PROCEEDINGS OFUGSAS-GU & BWELJOINT POSTER SESSION ON AGRICULTURAL AND
BASIN WATER ENVIRONMENTAL SCIENCES

2022

ORGANIZERS:

THE UNITED GRADUATE SCHOOL OF AGRICULTURAL SCIENCEGIFU UNIVERSITY
GIFU UNIVERSITY REARING PROGRAM
FOR BASIN WATER ENVIRONMENTAL LEADERS
CO-ORGANIZER:INTERNATIONAL CONSORTIUM OF UNIVERSITIES IN SOUTH AND
SOUTHEAST ASIA FOR THE DOCTORAL EDUCATION IN AGRICULTURALSCIENCE AND BIOTECHNOLOGY (ICGU12)

Date: November 9, 2022

UGSAS-GU & BWEL Joint Poster Session on Agricultural andBasin Water Environmental Sciences 2022
Organized by The United Graduate School of Agricultural Science, Gifu University (UGSAS-GU)
Gifu University Rearing Program for Basin Water Environmental Leaders (BWEL)PROGRAM
Wednesday, November 9
Time: 15:00-17:15
Timetable
15:00-15:05
15:10-16:45

Opening remarks
Prof. Ken Hiramatsu (Dean of UGSAS-GU)
Online poster presentation
Poster Core Time
Odd numbered posters：15:10-15:50
Even numbered posters：15:50-16:30
Free discussion time：16:30-16:45

16:45-16:50

Announcement of the winners of the Best Presentation Award

17:00-17:10

Best Presentation Award ceremony

17:10-17:15

Closing remarks
Prof. Fusheng Li (Head of the Promotion Office of BWEL)

Presentations
P01: Soil phosphate and potassium solubilization and Fusarium oxysporum inhibition
potential of soil fungi isolated from melon (Cucumis melo) rhizosphere in Central Java,
Indonesia
Desti Dian Amalina (Faculty of Agriculture, Sebelas Maret University, Indonesia;
Graduate School of Natural Science and Technology, Gifu University)
P02: Ensemble learning approach to predict soybean yield using UAV-based imagery andweather data
Luthfan Nur Habibi (UGSAS-GU)
P03: Study on the relationship between intumescence injury and calcium supply among
several tomato cultivars
Natassia Clara Sita (UGSAS-GU)
P04: Microbial fuel cell on different cultivation systems to reduce N2O and CH4 emission inIndonesia
Adhia Azhar Fauzan (UGSAS-GU)
P05: Sustainable pesticide management: Personalized simulation for Indian soil
Kishalay Chakraborty (UGSAS-GU)
P06: Rice yield prediction using UAV-based imagery and deep learning
Md. Suruj Mia (UGSAS-GU)
P07: Effects of transglutaminase on pasting properties and retrogradation of rice flour
Dang Thi Kim Lien (UGSAS-GU)

P08: Dissecting the role of N and S in Arabidopsis root architecture under Al stress throughphysio-genetics approach
Md. Abir Ul Islam (UGSAS-GU)
P09: Effect of exogenous gibberellin on regreening in Valencia orange fruit
Nichapat Keawmanee (UGSAS-GU)
P10: A coupled TOPMODEL-soil erosion model (TOPEROS): Towards having a compact
decision support tool for evaluating hydrological ecosystem services
Emmanuel Okiria (UGSAS-GU)
P11: Evaluation of physical and antioxidant properties of chitosan-based films combing withcurcumin nanoemulsion
Fakfan Luangapai (UGSAS-GU)
P12: Evaluation of the impact of climate change on river temperature
Khadiza Akter Mousumi (UGSAS-GU)
P13: Fate of plastic mulch residues in agricultural soil and its influence on soil property
response to soil amendment addition
Shiamita Kusuma Dewi (UGSAS-GU; BWEL)
P14: Highly accurate estimation of swell components using wave model
Shota Iguchi (Graduate School of Natural Science and Technology, Gifu University)
P15: High-resolution analysis of atmospheric optical fluctuations for laser communication
using WRF model
Naoya Shimpuku (Graduate School of Natural Science and Technology, Gifu University)
P16: Antibiotic resistance genes and 16S rDNA in large Johkasou treating residential area
domestic wastewater
Haoning Su (Graduate School of Engineering, Gifu University; BWEL)
P17: Statistical analysis of solar radiation under future climate in Chubu region
Tomoki Kitaoka (Graduate School of Natural Science and Technology, Gifu University)
P18: Diagnosis of soil fertility by remote sensing data and GIS
Jieli Zhou (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P19: Potential hosts and transfer of As, Cr, and Pb-induced resistance genes in soil under coalmining disturbanceYajie Wang (Graduate School of Engineering, Gifu University)
P20: Can activated carbon allow antibiotic resistance genes to access into its pores
Sri Anggreini (Graduate School of Engineering, Gifu University; BWEL)
P21: Improvement of membrane filtration performance by pre-coating with powdered
activated carbon
Wenqing Li (Graduate School of Natural Science and Technology, Gifu University; BWEL)
P22: Effect of bacteria on Uroglena sp. growth in surface water
Ramayandi (Graduate School of Engineering, Gifu University)

CONTENTS
P01: Soil phosphate and potassium solubilization and Fusarium oxysporum inhibition
potential of soil fungi isolated from melon (Cucumis melo) rhizosphere in Central Java,
Indonesia
Desti Dian Amalina, Sudadi, Widyatmani Sih Dewi, Hadiwiyono, Ningrum Meliyani
Pratama, Nadia Ismah Nur Rilansari, Rosalinda Hayyin Ayu Arsita, Komariah andHaruhisa Suga・・・・・・・・・・・・・p. 2
P02: Ensemble learning approach to predict soybean yield using UAV-based imagery andweather data
Luthfan Nur Habibi, Tsutomu Matsui and Takashi S.T. Tanaka ・・・・・・・・p. 4
P03: Study on the relationship between intumescence injury and calcium supply among
several tomato cultivars
Natassia Clara Sita, Asuka Kousaka, Risa Tamoi, Chiaki Ozawa, Yoshikazu Kiriiwa
and Katsumi Suzuki
・・・・・・・・・・・・・p. 6
P04: Microbial fuel cell on different cultivation systems to reduce N2O and CH4 emission inIndonesia
Adhia Azhar Fauzan, Ken Hiramatsu and Takeon Onishi ・・・・・・・・・・p. 8
P05: Sustainable pesticide management: Personalized simulation for Indian soil
Kishalay Chakraborty, Atsuhiro Shimada and Akio Ebihara ・・・・・・・・・p. 10
P06: Rice yield prediction using UAV-based imagery and deep learning
Md Suruj Mia and Takashi S. T. Tanaka
・・・・・・・・・・・・・p. 12
P07: Effects of transglutaminase on pasting properties and retrogradation of rice flour
Dang Thi Kim Lien and Takahisha Nishizu
・・・・・・・・・・・・・p. 14
P08: Dissecting the role of N and S in Arabidopsis root architecture under Al stress throughphysio-genetics approach
Md. Abir Ul Islam and Hiroyuki Koyama
・・・・・・・・・・・・・p. 16
P09: Effect of exogenous gibberellin on regreening in Valencia orange fruit
Nichapat Keawmanee, Gang Ma, Lancui Zhang, Masaki Yahata and Masaya Kato
・・・・・・・・・・・・・p. 18
P10: A Coupled TOPMODEL-Soil Erosion Model (TOPEROS): TowardsHaving a Compact
Decision Support Tool for Evaluating Hydrological Ecosystem Services
Emmanuel Okiria and Keigo Noda
・・・・・・・・・・・・・UnpublishedP11: Evaluation of physical and antioxidant properties of chitosan-based films combing withcurcumin nanoemulsion
Fakfan Luangapai, Methavee Peanparkdee and Satoshi Iwamoto ・・・・・・・p. 20
P12: Evaluation of the impact of climate change on river temperature
Khadiza Akter Mousumi, Takeo Onishi, Ken Hiramatsu and Toshifumi Imaizumi
・・・・・・・・・・・・・p. 22ⅰP13: Fate of plastic mulch residues in agricultural soil and its influence on soil properties
response to soil amendment addition
Shiamita Kusuma Dewi and Yongfen Wei
・・・・・・・・・・・・・p. 24
P14: Highly accurate estimation of swell components using ocean wave model
Shota Iguchi, Jun Yoshino and Tomonao Kobayashi ・・・・・・・・・・・・p. 26
P15: High-resolution analysis of atmospheric optical fluctuations for laser communication
using WRF model
Naoya Shimpuku, Jun Yoshino, Ichiro Tamagawa and Tomonao Kobayashi ・・p. 28
P16: Antibiotic resistance genes and 16S rDNA in large Johkasou treating residential area
domestic wastewater
Haoning Su, Shinya Okumura, Yasushi Ishiguro and Fusheng Li ・・・・・・・p. 30
P17: Statistical analysis of solar radiation under future climate in Chubu region
Tomoki Kitaoka, Jun Yoshino and Tomonao Kobayashi ・・・・・・・・・・・p. 32
P18: Diagnosis of soil fertility by remote sensing data and GIS
Jieli Zhou and Yongfen Wei
・・・・・・・・・・・・・p. 34
P19: Potential hosts and transfer of As, Cr, and Pb-induced resistance genes in soil under coalmining disturbanceYajie Wang and Fusheng Li・・・・・・・・・・・・・p. 36
P20: Can activated carbon allow antibiotic resistance genes to access into its pores
Sri Anggreini and Fusheng Li・・・・・・・・・・・・・p. 38
P21: Improvement of membrane filtration performance by pre-coating with powdered
activated carbon
Wenqing Li and Fusheng Li・・・・・・・・・・・・・p. 40
P22: Effect of bacteria on Uroglena sp. growth in surface water
Ramayandi, Sarkar Kanika, Nurul Syuhada Abdul Latif and Fusheng Li ・・・p. 42ⅱ

