. In Houay Lao
village, the ratio increased and continue to cultivate
maize due to the low price fluctuation and located ingood access.
Ratio of household with other cash crops
increased after the pavement of road. Local people
started to cultivated other cash crops as choices ofincome diversification. The introduction of cash crops
in this study was observed from aspects ofenvironmental degradation, increasing influence fromglobal market and influence from outsider such as
local government and NGO. Coffee was reported as acrop which highly contribute to area of other cash
crop land, where newly introduced and get supported
from international organization, especially in Houay
Su and Houay Lao village. Other cash crops were also
cultivated because some of species became todecrease during around 15 years and partly
disappeared already several years before the survey.
As a results, the land for “new cash crops” in this area
were established.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –Table 2. Changes of main livelihood activities in study area

Note: Year of first cultivation of pineapple in Houay Sanguan village is unclear.
mushroom was important exportation products tobring the highest value among all NTFPs in crossborder trade of China (Phounviosuk et al, 2013) This
study showed that commercialization of NTFPs,
especially for new NTFPs such red mushrooms,
related to road accessibility to village, and roaddevelopment brought drastic change of local
livelihood activities in forest.
The relationship between income diversity
and total income, which dominate with forest
resources, showed positive relationship. Regression
results showed that upland and paddy rice were notsignificantly related to income diversity in all villages.

Change of forest products collection and theircontribution to livelihood diversity

The ratio of household with NTFPs
increased from the village establishment. The mostimportant NTFPs were Mak Sam Sip and On Ling in
the beginning of village establishment followed by
Mak Naeng and Lok Lek. These products were
substantial part of local or cross-border trade. The
ratio of household with red mushroom drasticallyincreased after the year of road pavement except in
Houay Su villages because one household had
relatives with Chinese merchants and the influence
expanded to other villagers. It is reported that red
63

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –References

Maize had contributed to income diversity and total
income for household in village with poorer
accessibility. Other cash crops showed positive effecton income diversity in Houay Lao village, thisindicates that cash crop can be an important strategy
for income diversity. In addition to diversifying
income, it is also important for household survival in
overcoming shocks. This study highlighted that
NTFPs contribute both positively and negatively toincome diversity. However, if the households much
more collect red mushrooms, it showed much lessincome diversity. Although red mushrooms werehighly contributed to total income, this study suggests
keeping maintain other NTFPs is needed to diversify
their income.

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Conclusion
This study explored the history of
livelihood transition and analysis household strategy
to livelihood diversification. This study revealed thatin the beginning of village establishment local people
were still maintained subsistence activities combinedwith NTFPs collection in shifting cultivation systems.
NTFPs collection were considered to play animportant role for cash poorer accessibility villages
such in Bong, Houay Su, and Houay Sanguan village
and it was considered to be selected as a livelihood
activity to increase investment in local communities.
The results showed that diversity of income positively
correlated to total income and suggested that thepattern of livelihood diversification in this area tended
to be progressive.
Acknowledgment
We thank to the all staff of Department ofAgricultural and Forestry Office in Sone district andall villagers for the cooperation.

64

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –Agroforestry Practice in Volcano Prone Area: Findings from Mount
Merapi Indonesia
Zuhud Rozaki 1*, Nur Rahmawati 1, Oki Wijaya 1,
Atikah Fajriyah Mubarok 1, Masateru Senge 1, Mohd Fauzi Kamarudin 2
Department of Agribusiness, Universitas Muhammadiyah Yogyakarta
Universiti Teknikal Malaysia Melaka*Corresponding Author: zaki@umy.ac.id
1
2

SUMMARY
Mount Merapi is one of the most active volcano in Indonesia. Despite the risk and hazard, people
still staying in this volcano, many of them are working as farmers. Agroforestry is the agriculture system thatbeing practiced as economic contributor and also become volcano disaster mitigation strategies. The
presentation talk about agroforestry practices and challenges in Mount Merapi risk and hazard prone area ofIndonesia. Agroforestry has been practiced by farmers in Mt. Merapi risk and hazard prone area from older
generation. Each area in Mount Merapi has different type of agroforestry such as agrisilviculture, silvopasture,
and agrosilvopasture. In the village of Glagaharjo, Yogyakarta, this system has been used for land restorationor conservation. 139 agroforestry adopters were chosen from 269 respondents that have been randomlyinterviewed from four different areas around Mount Merapi. Statistical descriptive method using values ofmean and percentage were used. Additionally, multinomial Logistic regression was utilized to understand themodel. The findings of the study shows that the trend of agroforestry is increasing, especially in Glagaharjo,
after the 2010 eruption. Agroforestry system are also found to be practiced for mitigation strategies in facing
Mount Merapi hazards and risk, especially for crop protection, and land and water conservation. Promoting
agroforestry as one of the coping strategies in facing Mount Merapi hazards and risk is important as indicatedby the results of this study. Additionally, the gender perspectives on agroforestry also will be presented.
Keywords: Agroforestry; Mount Merap; Agriculture; Farmers’ lives

many of them are practicing AF. This system is aland use management system in which trees or
shrubs are grown around or among crops or
pastureland. This intentional combination ofagriculture and forestry has varied benefits,
including increased biodiversity and reduced erosion.
Jaza Folefack, Ngo Njiki, and Darr (2019) suggestthat using AF or mix cropping could increase yields
without over utilizing the natural forest. This
practice is suitable for mountainous area. Some areasin the world also use this method to cope andconserve the land that is prone to the volcano
eruption disaster (Utami et al., 2018). In Merapi, AF
is practiced for the reason of restoring the land thatwas impacted by previous volcano eruptions (Fitria& Banowati, 2018).
Suryanto et al. (2011) explain that AF hasa high level of diversity and as such, can increase
land productivity. Lasco et al., (2014) stated thatthat AF systems have emerged as having thepotential to enhance the resilience of smallholders to
disaster risk area. AF consists of several types andIntroduction
As an active volcano, Merapi often erupts.
This mountain is located in Central Java and covers
three regencies (Boyolali, Klaten and Magelang) andthe Special Region of Yogyakarta that covers one
regency (Sleman). Hardiansyah et al. (2020) explainthat Hhistorically, many people live in the area of
Merapi because the land is fertile. Beside theperceived danger of being a disaster prone area,
people who live in Mt. Merapi , enjoys the economic
blessing such as fertile land as well as tourists visit.
As stated by Dove (2008), people won’t move from
Merapi because of economic motivation and assets
preservation (Nofrita & Krol, 2014).
At Merapi, most people are farmers, dairy
farmers and sand miners, respectively (Paton et al.,2013) and many felt that it is important for them tokeep these livelihoods (Bakkour et al., 2015).
Antriyandarti, Ferichani, and Ani (2013) explain thatpeople in Merapi prone area need to develop theirabilities, skills and knowledge in agriculture because

65Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –variable (AF contribution).

each has its own characteristics regarding economic
the contribution and benefits environmentally(Henley, 2008). In Merapi itself, this system has
been practiced since a long time ago and continued
to the current generation. This study aims to describe
the AF practices and challenges in the Merapi prone
area.

Results and Discussion
1.

Demographics of respondents

From a total 139 respondents, malefarmers represent 80.58%. Regarding age, 79% of
respondents are in the age range of 28-40 and 41-53
years old.12.95% respondents are more than 64
years old, this age is categorized in non-productive
age. As with other countries, Indonesia also is facedwith the issue of ageing farmers since young people
are not interested to agriculture.
According to the Dinis et al. (2019),
education level is important for adoption ofagriculture system including adopting AF system.
The majority of Education of respondents’ iselementary school, which is 53.96%. The educationof four research areas are still dominated byelementary school graduated respondents. In
Jemowo and Tlogolele, only 5.71% and 7.14
respectively has Diploma or University certificate.
In terms of farming experience, 25.9% of
the respondents has 21-30 years’ of experience.
and16.55% respondents have experiences more than
41 years. For AF experience, the distribution arequite similar with 0-10 years’ of experience andfarming experience is 17.99%. As for the family
number of 2-4, 81% of the participants are in thiscategory.

Research Method
1. Study area

This study was conducted in Merapi prone
area which area coverage includes all different
direction of Merapi, East (Jemowo Village,
Tamansari Subdistrict, Boyolali Regency), North(Tlogolele Village, Selo Subdistrict, Boyolali
Regency), West (Krinjing Village, Dukun
Subdistrict, Magelang Regency) and South(Glgaharjo Village, Cangkringan Subdistrict,
Sleman Regency) respectively. National Agency for
Disaster Management (BNPB) classified the hazard
level of Merapi based on the radius with the top of
Merapi ring 1 (0-5 km), ring 2 (6-10 km), ring 3 (1015 km) and ring 4 (16-20 km) (BNPB 2010). This
study was taken in ring 2 because this area is theclosest area to Merapi that AF can be practiced.
Meanwhile ring 1 is owned by the government andis therefore prohibited for AF practice.
2. Sampling procedure and data collection

There are three main variables in thisstudy: demographic, AF characteristics, and AF
contributions. These variables or indicators were
measured with Likert scale and descriptive statistics.
These research instruments were analysed using thevalidity and reliability tests. Subsequently, in-depth
interviews and observations were done tocomplement the findings.

2.

AF characteristics
a. AF type

There are three types of AF system that are
being practiced by people in Merapi prone area
which are: agrisilviculture, silvopasture, andagrosilvopasture. These three types are found tomatch the people’s need, besides the available
natural resource. Agrisilviculture combine foodcrops and trees (Etshekape et al., 2018), silpasturecombines tree and livestock (Santiago-Freijanes etal., 2018), and agrosilvopasture combining foodcrops, trees and livestock (Russo, 1996). Each areaof Merapi has their own type of AF. Jemowo (East)is dominated by silvopasture, followed byagrosilvopasture and agrisilviculture respectively.
Meanwhile in Tlogolele (North), it is dominated byagrisilviculture, because this area is higher and theland is suitable for vegetables. Hence, applying
agrisilviculture benefitted the farmers. Glagaharjo

3. Data Analysis

To present the results, descriptive method
is used. Mean, frequency, and percentage were used
to describe the variables, and also to know thedifferent characteristics among four different areas.
Variables with Likert scale have been tested throughthe validity and reliability tests. Additionally,
logistic multinomial regression analysis was used toanalyse quantitative data to identify the best set of
quality independent variables (selected demographic
and AF characteristics) predicting the dependant66

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –severe damage from previous volcano eruptions.
Through AF, they can restore and conserve the
Glagaharjo land. AF also leads to conservation ofwater. Krinjing shows the highest mean in water
conservation. Additionally, trees from AF practices
can protect the food crops with the highest mean in
Glagaharjo. Kiyani et al. (2017) stated that, AF
adoption can increase the income, improves soil
fertility, and conserves water and soil.

has the same trend with Jemowo, which isdominated by silvopasture. This area is not suitablefor vegetables like Tlogolele.
b.

AF trends

AF easiness based on all areas are quite
similar which is around 4 in mean (Figure 1). Thissystem has already been practiced from respondents’parents of grandparents. Therefore, they feel that AF
is not difficult to be practiced. The average of landsize is less than half hectare. Jemowo has the lowest
average of land size while Tlogolele has the highest
average of land size.
The distance of AF plot to home varies andthe average distance is less than 1 km. The Merapi
mountainous area is challenging. Having farm land
far from home is taking time and more efforts fromthe farmers. AF plots which are less than 1 km is agood distance. In term of land fertility, Tlogolele ismost fertile as compared to others. This is proven by
the vegetables planted there. The lowest land fertility
is in Glagaharjo, an area more known as sandproducer due to the much deposit of sands from
eruption. Additionally, Glagaharjo is the most severe
damage from Merapi previous eruptions. Therefore,
this area has highest trend in the increasing of AF
practice. Farmers plant trees to conserve the land
after the volcano eruption swept away their land.

Figure 2. Average AF Income per Year in Millionsd.

Family income from AF consists offarming activities and livestock. Through Figure 2,
it can be seen that the average income per year fromAF practices are not much and ranges from IDR 10
million to IDR 28 million, or about IDR 0.8 million
to 2.5 million per month. However, AF is like
agriculture whereby the income is uncertain and will
be worse when eruption happened such asin
Glagaharjo. In this are, some land were burnt away
by Merapi eruption. When this event happens,, allfarmers are in high risk. This study too found that AF
practices are the farmers’ main livelihood.
Additionally, some farmers are practicing AF as asecond livelihood. The contribution of AF income
varies from around 58% to 90%. Glagaharjo shows
the lowest AF income contribution because thefarmers are losing their farm land, and many prefer
to work as a sand miner (Rahman et al. 2016).

Figure 1. AF Characteristicsc.

Family income from AF

AF contribution

AF contribution in Merapi prone areavaries which include as a food supplier, farming
income, land restoration/conservation, water
conservation and crop protection. AF adopters havetheir own reasons to adopt this system based on the
farmers needs and environmental conditions. AF to
supplies food for Jemowo with a mean of 3.66. Thissystem can help to generate income is more felt in
Krinjing with a mean of 3.56. Glagaharjo has highest
mean on land restoration/ conservation caused by theConclusions
AF practices in Merapi prone area havebeen started from the older generations. Therefore,
the current generation felt that AF practices are notdifficult as they are used to the system. There arethree types of AF that people are practicing:agrisilviculture, silvopasture, and agrosilvopasture.
This study shows that AF provide food or income

67Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 3 – AGROFORESTRY OF TROPICAL REGION –Etshekape, P. G., Atangana, A. R., & Khasa, D. P.
(2018). Tree planting in urban and peri-urban ofKinshasa: Survey of factors facilitating
agroforestry adoption. Urban Forestry and
Urban Greening, 30, 12–23.
https://doi.org/10.1016/j.ufug.2017.12.015
Fitria, A., & Banowati, E. (2018). Partisipasi
Masyarakat terhadap Pelestarian Hutan Lereng
Merapi Melalui Program Agroforestri Kopi diDesa Tlogolele Kecamatan Selo. Edu
Geography, 6(3), 162–169.
Hardiansyah, Muthohar, I., Balijepalli, C., &Priyanto, S. (2020). Analysing vulnerability ofroad network and guiding evacuees to shelteredareas: Case study of Mt Merapi, Central Java,
Indonesia. Case Studies on Transport Policy,
8(4), 1329–1340.
https://doi.org/10.1016/j.cstp.2020.09.004
Henley, D. (2008). Natural resource management:
Historical lessons from Indonesia. Human
Ecology, 36(2), 273–290.
https://doi.org/10.1007/s10745-007-9137-2
Jaza Folefack, A. J., Ngo Njiki, M. G., & Darr, D.
(2019). Safeguarding forests from smallholder
oil palm expansion by more intensive
production? The case of Ngwei forest(Cameroon). Forest Policy and Economics,
101(January), 45–61.
https://doi.org/10.1016/j.forpol.2019.01.016
Kiyani, P., Andoh, J., Lee, Y., & Lee, D. K. (2017).
Benefits and challenges of agroforestry
adoption: a case of Musebeya sector,
Nyamagabe District in southern province ofRwanda. Forest Science and Technology, 13(4),174–180.
https://doi.org/10.1080/21580103.2017.1392367
Lasco, R. D., Delfino, R. J. P., Catacutan, D. C.,
Simelton, E. S., & Wilson, D. M. (2014).
Climate risk adaptation by smallholder farmers:
The roles of trees and agroforestry. Current
Opinion in Environmental Sustainability, 6(1),83–88.
https://doi.org/10.1016/j.cosust.2013.11.013
Nofrita, S., & Krol, B. G. C. M. B. (2014). The
Livelihood Analysis in Merapi Prone Area After2010 Eruption. Indonesian Journal of
Geography, 46(2), 195.
https://doi.org/10.22146/ijg.5790
Paton, D., Okada, N., & Sagala, S. (2013).
Understanding Preparedness for Naturaland can become one of the mitigation strategies for
Merapi hazards. The AF system can become themethod for land and water conservation, and mostimportantly as coping strategies for Merapi hazards
and risks. Additionally, crops is used as protection
from the Merapi hazards. Increasing this system
practices in Merapi prone area will bring good
benefits for all parties. The challenge of this system
is education of AF for farmers in the Merapi prone
area. This is important to increase this system
practices and support the prevention actions against
Merapi hazards in the future. Understanding thecontribution of AF will lead more farmers to adopt
this system.
Acknowledgment
The authors would like to deliver big
gratitude to Universitas Muhammadiyah Yogyakarta
for supporting this research through internal research
grant.
Reference
Antriyandarti, E., Ferichani, M., & Ani, S. W.
(2013). Sustainability of Post-Eruption Socio
Economic Recovery for the Community on
Mount Merapi Slope through Horticulture
Agribusiness Region Development (Case Studyin Boyolali District). Procedia Environmental
Sciences, 17, 46–52.
https://doi.org/10.1016/j.proenv.2013.02.010
Bakkour, D., Enjolras, G., Thouret, J. C., Kast, R.,
Mei, E. T. W., & Prihatminingtyas, B. (2015).
The adaptive governance of natural disastersystems: Insights from the 2010 mount Merapi
eruption in Indonesia. International Journal ofDisaster Risk Reduction, 13, 167–188.
https://doi.org/10.1016/j.ijdrr.2015.05.006
BNPB (Badan Nasional Penaggulangan Bencana).
(2010). Peta zonasi bahaya (jarak radius 20 km)
dari Puncak Gunungapi Merapi (Hazard Map ofMerapi within 20 km radius). In BNPB (Badan
Nasional Penaggulangan Bencana) (Issue
November). https://bnpb.go.id/
Dinis, I., Simões, O., Cruz, C., & Teodoro, A.
(2019). Understanding the impact of intentionsin the adoption of local development practices
by rural tourism hosts in Portugal. Journal of
Rural Studies, 72(November 2018), 92–103.
https://doi.org/10.1016/j.jrurstud.2019.10.002
68

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Session 3 – AGROFORESTRY OF TROPICAL REGION –https://doi.org/10.1016/j.landusepol.2018.03.01
4Suryanto, P., Hamzah, M. Z., Mohamed, A., &Alias, M. A. (2011). Silviculture Agroforestry
Regime: Compatible Management in Southern
Gunung Merapi National Park, Java, Indonesia.
International Journal of Biology, 3(2).
https://doi.org/10.5539/ijb.v3n2p115
Utami, S. N. H., Purwanto, B. H., & Marwasta, D.
(2018). Land Management for Agriculture After
The 2010 Merapi Eruption. Planta Tropika:
Journal of Agro Science, 6(1), 32–38.
https://doi.org/10.18196/pt.2018.078.32-38

Hazards: Cross cultural comparison. Journal of
Integrated Disaster Risk Management, 3(1), 18–
35. https://doi.org/10.5595/idrim.2013.0051
Russo, R. O. (1996). Agrosilvopastoral Systems: A
Practical Approach Toward Sustainable
Agriculture. Journal of Sustainable Agriculture,7(4), 5–16.
https://doi.org/10.1300/J064v07n04_03
Santiago-Freijanes, J. J., Pisanelli, A., Rois-Díaz,
M., Aldrey-Vázquez, J. A., Rigueiro-Rodríguez,
A., Pantera, A., Vityi, A., Lojka, B., FerreiroDomínguez, N., & Mosquera-Losada, M. R.
(2018). Agroforestry development in Europe:
Policy issues. Land Use Policy, 76(March),
144–156.

69

INTERNATIONAL SYMPOSIUM ON A RECENT PROGRESS
IN FOREST ECOLOGY AND MANAGEMENT
2021

KEYNOTE SPEECH 2

Thursday, 11th November 2021
12:00 - 13:20

ORGANIZER:
THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCE, GIFU UNIVERSITY

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Keynote Speech 2

Masting Impact on Canopy Structure and Photosynthesis
in Beech Forests
Does Masting Perform as Gap?
Atsuhiro Iio

Agricultural faculty, Shizuoka University
Corresponding Author: iio.atsuhiro@shizuoka.ac.jp

SUMMARY
Small openings formed in forest canopy are called “gaps”. Gap formation promotes tree regeneration andgrowth, and thus is important event for determining species composition and productivity of future forest. In
addition to normal gap formed by tree death, forest canopy provides various types of gap; e.g., transient openings
formed by inter-species variations in leaf phenology (seasonal or phenological gap) and by insect outbreak. The
variety of gap increases spatio-temporal heterogeneity of forest structure and environments (i.e., niche partitioning),
which should play an important role for maintenance of species diversity and productivity in forest ecosystem.
However, studies for transient gaps are generally limited compared with those for normal gap, probably because ofdifficulty for detecting such transient impact on forest structure and function.
Masting is synchronous intermittent production of large seed crops in perennial plant populations. In
Fagus crenata forests, masting event generally occurs for 5 to 10 years intervals. This unique characteristic has
attracted interest of plant ecologists, and its ecological meaning and physiological mechanisms have been
investigated. On the other hand, several studies have shown that heavy masting reduces leaf amount of canopy layer,
most likely because of the high resource demands of fruit production, implying that masting can perform as transientgap. However, there are no studies evaluating masting event from such aspect. I therefore examined mastingdependent changes in canopy structure at Fagus crenata forest, and evaluated its impact on within-canopy lightenvironment and photosynthesis, with eco-physiological process-based model that can describe changes in canopy
structure at small branch scale.
Masting markedly reduced leaf area of sunlit part of canopy due to decreases in individual leaf area andshoot length of fruit-bared branches. Heavy masting event decreased stand leaf area index (LAI) by half, but LAI
returned to pre-masting level in following year. There were no clear differences in leaf photosynthetic capacity
between mast and non-mast years. Model simulation revealed that heavy masting drastically changes productive
structure of beech forest so that canopy photosynthesis decreased by half but photosynthesis in sub-canopy andunderstory layer increased two-three folds. When comparing masting with normal gap formation, canopy
photosynthesis was 20 % higher for masting than for normal gap formation, because masting reduced leaf area onlyin the upper part of canopy, which relieved significant leaf self-shading within the canopy and thus improved lightuse efficiency of photosynthesis, especially in the middle and lower part of the canopy. Masting may contribute toextend longevity not only for understory vegetations but also for shaded branches in the lower part of the canopy,
which largely differs with impact of normal gap.
Keywords: Masting; Fagus crenata; Gap; Structural heterogeneity; Canopy photosynthesis

72Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Keynote Speech 2

Forest Tree Breeding in Hokkaido, JapanSo Hanaoka

Hokkaido Regional Breeding Office, Forest Tree Breeding Center, Forestry and Forest Products
Research Insitute, Forest Research and Management Organization, National Research andDevelopment Agency
Corresponding Author: sohana@affrc.go.jp
SUMMARY
Forest tree breeding in Japan commenced in the 1950s with the selection and preservation of trees ofJapanese main forestry species, such as Cryptomeria japonica, Chamaecyparis obtua, Pinus densiflora, Abiessachalinensis, Larix kaempferi, Picea glehnii, etc. Progeny test sites of first generation-plus trees have been
established, and the selection of second generation-plus trees commenced in the 2000s based on tree growth andwood traits. In Hokkaido Prefecture, the northern part of Japan, variety development of second generation-plus treesof A. sachalinensis, L. kaempferi, Picea glhenii, etc. are required. However, it is important to reveal the heritabilityof target traits as the first step of variety development. Therefore, I have studied the heritability of tree growth andthe wood strength of those species. In this symposium, I will introduce the results of Picea glhenii. Also, reducing
the time and labor for trait measurement (phenotyping) is also an issue to promote forest tree breeding. Recently,
accurate and quantitative data have been required to combine genome and phenotype data. Therefore, technologicaldevelopment of phenotyping is also one of my works. I will introduce some of my results using UAV and AI.
Picea glehnii is an evergreen conifer species mainly distributed in Hokkaido. The forest area of P. glehnii
is about 180,000 ha, which is the third largest in Hokkaido. In my study, tree height and DBH data from six progeny
test sites were analyzed and higher heritability of tree height (0.16–0.77) and DBH (0.05–0.72) was revealed. Also,
the heritability of wood strength was estimated from the stress wave velocity data at 26–28 years after planting, andits heritability was 0.38–0.96. From these results, it was concluded that the growth and wood strength of P. glhenii
can be improved through breeding. Therefore, I selected 120 individuals of the second generation-plus trees basedon breeding values (Best linear unbiased prediction (BLUP) values). These trees will be planted in seed orchards,
and improved seedlings will be distributed in the future. Also, I studied the heritability of stem straightness, in otherwords, resistance to stem or basal bending due to snow damage. Environmental effect mainly explained thosevariations, and heritability was not necessarily high (0.025 and 0.012 for stem and basal bending, respectively). But
some genetic effects were detected, and I selected some superior trees resistant to stem or basal bending.
Regarding the technological development of phenotyping, I have developed a tree height measurement
application from the digital surface model, constructed by UAV images. Also, I have developed an AI (object
detection) model to detect cones on the tree crown of A. sachalinensis. These techniques will be utilized in the near
future.
Keywords: Heritability; Best linear unbiased prediction (BLUP); Variety development, Phenotyping; Picea glehnii

73

INTERNATIONAL SYMPOSIUM ON A RECENT PROGRESS
IN FOREST ECOLOGY AND MANAGEMENT
2021

SESSION 4
FOREST HYDROLOGY AND BIOGEOCHEMISTRY

Thursday, 11th November 2021
13:40 - 14:40

ORGANIZER:
THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCE, GIFU UNIVERSITY

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Seasonal Variations of Dissolved Organic Carbon Concentrations andFluxes in an Evergreen Broadleaved Subtropical Forest in Central
Japan
Siyu Chen 1*, Ruoming Cao 2, Shinpei Yoshitake 3, Toshiyuki Ohtsuka 2,4

School of Environment and Life Science, Nanning Normal University, China
River Basin Research Center, Gifu University, Japan
3
Faculty of Education and Integrated Arts and Sciences, Waseda University, Japan
4
United Graduate School of Agricultural Science, Gifu University, Japan*Corresponding Author: siyu9053@163.com
1
2

SUMMARY
Most studies of dissolved organic carbon (DOC) hydrological fluxes were established in deciduousand coniferous forests, little is known about the origin, dynamic, and fate of DOC in the evergreen broadleaved forest ecosystems, which are distributed widely in the subtropical and warm-temperate regions of East
Asia. Thus, we examined the variations of DOC concentration in throughfall, stemflow, and litter leachate inan evergreen broadleaved subtropical forest in Central Japan for nearly three years (Jun, 2016 – Apr, 2019),
and calculated the DOC fluxes of different forest hydrological fluxes to figure out the seasonal change patternsof DOC. All forest hydrological fluxes were highly enriched in DOC compared to bulk precipitation. DOC
concentrations in throughfall were significantly higher in spring (Mar-May) than the other seasons, while DOC
concentration in stemflow were highest in summer. However, DOC concentrations in bulk precipitation andlitter leachate did not show a statistically seasonal variation. Monthly DOC concentrations in stemflow andlitter leachate were significantly related to that in throughfall, thus the monthly DOC concentration change
pattern of stemflow and litter leachate was similar to that of throughfall. Furthermore, monthly throughfall
DOC concentration was positively correlated to dry weights of litterfall and flower, suggesting the canopy
exchanges (leaf emergency, leaf fall and florescence) had a great impact on throughfall DOC concentrationand had indirect impacts on DOC concentrations in stemflow and litter leachate. DOC fluxes variations in allhydrological fluxes were well described by bulk precipitation amounts, furthermore, DOC fluxes in throughfall
were also significantly controlled by its DOC concentrations. DOC fluxes in throughfall and litter leachateshowed a similar seasonal pattern, which were highest in summer or spring and lowest in winter, these seasonal
trends suggest that the increases in bulk precipitation can result in proportional increased in DOC fluxes input
to the forest floor. The results of our study suggest that canopy changes still have significant effects on DOC
concentration and DOC fluxes, despite there is no clear leaf and leafless season in the evergreen forest.
Keywords: Seasonal Variation; DOC; Throughfall; Carbon Vertical Transport; Stemflow

Considering the highest average NPP has beenreported for evergreen broad-leaved species
occupying mesic temperate environments in Japan,
it may also be a large contributor of DOC flux inputs
to the soil. Beyond that, previous studies found that
canopy structure is an important controlling factor
on throughfall patterns, broad-leaved deciduous
canopies have been shown to influence throughfall
patterns during the leafed season (Keim et al. 2005;
Staelens et al. 2006b). Moreover, some study
assumed that the multiple layers within the forest
canopy and differences in canopy phenology mightcomplicate throughfall patterns in tropical forests(e.g., Germer et al. 2006; Zimmermann et al. 2009;

Introduction
Evergreen broad-leaved (Lucidophyllous)
forests are distributed widely in the subtropical andwarm-temperate regions of East Asia. Numerous
studies have quantified and characterized fluxes of
DOC in the forest ecosystems at local, regional, or
national scale (Borken et al., 2011; Buckingham etal., 2008b; Van den Berg et al., 2012), but moststudies were established in deciduous and coniferous
forests (Michalzik et al., 2001, Kindler et al., 2011,
Camino-Serrano et al. 2014), little is known about
the origin, composition, and fate of DOC in theevergreenbroad-leaved
forestecosystems.
76Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –throughfall and stemflow ranged in 5–11 mg L–1 and
6–43mg L–1, respectively (Van Stan and Stubbins,2018), the annual mean DOC concentration inthroughfall (6.62 ± 1.31 mg L–1 in 2017, 6.10 ± 1.48
mg L–1 in 2018) and stemflow (11.95 ± 3.28 mg L–
1
in 2017, 12.26 ± 3.71 mg L–1 in 2018) in this study
consistent with previous reports. There was rare
research investigated the DOC concentration in litterleachate in the subtropical evergreen broadleaved
forest. However, the annual mean DOC
concentration in litter leachate (19.88 ± 4.20 mg L–1
in 2017, 26.98 ± 8.09 mg L–1 in 2018) was within therange of those investigated in the temperate forest
ecosystems (20–40 mg L–1, Michalzik et al. 2001),
but at low level.

Zimmermann and lsenbeer 2008). Although there isno clear leafed and leafless season in evergreen
forest, the changes of canopy phenology still have
impacts on the dynamics of DOC concentration inthroughfall, stemflow and litter leachate. To test the
hypothesis, we evaluated the variations of DOC
concentrations in throughfall, stemflow, and litterleachate of this evergreen forest, and quantified theannual contribution of DOC from different foresthydrological flux to the DOC input to the forest floor,aims to figure out the seasonal change patterns of
DOC.
Materials and Methods
The study site Mt. Kinka (35°26’ N,
136°47’ E, the peak is 329 m) is located in Gifu
Prefecture, central Japan. Almost all areas (597 ha)of Mt. Kinka consist of secondary natural forests(93%) and artificial coniferous forests (2%). In
particular, the lower slopes of Mt. Kinka are coveredby secondary evergreen broad-leaved forests that
predominated by Castanopsis cuspidata, which weremainly recovered after the World War II.
Samples of bulk precipitation, stemflow,
throughfall and litter leachate were collected twice amonth. Litterfall and flower were collected in using
litter traps (1 m2 area) that was set near the eachthroughfall collector for a total of nine litter traps.
Litterfall and flower were collected once a month.

Table1 Mean annual volume-weighted
dissolved organic carbon-1),hydrologicalflux (mm), DOC
flux (kg(DOC)
concentration
(mg
L
-1
-1
-1
-1
–1
ha yr ), net DOC flux (kg ha yr ), pH and EC (mS m ) in bulk
precipitation, stemflow, throughfall and litter leachate from January
2017 to December 2018.
Bulk Precipitation Stemflow
DOC concentration 2017
(mg L-1)
2018

Throughfall

Litter leachate

2.78 ± 0.40

11.95 ± 3.28

6.62 ± 1.31

19.88 ± 4.20

2.27 ± 0.16

12.26 ± 3.71

6.10 ± 1.48

26.98 ± 8.09

Hydrological flux
(mm)

2017

1864

67.72

1353.121506.87

2018

2157

74.14

1487.68

1702.93

DOC flux-1(kg ha-1 yr )2017

38.71 ± 4.20

9.01 ± 1.28

72.98 ± 5.15 307.84 ± 37.97

2018

54.03 ± 8.90

8.70 ± 1.56

82.01 ± 3.55 372.19 ± 54.61

net DOC flux
-1
(kg ha-1 yr )2017

38.71 ± 4.20

6.08 ± 2.61 48.20 ± 12.47 216.69 ± 72.46

2018

54.03 ± 8.90

5.66 ± 3.64 51.87 ± 11.19 301.21 ± 79.81

pH
EC (mS m-1)

Results and Discussions
There was a definite difference in DOC
concentration among precipitation, throughfall,
stemflow and litter leachate at both annual andmonthly scale (Table 1, Fig. 2), and the order of the
DOC concentrations in this study was always: litterleachate > stemflow > throughfall > precipitation.
The results are similar to those reported in otherforest ecosystems (Michalzik et al. 2001, Solinger etal., 2001, Chen et al. 2017). It is generally assumed
that the wash-off from the canopy, including
branches and trunks and leaching from the leaves are
the two major sources of DOC in throughfall andstemflow. The tree species and canopy structures cangreatly alter the amount, spatiotemporal patterning,and chemical properties of bulk precipitation via thevertical transport processes (Levia et al., 2011;
Pypker et al., 2011). Previous studies in subtropicalforests reported that DOC concentration in2017

5.69 ± 0.22

5.02 ± 0.21

5.35 ± 0.19

5.49 ± 0.23

2018

6.14 ± 0.16

5.19 ± 0.25

5.77 ± 0.14

5.50 ± 0.29

2017

4.41 ± 0.34

4.90 ± 0.36

5.17 ± 0.35

5.89 ± 0.55

2018

1.35 ± 0.07

3.87 ± 0.95

4.01 ± 0.98

6.92 ± 2.24

The concentrations of DOC in throughfall
were significantly higher in spring (Mar-May) thanthe other seasons. And monthly throughfall DOC
concentration was positively correlated to dry
weights of litterfall and flower. Additionally, Nitta
and Ohsawa (1997) reported that leaf fall of
Castanopsis had a high peak in May simultaneouslywith leaf emergence. The results observed inevergreen forest in this study are consistent withthose of a previous report that seasonal effects on
metabolic processes (such as changes in biologicalactivity and dry deposition) can affect theconcentrations of most nutrients in throughfall, withthe highest in leaves fall season in deciduous forest(Parker, 1983). Because stemflow is considered as aconcentrated version of throughfall (McColl, 1970)
and a significant relation was detected for themonthly concentrations of DOC between throughfall77Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –and stemflow (r = 0.482, p <0.05), the highest
seasonal DOC concentration of stemflow was foundin summer, monthly change pattern of stemflow was
similar to that of throughfall. Beyond that,significant and positive relationships were found inand litter leachate vs. in throughfall (r = 0.590, p<0.05). Taken together, we supposed that the canopy
exchanges (leaf emergency, leaf fall and florescence)
had a great impact on throughfall DOC
concentration and had indirect impacts on DOC
concentrations in stemflow and litter leachate.
The amounts of bulk precipitation thatreaches the forest floor, decomposition of litterfall,
and microbial activity are the major determinants ofthe concentrations of DOC in litter leachate (Kalbitz
et al., 2000; Long et al., 2015). In this study, monthly
DOC concentration in litter leachate was positively
correlated to temperature (r = 0.672, p < 0.001),
suggesting a significant biological control on the
DOC leaching from litterfall, which confirming theresults in previous studies in both laboratory
experiments and field studies (Andersson et al.
2000; Kalbitz et al. 2000; Solinger et al., 2001).
Beyond that, numerous studies found that litterleachate DOC concentration showed a positive
response to the litterfall dynamics in temperate

forests (Casals et al. 1995; Currie et al. 1996;
Michalzik and Matzner 1999). Statisticallysignificant correlation r = 0.388, p < 0.01) was also
found between litter leachate DOC concentrationand litterfall at monthly scale in this study.
Additionally, the statistically significant correlation
found between monthly litter leachate DOC
concentration and flower (r = 0.474, p < 0.05). As weknow, there was no previous research reported thecorrelation between litter leachate DOC
concentration and flower, we supposed that theamount of flower might also be an important sourceof DOC on the forest floor during the flower season.
In forest ecosystems, the forest floor has
been identified as a primary source for DOC (Cronanand Aiken, 1985, Currie et al., 1996, Qualls andHaines, 1991). At the study site, the annual DOC
fluxes input to the forest floor was 307.84 ± 37.97
kg–1 ha–1 yr–1 in 2017, and 372.19 ± 54.61 kg–1 ha–1
yr–1 in 2018, respectively, which were comparable tothe highest values recorded in temperate forests(100–482 kg–1 ha–1 yr–1, Michalzik et al. 2001, Fujii
et al. 2001), nevertheless, it was much higher thanthat reported in another evergreen broadleaved forest
in Japan (149.4 kg ha–1 yr–1, Fujii et al., 2011), thisresult may be explained by the differences of annual
precipitation amounts, vegetation types (lignin
concentration, C/N ratio), the amounts of litterfall( Godde et al. 1996, Currie and Aber 1997,
Michalzik et al. 2001, Fujii et al. 2001) and even the
difference of throughfall, considering 15.58 % of
total annual DOC fluxes was provided bythroughfall in 2017. And most of total annual DOC
fluxes was from litter leachate (70.02%, 216.69 ±72.46 kg–1 ha–1 yr–1 in 2017; 72.87%, 301.21 ± 79.81

Fig. 1 Monthly DOC concentration (mg/L) of bulk precipitation(BP), throughfall (TF), stemflow (SF) and litter leachate (LL) from
June 2016 to April 2019.

Fig. 2 Seasonal DOC flux (kg/ha) of bulk precipitation (BP),throughfall (TF), stemflow (SF) and litter leachate (LL) fromautumn in 2016 to spring in 2019.

78

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Acknowledgment

kg–1 ha–1 yr–1 in 2018), while stemflow only
attributed 1.97% of total annual DOC fluxes (6.08
kg–1 ha–1 yr–1) in 2017, indicating that the major
source of DOC input to soil depends on litter
decomposition.

The authors would like to thank Prof. T.
Otsuka for helpful advice. Special thanks for fieldassistance and laboratory assistance provided byindividuals at the River Basin Research Center, Gifu
University, Japan: Dr. R. Cao, Prof. F.S. Li. And
sugguestions by Pro. Yoshitake at Faculty of
Education and Integrated Arts and Sciences, Waseda
University.