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P01

Soil phosphate and potassium solubilization and Fusarium oxysporum
inhibition potential of soil fungi isolated from melon (Cucumis melo)rhizosphere in Central Java, Indonesia
Desti Dian Amalina1, Sudadi1, Widyatmani Sih Dewi1, Hadiwiyono2, Ningrum Meliyani Pratama1, Nadia Ismah Nur
Rilansari1, Rosalinda Hayyin Ayu Arsita1, Komariah1 and Haruhisa Suga3
1. Department of Soil Science, Faculty of Agriculture, Sebelas Maret University, Jalan Ir. Sutami 36A Surakarta,
Indonesia 57126
2. Department of Agrotechnology, Faculty of Agriculture, Sebelas Maret University, Jalan Ir. Sutami 36A Surakarta,
Indonesia 57126
3. Institute for Glyco-core Research (iGCORE), Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanINTRODUCTIONMETHODS

Melon (Cucumis melo) is one of important fruit crops inIndonesia. Melon production was 92,446 tons (15,724 ha) in2017 and 118,708 tons (6,773 ha) in 2018, that makes Indonesia
being the one of the largest melons producing country inSoutheast Asia (Badami et al. 2020). However, that number
only meets about 40% of domestic needs (Annisa andGustia2017). Farmers usually use chemical fertilizers toincrease melon yields, which in long term and continuous use
will lead to many disadvantages for soil and environment. Plant
growth will be disrupted, and melon yield will decrease.
Therefore, we need to change the pattern of cultivation that
leads to natural resources conservation and reduce soil quality
degradation by utilizing the soil beneficial microbes.
Melon needs a huge amount of nutrition for its growth.
Phosphorus (P) and potassium (K) are the essential nutrientsand required for all life stages of plant. P is being a structural
constituent of nucleic acid, as well as being involved inmetabolic energy transfer through Adenosine triphosphate(Olaniyi 2008) while K is involved in photosynthesis,
photophosphorylation enzyme activation, turgor maintenanceand stress tolerance. Adequate K nutrition has also been
associated with increased yields, fruit size and many things that
determined the fruit quality (Lester et al. 2010). These twonutrients are very important but their availability in soil is oftenvery low so they need the help of soil microbes to dissolve
them.
Melon plants are very susceptible to wilt disease caused by
Fusarium oxysporum. This disease will reduce the yield andquality of the fruit and even fail to harvest (Oumouloud etal.2013). The fungus F. oxysporum is a soil-borne pathogen thatcan survive for more than 10 years in the form ofchlamydiospores in the soil (Rahman et al. 2021). This makes
the disease quite difficult to control. Farmers usually usedchemical compounds to control this disease However, many
negative impacts such as environmental pollution, soil
poisoning, and soil degradation came from that (Zhao et al.
2011)
Biological control with soil microbes can be the alternative
treatment to control the Fusarium wilt disease. Many
antagonistic strains have been proved to be effective biocontrol
agents in controlled laboratory or greenhouse conditions (Zhaoet al. 2011). One of the microorganisms that used for the
biological control is a group of rhizosphere fungi, such as
species of the genera Trichoderma, Penicillium, Fusarium, andPhoma, which have ability to stimulate the plant immune
response upon enemy attack and growth promotion in crop
(Jogaiah et al. 2013). The aims of this study are to investigate
the rhizosphere fungi that have a beneficial effect for phosphate
and potassium solubilization and antagonistic potential to F.
oxysporum in melon crop.

This research was conducted in Laboratory of Soil Biology
and Biotechnology, Faculty of Agriculture, Sebelas Maret
University, Indonesia from April-August 2022.
Soil Sampling—Soil samples were taken by random
sampling with the survey method. Soil sampling was conducted
on melon plantation owned by melon farmers in three locations(Table 1). Soil samples were taken randomly at 4 points in eachcardinal point and replicated 3 times with a depth of 10—15 cm
in the root zone.
Table 1: Description of the soil sampling location
Location

Coordinates

Wonogiri
Regency
Sragen Regency

7o57'9.37” S
110o52'33.01” E
7o28'56,12” S
110o57'45,66” E
7o37'50.82” S
110o56'54.52” E

Karanganyar
Regency

Altitude(m asl)168

Soil Type
Vertisol

130

Vertisol

195

Alfisol

The isolation of soil fungi—The isolation of soil fungi wasconducted using the dilution method on PDA media. Fivegrams of soil was diluted in 45 ml of physiological solution andshaken until homogenous. The suspension then diluted by 10-5.
The suspension in the 10-3 and 10-5 dilutions was taken as much
as 0.1 ml using a sterile pipette and then spread into a Petri-dishthat already contained PDA media and leveled using a dry glass.
The isolation was replicated 2 times for each soil sample. The
Petri-dish was packed with wrapping paper and incubated atroom temperature for 7 days.
Ability to solubilize P and K—Identification of the ability tosolubilize P and K was conducted by placing the purified fungi
isolates into the specific media, namely Pikovskaya for P and
Alexandrof for K. The fungi that have the ability to solubilize
P and/or K are characterized by the presence of a clear
zone/halo zone that appear in the media around the growingcolonies.
Antagonistic potential—The antagonistic potential analysis
was carried out by biculture antagonistic test by placing thepurified fungi isolates and the F. oxysporum isolates side byside on PDA. The F. oxysporum isolates obtained from theLaboratory of Plant Pests and Disease, Faculty of Agriculture,
Gadjah Mada University, Indonesia. Observation was
conducted 7x24 hours after the isolate was incubated byobserving the growth direction of the fungi colony and F.
oxysporum.

RESULTS AND DISCUSSION
The results of this research shown that three isolates have thepotency to solubilize phosphate, five isolates have the ability tosolubilize potassium, and six isolate have the ability to inhibit2Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P01

the growth of F. oxysporum (Table 2). This means that the fungi
from the melon rhizosphere have the potency to be developed
into biofertilizer to help provide the soil nutrients and biologicalagents against the Fusarium wilt disease.
Table 2: Ability of the fungal isolates to solubilize nutrient
and to inhibit the growth of Fusarium oxysporum
IsolateOriginAbility tosolubilize
nutrient
P
K

KRA 1A

Karanganyar-+

25.00%KRA 1C

Karanganyar--

69.17%KRA 3A

Karanganyar

+

+

8.33%

WNG 2B

Wonogiri-+0%

SRG 1A

Sragen++20.83%SRG 1C

Sragen-+25.00%SRG 1B

Sragen--55.83%SRG 3B

Sragen--0%

SRG 3A

Sragen

+

-

0%

(d)

Percentage toinhibitF. oxysporum

ACKNOWLEDGMENTS
The authors would like to thank Sebelas Maret University,
which funded part of this research through the Mandatory
Research scheme for non-APBN funds for the fiscal year 2022.

REFERENCES
Annisa, P. and Gustia, H. (2017) Response of Growth andProduction of Melon Plants to Liquid Organic Fertilizer
Tithonia Diversifolia. Proceeding Nasional Seminar 2017,
Faculty of Agriculture UMJ, 104–114.
Badami, K., Daryono, B. S., Amzeri, A., and Khoiri, S. (2020).
Combining Ability and Heterotic Studies on Hybrid Melon(Cucumis melo L.) Populations for Fruit Quality Traits.
SABRAO Journal of Breeding and Genetics 52(4):402-417.
Jogaiah, S., Abdelrahman, M., Tran, L. S. P., and Shin-Ichi, I.
(2013). Characterization of Rhizosphere Fungi that Mediate
Resistance in Tomato Against Bacterial Wilt Disease.
Journal of Experimental Botany, 64(12):3829-3842.
Lester, G. E., Jifon, J. L., and Makus, D. J. (2010). Impact ofPotassium Nutrition on Postharvest Fruit Quality: Melon(Cucumis melo L) Case Study. Plant and Soil 335(1):117–
131.
Meike Jaya, R., and Joko, T. (2020). Plant Growth Promoting
and Antagonistic Potential of Endophytic Bacteria Isolated
from Melon in Indonesia. Plant Pathology Journal 19(3):200–210.
Olaniyi, J. O. (2008). Growth and Seed Yield Response of
Egusi Melon to Nitrogen and Phosphorus Fertilizers
Application. Journal of Sustainable Agriculture 2(3):255–
260.
Oumouloud, A., El-Otmani, M., Chikh-Rouhou, H., Claver, A.
G., Torres, R. G., Perl-Treves, R., and Álvarez, J. M.
(2013). Breeding Melon for Resistance to Fusarium Wilt:
Recent Developments. Euphytica 192(2):155–169.
Rahman, M. Z., Ahmad, K., Kutawa, A. B., Siddiqui, Y., Saad,N., Geok Hun, T., Hata, E. M., and Hossain, M. I. (2021).
Biology, Diversity, Detection and Management ofFusarium oxysporum f. sp. niveum Causing Vascular Wilt
Disease of Watermelon (Citrullus lanatus): A Review.
Agronomy 11(1310):1-24.
Zhao, Q., Dong, C., Yang, X., Mei, X., Ran, W., Shen, Q., andXu, Y. (2011). Biocontrol of Fusarium Wilt Disease for
Cucumis melo Melon Using Bio-organic Fertilizer. Applied
Soil Ecology 47: 67–75.

(b)Fig. 1: The halo zones appeared on Pikovskaya media
indicated that the isolates SRG 3A (a) and KRA 3A (b) havethe ability to solubilize phosphate.

(a)

(b)

Fig. 2: The halo zones appeared on Alexandrof media
indicated that the isolates SRG 1A (a) and WNG 2B (b) havethe ability to solubilize potassium.
Antagonistic potential—The antagonistic activity ofrhizosphere fungi against F. oxysporum that was grown on the
PDA showed that six of the nine isolates produced inhibitory
zones, this means that the six isolates could inhibit the growthand development of the F. oxsysporum (Fig. 3). The formationof inhibitory zones indicated the existence of antimicrobial or
antibiotic mechanisms of rhizosphere fungal isolates against F.
oxysporum (Meike Jaya and Joko 2020). The percentage ofability to inhibit the growth of F. oxysporum was shown in
Table 2. Isolate that has the highest ability to inhibit F.
oxysporum is KRA 1C (69.17%).

(a)

(b)

(f)

(g)
(h)
(i)
Fig. 3: In vitro antagonistic activity of rhizosphere fungi by
the biculture method against F. oxysporum (a) KRA 1A (b)KRA 1C (c) KRA 3A (d) WNG 2B (e) SRG 1A (f) SRG 1C(g) SRG 1B (h) SRG 3B (i) SRG 3A

Ability to solubilize P and K—Formation of the clear zone
around the colony on the Pikovskaya (Fig. 1) and Alexandrof(Fig. 2) medium indicated the ability of rhizosphere fungi tosolubilize phosphate and potassium.

(a)

(e)

(c)

3

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P02

Ensemble learning approach to predict soybean yield using UAV-based
imagery and weather data
Luthfan Nur Habibi1, Tsutomu Matsui2 and Takashi S.T. Tanaka2, 3
1. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
3. Artificial Intelligence Advanced Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
3. Recursive Feature Elimination (RFE). RFE is a wrapperbased feature selection method which selects a subset of
INTRODUCTION
variables according to the performance criteria of theEstimating soybean yield is key to improving farm
regression learner (Corrales et al., 2022). Importance values
management practices through precision agriculture. Remotefrom random forest regression were selected as the criteria.
sensing information such as spectral reflectance and vegetation
Multicollinearity tests using the Variance Inflation Factor (VIF)
indices (VIs) derived from Unmanned Aerial Vehicle (UAV)
values were performed after the selection procedures.
imagery has been proven to give precise information on grain
yield. However, spectral reflectance and VIs often have a highData analysis and statistics – The model accuracy waslinear correlation with each other. Consequently, appropriatecompared among five models, including three base learners
feature selection is required to construct the robust prediction(e.g., Random Forest, XGBoost, and Lasso Regression),model. Moreover, the inclusion of weather data into yieldstacked ensemble, and averaging-based ensemble. Lasso
prediction model may be valuable in improving the prediction
regression was used as a super learner in staked ensemble
accuracy. Therefore, this study aimed to explore the yieldmodel. Accuracy assessment of the established models wasprediction model’s performance using VIs and weather data.
conducted using three different methods of nested crossMany researchers reported that machine learning
validation, including:algorithms have been a more robust and efficient approach than
1. Leave-one-out of field (LOO-F)
conventional statistical analysis in predicting crop yield.
2. Clustering-based spatial cross-validation (CS-CV)
Ensemble learning technique combines several regression
3. Random cross-validation (R-CV)
learners to address overfitting problems caused by single
The best model was evaluated based on the coefficients ofpredictive models (Yoosefzadeh-Najafabadi et al., 2021). Thus,
determination (R2) and root mean square error (RMSE).
this study further aimed to evaluate the effectiveness of
RESULTS AND DISCUSSION
ensemble learning on the model accuracy to predict soybean
yield.
Feature selection – The feature selection aimed to reduce
the explanatory variables that could be redundant to the
MATERIALS AND METHODS
prediction model, which reduce the model’s capability. The
Field data collection – Soybean yields were surveyed withoriginal feature sets contain many collinear variables from the462 samples taken from seven soybean fields between 2018 andVIs and weather data. The results from the three feature
2021 in Gifu Prefecture, Japan, during the maturity stage. All
selection algorithms resulted in different sets of variables, assurveyed soybean samples have the same cultivar named
presented in Table 1 and Figure 1. MRMR chose the largest set‘Fukuyutaka’. Spectral data from UAV-based imagery wereof features with 11 features. In contrast, SBS and RFE chosealso taken from each field using two camera platforms, Parrot
only 7 and 4 features, respectively. All feature selection
Sequoia+ (2018–2019) and Altum MicaSense (2021), during
algorithms could remove perfectly collinear variables thattwo growing stages (R2 and R5). Moreover, weather data werepreviously existed in the original data set. However, all model
collected from the Agro-Meteorological Grid Square Data,
subsets still select collinear features reflected from the VIF
NARO (https://amu.rd.naro.go.jp/).
values that exceeds the standard value (below 10).
Analysis of UAV-based imagery – Vegetation indices
Table 1. Feature selection and multicollinearity results.
calculated from UAV-based imagery spectral data wereselected
based
on
the
Index
Database
Selected
Feature selection method
VIF range(https://www.indexdatabase.de/). In this research, we only
variables
Maximum Relevance Minimum
consider three bands (Green, Red, and Near-InfraRed) from the11
5.21 – 24.57
Redundancy (MRMR)
UAV imagery. A total of 64 features were extracted from UAV7
1.84 – 43476
based imagery data, including six raw bands and 58 VIs from
Sequential Backward Search (SBS)
two growing stages.
43.02 – 392
Recursive Feature Elimination (RFE)
Feature selection techniques – A combination of UAVbased imagery and weather data features creates a highTwo features from UAV-based imagery data, CIgreen (R5) anddimensional data set (106 features). Feature selection is a
NORMgreen (R5) were selected by all methods, indicating theprocess of selecting a subset of variables that contribute to theimportance of these features to the yield. Herrero-Huerta et al.,response. In this research, three types of feature selection were(2020) also found that CIgreen in the R5 growth stage was an
performed:
important covariate to predict soybean yield as it represents the
1. Maximum Relevance Minimum Redundancy (MRMR).
canopy chlorophyll content. Regarding to weather data, only
MRMR is a filtering-based feature selection which selects
three features out of 48 were selected, that is median value ofthe relevant features while controlling for the redundancy
solar radiation (at vegetative stage) and minimum value of
within the selected features (Zhao et al., 2019).
mean temperature (at reproductive stage) selected by MRMR,
2. Sequential Backward Search (SBS). This algorithm
and maximum value of mean temperature (at vegetative stage)
automatically selects a subset of features from all featuresfrom SBS.
and eliminates the one which minimizes performance(Shafiee et al., 2021)

4

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P02

Simple learners such as Lasso regression could perform better
if the objective is map extrapolation, as exhibited in the LOOF model approach. However, if the aim is to interpolate yieldmaps, a complex model from machine learning and an
ensemble model might be the appropriate method.
In conclusion, feature selection approaches can be used toreduce redundancy and highlight important features from VIs
and weather data to predict soybean yield. Moreover, a simple
statistical method might still be meaningful for predicting yieldaside from complex statistical algorithms from machinelearning.

Fig. 1: Venus diagram of variables selected by feature
selection methods.
Yield prediction model – Table 2 shows the prediction
accuracy of yield models from five regression learners withthree validation approaches. Overall, the yield was predictedwith RMSE values ranging from 53.54 to 274.39 kg/ha. The
best model was SBS-random forest R-CV model. Figure 2
shows an example of a predicted yield map.
Feature selection affected the accuracy of yield prediction
models. The model using variables selected by RFE performed
the best in the LOO-F approach. However, MRMR and SBS
performed better than RFE models in CS-CV and R-CV
approaches. The availability of weather variables in SBS and
MRMR models might improve the overall accuracy of models
because any weather data was not selected in RFE.
On average, models validated using the R-CV approach gave
the best results. However, in recent studies, many researchers
contend to use spatial CV method as it also accounts for the
spatial autocorrelation of response variables, which could bias
prediction maps, even though this idea is still controversial(Wadoux et al., 2021). On the other hand, validating the modelusing truly independent test data set, as we used in LOO-F,
might be the most reasonable way to validate the prediction
model in real practice.
Our main interest in this research was to explore theeffectiveness of ensemble learning technique. According to theresults, single learners outperformed the ensemble learners inalmost of all cases. This result contradicted with common
findings in the literature (Yoosefzadeh-Najafabadi et al., 2021).

Fig. 2: Example of predicted yield map from MRMRrandom forest model with CS-CV approach.

REFERENCES
Corrales, D.C., Schoving, C., Raynal, H., Debaeke, P., Journet, E.P.,
Constantin, J., (2022). A surrogate model based on feature selection
techniques and regression learners to improve soybean yieldprediction in southern France. Comput. Electron. Agric. 192.
Herrero-Huerta, M., Rodriguez-Gonzalvez, P., Rainey, K.M., (2020).
Yield prediction by machine learning from UAS-based mulit-sensor
data fusion in soybean. Plant Methods 16, 1–16.
Shafiee, S., Lied, L.M., Burud, I., Dieseth, J.A., Alsheikh, M., Lillemo,M., (2021). Sequential forward selection and support vector
regression in comparison to LASSO regression for spring wheat yieldprediction based on UAV imagery. Comput. Electron. Agric. 183,
106036.
Wadoux, A.M.J.C., Heuvelink, G.B.M., de Bruin, S., Brus, D.J.,(2021). Spatial cross-validation is not the right way to evaluate map
accuracy. Ecol. Modell. 457.
Yoosefzadeh-Najafabadi, M., Earl, H.J., Tulpan, D., Sulik, J.,
Eskandari, M., (2021). Application of Machine Learning Algorithms
in Plant Breeding: Predicting Yield From Hyperspectral Reflectancein Soybean. Front. Plant Sci. 11, 1–14.
Zhao, Z., Anand, R., Wang, M., (2019). Maximum relevance andminimum redundancy feature selection methods for a marketingmachine learning platform. Proc. - 2019 IEEE Int. Conf. Data Sci.
Adv. Anal. DSAA 2019 442–452.