Conclusions
The dynamics of DOC concentration andfluxes throughfall, stemflow and litter leachate in thestudied evergreen broadleaved forest in a subtropicalregion showed some differences from that indeciduous forest in temperate regions, where DOC
has been intensively studied. DOC concentrations of
all hydrological fluxes are not hydrologicallycontrolled. Significant positive correlations were
found among monthly DOC concentrations inthroughfall, stemflow and litter leachate. DOC
concentrations in throughfall and stemflow
exhibited a similar pattern. Moreover, monthly DOC
concentration in throughfall, stemflow and litterleachate positively responded to the canopy changes(litterfall and flower), these results demonstratingthat canopy change is one of the main controls of
DOC concentrations in throughfall, stemflow andlitter leachate.
DOC fluxes variations in all hydrologicalfluxes were well described by bulk precipitation
amounts, furthermore, DOC fluxes in throughfall
were also significantly controlled by its DOC
concentrations. DOC fluxes in throughfall and litterleachate showed a similar seasonal pattern, whichwere highest in summer or spring and lowest in
winter, these seasonal trends suggest that theincreases in bulk precipitation can result in
proportional increased in DOC fluxes input to theforest floor.
Together, the results of our study suggestthat canopy changes still have significant effects on
DOC concentration and DOC fluxes, despite there isno clear leaf and leafless season in the evergreen
forest. Although litter leachate was the primarysource of DOC inputs to forest floor, throughfall
DOC flux affected the litter leachate DOC flux in the
study site, thus, evaluating the proportion of DOC
fluxes from different hydrological fluxes input to theforest floor help us understand the dynamics of DOC
in the forest ecosystem better.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Difference of Water and Material Dynamics between Coniferous
Plantation and Deciduous Secondary Forest Watershed - Focusing on
Dissolved Organic and Inorganic Carbon
Taketo Uemura 1*, Takeo Onishi 2, Toshiyuki Otsuka 3,
Ken Hiramatsu 2

Graduate School of Natural Science and Technology, Gifu University
Faculty of Applied Biological Sciences, Gifu University
3
River Watershed Research Center, Gifu University*Corresponding Author: a4522002@edu.gifu-u.ac.jp
1
2

SUMMARY
To clarify the effects of coniferous plantation on the hydrological processes and the associated material
dynamics in the forest watershed, the dynamics of Dissolved Organic Carbon (DOC) and Dissolved Inorganic
Carbon (DIC), whose quantity and quality strongly depend on the water flow paths, were investigated by paired
catchment study between coniferous plantation (CP) watershed (0.60 km2) and deciduous broad-leaved secondary
forest (DB) watershed (0.72 km2). Stream discharge, DOC, DIC, Cl-, NO3-, SO42-, Na+, K+, Mg2+, Ca2+
concentrations, and fluorescence characteristics of DOC were measured at the outlet of each watershed. In addition,
we applied the same chemical analysis on throughfall, soil water at the depth of 10 cm and 50 cm, and spring water
as a substitute for groundwater, collected in each watershed. Daily concentrations of DIC, Na+, and Ca2+ of whichmain source can be considered bedrock, were clearly higher in the DB watershed than in the CP watershed, while
concentrations of DOC and NO3-, derived from organic decomposition near the soil surface, were slightly higher in
the CP watershed than in the DB watershed. The ratio of DIC to DOC (DIC/DOC) was higher in DB watershed thanin CP watershed, suggesting the contribution of component with high DIC content is significant in DB watershed.
During rainfall runoff periods, DOC and NO3- concentrations significantly increased with peak runoff in bothwatersheds. On the other hands, DOC were sometimes higher than DIC in CP watershed, while DOC were always
lower than DIC in DB watershed. These observations suggest that the ratio of runoff components flowing throughthe near surface zone tend to be higher in CP watershed during rainfall events. To demonstrate this hypothesis, we
conducted the End-Members Mixing Analysis (EMMA) using throughfall, soil water, and spring water(groundwater) as end-members to determine the contribution rate of each component.
Keywords: Paired catchment study; DOC; DIC; Flow path; EMMA

rainfall-runoff processes, and changes in discharge
and solution concentrations in stream water.
However, few studies have focused on the effects ofanthropogenic forest alteration on ecosystem
functions and the relationship between hydrologicalprocesses and material dynamics.
To evaluate the effects of coniferous
plantation on material dynamics, it is necessary to
elucidate formation mechanisms of water quality
based on water flow paths. Paired catchment study
between coniferous plantation and natural forest
watersheds will serve for this purpose. To estimate
the flow paths, substances that show characteristic
dynamics depending on hydrological processes areused as tracers, and the interrelationships between
their quantitative or qualitative dynamics andhydrological variables are evaluated. Recently, theIntroduction
Coniferous plantation, one of theanthropogenic alterations of forest ecosystems,
affects the hydrological processes in forest
watersheds. For example, the ratio of discharge toprecipitation is lower in evergreen coniferous
plantation watersheds than in natural deciduous
broad-leaved forest watersheds, caused bydifferences in interceptional evaporation orevapotranspiration cause differences in watershedwater runoff (Hisada et al., 2011; Nagasaka et al.,2015). These differences in hydrological processes
also affect material dynamics, such as carbon,
nitrogen, and inorganic ions through water flow.
Some studies on forest hydrology have reported
much information about water quality dynamics in81

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –cover 65% of the watershed, deciduous broad-leaved
forests 28%, and natural coniferous forests 2%.
Coniferous plantation is 50 to 60 years old. The
southern watershed has 0.72 km2, an average slopeof 33.1° and is located at 909 to 1278 m elevation.
The vegetation is a mixture of secondary deciduous
broad-leaved forests such as Quercus crispula andMagnolia obovata, and natural coniferous trees suchas Tsujopsis dolabrata and Chamaecyparis pisifera.
The mixed forest covers 91% of the watershed. The
geology and soil type in both watersheds is Nohi
rhyolite and brown forest soil respectively. Thus, thissite can be regarded as paired catchment with similar
climatic and geological conditions. A v-notch weirhas been installed at the outlet of each watershed,and stream discharge has been monitored. The
annual precipitation is about 2,500 mm, with highrainfall during warm season from May to November.
The mean annual air temperature is about 10°C.

dynamics of Dissolved Organic Carbon (DOC), suchas humic substances, and Dissolved Inorganic
Carbon (DIC), such as dissolved carbonic acid, havebeen actively studied due to the growing interest in
carbon cycle research, then their runoff process
based on water flow paths well understood (e.g.
Boyer et al., 1996; Hinton et al., 1998; Sakamoto etal., 1999; Katsuyama and Ohte, 2002; Kawasaki etal., 2002; Ohte and Kawasaki, 2004; Haga et al.,2007a; Haga et al., 2007b; Tsurita and Ishizuka,2013). These studies showed that DOC
concentrations increase during rainfall andsnowmelt period, when runoff components passing
through near the soil surface with rich organic matter
predominate. Moreover, DIC is supplied bydissolution of CO2 in soil air, deep soil, or leachingfrom bedrock. Thus, dissolved carbon dynamics instream water can be an effective indicator for
estimating water runoff processes.
Hooper et al. (1990) and Christophersen etal. (1990) proposed a method called End-Members
Mixing Analysis (EMMA), which assumes thatstream water is formed by the mixing of source water,such as groundwater and soil water in the watershed.
EMMA is useful for estimating the flow paths in thewatershed, since the contribution of each source
water can easily calculate from the concentrations of
substances in the stream water and source water.
In this study, to clarify the effects ofconiferous plantation on water and material
dynamics, we conducted the pair catchment study
between coniferous plantation (CP) watershed andnatural secondary deciduous broad-leaved forest(DB) watershed. We quantified the differences inwater and material dynamics in CP and DB
watershed. In addition, we compared material
dynamics focusing on DOC and DIC, and estimatedthe differences in water flow paths by EMMA.

Methods
1. Hydrological observation

Precipitation was measured using atipping bucket rain gauge installed at the outlet of
DB watershed. In addition, we obtained precipitation
data observed at AMeDAS Funayama Station,
Takayama City, Gifu Prefecture, to supplement
missing periods. Stream temperature and pressure
were measured at the v-notch weir in each watershedusing pressure logger (HOBO U20 model Water
Level Logger). Using the same type of logger, airtemperature and pressure was measured at thedownstream of the study site (elevation is 780 m).

Site Description
The study was conducted in paired
catchments located in Kuraiyama Experimental
Forest, Gifu Field Science Education and ResearchCenter, Faculty of Applied Biological Sciences, Gifu
University (Figure 1). The northern watershed has
0.60 km2, an average slope of 32.5°, with anelevation ranging from 926 to 1278 m. Coniferous
plantations, mainly Japanese cedar (Cryptomeria
japonica) and cypress (Chamaecyparis obtusa)

82

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –The measurement intervals were every 5 minutes
from May 22, 2020 to December 4, 2020, every 10
minutes from December 4, 2020 to April 14, 2021,
and every 3 minutes from April 14, 2021 to August
31, 2021. The stream water level was calculatedusing the observed water and air pressure, andconverted to stream discharge by applying weirformula.
𝑄𝑄 = 𝐶𝐶ℎ5⁄2

2
0.004
0.2
ℎ
𝐶𝐶 = 1.357 ++ �0.14 + 1⁄2 � � − 0.09�
ℎ𝐵𝐵
𝑊𝑊

3. Chemical analysis(1)

Water samples were filtered through a 0.7
μm pore glass fiber filter (Whatman GF/F) or a 0.45
μm pore syringe filter (ADVANTEC DISMIC®25CS), and kept refrigerated until analysis. DOC andDIC concentrations were analyzed using totalorganic carbon analyzer (SHIMADZU TOC-L,
TOC-V). Na+, K+, Mg2+, Ca2+, Cl-, NO3-, and SO42concentrations
were
measuredbyionchromatograph (DKK-TOA ICA-2000).
4. Estimate of the origin of runoff waterwhere Q is stream discharge (m3/s), C is the flow
coefficient (m1/2/s), h is the overflow depth (m), B is
the weir width (m), and W is the height from theriverbed to the bottom of the weir notch (m).

We estimated the origin of runoffwater
during rainfall events by End-Members Mixing
Analysis (EMMA). EMMA assumes thatstreamwater chemistry is determined by the mixing
of source water, such as rainwater, soil water, andgroundwater. First, we select two materials as tracer.
Then concentrations of these tracer in streamwaterand each source water (end-member) are plotted on
the same graph called mixing diagram. In the graph,streamwater would be plotted in a polygon
connecting each end-member, and the plot positionreflects the contribution rate of each end-member. In
case of using three end-members, the contribution
rate to streamwater is calculated by the next equation.

2. Water sampling

Streamwater samples were collected at theoutlet of each watershed using two automatic water
sampler (ISCO 6712 model). While one sampler
collects water once a day (CP: at 13:00, DB: at
12:00), another collects water once in a 2-hour when
stream water level rises during rainfall period.
Sampling started in May 22, 2020, and stopped
during winter season (December 5, 2021 to February
28, 2021).
To quantify how water quality change inhydrological processes, we collected rainfall outside
the forest (P), throughfall (TF), soil water (10 cm and50 cm depths), and spring water (SP) as a surrogate
index of groundwater. Rainfall was collected bythree 10 L collection bottles, with a 21 cm diameterfunnel, at the office (elevation is 750 m). To inhibit
microbial activity, 10 mL of 0.1 mg/L CuBr solution
was added to the collection bottles. Furthermore,glass wool was placed in the funnel neck, and a meshbag covered the top of funnel, to prevent possible
contaminations. For throughfall and soil watercollection, we set up one experimental slope in eachwatershed. Throughfall was collected by collection
bottles same as rainfall. Soil water was sampled by
the tension lysimeter (DIK-8392) at 10 cm and 50
cm depths. In each experimental slope, we placedcollectors on the upper, middle, and lower point of
the slope. Spring water was collected at one spring
point located near the experimental slope in eachwatershed. Sampling started in May 2021, with abiweekly sampling interval.

𝑓𝑓1 + 𝑓𝑓2 + 𝑓𝑓3 = 1
𝐶𝐶[𝐴𝐴]1 𝑓𝑓1 + 𝐶𝐶[𝐴𝐴]2 𝑓𝑓2 + 𝐶𝐶[𝐴𝐴]3 𝑓𝑓3 = 𝐶𝐶[𝐴𝐴]𝑠𝑠𝑠𝑠
𝐶𝐶[𝐵𝐵]1 𝑓𝑓1 + 𝐶𝐶[𝐵𝐵]2 𝑓𝑓2 + 𝐶𝐶[𝐵𝐵]3 𝑓𝑓3 = 𝐶𝐶[𝐵𝐵]𝑠𝑠𝑠𝑠(2)

where C[A] and C[B] are the concentrations of thetwo tracers A and B, f is the contribution rate of eachcomponent, subscripts 1, 2, and 3 indicate the endmembers, and st is the stream water. In this study,
rainfall component (rainfall and throughfall), soil
water component (10 cm and 50 cm depth) andgroundwater component (spring water) wereselected as the end-members, assuming that thesecomponents contribute to streamwater. We applied
the EMMA to streamwater during rainfall runoff
period collecting in July 2, and August 9, 2021. Two
different tracers were selected from the ninematerials potentially utilized for tracers. Thus, totalof 36 combinations of two chemical compositions
were tested. For the most promising tracer
combinations, we calculated each component
contribution rate, and the applicability of EMMA
were verified. The tracer concentrations for each
end-member were the arithmetic mean of the water
samples collected in May to September 2021. For
rainfall, throughfall and soil water, the arithmetic
83

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –mean of the upper, middle, and lower slope
concentrations were used.

with value 0.6 ± 0.4 mgC/L than in DB watershedwith value 0.4 ± 0.2 mgC/L. The ratio of DIC to
DOC concentration (DIC/DOC) was 2.0 ± 0.2 in CP
watershed and 5.6 ± 3.0 in DB watershed, withhigher values in DB watershed. Among otherdissolved ions, Na+, and Ca2+ showed clear
difference in concentration, with higher
concentrations in the DB watershed. Similar to DOC
concentration, NO3- concentration was slightly
higher in the CP watershed.

Table 1: Solute concentrations for both watersheds
Means ± standard deviations are shown.
CP
DB
DOC

(mgC/L)0.6 ± 0.4

0.4 ± 0.2

DIC

(mgC/L)

1.1 ± 0.2

1.8 ± 0.4

(mg/L)

1.9 ± 0.4

2.6 ± 0.5

+

Na
K

+

(mg/L)

0.4 ± 0.2

0.5 ± 0.1

2+

(mg/L)

0.1 ± 0.0

0.1 ± 0.02+

(mg/L)

0.6 ± 0.1

1.3 ± 0.3

Mg

1. Daily concentrations of streamwater
The streamwater concentrations from May
22, 2020, to August 31, 2021 are shown in Table 1.
DIC concentrations were clearly higher in DB
watersheds with 1.8 ± 0.4 mgC/L than CP watershedwith 1.1 ± 0.2 mgC/L. On the other hand, DOC
concentrations were slightly higher in CP watershed

2. Concentration dynamics during rainfall period
From the start of sampling in May 2020 toAugust 2021, 11 peaks in CP watershed, and 14
peaks in DB watershed out of 14 rainfall events werecollected. The significant increase in DOC and NO3concentrations was observed in both watersheds at
all rainfall runoff events. These increase roughly
paralleled to the timing of the stream discharge
increase. DIC, Na+, and Ca2+ concentrations, whichshowed clear differences between two watersheds,
didn’t show significant changes in concentrations,although in many cases there was a slight decreasein concentration as the runoff peak occurred. Due tothe rapid rise in DOC concentration, DIC/DOC

CP watershed

DB watershed

Ca

Cl-

(mg/L)

0.8 ± 0.2

0.7 ± 0.2

NO3-

(mg/L)

1.0 ± 0.5

0.9 ± 0.22-

(mg/L)

0.6 ± 0.4

0.9 ± 0.1

SO4

Results■ NO3-■ NO3-

Figure 2: Comparation of discharge, DOC, DIC and NO3- concentrations between CP and DB watershed during
rainfall period (July 2, 2021). Left side is CP watershed, and right side is DB watershed.
84Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –

CP

DB

CP

DB

CP

DB

Figure 3: Variations of DOC, DIC, and NO3- concentrations. Left side is CP watershed, and rightside is DB watershed. P: Rainfall (n = 8*), TF: Throughfall (CP: n = 9**, DB: n = 9***), SW10: 10 cm depth
soil water (CP: n = 6, DB: n = 6), SW50: 50 cm depth soil water (CP: n = 6, DB: n = 6), SP: Spring water (CP:n = 11, DB: n = 7), ST: Streamwater (CP: n = 322, DB: n = 359).
*: For NO3-, n = 6. **: For NO3-, n = 4. ***: For NO3-, n = 6.

during rainfall runoff became smaller in bothwatersheds. In CP watershed, however, DOC
concentration was sometimes higher than DIC
concentration. In contrast, in DB watershed, DOC
concentration was always lower than the DIC
concentration. Variation of DOC, DIC and NO3concentrations during the rainfall events on July 2,
2021, are shown in Figure 2.

3. Variation of concentrations in rainfall, throughfall,soil water, spring water, and streamwater
Variation of DOC and DIC concentrationsin the different water are shown in Figure 3.
Variability in dissolved carbon concentrations was
similar in CP and DB watersheds. DOC
concentrations were increased in the rainfall,
throughfall, soil water, and the highest concentration
is observed in surface soil water. Then decreased85

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –CP watershed and 30 % in DB watershed. After therainfall stopped, contribution of the spring water
increased again. The increment in the contribution
rate during peak runoff was more pronounced for thethroughfall component, which in some cases
exceeded the spring water contribution rate. The
mean contribution rate of throughfall was 9 % in CP
watershed and 23 % in DB watershed. Furthermore,
the maximum contribution rate of the throughfall
was 40 % in CP watershed and 69 % in DB
watershed, with the contribution of throughfall in theforest being higher in DB watershed. Soil watercomponent had the smallest contribution among thethree components, ranging from 2 to 12 % in CP
watershed and 0 to 6 % in DB watershed. In CP
watershed, soil water contribution persisted for sometime during the runoff recession period, while in DB
watershed was almost didn’t have contribution in thelate recession period. Furthermore, the contributionof soil water components was always about 5%
higher in CP watershed than in DB watershed. The
mean contribution rate of spring water is 87 % in CP
watershed and 81 % in DB watershed and tended toslightly in 50 cm depth soil water, the lowest
concentrations were observed in spring water.
Stream water showed a slight increase inconcentration compared to spring water. On theother hand, DIC concentration decreased through theprocess from rainfall to soil water, and the lowest
concentration was observed at 50 cm depth of soil
water. Then significantly increased in spring water
to a maximum concentration. In stream water, DIC
concentrations were lower than spring water, butalmost same or higher than rainfall and soil water.
The trend of high concentrations in soil water andrapid decrease in spring water was also observed inNO3-. However, in some cases, NO3- concentrations
were not detected in rainfall and throughfall. This
result was different from previous study in this site
(Ashik, 2021), thus there is uncertainty in theconcentration values. Na+, Mg2+, and Ca2+ increased
from soil water to spring water, which was similar to
DIC pattern.
4. Estimation of the contribution ratio of each endmember in runoff water

We found that DOC and NO3- werecommonly able to represent the occurrence andvariability of rainfall-runoff concentrations in bothwatersheds. Note that NO3- concentrations of rainfall
components are uncertainly, and the number of data
is small.
For example, Figure 4 shows the mixing
diagrams for event on July 2, 2021, using throughfall,
10 cm soil water, and spring water as end-members.
In both watersheds, the streamwater plots were
distributed around the spring water. During peakrunoff, however, the plots moved toward the straightline connecting throughfall and soil water endmember. As stream discharge diminished, the plots
were again plotted near the spring water. This trend
was also observed in the case of using rainfall or 50
cm depth soil water as the end-member, and in theevent of August 9, 2021.
We calculated the contribution rate for
Figure 4 and found that before the occurrence of thepeak runoff, the contribution of spring water was thelargest, accounting for more than 80 % of the stream
runoff. When the runoff volume increased, thecontribution of the throughfall and soil watercomponent increased, while spring water
contribution relatively decreased. The contribution
rate of spring water during peak runoff was 48% in●: Streamwater ◇: End-Member

Figure 4: Mixing diagram on July 2, 2021
rainfall period

86Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Since DOC and NO3- meet this requirement andshow clear concentration variations during rainfallrunoff events, they can be used as tracers for EMMA.
On the other hand, Na+ and SO42-, which have beenused as tracer in some studies, such as Katsuyama etal. (2001), couldn’t explain peak runoff in our study
site. This is probably due to the fact that theconcentration variation during rainfall runoff issmaller than those of DOC and NO3-.
The shift of stream water plots on the
mixing diagram indicates that the streamwaterchemistry approaches that of the rainfall and soil
water components as rainfall runoff occurs. During
recession period of runoff, the contribution of
rainfall and soil water components stops or
decreases, then streamwater chemistry returns tonear the spring water. A similar trend has beenreported in the forested watersheds of theheadwaters of the Shimanto River in Japan(Shinomiya et al., 2014). Therefore, the stream water
during rainfall runoff can be explained by the
mixture of rainfall, soil water, and spring water, andthe peak rainfall runoff is caused by the increased
contribution of rainfall and soil water components.
The contribution rate of each end-member
suggests that the contribution of soil-watercomponent might be greater in CP watershed than
DB watershed during rainfall-runoff events, which
supports the hypothesis that near-surface flow paths
are more prone to occur in CP watershed. Moreover,the contribution of soil water components is
continuously observed even in recession period. The
contribution of draining water from soil into stream
should significantly contribute to entire rainfall
runoff event. In the DB basin, the contribution ofthroughfall component was larger than that of springwater component, then we found no evidence ofsignificant contribution of groundwater component
in DB watershed. Since the contribution rates ofspring water and throughfall components vary in acomplementary, the relatively high contribution rateof throughfall might have caused a decrease in thecontribution rate of spring water. Katsuyama et al.
(2000) reported that the contribution of the rainfall
component was approximately 20 to 40 %. However,
our results were much higher than this value. For
rainfall and throughfall, microbial activity in thecollection bottles was considered to have an effect.
We found algae floating in some samples regardlessof the addition of CuBr. Therefore, there is

be relatively low in the DB watershed. The same
trend of variation in the contribution of each endmember was observed on August 9.
Discussion
Generally, DIC, Na+, Mg2+, and Ca2+
concentrations are higher in deeper soils and in
runoff components which experiences long contact
time with the bedrock. In contrast, DOC and NO3mainly produced in the near surface soil due to plant
litter deposition and microbial decomposition oforganic matter. In the study site, DOC and NO3concentrations were maximum in soil water, while
DIC, Na+, and Ca2+ concentrations in spring water
were significantly higher, indicating that DOC andNO3- decline, and increasing reaction of DIC, Na+,and Ca2+ occur during the process from surface soil
to spring water. This trend is commonly found in CP
and DB watersheds, suggesting that the sources ofdissolved materials are similarly distributed
regardless of vegetation in this site.
The fact that concentration of materials
mainly derived from the bedrock is high, and
DIC/DOC ratio in DB watershed is high suggest thatthe contribution of runoff components which have along contact time with the deep soil and bedrock,such as groundwater, is significantly large in DB
watershed than in CP watershed. The increase in
DOC and NO3- concentrations linked to runoff peaks
during rainfall period proves that rainfall runoff isformed by component passing through near the soil
surface with high DOC and NO3-. Large increase of
DOC concentrations in CP watershed during rainfall
suggests that near-surface flow paths during rainfall
in CP is much dominant then in DB, and thecontribution of near-surface component to stream
water is greater. The difference in the contribution ofnear-surface flow paths and deep soil or matrix flow
paths may create long-term and short-term
differences in material dynamics between
watersheds.
EMMA using DOC and NO3-explained
the occurrence of rainfall runoff due to the mixtureof three components: rainfall, soil water, and springwater. Both DOC and NO3- are extremely high in soil
water, and rainfall components or spring water isrelatively low. The tracers used in EMMA are
required to have sufficient concentration differences
between each end-members (Hooper et al., 1990).

87

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Japan Society on Water Environment. 30(11):657-660.
Hinton, M.J., Schiff, S.L., and English, M.C.
(1998) Sources and flowpaths of dissolved
organic carbon during storms in two forested
watersheds of the Precambrian Shield.
Biogeochemistry. 41: 175-197.
Hisada, S., Senge, M., Ito, K. and Maruyama, T.
(2011) Comparison of characteristics of waterbalance between evergreen coniferous anddeciduous broad-leaved forest. I D R E Journal.
271: 1-7.
Hooper, R.P., Christphersen, N. and Peters, N.E.
(1990) Modelling streamwater chemistry as a
mixture of soilwater End-Members – anapplication to the Panola Mountain catchment,
Georgia, U.S.A. J. Hydrol. 116: 321-343.
Katsuyama, M, Ohte, N., Uchida, T., Asano, Y. andKimoto, A. (2000) Effects of the Differences ofHydrological Processes on the Streamwater
Chemistry. J. Japan Soc. Hydrol. & Water
Resour. 13(3): 227-239.
Katsuyama, M., Ohte, N., and Kobashi, S. (2001) A
three-component End-Member analysis ofstreamwater hydrochemistry in a small Japanese
forested headwater catchment. Hydrological
Processes. 15: 249-260.
Katsuyama, M. and Ohte, N. (2002) Determining
the sources of stormflow from the fluorescence
properties of dissolved organic carbon in aforested headwater catchment. Journal of
Hydrology. 268: 192-202.
Kawasaki, M., Ohte, N., Nambu, K., Hobara, S.,
Okazaki, R., Katsuyama, M. and Kim, S. (2002)
The dynamics of DOC in the hydrologicalprocess in a forested watershed. Japanese
Journal of Limnology. 63: 31-45.
Nagasaka, A., Nagasaka, Y. and Ishikawa, Y. (2015)
Export of stream dissolved organic carbon andinorganic nitrogen at two headwater catchmentswith different forest types in central Hokkaido,
Japan. Bulletin of the Hokkaido Forestry
Research Institute. 52: 11-22.
Sakamoto, T., Takahashi, M., Terajima, T., Nakai,Y., and Matsuura, Y. (1999) Comparison of theeffects of rainfall and snowmelt on the carbon
discharge of a small, steep, forested watershedin Hokkaido, northern Japan. Hydrological
Processes. 13: 2301-2314.

uncertainty in the concentration values of rainfall
components. Furthermore, it is also possible that theend-member and tracer are chosen incorrectly.
For further improvement of accuracy of
EMMA, silicate, which is leached during thechemical weathering process of bedrock minerals
and is rarely used by organisms, can be used as a
tracer.
Acknowledgment
We would like to acknowledge the support
provided by members of the Laboratory of
Watershed Management, Laboratory of Otsuka,
River Watershed Research Center, Laboratory ofMontane Forest Ecology and Management, Mr.
Akinori Tsuzuki and Mr. Masaya Aoki, Gifu Field
Science Education and Research Center, Gifu
University. We also would like to acknowledge
Professor Kosugi, Kyoto University, and Professor
Katsuyama, Kyoto Prefectural University, for the
proposal of EMMA.
References
Ashik, T. (2021) Paired catchment study of nitrogendynamics in cool-temperate broadleaved
deciduous and evergreen coniferous forests,
central Japan. Master’s thesis of Graduate
School of Natural Science and Technology, Gifu
University.
Boyer, E.W., Hornberger, G.M., Bencala, K.E., andMcKnight, D. (1996) Overview of a simple
model describing variation of dissolved organiccarbon in an upland catchment. EcologicalModelling. 86: 183-188.
Christophersen, N., Neal, C., Hopper, R.P., Vogot,
R.D. and Anderson, S. (1990) Modellingstreamwater chemistry as a mixture of soilwater
End-Members – a step towards secondgeneration acidification models. J. Hydrol. 116:
321-343.
Haga, H., Nishida, K. and Sakamoto, Y (2007a)
Effects of flow paths on dissolved organiccarbon concentrations in a forest stream.
Journal of Japan Society on Water Environment.
30(10): 573-578.
Haga, H., Nishida, K. and Sakamoto, Y (2007b)
Dissolved organic carbon concentrations in aforest stream and hydrological processes in ahillslope during storm flow periods. Journal of88Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Tsurita, T. and Ishizuka, S. (2013) Dissolved
organic- and inorganic- carbon dynamics in asmall watershed of Japanese cedar and cypress
plantation in northern Kyushu. Kyushu J. For.
Res. 66: 97-98.

Shinomiya, Y., Yamada, T., Inagaki, Y., Yoshinaga,
S. and Torii, A. (2014) Evaluation of annual
nitrate nitrogen load and its runoff processes ina forested catchment in the Shimanto River
headwaters. Journal of Japan Society on Water
Environment. 37(3): 91-101.

89

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Estimating of Run off Using SCS-CM Model in Gunung Bromo
Education Forest Discharging to Kalijirak River
Dwi Priyo Ariyanto 1,2,*, Tio Egar Nurhutomo 3,
Mujiyo 1, Komariah 1

Soil Science Department, Faculty of Agriculture, Universitas Sebelas Maret, Indonesia
Forestry Education and Training Center, Universitas Sebelas Maret, Indonesia
3
Students of Agrotechnology Study Program, Faculty of Agriculture, University ofSebelas Maret Surakarta*Corresponding Author: dp_ariyanto@staff.uns.ac.id
1
2

SUMMARY
This study aims to calculate the effective runoff volume estimation at Gunung Bromo Education
Forest of Universitas Sebelas Maret, Indonesia based on the SCS-CN formula. Based on the formula used, aslope map and land cover map are needed as the basis for calculating the volume of runoff. Slope map isclassified into 5 classes and land cover map is divided into 6 land cover. Overlay process is carried out to get
the final result of the land map unit. Field surveys were carried out to obtain data on slopes, types of cover,and laboratory analysis was carried out to obtain soil texture data. The results showed that the estimatedresearch location had an effective area of 83.69 hectares or about 62% and a runoff volume of 696,321.45 m3.
The results of this study can be used as basic data to make an appropriate hydrological management plan.
Keywords: Forest; KHDTK Gunung Bromo; Land cover; Runoff; SCS-CN model
region varies depending on the biophysicalwatershed (Hanifiyani, 2015) The purpose of forest
management by taking into account hydrological
data is to maintain sustainable water management, sothat hydrological data can be explored for optimalhydrological governance on land, as well as areference for the prevention of adverse impacts
resulting from runoff.
Gunung Bromo Education forest is an areathat has a corrugated topography in which the riverflows. However, there is no information regarding
the effective contribution of Gunung Bromo
Education Forest volume that can be donated to subwatershed Kaljirak through runoff in the area. Oneof the models used to predict the volume of runoff ofa watershed by the SCS-CN (Soil Conservation
Service Curve Number) method. This study aims toknows the effective area estimate of Gunung Bromo
Education Forest, the volume of runoff contributed
to sub-watershed Kalijirak, and the influence of theenvironment along with the relationship of runoff
volume parameters.

Introduction
Watershed management of is an important
aspect as a way to maintain a unity of upstream anddownstream areas, due to the reciprocal relationshipbetween the two. Medawaty, Iriani & Nuraeni (2014)
explained that several watersheds in Indonesia havedecreased river conditions, due to the not optimal
management of land function impacts into
settlements from local natural resources that cause
land degradation. Many phenomena arecharacteristic of the occurrence of degradation.
According to (Isrun, 2009) the characteristics of landdegradation include the discharge of river water inthe dry season is very low, acceleration of
sedimentation in lakes and irrigation networks, aswell as a decrease in water quality. Therefore,watershed management needs to be done in order toprovide benefits.
The increasing biophysical function ofwatersheds in the form of land cover today fromforest areas to plantations, agriculture, andsettlements resulted in changes to the water system
conditions. Optimization of forest area will affect theavailability of water in sub-sub-watersheds forests
can significantly affect watershed water systems,
although the optimal cover area (forest) in each

Materials and Methods
The research of runoff volume wasconducted at Gunung Bromo Education Forest (or inIndonesian named KHDTK Gunung Bromo UNS)

90

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –located in Karanganyar District, Central Java
Province, Indonesia, from December 2020 toJanuary 2021. Primary data is obtained through fieldobservation surveys, as well as laboratory analysis.
Rainfall data was taken from Jumantono weather
station which 6 km from Gunung Bromo Education
Forest between 2009 to 2018. The determination of
the sample point is done by purposive sampling of
the results of land unit obtained from the overlay ofland cover maps and slope maps. The land cover
map was obtained from Forestry Education andTraining Center. The slope map of the slope isprocessed from Indonesian Geospatial Information
Agency in scale of 1:25,000.
Sampling is done by sampling the soil
continuously in the topsoil layer (depth 0 - 20 cm)
using a hoe in each land unit specified. Overlaying
the map using a scale of 1:12,000 produces a land
unit map with 16 sample points. Observations
observed in the field in the form of data type closure,land management, and hydrological conditions.
While the data in the laboratory analysis in the formof soil texture which is then collected for calculation
using SCS-CN equation to obtain an estimate ofrunoff volume at the research site. Estimating runoff
volume using the SCS–CN method is supported by
the potential retention value of the runoff area.

results of the broad analysis of Catchment Area arepresented in Table 1.
Table 1: The effective runoff area per land unit
Land Unit
Mixed (25-45%)
Mixture (>45%)
Mahogany (25%-45%)
Mahogany (>45%)
Season (25%-45%)
Season (>45%)
Former nursery (>45%)
Pinus (<8%)
Pinus (9%-15%)
Pinus (16%-24%)
Pinus (25%-45%)
Pinus (>45%)
Mahogany Pine (<8%)
Mahogany Pine (9%-15%)
Mahogany Pine (25%-45%)
Mahogany Pine (>45%)

Land
area
ha
18,36
12,14
1,70
1,97
6,53
3,64
3,10
0,96
1,79
5,35
38,45
26,89
0,12
3,87
12,86
11,81

Effective
runoff area
ha
%
14,40
78
11,10
91
1,70
100
1,97
100
5,52
84
3,44
94
3,10
100
0,95
99
0,68
38
1,86
35
14,16
37
1,02
38
0,01
1
1,68
43
10,31
80
11,81
100

Results and Discussion
1. Wide effective volume runoff KHDTK Gunung
Bromo
The delineation process is made with theprinciple of topographic data extraction to obtain
input values determining the characteristics ofwatershed hydrology (Purwono, Hartanto, Prihanto,& Kardono, 2018). Overland flow or flow runoff, toknow the form of runoff flow and the area intended
water when covering based on relief form
topography or soil height. The direction of runoff byusing the D8 algorithm method. Determination of
topographic direction based on pixel algorithm
based on height and then compared between 1 pixel
to 8 pixels around it (Indarto, Wahyuningsih, Usman,&Rohman, 2008). After obtaining the results area ofwater catchment, then calculate the area using
calculate geometry.
Determination of the effective area valueof each land unit by combining or intersect effective
wide of Gunung Bromo Education Forest area. The

Figure 1: Graph of effective runoff volume per year

2. Effective runoff volume
The main purpose of effective runoff
volume estimation is to obtain specific and reliable
information for hydrological management of a landarea. Factors that affect the volume of runoff include
factors of land use type, factors of land management,
hydrological condition factors, factors of soilhydrology group. The data obtained is then used for
the SCS-CN equation and generates the averagevalue of runoff volume per year per SPL region and91

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –with a total of 115.81 mm / m2/ year, and the highest
runoff volume contributor cover at pine land coverwith 1036.76 mm / m2/ year. There is a noticeable
difference in the type of cover of pine-mahogany andpine land against former seedlings and mixtures, andmahogany and land land a season there is no realdifference to other land cover. This is because
according to (Verrina, Anugrah, & Sarino, 2013)
land that has dense vegetation, rainwater that falls
will be stuck to vegetation and seep into the soil, sothat runoff flows small, so there are other factors that
affect the volume of runoff. The conditions in thisstudy are not uniform slope levels on each land coverthat makes a noticeable difference to the value ofrunoff volume.

is presented in Figure 1.
The total effectiveness of the averagerunoff volume of Gunung Bromo Education Forest
in the last 10 years was able to account for thevolume of runoff by 63% with the total effective
volume of 698,334.95 m3 of the total catch of runoff
volume of Gunung Bromo Education Forest(1,117,264.34 m3). This is because the contour
shape of the soil on Gunung Bromo Education Forest
is hilly, causing the runoff volume does not lead to
sub-watershed Kalijirak. According to Bambang,2010, runoff comes from the highest points andmoves towards the lower points in a perpendicular
direction with contour lines.
3. Effect of environmental parameters on effective
runoff volume
There is an environmental influence fromthe results of runoff volume value where after
statistical tests, it is obtained that the environmental
results that affect the magnitude of the runoff volume
value of each land unit is the influence of land coverand the influence of slope. According to (Sarminah
et al., 2018) different levels of vegetation density
types, can suppress the rate of surface runoff, so thetighter the heading of vegetation covering thesurface, the lower the rate of surface runoff. Whileon the slope in accordance with the statement(Suryanto & Wawan, 2017) slope has an impact on
the rate of surface flow the more tilted a land, thehigher the runoff rate influenced by gravitational
forces. The results of One Way Anova test thatshowed the influence of land cover environment andland slope on the volume of runoff effectively thentested further DMRT statistics (Table 2).

Table 3: Effect of slope on effective runoff volume per
year
Slope
3 (16%-24%)
2 (9%-15%)
1 (0%-8%)
4 (25%-45%)
5 (>45%)DMRT test results of land slope classpresented in table 4.18 show a noticeable differencein each of the 5 classes of land slope to the volumeof effective runoff. where the slope of the land class3 (16%-24%) has the lowest runoff volume
contributor factor with a total of 179.83 mm/m2/year,
while the highest at slope 5 with a runoff volume of
934.67 mm/m2/year. There is a noticeable differencein each of the 5 grades of slope to the volume ofeffective runoff whereas in the slope class 3 (16%24%) and slope 2 (9%-15%) there is a noticeable
difference in the slope class 4 (25%-45%) and 5(>45%), while at a slope of 1 (0%-8%) has a
noticeable difference to the slope of 5 (>45%)
according to (Suryanto & Wawan, 2017) the slopeclass of the land has a difference in the angle ofweight force (gravity) The difference in weight force
is getting bigger in line with the increasing tilt of theground surface from the horizontal plane. Thisweight force is an absolute requirement of theprocess (transportation) of water to a lower height.
The conditions in this study are not uniform slope
levels on each land cover that causes there to be a
noticeable difference in the value of runoff volume
that is not in accordance with existing theories.