Table 2. Results of regression learners trained with subset of variables selected by feature selection methods anddifferent validation approaches.
Leave-one-out of field
R2
RMSE
Maximum Relevance Minimum Redundancy (MRMR)
Random Forest
0.22
68.67
0.15
72.88
XGBoost
Lasso
0.36
61.24
Ensemble stacking-48.46274.39Ensemble averaging (equal weight)
0.29
65.62
Sequential Backward Search (SBS)
Random Forest
0.24
64.11
XGBoost
0.27
66.62
Lasso
0.36
60.01
Ensemble stacking
0.17
68.21
Ensemble averaging (equal weight)
0.32
61.31
Recursive Feature Elimination (RFE)
Random Forest
0.33
62.45
XGBoost
0.23
68.31
Lasso
0.40
58.38
Ensemble stacking
0.32
63.24
Ensemble averaging (equal weight)
0.35
61.03
Model subset

The best models for each case are highlighted with bold letters

5

Clustering spatial-CV
R2
RMSE

Random CV
R2
RMSE

0.66
0.65
0.65
0.64
0.60

54.21
54.62
54.43
55.13
58.94

0.68
0.66
0.66
0.67
0.67

53.58
54.96
54.67
54.23
54.29

0.65
0.66
0.64
0.65
0.60

54.61
54.35
55.26
54.44
59.26

0.68
0.64
0.66
0.67
0.67

53.54
56.38
54.77
54.21
54.93

0.62
0.59
0.63
0.63
0.57

57.28
59.07
55.91
55.73
61.52

0.64
0.56
0.65
0.66
0.65

55.98
62.26
55.42
55.09
56.60

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P03

Study on the relationship between intumescence injury and calcium supply
among several tomato cultivars
Natassia Clara Sita1,2, Asuka Kousaka2, Risa Tamoi2, Chiaki Ozawa3, Yoshikazu Kiriiwa2,3 and Katsumi Suzuki2,3
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
Russet Burbank potato suggested that intumescence injury is
related with the Ca concentration in the leaf (Schabow andPalta, 2019). In this study, it was clarified that Ca concentrationin the shoot is related to the intumescence injury on tomato
leaves.
The cultivar variations tomato shoot Ca content andsusceptibility to intumescence injury under various Caconcentration supplies were described in Fig 1. Based on their
sensitivity to intumescence injury, tomatoes were divided into
three groups: tolerant (‘CF Momotaro York’ and ‘Momotaro
Select’), moderate (‘Momotaro Natsumi’ and ‘Misora 64’); andsensitive (‘CF Rinka 409’ and ‘Reika’). When subjected to low
Ca concentrations, the sensitive cultivars suffered from highdegree of intumescence injury while the moderate cultivars
developed less. However, despite the low Ca level in tomato
shoots, tolerant cultivars only had minimum degree of
intumescence. Along with the increasing concentration of Ca
supply, the degree of intumescence injury decreased in all
cultivars. This experiment showed that Ca concentration
treatment significantly affected the Ca content in the shoot andthe degree intumescence injury. According to a previous study
about the use of supplemental addition of Ca to reduce
intumescence injury in Russet Burbank potatoes (Schabow andPalta, 2019), application of a high Ca concentration supply canlower the incidence of intumescence injury in tomatoes.

INTRODUCTION
Intumescence injury is a physiological disorder characterizedby irregular outgrowth and elongated epidermal or parenchymal
cells on the adaxial and/or abaxial surface of leaves. In
tomatoes, this phenomenon occurs when ultraviolet (UV) lightis lacking and the humidity is high, and the severity increases
over time (Suzuki et al., 2020). Calcium (Ca) is a macronutrientthat serves as an intracellular messenger, as well as providing
structural support for the cell wall and membranes (Marschner,
1995; White and Broadley, 2003). According to previous
studies, there were variances in the occurrence of intumescenceinjury across various tomato cultivars (Ozawa et al. 2018), andthis incidence was discovered to be a Ca-related injury(Schabow and Palta, 2019). Ca foliar spray was found to repress
the incidence of blossom-end rot in tomatoes (Mazumder et al.,2021), but its effect on intumescence injury was still unknown.
This study aimed to investigate the relationship between Ca
supply and intumescence injury among various tomato cultivars
and to mitigate intumescence injury in tomatoes by Ca foliar
spray.

MATERIALS AND METHODS
Experiment 1: Six different cultivars of tomato (Solanum
lycopersicum) were used in this study (Fig. 1). After 7 days ofsowing, the seedlings were transferred into artificial climate
chamber and were supplied from the bottom with nutrient
solution (Ohtsuka Ekihi SA formula ½). The treatment wasstarted after one week of cultivation, with three different Ca
concentration, which were 0.5 me (low Ca), 4.5 me (control)and 14.5 me (high Ca). Tomatoes were conditioned in highhumidity and lack of UV irradiance, and intumescence injury
was observed after 3 days. Experiment 2: The Ca foliar sprayexperiment was performed using cultivar ‘CF Rinka 409’. In
this experiment, tomatoes were conditioned under low UV andhigh humidity conditions for 7 days. The Ca foliar spraysolution was sprinkled into adaxial and abaxial surface oftomato leaves with five different treatments: no spray (control),DW (distilled water spray), Ca-1x (spray once), Ca-2x (sprayonce every three days) and Ca-7x (daily spray for a week). To
investigate the intumescence injury, tomato leaves were stainedby Toluidine blue O, scanned, and the degree of intumescence
was calculated as the percentage of stained leaf area using
ImageJ software. Ca concentration in tomato shoots wasquantified by using Inductively Coupled Plasma (ICP)
Spectrometer in both experiments.

Fig. 1: Cultivar differences in susceptibility to intumescenceinjury and Ca content of tomato shoots at different Ca
concentration supplies. (Black: tolerant cultivars; grey:
moderate cultivars; white: sensitive cultivars).
The effect of Ca-spray on the intumescence injury in tomato
shoots of cultivar ‘CF Rinka 409’ was also examined (Fig 2).
The intumescence injury was found highest when no foliar Ca
was applied, and daily application of Ca spray will significantly
reduce this incidence. According to Kumazaki et al. (2010) in
Ueno et al. (2018), spraying leaves with a potassium (K)solution helps prevent leaf tips from dying from a K deficit.
With a regular supply of foliar Ca, intumescence injury in thepresent study was greatly reduced. Therefore, it is

RESULTS AND DISCUSSION
Intumescence injury is a physiological disorder that
commonly occurs in the Solanaceae family of plants, including
tomatoes (Lang and Tibbitts, 1983), potatoes (Douglas, 1907),
and sweet potatoes (Craver et al., 2014). Previous study in6Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P03

Douglas, G. E. (1907) The Formation of Intumescences on
Potato Plants. Botanical Gazette. 43: 233–250.
Lang, S. P. and Tibbitts T. W. (1983) Factors Controlling
Intumescence Development on Tomato Plants. Journal of theAmerican Society for Horticultural Science. 108: 93-98.
Kumazaki, A., Kawakami, M., Asano, Y., and Suzuki T. (2010)
Effect of Fertilization Method to Suppress Burned Leaf Tipof Tomato. Hort. Res. (Japan) 9 (Suppl. 1): 135 (In Japanese).
Ozawa, C., Tonooka, C., Kiriiwa, Y., and Suzuki K. (2018)
Cultivar Difference in Occurrence of Edema in Tomato.
Hort. Res. (Japan) 17: 436. (in Japanese).
Marcshner, H. (1995) Mineral Nutrition of Higher Plants, 2ndedn. Academic Press.
Mazumder, M. N. N., Misran, A., Ding, P., Wahab, P. E. M.,
and Mohamad. A. (2021) Preharvest Foliar Spray of Calcium
Chloride on Growth, Yield, Quality, and Shelf Life
Extension of Different Lowland Tomato Varieties in
Malaysia.
Horticulturae.
7,466.
https://doi.org/10.3390/horticulturae7110466
Schabow, J. E. and Palta J. P. (2019) Intumescence Injury in
The Leaves of Russet Burbank Potato Plants is Mitigated by
Calcium Nutrition. American Journal of Potato Research. 96:
6–12.
Suzuki, K., Ozawa, C., and Kiriiwa Y. (2020) MorphologicalStudy on the Incidence of Intumescence Injury in Tomato
Plant Leaves. Hort. J. 89: 567-574.
White, P. J. and Broadley M. R. (2003) Calcium in Plants.
Annals of Botany. 92: 487-511.
Ueno, H., Maeda, T., Katsuyama, N., Katou, Y., Matsuo, S.,
Yano, K., Ando, A., Nagasuga, K., Yamada, M. and.
Imanishi S. (2018) Cation Measurements and Gene
Expression Analysis Suggest Tomato Leaf Marginal
Necrosis is Caused by a Jasmonate Signal Induced by K+
Starvation in The Tip Region of Leaflets. The Horticulture
Journal. 87: 206-213.

recommended that the frequency of foliar spraying be
increased.

Fig 2: Correlation between Ca content and intumescenceinjury in tomato leaves with foliar Ca spray applied. (Ctrl:
no foliar spray; DW: distilled water foliar spray; Ca-1x: Ca
foliar spray once in a week; Ca-2x: Ca foliar spray twice ina week; Ca-7x: daily Ca foliar spray for one week).
Based on the experiment, it can be concluded that different
cultivars of tomato developed different degree of intumescenceinjury susceptibility, and Ca concentration supplies affected
their severity The application of Ca foliar spray was also found
to reduce the intumescence injury incidence in tomatoes.

REFERENCES
Craver, J. K., Miller, C. T., Williams, K. A., and Boyle D. L.
(2014) Characterization And Comparison of Lesions on
Ornamental Sweet Potato ‘Blackie’, Tomato ‘Maxi Fort’,
Interspecific Geranium ‘Caliente Coral’, and Bat-Faced
Cipher ‘Tiny Mice’. J. Amer. Soc. Hort. Sci. 139: 603–615.

7

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P04

Microbial fuel cell on different cultivation systems to reduce N2O and CH4
emission in Indonesia
Adhia Azhar Fauzan1, Ken Hiramatsu2 and Takeon Onishi2
1. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan

MATERIALS AND METHODSINTRODUCTION

Two different treatments will be analyzed in this study:
without MFC (control) and with MFC. Using the conventionaland cultivating systems from Indonesia, "Jajar Legowo". Using
3 m x 2 m per plot. Spacing 25 cm x 25 cm for conventional
system and (25 cm x 50 cm) x 12.5 cm for Jajar Legowo system.
Carbon graphite-felt melt (S-221; Osaka Gas Chemical Co.,
Ltd.) will use as an anode with a total surface area of 100 cm2(length 10 cm x length 10 cm x thickness 1 cm). As a cathode,a graphite rod (C-072591; Nilaco Corporation) with a length of
10 cm and diameter of 0.5 cm upper the water.
The Eh meter (PRN-41; Fujiwara Factory Co., Ltd.) will be
use to measure the redox potential. Applying a closed chamber
method, gas was sampled once per week. N2O and CH4
concentrations will be measured by gas chromatography (GC2014; Shimadzu CO., Ltd.). Gas samples will be taken at 15minute intervals between 7 and 8 am.
To determine the mass balance of nitrogen in this study, we
will analyze nitrogen in soil and water.

In Rice paddy fields there is a reduction of NO3 (Nitrate) toform N2O (Nitrous oxide) and a reduction of CO2 (Carbon
dioxide) to form CH4 (Methane). N2O and CH4 are greenhousegasses playing a key role in global warming with a global
warming potential (GWP) that is 265 and 28 times higher thanthat of CO2 per unit mass for 100 years scale (Yang et al., 2019).
The availability of oxygen in the soil of rice paddy fields is very
important for microbes because oxidants are used as electron
acceptors from microbial activity. Anaerobic conditions in rice
paddy fields cause low oxygen availability. The availability ofoxygen in the soil is influenced by the concentration of soiloxidants such as NO3, SO4, Fe2O3, MnO4, and CO2. The low
oxygen content in the soil causes redox to occur. Although not
as frequently observed as pH, redox also highly influenceschemical and biological processes in soil.
A microbial Fuel Cell (MFC) is a device using metabolic
catalysis of microorganisms to generate electric energy byutilizing organic matter in the environment (Logan et al., 2006).
The electrons and protons were produced by the substrate thatwas used by the electricians at the anode. An external circuit
and internal texture make the electrons and protons travel
through them to the cathode, where eventually oxygen reacts tothem, then produces electricity (Zhang et al., 2021). In short,
MFC can function as electron acceptors as a result of microbialactivity. The presence of MFC as an electron acceptor will
prevent microbes from using the oxidants in the soil.
Indonesia is the world’s third-largest rice producer and oneof the world’s biggest rice consumers. Indonesia's climaticconditions allow rice to be cultivated throughout the year. The
Rice area in Indonesia in 2010 is 13.2 million ha, representing
24% of the total agricultural area in Indonesia. Rice yield
increased slightly from 4.3 t/ha in 1995 to 5 t/ha in 2010. 84%
of the total rice field in Indonesia in 2012 were irrigated andflooded(International Rice Research Institute., 2013). The total
area and irrigation treatment in rice fields in Indonesia have thepotential for large CH4 and N2O emissions. Understanding andquantifying the N2O and CH4 budget is important for assessing
realistic pathways to mitigate climate change. Indonesia hasdeveloped a cultivation system to increase rice production. It iscalled “Jajar Legowo”. The idea is to create an open row after
several regular rows. For compensating the open row, plantspacing within each of the edge rows is half of the normal plantspacing. Adjusting plant spacing is one of the importantagronomic practices for increasing crop yield and reducing
plant competition with weeds and plant-to-plant competition
for available water and nutrient and light.
The occurrence of an increase in redox in flooded soil with
MFC proves that MFC has succeeded in becoming an electron
acceptor in anaerobic conditions of rice paddy soil (Ranatunga
et al., 2018). The absence of NO3 and CO2 as electron acceptors
makes these oxidants available and can be utilized by plants.
The utilization of NO3 and CO2 as elements for plant growth isestimated to suppress N2O and CH4 emission production.
Furthermore, it is hoped that the emission of N2O and CH4 will
decrease.

Fig.

1: conventional cultivation systems. Source:
https://www.goodnewsfromindonesia.id/2021/05/07/miliki-jutaanhektare-lahan-inilah-10-provinsi-dengan-lahan-sawah-terluas-diindonesiaFig.

2: Jajar Legowo cultivation systems. Source:
https://tabloidsinartani.com/detail/indeks/agri-penyuluhan/15688Kenali-Kembali-Sistem-Jajar-Legowo

8Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P04

J. (2019). Biochar improved rice yield and mitigated CH4
and N2O emissions from paddy field under controlled
irrigation in the Taihu Lake Region of China. Atmospheric
Environment, 200, 69–77.
https://doi.org/10.1016/j.atmosenv.2018.12.003
Zhang, K., Wu, X., Wang, W., Chen, J., Chen, J., & Luo, H.
(2021). Roles of external circuit and rhizosphere location inCH4 emission control in sequencing batch flow constructedwetland-microbial fuel cell. Journal of Environmental
ChemicalEngineering,9(6).
https://doi.org/10.1016/j.jece.2021.106583

REFERENCES
International Rice Research Institute. (2013). Rice almanac :source book for one of the most economic activities on
Earth.
Logan, B. E., Hamelers, B., Rozendal, R., Schröder, U.,
Keller, J., Freguia, S., Aelterman, P., Verstraete, W., &Rabaey, K. (2006). Microbial fuel cells: Methodology andtechnology. In Environmental Science and Technology (Vol.
40, Issue 17, pp. 5181–5192).
https://doi.org/10.1021/es0605016
Ranatunga, T., Hiramatsu, K., & Onishi, T. (2018).
Controlling the process of denitrification in flooded ricesoils by using microbial fuel cell applications. Agricultural
Water Management, 206(April), 11–19.
https://doi.org/10.1016/j.agwat.2018.04.041
Yang, shihong, Xiao, Y., Sun, X., Ding, J., Jiang, Z., & Xu,

9Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P05

Sustainable pesticide management: Personalized simulation for Indian soil
Kishalay Chakraborty1, Atsuhiro Shimada1 and Akio Ebihara1
1. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan
Organic Carbon, EC, Macro and micronutrients, etc. The data
is published in SHC Portal and available for free. The data is
INTRODUCTION
downloaded from the portal for the North-East part of India andprocessed using Python scripts to extract the pH and Organic
Food security is a major challenge in the 21st Century. Due
Carbon concentration which are useful for this study.
to the increasing population and decreasing agricultural land
share, we will be in scarcity of food in the next few years. To
Soil Texture and Porosity— Soil texture is the ratio between
tackle this issue, steps were taken in the last century. Many ofthree types of soil particles Sand, Silt, and Clay. This data isthem are currently considered detrimental, due to theircollected in the form of a NetCFD file from the GLDAS
irreversible impact on human health, the environment, and also
Fractions of clay, sand, and silt Database (Rodell et al., 2004)
soil health leading to a long-term loss in productivity. The
unregulated use of pesticides is a major factor among them.
at a spatial resolution of 0.25 degrees. The data is processedwith Python script and netCDF4 library.
There is a lack of awareness about application methods andquantity. The key stakeholders in the agriculture processes, theTime series Rainfall and Temperature data — Time-series
farmers, are mostly unaware of the effects of pesticides on
health, the environment, and soil. The effect of pesticide
Rainfall (Pai et al., 2014) and Temperature (Bhaskar Rao et al.,2009) data are collected from the Indian government weather
residues on soil may be predicted by mechanistic methods bymodeling the soil conditions in a simulation system. This may
portal. The daily minimum, maximum and average temperature
are available at the spatial resolution of 0.25 and 1.0 degrees
be a useful tool for farmer awareness.
Tang F. H. M. et.al. reported a pesticide residue simulator
respectively. The data is available as binary files which areusing a Global perspective by simulating soil texture, weather,processed using the ‘c’ scripts provided along with the data. The
binary files are converted to CSV format and then processed
pesticide application, etc. in BRTSIM software (Tang & Maggi,2021). La Cecilia reported the simulation of Glyphosate
using the custom python scripts.
degradation in soil(la Cecilia & Maggi, 2018). Hendrik
Rathjens modeled pesticide and Metabolite concentration inTime series solar radiation data— Solar radiation is thesource of energy on earth. The visible ray or shortwave
soil using the SWAT+ tool (Rathjens et al., 2022). Victoria
Kolupaeva modeled the transport of pesticides and water in theradiation reaches the earth's surface and heats the earth's
surface. The heat is also reflected from soil to the atmospheresoil(Kolupaeva et al., 2022).
India is a country with a large population and very diverse
as long-wave radiation. CRU JRA v2.3 (Kobayashi et al.,2015)database is collected which provided both shortwave andagriculture systems and climatic conditions. The same
pesticide protocol does not apply to all soil and climatic
longwave radiation of the earth's surface at 0.5 degrees spatial
resolution at an interval of 6 hours. The data is available as aconditions. Personalized pesticide protocol is required forindividual farms, based on their soil and climatic conditions. A
NetCFD file and processed using python and netCFD4 libraries.
The unit of both shortwave and longwave radiation are Watt/m2
firm-level personalized simulation system for Indian conditions
will be developed in this work. Soil/climate properties and theor J/s m2.
application rate of pesticide species will be the input fordeveloping the model. The system will predict the time required
Time series potential evapotranspiration data— CRU
TS4.06 database (Harris et al., 2020) contains time-series data
for the pesticide to degrade. This information will help toof potential evapotranspiration as a daily average at monthly
manage pesticide application sustainably and safely.
intervals. The data is represented as mm/day. This dataset is
The government of India developed a database of soil
also available as a NetCFD file and processed using python andparameters by lab testing soil samples from individual farmers.
netCFD4 libraries.
(Soil Health Card, n.d.). This individual farmer's data will be
used in this study to make the personalized model. The data
Pesticide application data — Pesticide application data is
generated from this research may produce a heatmap of highpesticide concentration areas in India. This data may be helpful
collected from the Government of India Ministry of Agriculture
for Government and other regulatory agencies in developing
and farmers welfare, directorate of plant protection, quarantine
pesticide-related policies in those areas. This work will be a
& storage Statistical database. The data is available in excel
small step towards sustainable agriculture.
files for 2017-18 to 2021-22 in the form of State-wise
consumption of chemical pesticides [Ccons in Metric Ton] andPesticide wise Total consumption in the country. Total
MATERIALS AND METHODS
agricultural land use is accessed from Area and production
statistics Ministry of Agriculture and Farmers welfare's Area
Simulation tool – BRTSim — BRTsim is a Computational
Production and Yield Information System. [ACultivation insolver for biochemical reactions and transport of fluids in
Hactere]. State-wise average pesticide application rate [Ravg] isporous and nonporous media. In this study, BRTSim will becalculated from Consumption [Ccons] and area [ACultivation] as
used to model the soil based on soil property and weather data
Equation1. The average pesticide application rate is plotted inand simulate the degradation and movement of Pesticide
Fig. 1.
compounds in soil with water.
େୡ୭୬ୱ ୶ ଵ଴଴଴Ravgൌ[in Kg/m2](1)୅େ୳୪୲୧୴ୟ୲୧୭୬ ୶ଵ଴଴଴଴
Soil Health Card (SHC) data— The government of India
launched this program to test individual farmers’ land for
nutrient parameters and scientifically prescribe crops relevant
to fertilizer requirements. This scheme has tested 5,47,46,153
samples in the laboratory for 12 soil parameters including pH,

10

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P05

Fig. 4: Test case 3 Result. Individual data points

DISCUSSION

Fig. 1: Average Pesticide application rate 2017-2021
Development of model — The model is developed byfollowing Tang & Maggi (2021) and introducing new input
data. The system is divided into 2 atmospheric layers and 4 soil
layers F01-F04. The pesticide ‘Acephate’ is selected randomly
to simulate using this system.