Table 2: The Effect of land cover on effective runoff
volume per year
Land CoverFormer Nursery
mixturemahogany
A season
Pinus-Mahogany
Pinus

Runoff volume (mm/m2)
179,83a324,47a359.66ab
802,97bc
934,67c

Runoff volume (mm/m2)
115.81a231.61a431.60ab485.20ab829.60bc
1,036.76bc

DMRT test results of land cover presented
in Table 3 showed a noticeable difference in each of
the 6 types of land cover to the volume of effective
runoff, where the type of land cover used seedlings
have the lowest end runoff volume contributor factor
92

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Conclusions

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Aliran Sungai ( Das ) Studi Kasus : Kawasan
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Aliran Sungai (DAS). Restorasi Sungai:
Tantangan Dan Solusi Pembangunan
Berkelanjutan, 490–504. Surakarta: Badan
Informasi Geospasial.
Sarminah, S., Prititania, F. S., Kehutanan, F.,
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Jurnal Teknik Sipil Dan Lingkungan, 1(1).

Effective runoff area of Gunung Bromo
Education Forest account for 83.71 ha which flowed
698,334.95 m3 per year. Land cover and slope
influence to runoff volume in Gunung Bromo
Education Forest.
Acknowledgment
This work was supported by Universitas
Sebelas Maret Research Grant (PUT-UNS) financial
year 2019-2021.
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hidrologi menggunakan model swat di sub das
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L. (2008). Pembuatan Jaringan Sungai Dan
Karakteristik Topografi Das Dari DEM- JATIM.
Media Tek Sipil. MEDIA TEKNIK SIPIL, (37),
99–108.
Isrun. (2009). Analisis tingkat kerusakan lahan padabeberapa sub das di kawasan danau poso. Media
Litbang Salteng, 2(1), 67–74.
Medawaty, I., Iriani, L. Y., & Nuraeni, R. (2014).
Penerapan Teknologi Pengolahan Air Secara

93Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –The Runoff and Sediment Response Due to Land Use Change in the
Lubuk Paraku Sub-Watershed
Edwina Zainal 1*, Zufrimar 1, Zahrul Umar 1, Irfan Andika S 1

Faculty of Civil Engineering and Planning, Bung Hatta University*Corresponding Author: edwinazainal@bunghatta.ac.id
1

SUMMARY
Batang Arau catchment area consists of several tributaries, with the main river being the Batang
Arau River. The upstream of the Batang Arau catchment area is Lubuk Paraku sub-watershed which is in thenortheast of Padang City, with a catchment area of 25.04 km2 approximately. In this study, the land-use change
from 2007 to 2017 was identified by Arc-GIS software and examined the impact of land-use changes on runoff
response and sediment load. The Rational Method was used to express a relationship between rainfall runoff
in the catchment area to analyze the rainfall-runoff response concerning land-use change. The sedimentcapacity was estimated by the Yang, Ackers & White, and Engelund & Hansen method. The results showed
the decreasing of dense forest and the increasing of the runoff and sediment capacity. The rate of runoff is107.86 m3/s and 121.68 m3/s, in the years 2007 and 2017, respectively. The sediment capacity was 36266.33
t/y and 39730.19 t/y, in the years 2007 and 2017 that estimated by Engelund and Hansen method.
Keywords: Land-use change; Rainfall-runoff analysis; Sediment load; Lubuk paraku

use conversion in the water catchment area.
Nurhamidah, et al., (2018) showed the upstream of
the Batang Arau catchment area continued to decline
due to the land-use conversion. The increasing
activity in the upstream area will have an impact on
the midstream to the downstream area in the changesof runoff fluctuations and sediment load. Runoff
response generally depends on the shapes, length,
slopes, perimeter, and areas of the watershed. In theabsence of runoff records and other relatedhydrological information, attempts have been madeto relate runoff to the watershed geometry using
various techniques.
Sediment in the river channel will cause
flood water levels to rise. According to the Padang
City Bappeda (2014), the flooding that occurred in
Padang City was indicated by the damage of the riverbodies in the upstream area.
Base on the description above, it isnecessary to identify the runoff and sediment
response due to land-use change in the upstream of
the Batang Arau catchment area in order tounderstand the hydrological process and predict thehydrological behavior.

Introduction
The land-use change in the upstream
region is recognized to have a significant influenceon the hydrological function of the catchment area.
The hydrological function of the catchment area isthe ability to absorb, hold, store, and slowly drain thewater to create a balance of a water system.
Batang Arau catchment area consists ofseveral tributaries, with the main river is the Batang
Arau River and one of the major rivers in Padang
City. The upstream of the Batang Arau catchment
area is Lubuk Paraku which is located in thenortheast of Padang City. It is dominated byconservation areas and protected forests such as the
Botanical Forest Park Dr. Mohammad Hatta and the
Nature Reserves Barisan I (BPDAS Agam Kuantan,
2011). A small part of the Lubuk Paraku is converted
by the community into settlements, rice fields, andagricultural areas (Rebecha P., et al., 2015; Endes N
D, et al., 2014). It is also a tourist destination areaand flyover construction planned area (RPJMD,
2019).
The RPJMD (Local Government
Medium-term Development Plans 2019-2024) ofPadang City carried out prioritizes economic
development, such as tourist areas, and is supported
by increasingly improved road access. Thisdevelopment is a response to the increasing of land-

Methods
1. Study area

The Lubuk Paraku sub-watershed (figure1) is located in Indarung Village, Lubuk Kilangan

94Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –District. The area has a slope from gentle (8-16%) tovery steep (> 40%) with a dominant hilly topography.
The main river of Lubuk Paraku watershed is Lubuk
Paraku River, with a length of 10.29 km. The
catchment area in the Lubuk Paraku is relatively
small, covering an area of 25.04 km2 or 14.71% of
the Batang Arau watershed total area.
The Lubuk Paraku sub-watershed area
dominates by hilly areas, mountains, primary andsecondary forests with undulating topography.
According to the Regional Development Planning
Agency (Bappeda) of Padang City (2014), the land
use in the Lubuk Paraku sub-watershed protect
forest and tourism forest, such as Bung Hatta Grand
Forest Park. In addition, other land uses are
fields/moorlands, rice fields, abandoned land andsettlements. The soil in this area is intensively
washed by rainwater, so the soil surface looks a bitpale and rough.

Figure 1. Batang Arau watershed and Lubuk Paraku
sub-watershed

3. Sediment load analysis

This research compares several methodsof estimating the total sediment load, namely Yang
Method, Ackers and White’s Method, and Method ofEngelund and Hansen. Sediment parameter data
inventory is done by taking water samples toestimate the concentration of total load sediment. In
addition, measurement of river hydraulic parameteris needed, including flow velocity. Tabel 1 shows theformula that used for calculation method.

2. Rainfall-runoff analysis

The rainfall data from 2003 until 2017 was
taken from the Office of Management of Water
Resources of West Sumatera. This study used the
maximum daily rainfall data from the Ladang Padi
rainfall station. It produces rainfall plan and rainfall
intensity at a certain return period.
Determination of land-use change in 2007
and 2017 by Arc-GIS software is used to determine
the flow coefficient in the Lubuk Paraku sub
watershed in order to estimate the amount of runoff
generated by the rainfall plan. It is used in rational
formula to estimate the runoff debit.

95

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Result and Discussion
1. Rainfall Analysis

Table 2. The Average of The Maximum Daily Rainfall

Figure 2. Rainfall Station in Batang Arau catchment
area

Figure 1 shows the each rainfall station(Gunung Nago, Batu Busuk, Simpang Alai and
Ladang Padi) that may influencing the Batang Araucatchment area on ArcGIS application. According tofigure 1, in this study, the average rainfall data was
obtained from the maximum daily rainfall data in agiven year only from Ladang Padi rainfall station(upstream region). The maximum daily rainfall for
fifteen years in the study area is shown in table 2.
The highest rainfall occurred on 2006 at 155 mm,
while the lowest occurred on 2007 at 75 mm.
The maximum rainfall data was analyzed
with a certain return period in order to show the
relationship between the magnitudes of the extreme
events rainfall to the frequency of occurrence byusing a probability distribution. There were four
methods that were commonly used, such as theNormal, Log-Normal, Gumbel and Log Pearson III.
According to the result and the goodness of fit testof chi-square and the Smirnov-Kolmogorov, theNormal distribution was generally appropriate forthe Lubuk Parake watershed.

R  24 
I = 24  
24  t 

Year

The Maximum Daily Rainfall (mm)

2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017

105
95
118
155
75
80
145
109
118
117
125
125
76
118
122

2. Land-Use Change

Land use is the utilization of land
resources by humans for various purposes andactivities such as industrial, residential, recreational,transport, and agriculture (Azlini et al., 2015). The
Lubuk Paraku watershed is a water catchment andprovides water resources for domestic use
downstream, industrial, and agricultural. Theserivers serve as freshwater sources, irrigation for
agriculture purposes, and are for recreational
activities (Endes N D, et al., 2014).
Table 3. Land-use Change
Land-use
Bare ground
Scrub//Shrub(4)

The Monobe equation (4) is used to
calculate the rainfall intensity (I in mm/hour). It
provides a mathematical relationship between the
duration (t in hour) and observed rainfall (R in mm).
In the study area, the rainfall intensity is 42.52
mm/hour for 2 year return periods.

2017
Area (km2)

1.92

0.99

0.84

0.45

Dense forest

16.80

14.77

Paddy field

0.98

1.63

Farm and
shrub

0.76

1.28

House

1.88

3.38

Industrial

1.28

1.85

Dry landfarming

2/3

2007
Area (km2)

0.76

1.06

The dense forest in the Lubuk Paraku
watershed has been decreased from 16.80 km2 to14.77 km2. On the other side, the settlement
significantly increased. Table 3 showed a significant
association between the increase of development96Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –land use and the decrease of forest areas. In anotherstudy by Maryam A, et al. (2020), a significant landuse change in the upstream of Batang Araucatchment areas from 2010 to 2018 has beenreported, where the Lubuk Paraku area has increasedin the settlement and infrastructure.
Inefficient law enforcement has caused
many illegal activities, which trespass the forest
reserve and riverbank area. The unsustainable
developing methods (settlement on a steep slope,
recreational, transport, agricultural, soil erosion,
landslide, and high sedimentation rate in theriverbed) have created major ecological disturbancesin the Lubuk Parau sub-watershed.

4. Sediment analysis

Chih Ted Yang (1972) and T A Gunawan
et al. (2018) explained that sediment load estimation
based on the concept of energy exchange, such as theEngelund-Hansen, Yang, and Bagnold method are
more accurate, reliable, and simpler than otherapproaches. The result of the total sediment transportestimation can be seen in table 5. The comparison of
the methods has not provided satisfactory results.
However, the result shows the highest rate of
sediment load by the Engelund and Hansen method.
Tipani U S, et al. (2018) has reported thatthe study area is located in a highland elevation,
more than 40% of the total area has an elevation of
more than 1000 m and has steep slopes with agradient greater than 250. Therefore, the Lubuk
Paraku has a high risk of soil erosion and is notsuitable for development. It also emerged from thestudy of Benjamin K K, et al. (2020); F M Ziadat, etal. (2013), that the rate of soil loss increased with theincrease in slope steepness and slope length. In
addition, the Lubuk Paraku sub-watershed area isalso prone to landslides due to heavy rainfall andheavy runoff. The runoff rates cause an increase in
shear stress on the soil surface, leading to increased
silt delivery (Syed A A and Hamelmal H, 2016).

3. Runoff analysis

The Rational method was used to estimate
surface runoff potential in an ungauged watershed of
the upper Batang Arau River basin. The Rational
method is used for small watershed areas (C.
Thomson, P.E., 2019). The rational formula is shownin equation 5 to estimate the peak of runoff (Q inm3/sec) at a specific location in a watershed as afunction of the drainage area (A in ha), mean rainfall
intensity (I in mm/hour), and runoff coefficient (C)for a certain rainfall duration. The peak flow in the
study area are summarized in table 4.

Q = 0.002778CIA

Table 5. Sediment Estimation(5)

Table 4. Runoff Estimation
Year

Runoff (m3/s)

2007

107.862017

121.68Year

Land-use changes greatly influence thesurface runoff into water bodies. The decreasing ofdense forests can cause repeated runoff and erosion
into water bodies, particularly during rainfall. The
increasing runoff during precipitation due todecreased infiltration rate into the soil caused landchanges/landslides (Mufubi A, et al., 2016; Edwina
Z, et al., 2016). Open farming has the highest erosion
rate estimated, and more than 69% of the rainfall
becomes runoff that flows into the river (BY
Aminudin, et al., 2005). Furthermore, this study also
demonstrates an approach to generate reliable runoff
data for vulnerable ungauged river basins.

Total Sediment Load Method(ton/year)
Yang

2007

2270.952017

2286.46

AckersandWhite

EngelundandHansen51.64

39730.19

49.81

36266.33

Conclusions
This study investigated the runoff andsedimentation response due to land-use change inthe Lubuk Paraku sub-watershed by using therational method and sediment load estimation. It isevident from the findings of the study that the Lubuk
Paraku sub-watershed had land-use change from2007 to 2017 and affects the generated runoff andsediment load. Soil erosion, landslides, agriculture
activities, industrial, residential, and unplanneddevelopment associated with land-use change havesignificantly influenced the Lubuk Paraku subwatershed. Continuous land-use changes without a97

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
Session 4 – FOREST HYDROLOGY AND BIOGEOCHEMISTRY –Transport Estimation. 11th Australian Fluid
Mechanic Conference. Hobart. 175-178.
Madhatilah, and Rusli HAR. (2020) Analisis Debit
Air Limpasan Permukaan (Run Off) Akibat
Perubahan Tata Guna Lahan Pada DAS Kuranji
dan DAS Batang Arau Kota Padang. Junal Bina
Tambang, Vol. 5, No. 1: 178-189.
Maryam Afifa, Afla Dina, Elvi Roza Syofyan, andWisafri. (2020) Analisa Debit Andalan Pada
DAS Batang Arau Dengan Menggunakan Model
Soil and Water Assessment Tool (SWAT). Jurnal
Ilmiah Poli Rekayasa, Vol. 16, No.1: 34-42.
Mufubi Agaton, Yudi Setiawan, Hefni Effendi.
(2016) Land use/land cover change detection inan urban watershed: a case study of Upper
Citarum Watershed, West Java Province,
Indonesia. Procedia Environmental SciencesVol. 33:654-660.
Nurhamidah, Ahmad Junaidi, and Muhammad
Kurniawan. (2018) Tinjauan Tata Guna Lahan
Terhadap Limpasan Permukaan, Kasus: DAS
Batang Arau Padang. Jurnal Rekayasa Sipil(JRS-UNAND), 14(2): 131-138.
Padang City Government. (2019) Local
Government Medium Term Development
Medium Plan 2019-2024 (RPJMD Kota
Padang).
Rebecha Prananta, Endes N. Dahlan, and Omo
Rusdiana. (2015) Penilaian dan Pemanfaatan
Sumberdaya Air Sub DAS Lubuk Paraku Kota
Padang, Sumatera Barat (Assessment andUtilization of Water Resource in Lubuk Paraku
Sub Watershed, Padang City, West Sumatera).
Jurnal Penelitian Hutan dan Konservasi Allam,
12 (1): 19-31.
Syed Ahmad Ali and Hamelmal Hagos. (2016)
Estimation of soil erosion using USLE and GIS
in Awasa Catchment, Rift valley, Central
Ethiopia. Geoderma Regional, Vol. 7, Issue 2:
159-166.
T A Gunawan, A Daud, H Haki, and Sarino. (2019)
The Estimation of Total Sediments Load inRiver Tributary for Sustainable Resources
Management. IOP Conf. Series: Earth andEnvrinmental Science 248/012079
Tipani U S, Taufiq H, Hartati, Zahrul U. (2018)
Kajian Sedimentasi pada Muara Sungai Batang
Arau Kota Padang. Jurnal Ilmiah Rekayasa Sipil
Vol. XV, No.1: 37-44.

proper development plan and law enforcement may
threaten the sustainability of the the Batang Arauwatershed.
Reference
Azlini Razali, Sharifah Norkhadijah Syed Ismail,Suriyani Awang, Sarva Mangala Praveena, andEmilia Zainal Abidin. (2018) Land use change
in highland area and its impact on river water
quality: areview of case studies in Malaysia.
Springer: Ecological Processes 7: 19.
Benjamin K K, Lalit K, and Richard K. (2020)
Impact of Land Use/Cover Changes on Soil
Erosion in Western Kenya. MDPI,
Sustainability, 12, 9740.
BY Aminudin, WY A Wan, UB Cheah, MH
Ghulam, M Zulkefli, RB Salama. (2001) Impact
of intensive highland agriculture on theecosystem. Journal of Tropical Agricuulture
Food Science, Vol. 29, No. 1: 69-76
C. Thomason, P.E. (2019) Hydraulic Design
Manual, Texas Department of Transportation.
Central Management of Regional River Flow
(BPDAS) Agam Kuantan. (2011) Identifikasi
potensi penyedia, pengguna dan karakter jassa
DAS untuk pengembangan cost sharing hulu
hilir SWP DAS Arau, Padang.
Chih Ted Yang. (1972) Unit Stream Power andSediment Transport. Journal of the Hydraulics
Division, Vol. 98, Issue 10: 1805-1826
Edwina Zainal and K. Toshiharu, (2016)
Investigation of Long Term Evapotranspiration
by Using Hamon Equation Factor and NDVI
Data in Forest Plantations Area, China-USA
Business Review, Vol. 15 No. 10, pp 494-506.
Endes N. Dahlan, Rebecca P, and Omo Rusdiana.
(2014) Pemanfaatan Sumberdaya Air di Sub
DAS Lubuk Paraku Sumatera Barat (Water
Resourece Utilization in Lubuk Paraku Sub
Watershed, West Sumatera). Media Konservasi,
19 (1): 30-40.
F.M Ziadat and A.Y Taimeh. (2013) Effect ofRainfall Intensity, Slope, Land use andAntecedent Soil Moisture on Soil Erosion in an
Arid Environment. Land Degradation &Development, Vol. 24, Issue 6: 582-590
Mehdi Habibi and Muttucumaru Sivakumar. (1992)
Review of Selected Methods of Sediment98

UGSAS-GU & BWEL JOINT POSTER SESSIONON AGRICULTURAL
AND BASIN WATER ENVIRONMENTAL SCIENCES

2021

POSTER PRESENTATIONS
Thursday, 11th November 2021
15:00 - 17:10

ORGANIZERS:
THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCE,
GIFU UNIVERSITY&
GIFU UNIVERSITY REARING PROGRAM
FOR BASIN WATER ENVIRONMENTAL LEADERS

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P01

Effects of curcumin nanoemulsion on the water vapor permeability andantioxidant activities of chitosan-based films

Fakfan Luangapai 1, Methavee Peanparkdee2, and Satoshi Iwamoto1,3
1. Division of Science of Biological Resources, The United Graduate School of Agricultural Science, Gifu University, 1-1
Yanagido, Gifu 501-1193, Japan.
2. Department of Food Science and Technology, Faculty of Agro-Industry, Kasetsart University, Bangkok 10900, Thailand.
3. Department of Applied Life Science, Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193,
Japan.
curcumin oil. The modified films were conditioned in achamber for 48 h at 50% relative humidity (RH).
INTRODUCTION
The development of renewable and environmentally friendly
Preparation and characterization of CurNEs
resources is critical concerned because non-biodegradable
Curcumin powder (70 mg) was dispersed in 10 mL of MCT
plastic wastes affected environmental issues. Biopolymer
oil to form the oil phase. Tween 80 and distilled water werematerials can be a viable solution by partially substituting nonmixed at 1:8 to form the aqueous phase. Appropriate
biodegradable materials and perform as excellent carriers ofproportions of the oil and aqueous phases and ultrasonication
functional additives. Chitosan (CH), a deacetylated derivative
times as shown in Table 1. Under the various conditions, theof chitin, is considered as environmentally benign biopolymer.
size distributions and zeta potentials of the oil droplets were
CH has wide range of applications owing to its
measured on a Zetasizer Nano ZS (Malvern, UK) at 25 °C.
biocompatibility,
biodegradability,andantimicrobial
properties. Nevertheless, the chitosan films demonstrate
Table 1 Ratio of oil to aqueous phase and ultrasonication
undesirable properties, such as low mechanical strength andtime of curcumin nanoemulsion.
high permeability of water vapor transmission, which limit their
Conditions
Ultrasonic time (min)Oil: Aqueous
function and efficiency. To overcome these shortcomings andphaseachieve additional functionalities, several studies have
CurNE115
1:4
attempted to incorporate hydrophobic lipid compounds in CH
films (Peanparkdee et al., 2016).
CurNE230
1:4
Curcumin, obtained from the extraction and purification of
CurNE390
1:4
Curcuma longa, has been used as a spice, coloring agent,
CurNE4120
1:4
nutritional supplement, and herbal medicine. It has a wide range
CurNE515
1:6
of beneficial properties, including antioxidant, antiCurNE615
1:8
inflammatory, anti-cancer, and antimicrobial properties.
CurNE715
1:9
However, the utilization of curcumin is restricted due to poor
solubility and rapid hydrolysis. Numerous approaches have
Determination of the WVP of CH-based films
been reported to increase bioavailability of curcumin, such as
The water vapour transmission rate (WVTR) was evaluated,nanoemulsion, nanoliposomes, and solid lipid nanoparticles.
following the test method of ASTM (2007). The film samples
Herein, the study focused on the nanoemulsion technique, awere coated over cups (diameter 5 cm) containing silica gel.
promising approach to overcome dissolubility, stability, andThe film-covered cups were placed in a chamber that was set tobioactivity of various oil-soluble compounds owing to their
25 °C and 75% RH. WVTRs (gm−2d−1) of the films were
small droplet size and high kinetic stability. As effective points
determined from the slope of the regression analysis of theof curcumin and nanoemulsion technique, they had highmoisture weight gain (Δw) obtained through a film area (A)potential to be incorporated with chitosan-based films in orderduring a definite period (Δt) upon attaining the steady-state (Eq.
to improve the films’ properties (Luangapai et al., 2019).
(1)). The WVTRs of the films were subsequently calculate
Hence, this work aimed to develop the capabilities of water
WVP via Eq. (2). At least five replications of each modified
vapor permeability and enhance antioxidant activities of thefilm were tested (gmmm−2d−1kPa−1), x is the film thickness
films by incorporating with curcumin nanoemulsion (CurNEs).
(mm), and Δp is the difference between the partial water vapour
Thereafter, the effects of CurNEs on the various preparations
pressures of the inner (p1) and outer (p2) surfaces of the film inwere determined through physical properties such as color,the chamber (kPa).
water vapor permeability (WVP), glass-transition temperature
Δw
WVTR =
Eq. (1)(Tg), and antioxidant activities.
AΔt

MATERIALS AND METHODSWVP = WVTR

Preparation of the CH-based films
CH powder (2 g) was dissolved in a 1% acetic acid solution.
Glycerol was added to the film-forming solution containing
30% w/w CH powder. The solution was heated in a water bathshaking incubator for 4–6 h at 50 °C and 120 oscillation/min.
The sample solutions were ultrasonicated for 15 min. The
modified CH films were prepared via four approaches. First,
CH in the film-forming solution as the control sample. Second,
CH–NE was the film-forming solution blended with CurNEs.
The CH and CH–NE film solutions were separately
homogenized and cast on a metal plate. CH–CNE and CH–CO
were prepared by coating the surface with CurNEs andxΔpEq. (2)

Determination of the antioxidant activities of CH-based films
The sample (1 mL) was added to the ABTS working solution(4 mL). The mixture was incubated in the dark for 30 min. The
ABTS radical scavenging activity (RSA) was calculated
employing Eq. (3) and expressed as mmol Trolox/g weight ofthe dried film:
Eq. (3)
RSA(%) = (Acontrol − Asample )⁄Acontrol × 100
where Acontrol and Asample are the absorbance of the blank andsample.
Determination of the color of CH-based films
The color parameters (L*, a*, and b*) of the films were
calculated on a spectrophotometer (RM200QC, X-rite, USA).

100

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P01
Six spots were randomly selected on the sample films, and threefilm samples were utilized for each replication. A white plateused to measure background and standard calibrations.

lesser than that of CH–NE because of their lower potential
intermolecular interactions and hydrophobicity.
Determination of the color of CH-based films
The color properties can be detected through the L*, a*, andb* values. The L*, a*, and b*coordinates indicate the lightness
and darkness, green to red, and yellow to blue colors,
respectively. CH–NE exhibited the lowest L* (76.63 ± 1.75),
although it exhibited the highest a* (10.0 ± 1.94) and b* (54.58± 1.06) values. Thus, the probable appearance of CH–NE could
be a yellowish red, whereas CH, CH–CNE, and CH–CO were
yellowish green. The color properties might be affected by themethod of in corporations.

Determination of the thermal properties of CH-based films
The thermal properties were determined employing a
DSC6200/EXSTAR6000 apparatus an inert nitrogen
atmosphere at −50 to 150 °C and a heating rate of 10 °C/min.

RESULTS AND DISCUSSION
Size distributions and zeta potential of CurNEs
The mean diameters and zeta potential values of CurNEs arelisted in Table 2. CurNE7 exhibited the smallest mean diameter(307.53 ± 6.30 nm) (p ≤ 0.05). The variety of the size
distribution corresponded to the ratio of the oil to the aqueous
phases, the increasing adsorption of the concentration of theaqueous phase, the decrease in the interfacial tension, and theformation of fine droplets. CurNE7 exhibited the highest
absolute value (−15.43 ± 0.74 mV) (p ≤ 0.05). Moreover, theultrasonication time is a crucial factor in preparing NE because
prolonged time might increase the temperature from thecavitation-induced thermal effect, thus inducing thedeterioration and aggregation of neighboring droplets (Agrawal
et al., 2017). Since CurNE7 exhibited a concise ultrasonication
time (15 min) and demonstrated higher stability and uniformity
compared with the other conditions, it was selected andintegrated with the CH films to further investigations.

Determination of the thermal properties of CH-based films
CH exhibited a Tg of ~80.71 °C, however, the Tg of the CH–
NE film sample shifted to 71.34 °C, whereas those of CH–CNE
and CH–CO shifted slightly to 78.82 and 83.42 °C,
respectively. It is well known that the Tg is an importantcriterion for the miscibility of the components. In a completely
miscible blend of two compounds, only one Tg was observed.
The results indicating good miscibility by performing one Tg
and forming of new hydrogen bonding networks appear.
Furthermore, the decrease of Tg of CH–NE corresponded to theinteraction between CH and CurNEs in the amorphous regions.
The modification of CH by small amounts of additives, such asNEs, is induced owing to the plasticization effect, thusincreasing the chain mobility in the amorphous regions, anddecreasing the degree of crystallinity (Luangapai et al., 2021).
In summary, to develop CH-based films by incorporating
CurNEs on various preparations. CH–NE exhibited good
appearance, enhanced WVP, and antioxidant activities.
Additionally, the thermal analysis revealed that the films
exhibited considerable miscibility of the components from CH
and CurNEs incorporation. The results initiated and promoted
an interesting development as an active ingredient for
producing food packaging materials.

Table 2 Mean diameters and zeta potential of curcumin
nanoemulsion.
Condition

Mean diameter (nm)

Zeta potential (mV)CurNE1CurNE2CurNE3
CurNE4CurNE5CurNE6CurNE71023±13.00a-0.88±0.36b-0.69±0.26b-0.50±0.33b-0.52±0.48b-0.08±0.21b-0.73±0.38 b-15.43±0.74a

758.7±3.68b

360.67±2.29e378.20±5.59d
533.23±3.68c
367.50±5.20de
307.53±6.30f

Table 3 WVP, antioxidant activities and Tg of CH-based
films composited with curcumin nanoemulsion and/or oil.
WVP
mmol Trolox/g
Tg
Samples(g mm m−2 d−1 kPa−1)
film
(℃)
CH
0.130±0.010a
0.39±0.02d
80.7
CH-NE
0.049±0.001d
2.11±0.09a
71.3
CH-CNE
0.087±0.001b
0.58±0.03c
78.8
CH-CO
0.077±0.001c
0.73±0.00d
83.4*Different letters in the same column indicate significantly different (p ≤ 0.05).

Determination of the WVP of CH-based films
The CH–NE condition exhibited the lowest WVP (0.049 ±0.001 gmmm−2d−1kPa−1), followed by CH–CO, CH–CNE, andCH. The improved WVP of CH–NE might be due to thepresence of CurNEs in the inner film structure enhanced thewater barrier properties by increasing the tortuosity, thereby
generating the resistance of water vapour through the film.
Conversely, CH–CNE and CH–CO exhibited higher WVP thanCH, however, compared with CH–NE, CH–CNE and CH–CO
exhibited lower efficiencies, which were attributed to the weak
structural integrities of the surface of the film.

*Different letters in the same column indicate significantly different (p ≤ 0.05).

REFERENCES
Agrawal, N., Maddikeri, L., and Pandit, B. (2017) Sustained
release formulations of citronella oil nanoemulsion using
cavitational techniques. Ultrasonics Sonochemistry 36: 367374.
Luangapai, F., Peanparkdee, M., and Iwamoto, S (2019).
Biopolymer films for food industries: Properties,
applications, and future aspects based on chitosan. Reviewsin Agricultural Science 7: 59-67.
Luangapai, F., Peanparkdee, M., and Iwamoto, S Effects of acurcumin nanoemulsion on the physico ‐ chemical
properties of chitosan‐based films. International Journal of
Food Science & Technology.
Peanparkdee, M., Iwamoto, S., and Yamauchi, R (2016).
Microencapsulation: A review of applications in the food andpharmaceutical industries. Reviews in Agricultural Science
4: 56-65

Determination of the antioxidant activities of CH-based films
The CH–NE condition exhibited five times higher
antioxidant activity than CH. Moreover, CH–NE exhibited thehighest antioxidant activity probably because of the reductionin the droplet size of the CH-incorporated NE, which promoted
specific surface interaction, and afforded accelerated and very
efficient free radical absorption. Additionally, the reduction in
the size of the oil droplets improved the mass transfer andincreased the release rate, dispersion, and kinetically stablesuspension. Conversely, CH–CNE and CH–CO also
demonstrated antioxidant effects, although their effects were101

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P02

LAMP-FLP for detection of thiophanate-methyl resistant strains ofFusarium fujikuroi in Japan
FangJing Li1, Ryoji Komura2, Chiharu Nakashima2, Masafumi Shimizu3, Koji Kageyama4 and Haruhisa Suga5
1. The United Graduate School of Agricultural Science, Gifu University, Gifu 501-1193, Japan
2. Graduate School of Bioresources, Mie University, Tsu, Mie 514-8507, Japan
3. Fac. of Applied Biological Sciences, Gifu University, Gifu 501-1193, Japan
4. River Basin Research Center, Gifu University, Gifu 501-1193, Japan
5. Institute for Glyco-core Research (iGCORE), Gifu University, Gifu, 501-1193, Japan
conducted for each concentration. After five days of growth at
25℃, colony radius was measured. The minimum inhibitory
INTRODUCTION
concentration (MIC) was determined by the growth ability ofFusarium fujikuroi is the pathogen of rice bakanae disease,the strain, and the half-maximal effective concentration (EC50)
which results in 3.0–95.4% yield losses (Gupta et al. 2015).
were calculated based on the colony radius.
Infected plant often exhibits abnormal stem elongation andyellowish leaves. Gibberellin (GA) production in F. fujikuroi
PCR and sequencing
is considered as a main factor contributing to symptoms ofbakanae disease (Singh and Sunder 2012). In addition to GA,
PCR amplification for β1-tubulin and β2-tubulin was
F. fujikuroi is known to produce mycotoxin fumonisin (FUM),
performed using AmpliTaq ® DNA Polymerase Kit ((Thermo
posing a health risk to mankind and animals (Morgavi andFisher Scientific, Waltham, MA, USA)). The PCR products
Riley 2007). Based on phylogenetic and GA and FUM
were purified by Nucleospin ® Extract Kit II Gel (Machereyproduction analyses, F. fujikuroi is subclassified into GA andNagel, Düren, Germany), sequenced by Big-Dye terminator
FUM groups (G- and F-groups) (Suga et al. 2019). G-group
V3.1 with cycle sequencing kits (Thermo Fisher Scientific).
strains produce a high amount of GA and are FUM nonproducers, whereas F-group strains produce a little or no GA
LAMP-FLP reaction
but a high amount of FUM (Suga et al. 2019).
Thiophanate-methyl (TM), a benzimidazole fungicide, has
LAMP primers and probe sets were designed according tobeen used to control F. fujikuroi for some decades.
Komura et al. (2018). The LAMP reaction was performed
Representative benzimidazole fungicides include benomyl,using an LF-8 detecting instrument (Nippon Gene) at 66°C for
carbendazim, and TM. The mode of action of benzimidazole
35 min. DNA amplification was measured by the turbidity offungicides is binding to tubulin, the sub-unit of microtubules
the LAMP products. An annealing curve from 95°C to 35°C
that involved in cellular processes. Two tubulin genes are
based on the quenching time was then analyzed, using LF-8
presented in F. fujikuroi (β1-tubulin (gene ID: FFUJ04397)
Manager software and the LF-8 instrument. The experimentand β2-tubulin (FFUJ03388)) (Wiemann et al. 2013).
was repeated twice, for confirmation of result consistency.
Due to repeatedly application of benzimidazole fungicides,
their resistance has been reported in F. fujikuroi. In most
RESULTS AND DISCUSSION
cases, resistance is correlated with a point mutation in a βtubulin gene, which results in an amino acid change at the
TM sensitivity
fungicide binding site. An E198V or F200Y mutation in β2tubulin was discovered to be responsible for carbendazim
The TM sensitivities of nine strains (five strains wereresistance in Chinese F. fujikuroi (Chen et al. 2014). However,isolated in this study. Three strains were from Suga et al.
there is no report for TM resistance in F. fujikuroi.
(2019)) were investigated (Table 1). The MIC of TM
Clarification of the fungicide resistance mechanism, andsensitive (TMS) strains were 1.0–10.0 μg mL-1 and weredevelopment of molecular method for monitoring resistancehigher than 100 μg mL-1 in TM resistant (TMR) strains. EC50
based on the mechanism, are important for the continuing
in TMS strains (3.3–4.1 μg mL-1) was lower than that in TMR
application and the development of new fungicide. A loopstrains (7.1–12.7 μg mL-1). According to previous study (Sugamediated isothermal amplification-fluorescent loop primer
et al. 2019) and the current results, most of G-group strains(LAMP-FLP), is detection method of a specific single
were resistance to TM, while no of F-group strains were TMnucleotide polymorphism (SNP). In recent years, LAMP-FLP
resistant except for one anomalous strain Gfc1521033 (Table
has been applied to the detection of fungicide resistance in the1). TMR selection pressure might impose on the G-group
Fusarium head blight pathogen of wheat (Komura et al. 2018).
strains due to bakanae pathogen with high GA production.
In this study, mutations in β2-tubulin that correlated withTM resistance were investigated in Japanese F. fujikuroi, andMutation in β-tubulin
a LAMP-FLP method was developed for detection of TMresistant strains.
Comparison of the β1- and β2-tubulin sequences between
TMS and TMR strains indicated that no amino acid
MATERIALS AND METHODS
substitution occurred in β1-tubulin, and either E198K or
F200Y mutation in β2-tubulin was detected in TMR strains
TM sensitivity(Table 1). This result revealed that the mutations in β2-tubulin
are associated with TM resistance in Japanese F. fujikuroi.
A mycelial plug with 4 mm diameter was placed at theβ-tubulin as the target site of benzimidazole fungicides
center of the sterilized PDA medium supplemented with 0,
has been studied intensively. Previous research suggests that
0.25, 0.5, 0.75, 1, 10, and 100 μg mL-1 TM. Three repeats
β1-tubulin functions on sexual reproduction, whereas β2weretubulin is important for vegetative growth (Liu et al. 2013).
Y50D mutation in β1-tubulin was found responsible to102Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P02
benomyl resistance in Fusarium verticillioides (Liu et al.
2013), while mutations E198V and F200Y in β2-tubulin werecorresponded to carbendazim resistance in F. fujikuroi.
Present study also revealed that TM resistance is caused by themutations in β2-tubulin, possibly due to the importance of β2tubulin in vegetative growth.

they have either E198K or F200Y. Previous research indicatedthat LAMP-FLP was successfully detect F167Y, E198Q, andF200Y in β2-tubulin of benzimidazole resistant F.
graminearum (Komura et al. 2018). LAMP-FLP developed inthis study also useful for diagnosis of TM resistance inJapanese F. fujikuroi.

REFERENCES
Chen, Z. H., Gao, T., Liang, S. P. et al. (2014) Molecularmechanism of resistance of Fusarium fujikuroi tobenzimidazole fungicides. FEMS Microbiology Letters 357:
77-84.
Gupta, A. K., Solanki, I. S., Bashyal, B. M. et al. (2015)
Bakanae of rice – an emerging disease in Asia. Journal ofAnimal and Plant Sciences 25: 1499-1514.
Komura, R., Kawakami, T., Nakajima, K. et al. (2018)
Simultaneous detection of benzimidazole-resistant strains ofFusarium head blight using the loop-mediated isothermal
amplification-fluorescent loop primer method. Journal of
General Plant Pathology 84: 247-253.
Liu, S., Duan, Y., Ge, C. et al. (2013) Functional analysis of
the beta2 - tubulin gene of Fusarium graminearum and thebeta-tubulin gene of Botrytis cinerea by homologous
replacement. Pest Management Science 69: 582–588.
Morgavi, D. P., and Riley, R. T. (2007) An historical overview
of field disease outbreaks known or suspected to be causedby consumption of feeds contaminated with Fusarium
toxins. Animal Feed Science and Technology 137: 201-212.
Suga, H., Arai, M., Fukasawa, E. et al. (2019) Genetic
differentiation associated with fumonisin and gibberellin
production in Japanese Fusarium fujikuroi. Applied andEnvironmental Microbiology 85: e02414-18.
Singh, R., and Sunder, S. (2012) Foot rot and bakanae of rice:an overview. Review of Plant Pathology 5: 566–604.
Wiemann, P., Sieber, C. M. K., von Bargen, K. W. et al.
(2013) Deciphering the cryptic genome: genome-wide
analyses of the rice pathogen Fusarium fujikuroi reveal
complex regulation of secondary metabolism and novel
metabolites. PLoS Pathogens 9: e1003475.
Zhang, Z., Chen, Z., Hou, Y.et al. (2015) PIRA-PCR for
detection of Fusarium fujikuroi genotypes withcarbendazim- resistance alleles. Plant Disease 99: 12411246.

Fig. 1: Thiophanate-methyl resistance and sensitivity andcorresponding mutation at positions 198 and 200 in β2tubulin as determined by LAMP-FLP method in F.
fujikuroi. Fifteen and 22 of 37 TMR strains were detectedwith E198K and F200Y mutation, respectively. No
mutation was detected in 56 TMS strains.
LAMP-FLP analyses
In this study, 93 F. fujikuroi strains were used for LAMPFLP analyses. TM sensitivity of them were previously
conducted by Suga et al. (2019). Thirty-seven TMR strains
and 56 TMS strains were determined. LAMP-FLP method
showed that all of the 37 TMR strains have either of E198K or
F200Y mutations, and that none of the 56 TMS strains has
these mutations (Fig. 1). LAMP-FLP was successfully
detected E198K and F200Y mutations in TMR strains.
Molecular method can monitor the resistance faster thanconventional methods. A primer-introduced restriction
analysis PCR (PIRA-PCR) was developed to identify E198L
and G235G substitution in β2-tubulin in carbendazim-resistantF. fujikuroi strains (Zhang et al. 2015). However, PIRA-PCR
is unable to identify TMR strains in Japanese F. fujikuroi as

Table 1. Phenotypic and molecular characteristics and thiophanate-methyl sensitivity of strains of Fusarium fujikuroi.

StrainSubgroupa

Thiophanatemethyl sensitivityb

MIC(μg mL-1)

EC50(μg mL-1)c

Amino acid in
β2-tubulin
198 th

198 thReference

MAFF235949

G-groupTMS

1<MIC<10

4.07 (0.93)

EF

Gfc1524102

F-groupTMS

1<MIC<10

3.85 (0.93)

EF

Suga et al. 2019
This study

Gfc1524502

F-groupTMS

1<MIC<10

3.66 (0.93)

EFThis study

Gfc0825009

F-groupTMS

1<MIC<10

3.30 (0.93)

EF

Suga et al. 2019

Gfc1524101

G-group

TMR

100<MIC

12.74 (0.97)

KFThis study

Gfc1524102Re

G-group

TMR

100<MIC

10.79 (0.98)

K

F

This study

Gfc1524502Re

G-group

TMR

100<MIC

10.55 (0.97)

KFThis study

Gfc0801001
Gfc1521033

G-group
F-group

TMR
TMR

100<MIC
100<MIC

7.09 (0.95)
9.35 (0.95)

E
E

Y
Y

Suga et al. 2019
H. Watanabea The gibberellin and fumonisin groups (G- and F-groups) are subgroups in F. fujikuroi (Suga et al. 2019).
b Thiophanate-methyl resistant (TMR) strains grew on PDA with 100 μg mL-1 with thiophanate-methyl. Thiophanate-methyl
sensitive (TMS) strains did not.
c EC50 for thiophanate-methyl. The correlation coefficient is indicated in parenthesis.

103Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P03

Changes in chlorophyll and carotenoid profiles and gene expression during
fruit regreening in Valencia orange
Nichapat Keawmanee1,2, Gang Ma2,3, Lancui Zhang2, Masaki Yahata2,3, and Masaya Kato2,3*
1. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu-shi, Gifu 501-1193, Japan
2. Department of Bioresource Sciences, Faculty of Agriculture, Shizuoka University, 836 Ohya, Suruga, Shizuoka 4228529, Japan
3. Graduate School of Integrated Science and Technology, Shizuoka University, 836 Ohya, Suruga, Shizuoka 422-8529,
Japan

INTRODUCTION

significantly higher, while the contents of lutein and β-carotene
were significantly lower in the bottom part than the top part of
the fruit during regreening (Fig 1B). In this study, theexpression of seven chlorophyll biosynthetic genes, six
chlorophyll degradation genes, and eleven carotenoid
biosynthetic genes was investigated. The up-regulation ofchlorophyll biosynthetic genes and down-regulation ofchlorophyll degradation and carotenoid biosynthetic genes
were observed in the fruit flavedo during regreening. In
comparison with the bottom part of the fruit, the expression ofgenes involved in chlorophyll biosynthesis (CitCHLH,
CitCHLM, CitPORA, CitCS, and CitCAO) was significantly upregulated in the top part (Fig. 2A), while the expression ofchlorophyll degradation genes (CitCLH2, CitSGR, andCitRCCR) was significantly down-regulated in the top part inJune (Fig. 2B). The expression of carotenoid biosynthetic genes(CitPSY, CitZDS, CitCRTISO, CitLCYb1, CitLCYb2, andCitHYb) were significantly lower in the top part compared withthe bottom part. In contrast, the expression of CitLCYe andCitHYe in the top part were significantly higher than that in thebottom part in June (Fig. 3).

MATERIALS AND METHODS

Total chlorophyll contents (mg/g FW)

Fruits of ‘Valencia orange’ (Citrus sinensis Osbeck)
harvested in April, May, and June from Fujieda farm of
Shizuoka University were used as plant materials. After
harvest, the flavedos in the top and the bottom parts of the fruit
were sampled separately, immediately frozen in liquid nitrogen,and kept at -80 °C until use. Chlorophyll was extracted fromflavedos using N,N-dimethylformamide and calculated
according to Moran’s method (1982). The contents of major
carotenoids, all-trans-vioxanthin (T-vio), 9-cis-vioxanthin (Cvio), lutein (Lut), β-cryptoxanthin (β-Cry), and β-carotene (βCar), were extracted from the flavedos following Kato’s
method and measured by HPLC (Kato et al., 2004). Regarding
gene expression, the total RNA was extracted by using phenolchloroform according to Kato’s method (Kato et al., 2004). The
gene expression analysis was carried out by real-time PCR
using TaqMan probes and the set of primers for carotenoid
biosynthetic genes (CitPSY, CitPDS, CitZ-ISO, CitZDS,CitCRTISO, CitLCYb1, CitLCYb2, CitLCYe, CitHYb, CitHYe,
and CitZEP), chlorophyll biosynthetic genes (CitGGDR,CitCHLH, CitCHLM, CitCHL27, CitPORA, CitCS, andCitCAO), and chlorophyll degradation genes (CitCLH1,
CitCLH2, CitSGR, CitPPH, CitPAO, and CitRCCR).

A
25
20

Chl a
Chl b

*

15

Total carotenoid contents (mg/g FW)

Citrus fruit accumulate a diversity of pigments at different
mature stages. In immature fruit, chlorophylls were massively
accumulated in the flavedo. During the maturation,
chlorophylls contents decreased rapidly along with the gradualincreases in the contents of carotenoids in the flavedo of citrus
fruit (Young and Erickson, 1961). In some cultivars, especially
Valencia orange, when the fruit is left on the tree till late springor summer season, the color of the fruit will reverse from orange
to green, and this process is called regreening (Caprio, 1956). It
was reported that the regreening was correlated with thedecreases of carotenoids and the increases of chlorophylls(Thomson et al., 1967). This study aimed to investigate thechanges of chlorophyll and carotenoid profiles and gene
expression involved in chlorophyll and carotenoid metabolisms
in the top and the bottom parts of Valencia orange during theregreening.

*

10
5
0

Top
April

BottomTop

May

Bottom

B
500

*

T-vio
C-vio
Lut
b-Cry
b-Car

400
300

**

200
100
0

Top

June

April

BottomTop

May

BottomJune

Fig. 1: The contents of chlorophylls (A) and carotenoids (B) in top
and bottom parts of fruit flavedo.
A

TopmRNA levels (arbitrary units)

80

CitCHLH

160

CitCHLM

40

CitCS

1200

120

45

30

900

40

80

30

20

600

20

40

15

10

300

0

1

0

1

B
mRNA levels (arbitrary units)

CitCLH2

0

1

Top

200

The regreening of citrus fruit was observed during theexperiment period, especially in June. The color of the flavedo
reversed gradually from orange to green, which was related tothe increase of chlorophylls contents and decrease of
carotenoids contents. In the different parts of the fruit, the result
showed that the contents of chlorophyll a, b, and total
chlorophyll in the top part were higher than the bottom part of
the flavedo (Fig. 1A). In contrast, the contents of total
carotenoid, 9-cis-violaxanthin and β-cryptoxanthin were

CitPORA60

0

RESULTS

Bottom60

40

CitSGR

80

30

60

100

20

40

50

10

20

1

0

1

Bottom

150

0

0

1

CitCAO

1

0

CitRCCR

1

Fig. 2: The expression of genes involved in chlorophyll biosynthesis(A) and chlorophyll degradation (B) in top and bottom parts of fruit
flavedo (June).

104Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P03
TopmRNA levels (arbitrary units)

40

CitPSY

40

CitZDS

CitCRTISO

320

30

30

30

240

20

20

20

160

10

10

10

80

0

0

1

1

0

0

1

600

CitHYb 160

120

450

120

15

80

300

80

10

40

150

40

5

160

0

CitLCYb2

1

01(2004) report that the expression of CitLCYe was high in thegreen fruit and it decreased when the fruit turned orange.
In conclusion, the results presented in this study suggested
that the reduction of β,β-xanthophylls (β-cryptoxanthin,zeaxanthin, all-trans-violaxanthin, and 9-cis-violaxanthin) was
related to the down-regulation of carotenoid biosynthetic genes,
while the accumulation of chlorophyll was related to the upregulation of chlorophyll biosynthetic genes and downregulation chlorophyll degradation genes in the flavedo during
the regreening process. In addition, the regreening was moreevident in the top part of the fruit than the bottom part, whichindicated that the regreening process might start from the top
part of the citrus fruit.

Bottom40

0CitLCYe

1

CitLCYb1

1

20

0

CitHYeACKNOWLEDGMENTS
This work was supported by KAKENHI Grant Numbers
JP20H02976 (to M.K.) from Japan Society for the Promotionof Science (JSPS).

1

Fig. 3: The expression of genes involved in carotenoid biosynthesis
in top and bottom parts of fruit flavedo (June).

DISCUSSION

REFERENCES

In general, the citrus fruit turn from green to orange in thewinter season. In Valencia orange, when the fruit was left on
the tree till spring or summer season, it will reverse from orange
to green (Caprio, 1956). In plants, the regreening process was
accompanied with the reversion of chromoplasts (carotenoid)
to chloroplasts (chlorophyll) and the formation of new
chloroplasts (MacKinney, 1961). In the immature fruit, the fruit
flavedos accumulated high levels of chlorophyll a, chlorophyllb, and β,ε-carotenoids (α-carotene and lutein). In the mature
fruit, the contents of chlorophyll a, chlorophyll b, and β,εcarotenoids decreased, and the accumulation of β,β-carotenoids(β-cryptoxanthin, zeaxanthin, all-trans-violaxanthin and 9-cisviolaxanthin) was observed. (Kato et al., 2004; Ma et al., 2016;Rodrigo et al., 2013). In the present study, the result showedthat the contents of chlorophylls were induced, while thecontents of carotenoids were decreased in flavedo during theregreening process. Moreover, the regreening was more
obviously in the top part than the bottom part of the fruit.
Similarly, Coggins and Lewis (1962) reported that the content
of chlorophyll was higher in the stem end area than the apicaland equatorial area of the flavedo.
The accumulation of chlorophylls and carotenoids wasregulated by the expression of genes involved in chlorophylland carotenoid metabolisms. In citrus fruit, the syntheses ofchlorophyll and carotenoid share a common precursor. They areboth derived from geranylgeranyl diphosphate (GGPP). The
GGPP was converted into chlorophyll a catalyzed by thegeranylgeranyl reductase (GGDR) and the tetrapyrrole
biosynthetic genes. In the meanwhile, the condensation of
GGPP to form phytoene by phytoene synthase (PSY), which
was the first key step in the carotenoid biosynthesis (Alós et al.,2006; Ma et al., 2021). During fruit ripening, the expression ofchlorophyll biosynthesis genes (CitGGDR) was downregulated, while up-regulation of carotenoid biosynthesis genes(CitPSY, CitPDS, CitZDS, CitLCYb, and CitHYb) and
chlorophyll degradation (CitPAO) contributed to carotenoid
accumulation in the flavedo of citrus fruit (Kato et al., 2004 andAlós et al., 2006). In this study, the up-regulation of chlorophyll
biosynthetic genes (CitCHLH, CitCHLM, CitPORA, CitCS, andCitCAO) and simultaneous down-regulation of carotenoid
biosynthetic genes (CitPSY, CitZDS, CitCRTISO, CitLCYb1,
CitLCYb2, and CitHYb) and chlorophyll degradation genes(CitCLH2, CitSGR, and CitRCCR) were observed in the top part
during regreening. In addition, the expression of CitLCYe was
up-regulated in the top part of the fruit. Similarly, Kato et al.

Alós, E., Cercós, M., Rodrigo, M.J., Zacarías, L., and Talón, M.
(2006) Regulation of color break in Citrus fruits. Changes inpigment profiling and gene expression induced by
gibberellins and nitrate, two ripening retardants. Journal of
Agricultural and Food Chemistry 54: 4888-4895.
Caprio, J.M. (1956) An analysis of the relation between regreening of Valencia oranges and mean monthly
temperatures in Southern California. Proceedings of theAmerican Society for Horticultural Science 61: 222-35.
Coggins, C.W. and Lewis, L.M. (1962) Regreening of Valencia
orange as influenced by potassium gibberellate. Plant
Physiology 37: 625-627.
Kato, M., Ikoma, Y., Matsumoto, H., Sugiura, M., Hyodo, H.,
and Yano, M. (2004) Accumulation of carotenoids andexpression of carotenoid biosynthetic genes during
maturation in citrus fruit. Plant Physiology 134: 824-837.
MacKinney, G. (1961) Coloring matters, In "The Orange, Its
Biochemistry and Physiology". (W. B. Sinclair, Ed.) Univ. of
Calif. Printing Dept. 302-333.
Ma, G., Zhang, L.C., Yungyuen, W., Tsukamoto, I., Iijima, N.,
Oikawa, M., Yamawaki, K., Yahata, M., and Kato, M. (2016)
Expression and functional analysis of citrus carotenehydroxylases: Unraveling the xanthophyll biosynthesis in
citrus fruits. BMC Plant Biology 16: 148.
Ma, G., Zhang, L.C., Kitaya, Y., Seoka, M., Kudaka, R.,
Yahata, M., Yamawaki, K., Shimada, T., Fujii, H., Endo, T.,
and Kato, M. (2021) Blue LED light induces regreening in
the flavedo of Valencia orange in vitro. Food Chemistry 335:
127621.
Moran, R. (1982) Formulae for determination ofchlorophyllous
pigments
extracted
withN,Ndimethylformamide. Plant Physiology 69: 1376-1381.
Rodrigo, M.J., Alquézar, B., Alós, E., Lado, J., and Zacarías, L.
(2013) Biochemical bases and molecular regulation ofpigmentation in the peel of Citrus fruit. Scientia
Horticulturae 163: 46-62.
Thomson, W.W., Lewis, N., and Coggins, C.W. (1967) The
reversion of chromoplasts to chloroplasts in Valencia
oranges. Cytologia 32: 117-124.
Young, L.B., and Erickson, L.C. (1961) Influence oftemperature on color change in Valencia oranges.
Proceedings of the American Society for Horticultural
Science 78: 197-200.

105

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P04

Modified newmark approach for evaluation of earthquake-induced
displacement of earthdams
Phuong Le-Hong1, Shin-ichi Nishimura2, and Tatsuro Nishiyama2
1. The United Graduate School of Agricultural, Gifu University, Gifu, Japan
2. Faculty of Applied Biological Sciences, Gifu University, Gifu, Japan=FS

INTRODUCTION

M R R ∑ C ⋅ l + (W ⋅ cos α − u ⋅ l − kh ⋅ W ⋅ sin α ) tan ϕ =MDΣ W ⋅ xg + kh ⋅ W ⋅ y g 

(1)

By assigning unity to Fs, the equation can be rearranged interms of the yield seismic intensity as

Newmark (1965) proposed a simple approach for evaluating
the potential deformation of earth dams and embankments due
to earthquake shaking based on a rigid slide block above theslippage surface, known as the conventional Newmark sliding
block approach. Due to its simplicity and efficiency, theconventional Newmark approach has been widely applied inpractice. The procedure requires that the value of the yieldacceleration or yield seismic intensity be determined for the
potential failure surface using conventional limit equilibrium
methods. The ordinary method of slices, or Fellenius method,is commonly used in the conventional Newmark approach to
calculate the factor of safety as well as the yield seismic
intensity. However, this method ignores the forces between
the slices needed to facilitate the calculation. When comparedto the rigorous methods, which take into account the forces
between the slices, the factor of safety of the Fellenius method
is smaller (Duncan and Wright 1980). Therefore, it can bepredicted that the amount of displacement calculated by theconventional Newmark approach will be larger than that
calculated by a strict method.
As is known, the acceleration time history data is theacceleration data measured at certain time intervals of anearthquake. At any given time interval, when the seismic
acceleration exceeds the yield acceleration, the sliding mass
will slip. However, in the conventional Newmark approach
used at present, the change in the position of the sliding massat that interval is not considered much in the analysis.
Therefore, the fixed sliding mass position in the conventional
Newmark approach may lead to unreliable estimations ofseismic slope deformations. Nishimura et al. (2020)
considered the changing of the sliding mass position throughthe re-division of the sliding mass as the sliding mass moves at
the time steps of the acceleration time history data. However,they also used the Fellenius method to calculate theearthquake-induced permanent displacement.
In this study, therefore, a modified Newmark approach is
proposed to evaluate the seismic displacement of an earth dam
based on the changing position of the sliding mass over time.
In the proposed procedure, the change in the sliding mass
position is taken into account by re-dividing the slip mass inthe time step of the slip occurrence. Moreover, a relatively
easy method among the strict methods is used, namely, thesimplified Bishop method, to evaluate the yield seismic
intensity. Seismic displacements are evaluated and compared
for the Fellenius and simplified Bishop methods in bothconventional and modified Newmark approaches.

ky =R ∑ C ⋅ l + (W ⋅ cos α − u ⋅ l ) tan ϕ  − ∑W ⋅ xg
Σ W ⋅ y g + W ⋅ sin α ⋅ tan ϕ 

(2)

The simplified Bishop method is a stricter method than theFellenius method, in which normal interaction forces between
adjacent slices are assumed to be collinear and the resultant
interslice shear force is zero. By considering horizontal
seismic intensity kh, the original formulation by Bishop(1955) can be rewritten as
M R R ∑ {c ⋅ b + (W − b ⋅ u ) tan ϕ} mα =MDΣ W ⋅ xg + kh ⋅ W ⋅ y g tan ϕ ⋅ sin α
mα cos α +
=
Fs

Fs*
=

(3)

In a manner similar to the Fellenius method, the yieldseismic intensity based on the Bishop method is obtained ask *y =R ∑ {c ⋅ b + (W − u ⋅ b) tan ϕ} mα  − ∑W ⋅ xg
ΣW ⋅ y g(4)

As mentioned earlier, the changing position of the sliding
mass over the time intervals in the seismic data is ignored in
the conventional Newmark sliding block approach. Thus, thevalues for xg and yg in Eqs (2) and (4) do not change alongwith the other parameters for the slip surface and sliding mass,
resulting in yield seismic intensity that does not change overtime. In addition, when applying the method of slices(Fellenius or Bishop method) in the conventional Newmark
approach, the sliding mass above the slip surface is only
subdivided once into vertical slices at the initial static state.
However, when the sliding mass moves, the edges of the slice
are no longer vertica as they were at first.
A modified Newmark procedure is developed to address theabove-mentioned drawbacks. The determination of the yieldseismic intensity is carried out in this modified version basedon the Fellenius and Bishop methods. The effect of thechanging position of the sliding mass during an earthquake on
the predicted permanent displacements is also considered.

VERIFICATION
To verify the applicability of the modified Newmark
procedure, an agricultural earth-fill dam, the Ijira Dam, 18 m
in height and located in Yamagata City, Gifu Prefecture,
Japan, was analyzed. This dam was under repair from 2016 to2017, but this study only focuses on analyzing it before its
repair. As a means of verification, the results obtained by the
proposed approach were compared with those obtained fromthe conventional Newmark approach. The input acceleration
time history in the horizontal direction, used in this study andbased on the EW component of the 2011 off the Pacificcoast of Tohoku-Pacific Earthquake.

THE NEWMARK METHODOLOGY
Factor of safety Fs in the ordinary method of slices, or theFellenius method, is given by the ratio of resisting moment MR
and driving moment MD, as106

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P04
Fellenius : 0.292 m
Bishop : 0.126 m

20
10

7(a)

Fellenius : 0.260 m
Bishop : 0.103 m

20
10

7(a)

7(a)

7(a)

0

0
0

20

40

a) Case 1

20
10

60

0

80

Fellenius : 1.413 m
Bishop : 0.642 m

40a) Case 1

20
10

7(b)

7(b)

20

60

80

Fellenius : 0.868 m
Bishop : 0.450 m
7(b)

7(b)

0

0
0

20

40

b) Case 2

20
10

60

0

80

Fellenius : 7.961 m
Bishop : 1.777 m

7(c)

20

40

b) Case 2

20
10

7(c)

60

80

Fellenius : 1.521 m
Bishop : 0.921 m
7(c)

7(c)

0

0
0

20

40

c) Case 3

60

0

80

20

40

c) Case 3

60

80

Fig. 1: Sliding displacement results with Conventional
Newmark approach

Fig. 2: Sliding displacement results with Modified
Newmark approach

Figure 1 presents the results of the sliding displacement
based on the Fellenius and Bishop methods for the threecalculation cases when the conventional Newmark approach isapplied. It can be seen that, in Case 1, the sliding displacement
is small with both Fellenius and Bishop methods. The
difference between the two methods is not significant, with theresults of the Fellenius method being slightly larger than thoseof the Bishop method by about 0.16 m. However, in Cases 2
and 3, since the shear strength parameters of soil material No.
7 are assumed to have decreased, the sliding displacementswith both methods are larger than in Case 1, especially that ofCase 3. The application of the Bishop method yielded smaller
results than the Fellenius method, as was analyzed in section
2. Moreover, it was found that, with the conventional
Newmark approach, the difference between the Fellenius andBishop methods is large when the sliding mass moved asmuch as it did in Case 3 (Fig. 1c ). The results of the sliding
displacement with the Bishop and Fellenius methods for thethree cases are shown in Fig. 2 according to the modified
Newmark approach. It can be clearly seen that the results of
the Bishop and Fellenius methods have decreased, especiallythose with the Fellenius method in Case 3. The difference
between the Fellenius and Bishop methods is no longer aslarge as with the conventional Newmark approach. This
proves that the re-division of the sliding mass in the modified
Newmark approach is effective with both Bishop andFellenius methods.
According to the design guidelines for land reclamation
projects, "reservoir maintenance" (MAFF 2015), it is difficult
to give specific values for the allowable settlement ofreservoirs, but they include (1) the difference in elevation
between the top of the dam and the normal water level (NWL),(2) the difference in elevation between the top of the dam andthe designed high water level (HWL), and (3) 1.0 m (taking
into account the extra height and freeboard). It can be seenthat with hypothetical Cases 2 and 3, when calculating
according to the conventional Newmark approach, it is highly
possible that the results of the sliding displacement will belarger than the allowable settlement, especially with theFellenius method. Accordingly, no guarantee can be given forthe dam’s seismic performance against level 2 earthquake
motion. This will lead to a costly, wasteful solution for
ensuring the dam's performance against level 2 earthquake
motion. However, when applying the modified Newmark
approach, the results of the sliding displacement are smaller
than the allowable settlement with both Fellenius and Bishop
methods for Case 2, and with the Bishop method for Case 3.
Therefore, the application of the modified Newmark approach,combined with the Bishop method, will provide a lessconservative design solution.

CONCLUSIONSIn this study, a modified Newmark approach has beenpresented to evaluate the earthquake-induced permanent
displacement of an earth dam based on the changing positionof the sliding mass by re-dividing the sliding mass by time
steps when slip occurs. Comparing the results of the modified
and the conventional Newmark approaches, there is a
noticeable difference between the obtained displacements. The
difference confirms that ignoring the change in the slip surface
position during an earthquake can produce an inconsistentyield seismic intensity (constant value), and result in an overly
conservative displacement estimate. Through verification for
one agricultural dam, with two hypothetical cases (cases 2 and3), the displacement results, according to the conventional
Newmark approach, may be outside the allowable settlement
range with both methods Fellenius and Bishop. But with themodified Newmark approach, the values of that displacement
are still within that range with both methods Fellenius andBishop for case 2 and with the Bishop method for case 3. Thus
the modified Newmark approach, combined with thesimplified Bishop method, a method that is stricter than theFellenius method, leads to a more realistic assessment of thepermanent displacement in seismic slope stability analyses.

REFERENCES
Bishop A. W., (1955) The Use Of The Slip Circle in The
Stability Analysis of Slopes, Geotechnique, Vol. 5, No. 1,
pp. 7–17.
Duncan J. M. and Wright S. G., (1980) The Accuracy of
Equilibrium Methods of Slope Stability Analysis,
Engineering Geology, Vol. 16, No. 1–2, pp. 5–17.
Ministry of Agriculture, Forestry and Fisheries of Japan(MAFF), (2015) Land Improvement Project Design
Guideline “Reservoir maintenance.”, pp. 125-129.
Newmark N. M., (1965) Effects of Earthquakes on Dams andEmbankments, Geotechnique, Vol. 15, No. 2, pp. 139–160.
Nishimura S., Nishiyama T., Hiramatsu K., and Senge M.,(2020) Newmark’s Method Considering Redivision of
Sliding Mass: Application to Agricultural Fill Dam, The
Japanese Society of Irrigation, Drainage and RuralEngineering, Vol. 88, No. 2, pp. 213- 218. (In Japanese)107Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P05

Development of a HS-SPME/GC-MS method for the extraction andidentification of the volatile compounds in coffee
Fawzan Sigma Aurum1,2, Teppei Imaizumi1, Manasikan Thammawong1 and Kohei Nakano1*
1. The United Graduate School of Agricultural Science, Gifu University, 1 – 1 Yanagido, Gifu, 501-1193 JAPAN2. Assessment Institute for Agricultural Technology of Bali, Indonesian Agency for Agricultural Research andDevelopment, Ministry of Agriculture, Denpasar, Bali, 80222 INDONESIAINTRODUCTION

Sample preparation and volatile extraction
Coffee powder sample 1 gram was transferred into a 20 mL vialwith a laminated PTFE screw cap.
The procedure of extracting the volatile compounds was
based on headspace extraction. The internal standard (10 µl of
1µl/ml 3-heptanone) was spiked to the sample vial before
incubation. A thermostatic system was used to incubate the vial.
Two optimization temperatures were employed for volatile
extraction, i.e., 70○C and 50○C. Firstly, the vials were incubatedfor 10 minutes. Then, the SPME-arrow needle was injected into
the vial for extraction for 10 minutes for both temperature
treatments. Volatile extraction was done using SPME-Arrow
PDMS (O.D.: 1.1 mm, film thickness: 100 µm, length: 20 mm)
purchased from Shimadzu (Shimadzu Corporation, Kyoto,
Japan). Prior to extraction, the SPME-Arrow was equilibrated
at GC Injector port 250○C for 15 minutes.

The unique flavor and aroma of coffee can be attributed to its
volatile compounds and other metabolites. Volatile metabolites
compounds of coffee can be extracted using various methods.
Steam distillation and solvent extraction are often used.
However, both methods may lead to thermal artifacts (Prososki
et al., 2007). Currently, Head Space Solid Phase Micro
Extraction (HS-SPME) has been widely employed to extract
volatiles of coffee.
Compared to liquid-liquid extraction, such headspaceextraction techniques have several advantages, including
solvent-free sample preparation, a quick extraction time, a little
volume of sample required, and fewer labor requirements. The
SPME is based on compound ab/adsorption on a fiber covered
with a polymer, commonly known as the extraction phase.
Several polymers with varying polarity are presently available,
and compound desorption in GC is often accomplished using athermal technique. Polar chemicals are sorbed on polar fibers,
whereas nonpolar compounds are sorbed on nonpolar fibers.
Aside from the fiber's composition, numerous additional factors
such as extraction temperature and length, equilibration time,
desorption time, and agitation can all impact the SPME
technique.
A novel SPME-Arrow technology has emerged in recent
years. This method employs a greater amount of sorbent
material to increase detection limits in SPME fibers, which areless fragile than standard SPME fibers. Since its inception, the
SPME-Arrow approach has been used to evaluate the volatile
constituent in fish sauce (Song et al., 2019), contaminated water(Kremser et al., 2016), and wastewater (Helin et al., 2015).
Based on our literature study, SPME-Arrow has never beenutilized for the volatile compound evaluation of roasted coffee.
This research aims to determine the optimum extraction
temperature of the SPME arrow using HS-SPME for volatilecompounds identification. This study uses coffee from the classof Coffea Arabica and Coffea Canephora P (Robusta) collectedfrom four famous coffee-producing locations in Indonesia.
Volatile profiling was done by the GC-MS system employing
an untargeted method. Therefore, to process and extract the
informative data, chemometrics modeling was used.

GCMS experimentation
The volatile compounds analysis from the coffee head
space were performed using a GC 2030 Nexis (Shimadzu,
Japan) coupled to a Shimadzu Mass Spectrometer-TQ8040 NX(Shimadzu, Japan) with the column: 30 m x 0.250mm x 1.0 µm
DB-5 (Agilent Technology, Inc, US). Injection system: Splitless. Temperature program employed was as follows: 0-4 min(40 °C); 5-25 min at 10 °C/min to 250 °C; hold at 250 °C (5min). The carrier gas used was Helium with the flow rate at 1.5
ml/min. The molecular fragmentation was obtained by electron
ionization (EI) 70eV with scan interval: 1.0 sec. Scan mode
employed was full scan, in the range: 40 – 400 amu. Total ion
chromatograms (TIC) were processed using the built-in
processing software.
Data Processing
The mass spectra from Shimadzu dedicated file wereconverted to NETcdf for ease of analysis. Then the fragment
ion of each compound was compared to the database (NIST17
and open source library at RIKEN PRIME) to identify thecompounds. Data deconvolution and alignment was done in
MSDIAL using the n-alkane standard (Tsugawa et al., 2015).
Furthermore, the identification was accomplished bycomparing the retention indices (RI) of volatiles under theexperimental condition with the data from the databases. Only
compounds with match probability > 80% (considered an
acceptable match) were included. Data were normalized by theinternal standard followed by data rearrangement in R-Studio.
Multi-variative data analysis was done using Random
Forest (RF) in R employing the RandomForest package (Liawand Wiener, 2002). The features were auto-scaled, the numberof trees selected was 250, the number of features tried at eachsplit was 8. The top important metabolites features wereselected based on the Decreased Mean Accuracy algorithm
from the RF model.
The model was built based on the training samples (N = 6)
for each coffee origin. The model was then applied to predictthe coffee origin of the validation samples (N = 3). Training andvalidation samples were coffee from different villages from thesame location. The most optimum temperature extraction

MATERIALS AND METHODS
Materials
Coffee beans from 4 locations in Indonesia of the crop year
2020 were used (Arabica Gayo - Aceh, Arabica Kintamani –Bali, Robusta Lampung – Sumatera, Robusta Jawa – West
Java). Sample was roasted in a small batch roaster machine
capacity of 500 gram (WE 600I, Indonesia) using controlledtemperature at 200oC for 12 minutes, followed by fan-assisted
cooling 10 minutes. Coffee was ground into powder followedby screening with Ø 425 µm test sieve (Retsch, UK). Coffee
sample was stored at -80oC until further use. The chemical
reagent used was 3-heptanone purchased from Sigma-Aldrich(Merck KGAa: St. Louis, Missouri, United States)108

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P05
method was selected based on the comparison of the accuracy
and area under the curve from both models.

pyrazines, the Maillard-reaction products, are generated byvarious chemical processes, including heat oxidation of lipids
and thermal degradation of carbohydrates.
However, several volatile compounds selected as importantfeatures were overlapping in the two treatments. For instance,
Ethoxybenzene and 6-Amino-6-methylfulvene which areshared in both treatments. Both compounds are aromatic with apleasant smell. However, most of the selected features are
unique to each treatment. The first important volatile for 50oC,
4-Ethyl-2-methoxyphenol, is included in the phenolicconstituent associated with flavor in cooked foods, resulting ina roasted-like aroma. On the other hand, the first importantvolatile extracted by the 70oC method was from the pyridazine
group. It is a heterocyclic aromatic organic compoundcommonly found in coffee.
The higher extraction temperature shows a higher
classification of coffee origin. This result may be associated tothe compounds having a higher boiling point. It is commonly
understood that brewing coffee at a higher temperature below
the water boiling point induced pleasant aroma and flavor.
The performance of the Random Forest model evaluatedusing several accuracy parameters shows that extracting thevolatile at 70oC for 10 minutes results in better classification.
Therefore, this information is beneficial for the future study ofcoffee classification based on volatile metabolites profile.

RESULTS AND DISCUSSION
Data deconvolution and alignment in MS-DIAL identified
125 references matched and 409 unknown compounds for thecoffee volatiles extracted at the temperature of 70○ C for 10
minutes. Another extraction temperature, 50○ C for 10 minutes,
resulted in 112 references matched and 380 unknown
metabolites. After removing the redundant features and filtering
the detected features with blank, 78 compounds and 77
compounds were determined for 70○C and 50○C extraction
temperature, respectively.
Model evaluation was performed using several indicators
as indicated in Table 1. Based on the model evaluation, theresults of the study showed that extraction temperature at 70○C
for 10 minutes performed better than 50○C. Prediction accuracyof the model based on the validation sample was performed by
employing a confusion matrix. Out of Bag (OOB), estimate
error rate is a way to measure the error prediction of an RFmodel. Multiclass Area Under the Curve was performed toevaluate the class separation based on the RF model.

REFERENCES

Table 1. Random Forest model performance parameter basedon the extraction times
Extraction
Prediction
OOB
Multiclass
Temp
Accuracy
Error rate
AUC
50○C
91.67%
16.67%
0.972
70○C
100%
0.00%
1.000

Helin, A., Rönkkö, T., Parshintsev, J., Hartonen, K., Schilling, B., Läubli, T., &Riekkola, M. L. (2015). Solid phase microextraction Arrow for the
sampling of volatile amines in wastewater and atmosphere. Journal of
Chromatography A, 1426, 56–63.
Kremser, A., Jochmann, M. A., & Schmidt, T. C. (2016). PAL SPME Arrow Evaluation of a novel solid-phase microextraction device for freely
dissolved PAHs in water. Analytical and Bioanalytical Chemistry,
408(3), 943–952.
Liaw A. and M. Wiener. (2002). Classification and Regression by randomForest.
R News 2(3), 18-22.
Prososki, R. A., Etzel, M. R., & Rankin, S. A. (2007). Solvent type affects thenumber, distribution, and relative quantities of volatile compounds
found in sweet whey powder. Journal of Dairy Science, 90(2), 523–
531.
Song, N. E., Lee, J. Y., Lee, Y. Y., Park, J. D., & Jang, H. W. (2019).
Comparison of headspace–SPME and SPME-Arrow–GC–MS methods
for the determination of volatile compounds in Korean salt–fermented
fish sauce. Applied Biological Chemistry, 62(1).
Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M.,
VanderGheynst, J., Fiehn, O., & Arita, M. (2015). MS-DIAL: dataindependent MS/MS deconvolution for comprehensive metabolome
analysis. Nature Methods, 12(6), 523–526.