Using BRTSim software, Chemical residue for the pesticide‘Acephate’ in Indian soil conditions for 84 farmers samples
were simulated in this work. Although all the parameters are
not introduced yet in this system. As a result, in Case 3 themajor factor seems to be the pesticide application rate only.
Furthermore, data from 47 individual pesticides are available tobe simulated. We are preparing the system to introduce all theparameters together and apply time series input pesticideconcentration to make the system robust.

RESULTS
Test case 1 — In test case 1 pesticide is applied only at time
t=0 at the top soil layer. The model is simulated for 5 years. The
gradual movement of pesticide (and gradual decay) from one
layer to another can be observed in Fig. 2.

ACKNOWLEDGMENTS
We are grateful to Prof. Federico Maggi, The University ofSydney, for advice on the use of the BRTSim software, whichispublicly
availableathttps://sites.google.com/site/thebrtsimproject/home.
We acknowledge the support and guidance from Professor
Naoto Ogawa from Shizuoka University, Shizuoka, Japan.

REFERENCES
Bhaskar Rao, Srinivas, D., & Ratna, S. B. (2009).
Development of a high resolution daily gridded
temperature data set ( 1969 – 2005 ) for the Indian region.
Atmospheric Science Letters, 10(October), 249–254.
Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020).
Version 4 of the CRU TS monthly high-resolution gridded
multivariate climate dataset. Scientific Data, 7(1), 1–18.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M.,
Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C.,
Endo, H., Miyaoka, K., & Kiyotoshi, T. (2015). The JRA55 reanalysis: General specifications and basic
characteristics. Journal of the Meteorological Society ofJapan, 93(1), 5–48.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai,
N. S., & Mukhopadhyay, B. (2014). Development of a
new high spatial resolution (0.25° × 0.25°) long period(1901-2010) daily gridded rainfall data set over India andits comparison with existing data sets over the region.
Mausam, 65(1), 1–18.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J.,
Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B.,
Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P.,
Lohmann, D., & Toll, D. (2004). The Global Land Data
Assimilation System. Bulletin of the American
Meteorological Society, 85(3), 381–394.
Tang, F. H. M., & Maggi, F. (2021). Pesticide mixtures in soil:
A global outlook. Environmental Research Letters, 16(4).
https://doi.org/10.1088/1748-9326/abe5d6

Fig. 2: Test case 1 Result. F01-F04: Soil Layers top to
BRZ(Bellow Root Zone) respectively.
Test case 2 — In this case pesticide is applied repeatedly
over the entire 5 years simulation time. The result is shown in
Fig.3. The exponential decay of concentration is not observedin this case due to continuous pesticide input.

Fig. 3: Test case 2 Result. F01-F04: Soil Layers top to
BRZ(Bellow Root Zone) respectively.
Test case 3 — In this case, a total of 84 land samples are
simulated by applying respective Average pesticide
application, soil texture, pH, and Organic Carbon Content for
3 years duration. The final pesticide concentration for theindividual data point is shown in Fig. 411

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P06

Rice yield prediction using UAV-based imagery and deep learning
Md Suruj Mia1,2 and Takashi S. T. Tanaka2,3
1. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
3. Artificial Intelligence Advanced Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japanfeasibility of the CNN model for on-farm experimentation(OFE) with different fertilization treatment plots.
INTRODUCTION
Rice, a cereal crop is the most widely consumed staple foodfor half of the world’s population (Zhou et al., 2022) mostly in
Asia and Africa. To fulfill present and future demands, there isan increasing need to enhance yield gains per unit land area
while preserving natural resources (Grassini et al., 2013). In
agricultural sector timely, non-destructive, inexpensive, andreliable yield forecast at large scale is important andprerequisite for preventing climate risk and ensuring foodsecurity, especially with climate change and extreme events(Kang et al., 2009).
To predict yield before harvesting a variety of methods havebeen presented and used, including ground-based field surveys,
farmers’ expert knowledge, crop growth models (A.J.W. de
Wit, C.A. van Diepen, 2008), remote sensing-based methods(Wang et al., 2010), and crop growth models combined withenvironmental factors and remote sensing data (Betbeder et al.,2016). The usual statistical approach should not be utilized toanalyze data collected on farms as it does not meet therequirements of the experimental design (Zhou et al., 2021).
Since 1970s, satellite data have been extensively employedwith varying spatial, temporal, and spectral resolutions for
agricultural yield prediction over the glove (Battude et al.,2016). However. clumsy geographical resolution and subpar
temporal sampling, make it difficult to use satellite data foryield prediction (Wang et al., 2010). As compared withcompared airborne and satellite platforms, Unmanned Aerial
Vehicles (UAVs) has significantly promoted for data collecting
due to the superior spatial, spectral, and temporal resolution(Maimaitijiang et al., 2020). A reliable technique formonitoring agricultural crops is the extraction of visual indices(VIs) and color indices (CIs) from multispectral and RGB
images (Zhou et al., 2022). The physiological and geometricproperties of vegetation such as leaf chlorophyll content, leaf
area index (LAI), nitrogen concentration, plant height, biomassyield and grain yield can be estimated from canopy spectral
information derived from UAV-based multispectral andhyperspectral imagery (Maimaitijiang et al., 2020).To attainprecise yield estimation for a variety of crops, many statisticaland machine learning (ML) approaches have been developed.
Deep Learning (DL) can automatically learn representations
from data using a multi-layer architecture, which supports
complex nonlinear functions that are learned from thehierarchal outputs of the previous layers. In many fields, DL
outperforms conventional feature extraction techniques in thedomain of image and semantic recognition, natural language
processing and video analysis (Ji et al., 2013). According to
Krizhevsky et al. (2012), convolutional neural network (CNN)
is the most effective DL technique for image classification andregression tasks. The CNN architectures receive input images
with dimensions of width, height, and depth, and extract thefeatures through a sequence of operations including
convolution and pooling (Yang et al., 2019).
The objective of this study was to develop rice yieldprediction model using CNN and UAV-based high-resolution
multispectral images during the heading stage. Thus, we
compared the prediction accuracy between traditional linear
model based on VI and CNN model. We further explored the

MATERIALS AND METHODS
Rice was grown at different location of Japan from 2017–2021. To evaluate different fertilizer application rates, strip
trials were performed at nine fields in different locations ofGifu, Japan from 2020–2021. In OFEs, 8-row rice transplanterswith a 2.4-m working width were used to adjust the application
rates of basal fertilizer. The nitrogen (N) fertilizer was appliedin the OFE fields at a rate of 0, 250, 300, 350, and 400 kg ha–1.
Image data for yield prediction of rice was collected by using
two multispectral cameras (Sequoia+, Parrot, Paris, France andRededge-Altum, MicaSense, Seattle, USA) mounted on a UAV
platform at the heading stage. Using structure-from-motion
software (Pix4D mapper version 4.4.12, Pix4D, Prilly,
Switzerland), the captured multispectral images were processedto generate reflectance imagery. The ground sample distance(GSD) ranged from 0.01 to 0.06 m per pixel. A total of 641
plant samples with georeference were harvested within an
approximately of 1-m2 area. Utilizing those georeferenced yielddata and corresponding 1-m2 images clipped from thereflectance imagery, CNN models for crop yield prediction
were developed. The input bands were green, red, andnearinfrared. The total dataset was randomly split into training(59.90%, n = 384), validation (19.97%, n = 128) and test
(20.13%, n = 129) datasets. Using AlexNet and CNN
architecture the CNN model was built. The patience, epoch,
batch size, and learning rate were 15, 100, 32, and 0.001,
respectively. The training dataset was augmented by rotating
and flipping the resized images. The reflectance maps forprediction were split into 1-m2 images.

RESULTS AND DISCUSSION
The relationship between observed and predicted yield areshown in Figure 1 (a) and (b) for AlexNet and CNN
architecture, respectively. The root mean square error (RMSE)
in the predicted rice yield for Alexnet architecture-based Model
was 0.691, 0.945 and 0.748 t ha–1 for the training, validation,and test datasets, respectively, while the percentage of error(RMSPE) was 13, 18 and 16 for the training, validation, andtest datasets, respectively. The RMSE in the predicted rice yieldfor CNN architecture-based Model was 0.854, 1.054 and 0.943
t ha–1 for the training, validation, and test datasets, respectively
and the percentage of error (RMSPE) was 15, 20 and 18 forthe training, validation, and test datasets, respectively. The R2
for Alexnet architecture-based Model was 0.691, 0.543, and
0.614 for the training, validation, and test datasets, respectively
and for CNN architecture-based Model was 0.598, 0.431, and
0.533 for the training, validation, and test datasets, respectively.
Table 1 shows the performance of both architectures. The
AlexNet architecture-based CNN model is outperformed
regarding R2 and RMSE. The RMSE and R2 values of themodels specify that the AlexNet architecture-based model canestimate the rice grain yield more accurately than CNN
architecture-based model from the multispectral images
collected by UAV at heading stage.

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Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P06

Fig. 2 (b): Spatial variation of yield on OFE-field derived
from the CNN architecture-based CNN model.
Fig. 1 (a): Relationship between observed and predictedgrain yield derived from the Alexnet architecture-based
CNN model.