Furthermore, the model can be employed to find theimportant features that lead to the classification. Table 2 shows
the top 10 important features for both extraction temperatures.
The important volatile compounds were selected based on the
Mean Decrease (MD) Accuracy score and Mean Decrease(MD) Gini value. The important metabolites at the extraction
temperature of 50○C showed differ to 70○ C.
Overall, the composition of the top ten importantcompounds are from the furans, phenols, pyrazines, andpyridines. These compounds are available in roasted coffee
together or bind with hydrocarbons, acids and anhydrides,
esters, alcohols, and sulfur compounds. These compounds varyin concentration and sensory potency, which explains why
different coffee types have distinct flavors. Furans andTable 2. Top ten important volatile compounds of coffee using extraction temperature of 70○C and 50○C based on Random Forest
Extraction Temperature 50○ C
Volatiles (IUPAC)

RI

RT

Extraction Temperature 70○ C

m/z

MD
Accur
acy*

MD
Gini
**

Volatiles (IUPAC)

MD
Accu
racy*

MD
Gini
**

RI

RT

m/z

1224.7

17.4

118.1

4.944

0.615

1120.2

15.7

119

4.707

0.597

832.7
1328.61329.1963.0
871.6
1066.0
8

10.1
18.9
18.95
12.85
10.9
14.773

96.0
150.1
150.1
107.1
94.0
95.04

4.629
4.612
4.431
4.264
4.081
4.015

0.564
0.648
0.528
0.510
0.540
0.486

959.1
12.78
872.03
10.9
94.05
3.865
0.498
2,5-Dimethyl-1H-pyrrole
979.0
13.17
2-Methyl-5-(methylthio)
1234.9
17.5
81.05
3.79
0.505
furan
Note: * Mean Decrease (MD) Accuracy based on Random Forest Model; ** Mean Decrease (MD) Gini based on Random Forest Model

43
94.05

3.97
3.955

0.563
0.471

4-ethyl-2-methoxy-phenol

1291.1

18.4

137.1

5.231

0.851

4-Methylthiazole

815.56

9.74

99

5.184

0.973

Ethoxybenzene
Phenol, 2-methoxy2-Heptanol
2-Furanmethanol-acetate
6-Amino-6-methylfulvene

979.0

13.17

94.05

1099.44899.79992.32
964.2

15.33
11.58
13.4
12.8

109.0545.5
81.05
107.07

4.866
4.302
4.206
4.136
4.098

0.714
0.630
0.565
0.495
0.587

2-Ethyl-6-methylpyrazine

1001.6

13.58

121.1

3.966

0.498

109

2H- Cyclopenta[d]pyridazine
1-methyl-1H-pyrrole-2acetonitrile
Furfural
3,5-Diethylphenol
2-Ethyl-4,5-dimethylphenol
6-Amino-6-methylfulvene
2,5-dimethyl -1H-Pyrrole
3-Hydroxypyridinemonoacetate
1-Propionylethyl acetate
Ethoxybenzene

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P06

Development of a program for constructing ortholog data set usingtaxonomic information
Tomoaki Watanabe1 and Tokumasa Horiike2
1. The United Graduate School of Agricultural Science, Gifu University
2. Department of Bioresource Sciences, Shizuoka University
groups phylogenetic trees and taxonomic information. It is also
possible to infer the species phylogenetic trees based on the
INTRODUCTION
created ortholog dataset (default: on).
Orthologs, which are genes derived from speciation, have
We evaluated the performance of the improved Ortholog
been used for species phylogenetic inference because theirFinder with evolutionary simulated protein sequences as
phylogenetic relationships correspond to that of species. The
follows. First, we generated gene phylogenetic trees from therRNA gene is a typical example of such genes (Woese et al.
model phylogenetic tree by Zombi (Davín et al. 2019). In this1990), but there are also some problems in using it for species
process, gene duplications occurred with a certain probability
phylogenetic inference because the number of rRNA genes in a
in each gene tree. Next, we generated protein sequence data
genome may differ between species. In such a case, the number
based on each gene phylogenetic tree using INDELibleof the genes is considered to have increased due to gene
(Fletcher and Yang 2009). Then, we randomly deleted the
duplication. Genes derived from gene duplication are called
paralogs in each generated homolog group, leaving one. This
paralogs and genes generated by gene duplication before
makes all out-paralogs hidden out-paralogs. Using the sequencespeciation are called out-paralogs in particular. It has been andata generated by the above process, we executed Ortholog
important issue to distinguish orthologs and out-paralogs
Finder to construct an ortholog dataset (previous version),
because the phylogenetic relationship of the out-paralogs does
improved Ortholog Finder, OMA (Altenhoff et al. 2019), andnot correspond to that of species because out-paralogs remain
OrthoFinder (Emms and Kelly 2019) programs, respectively.
information of gene duplication (Tekaia 2016). In recent years,
For each ortholog dataset, we calculated “Recall” (proportion
with the rapid increase in the amount of available sequenceof inferred orthologs to all orthologs), “Precision” (proportion
information in databases, the methods of using multipleof true orthologs to inferred orthologs), “Tree” (proportion oforthologs for species phylogenetic inference have become
inferred orthologs whose phylogenetic tree matched the species
popular to reduce the effects of errors in individual ortholog
phylogenetic tree), and “Species” (proportion of species
inference and increase the accuracy of the analysis (Gadagkar
phylogenetic trees inferred from the orthologs data set thatet al. 2005). However, these are not fundamental solutions tomatched the model phylogenetic tree) for each ortholog dataset.
the problem of distinguishing paralogs. Therefore, there is still
The numbers of iterations for the analyses were set to 100. We
a strong need for methods to construct highly accurate ortholog
also evaluated the performance of the programs using real
datasets that exclude paralogs. Although several programs toprotein-sequence data provided by Quest for Orthologs
construct orthologs datasets are already reported, they were(Altenhoff et al. 2016). In this test, the 78 species to be analyzed
mainly developed for prediction of gene function andwere selected from three domains: bacteria, archaea, andconsequently allow for the contamination of out-paralogs andeukaryotes. The accuracy of ortholog inference was evaluatedespecially those in which one gene is deleted after geneby the percentage of agreement between the true phylogenetic
duplication (called hidden out-paralogs, Horiike et al. 2016;tree of the species (provided by Quest for Orthologs) and theTrain et al. 2017) are prone to contamination. Therefore, thephylogenetic tree of the inferred orthologs in the test of Quest
existing programs are not suitable for constructing datasets forfor Orthologs.
species phylogenetic analysis. For these reasons, "Ortholog
Finder" which is the first program to construct orthologs data
RESULTS AND DISCUSSIONS
set that can remove out-paralogs and even hidden out-paralogs
are developed in previous research (Horiike et al. 2016).
The result of the performance tests with simulated protein
Ortholog Finder was able to detect out-paralogs from thesequences is shown in Fig. 1. The sequences were generated
phylogenetic tree of candidate ortholog groups using taxonomic
based on a model species tree with 32 OTU (Operational
information such as out-groups and in-groups. However, there
Taxonomic Unit). The ortholog dataset generated by improved
was a limitation in the removal of hidden out-paralogs because
Ortholog Finder has a higher percentage in “Precision” than
the available taxonomic information was limited to two. Inthat generated by the original Ortholog Finder, OMA, andaddition, there were technical problems such as long running
OrthoFinder. This suggests that each ortholog in the improved
time, inability to handle large-scale analysis, and low
Ortholog Finder dataset are more reliable than that in themaintainability due to using many kinds of development
ortholog dataset generated by the other programs. As shown inlanguage. Therefore, the purpose of this study was to develop a"Species" of Fig. 1, the phylogenetic tree using the improved
new ortholog dataset construction program that solves these
Ortholog Finder dataset has the highest percentage of species
problems and to evaluate its performance.
phylogenetic trees that matched the model phylogenetic treeout of 100 iterations. This suggests that the ortholog dataset
constructed by the improved Ortholog Finder can be used to
METHODS
inference the phylogeny of species more accurately than the
The ortholog inference of the improved Ortholog Finder isortholog datasets generated by the existing programs.
composed of the following five processes. 1) Prediction and
The agreement between phylogenetic trees of each ortholog
removal of horizontally transferred genes from the input
and the species phylogenetic tree was low overall. However,sequences (default: off). 2) Calculation of similarity scores
the accuracy of species phylogenetic inference was much
between all sequences by the homology search program andhigher in the improved Ortholog Finder, suggesting that thenormalization of each score (Emms and Kelly 2015). 3)
negative effect of such a low agreement percentage on species
Clustering of genes to create candidate ortholog groups. 4)phylogenetic inference is small. In addition, Recall was also
Phylogenetic tree inference of candidate ortholog groups. 5)
higher in the previous Ortholog Finder than in the improved
Detection and removal of out-paralogs using candidate ortholog110Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P06
Ortholog Finder. This is because the previous Ortholog Finderused only two taxonomic information for ortholog inference
and the threshold for ortholog detection was low. Since thethreshold of ortholog detection becomes stricter when moretaxonomic information is used, the improved Ortholog Finder
had lower Recall than the others. However, Fig 1. shows thestrict threshold of improved Ortholog Finder leads to accurate
species phylogenetic inference. Also, OMA and OrthoFinder
are high recall than improved Ortholog Finder. This is because
the paralog detection methods are different. OMA uses a
comparison of the evolutionary distance of candidate
orthologs to detect out-paralogs. A very simple explanation of
the principle is that the evolutionary distance between
orthologs is smaller than between out-paralogs. OrthoFinderinfers the rooted phylogenetic tree of species from the inferredorthogroups and detects paralogs by comparison with thespecies tree. Both methods cannot accurately detect outparalogs under the conditions of this evaluation test because
duplicate genes must remain in order for out-paralog
detection. Therefore, almost all genes are not removed and the
Recall is high while Precision is low. This means that thedifference in the Recall of each program means that only
Ortholog Finder was able to remove more out-paralogs
correctly. We constructed an ortholog dataset for “2018’s
Quest for Orthologs protein dataset” and performed thebenchmark test. The results of this test show RF (Robinson
Foulds) distance between the species phylogenetic tree for
LUCA (Last Universal Common Ancestor) and thephylogenetic trees of orthologs generated by each program(Fig. 2). RF distance is a scale for the similarity between two
phylogenetic tree topologies. If the topologies for two
phylogenetic trees are closer, the RF distance is smaller.
Ortholog Finder has the smallest RF distance to the species
phylogenetic tree. This indicates that Ortholog Finder has highaccuracy in ortholog inference and is the program that canconstruct the most suitable ortholog dataset for species
phylogeny inference. We evaluated the performance of the
program using simulated ortholog data and publishedsequence data provided by Quest for Orthologs. The results ofthe performance tests using a simulation program suggest that
improved Ortholog Finder can accurately remove paralogs
even under severe conditions that many genes that are
duplicated before speciation have been deleted differentially.
The percentage in "Precision" and "Species" of improved
Ortholog Finder (provided ten taxonomic information) was
higher than improved Ortholog Finder (provided threetaxonomic information), respectively. This result suggests thatthe accuracy of orthologs inference was further enhanced byusing more taxonomic information. Based on the results of theQuest for Orthologs benchmark test, the improved Ortholog
Finder showed a smaller RF distance than that of the existing
programs. This indicates that the improved Ortholog Finder
also can accurately infer orthologs under real conditions where
out-paralogs and hidden out-paralogs are mixed. It shows thatthe improved Ortholog Finder is particularly suitable forconstructing ortholog data sets for phylogenetic inference ofspecies, which is our motivation.

Emms, D. M. & Kelly, S. (2015) OrthoFinder: solving
fundamental biases in whole genome comparisons dramatically
improves orthogroup inference accuracy. Genome Biology 16.
Emms, D. M. & Kelly, S. OrthoFinder: Phylogenetic orthology
inference for comparative genomics. (2019) Genome Biology.
20.
Fletcher and Yang (2009) INDELible: A Flexible Simulator of
Biological Sequence Evolution. Molecular Biology and
Evolution. 26. 1879–1888.
Gadagkar S, et al. (2005) Inferring species phylogenies frommultiple genes: Concatenated sequence tree versus consensus
gene tree. Journal of Experimental Zoology, 304:64-74.
Horiike T et al. (2016) Ortholog-Finder: A Tool for
Constructing an Ortholog Data Set. Genome Biology and
Evolution. 8. 446-457.
Tekaia, F. (2016) Inferring orthologs: Open questions andperspectives. Genomics Insights 9.
Train et al. (2017) Orthologous Matrix (OMA) algorithm 2.0:
more robust to asymmetric evolutionary rates and more scalable
hierarchical orthologous group inference. Bioinformatics. 33.
i75-i82.
Woese C et al. (1990) Towards a natural system of organisms:
proposal for the domains Archaea, Bacteria, and Eucarya.
Proceedings of the National Academy of Sciences. 87: 4576–
4579.

Fig. 1: The result of performance test using simulation
sequences based on the 32OTU model tree. For each
evaluation item, the results of five different programs areshown. From left to right, the programs are improved
Ortholog Finder (provided three taxonomic information),
improved Ortholog Finder (provided ten taxonomic
information), previous Ortholog Finder, OMA, andOrthoFinder. The vertical axis represents the percentage(%) of each item.

REFERENCES

Altenhoff, A. M. et al. (2016) Standardized benchmarking in
the quest for orthologs. Nature Methods 13:425-430.
Altenhoff, A. M. et al. OMA standalone: Orthology inference
among public and custom genomes and transcriptomes. (2019)
Genome Research. 29
Davín A. A. et al. (2019) Zombi: a phylogenetic simulator oftrees, genomes and sequences that accounts for dead linages.
Bioinformatics 36.1286-1288.

Fig. 2: The result of Quest for Orthologs benchmark test
(Generalized Species Tree Discordance Benchmark ofLuca). Each program is indicated by a separate mark.
(OF2 is a yellow-green diamond). The vertical axis
represents the mean Robinson Foulds distance between thespecies phylogenetic tree and the orthologous phylogenetic
tree.

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UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P07

Visualization of low molecule phenol (LMP) and copper naphthenate in oilpalm veneer using X-ray microtomogrpahy
Ayuni Nur Apsari1*, Eko Sudoyo2, Eka Mulya Alamsyah3, Kenji Kobayashi4, and Takashi Tanaka4
1. The United Graduate School of Agriculture Science, Major of The Utilization of Biological Resources, Gifu University,
Yanagido 1-1, Gifu City 501-1193 Japan
2. Research and Development Division, PT Sumber Graha Sejahtera, Sampoerna Strategic Square, North Tower, 21st
Floor, Jl. Jend. Sudirman Kav. 45-46, Jakarta 12930, Indonesia
3. Post Harvest Technology Study Program, School of Life Sciences and Technology, Institut Teknologi Bandung, Jalan
Ganesha No. 10 Bandung 40132 Indonesia
4. College of Agriculture, Academic Institute, Shizuoka University, Ohya 836, Suruga-ku, Shizuoka, 422-8529 JapanOne oil palm veneer treated by LMP, copper naphthenate,
and the untreated oil palm veneer with dimension 10 mm x
100 mm x 2.21 mm were arranged as Fig. 1 for X-ray
microtomography scanning. The L4320 lens type with F.O.V
14 mm x 10 mm, and molybdenum X-ray target were used for
X-ray scanning. The X-ray tube voltage and tube current wereset automatically at 50 kV and 24 mA, respectfully. The
resolution of the image was 8.800 µm/px (Binning 2), 8 s
exposure time, angular step at 0.225° (with 800 projections
equaling 180º ÷ 800 = 0.225° angular steps), and the distance
between the sample and lens was 8 mm. After scanning, we
auto-centered the reconstruction image to minimize the blurry
image due to sample shifted during the rotation of the X-ray
scanning process.

INTRODUCTION
The abudant of oil palm trunk has a prospective resource forthe plywood industry. The oil palm is a monocotyledonous
species that has different anatomy with dicotyledonous wood.
The anatomy of the oil palm trunk is contains around 50% of
soft parenchyma tissue and vascular bundles (Schmidt et al.,2016 and Loh et al., 2011).
The prospective veneer material from oil palm has aninferior quality in dimentional stability, low strength, and lessdurability. However, the oil palm is more porous than thecommon wood. The anatomy of oil palm makes it easier toabsorb the chemical modification such as low molecule phenol(LMP) to enhance the dimensional stability, especially inloose side of the veneer (Loh et al., 2011).
The veneer also need to be protect from the decay fungi.
The copper based preservative is famous to be used as woodpreservative. The recent plywood industry used copper
naphthenate to increase their veneer durability, especially toprotect veneer surface against staining fungi (Civardi et al.,2016). The Schmidt et al. (2011) investigated the mass lossof oil palm wood by fungus Coniophora puteana up to 61%.
The investigation was done using transmission electron
microscopy (TEM), light microscopy, and UV techniques.
Those techniques were stained and/or destruct the sample forinvestigation.
The appropriate techniques is needed to check the quality ofthe treated oil palm veneer using LMP and copper naphthenate
non-destructively and without any additional material(chemically). X-ray microtomography might be the promisingtechnique to perform non-destructive testing to the treated oilpalm veneer without any additional material. It gives a highresolution image and can be form into 3D image to deeply
understand the microdistribution of material in wood (Oishi
and Tanaka, 2020).
This study was conducted to determine the applicability of
X-ray microtomography to visualize the micro distribution of
LMP and copper in treated oil palm veneer non-destructively
and without any additional material during X-ray scanning.

Fig. 1: The schematic of the X-ray microtomography
The gray value plot profile and calculation of it were
performed using imageJ 1.48v software to the 2D X-ray
image. The deeper observation of the LMP and Cu distribution
was performed using VGstudio software, specially using Xray image stack in 3D form.

RESULTS AND DISCUSSION
The distribution of LMP and Cu in the treated oil palm
veneer was successfully visualized using the X-ray
microtomography. The difference among LMP treated veneer,copper naphthenate treated veneer, and untreated veneer canbe seen clearly from the 2D X-ray image and the gray value
plot profile. The 2D image also show the shrinking andswelling phenomena happened in the treated veneer comparewith the size of the untreated veneer.
The micro distribution of LMP mostly in the parenchyma of
the LMP treated veneer, and seem distributed evenly in theveneer. The brightness level almost same for all part according
to the gray value peak in the plot profile. The micro
distribution of the Cu in the copper naphthenate treated veneer
can be seen from the brightest color of the vascular bundle’s
border in the copper naphthenate treated veneer (Fig. 2).

MATERIALS AND METHODSOil palm veneer was treated by LMP (solid content 27.11%)
and copper naphthenate (metal content 8%). The LMP
treatment was conducted using impregnation process. The
pressure was kept at 5 kg/cm2 for 30 minutes using pump
Krisbow KW20-425. The copper naphthenate treatment was adipping process for one second. After modification treatment
to the veneer, all the veneer (including untreated-veneer) wereoven-dried at 103℃ for 15 hours. The modification treatment
was followed the plywood industrial method.

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UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P07

Fig. 3: (a) Up-right-front three-dimensional (3D) X-ray
image stack of oil palm veneer, (b) 2D image of transverse
section (obtained from the blue cut section of the 3D
image, 226% zoom), and (c) 2D image of a radial section(obtained from the green cut section of the 3D image)Fig. 2: Two-dimensional (2D) X-ray image of the oilpalm veneer. Scale: 2.64 mm

ACKNOWLEDGMENTS
The authors express their appreciation to JSPS KAKENHI
for funding (Fund No.: 20K06163). We would like to express
our big appreciation to Takeshi Arizono for performing andexplaining the LMP and copper naphthenate materials and theapplied process. We thank PT. Sumber Graha Sejahtera"Sampoerna Kayoe" for providing the material and facility for
conducting the LMP and copper naphthenate treatments.

The X-ray image show clearly the swelling phenomenon of
the parenchyma in the LMP treated oil palm veneer, and theshrinking phenomenon of parenchyma in the copper
naphthenate treated oil palm veneer. The probability of LMP
and Cu as the reagent distributed in the treated oil palm veneer
can be seen from the peak of the gray value plot profile. The
treated veneer has many higher peaks (higher gray value) thanthe peak of untreated oil palm veneer. The average gray valueof copper naphthenate treated veneer was the highest(18900.15441), followed by LMP treated veneer(17973.6965), and the lowest was the untreated veneer’s
average gray value (15566.27132).
The anatomy of the oil palm different with the anatomy ofwood. The oil palm anatomy contains of vascular bundle andparenchyma. The oil palm also has crystalline silica in theparenchyma. The observation from X-ray image stack in 3D
form show the existence of the crystalline silica in both treatedveneer and untreated veneer (Fig. 3). The parenchyma of oilpalm has <20% klason lignin that contaminated withcrystalline silica (Tomimura, 1992).

REFERENCES
Civardi C, Van den Bulcke J, Schubert M, Michel E, Butron
MI, Boone MN, et al. (2016). Penetration and effectivenessof micronized copper in refractory wood species. PLoS
ONE.
11
(9),
e0163124.
https://doi.org/10.1371/journal.pone.0163124
Loh, Y. F., Paridah, M. T., Hoong, Y. B., and Yoong, A. C. C.
(2011). Effects of treatment with low molecular weightphenol formaldehyde resin on the surface characteristics ofoil palm (Elaeis quineensis) stem veneer. Materials &Design, Volume 32, Issue 4, Pages 2277-2283.
https://doi.org/10.1016/j.matdes.2010.11.014
Oishi, A., and Tanaka, T. (2020). Development of anondestructive imaging method for morphologicalcharacterization of adhesive bondlines in wood-basedmaterials using x-ray micro-computed tomography. 日本接着学会誌 第57巻 4号 別刷. 145–151, 18–24.
Schmidt, O., Bahmani, M., Koch, G., Potsch, T., and Brandt,
K. (2016). Study of the fungal decay of oil palm wood using
TEM and UV techniques. International Biodeterioration &Biodegradation,
Volume111,
Pages
37-44.
https://doi.org/10.1016/j.ibiod.2016.04.014
Tomimura, Y. (1992). Chemical characteristics and utilizationof oil palm trunks. Jarq-japan Agricultural Research
Quarterly, 25, 283-288.

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UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P08

Generalized pollination and floral scent chemistry in the invasive
Coreopsis lanceolata
Muhammad Arifin1 and Tomoko Okamoto1, 2,
1. The United Graduate School of Agricultural Science, Gifu University, Japan
2. Faculty of Applied Biological Science, Gifu University, Japan
microscope and standard book references were used for
identification. We noted the number of attached pollen grain
and location of pollen load on specimen’s bodies during theidentification.

INTRODUCTION
The invasive alien species pose threats to the reproductionof native plants due to their ability to compete for local
pollinators (Courchamp et al. 2017; Muñoz et al. 2008). The
floral traits have been reported to facilitate invasive species
successfully colonizing the local plant-pollination web(Gibson et al. 2012; van Kleunen et al. 2015). Therefore,
investigation on floral traits as key factors supporting
invasiveness is crucial to explain the mechanism ofinvasiveness.
Coreopsis lanceolata (Asteraceae) is an herbaceousperennial plant native to North America (Samfield et al. 1990).
It was introduced to Japan in the 1880s as ornamental plant
and later used for landscaping (Suzuki et al. 1990). This
species spreads rapidly in Japan and is currently registered as
the 100 worst invasive alien species based on the Invasive
Alien Species Act. C. lanceolata produces numerous flower
densities of about 30 capitula per 100 cm2 (Zeng et al. 2021).
The flowers produce large amounts of seeds as long-distancedispersal strategy, in which the seeds are easily transferred bywind or water. The plants can also use rhizome as shortdistance dispersal strategy (Batianoff et al. 2002).
C. lanceolata are self-incompatible. Consequently, seedproduction has relied on pollinators. However, the floral traits
involved in the attraction of pollinators for seed production are
overlooked. This study investigated floral traits related to thereproductive biology of C. lanceolata in Japan. The aims wereto answer the following questions: (i) Which insect
assemblages visit C. lanceolata? (ii) How important are thevolatile compounds for the attraction of visitors?

Floral scent collection and analysis- Floral scent collections
were conducted from fresh flowers growing in Tsubaki Bora
population using a dynamic headspace technique (Raguso etal. 1998). The floral scent collections were captured for twohours during the peak of flower visitor activities in 1000h1400h. During the collection, two capitula from eachindividual were enclosed in low-density polyethylene bags.
Overall, we sampled six capitula from three individuals. The
sorbent cartridge made from Tenax TA (60 mg; mess 80–100;
GL Science, Tokyo, Japan) was used to adsorb the volatiles in
the headspace.
Collected volatile samples were eluted from the adsorbent
with n-hexane (1.5 mL) and were carefully evaporated by N2
flow until it reached 20 µL at room temperature. A
microsyringe was used to take an aliquot of 1 µL sample. As astandard for analysis, 1 µL of n-eicosane (concentration of 10ng .uL-1) was added to the sample. The analysis was
performed using gas chromatography-mass spectrometry (GCMS) (GC-2010, MS: Shimadzu, Tokyo, Japan) installed with
Rtx-5sil MS capillary column (30 m × 0.25 mm; film
thickness, 250 µm; Restek, Restek Corporation, USA).
Helium was used as the carrier gas.
To discover the floral scent compound, we compared thevolatiles produced by flowers, branches without flowers, andambient air. The compounds were discovered and identified
by matching the fragments to those in the National Institute ofStandards and Technology NIST 05 and NIST 05s libraries.
We also estimated retention indices for all compounds using
n-alkane standards and compared them to those reported inprior research and the NIST chemistry webbook(https://webbook.nist.gov/chemistry/). Emitted rate of eachcompound was determined by comparing the GC results to theinternal standards.

MATERIALS AND METHODS
Study species and sites- Coreopsis lanceolata (Asteraceae)
is an herbaceous perennial up to 70 cm in height, which foundinvading riverbanks, roadsides, and wastelands in almost
entire Japan. These plants produce flower head, namely acapitulum (plural capitula), from May to July. The capitulum
is composed of numerous ray and disk florets arranged into awhole inflorescence that looks like a single flower. The
capitula are bright yellow and 4–6 cm in diameter (Batianoff
et al., 2002).
We conducted the study of C. lanceolata on three
populations in Gifu Prefecture, Japan, from May to July 2021.
The populations, namely Ajiki (35°29'47.0"N 136°43'37.1"E;
26 m. asl), Hirai (35°33'20.0"N 136°43'11.6"E; 76 m. asl) andTsubaki Bora (35°28'18.6"N 136°45'50.4"E; 18 m. asl) weremore than 4 km apart each other.

RESULTS & DISCUSSION
Flower visitor assemblages- Species-rich of insects wereobserved visiting C. lanceolata. Overall, we recorded 261
insects belonging to at least 12 families of five orders. The
flower visitors were observed diurnally in all populations, inwhich the peak daily visitation was between 1000h to 1400h.
Hymenoptera made the most significant portion of visitation(c 59-66%), followed by Diptera, Lepidoptera, Coleoptera,and others, respectively (Fig. 1). Most of the flower visitors
carried pollen on their bodies. Compared to visitors among
populations, the halictid and megachilid bees were the mostcommon and important visitors for C. lanceolata. The halictid
bees, such as Halictus spp. and Lasioglossum spp. were small
to medium in body size, though they effectively carried pollen
on whole bodies, mainly on the scopa under the abdomen.
This pollen was readily transferred across flowers (Parker etal., 2015). In contrast, the recent studies of C. lanceolata inintroduced range of China revealed that C. lanceolata
significantly attracted Apidae, especially Apis cerana (c 45%
of total specimens), to guarantee seed reproduction (Zeng et

Flower visitor observation and analysis- Flower visitor
observations on C. lanceolata were conducted on cloudy andsunny days with low wind speeds between 0700h-1830h. All
populations were growing on the roadside (300-800 m long).
Therefore, transect sampling was performed. We clustered theflowers into four to seven sampling points for each transect
and made 10 mins observations of each, in turn. The hand net
was used to capture the flower visitors which landed on flower
parts. Subsequently, flower visitors were deposited into 30 mltransport tube and kept at -20oC until identification. The stereo

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UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P08
al. 2021). Even though the honeybee A. cerana also exist inJapan, surprisingly, our study revealed that A. cerana was lessabundant as the visitor in all populations (Fig. 1). Instead,halictid and megachilid bees were the most abundant. Further
study regarding the insect assemblage’s composition
surrounding the C. lanceolata population probably helps
answer these phenomena.

Floral scent- The floral scents emitted from C. lanceolata
were listed in Table 1. The emission rate of floral compounds
was 4.42 ± 2.99 ng .h-1.capitulum-1 (N=3). In total, we detected
at least 14 unique compounds belonging to benzenoids,
monoterpenes, and sesquiterpenes emitted from the flower.
Sesquiterpenes were the predominantly abundance volatiles,
which collectively made up more than half of the relative
amount of volatiles. All of the identified compounds were
found in the floral scents of other angiosperms (Knudsen
1993; Knudsen et al. 2006). These compounds are likely to be
attractive for general insects. Our study supported theevidence that floral scent is more likely a key component
which plays the role for pollinator attraction (Whitehead et al.
2009). However, the identified compounds (Table 1) which
we presented here were the preliminary work. Further works
on confirmation with the authentic standard compound areneeded to determine the compounds.

Fig.1: Comparison of flower visitor assemblages of C.
lanceolata among populations
Most notably, our results highlighted the ability of invasive
C. lanceolata to conduct the generalist pollination system. The
flower of C. lanceolata was typically an open plate shapeflower in which the pollen and nectar were exposed and easily
accessed by the visitors. These results support previous studies
that invasive plants attract generalist pollinators forreproduction (Montesinos et al. 2016; Ward et al. 2013). Mostof the flower visitors were native species, and this could be ashred of evidence that C. lanceolata successfully integrated
into the native pollination web (Memmott et al. 2002). The
integration with native pollinators was more likely to be a vital
key that allowed C. lanceolata to produce large seeds,
although they were basically self-incompatible.

In conclusion, this study lay the evidence that C. lanceolata
utilized generalist pollination system to avoid reproductive
failure. Moreover, it is noteworthy that the scent profiles playa role in attracting general native visitors to C. lanceolata.

ACKNOWLEDGMENTS
The first authors, M.A., would like to express gratitude to
The Ministry of Education, Culture, Sports, Science, andTechnology (MEXT) Japan for sponsoring a scholarship for
Ph.D. study.

REFERENCES
Batianoff, G. N., & Halford, D. A. (2002) Coreopsis lanceolata L. (Asteraceae):
Another Environmental Weed for Queensland and Australia. Plant Protection
Quarterly 17(4): 168–169.
Courchamp, F., Fournier, A., Bellard, C., Bertelsmeier, C., Bonnaud, E.,
Jeschke, J. M., & Russell, J. C. (2017) Invasion Biology: Specific Problems
and Possible Solutions. Trends in Ecology and Evolution 32(1): 13–22.
Gibson, M. R., Richardson, D. M., & Pauw, A. (2012) Can Floral Traits Predictan Invasive Plant’s Impact on Native Plant-Pollinator Communities? Journalof Ecology 100(5): 1216–1223.
Knudsen, J. (1993) Trends in Floral Scent Chemistry in Pollination Syndromes:
Floral Scent Composition in Moth-Pollinated Taxa. Botanical Journal of theLinnean Society 113(3): 263–284.
Knudsen, J. T., Eriksson, R., Gershenzon, J., & Ståhl, B. (2006) Diversity andDistribution of Floral Scent. The Botanical Review 72(1): 1–120.
Memmott, J., & Waser, N. M. (2002) Integration of Alien Plants into a Native
Flower-Pollinator Visitation Web. Proceedings of The Royal Society B:
Biological Sciences, 269(1508): 2395–2399.
Montesinos, D., Castro, S., & Rodríguez-Echeverría, S. (2016) Two Invasive
Acacia Species Secure Generalist Pollinators in Invaded Communities. Acta
Oecologica 74: 46–55.
Muñoz, A. A., & Cavieres, L. A. (2008) The Presence of a Showy Invasive
Plant Disrupts Pollinator Service and Reproductive Output in Native Alpine
Species Only at High Densities. Journal of Ecology 96(3): 459–467.
Parker, A. J., Tran, J. L., Ison, J. L., Bai, J. D. K., Weis, A. E., & Thomson, J. D.
(2015) Pollen Packing Affects the Function of Pollen on Corbiculate Bees but
Not Non-Corbiculate Bees. Arthropod-Plant Interactions 9(2): 197–203.
Raguso, R. A., & Pellmyr, O. (1998) Dynamic Headspace Analysis of Floral
Volatiles: A Comparison of Methods. Oikos, 81(2): 238–254.
Samfield, D. M., Zajicek, J. M., & Cobb, B. G. (1990) Germination ofCoreopsis lanceolata and Echinacea purpurea Seeds Following Priming andStorage. Hortscience 25(12): 1605–1606.
Suzuki, K., & Kondo, M. (1990) A Method of Plant Seeding and Maintenancewith Wildflower. Journal of the Japanese Society of Revegetation Technology
16(3): 46–57.
van Kleunen, M., Dawson, W., & Maurel, N. (2015) Characteristics ofSuccessful Alien Plants. Molecular Ecology, 24(9): 1954–1968.
Ward, M., & Johnson, S. D. (2013) Generalised Pollination Systems for Three
Invasive Milkweeds in Australia. Plant Biology 15(3): 566–572.
Whitehead, M. R., & Peakall, R. (2009) Integrating Floral Scent, Pollination
Ecology and Population Genetics. Functional Ecology 23(5): 863–874.
Zeng, J.J., Zhou, B., & Wang, N. (2021) Comparing the Reproductive
Biological Characteristics of the Alien Invasive Coreopsis lanceolata toThose of the Non-Invasive Alien Congener Coreopsis tinctoria. Plant Species
Biology 36(3): 379–389.

Table 1. Floral scent chemistry of C. lanceolata in Tsubaki
Bora population expressed as percent relative peak area
Compounds♯

Retention
indices

Relativeamount (%)(mean ± SD,
N=3)7.64 ± 2.83

Benzenoids
2-Phenylacetaldehyde

1045

Methyl phenylacetate1176

0.61 ± 0.65

Methyl salicylate

1195

0.43 ± 0.41

Monoterpenes
o-Cymene

1026

1.62 ± 1.31

(E)-Linalool oxide, furanoid

1087

0.75 ± 0.70

Z-Verbenol

1165

15.37 ± 6.85
17.90 ± 8.19

Sesquiterpenes
β-Bourbonene

1391

β-Elemene1394

4.43 ± 1.13

β-Cedrene

1427

18.05 ± 4.48

β-Bisabolene

1512

25.01 ± 3.95

m/z [43, 119, 41, 109, 81]1259

1.23 ± 1.08

m/z [119, 132, 105, 41]1485

2.54 ± 1.65

m/z [41, 69, 132, 134, 119]1682

2.35 ± 0.80

Unidentifieds*

m/z [43, 58, 71, 57, 59, 41]
1842
2.08 ± 1.11
Total emission rate (mean ± SD,
4.42 ± 2.99
ng .h-1.capitulum-1)♯Identification of compound was performed by comparing thesimilarity of mass spectrum of focal compound in libraries, andRetention Indices (RI) reported in the NIST Chemistry WebBook*Ion fragments are lined from the stronger for unidentified
compounds

115Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P09

Inhibition effect of different sorbent materials on transfer ofcesium and arsenic from contaminated soil to vegetation
Shiamita Kusuma Dewi1 and Yongfen Wei2
1. The United Graduate School of Agricultural Science, Gifu University, Japan
2. River Basin Research Center, Gifu University, Japan

Pot Experiment (Fig.1) – CsCl and Na2HAsO4 solution
were uniformly mixed with soil. The contamination
concentration of the soil used for the experiment was set as
300 mg kg-1 for arsenic and 50 mg kg-1 for cesium. The soil
was well mixed with sorbent materials, and the mixing ratio
for each sorbent to the soil was set at 5%. 500 g ofcontaminated soil was placed in plastic pot and no additive
applied soil was used as control for comparison. Eachtreatment had two replicates. Totally, 48 pots were prepared

INTRODUCTION
Arsenic (As) in soil is the major source for arsenic uptake ofcrops. Inorganic arsenic (As) such as arsenite and arsenate are
toxic to humans and is considered a carcinogen by theInternational Agency for Research on Cancer.
Radio cesium represents in water, soil, and air which
threatening human health and the environment. It acts directly
on living organisms from external sources, or it becomesincorporated through the food chain or both. The plant forms
the base of the food chain. Therefore, it is essential to inhibit
its transfer to vegetation.
Due to simplicity, low cost, high effectiveness, and being
environmentally friendly, reducing the bioavailability of theseheavy metal (iod) in soil by adding promising sorbent
materials such as activated carbon, biochar, and wood ash canbe considered as an economically feasible approach.
Activated carbon (AC), having a high surface area, anddeveloped porosity, is a low-cost and effective adsorbent for awide range of pollutants. (Mohan et al.,2006; Marsh etal.,2006). Biochar is an eco-friendly product that has anegative surface charge, high porosity, large specific surface
area, and abundance of functional groups on the surface, it has
been considered as a low-cost material that can be used incarbon sequestration, soil improvement, waste management,
and remediation areas (Wang et al. 2018; Ruan et al. 2019).
Wood ash contains organic matter, Ca, Mg, Fe, and otherminerals that can immobilize toxic metal (iod) in soil.
In this study, to evaluate the ability of a different types ofsorbent on inhibiting arsenic and cesium transfer fromcontaminated soil to vegetation, pot experiment is designed
and carried out.

SSA (m2 /g)

1368.800 206.500

-

4.200

5% CAC

Potting Soil

5% WAC
5% Biochar
5% Wood ash

Fig 1. Design of Experiments
Sample analysis – This research experiment was
conducted outdoor. Komatsuna was cultivated in greenhouse
Gifu University for 73 days and no additional fertilizer wasadded during the entire growing period. The vegetation waswatered with river water and the water leached from thepotting soil was collected on harvest day. After harvest, thevegetation was washed and, then further hot-dried in the ovenat 80°, followed by shattering by crusher and digested withnitric acid in the autoclave. Arsenic and cesium concentrationof the digested sample from the different parts was measured
using inductively coupled plasma mass spectrometry (ICPMS, Agilent 7700X).
Statistical analysis – Statistical analysis are conducted using
SPSS 24.0 (p≤ 0.05). All data are expressed as means ±standard errors. Significant differences are determined using
one-way analysis of variance (ANOVA) by Tukey’s multiple
comparisons test.

RESULTS
Plant growth status evaluation – As shown in Table 2.,
depending on the contaminant(s) the dry weight of harvested
komatsuna range from 0.416 to 0.884 g pot-1 (Control;
average: 0.618 g pot-1), 0.052-0.295 g pot-1 (Cs: 50 mg kg-1;
average: 0.161 g pot-1), 0.055-0.191 g pot-1 (As: 300 mg kg-1;
average: 0.103), 0.138-0.287 g pot-1 (AsCs; average: 0.160 g
pot-1), respectively. It was found that the contamination of As
and Cs suppressed the growth of the most komatsuna, and that
only CAC, BAC, WAC addition in AsCs contamination level
which increased the dry weight of komatsuna compared tocontrol.

Table 1. Properties of soil and sorbents used in this study

BAC
Biochar Wood Ash CAC
WAC
10.2009.200
11.8006.700
7.800
463.000 777.000 463.000 1204.000 95.600
15.018 37.430
2.244
22.368 18.292
7.360
6.280
6.390
9.780
8.390
0.486
0.477
0.020
0.604
0.549

Single Pollutant
Cs: 50 mg/kg

Multiple
Pollutants (Cs: 50
mg/kg and As:300
mg/kg)

Soil and additive samples – the mixture of two commercial
products: Gardening soil and Decomposed granite soil mixed
in a dry weight ratio of 3:2 was used as soil sample. Fiveselected sorbent materials including bamboo activated carbon(BAC), wood activated carbon (WAC), coal activated carbon(CAC), biochar, and wood ash, were used in the study. The
properties of the samples of soil and sorbents are summarizedin Table 1. It can be seen from the table that the properties ofsoil samples and sorbents are quite different.
Soil
6.100
81.20012.361
11.5303
Pore volume (cm /g)

5% BAC

Single Pollutant
As: 300 mg/kg

MATERIALS AND METHODSParameterpH
EC (mS/m)
OM (%)
CEC (cmol/kg)

No Pollutant

1168.000 1500.900

Note: All value represented the averaged values. EC: electricalconductivity, OM: organic matter, CEC: cation exchange capacity,
SSA: specific surface area, BAC: bamboo activated carbon, CAC:
coal activated carbon, WAC: wood activated carbon

116Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P09

0.416 ± 0.008

Bamboo Activated Carbon (BAC)

0.545 ± 0.021

Coal Activated Carbon (CAC)

0.731 ± 0.030

Wood Activated Carbon (WAC)

0.619 ± 0.274

Biochar0.884 ± 0.045

Wood ash

0.513 ± 0.062

Cs

0.295 ± 0.006

Bamboo Activated Carbon (BAC)

0.053 ± 0.001

Coal Activated Carbon (CAC)

0.243 ± 0.001

Wood Activated Carbon (WAC)

0.172 ± 0.083

Biochar0.052 ± 0.008

Wood ash0.150 ± 0.024

As

0.191 ± 0.073

Bamboo Activated Carbon (BAC)

0.113 ± 0.008

Coal Activated Carbon (CAC)

0.101 ± 0.000

Wood Activated Carbon (WAC)

0.070 ± 0.000

Biochar0.090 ± 0.001

Wood ash0.055 ± 0.009

AsCs

0.138 ± 0.000

Bamboo Activated Carbon (BAC)

0.165 ± 0.032

Coal Activated Carbon (CAC)

0.287 ± 0.067

Wood Activated Carbon (WAC)

0.183 ± 0.000

Biochar0.119 ± 0.003

Wood ash

0.070 ± 0.011

0.618

0.103

0.16

abc

a

As amount mg/L

240

160

0.8

120
a

a

a

a

a

a

80

0

Plant uptake

b

b
a

b
a

0.8

240
b

200
160
120
80

0.4

40
0

Leached water

In summary, all data revealed that adding sorbent materials
into contaminated As and Cs soil could be inhibiting As andCs transfer to plant (BAC and CAC) but also could beallowing the As and Cs soluble to plant uptake and leachedwater. Further investigation needs to be conducted toinvestigate the factor that affect the solubility of arsenic andcesium in soil due to the addition of sorbent materials.

40

0Leached water

CONCLUSION

200

1.2

0.4

80

Leached water

1.6

Plant uptake

The addition of different sorbent materials in As
contaminated and Cs contaminated soil mostly decreased the
dry weight of komatsuna. Compare to control only BAC,
CAC, and WAC in AsCs contaminated soil increased the dryweight in AsCs contaminated soil. As shown in Fig.2 As and
Cs concentration in vegetation was no different between
control and sorbent materials additive treatment, only BAC
and CAC significantly decreased Cs concentration in the plant.
This figure indicated that arsenic and cesium were soluble forplant uptake. Arsenic and cesium that are uptaken by plants
will suppress the growth of the komatsuna plant. Some
researchers also report that at higher concentration, uptake
nutrient element inhibits in roots, therefore, the growth of thewhole plant hampered and biomass decreased and caused
physiological disorders (Wells B, 1997).
For As and Cs concentration in leached water, the mostresult showed that addition of sorbent materials gave nosignificant differences between the control and treatmentgroup. Despite of decrease arsenic and cesium amount inleached water, some of addition of sorbent materials tend toincrease the amount of As/Cs in it.

0

Plant uptakeb)

0

DISCUSSION

40

0

40

Fig.3 Amount of Cs uptaken by plant and in leached water under (a)single pollutant (Cs:50 mg/kg), (b) multiple pollutants (As:300 mg/kg;
Cs:50 mg/kg)

As amount mg/kg

ab

80

1.6

1.2

200

120

Plant uptake

As amount mg/kg

As amount mg/L

0.4

120
c

ab

160

0

160
d

ab
a

0.4

b)

200

bc

ab

0.8

0.161

Amount of arsenic and cesium in vegetation and leachedwater - The amount of Cs and As in the vegetation andleached water under multiple pollutants (contamination levelCs:50 mg/kg and As: 300 mg/kg) is shown in Fig.2 and Fig.3.
All sorbent materials were not significantly different
compared to the control group. Only the addition of BAC and
WAC which significantly decreased the cesium amount in thevegetation. For leached water, BAC and wood ash showed
increased As concentration significantly in leached water.
While only wood ash treatment showed significantly increased
Cs amount in leached water
1.6
240
a)

0.8

1.2

240

b

b

0

Value represents the mean ± SEM. BAC: bamboo activated carbon,
CAC: coal activated carbon, WAC: wood activated carbon.

1.2

1.6

Cs amount mg/kg

No addition

Cs amount mg/L

control
Only Cs:50 mg/kg
Only As :300 mg/kg
Both Arsenic andCesiuma)

Average(g/pot)Cs amount mg/L

Dry weight(g/pot)Treatment

Cs amount mg/kg

Table 2. Dry weight of plant under different addition of sorbent
materials

Leached water

Fig.2 Amount of As uptaken by plant and in leached water under (a)single pollutant (As:300 mg/kg), (b) multiple pollutants (As:300
mg/kg; Cs:50 mg/kg)

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UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P10

Ensemble prediction of solar irradiance in tropical regions
Daiki Harada1, Naoki Moriai1 and Tomonao Kobayashi2
1. Graduate School of Natural Science and Technology, Gifu University
2. Faculty of Engineering, Gifu University
Step 2In this step, ensemble prediction is performed to further
INTRODUCTION
improve the accuracy. Ensemble prediction is a method of
performing numerical weather prediction with multiple initial
Solar irradiance is high in tropical regions (Fig. 1). The
values and statistically processing the results. In this study, we
introduction of photovoltaic systems in these regions is
set multiple initial times and make predictions using the
expected to contribute to the realization of a low-carbon society
combination of options optimized in step 1. Specifically, three
and the stable supply of electricity. For efficient operation ofpredicted values can be obtained every day by making
the power grid to which the photovoltaic system is connected,
predictions for three days including the current day. Of thethe predicted value of the solar irradiance by the numerical
three, the intra-day forecast, the next-day forecast, and the 2weather prediction model may be used. In this case, theday ahead forecast are made in order from the newest forecast
prediction accuracy of the numerical weather prediction model
start time. The method of statistical processing will be
has a great influence on the efficient operation of the power
examined in the future.
grid.
In this study, the solar irradiance is predicted by theTargets area and computation settings
numerical weather prediction model.
The calculation area is shown in Fig. 2. In this study, threeareas centered on the National Electronics and Computer
Technology Center (NECTEC) in Thailand were set, andcalculations are performed by two-way nesting. Two-way
nesting not only reflects data from a large area to a small area,
but also feeds back the calculation results of the small area tothe calculation of the large area. The horizontal resolutions ofregion 1, 2 and 3 are 18 km, 6 km, and 2 km, respectively, andthe number of grids was 100 × 100, respectively, and the solar
irradiance in whole the year 2017 was calculated. FNL data (3hour interval, 0.25 ° grid) from the National Centers forEnvironmental Prediction (NCEP) was used as input data.

Fig. 1: Distribution of solar irradiance

MATERIALS AND METHODSIn this study, we aim to improve the prediction accuracy ofsolar irradiance in tropical regions by the Weather Research andForecasting (WRF) model (Skamarock et al., 2008). WRF hasa physical process for calculating clouds, radiation, and etc. In
addition, there are several option schemes for each physicalprocess. In order to make accurate calculations, it is necessary
to use the appropriate schemes according to the climaticconditions of the target area. In this study, the accuracy of solar
irradiance calculation is improved in the following two steps.

Fig. 2: Target domains for weather forecasting

Step 1In this step, appropriate combination of physical processschemes is derived for getting the highest WRF calculation
accuracy in the tropics (option optimization). In this study,
simulations were performed in two cases (case 1 and case 2). In
case 1, a combination of schemes according to Hadrien et al.
(2018), which is considered to have high calculation accuracyof solar radiation intensity in Singapore, was set. As case 2, a
combination of options modified from case 1 to improveaccuracy in Thailand for the six processes of cloud physics
process, long wave radiation process, shortwave radiation
process, cumulus process, surface process, and planetary
boundary layer process. The modification was based on the
WRF model optimization method performed by Arasa et al.
(2016) for Spain.

RESULTS AND DISCUSSIONS
Step 1
Table 1 shows statistical errors of the initial settings (case 1)
and the optimal case (case 2). In addition, Fig. 3 shows a timeseries diagram comparing the observed and calculated values,
and the three days from October 1st to October 7th, 2017 aretargeted so that the changes in the values can be clarified. Since
the solar radiation intensity peaks at noon, the time-series
diagram shows a roughly upward convex change. Small
fluctuations indicate the effect of clouds.
By optimizing the options, it can be read that the observed value
is approaching. The accuracy of cloud prediction is still low.

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P10
Table. 1: Annual correlation coefficients and RMSEs
between WRF forecasting and observation
case1
case2
Corr
0.773
0.814
RMSE(W/㎡) 241.5
204.7

Table. 2: Annual RMSEs for each prediction
RMSE(W/m2)

Intra-Day Next-Day 2-Day ahead
174.9
175.3
183.3

In addition, the ensemble prediction revealed that the valuechanges significantly depending on the time when theprediction is started.

Step 2The statistical error of each predicted value is shown in Table
2, and the time series diagram is shown in Figs. 4 and 5. Eachtime-series chart is in the three Thai seasons (dry season andrainy season) on behalf of a week. The period was from
December 11th to 17th, 2019 and from July 11th to 17th, 2020,
respectively.
From Fig. 4, it can be seen that the error of each predictedvalue is small and the calculation can be performed accurately
in the dry season. On the other hand, from Fig. 5, it can be seenthat the error of each predicted value is large in the rainy season,and it deviates greatly from the observed value.

REFERENCES
Arasa, R., I. Porras, A. Domingo-Dalmau, M. Picanyol, B.
Codina, Mª Ángeles González, and J. Piñón.: Defining aStandard Methodology to Obtain Optimum WRF
Configuration for Operational Forecast:Application over thePort of Huelva, Atmospheric and Climate Sciences, Vol.6,
pp.329-350, 2016
Skamarock, W.C., J. B. Klemp, J. Dudhia, D.O. Gill, D. M.
Barker, W. Wang, and J.G. Powers.: A description of theadvanced research WRF version 3. NCAR Tech. Note. TN475+STR, 1-96, 2008
Verbois, H., R. Huva, A. Rusydi, and W.Walsh.: Solar
irradiance forecasting in the tropics using numerical weather
prediction and statistical learning, Solar Energy, Vol.162, pp.
265-277, 2018

CONCLUTIONSIn this study, we improved the accuracy of solar radiation
prediction using a numerical weather prediction model. By
optimizing the options of the physical process, it is possible tocapture the occurrence of clouds to some extent.

Fig. 3: Solar irradiance (from Oct. 1st, 2017 to Oct. 7th. NECTEC)

Fig. 4: Ensemble prediction of solar irradiance in the dry season (from Dec. 11th, 2019 to Dec. 17th. NECTEC)

Fig. 5: Ensemble prediction of solar irradiance in the rainy season (from Jul. 11th, 2020 to Jul. 17th. NECTEC)

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Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P11

Short-term prediction of cloud movements with meteorological satellite
images processed in spectrum space
Yuto Hara1, Rina Takada1, Tomonao Kobayashi1 and Jun Yoshino1
1. Gifu University, Gifu, JAPANphase shifting of spectrum components and clouddevelopments are corresponds to amplitude changing of the
INTRODUCTION
components. From these relations, the travelling anddeveloping of clouds can be predicted easily in the spectrum
Many photovoltaic (PV) systems are already installed inspace.
worldwide in these days. The PV output fluctuates by changing
solar irradiance, and then, the PVs which are connected toelectric grids cause of instability of power on the grids. One of
the ways for avoiding the risk on the grids is that the solar
irradiance and PV output are predicted, and the predicted
information is utilized for management of the electric grids.
There are several methods for prediction of weather and solar
irradiance, e.g. numerical weather simulation, and image
processing of cloud images. Rerez et al. (2010)[1] shows
prediction errors with several prediction methods as shown in
Figure 1. This figure indicates that the statistical error RMSE of
the prediction with image processing technique is lower thanthe one with a numerical weather simulation from the beginning
to 6-hour ahead. It means that the image processing technique
can predict with higher accuracy than the numerical weather
Fig. 2: Flow chart of cloud image prediction
simulation in short-term prediction.
By following to this result, the image processing technique
Two dimensional Fast Fourier Transform (FFT) is used forwith meteorological satellite images is developed in this study.
transforming between the physical space and the spectrum one,As the original approach in this study, the satellite images arebecause grid sizes in the physical space and interval of grounddeveloped temporally to the future ones in a spectrum space.
frequency in the spectrum space are constants. The linear
regression or Maximum Entropy Method (MEM) is applied forthe time-development processes in the spectrum space. Whenthe linear regression is applied, the timeseries of amplitudes andphases of spectrum components are analysed with Least Square
Method (LSM) and extrapolated for future prediction. In casewhen MEM is applied, the amplitudes and phases of spectrum
components are extrapolated with MEM, just the same as thelinear regression. The reason why MEM is employed for timedevelopment, not FFT, is that it can transform with highaccuracy also with small number of data, and it also canevaluate spectrum components with arbitrary frequencies.
— Target images of clouds
The satellite images taken by HIMAWARI-8[2] are used foranalysis in this study. The satellite observes with 16 bands,
from 0.47 to 13.28 μm in electric magnetic wavelength. In
this study, the images in Band-3 are used. They are visible lightimages with 0.5 km in horizontal resolution and 2.5 minutes intime interval of images around Japan Islands.

Fig. 1: Prediction accuracies of solar irradiance withdifferent methods and their variations in forecasting period(Rerez et.al, 2010[1])

METHODS— Image processing
The image processing method for predicting future cloud
images from the current ones is developed. The images aredeveloped temporally for prediction of the images in spectrum
space as explained in the previous section. Figure 2 shows aflow chart of transforming and developing of cloud images forprediction in this study.
First the successive cloud images are transformed with
Fourier Transform to spectrum space. Next in the spectrum
space, the spectrum images are developed temporally. We tried
two ways for this time-development. One is a linear timedevelopment, and the other is spectrum one. After thedevelopment, the time-developed spectrum images are return tothe physical space with inverse Fourier Transform. In the
spectrum space, cloud advections are corresponding to the

RESULTS AND DISCUSSIONSThe successive satellite images taken by Himawari-8 for 2
hours, from 2 hours before the current with 2.5-minute interval,
are used for prediction of future cloud patterns in this study.
Figure 3 shows the resulted images in this study.
In Figure 3, cloud images taken by HIMAWARI-8 from10:00 to 12:00 in 2019/02/02 with 0.5 km in horizontal
resolution are used as input and are predicted with the linear
regression models or MEM after 1 hour ahead from 12:00 to
13:00 in the same day. The cloud images on the upper row in
the figure are observed ones, and the one at the lower row is thepredicted cloud images. The labels [A] to [D] is timestamps;[A] and [B] are 10:00:00 and 10:57:30, respectively, andcurrent times. [C] and [D] are 12:00:00 and 12:57:30, and in120

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2019: P11
prediction. The observation images in time [C] and [D] in theupper row in the figure are for reference of the prediction.
As shown in Figure 3, the advection and development ofclouds are predicted by both LSM and MEM near the center ofthe images. On the other hand, the predicted images become
blurred, and their borders becomes fuzzy in prediction.
Table 1 shows the statistical errors, correlation coefficientsand RMSE, of the predicted images to the original satellite one.
From this table, it is found that the prediction with the linear
regression, LSM is higher accurate than the one with MEM, andthe accuracy becomes lower as time-developing from [C] to[D]. This reason may be that each component in the spectrum
space is developed in time individually, and the phases haveshifted each other when the spectrum components aretransformed inversely to the physical space. This prediction
method still contains parts for development.

grid management. This method is an image processing of cloud
patterns taken by a meteorological satellite and predict future
cloud patterns by developing images temporally in the spectrum
space. Advection and development of clouds are corresponding
to phase shifting and increasing amplitude of spectrum
components, respectively, and the future cloud patterns can bepredicted in the spectrum space. The linear regression or MEM
is applied for the time-development in the spectrum
components of the images. The proposed method predicts the
future cloud images, however the images are blurred. The low
quality in the prediction may be caused in time-developing
process.

REFRENCE[1] Rerez et.al, Validation of short and medium term
operation solar radiation forecasts in the US, Solar Energy, vol.
84, issue 12, pp. 2161-2172, 2010[2] Japan Meteorological Agency: Satellite Imagery,https://www.jma.go.jp/, access 2021/07/30[3] R. Takada: Short-term prediction of cloud movementswith meteorological satellite images processed in spectrum
space, Bachelor Thesis, Gifu University, 2020 (in Japanese)

CONCLUSIONS
Predictions of solar irradiance and the PV outputs are
effective for management of electric power grids whichcontains unstable generations from PV systems. In this study,
the short-term prediction method of the solar irradiance for the[A] Observation[B] Observation[C] Observation[D] Observation[C] Prediction (LSM)[C] Prediction (MEM)[D] Prediction (LSM)[D] Prediction (MEM)

Fig. 3: Images observed with a meteorological satellite (images in upper row) and future cloud images (images in lower row)
predicted with linear regression (LSM) or Maximum Entropy method (MEM). [A] to [D] indicate observation or prediction
time; [A] 10:00:00, [B] 10:57:30, [C] 12:00:00, [D] 12:57:30
Table 1. Statistical error indices of predictions
Prediction by LSM([C] 12:00:00)

Prediction by MEM([C] 12:00:00)

Prediction by LSM([D] 12:57:30)

Prediction by MEM([D] 12:57:30)

Correlation coefficient

0.699

0.324

0.322

0.179

RMSE

0.110

0.136

0.173

0.136

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UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P12

Application of Kalman filter to solar irradiance forecasting witha meteorological model in tropical region
Naoki Moriai1, Daiki Harada1 and Tomonao Kobayashi2
1. Graduate School of Natural Science and Technology, Gifu University
2. Faculty of Engineering, Gifu University
Where H is the transformation matrix and 𝜈𝜈𝑘𝑘 is a noise
associated with the observed values (normal white noise with
INTRODUCTION
mean 0 and variance R). Eqs. (1) and (2) are called the stateHuge amount of photovoltaic (PV) systems are alreadyspace representation equations. In this study, a linear systeminstalled in worldwide. In the tropical region, solar irradiance ismodel for evaluation of the adjusted solar irradiance 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊−𝐾𝐾𝐾𝐾
usually high, and the region has advantage for PV generations.
with Kalman filter from the forecasted one 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊 with WRF is
Because the PV output fluctuates by changing of weather andemployed, and indicate it as follows;
solar irradiance, PVs which are connected to electric grids(3)
𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊−𝐾𝐾𝐾𝐾 = 𝑎𝑎𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊
cause of instability of power on the grids. As one of solutionsof this problem, the weather and solar irradiance are forecasted
Where 𝑎𝑎 is a state variable of this model. From thiswith a numerical weather model and are utilized for appropriate
formulation, A and B in Eq. (1) are unitary matrices in thisgrid management. For better gird management, the weather andstudy. In order to improve the accuracy, it is necessary to set asolar irradiance with higher accuracy is required. However, thevariable 𝑞𝑞𝑎𝑎 for the noise of the state equation and the variable
meteorological physical processes in the atmosphere in thetropical region is complicated and the accuracy of the weather
R for the noise of the observation equation. In this study, 𝑞𝑞𝑎𝑎
and solar irradiance forecasting with the numerical model isn't
and R are changed in the range of 10-10 to 1010 and 10 to 1021,
high. Harada (2020) optimized physical parameters in a
respectively, and the annual RMSE of the solar irradiances isnumerical weather model to improve the accuracy of
calculated, and the optimal values for the variances are found.
predictions of solar radiation intensity in tropical regions. In his
Computational conditions
study, the correlation coefficient between observed and
The resolution of the finest domain, Domain 3 is 2 km. The
forecasted solar irradiances was improved. However, RMSE
National Electronics and Computer Technology Center
between the irradiances remained large. The forecasted(NECTEC) of Thailand is located at the center of the domain,
irradiance is seemed to overestimate compare with the observed
and the solar irradiance is observed at the center. Domain 1 (18one, and it may be caused of the large RMSE. In this study, a
km resolution), Domain 2 (6 km resolution) and Domain 3 are
postprocessing of the weather model is introduced and removed
nested each other for computation of weather. The computation
the bias from the solar irradiance forecasted with the model.
period is from 2019/07/01 to 2020/09/30, but the first threemonths are not included in the output because they are the spinup period of the state variable 𝑎𝑎 in Kalman filter, and the
METHODOLOGY
output period is one year from 2019/10/01 to 2020/09/30. The
In this study, the solar irradiance is forecasted with aanalysis data and forecasted data until 72-hour ahead of NCEP
numerical weather first, and the irradiance is adjusted with aoperational Global Forecast System (GFS) 0.25-degree grids
postprocessing.
(horizontal resolution 0.25°× 0.25°, 1 hourly interval) is used as
Numerical weather model, WRF
input data for initial and boundary values of WRF forecasting.
As a numerical weather model, WRF (The Weather Researchand Forecasting) is used. WRF is a three-dimensional, fullycompressible, non-hydrostatic numerical weather model
RESULTS AND DISCUSSIONS
developed by the National Center for Atmospheric Research,
The correlation coefficients and RMSEs of the forecasted
the National Centers for Environmental Prediction, and othersolar irradiance 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊 with WRF and the adjusted one
organizations.
𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊−𝐾𝐾𝐾𝐾 with Kalman filter are summarized in Table 1. By
Kalman filter as a postprocessing
applying Kalman filter as a postprocessing of WRF forecasting,
As a postprocessing of WRF simulation, Kalman filter isthe correlation coefficient remains the same, but the RMSE
introduced to reduce the overestimation and adjust the
reduces about 52 W/m², and this result indicates that theforecasted solar irradiance. Kalman filter is employed widely inaccuracy of the computed solar irradiance is improved withengineering field. Liu et al. (2014) also applied it to the solar
Kalman filter.
irradiance forecasting. As following to their study, theFigure 1 shows time variation of the state variable 𝑎𝑎 ofmathematical model of the filter is set up. Here, a variable x in
Kalman filter. As a reference, result of monthly regressiona system is consider. The development of x from the previous
analysis of the forecasted and observed solar irradiances is also
step can be indicated with the following equation.
plotted on the figure. By referring of the linear system model of
The equation of state:
Kalman filter, Eq. (3), the following equation is introduced as a(1)
𝑥𝑥𝑘𝑘 = 𝐴𝐴𝑥𝑥𝑘𝑘−1 + 𝐵𝐵𝜔𝜔𝑘𝑘model of this analysis.
(4)
𝐼𝐼𝑂𝑂𝑂𝑂𝑂𝑂 = 𝑎𝑎𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊
Where 𝑥𝑥𝑘𝑘 is the state quantity at step k, A and B are thetransformation matrices, and 𝜔𝜔𝑘𝑘 is a noise (normal white
Where 𝐼𝐼𝑂𝑂𝑂𝑂𝑂𝑂 is the observed solar irradiance, and 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊 isnoise with mean 0 and variance 𝑞𝑞𝑎𝑎 ) about the state quantity. If
the forecasted one by WRF value, and 𝑎𝑎 is the coefficient of
we assume that a part of the state quantity 𝑥𝑥𝑘𝑘 is observed, thethe model. The trend of the state variable 𝑎𝑎 in Kalman filter isrelation between the state quantity 𝑥𝑥𝑘𝑘 and the observed value
similar to the one of the coefficient 𝑎𝑎 of the regression
𝑧𝑧𝑘𝑘 can be shown as follows;
analysis. This result explains that Kalman filter works properly.
The equation of observation:
The state variable 𝑎𝑎 of Kalman filter is updated every day, and(2)𝑧𝑧𝑘𝑘 = 𝐻𝐻𝑥𝑥𝑘𝑘 + 𝜈𝜈𝑘𝑘
it vibrates in short period as in the figure, however it doesn’tshow sudden changes. In dry season (November to March), thevariable 𝑎𝑎 is relatively high, but the value moves around 0.8

122Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P12
1.0 in Figure 4, and it indicates that the forecasted irradiancewith WRF, 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊 is overestimated compared with the observed
one 𝐼𝐼𝑂𝑂𝑂𝑂𝑂𝑂 , the same as the explanations with Figures 3 and 4.
On the other hand, the gradient of the red line is a little lower
than 1.0, and it leads that Kalman filter adjusts the overestimate
in WRF forecasting and evaluates appropriate irradiance.

through a year. As explained before, the state variable 𝑎𝑎 is
updated every day, and it contributes to the improvement of theforecasting accuracy as indicated in Table 1.
Table 1: Annual correlation coefficients and RMSEs
between observed and WRF forecasting

Forecasted solar irradiance
𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊 with WRF

Adjusted solar irradiance
𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊−𝐾𝐾𝐾𝐾 with Kalman filter

Correlation
Coefficient

RMSE (W/m2)

0.831

216.0

0.831

163.9

Fig.4: Correlation map
between observed solar
irradiance 𝑰𝑰𝑶𝑶𝑶𝑶𝑶𝑶
andforecasted one
𝑰𝑰𝑾𝑾𝑾𝑾𝑾𝑾
with WRF.

Fig.5: Correlation map
between observed solar
irradiance 𝑰𝑰𝑶𝑶𝑶𝑶𝑶𝑶
andadjusted one 𝑰𝑰𝑾𝑾𝑾𝑾𝑾𝑾−𝑲𝑲𝑲𝑲
with Kalman filter.

CONCLUTIONSIn this study, Kalman filter is employed as a postprocess ofthe numerical weather model WRF, and the forecasted solar
irradiance with WRF is improved using it. The target is tropicalarea, Thailand. The WRF overestimates the solar irradiance, but
the overestimation is removed by Kalman filter andappropriated one is evaluated. By applying Kalman filter, thecorrelation coefficient of the computed irradiance with WRF to
the observed one remains the same, but its RMSE is reduced byabout 52 W/m² and is improved. The state variable 𝑎𝑎 ofKalman filter varies in time, but it doesn’t show big changes.
Its value is around 0.8 through a year. The WRF cannot
represent sudden-change weather in tropical area, e.g. squall, or
cannot follows the change well, and it causes of reducing
forecasting accuracy. This error isn’t corrected with Kalmanfilter presented in this study.

Fig.1: Timeseries of state variable 𝒂𝒂 of Kalman filter andcoefficients 𝒂𝒂 of monthly regression analysiys.

Figures 2 and 3 show daily solar irradiances in clear days andcloudy days, respectively. Their periods are from 2020/01/30 to2020/02/01, and 2020/06/15 to 2020/06/17. In the figures,
observed irradiance 𝐼𝐼𝑂𝑂𝑂𝑂𝑂𝑂 , forecasted one 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊 with WRF,
and adjusted one 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊−𝐾𝐾𝐾𝐾 with Kalman filter are plotted. In
each figure, the forecasted irradiance 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊 is overestimatedcompare with the observed one 𝐼𝐼𝑂𝑂𝑂𝑂𝑂𝑂 . The adjusted irradiance
𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊−𝐾𝐾𝐾𝐾 is reduced from the forecasted one 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊 by
applying Kalman filter, and fits well with the observed one𝐼𝐼𝑂𝑂𝑂𝑂𝑂𝑂 .
The adjusted irradiance 𝐼𝐼𝑊𝑊𝑊𝑊𝑊𝑊−𝐾𝐾𝐾𝐾 represents the observed one
𝐼𝐼𝑂𝑂𝑂𝑂𝑂𝑂 better in clear days as shown in Figure 2 than in cloudy
days as in Figure 3. It is caused that the weather sometimes
changes suddenly, e.g. squalls in the cloudy days in tropicalregion, Thailand, but WRF cannot simulate the sudden-change
weather or cannot follow the changes well. This error isn’t
correct with Kalman filter presented in this study.
Figures 4 and 5 show the correlation maps between theobserved and the forecasted irradiances, and between theobserved and the adjusted ones, respectively. The lines withgradient 1 are plotted as a reference. Interval of the plotted data
is 10 minutes, therefore the error seems large in these figureswhen the WRF cannot follow the sudden change of the weather
as found in Figure 3. If the timeseries of solar irradiances aresmoothed and the interval becomes longer, the error in the
figures may be reduced. In the figures, the results of regression
analysis are indicated with red lines. Its gradient is larger than

REFERENCES

Harada, D: Optimisation of a weather forecasting model for theaccurate calculation of solar radiation intensity in the tropics,
Bachelor Thesis, Gifu University, 2020. (in Japanese)
Skamarock, W. C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M.
Barker, W. Wang, and J.G. Powers: A description of theadvanced research WRF version 3. Tech. Note TN475+STR, 1-96. 2008.
Liu, Y., S. Shimada, J. Yoshino, T. Kobayashi, K. Furuta, and
Y. Miwa: Solar irradiance forecasting using the WRF modeland Kalman filter, Grand Renewable Energy 4 p., 2014.
National Centers for Environmental Prediction: NCEP Products
Inventory – Global Products, https://www.nco.ncep.noaa.
gov/pmb/products/gfs/, access 2021/07/23

Fig.2: Timeseries of observed solar irradiance 𝑰𝑰𝑶𝑶𝑶𝑶𝑶𝑶 , Fig.3: Timeseries of observed solar irradiance 𝑰𝑰𝑶𝑶𝑶𝑶𝑶𝑶 ,forecasted one 𝑰𝑰𝑾𝑾𝑾𝑾𝑾𝑾 with WRF, and adjusted one 𝑰𝑰𝑾𝑾𝑾𝑾𝑾𝑾−𝑲𝑲𝑲𝑲 forecasted one 𝑰𝑰𝑾𝑾𝑾𝑾𝑾𝑾 with WRF, and adjusted one 𝑰𝑰𝑾𝑾𝑾𝑾𝑾𝑾−𝑲𝑲𝑲𝑲
with Kalman filter in successive three clear days.
with Kalman filter in successive three cloudy days.

123Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P13

Bacterial activity and EPS affect the performance of household wastewater
treatment facility
Haoning Su1, Miya Ogata3, Yuuki Okochi 2, Shinya Okumura 2, Yasushi Ishiguro 3 and Fusheng Li 3
1. Graduate School of Engineering, Gifu University, Japan
2. Kasugai Office, Aichi Prefectural Johkasou Association, Kasugai, Aichi, Japan
3. River Basin Research Center, Gifu University, JapanINTRODUCTION

polymeric substance (EPS) of sludge samples were extractedusing the modified method described by Liang et al. (2010) andmeasured by the colorimetrical methods (Basuvaraj et al.,2015).

On-site wastewater treatment facilities were regarded asalternative technology of centralized wastewater treatment
plants (WWTPs) for single households. The performance of thisfacility is greatly affected by activity of microbial communities
which contributed to the oxidation of carbon and ammonia,
reduction of nitrate. However, due to the limitation of volume
and structure, it is difficult to achieve the continuous discharge
and return of accumulated sludge compare with general
WWTPs and no study is known to us investigating the
microbial activity related with the performance of household
wastewater treatment facility. In addition, the adhesion andaggregation properties of microbial cells are closely related to
extracellular polymeric substances (EPS), which may have aclose relationship with settleability of sludge and affect the final
liquid-solid separation step. In this study, 15 units of household
wastewater treatment facility were investigated to treat single
household wastewater, and the sampling was conducted basedon different periods after desludging operation reflects todifferent sludge retention time (SRT) among 22 to 586 days, as
the sludge in this facility which have a much longer retention
time than the sludge in WWTPs. Thus, specific objectives ofthis research were to: 1) evaluate the performance of this single
household wastewater treatment facility and analyze thecorrelation of each water quality indexes by principal
components analysis (PAC) and agglomerative hierarchicalclustering (AHC); 2) identify relationships between treatment
efficiencies and the microbial communities and EPS ofaccumulated sludge.

(3) Statistical analysis
Measurement results in the series of water quality were used as
the pooled data for analysis after standardization treatment of
the obtained data for each index. Then, the principal component
analysis (PCA) and agglomerative hierarchical clustering(AHC) were performed using the SPSS software (IBM SPSS
Statistic 21.0, IBM Corp., Armonk, NY) for better
understanding the interrelations of all measured water quality
indexes (including SS, turbidity, BOD, COD, DOC, UV260,
UV390, nitrogen and phosphorus).

RESULT AND DISCUSSION(1) Water quality evaluation by PCA and AHC
The results of main physicochemical indexes of the effluent
from 15 treatment facilities are shown in Fig 1.

METHODS(1) Treatment facilities and sample collection
This research was conducted in residential areas (Aichi
prefecture, Japan). 15 typical household wastewater treatment
facilities in these areas, using fluidized bed biofilm treatment
method with the same structure, were selected to represent thedifferent sludge retention time namely as short sludge retention
time (S-SRT), middle sludge retention time (M-SRT) and longsludge retention time (L-SRT).

Fig. 1: Results of main physicochemical indexes of theeffluent from 15 units during July 2020 – December 2020
Each treated water quality results scattered in a larger range,
while the analyze with more specificity was conducted by PCA
and AHC to clarify the correlation between each index and thefactors that affect the treated water quality. Preliminary PCA
results of component loadings together with previous study
indicated that the treated water quality is affected by bothsuspended solids and dissolved organic matter, while results ofcomponent scores and cluster analysis showed that the facilityof S-3 had a high score in the first principal component andformed one single cluster by itself, which was significantly
different from the other 14 facilities. From these, we reanalyzed
the data without outlier of S-3. There was no change in theoverall trend that the 1st component was dominated bysuspended solids while the 2nd component was dissolved
organic matters. Reanalyzed PCA (component scores) and
AHC results as shown in Fig. 2 (a) and Fig. 2 (b), remained 14(2) Sample analysis
For water samples, transparency, turbidity, pH, biologicaloxygen demand (BOD) for particulate BOD (PBOD) anddissolved BOD (DBOD), chemical oxygen demand (CODCr)
for particulate CODCr (PCOD) and dissolved CODCr (DCOD),
total nitrogen (TN) for particulate N (PN) and dissolved N (DN)
and total phosphorus (TP) for particulate P (PP) and dissolved
P (DP) were measured. Dissolved organic carbon (DOC) (TOCVws, Shimazu Co., Japan), and ultraviolet absorbance at 260
nm (UV260) and 390 nm (UV390) (UV-2600, SHIMADAZU,Japan) were measured for the samples after filtration through0.2 μm membrane filters. For sludge samples, the activity ofbacteria in sludge was analyzed based on dehydrogenase
activity (DHA) (Zhou et al., 1996) and specific oxygen
utilization rate (SOUR) (Mark C et al., 2016). Extracellular124

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P13(3) Characteristics of accumulated sludge based on
bacterial activity and EPS
As shown in Fig. 4 (a) and Fig. 4(b), the accumulated sludgeof Group 1 had relatively higher bacterial activity which wascharacterized by dehydrogenase activity (a) and specificoxygen utilization rate (b), compare with the sludge of Group 2
and 3. Furthermore, as EPS quantified as loosely bound (LBEPS) and tightly bound (TB-EPS) respectively, shown in Fig.
4 (c) and Fig. 4 (d). Sludge of Group 1 had more content of TBEPS with strong binding between the cells but less LB-EPS. On
the contrary, sludge of Group 2 contains more LB-EPS than
TB-EPS relatively.

facilities were classified to be 3 groups named as Group 1,
Group 2 and Group 3.
4

Group3

a

得点図
M-4
2

PC2 (20.6%)

M-3
S-2

S-1
-4

-2

0

L-5

Group1

L-2

L-1

M-2 0

S-6

Group2

L-3

L-4

2 S-4

4

M-1 S-5
-2

a

b

c

d

-4

PC1 (32.5%)bFig. 4: Characteristics of accumulated sludge based on
bacterial activity (a: dehydrogenase activity; b: specificoxygen utilization rate) and EPS (c: LB-EPS; b: TB-EPS)
Sludge contains more content of TB-EPS than LB-EPS andbehaves as higher microbial activity as well. At the same time,
the sludge volume index (SVI) of this kind of sludge (Group 1)
showed lower values which means sludge particles are easily
aggregated to be the settleable flocs. However, for the sludge ofGroup 2 and Group 3, lower microbial activity and more LBEPS contents may affect the aggregation of sludge flocs andlead to the high concentration of suspended solids in the treatedwater.
The findings at this stage clarified the contribution of theactivity of bacteria and different contents of EPS inaccumulated sludge to the performance of household
wastewater treatment facility. Higher microbial activity andmore TB-EPS content in sludge may be beneficial to the treatedwater quality by promoting the biological degradation ofdissolved organic matters and settleability of sludge.

Fig. 2: Results of the statistical analysis using observed data
for treated water (a: component scores of PCA; b:
agglomerative hierarchical clustering)(2) Treated water quality of each group
The comparison of the treated water quality of each group is
shown in Fig. 3. The treated water quality of Group 1 tended tobe better than other two groups, and the variability of the data
was smaller. The treated water quality of Group 2 was not as
good as that of Group 1, but the variability of the data wassmaller than that of Group 3.

REFERENCES
Zhou, C., Yin, J., (1996) A method for measurement of ttcdehydrogenase activity. Acta Scientiae Circumstantiae.
CNKI:SUN:HJXX.0.1996-04-003
Yoshiyama, K., Mellard, J. P., Litchman, E., and Klausmeier,C. A. (2009) Phytoplankton Competition for Nutrients andLight in a Stratified Water Column. American Naturalist
174:190-203.
Mark C. M. van Loosdrecht;Per H. Nielsen;Carlos M.LopezVazquez;Damir Brdjanovic.,2016.Experimental Methods in
Wastewater Treatment. IWA PUBLISHING.3.5.3.2.
Liang, Z.W., Li, W.H., Yang, S.Y., Du, P., 2010.Extraction andstructural characteristics of extracellular polymeric
substances (EPS), pellets in autotrophic nitrifying biofilm
and activated sludge. Chemosphere 81,626–632.
Basuvaraj, M., Fein, J., Liss, S.N., 2015. Protein andpolysaccharide content of tightly and loosely boundextracellular polymeric substances and the development of a
granular activated sludge floc. Water Res. 82, 104-17.

Fig. 3: Comparison of water quality of each group125Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P14

Steam gasification of woody biomass with renewable hydrogen
Sheng-Wen Xiao1 and Kazuhiro Kumabe2,
1. Graduate School of Natural Science and Technology, Gifu University; BWEL
2. Faculty of Engineering, Gifu University
changes under the conditions of introducing hydrogen, carbon
particles with a purity of 99.9% and a particle size of 20 microns
INTRODUCTION
were used as raw materials in the steam gasification (700900°C) using the apparatus shown in Fig. 2.