REFERENCES
A.J.W. de Wit, C.A. van Diepen, Crop growth modelling andcrop yield forecasting using satellite-derived meteorologicalinputs, International Journal of Applied Earth Observation
and Geoinformation, Volume 10, Issue 4
Battude, M., Al Bitar, A., Morin, D., Cros, J., Huc, M., Sicre,
C.M., Le Dantec, V., & Demarez, V. (2016).
Betbeder, J., Fieuzal, R., & Baup, F. (2016). Assimilation of
LAI and Dry Biomass Data From Optical and SAR Images
Into an Agro-Meteorological Model to Estimate Soybean
Yield. Ieee Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, 9, 2540-2553
Collobert, R., Weston, J., 2008. A unified architecture fornatural language processing: deep neural networks withmultitask learning. Proc. 25th Int. Conf. Mach. Learn. 160–
167. https://doi.org/10.1145/1390156.1390177.
Grassini, P., Eskridge, K. M., and Cassman, K. G. (2013).
Distinguishing between yield advances and yield plateaus inhistorical crop production trends. Nat. Commun. 4:2918. doi:
10.1038/ncomms3918
Ji, S., Yang, M., Yu, K., 2013. 3D convolutional neuralnetworks for human action recognition. Pami 35, 221–231.
https://doi.org/10.1109/TPAMI.2012.59.
Kang, Y., Khan, S., & Ma, X. (2009). Climate change impacts
on crop yield, crop water productivity and food security–A
review. Progress in natural Science, 19, 1665-1674
Krizhevsky, A., Sulskever, Ii., Hinton, G.E., 2012. ImageNetclassification with deep convolutional neural networks. Adv.
NeuralInf.
Process.
Syst.
60,
84–90.
https://doi.org/10.1145/3065386.
Maimaitijiang M., Sagan V., Sidike P., Hartling S., Esposito F.,
Felix B. Fritschi, Soybean yield prediction from UAV using
multimodal data fusion and deep learning, Remote SensingofEnvironment,
Volume237,
2020,
https://doi.org/10.1016/j.rse.2019.111599.
Qi Yang, Liangsheng Shi, Jinye Han, Yuanyuan Zha, Penghui
Zhu, Deep convolutional neural networks for rice grain yield
estimation at the ripening stage using UAV-based remotelysensed images, Field Crops Research, Volume 235, 2019,
Pages 142-153. https://doi.org/10.1016/j.fcr.2019.02.022
Wang, K., Franklin, S.E., Guo, X., & Cattet, M. (2010). Remotesensing of ecology, biodiversity and conservation: a review
from the perspective of remote sensing specialists. Sensors,
10, 9647-9667
Xinbin Zhou, Yusuke Kono, Aung Win, Tsutomu Matsui &Takashi S. T. Tanaka (2021) Predicting within-field
variability in grain yield and protein content of winter wheat
using UAV-based multispectral imagery and machinelearning approaches, Plant Production Science, 24:2, 137151, DOI: 10.1080/1343943X.2020.1819165
Zhou, J.; Lu, X.; Yang, R.; Chen, H.;Wang, Y.; Zhang, Y.;
Huang, J.; Liu, F. Developing Novel Rice Yield Index Using
UAV Remote Sensing Imagery Fusion Technology. Drones
2022, 6, 151. https://doi.org/ 10.3390/drones606015

Fig. 1 (b): Relationship between observed and predictedgrain yield derived from the CNN architecture-based CNN
model.
Table: Results of prediction models based on RGB images

r2
RMSE (tha-1)
RMSPE (%)

Alexnet architecture
Train
Test
0.691
0.614
0.748
0.858
13
16

CNN architecture
Train
Test
0.598
0.533
0.854
0.943
15
18

By using the developed models, the yield prediction was
done on OFE field. Figure 2 (a) and 2 (b) shows the predicted
yield maps of one of the investigated fields with the AlexNet
and CNN architecture-based model, respectively. The
estimated yield map in both prediction models clearly displayed
spatial variability. Both models identified the different fertilizer
treatments in the field and low estimation in the boundary side.
CNN architecture-based model tended to overestimate to whole
field than Alexnet architecture-based model.

Fig. 2 (a): Spatial variation of yield on OFE-field derived
from the Alexnet architecture-based CNN model.

13

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P07

Effects of transglutaminase on pasting properties and retrogradation of riceflour
Dang Thi Kim Lien1,2 and Takahisha Nishizu2
1. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. Faculty of Applied Life Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Pasting properties of rice flour—Three grams of rice flourand 25 g of water were put into RVA canisters. TG was added
INTRODUCTION
to this mixture at the concentration of 0.5 and 1 % w/w ofprotein. The mixture was incubated for 30 mins at 55°C in a
Rice is one of the most important food sources in human dietwater bath. The pasting properties of samples were determined
for many reasons. It is the primary consumption of more thanwith a Rapid Visco Analyser (RVA) Super 3 (Newport
half of the world’s population. Products prepared from rice
Scientific Inc., Australia). The temperature profile involved anflour always remain highly referable due to the digestibility andinitial 10 s high-speed (960 rotations min−1) stir that dispersed
hypoallergenic properties. However, the critical concern about
the sample prior to the beginning of the measuring phase at 160
rice flour prepared products is the deterioration of starch during
rotations min−1. Temperature was held at 50 °C for 1 min andstorage caused by retrogradation.
then raised to 93 °C in 4 mins, held for 7 min, cooled to 50 °C
Most of the studies about retrogradation focused on thein 4 mins, and held for 3 mins.
effects on starch component, but the effects on proteins whichis the second abundance component have not been investigated
XRD samples preparation and measurement —The samples
deeply. From previous studies, it showed that protein fractions(1:2 dry matter of rice flour-to-water ratio) with TG 0.5 and 1 %and its structure in rice affected the physical characteristics of
w/w of protein were incubated for 30 mins at 55°C in a water
cooked rice (Furukawa, 2008). Therefore, the modification onbath, then gelatinized by steaming in an autoclave at 104°C for
protein molecules possibly affects the product properties
30 mins. Subsequently, the gelatinized samples were stored atduring.
4°C for 5 and 7 days to allow them to retrograde. Afterward,
Transglutaminase (TG) is protein-glutamine c-glutamylthese samples were lyophilized. The freeze-dried samples weretransferase (EC 2.3.2.13) that catalyzes the cross-linking
ground, and then the powder samples were passed through areaction between glutamine and lysine. It has been successfully
100-µm sieve before XRD.
applied to alter the technological and functional properties ofFreeze-dried rice gel powder was added 0.8% Zinc Oxide forfood matrices containing proteins, resulting in improvements ofstandardization and 0.7 times of water (w/w of rice flour) rightelasticity, water holding capacity and other functional
before measurement. The XRD was conducted using aproperties. The action of TG on rice protein can possibly
SmartLab X-ray diffractometer (Rigaku Co., Japan) equipped
affect properties of rice flour during processing. Renzetti et al.
with a copper tube operating at 45 kV and 200 mA.
(2007) has also proved that TG modified main protein fractions
Measurements were collected at room temperature with ain rice flour and resulted in the formation of high molecularscanning rate of 2°/min and a diffraction angle range of 10–37°
polymers which improved water holding capacity in rice batter.
(2-Theta° range), where theta is the angle of incidence of the XIn addition, the patent on application of TG in rice cooking
ray beam on the sample. The degree of crystallization was(Kitagawa, 2002) shown its effectiveness on maintaining theanalyzed using Origin 2022 software (OriginLab Co., USA).
texture of cooked rice in excessive amount of water andconsequence to an reduce in retrogradation process. However,
Statistical analysis— Statistical analysis was performed withthere is no report about the effects of TG on pasting properties
SPSS 26.0 software (IBM, USA) on all tests using a one-way
and retrogradation tendency of rice flour which are significant
ANOVA and Tukey’s post hoc test to detect significant
factors in predicting and controlling the quality of rice products.
differences.
The aim of this paper is to investigate the effects of TG on
starch pasting properties and starch retrogradation, and thus get
a better insight on the application of TG in rice products.

RESULTS and DISCUSSION

Pasting properties—The pasting behaviors of control and
TG treated samples were showed in table 1. The addition of TG
significantly increased peak, breakdown and final viscosity.
The higher peak viscosity with TG treatments was also alignedwith the result of Palabiyik et al. (2016) who used TG and otherenzymes to modified the pasting properties of gluten-free flour.
It was possible that the modification of protein structure andstrengthening the gel network via cross-links by TG activity has
affected the starch swelling and increased the initial viscosity.
The significant higher breakdown value with increasing TG
concentration may refer to the surface modification of proteins.
Ribotta & Rosell (2010) also reported that incorporation of TG
treated-protein significantly increased peak and breakdown
viscosity of starch gel. The increase in viscosity during thecooling period (setback) indicates the tendency of thereassociation of amylose present in the hot paste. The
significantly decreased in setback by TG treatment indicated alower tendency of the starch granules to retrograde (Sandhu et

MATERIALS AND METHODS
Materials—Rice flour contained 6% of protein (KumamotoCo., Japan) was purchased from local supermarket. Activa TGK contained 1% of microbial Transglutaminase was provide by
Ajinomoto Co. (Tokyo, Japan).
Transglutaminase purification—Activa TG-K preparation
contained 1% of TG, 75% Calcium lactate, 24% dextrin andothers. These background substances may interfere the results,
therefore, purification was required. Two grams of Activa TGK was dissolved in 10 ml of 0.1 M Tris/HCl buffer (pH 7.5).
The mixture was centrifuged at 15,000 rpm at 4°C for 30 mins.
The supernatant was collected and dialyzed at 4°C for 6hrs in
Tris/HCl buffer (pH 7.5) solution. The solution was changed
every 2hrs. The obtained solution was used as a 0.2% TG
solution for enzymatic reaction.

14

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P07

al. 2007). However, some papers have reported an insignificant
change in pasting properties when TG was applied in rice flour
and whey protein blend (Marco and Rosell, 2008). This could
be explained by the different action of TG with different typeof protein.

of TG has not reached optimum amount to alter the
retrogradation behavior of rice gel during storage. Also, further
studies need to be conducted to understand the mechanism of
TG action on rice flour properties.

Table 1. Effects of TG treatment on pasting parameter ofrice flour.
SamplePeak
Breakdow Final Vis SetbacksVisn(mPa.s)(mPa.s)(mPa.s)(mPa.s)Control 2515.33
1202.67a
2602.00a 1289.33
ba
TG 0.52743.67
1285.67b
2700.33b 1242.33
ba
1400.00c
2649.00a 1236.67
TG 12812.33bbaMeans of triplicate. Values within a column that have different
letters indicate a significant difference (P = 0.05)
TG 0.5: Sample treated with 0.5 % of TG
TG 1: Sample treated with 1 % of TG

REFERENCES
Furukawa, S. (2008). Rice Proteins Bring Good Relationshipbetween Eating Quality of Cooked Rice and Brewing
Suitability of Sake. Journal of The Brewing Society of Japan,
103 (3), 145-149.
Kitagawa, T., Kawasaki, M., Hiki, Y., & Nemoto, K. (2002).
Production method of highly hydrous rice (Japan. PatentNo.JP331008B2).
Marcoa, C., & Rosell, C. M. (2008). Effect of different protein
isolates and transglutaminase on rice flour properties. Journalof Food Engineering, 84(1), 132-139.
Palabiyik, I., Yildiz, O., Toker, O. S., Cavus, M., Ceylan, M.
M., & Yurt, B. (2016). Investigating the addition of enzymes
in gluten-free flours–The effect on pasting and texturalproperties. LWT-Food Science and Technology, 69, 633641.
Renzetti, S., Dal Bello, F., & Arendt, E. K. (2008).
Microstructure, fundamental rheology and baking
characteristics of batters and breads from different glutenfree flours treated with a microbial transglutaminase. Journalof Cereal Science, 48(1), 33-45.
Ribotta, P. D., & Rosell, C. M. (2010). Effects of enzymatic
modification of soybean protein on the pasting andrheological profile of starch – protein systems. Starch ‐Stärke, 62(7), 373-383.
Sandhu, K. S., Singh, N., & Malhi, N. S. (2007). Some
properties of corn grains and their flours I: Physicochemical,
functional and chapati-making properties of flours. Food
Chemistry, 101(3), 938-946.