In order to alleviate global warming, the development ofsustainable energy has become the mainstream. However, when
new power generation technologies such as wind power andsolar power are connected to the power system, one of the
problems arises. The resonance caused by the uncertainty of thewind power generation cycle will have a huge impact on thestability of the power system (Brouwer et al. 2014). Usinghydrogen as an energy carrier for renewable energy
technologies such as wind power can solve this problem (Basile
and Iulianelli 2014).
On the other hand, the process of liquid fuel production via
the steam gasification of woody biomass is developed(Kumabe, K. et al. 2014). In the steam gasification, CO2 is
produced due to the water-gas shift reaction ( CO + H2 O ⇋CO2 + H2 ). Now, the introduction of hydrogen, derived fromrenewable energy, into the steam gasification is tried to reduce
the CO2 emission.
In the study, first, the effect of the addition of hydrogen to
the simulated syngas, produced from the steam gasification, on
the product gas composition was examined. Next, the effect of
the addition of hydrogen to the steam gasification on the
product gas composition was investigated using fixed-bed
reactors of both a semi-batch (gas flow type) and continuous
reactor.

Thermocouple

𝐻2

Ice

Pump

Ice

Gas meter

Silica gel
Ice

Gas bagGC

Fig. 2: Experimental apparatus of semi-batch fixed-bed
reactor for carbon steam gasification.
The reaction conditions of carbon steam gasification
without/with H2 introduction using the semi-batch fixed-bed
reactor are shown in Tables 1 and 2, respectively.
Table 1: Reaction conditions of carbon steam gasification
without H2 introduction using semi-batch fixed-bed reactor.
Temperature
700, 800, and 900°C
Gas pressure
1 atm
Mass of carbon particles
1g
Flow rate of water(g)62.22 mL/min
100 mL/min
Flow rate of N2 (g)Time of reaction
60 minTable. 2: Reaction conditions of carbon steam gasification
with H2 introduction using semi-batch fixed-bed reactor.
Temperature
700, 800, and 900°C
Gas pressure
1 atm
Mass of carbon particles
1g
Flow rate of water(g)62.22 mL/min
50 mL/min
Flow rate of N2 (g)50 mL/min
Flow rate of H2 (g)Times of reaction
60 minThermocouple

Electric furnace

Gas meter
Ice

Empty

Electric furnace

In order to study the influence of the introduction ofhydrogen on the gas phase reaction of the synthesis gas between
600 and 900°C, the simulated synthesis gas (CO: 25.00 vol%,CO2: 7.97 vol%, H2: 24.90 vol%, and N2 base) was first used tocarry out experiments with the hydrogen introduction (itsconcentration: 25, 35, 50, and 75 vol%) using the apparatus, as
shown in Fig. 1.

𝐻12HeaterQuartz wool

MATERIALS AND METHODS

1
Tzohbt𝐻2 𝑂

𝑁2Carbon particles

In the semi-batch fixed-bed reactor, the molar ratio of H2O/C
in the reaction is not steady. Considering the gas density aroundthe carbon particles and the high diffusion of hydrogen that
affect the kinetic of surface reaction (Li et al. 2012), it isdecided to use the method of continuous feeding of carbon inorder to steady the molar ratio of H2O/C in the reaction, as
shown in Fig. 3.

Gas bagGC

Fig. 1: Experimental apparatus for introducing hydrogen
into simulated synthesis gas.
In the second stage, in order to study the reaction mechanismof the steam gasification of carbon in woody biomass and the126Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P14

𝐻2

𝑁2

Thermocouple

Feeder𝐻2 𝑂

Carbon particles

HeaterElectric furnace

Thermocouple

Pump

Quartz wool

Gas bag

Empty Silica gel
Ice

Ice

Ice

GC

Fig. 5: Change in production of CO2 with time for three setsof reaction condition in semi-batch fixed-bed carbon
gasification.

Gas meter

Fig. 3: Experimental apparatus of carbon continuous
supply steam gasification.

It is currently in the carbon continuous feed gasification
experiment, and the data of carbon continuous feed will begiven in the future progress. And in the next stage, a continuous
feed gasification experiment of woody biomass will be carried
out.

RESULTS AND DISCUSSION
Based on the gas phase reaction, the experiment using
simulated biomass gasification gas and introducing hydrogen
under the condition of 600-900°C has obtained relatively clear
results. The relationship of the increasing concentration of H2
to the reduction ratio of CO2, defined as the division of theinitial CO2 concentration (7.97 vol%) into the concentration ina product gas, is obtained, as shown in Fig. 4.

CONCLUSIONS
Through experiments based on the gas phase reaction and thecurrent preliminary simulation of the impact of the introductionof hydrogen on the carbon particles on the gasification process
of woody biomass, it can be preliminarily understood that in thegas phase reaction, the introduction of hydrogen at thetemperatures of 600-700°C is the most effective way to reduce
CO2 emissions. In the particle surface reaction stage, theintroduction of hydrogen will print CO2 production, while also
inhibiting the carbon reaction. At 800°C, it has the mostsignificant CO2 suppression effect. At 900°C, while ensuring
the suppression effect, the carbon reaction rate will also begreatly improved.

ACKNOWLEDGMENTS

REFERENCES
Fig. 4: Relationship of increasing concentration of H2
to the reduction ratio of CO2.

Brouwer, A.S., Broek, V.D.M., Seebregts, A., and Faaij, A.
(2014) Impacts of Large-scale Intermittent Renewable
Energy Sources on Electricity Systems, and How These canbe Modeled. Renew. Sustain. Energy Rev. 33:443-466.
Basile, A. and Iulianelli, A. (2014) Advances in Hydrogen
Production, Storage and Distribution. Woodhead Publishing.

With the increase of hydrogen concentration, the production of
CO2 is obviously reduced, because in the water-gas shift
reaction, hydrogen as a product will inhibit the progress of thereaction, reducing the CO2 production.
Regarding the influence of temperature, between 600 and
700°C, the CO2 reduction effect caused by the introduction ofhydrogen is the most significant, and this is similar to any
proportion of H2 introduction.
In the semi-batch fixed-bed carbon steam gasification withthe three sets of experiments at different temperatures, as shownin Fig. 5, the introduction of hydrogen significantly reduced theoutput of CO2 due to the reaction rate of carbon.

Li, Q., Xu, J., Gore, J. (2012) Multiphysics modeling of carbon
gasification processes in a well-stirred reactorwith detailed
gas-phase chemistry. Combustion and Flame 159:1693-1707

127

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P15

Adsorbability of organic matter released from drinking water treatment
sludge onto activated carbon
Sri Anggreini1, Maulana Yusup Rosadi1, Miya Ogata2 and Fusheng Li2
1. Graduate School of Engineering, Gifu University, Gifu, 501-1193, Japan
2. River Basin Research Center, Gifu University, Gifu, 501-1193, JapanINTRODUCTION

with a designated dose within the range of 0-1.0 g/L. After 72
hours of shaking, the samples were filtered through a cellulose
acetate membrane filter (0.2 µm) to remove AC. The
concentration of OM was quantified as DOC (TOC 5000,
Shimadzu Co.) and the UV260 (Model UV-2600, Shimadzu
Co.), respectively. The composition of OM was analyzed using
a fluorescence spectrophotometer (RF-5300, Shimadzu Co.).
The adsorption data were analyzed using the modified
Freundlich isotherm model by Eq. (1).

Organic matter (OM) in water is a complex mixture oforganic compounds including humic substances, proteins,carbohydrates, lipids, and anthropogenic organic pollutants.
OM causes several problems in the drinking water supply,
including color and odor, the formation of carcinogenic
disinfection by-products (DBPs), and promoting microbial
growth in the water distribution system. Drinking water
treatment produces residue known as drinking water treatment
sludge (DWTS) which consists of flocs settled and detained in
the sedimentation and filtration process, respectively. DWTS isgravity concentrated and the water is separated and recycled as
raw water. OM in DWTS may affect the drinking water
treatment process and finished water quality.
Activated carbon (AC) adsorption is considered as the mosteffective method to remove OM in water and its application isincreasing. So far, many studies have been focused on thecharacteristics of OM released from DWTS based on
instrumental analysis (Ahmad et al. 2016, Xu et al. 2017,
Rosadi et al. 2020). However, the study based on the treatability
by adsorption process is limited. Therefore, the objective of thisstudy was to investigate the adsorbability of OM released from
DWTS under different storage conditions. For this purpose,
OM that released from DWTS under two different temperatures(20 and 40oC) and two oxygen conditions (with aeration andwithout aeration) was prepared and used for batch adsorption
experiment. OM before and after adsorption was evaluatedbased on dissolved organic carbon (DOC), UV260, andfluorescence excitation-emission matrix (EEM).

𝑞𝑞 = 𝐾𝐾 �𝐶𝐶𝐷𝐷𝐴𝐴𝐴𝐴

�

1
𝑛𝑛

(1)

where, q is equilibrium solid phase concentration, C isequilibrium liquid phase concentration, DAC is activated carbon
dose, and K and 1/n are the adsorption parameters reflecting theadsorption strength of adsorbate and its affinity with theadsorbent, respectively.

RESULTS AND DISCUSSION
Composition changes based on OM before and after
adsorption – The fluorescence EEM results are shown in Fig.
1. Three peaks were detected such as P1 (protein-like), P2(fulvic-like), and P3 (humic-like). The two corresponding
apparent peaks of humic molecules (P2 and P3) graduallydisappeared. However, the intensity of P1 could still be seen
even the AC doses increasing to 1 g/L, suggesting protein-like
substances were less adsorbable than humic ones. Gu et al.
(1995) reported that OM fractions with larger MW (>3000 Da)
have higher adsorption affinity and capacity compared with thesmaller ones (<3000 Da). Similar phenomena were also
observed during the adsorption of humic molecules (larger size)and protein-like substances (smaller size) onto AC.

MATERIALS AND METHODS
Incubation experiment – The DWTS was collected from adrinking water treatment in Japan. This plant uses a rapid sandfiltration system with a capacity of about 200,000 m3/day. The
DWTS was transported to the laboratory and incubated under
the condition with and without aeration at the temperatures of
20ºC and 40ºC. Five water samples were prepared, namely raw
DWTS, without aeration 40oC (WOA-40), with aeration 40oC
(WA-40), without aeration 20oC (WOA-20), and with aeration
20oC (WA-20). After 7 days incubation, centrifugation was
conducted, and the supernatant was filtered through a 0.2 µm
cellulose acetate membrane prior to being subjected to analysis.

Fig. 1 Fluorescence EEM of OM at condition of without
aeration of 40oC (WOA-40), (a) before and (b) after
adsorption (DOC = 9.73 mg/L).

Activated carbon – Filtrasorb-400 granular activated carbon(Calgon Co., USA) after crushing to powder below 45 µm wasused in this study. The granular AC was washed with Milli-Q
for 7 times and boiling for 1 hour before drying in an oven at
105oC. The granular AC was crushed and sieved with sizes
below 45 µm, followed by washing with Milli-Q for removal offine particles and drying in the oven at 105oC overnight.

Adsorbability of OM based on the residual concentration
profiles – The residual rate of UV260 was much lower than DOC
as shown in Fig. 2. DOC reflects all organic molecules(adsorbable and less or non-adsorbable) that present in DWTS,
while UV260 only reflects those possessing UV-absorbing
features in DWTS. Therefore, AC was effective to remove UVadsorbing substances than non-UV adsorbing substances.
Previous study also reported that AC was known to adsorb
preferentially the UV260 adsorbing substances, which yielded
the great reduction of SUVA values. The residual rates basedon the fluorescence EEM components are displayed in Fig. 3.

Batch Adsorption experiment – Batch adsorption
experiments were conducted according to the bottle-point
method of variable AC doses in 500-ml flasks sealed with
Teflon-lined rubber septa. To each flask, 100 ml of the water
sample was added, followed by the addition of the adsorbent

128Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P15
Table 1. Parameters of modified Freundlich isotherm based on different indices.
Indices
DOC

Parameters
Kf (mg/g)1-1/n
1/n

UV260

WA-40

6.89

4.05

WA-20
5.57

(6.81-7.75)c
0.67(11.37-12.86)d0.54(6.38-7.45)bc
0.67(3.88-4.15)a0.78(3.75-6.19)ab
0.73(0.48-0.84)a(0.50-0.68)a(0.63-0.81)a(0.77-0.81)a

0.985

0.995

0.979

0.980

0.994

Kf [m1/n-1/(g/L)1-1/n]

48.72

51.27

35.93

22.49

33.20

R

Kf [QSU/(g/L)]1-1/n

(P1)
1/n
R2
Kf [QSU/(g/L)]1-1/n

(P2)
1/n
R2
Humic-like(P3)

WOA-2012.07(0.65-0.71)a2

Fulvic-like

WOA-40

7.42

R2

1/n

Protein-likeRaw DWTS

Kf [QSU/(g/L)]1-1/n
1/n
R2(47.05-53.13)c
0.36(50.71-54.59)c (36.12-39.01)b
0.46
0.58

(21.41-24.93)a
0.55(32.62-33.38)b0.46(0.34-0.41)a
0.967(0.44-0.51)ab
0.986(0.53-0.62)b
0.996(0.45-0.52)ab
0.984(0.54-0.62)b
0.985

2.67

4.38

2.65

1.61

2.39

(1.68-4.31)a(3.85-6.54)a(2.02-4.05)a(1.09-2.84)a(1.79-3.10)a

0.67

0.57

0.68

0.69

0.70

(0.50-0.73)a(0.49-0.69)a(0.57-0.79)a(0.62-0.83)a(0.56-0.71)a

0.997
5.28

0.976
8.95

0.991
5.69

1.000
4.25

0.985
5.72

(4.67-7.47)ab
0.61(7.99-10.34)b
0.56(5.20-7.22)ab
0.66(3.97-5.10)a0.60(4.73-6.47)ab
0.67(0.63-0.71)ab(0.49-0.60)a(0.56-0.72)ab(0.56-0.69)b(0.60-0.70)ab

0.990
6.81
(5.99-7.07)ab
0.42

0.971
14.41
(13.89-15.62)b
0.60

0.991
8.10
(6.82-10.77)ab
0.62

0.968
5.07
(4.69-6.25)a
0.46

0.984
6.87
(5.89-7.19)ab
0.42(0.38-0.53)a(0.35-0.68)b(0.56-0.65)a(0.37-0.51)ab(0.34-0.45)ab

0.986

0.994

1.000

0.993

0.999

Different letters (a,b,c,d) given behind the data shown the differences between the groups in the same row are statistically significant (Tukey's HSD
test, p<0.05). The letter 'ab' or 'bc' means the differences of the values with the letters of 'a' and 'b' or 'b' and 'c' are not statistically significant.

The residual rates of P3 were much lower than those of P2,
followed by those of P1. The results demonstrated that humiclike was more preferably removed by AC than fulvic-like,followed by protein-like substances. The stronger adsorptionof humic-like than fulvic-like and protein-like is probably due
to the larger molar mass of humic-like molecules, resulting ina large number of reactive groups present per molecule. An
important factor in the stronger adsorption strength of humiclike substances is the larger number of reactive ligands on
humic-like molecules than fulvic-like molecules. A humic-like(13.2 kDa) molecule contains 32 carboxylic and 32 phenolicgroups. However, fulvic-like (0.683 kDa) contains 4
carboxylic and 1 phenolic group (Weng et al. 2007).
DOC

Residual rate

1.0

1000

= 9.73 mg/Lq (mg/g)

1.2

20 > WA-40 for DOC-based, indicating OM released without
aeration possessed higher adsorbability than with aeration
conditions. For fluorescence EEM components at condition of
WOA-40 as examples, the K value for P3 (14.41 (QSU/(g/L))11/n) was generally higher than those for P2 (8.95 (QSU/(g/L))11/n) and P1 (4.38 (QSU/(g/L))1-1/n). This result is consistent
with the residual rates of EEM components mentioned
previously. It suggests that protein-like substances were lessadsorbable than humic-like substances. The differences inmolecular weight, functional group, and hydrophobic orhydrophilic feature of OM were probably the reason behindthe differences in adsorbability of OM released from DWTS.

UV260 = 22.20 m-1

0.8
0.6
0.2
0

0.2

0.4
0.6
0.8
AC dosage (g/L)

1

Residual rate

0.8

0.4
0.2
0.0

0.2

0.4
0.6
0.8
AC dosage (g/L)

1.0

10
100
C/DAC (mg/g)

1000

The adsorbability of OM released from DWTS wasinvestigated. The OM contains more constituting components
with higher adsorbability and varied greatly in different
temperature and oxygen conditions. Compositional analysis
by fluorescence EEM indicated that humic-like and fulvic-like
substances have higher adsorbability than protein-like
substances.

0.6

0.0

1

CONCLUSIONS

P1 = 2.85 QSU
P2 = 5.25 QSU
P3 = 1.52 QSU

1.0

0.1

Fig. 4 Modified Freundlich isotherm for OM based on
DOC (WOA-40).

1.2

Fig. 2 Residual rates of OM vs. AC dosages (WOA-40).
1.2

y = 12.067x0.5382
R² = 0.9952

10
1

0.4
0.0

100

1.2

Fig. 3 Residual rates of fluorescence EEM components vs.
AC dosages (WOA-40).

REFERENCES

Adsorbability of OM based on the adsorption strength – The
estimated parameters of modified Freundlich isotherm aresummarized in Table. 1 and displayed in Fig. 4. The K values
followed the order WOA-40 > raw DWTS > WOA-20 > WA-

Weng, L., H, W., Riemsdijk, V., Hiemstra, T. (2007) Adsorptionof humic acids onto goethite: Effects of molar mass, pH andionic strength. J. Colloid Interface Sci 314:107-118.

129

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P16

Coexisting and competing algal species with moldy odor-causing
blue algae in Nagara River
Wenqing Li1, Haoning Su2, Shoma Wakamiya3 and Fusheng Li4
1. Graduate School of Natural Science and Technology, Gifu University, Japan
2. Graduate School of Engineering, Gifu University, Japan
3. Faculty of Engineering, Gifu University, Japan
4. River Basin Research Center, Gifu University, Japan
from the figure that the number concentration of
Cyanophyceae was larger in the period from July to
September, with the largest emerging in August, a trend
clearly different from the trend of the total algae shown in Fig.
1a and the trends of the dominating three algal families
observed in the river, Bacillariophyceae, Chlorophyceae andFlagellata. A relatively larger number changes over the past
20 years was also confirmed for the moldy odor-causing algalfamily in the month of its richest occurrence (i.e., in August).

INTRODUCTION
Odor and smell are important items that greatly affect thequality of tap water and are highly concerned by consumers
and drinking water suppliers. As the major compounds
causing moldy odor even at very low concentration levels, forinstance, several ng/L, 2-methlisoborneol (2-MIB) andgeosmin are mainly produced by Cyanophyceae (blue algae)
and actinomycetes as their metabolic products. Understanding
the coexisting and competing of these microorganisms withother algal and bacteria species in the tap water sources(rivers, lakes and reservoirs) is very important for better
management of the sources and for controlling their growthand presence in water. In this study, analysis was conducted
for the measurement results of eight families of algal species
in the water intake of Nagara River and the coexisting andcompeting of the moldy odor-causing blue algae with otheralgal species were evaluated.

a

MATERIALS AND METHOD
The measurement results of algal species in the following
eight families were analyzed: Cyanophyceae, Flagellata,
Chlorophyceae,
Bacillariophyceae,
Cryptophyceae,
Chrysophyceae, Dinophyceae, and Euglenophyceae. The
results were obtained for water samples collected once amonth from the water intake at the downstream of Nagara
River over the past 20 years (1999-2019). Measurement was
conducted through optical microscope observation. The
analysis of the measurement results was conducted based onprincipal component analysis (PCA) and agglomerative
hierarchical clustering (AHC) (IBM SPSS Statistic 21.0).

b

Fig. 1. The monthly changes of the number
concentration of algae: (a) the total eight families ofalgae, (b) Cyanophyceae

RESULTS and DISCUSSION
The monthly changes of the number concentration of thetotal algae (eight families) of at the Nagara River water intake
for 20 years are shown in Fig. 1a. The mean number
concentration showed distinct increase from May, reached its
highest in June and then fluctuated with comparatively higher
values till September. The mean number concentration
remains stable at lower levels from December to April. Of thetotal eight observed algal families in the past 20 years,
Bacillariophyceae, Chlorophyceae and Flagellata were foundto be the dominating species, contributing greatly to the total
algal species present in the river. In addition, it is clear fromthe box chart that, for all months in the period from April to
October, the changes in the algal number were relatively large,
indicating that the number concentration of algae fluctuatedgreatly in the past 20 years.
The monthly changes of the number concentration of
Cyanophyceae, some species of which are well blamed fortheir significant contribution to the occurrence of moldy odor
in most surface water sources, in the water intake of Nagara
River in the past 20 years are shown in Fig. 1b. It can be seen

1.0

Dinophyceae
Cryptophyceae
Cyanophyceae

0.5

Chlorophyceae
Bacillariophyceae
Chrysophyceae
-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.0

0.2

Flagellata
0.4

0.6

0.8

1.0

PCA1 (24.6%)

Euglenophyceae-1.0

PCA2 (20.4%)-0.5

Fig. 2. Relations among eight algal families
analyzed by principal component analysis

130Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P16
number concentrations in August of the past 20 years werecompiled and analyzed by principal component analysis. The
analysis result is shown in Fig. 4. Cyanophyceae andEuglenophyceae showed a strong negative correlation in boththe first and second principal components. In addition, in thesecond principal component, the relation between
Cyanophytaaceae, Dinophyceae and Cryptophyceae was also
found more obvious. Agglomerative hierarchical clustering
was also performed on the basis of data of August alone. As
shown in Fig. 5, the group relation between Cyanophyceae
and Euglenophyceae was still the furthest. Cyanophytaaceae,
Dinophyceae and Cryptophyceae belonged to the same group,and Cyanophyceae and Dinophyceae were more closely
related. The result showed that, in August, the coexisting
relation between Cyanophyceae and Dinophyceae was more
profound than the coexisting relation between Cyanophyceae
and Cryptophyceae. With this analysis approach,
Cyanophyceae and Euglenophyceae also revealed acompetitive relation.
By conducting principal component analysis andagglomerative hierarchical clustering for the number
concentration data of eight algal families in the past 20 years,
we found that a coexisting relation of Cyanophyceae withDinophyceae and Cryptophyceae, and a competing relation of
Cyanophyceae with Euglenophyceae probably exist in thewater of the source water intake located in the downstream of
Nagara River. Further analysis will be conducted based on thedominating species extracted from all algal families. In
addition, analysis on the likely interactions between algal
species against the concentrations and composition ofnutrients will be conducted along with the likely relations
between algal species and bacteria.

Fig. 3. Relations among eight algal families analyzed
by agglomerative hierarchical clustering
To evaluate the coexisting and competing relations of the
moldy odor-causing Cyanophyceae with other algal families in
the water intake of Nagara River, principal component
analysis and agglomerative hierarchical clustering wereconducted by using the number concentration results of theeight algal families observed for the past 20 years. The resultof principal component analysis is shown in Fig. 2, and that of
the agglomerative hierarchical clustering is shown in Fig. 3.
As shown in Fig.2, although Cyanophyceae was negatively
correlated with other algae in the first principal component, its
load rate in the first principal component was very small. In
the second principal component, Cyanophyceae correlated
negatively with Euglenophyceae; while it correlated positively
with Dinophyceae and Cryptophyceae. The clustering result in
Fig. 3 showed that Cryptophyceae and Dinophyceae weremore strongly correlated, but the algal family of
Cyanophyceae also fell in the group. It could be seen that
Cyanophyceae was the family most distant from the family of
Euglenophyceae. This is consistent with the result of theprincipal component analysis, thus revealing the likely
coexisting relations of Cyanophyceae with Dinophyceae and
Cryptophyceae, and the likely competing relation of
Cyanophyceae with Euglenophyceae.
Since the family of Cyanophyceae is most abundant inAugust, to further investigate the coexistence and competition
1.0

DinophyceaeICyanophyceae

Cryptophyceae
0.5

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

Chrysophyceae
0.0

0.2

0.4

Fig. 5. Relationship among eight algal families inAugust analyzed by agglomerative hierarchicalclustering

Chlorophyceae
Bacillariophyceae
Flagellata
0.6

0.8

1.0

Euglenophyceae

PCA1 (23.9%)

REFERENCES-1.0

PCA2 (30.0%)-0.5Guo, Q.Y., Zhang, X.H., Li, X., Chen, T.M., Yang, B.R.,
Ding, C., Wang, C.M., Pan, M., Ma, W.X., Yu, J.W.,2021.
Variation and mitigation of musty, septic, chemical, grassy,
fishy odors and corresponding odorants in a full-scale
drinking water treatment plant with advanced treatments.
Chemosphere 269, 128-691.

Fig. 4. Relations among eight algal families inAugust analyzed by principal component analysis
of Cyanophyceae with other algal species, the data of algal

131

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P17

Fate and effect of microplastic and heavy metal in agricultural soil
added with biochar
Rahman Fariha1, Shiamita Kasuma Dewi2, Zaw Min Han3 and Wei Yongfen4
1. Graduate School of Natural Science and Technology, Gifu University, Japan
2. The United Graduate School of Agricultural Science, Gifu University, Japan
3. Graduate School of Engineering, Gifu University, Japan
4. River Basin Research Centre, Gifu University, JapanINTRODUCTION

grounded to 2 mm sieves, and mixed with cow manure (0.50 g
per earthworm) was added to soil surface per week (Xu et al.,2013). A deionized water was added to maintain the water
holding capacity. The epigenic earthworm species E. fetida waschosen for this experiment due to its wide tolerance against
environmental variables and survive metal contaminated area.
Adult E. fetida with weight from 93 to 94 mg were purchasedfrom a local earthworm culture farm. Before the incubation
experiment worms were rinsed with deionized water anddepurated with a hungry treatment. The body weight ofindividual was recorded before being transferred intoexperimental reactor. Rice husk biochar were brought fromlocal market.

Globally, microplastics (MPs) pollution in the agroecosystem
has increasingly become an environmental issue (Tunali et al.,2020). MPs are defined as plastics smaller than 5 mm in size
and mainly originate from synthetic particles andenvironmental degradation. The extensive application of sludge,
compost, and mulch films directly introduce MPs into
agricultural fields (Zhang et al., 2020a.) The negative biologicaleffects of MPs are thought to be induced by two mechanisms
first, small-sized MPs are accidently ingested, causing direct
damages to organisms (Chen et al., 2019). Secondly, MPsprovide high specific surface area upon which, otherenvironment pollutants may adsorb; thus, the MPs act as
vectors transferring these contaminants such as organic
compounds, heavy metals (Liu et al., 2019). Among theadsorbed toxic contaminants on MPs, heavy metals are toxic
key contaminants of inorganic nature. Moreover, heavy metals
pollution in soil environment and surface water has become
another major challenge across the world. Over the past few
decades, Cadmium (Cd) pollution has attracted worldwide
attention. Excessive amounts of Cd can damage soil and aquatic
ecosystems, affect organism growth and activity, and bringthreat to downstream animals and human health. Biochar the
carbon-rich material is recognized as a versatile reagent forcontaminated land remediation owning to its high capacities tobind with heavy metals.
Although individual toxicity of MPs and Cd to organism andsoil has been widely studied, limited knowledge is available on
the interactions between cadmium and MPs, as well as potential
impacts from their combinations. Moreover, some studies also
have speculated that MPs presence could increase thebioavailability of other soil contaminants (Zhou et al., 2020b),
but experimental analysis and concrete data have not been
provided. Accordingly, it is worth and interesting to study
whether MPs could increase the availability of Cd soil.
In the current study, experiment was conducted with earthworm
co-exposure to MPs and Cd. The objective of this study wasi) to determine the joint toxicity of MPs and Cd to the soil
fertility and earthworms Eisenia fetida by evaluating theindividual growth changes with biochar application.

Experimental set up
The pretreated E. fetida were randomly transferred into reactor,
each reactor containing 15 E. fetida and 400 g soil. 6 holes wasmade in the lid for ventilating. Eight experimental groups were
set up: control group (without MPs and Cd); T1, (Clean soil +BC); T2= Cd only; T3= (Cd+ BC); T4 = (Cd+MPs3%); T5=(Cd+MPs10%);
T7=(Cd+MPs3%+BC);
T6 =(Cd+MPs10%+BC). Each treatment was prepared in triplicate.
The reactor was placed in a dark room with a temperature of
25 °C ± 0.5 °C. The exposure experiment was carried out for 2
months; soils and E. fetida were collected at every 7 days after.
Earthworm growth parameters
Earthworms were gently taken out from each reactor by hand
and washed with pure water for removing the adhering
materials from their body every seven days. Earthworms wereplaced to a wet adsorption paper and then weighted on a live
weight basis. The growth rate of the earthworms was calculated,
Growth rate (%) = (Morg.2-Morg.1/Morg.2)/ t(where, Morg,1 and Morg,2 refer to initial body weight (mg) andfinal body weight (mg) of E. fetida, and t refers to the exposuredays (Huerta Lwanga et al; 2017a.).
Soil parameters
The pH and electrical conductivity (EC) were measured byusing the aqueous solution of pulverized dry sample with
deionized water (w/v = 1/10) after shaken for 2 h (Cui et al.,2018). The organic matter content (loss on ignition) was
measured by combusting the dried samples in a muffle furnace(Yamato, Japan) at 600 °C for 2 h. Total nitrogen and Total
phosphorus was measured by using the molybdenum blueabsorption method with a spectrophotometer at the designatedwavelength of 220nm and 880 nm respectively (Huang et al.,2012).

MATERIALS AND METHODS
Soil, MPs, and earthworms Preparation:
The clean soil used in this study was topsoil (0-20 cm) collectedfrom the university crop field area Gifu, Japan. After naturally
air dried for 3 days, soils were sieved through a 2-mm meshafter removing stones and plant residues. Mulching sheet
polythene used in this experiment as a microplastics with theparticle size (≤5 mm) and the concentration was 3% and 10%.
Artificially Cd-contaminated soil was obtained by spiking Cd(serving as Cd (NO3)2) in the clean soils. Briefly, Cd solution
mixed with distilled water and sprayed into soils to obtain a
final concentration of 5 mg kg -1. Soils were then incubated atroom temperature (250C) for 30 days. Soils were then air-dried,

RESULTS AND DISCUSSION

Effects of contaminates on growth of E. fetida
Co-exposure to MPs + Cd and singly exposure to Cd bothdecreased growth rate compared to control (Fig. 1). T2 and T6
treatments (D14 and D21) was lower (P<0.05) than that incontrol. On the contrary, the growth rate slightly increased
when applied biochar. T1 shows significantly higher in D14 and132

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P17
D21 compared to control. Addition of biochar increases thegrowth rate in all treatments (Fig.1). The degradation of MPs
has just begun from 21st days, Fig.2. As indicated in previous
studies, MPs have great potential to accumulate metal
contaminants on their surfaces from the ambient environment
due to strong hydrophobicity and higher specific surface area,and pollutants can easily be released inside of organisms afteringestion (Bakir et al., 2014). Thus, the Cd concentration in E.
fetida and bio accessibility of Cd might have been enhanced byMPs, an explanation for the lower growth rate recorded in MPs+ Cd group in this current study. The positive effect of biochar
on E. fetida growth can be attributed to its soil-conditioning
effects in terms of increasing soil water holding capacity,
nutrients bioavailability, and microbial activities.

Fig.3 Organic matter content (%) of soils under various treatments

Effects of contaminates on soil property
The OM of all treatments were measured on D7, D14 and D21.
T1 shows significantly higher in D14 and D21 compared tocontrol. T4 and T6 had showed similar OM contents (lower thanBiochar amendments). Addition of biochar increases the OM in
all treatments (Fig.3). MPs has a linear hydrocarbon structure,large molecular size, lack of functional groups and highhydrophobicity, which make the synthetic polymer quite
resistant to degradation under natural field conditions. As aresult, the SOM of MPs treatment remained stable. On the otherside, the use of biochar also increases the soil organic matter insoil in all treatments (p <0.05). Due to its high carbon source
helps to enhance soil organic matter in soil.
On the other hand, no differences (total nitrogen, TN) wereobserved in 7th day among the treatments. In 14th days, TN
contents were significantly lower in T2 and T6 and highest TN
were observed in T and T2 treatments. In 21st day TN contentsslightly increased in all treatments. Nitrogen (N) is essential tomanage agricultural soil health and crop productivity However,there are limited studies about the combine effects of MPs andCd on the dynamics of soil nitrogen in agro-ecosystem. In Cdcontaminated soils, soil nitrogen is inhibited and usually block
nitrogen supply for plants.
25.00

Growth rate (%)

20.00
15.00

a

ab
ab

a
a

7day

a

ac

c

14 day

ac

21day

bc
abc

c

a

abc

bc
a

Fig.4 Total nitrogen content (mg g-1) of soils under various treatments
REFERENCES
Bakir, A., Rowland, S.J., Thompson, R.C., 2014. Enhanced desorptionof persistent organic pollutants from microplastics under simulated
physiological conditions. Environ. Pollut. 185, 16–23.
Chen, Q.Q., Li, Y., Li, B.W., 2019. Is color a matter of concern during
microplastic exposure to Scenedesmus obliquus and Daphnia
magna? J. Hazard. Mater. 383, 121224.

Cui, G., Li, F., Li, S., Bhat, S.A., Ishiguro, Y., Wei, Y.,
Yamada, T., Fu, X., Huang, K., 2018. Changes of quinolone
resistance genes and their relations with microbial profiles
during vermicomposting of municipal excess sludge. Sci. Total
Environ. 644,494–502.

Huang, K., Li, F., Li, J., Helard, D., Hirooka, K., 2012. Rapid
Vermicomposting of Fresh Fruit and Vegetable Wastes Using
Earthworm Eisenia Foetida. J. Japan Soc. Civ. Eng. Ser. G(Environmental Res. 68, III_113-III_120.
Huerta Lwanga, E.H., Gertsen, H., Gooren, H., Peters, P., Salanki, T.,van der Ploeg, M., Besseling, E., Koelmans, A.A., Geissen, V.,
2017a. Microplastics in the Terrestrial Ecosystem: Implications for
Lumbricus terrestris (Oligochaeta, Lumbricidae). Environ. Sci.
Technol. 50, 2685–2691.
Liu, P., Lu, K., Li, J., Wu, X., Qian, L., Wang, M., Gao, S., 2019. Effectof aging on adsorption behavior of polystyrene microplastics forpharmaceuticals: Adsorption mechanism and role of aging
intermediates. J. Hazard. Mater. 384, 121193.
Tunali, M., Uzoefuna, E.N., Tunali, M.M., Yenigun, O., 2020. Effectof microplastics and microplastic-metal combinations on growth andchlorophyll a concentration of Chlorella vulgaris. Sci. Total
Environ. 743, 140479.temperate forested basin in northern Japan.
Hydrological Proceses, 15(10), 1817-1828.
Xu, D., Li, C., Wen, Y., Liu, W., 2013. Antioxidant defense system
responses and DNA damage of earthworms exposed to
Perfluorooctane sulfonate (PFOS). Environ. Pollut. 174,121–127
Zhang, L., Xie, Y., Liu, J., Zhong, S., Qian, Y., Gao, P., 2020a. An
overlooked entry pathway of microplastics into agricultural soils
from application of sludge-based fertilizers. Environ. Sci. Technol.
Zhou, Y., Liu, X., Wang, J., 2020b. Ecotoxicological effects of
microplastics and cadmium on the earthworm Eisenia foetida. J.
Hazard. Mater. 392, 122273.

abc
ac

c

abc
a

10.00

5.00
0.00

Fig.1 Growth status of earthworms under various treatmentsabFig.2 SEM image of MPs in soils of 7th days (a) and 21st days (b) after
mixing133

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P18

MODIS-based estimation of carbon sequestration of different vegetation in
Qilian Mountains, China
Zhou Jieli1, Zhang Fuping2 and Wei Yongfen3
1. Graduate school of Natural Science and Technology, Gifu University
2. Shaanxi Normal University, China
3. River Basin Research Center, Gifu University

INTRODUCTION

In this study, NPP was estimated based on the CASA model.
The CASA model has the characteristics of high computational
efficiency and a few input parameters, it is widely used inregional or global scale NPP estimation. And many studies haveobtained satisfactory estimation results based on the CASA
model. In this study, NPP was estimated based on the following
equations ((1)-(10)):
NPP (x, t) =APAR(x, t) ×ε(x, t)(1)
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴(𝑥𝑥, 𝑡𝑡) = 𝑆𝑆𝑆𝑆𝑆𝑆(𝑥𝑥, 𝑡𝑡) × 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹(𝑥𝑥, 𝑡𝑡) × 0.5(2)(3)
𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 (𝑥𝑥, 𝑡𝑡) = 𝛼𝛼𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 + (1– 𝛼𝛼)𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁(𝑥𝑥,𝑡𝑡)–𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑖𝑖,𝑚𝑚𝑚𝑚𝑚𝑚
𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 (𝑥𝑥, 𝑡𝑡) =×

Vegetation can be thought of as a carbon sink and play animportant role in climate change and global warming. Accurate
estimation of vegetation carbon sequestration capacity canprovide a scientific basis for formulating more scientific andreasonable carbon management measures.
Qilian Mountains have diverse vegetation and are an
important ecological barrier in western China. At present, theestimation of carbon sequestration in the Qilian Mountains ismostly for single vegetation, and most of the studies are for small
areas (F. Jin and Z. Zhang, 2005). In contrast, studies on theestimation of carbon sequestration for the entire Qilian
Mountains and different vegetations are rarely reported.
Therefore, in this study, by using satellite data based on net
primary productivity (NPP) estimate model, the carbon
sequestration of different vegetations in the Qilian Mountains in2005 and 2015 was estimated, aiming to provide relevant
departments with the research area’s carbon sequestration inrecent years and a scientific and intuitive reference for regional
ecological protection.

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑖𝑖,𝑚𝑚𝑚𝑚𝑚𝑚 –𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑖𝑖,𝑚𝑚𝑚𝑚𝑚𝑚(𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑚𝑚 – 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑚𝑚 ) + 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑚𝑚
𝑆𝑆𝑆𝑆(𝑥𝑥,𝑡𝑡)–𝑆𝑆𝑆𝑆𝑖𝑖,𝑚𝑚𝑚𝑚𝑚𝑚
× (𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑚𝑚 −𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑆𝑆𝑆𝑆 (𝑥𝑥, 𝑡𝑡) =𝑆𝑆𝑆𝑆𝑖𝑖,𝑚𝑚𝑚𝑚𝑚𝑚 –𝑆𝑆𝑆𝑆𝑖𝑖,𝑚𝑚𝑚𝑚𝑚𝑚

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑚𝑚 )+𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑚𝑚𝑚𝑚𝑚𝑚
1+𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁(𝑥𝑥,𝑡𝑡)
𝑆𝑆𝑆𝑆(𝑥𝑥, 𝑡𝑡) =1–𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁(𝑥𝑥,𝑡𝑡)

(4)

(5)
(6)

ε(x, t) = 𝑇𝑇ε1 (x, t) × Tε2 (x, t) × Wε (x, t) × εmax
(7)
𝑇𝑇𝜀𝜀1 (𝑥𝑥, 𝑡𝑡) = 0.8 + 0.02 × 𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜 (𝑥𝑥) − 0.0005 × [𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜 (𝑥𝑥)]2(8)
𝑇𝑇𝜀𝜀2 (𝑥𝑥, 𝑡𝑡) = 1.184/{1 + 𝑒𝑒𝑒𝑒𝑒𝑒[0.2 ×(𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜 (𝑥𝑥)– 10– 𝑇𝑇(𝑥𝑥, 𝑡𝑡))]} × 1/{1 + 𝑒𝑒𝑒𝑒𝑒𝑒[0.3 ×(– 𝑇𝑇𝑜𝑜𝑜𝑜𝑜𝑜 (𝑥𝑥)– 10 + 𝑇𝑇(𝑥𝑥, 𝑡𝑡))]}(9)𝑊𝑊𝜀𝜀 (𝑥𝑥, 𝑡𝑡) = 0.5 + 0.5 ×

𝐸𝐸(𝑥𝑥,𝑡𝑡)

𝐸𝐸𝑝𝑝 (𝑥𝑥,𝑡𝑡)(10)where,APAR(x, t): the photosynthetically active radiation absorbed by
pixel x in month t (gC/m2).
ε (x, t): the actual light energy utilization rate (gC/MJ) of pixel x
in month t.
SOL (x, t): the total solar radiation at pixel x in month t (gC/m2).
FPAR (x, t): the absorption ratio of the vegetation layer to theincident photosynthetically active radiation. The constant 0.5 in(1) represents the ratio of the effective solar radiation that can beused by vegetation to the total solar radiation.
FPARmin and FPARmax: 0.001 and 0.95, respectively.
SRi,max and SRi,min : the 95% and 5% inferior percentiles of
NDVI of the i-th planting type, respectively.α is 0.5.
Tε1(x, t) and Tε2(x, t): the stress effects of low temperature andhigh temperature on light energy utilization.
Wε (x, t): the water stress coefficient, reflecting the influence ofwater conditions.
εmax : the maximum light energy utilization rate under the ideal
conditions (gC/MJ).
Topt(x): the optimum temperature for plant growth, which isdefined as the monthly average temperature (℃) when the NDVI
value reaches the highest in a certain area in a year.
E (x, t): the actual evapotranspiration of the area (mm).
Ep (x, t): the regional potential evapotranspiration (mm).

Fig.1 Study area

DATA AND METHODS— DataTo estimate the carbon sequestration of different vegetationsin 2005 and 2015, the following data were used:(1) Land use data: collected from the Resource andEnvironmental Science Data Center of the Chinese Academy of
Sciences.
(2) DEM data: collected from ASTGTM data of the
Geospatial Data Cloud, with a spatial resolution of 30m.
(3) MODIS-NDVI data set: collected from NASA’s 16-day
synthetic data, with a spatial resolution of 500m.
(4) Evapotranspiration data: collected from NASA’s 8-day
synthetic data, with a spatial resolution of 500m.
(5) Meteorological data: collected from the China
Meteorological Science Data Sharing Network.

RESULTS AND DISCUSSION— Land cover classification—Method134

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P18
According to the field investigation and existing materials, theland cover in the study area was classified into four categories:
cultivated land, woodland, grassland, and others.
Table 1 Classification results
Land Area/（×104hm-2）
Land CoverLand cover change
2005
2015
(2005 - 2015)

the amount of carbon sequestered by vegetation was calculated
and its distribution was shown in Fig. 2 and Fig. 3.

Cultivated land
62.41
63.02
0.98
Woodland
165.04165.3
0.16
Grassland
837.26 837.38
0.01
Others878.79 877.76-0.12
Table 1 shows that vegetations occupied more than half of thestudy area. Among the vegetations, grassland had the largest area,
followed by woodland, cultivated land. From 2005 to 2015, thearea of cultivated land increased the most, followed by woodland,grassland. The land for others (including water, construction
land, and unused land) decreased.

Fig. 2 Carbon sequestration distribution map in 2005

—NPP estimation results
The estimated were average NPP and total NPP of the study
area in 2005 and 2015, respectively, shown in Table 2 and Table
3.
Table 2 Average NPP estimated for different vegetation in the
study area
Land Cover

2005

2015

Average NPP

Average NPP（t · hm · a ）-2-1

Cultivated land
Woodland
Grassland
Others（t · hm-2· a-1）

5.03

5.63

5.19

5.31

3.82

4.37

1.36

1.56

Fig. 3 Carbon sequestration distribution map in 2015
In terms of spatial distribution (Fig. 2 and Fig. 3), the carbon
sequestration values in the eastern part of the study area arehigher than that in the western part, which is consistent with thefact that the ecological system in the eastern part of the study
area is better than that in the western part. As can be seen,
compared to 2005, Carbon sequestration increased significantly
in 2015. It can be inferred from this that the ecologicalconditions in the Qilian Mountains have been improving inrecent years.
Many areas in the study area have not been developed andutilized due to soil quality and environmental factors, especiallyin the western part, and the amount of carbon sequestration invegetation is small. Therefore, how to improve the unused landthrough technology and increase the vegetation coverage is aproblem worthy of further exploration. Due to the large area ofgrassland, the contribution to carbon sequestration in the study
area is great. Therefore, the study area must take further
measures.

Table 3 Total NPP estimated for different vegetation in the
study area
Land Cover

Cultivated land
Woodland

2005

2015

Total NPP

Total NPP

（×104 t/ a）（×104 t/ a）

313.92354.8

Grassland

856.56877.743198.33

3659.35

Others1402.931604.92

Total

5771.746496.81As shown in Table 2, the average NPP values of cultivatedland and woodland are higher, than that of grassland and others.
Table 3 shows that the total NPP of different vegetations has
increased obviously from 2005 to 2015. Besides, grassland has
the highest total NPP value due to its large area. Although thevegetation area only occupied about half of the study area, thevegetation NPP occupied about three-quarters of that in the study
area.

REFERENCES
F. Jin, Z. Zhang. (2005) Value evaluation of forest ecosystemservices of Qilian Mountain in Gansu province. Science of Soiland Water Conservation.
National Bureau of Statistics. (2015) Gansu Statistical
Yearbook. Beijing: China Statistics Press.

—Estimation of carbon sequestration
Accordingtothephotosynthesis
equation:
6CO2+6H2O→C6H12O6+6O2, to the production of 1kg organic
matter can fix 1.63kg CO2. So, based on the NPP of vegetation,135

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P19

Association of bacterial community with heavy metals in soils of different
coal mining areas
Yajie Wang1, Zãw Min Han1, Yongfen Wei2 and Fusheng Li1,2
1. Graduate School of Engineering, Gifu University
2. River Basin Research Center, Gifu University

INTRODUCTION

single heavy metal pollution index, CD is the measured heavy
metal concentration, CR is the geochemical background value.
The calculated RI of heavy metals were used in the examinationof relationships between heavy metals and bacterial
community. The examination was conducted by Spearman
correlation analysis, variance-partitioning analysis (VPA) anddistance-based redundancy analysis (dbRDA).

Coal as one of the main fossil fuel resources has played
important role in the world in supporting energy supply andsocial activities. Heavy metals including chromium (Cr),
cadmium (Cd), nickel (Ni), copper (Cu), zinc (Zn), arsenic(As), lead (Pb), and mercury (Hg) are ubiquitous in the coal.
Exploitation of coal could result in soil pollution, even though
the extent of pollution may differ with the condition ofmanagement and the exploitation technology. Heavy metals insoil can enrich through the food chain, and endanger humanhealth and life, eventually. In soil, bacteria are the mostabundant organism, which are sensitive to the content of heavy
metals (Du et al., 2018). The bacterial richness, diversity andcommunity structure are likely to differ significantly in the soil
with different types and concentrations of heavy metals, sobacteria are possible to be better bio-indicators than other larger
organisms. A better understanding on the effect of heavy metals
on bacterial community is critical to optimize such monitoring
methods and remediation schemes.

RESULTSThe concentrations of the targeted heavy metals in soil ofcoal mines are summarized in Table 1. In this table, theanalytical results of pH, EC, moisture, total phosphorus (TP),total nitrogen (TN) and organic matter (OM) are also included.
The obtained results showed distinct variations in different soil
samples.
The result of RI analysis of the heavy metals in soil of coalmines (will be provided in poster) showed that the overall
potential ecological risk differs greatly in different samples, andfell in the range of 26.4-75.3, the highest value is of M3. The
average ecological risk of individual heavy metal was of theorder: Cd (28.2)> Ni (3.75)> As (3.26)> Cu (3.04)> Pb (2.96)>
Cr (1.12)> Zn (0.49). The ecological risk for Cd account for
66% of the overall ecological risk, on average. The ecologicalrisk for Cd fell in the range of 16.8-61.1, the highest value isalso of M3. These results addressed that Cd was the key
influence factor to cause the potential ecological risk in the soilof coal mines.
The richness of bacteria in soil of each coal mine are shownin Fig. 1. The richness of M1 is significantly higher than that ofother coal mines, and the richness of M5 is the lowest. There
are no significant differences between the richness of M5, M4
and M3. The correlation analysis (will be presented in poster)
showed Zn, Cr, and Cd have inverse relationships with bacterial
richness, indicating the effect of Zn, Cr and Cd on reducing thetotal bacteria in soil of coal mines.

MATERIALS AND METHODS
six coal mines in Luliang, Shanxi Province of China withdifferent annual output (M1: 0.6, M2: 1.2, M3: 1.5, M4: 1.8,
M5:3.0 M6: 8.0×106 t) were selected as the research area.
Surface soil (1-5cm) samples on three different sites (0, 1, and
3km away the mining area) of each coal mine were collected,
and were labeled as A, B and C, respectively. The collected soil
samples were frozen dried, sieved, and homogenized. The soil
samples were then used for quantification of organic matter(OM), total nitrogen (TN), total phosphorus (TP), 7 heavy metalelements (Cr, Ni, Cu, Zn, As, Cd, and Pb), and bacterial
community analysis based on genomic DNA. The genomic
DNA was extracted using the DNA extraction kit (MOBIO,
USA). The absolute abundance of DNA based on 16S rDNA
was measured through quantitative real-time PCR (qPCR)
following the procedures described in the literature (Li et al.,2021). The bacterial diversity and community structure wereanalyzed through high-throughput sequencing technology. The
v4-variable region of the 16S rRNA gene was amplified by
PCR with primers (515F-806R). The amplified genes weresequenced on the Illumina MiSeq platform. At 97% similarity
level, the out representative sequences in the obtained highquality sequences were taxonomically analyzed by RDP
classifier Bayesian algorithm and compared with Silva database
to obtain the community information at each classifiable level.
The potential ecological risk index (RI) introduced byHakanson (1980) is used to assess the degree of ecological risk
caused by heavy metal concentrations in soil. The formulas are
as follows:

5.00E+09

16S rDNA (copies/g-dry)

4.50E+09i

4.00E+09
3.50E+09
3.00E+09bb

2.50E+09
2.00E+09ab

1.50E+09
1.00E+09

ab

a

M4

M5

5.00E+08
0.00E+00

M1

M2

M3

M6

Coal mines

Fig.1: Total bacteria measured based on 16S rDNA in soil
samples of the mines (M1-M6). The different letters indicate
the difference is statistically significant (p<0.05).

RI = ∑𝑛𝑛𝑖𝑖 Er = ∑𝑛𝑛𝑖𝑖 (Tr x C f )iii
C f = CD/CR
Where 𝑛𝑛 is the number of heavy metals, Er is the single index
of ecological risk factor of individual heavy metal, Tr is theheavy metal toxic response factor (Hakanson, 1980), Cf is theiciAlpha diversity indices varied between different soil
samples. The variation of Shannon and Simpson was 7.3710.16 and 0.956-0.997, respectively. The Chao 1 index and Ace

136

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P19
In total, 86 bacterial phyla were identified from soil samples.
The relative abundance of the top 20 phyla were chosen for thehistogram analysis (Fig. 2). Proteobacteria, Acidobacteriota,
Firmicutes, Actinobacteria, Bacteroidetes were dominating
phyla, which accounted for 38.6%, 11.8%, 4.9%, 7.0% and3.8%, respectively, on average. The relative abundance of eachphylum in different soil samples were different.
The result of dbRDA of heavy metals and bacterial
community structure at the phylum level was presented in Fig.
3. The analysis showed that Ni, Cu, Cr, and Zn signify a greater
impact on bacterial community structure. dbRDA1 anddbRDA2 explained 67.85% and 18.92% of the total variation in
the community structures, respectively. Ni (r = 0.98), Cr (r =0.96), Cd (r = 0.96), and As (r = 0.96) were significantly
correlated with dbRDA1, whereas Zn (r = 0.96) and Cu (r =−0.93) were significantly correlated with dbRDA2. The
analysis of the combined effect of heavy metals on bacterial
community structure (will be presented on poster) showed Ni
and Cu had highest correlation with bacterial community
structure, indicating the major roles of Ni and Cu in affecting
community structure.
This study highlights the effects of heavy metals on thebacterial community in soil of coal mining areas. The changesof bacterial community are associated with Cd, and also closely
associated with Zn, Cr, Ni and Cu. Different elements showed
different effects on bacterial richness, diversity and community
structure. Zn affects the bacterial richness, diversity andcommunity structure. Cr and Cd mainly affect the bacterial
richness. Ni and Cu show great effect on bacterial community
structure.

index ranged from 3637.9-4914.5 and 3693.5-5008.9,
respectively. The correlation analysis (will be provided inposter) showed Zn has inverse relationships with Shannon
index and Simpson index, suggesting the role of Zn incontrolling the bacterial diversity in the soil of coal mines.

Fig. 2: Bacterial community for the top 20 dominant taxa at
phylum level in the soil samples of the mines (M1-M6).
M2.C
M4. B
M5.C
M1. C
M6. C

M3.C

REFERENCES

M6. B
M4. A

Du, H., Harata, N., Li, F. (2018). Responses of riverbed
sediment bacteria to heavy metals: Integrated evaluation
based on bacterial density, activity and community structure
under well-controlled sequencing batch incubation
conditions. Water Research, 130: 115-126.
Hakanson L. (1980). An ecological risk index for aquatic
pollution control: a sedimentological approach. Water
Research, 14(8): 975–1001
Li, W., Su, H., Li, J., Bhat, S. A., Cui, G., Han, Z. M., … Li, F.
(2021). Distribution of extracellular and intracellular
antibiotic resistance genes in sludge fractionated in terms ofsettleability. Science of The Total Environment, 760:
143317.

M2. A

M1. A

M5. B
M5. A

M2. B

M6. A

M3. B
M1. B
M3. A
M4. C

Fig. 3 Effect of heavy metals on bacterial community
structure at phylum level. A, B and C represent thedifferent sampling sites in each mine.

Table 1. Physiochemical properties and heavy metal content of soil samples of the mines (M1-M6).

*TN, TP and OM represent total nitrogen, total phosphorus and total organic matter in soil.

Coal mineIndex unit
pH
EC
mS/m
Moisture
%
OM
g/kg
TP
mg/kg
TN
mg/kg
Cr
Pb
Ni
mg/kg
Cu
Zn
Cd
As

M1
range

mean

M2
range

mean

M3
range

mean

M4
range

mean

M5
range

mean

M6
range

mean

8.28-8.53
158-21422-27.4
15.4-17.7
2.4-2.7
520-620
29.8-33.8
14.7-15.8
16.9-21.2
12.1-14.0
28.8-43.7
0.08-0.10
3.32-3.75

8.47
179
24.3
16.8
2.5
537
31.7
15.3
20.0
13.2
35.1
0.09
3.44

8.39-8.65
140-177
23.6-25.5
15.1-17.9
2.4-2.9503-528
21.2-36.2
10.1-16.4
14.7-20.9
12.8-14.3
27.4-33.5
0.07-0.10
2.15-5.06

8.51
153
24.6
16.3
2.6
514
27.5
12.6
17
13.6
29.8
0.08
2.97

8.64-8.94
67-18522.5-25.1
18.4-21.5
2.2-3.2
516-61336.1-47.9
15.6-22.8
15.2-21.0
13.8-18.2
35.4-59.5
0.09-0.20
2.04-4.95

8.79
106
23.8
20.2
2.7
558
38.4
18.3
20.0
14.8
44.9
0.13
3.23

8.19-8.76
153-195
23.2-26.4
16.9-20.1
2.1-2.2
498-58735.6-37.8
15.4-16.3
20.5-26.1
13.2-14.6
33.8-34.9
0.09-0.10
3.36-4.63

8.34
176
25.5
19.1
2.1
532
36.6
15.8
23.7
13.8
34.2
0.09
4.14

8.42-8.76
150-173
21.8-24.6
17-19.2
2.1-2.4
532-632
38.8-43.9
14.6-17.4
19.0-23.7
12.8-16.6
36.1-40.8
0.09-0.10
4.54-5.39

8.51
164
23
17.9
2.3
585
41.9
16.3
21.8
15.1
38.9
0.09
5.12

8.34-8.62
143-168
21.2-29.5
18.3-20.5
2.3-3.0519-620
22.2-31.9
10.3-15.0
22.2-31.9
9.2-12.7
19.8-53.7
0.06-0.10
2.30-3.38

8.5
168
23
19.1
2.8
562
29.7
14.2
18.6
12.1
35.5
0.09
3.02

137

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P20

Changes of 16S rDNA and antibiotic resistance genes in the drinking
water treatment process
Nadya Diva Sagita1, Sutra Maysaroh1, Maulana Yusup Rosadi 1 and Fusheng Li2
1. Graduate School of Engineering, Gifu University, Japan
2. River Basin Research Center, Gifu University, JapanINTRODUCTION

Thermal Cycler Dice TP800 (Takara, Japan) using TB Green
assays with a final volume of 25µL. Each qPCR mixture
contained 12.5 µL of TB Green Premix Ex Taq (Tli RNaseH
Plus), 0.5 µL of each 10 µM primer, 9.5 µL of sterile Milli-Q
water, and 2 µL of DNA template. DNA templates were
included in triplicate, while negative controls (DNA-free water)and serial dilutions of standard plasmids were included in
duplicate in each qPCR run. The standard curve of targeted
genes was generated by 10-fold dilutions of the plasmidcarrying the targeted gene. The copy number of each gene wascalculated from the corresponding standard curve using the CT
value of each gene in the qPCR runs.

The intensive use of antibiotics has resulted in the
proliferation of antibiotic-resistant bacteria (ARB) andantibiotic resistance genes (ARGs), which have become
emerging contaminants. It is estimated that antibiotic resistancewill cause up to 10 million deaths annually by 2050 if no action
is taken from now (O’ Neil, 2014). Previous studies havedetected ARGs pollution in aquatic environments such as
surface water and groundwater (Wu et al., 2020). Moreseriously, various ARGs have been detected in the drinking
water source and potable water (Su et al., 2018), posing a severe
threat to human health. Therefore, it is crucial to understand
ARGs’ abundance in water sources and their removal indrinking water treatment plants (DWTPs).
Numerous studies have been conducted to investigate thereduction of ARGs in DWTPs. Su et al. reported that the
sedimentation process could remove 55% of ARGs, and thesand filtration process achieved the removal value of 67% (Suet al., 2018). In contrast, chlorination resulted in the enrichmentof ARGs (Xu et al., 2016). However, these studies only focusedon the removal efficiencies of the water treatment units, andinformation regarding the relationship of water quality
parameters with changes in the abundance of ARB and ARG is
still unclear, requiring further investigation.
Accordingly, the main objective of this study was toinvestigate changes in the number of bacteria represented by the
16S rDNA and antibiotic resistance gene in the drinking water
treatment process. The correlations between gene changes andother water quality parameters were also evaluated.

RESULTS AND DISCUSSION
All of the targeted genes were detected in drinking water
sources (Fig. 1). In the raw water of DWTP A and B, theconcentration of 16S rDNA ranged from 109 copies/L to 1010
copies/L. Meanwhile, the concentration of two classes of ARGs
ranged from 105 copies/L to 107 copies/L. Overall, theprocessing unit obtained an ARGs removal of 99.74% in
DWTP A and 98.02% in DWTP B. Both of the target ARGs
were still detected in the purified water. The total
concentrations of the target ARGs in the final effluent of DWTP
A were 1.33×103 copies/L, and 1.40×104 copies/L for DWTP
B. ARGs in purified water indicate that the existing technology
in DWTP A and B cannot eliminate ARGs in drinking water.
According to the results shown in Table 1 and Table 2,intermediate-chlorination removed the most ARGs in DWTP
A, followed by sedimentation and sand filtration. In contrast,
the post-chlorination process removed no ARGs and increased
the individual ARGs’ abundance. In DWTP B, theintermediate-chlorination removed the most ARGs, followedby post-chlorination, sand filtration, and sedimentation.
Both DWTP A and DWTP B could remove ARGs by the
sedimentation process. Su et al. (2018) reported a similar result
on removing twenty-one ARGs, including sulfonamide andtetracycline resistance genes, where the sedimentation processeffectively removed ARGs (55%) (Su et al., 2018). Since 16S
rDNA had a strong positive correlation with ARGs in DWTP A(> 0.98), bacterial biomass was essential in ARG fate.
According to the results shown in table 3, the decline of ARGs
and intl1 abundance might be linked to the removal of ARBcontained ARGs along with the deposition of suspended solids,
indicated by the decrease of 16S rDNA. The aggregating
function might explain the ARGs reduction efficiency. The
added coagulant/flocculant would cause microorganisms andsmall suspended particles to form large granules (flocs) and lose
their original stability. After that, the flocs would rapidly
precipitate to the bottom of the sedimentation tank due to thegravity.
The total ARGs in DWTP A and DWTP B decreased
significantly after the intermediate-chlorination process (p <0.05). It can be seen from the Pearson correlation shown in
Table 3 and Table 4 that the relationship between the targeted
genes and the chlorine residue had strong negative correlations.
The correlation suggests that the addition of chlorine impacted
reducing the abundance of ARGs in the water. Inactivation of

MATERIALS AND METHODS
Sample collection
All samples in this study were collected from August 2020 to
December 2020 in two different DWTPs located in Aichi
Prefecture, Japan. DWTP A has 76,500 m3/day, and DWTP B
has 344,300 m3/day capacity. The treatment processes include
coagulation-flocculation, sedimentation, filtration influent(intermediate-chlorination), rapid sand filtration, and purified
water tank (after post-chlorination).
DNA extraction
Water samples (0.1–0.5 L) were filtered through 0.2-μm
polytetrafluoroethylene membrane filters (Millipore, Ireland)
using a vacuum filtration apparatus. The filters were then cut
into small pieces, and DNA was extracted using the PowerSoil
DNA Isolation Kit (Mobio, USA), following the
manufacturer’s protocol. All the extracted DNA samples werestored at -20℃ until quantitative analysis.
ARGs quantification
Quantitative PCR (qPCR) was used to quantify the abundanceof 16S rDNA, ARGs conferring resistance to sulfonamide andtetracycline antibiotics (sul1 and tetG), and class 1 integron
genes (intl1). The 16S rDNA was quantified as a measure of
total bacterial load. sul1 and tetG were chosen because they are
the most common ARGs detected in surface water. The intl1
gene was also quantified as an indicator of potential horizontal
gene transfer for ARGs. The qPCR assays were conducted on a138

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P20
ARGs mostly depends on the damage of DNA structure.
Chlorine-based disinfection was added to the water in thechlorination process, and hypochlorous acid (HOCl), a
powerful oxidant, was produced. Because of its electricalneutrality, HOCl can easily penetrate the cell wall andmembrane of bacterial cells through passive diffusion. The
reaction of HOCl with proteins and amino acids leads to protein
fragmentation due to the cleavage of peptide bonds, which
results in the irreversible aggregation of essential bacterial
proteins and causes bacterial death, characterized by a reductionin the value of 16S rDNA. HOCl might also attack the DNA,leading to DNA breakage and impairment of DNA synthesisthat caused the decline in the abundance of ARGs and intl1(Nizer et al., 2020).
The abundance of all the targeted genes in DWTP A and
DWTP B were decreased after the filtration process. The
reduction could be due to quartz in the sand filter, which could
filter and remove the suspended solids and waterborne
pathogens from water. This result was consistent with theprevious research that mentioned that the effect of sandfiltration was superior in removing the absolute abundance of
ARGs (67%) (Su et al., 2018).
All targeted ARGs in DWTP A gained an enrichment of 99%
for total ARGs in the post-chlorination unit. The lower removal
value of ARGs in post-chlorination indicates that ARBs’
response to chlorine varies depending on concentration levels.
Following Xu et al., a low chlorine dose (± 0.25 mg-Cl2/L)
would be more likely to damage the bacterial cells thaninactivate the DNA.

From various processes, chlorination was more effective ineliminating ARGs than coagulation, sedimentation, and sandfiltration, judging from the process’s removal rates for thetargeted genes. The removal rates for the targeted genes showedthat the intermediate-chlorination applied after sedimentation
was more critical in eliminating ARGs than the post
chlorination.
Table 1 The percent removals of targeted genes in the water
treatment units of DWTP A (%).
Sedimentation

Intermediate
chlorination

Filtration

Postchlorination

84.6
87.8
90.1

99.8
86.4
61.5

57.6
78.1
98.0

-25.8
-102.4
-24.6

78.5

99.9

23.3

61.8

sul1
tetG
intl1
16S
rDNA

Table 2 The percent removal values of targeted genes in thewater treatment units of DWTP B (%).
Sedimentation

Intermediate
chlorination

Filtration

Postchlorination

27.1
34.1
26.3

97.9
59.6
99.9

70.9
31.0
43.8

50.3
54.8
-8.3

49.4

97.7

86.4

74.1

sul1
tetG
intl1
16S
rDNA

Table 3 Correlation between targeted genes and water
quality parameters in DWTP A.
sul1
tetG
intl1
16S rDNA

Turbidity
0.99
0.99
0.99
0.98

Suspended solid
0.96
0.98
0.97
0.98

Free chlorine
-0.89
-0.85
-0.70
-0.89

Table 4 Correlation between targeted genes and water
quality parameters in DWTP B.
sul1
tetG
intl1
16S rDNA

Turbidity
0.73
0.99
0.73
0.99

Suspended solid
0.76
0.99
0.75
0.99

Free chlorine
-0.94
-0.82
-0.95
-0.94

REFERENCE
Nizer, W. S. da C., Inkovskiy, V., & Overhage, J. (2020).
Surviving reactive chlorine stress: Responses of gramnegative bacteria to hypochlorous acid. Microorganisms,
8(8), 1–27.
O’ Neil, J. (2014). Review on Antibiotic resisitance.
Antimicrobial Resistance : Tackling a crisis for the health andwealth of nations. Health and Wealth Nations, 1–16.
Su, H. C., Liu, Y. S., Pan, C. G., Chen, J., He, L. Y., & Ying,
G. G. (2018). Persistence of antibiotic resistance genes andbacterial community changes in drinking water treatment
system: From drinking water source to tap water. Science of
the Total Environment, 616–617, 453–461.
Wu, D. L., Zhang, M., He, L. X., Zou, H. Y., Liu, Y. S., Li, B.
B., Ying, G. G. (2020). Contamination profile of antibiotic
resistance genes in ground water in comparison with surface
water. Science of the Total Environment, 715.
Xu, L., Ouyang, W., Qian, Y., Su, C., Su, J., & Chen, H. (2016).
High-throughput profiling of antibiotic resistance genes indrinking water treatment plants and distribution systems.
Environmental Pollution, 213, 119–126.

Fig. 1 Absolute abundances of targeted genes during
treatment process. The different letters indicate thedifference between two groups is significant based on the
one-way ANOVA (p < 0.05). RW: raw water; SE:
sedimentation effluent; FI: filtration influent (with Cl2
addition); FE: filtration effluent; PW: purified water (after
post-chlorination).

139

Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P21

Changes of dissolved organic matter from source to treated water indrinking water treatment plant: Evaluation based on fluorescence
excitation-emission matrix analysis
Maulana Yusup Rosadi1, Nadya Diva Sagita1, Sutra Maysaroh1 and Li Fusheng2
1. Graduate School of Engineering, Gifu University
2. River Basin Research Center, Gifu University381) and Peak 4 (tryptophan-like: Ex/Em: 275–285/320–350)
and humic-like peaks: Peak 3 (humic-like: Ex/Em: 320–
INTRODUCTION
360/420–460) and Peak 5 (humic-like: Ex/Em: 230–260/400–480) (Rodríguez-Vidal et al., 2021). The humification index
Dissolved organic matter (DOM) is a mixture of organic(HIX) indicates the degree of DOM humification and wascompounds with different physiochemical properties that cancalculated as the area under the emission spectra of 435 to 480
originate from microorganisms and/or terrestrially derived
nm divided by the sum of the emission spectra of 300 to 345
substances with large amounts of aromatic carbon and
nm and 435 to 480 nm, at an excitation wavelength of 254 nm.
phenolic contents (i.e., humic substances), and can make up
The fluorescence index (FI) indicates microbial (FI > 1.9) orconcerns related to the effectiveness of water treatment
terrestrial source (FI < 1.4) of DOM and was calculated as theprocesses. Conventional monitoring techniques, such as
fluorescence intensity ratio of the emission wavelengths of
specific ultra-violet absorbance at 254 nm, chemical oxygen
470 nm and 520 nm, at an excitation wavelength of 370 nm
demand, and dissolved organic carbon (DOC) has been widely(Líden et al. 2017).
used in many drinking water treatment plants to assess theremoval efficiency of DOM during drinking water treatment(Li et al. 2020, Moyo et al. 2019). However, these parameters
are not capable to reflect the composition of DOM that arecomplex and have a wide range of molecular structure, size
distribution, hydrophobicity, and aromaticity (Li et al. 2020).
Therefore, the sensitive analytical approaches are required toprovide more detail on the reactivity and treatability of DOM.
Fluorescence excitation emission matrix (EEM)
spectroscopy has been used for a rapid and highly sensitive
analytical technique to determine the dynamics of DOM andcan supply the information of DOM quality in various types ofwater. Since the fluorescence EEM analysis is more sensitive
to UV-vis absorbance, it is important to examine the changes
Fig. 1: Flowchart of the treatment process in the drinking
of DOM to ensure its reactivity and treatability throughout thewater treatment plants. The circle icons represent thedrinking water treatment. This study aims to demonstrate thesampling points from each treatment unit. RW: raw
changes of DOM compositions in different physicochemical
water; CE: coagulation effluent; SE: sedimentation
processes of drinking water treatment plant using fluorescence
effluent; FI: filtration influent; FE: filtration effluent: DE:
EEM analysis for assessing the drinking water quality.
chlorination effluent; PW: purified water.

MATERIALS AND METHODSRESULTS AND DISCUSSION(1) Flowchart of drinking water treatment plant andsample collection
Water samples from a drinking water treatment plant werecollected monthly from July 2020 to December 2020 in Aichi
prefecture, Japan. Fig. 1. shows the flowchart of the drinking
water treatment plant with a design capacity of 87,500 m3, andthe samples were collected from the inlet and outlet of each
unit process. The raw water of the plant originates from open
reservoir. The plant uses polyaluminum chloride forcoagulation with dose is around 25 mg/L. 10 mg/L ofactivated carbon is added in raw water. Sodium hypochlorite
is injected in mid- and post-chlorination (0.5–1.2 mg/L).

(1) DOM composition in different treatment units(2) Sample Analysis
The collected water samples were filtered through 0.2-μm
cellulose acetate membrane filter (Advantec, Japan). Filteredsamples were analyzed for DOC using TOC-Vwet analyzer(Shimadzu, Japan). The fluorescence EEM was analyzed using
RF-5300 spectrofluorometer (Shimadzu, Japan). The peakpicking method was used to describe the obtained
fluorescence peaks. The fluorescence peaks can be classified
into protein-like peaks: Peak 1 (tyrosine-like: Ex/Em: 220–237/305–320), Peak 2 (tryptophan-like: Ex/Em 215–237/340–

Fig. 2: Changes of DOM composition in different
treatment units (n = 12).
Fig. 2 shows the changes of DOM composition throughout
the treatment units. Raw water feeding the drinking water
treatment contained high proportion of Peak 3 (56% of thetotal fluorescence intensity) that attributed to the presence of140Proceedings of International Symposium on a Recent Progress in Forest Ecology and Management 2021
UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2021: P21
terrestrially derived humic substances. The presence oftyrosine-like (Peak 1) and tryptophan-like (Peak 2 and 4)
might be attribute to some biological activity associated withmicrobes and planktons (Rodríguez-Vidal et al., 2021).
Coagulation, sedimentation, and sand filtration caused adecrease in fluorescence intensity, showing a better removal of
humic-like substances than tyrosine-like and tryptophan-like
substances. The increase and decrease of fluorescenceintensity during post-chlorination indicates the desired
primary reaction between chlorine and DOM fractions. The
higher fluorescence intensity for PW can be explained by theincrease of tryptophan-like Peak 2 and humic-like Peak 3
intensity due to the breakdown of aromatic structures in humic
molecules into smaller compounds.

the composition and structure of fluorescence DOM and theirchanges throughout the treatment process.
(3) Changes of HIX and FI in RW and PW
Although, the removal of humic-like substances washigher by coagulation, sedimentation, and sand filtration
process, the high HIX value in PW (Fig. 4) could be explainedby the humification process that occur by the remaining
humic-like substances and coincide with the decay of theresidual chlorine. High FI in the PW indicates that most of
terrestrially derived DOM was removed along the treatment. It
is known that biopolymers and humic substances has arelatively high removal during the conventional treatment(Líden et al. 2017), thus explaining the increasing FI.
Fluorescence EEM analysis can rapidly and sensitively
detect the fluorescence DOM during drinking water treatment
process. Understanding the dynamics of fluorescence DOM isnecessary to monitor and control the water quality since the
DOC is strongly correlated to its fluorescent components. The
findings can provide the important information that can beused to optimize the water treatment process to ensure thedrinking water quality.

(2) Selective removal of DOM after coagulation and postchlorination

Fig. 4: Values of HIX and FI in RW (raw water) and PW(purified water) (n = 12).

REFERENCES
Lidén, A., Keucken, A., and Persson, K.M. (2017) Uses offluorescence excitation-emissions indices in predicting
water treatment efficiency. Journal of Water Process
Engineering 16:149–257.
Moyo, W., Chaukura, N., Msagati, T.A.M., Mamba, B.B.,
Heijman, S.G.J., and Nkambule, T.T.I. (2019) The
properties and removal efficiencies of natural organic
matter fractions by South African drinking water treatment
plants. Journal of Environmental Chemical Engineering
7(3):103101.
Rodríguez-Vidal, F.J., García-Valverde, M., Ortega-Azabache,B., González-Martínez, A., and Bellido-Fernández, A.
(2021) Using excitation-emission matrix fluorescence toevaluate the performance of water treatment plants fordissolved organic matter removal. Spectrochimica Acta
Part A 249:119298.
Yang, X., Zhou, Z., Raju, M.N., Cai, X. and Meng, F. (2017)
Selective elimination of chromophoric and fluorescentdissolved organic matter in a full-scale municipal
wastewater treatment plant. Journal of Environmental
Science 57:150–161.

Fig. 3: Relationship between the removal of DOC andfluorescence intensity (FI) of DOM after (A) coagulationand (B) post-chlorination (n = 12).
Higher DOC concentration link to the high contribution of
humic substances, preferentially removed by coagulation due
to their high molecular weight (Moyo et al., 2019) (Fig. 3). In
contrary, post-chlorination process was more effective toeliminate fluorescence DOM than DOC. Chlorination could
increase DOC concentration by the hydrolysis of particulateorganic matter and degradation of chlorine-killed bacteria,
resulting in the negative removal value. As for the
fluorescence DOM, Peak 5 was highly removed bychlorination than other components through the oxidation of
unsaturated and conjugated groups (Yang et al., 2017), leading
to the differential removal among the fluorescence DOM. The
negative removal rate of fluorescence DOM suggests the plant
are less effective in removing fluorescence DOM in thechlorination process. The raw water quality might influence141

9th UGSAS-GU & 9th IC-GU12IC-GU12Country University/ Institution
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Gifu University

Kouichirou Aoki
Section HeadThe United Graduate School of Agricultural Sciencegjab00024@jim.gifu-u.ac.jp
Yoko Takahashi
LeaderThe United Graduate School of Agricultural Sciencegjab00025@jim.gifu-u.ac.jp
Shigeno Kurimoto
Secretary
The United Graduate School of Agricultural Sciencegjab00046@jim.gifu-u.ac.jp
Postal address: 1-1 Yanagido, Gifu 501-1193, JAPAN
Phone: +81-58-293-2984
Fax:+81-58-293-2992

The United Graduate School of Agricultural Science, Gifu University
1-1 Yanagido, Gifu 501-1193, Japan
Tel: +81-58-293-2984 (or, 2985)
E-mail: renno@gifu-u.ac.jp
Home Page: http://www.ugsas.gifu-u.ac.jp/eng/

Promotion Office of Gifu University Rearing Programfor Basin Water Environmental Leaders, Gifu University
1-1 Yanagido, Gifu 501-1193, Japan
Tel: +81-58-293-2085
E-mail: bwel@green.gifu-u.ac.jp
Home Page: https://www.green.gifu-u.ac.jp/BWEL/eng/index.html

ISBN978-4-909365-07-1