Retrogradation of storage rice gel—Data from XRD showedthat the retrogradation degree increased with the increase ofstorage day. This could be explained by the continuous crosslinking and recrystallization of starch molecules to form anordered structure during the retrogradation. Compared with therice gel without TG, rice gel with added TG was lower inretrogradation degree after 5 days storage. However, there wasinsignificant difference between samples after 7 days storages.
Renzetti et al. (2007) proposed that a strengthened protein
network consequent to protein cross-linking has improved
ability to trap water in rice bread and increased in water holding
capacity of rice batter, so a possible explanation for this paper
results might be the enhance in water holding capacity due to
TG action inhibited the retrogradation of rice gel.
In conclusion, the TG treatment had significant effects on
pasting properties of rice flour, but it shown slight effect onretarding the retrogradation. It is possible that the concentration15

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P08

Dissecting the role of N and S in Arabidopsis root architecture under Al
stress through physio-genetics approach
Md. Abir Ul Islam1,2 and Hiroyuki Koyama2
1.
The United Graduate School of Agricultural Science, Gifu University, Gifu 501-1193, Japan
2.
Faculty of Applied Biological Sciences, Gifu University, Gifu 501-1193, Japanagar, 1% sucrose and 1X Gamboge (Sigma-Aldrich) solution
for seedling better growth and development. The agar plate
INTRODUCTION
media along with the accessions and mutant seeds put
vertically in growth chamber. The growth chamber
A significant barrier to crop development, soil acidity
environment was set by following Kobayashi et al. 2007
(having a pH of less than 5.0), has gradually increased due toprotocol. After 10 days of growing, we selected 5 plants fromnatural and/or man-made activities (Von Uexkull and Mutert
each accession and put them in water agar plates and took1995). This detriment increases the solubilization of
picture with a digital camera (Canon EOS KissX5) with a fixed
Aluminum (Al) into trivalent form (Al3+) represents a major
focal length and magnification. Then we used EZRhizo
detrimental aspect of economic yield loss of major crops
software to find out adverse effect of Al in plants.
(Kinraide and Parker, 1990; Yokota and Ojima 1995). This
Al3+ triggered biosynthesis of ethylene and reactive oxygen
species (ROS), programmed cell death (PCD) and resulted ininhibition of root growth and development (Reviewed at
Alarcon-Poblete et al. 2018). This root inhibition limits theforaging of macro- and micro-mineral nutrients from the soil
solution. Recently, detrimental effects of Al have been
mitigated with mineral supplements like nitrogen (N) (Liu etal. 2014). Although one of the Al species that contains sulfur,
AlSO4-, is not poisonous to plants, the key macronutrient
sulfur (S) has not yet been studied for its potential ameliorative
effects under Al stress circumstances (Kinraide 1995). S
containing fertilizer Gypsum is commonly used for the abiotic
stress tolerance in plants (Carvalho and Van Raijo, 1997; Moraet al., 1999). S containing amino acids like cysteine andmethionine help plants to overcome abiotic stress like heavy
metals, salinity, etc. (Huang et al. 2021). But the mechanismof S behind the stress alleviation is poorly understood.
Therefore, we are trying to find out the molecular mechanismof the S and N during Al3+ toxicity. Recently, our laboratory
has found some Arabidopsis accessions and mutants (S and N
transporter mutant) differential root system architecture (RSA)
amid Al stress (Fig. 1). Moreover, some accessions showedsulfate sensitivity and overproduction of lateral roots. Fromthat point of view, we want to examine the combined effect of
N and S effect on Al3+ toxicity. We will treat plants with shortand long-term Al toxicity along with treatments to find out the
N and S assimilatory mechanism in tolerance.
Finally, we will try to find out our treatment's effect on theroot architecture and also investigate the S-assimilatory
pathway dependency on the N-assimilation pathway during
amino acid production under Al toxicity. At the end of thestudy, we hope to find out
I.
Responsible S assimilatory genes on Al stress.
II.
Complementary expression of N and S assimilation
effect genes and enzymes under Al stress.

RESULTS

Fig. 1: Clustering Arabidopsis accessions and knock outline in strong Al stress.
Table 1: Optimized of pH in ½ MS media for Al stress.
Initial
pH

After
Auto
clave

50 µM
Al

5.5
6
6.5
7
7.7
8.0

5.07
5.48
6.035
6.494
6.907
7.150

Control
Control
5.2

100
µM
Al

200
µM
Al

250
µM
Al

300
µM
Al

5.145

4.568

5.072
4.943

Table 2: Optimized pH in ¼ MS media for Al stress.
InitialpH
5.4

After Auto clave

7.0

6.450

7.4

6.902

75 µM Al100 µM Al

5.021
4.945
4.904

MATERIALS AND METHODS
Plant materials and growth conditions—Arabidopsis(Arabidopsis thaliana) accessions Col-0 (Wild type), STOP1
mutant, ALS3-KO and Ler-1 were used in the first experimentfor Al concentrations optimization. For optimizing theconcentrations first, we selected 5.0 and then we selected 4.5
as our final pH. Optimization of pH along with or without Al
was done by using Msogoya et al. 2008 protocol by using
Murashige and Skoog (MS) commercial salt (Table 1 and 2).
The concentrations of the Al were 0 µM, 100 µM, 200 µM, 250µM and 300 µM. Furthermore, we manually prepared the MS
media stock solution and selected the strength as ½ MS, 1%

Fig. 2: Observation of Al stress in root system architecturein MS media.

16Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P08

dicotyledonous species. Physiologia Plantarum, 79(2), 283288.
Kobayashi, Y., Hoekenga, O. A., Itoh, H., Nakashima, M.,
Saito, S., Shaff, J. E., ... & Koyama, H. (2007).
Characterization of AtALMT1 expression in aluminuminducible malate release and its role for rhizotoxic stress
tolerance in Arabidopsis. Plant Physiology, 145(3), 843-852.
Liu, B., Luo, C., Li, X., Gray, L., Zhang, F., Liu, M., ... & Lei,
B. (2014). Research on the threshold of aluminum toxicity
and the alleviation effects of exogenous calcium, phosphorus,
and nitrogen on the growth of Chinese fir seedlings underaluminum stress. Communications in soil science and plant
analysis, 45(1), 126-139.
Mora, M. L., Schnettler, B., & Demanet, R. (1999). Effect ofliming and gypsum on soil chemistry, yield, and mineralcomposition of ryegrass grown in an acidic Andisol.
Communications in soil science and plant analysis, 30(9-10),1251-1266.
Shen, C., Fu, H. L., Liao, Q., Huang, B., Fan, X., Liu, X. Y., ...
& Huang, Y. Y. (2021). Transcriptome analysis andphysiological indicators reveal the role of sulfur in cadmium
accumulation and transportation in water spinach (Ipomoea
aquatica Forsk.). Ecotoxicology and Environmental Safety,
225, 112787.
Von Uexküll, H. R., & Mutert, E. (1995). Global extent,
development and economic impact of acid soils. Plant andsoil, 171(1), 1-15.
Yokota, S., & Ojima, K. (1995). Physiological response of root
tip of alfalfa to low pH and aluminium stress in water culture.
In Plant-Soil Interactions at Low pH: Principles andManagement (pp. 329-331). Springer, Dordrecht.

DISCUSSION
Aluminum stress could not observe in the MS agar plate
media because of Phosphate (Pi) present in the commercial
MS salt (Fig. 2). Pi precipitation occurs in case of high pH
when we tried to optimized Al concentration to pH 5.0 that
resembled with the finding with da Silva Cerozi &Fitzsimmons (2016). As a result, we remove the phosphateconcentration from the MS salt. After getting the Al stress insolid media we will try to see the treatment effects on

ACKNOWLEDGEMENT
We acknowledge the Ministry of Education, Culture, Sports,
Science and Technology, Japan.

REFERENCES
Alarcón-Poblete, E., Inostroza-Blancheteau, C., Alberdi, M.,
Rengel, Z., & Reyes-Díaz, M. (2018). Molecular regulationof aluminum resistance and sulfur nutrition during root
growth. Planta, 247(1), 27-39.
Carvalho, M. C. S., & Van Raij, B. (1997). Calcium sulphate,
phosphogypsum and calcium carbonate in the ameliorationof acid subsoils for root growth. Plant and Soil, 192(1), 3748.
da Silva Cerozi, B., & Fitzsimmons, K. (2016). The effect ofpH on phosphorus availability and speciation in an
aquaponics nutrient solution. Bioresource technology, 219,778-781.
Kinraide, T. B. (1991). Identity of the rhizotoxic aluminium
species. In Plant-soil interactions at low pH (pp. 717-728).
Springer, Dordrecht.
Kinraide, T. B., & Parker, D. R. (1990). Apparent
phytotoxicity of mononuclear hydroxy‐aluminum to four17Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P09

Effect of exogenous gibberellin on regreening in Valencia orange fruit
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

pale orange and then turned green. It was related to the increaseof chlorophylls contents (Fig. 1A) and decrease of carotenoids
contents (Fig. 1B). In GA treatment, the result showed that thecontents of chlorophyll a, b, and total chlorophyll weresignificantly higher than the control at 6th weeks. In contrast,
the contents of total carotenoid, T-vio, C-vio, β-Cry, and β-Car
were significantly low levels in the GA treatment comparedwith the control at the 4th week.

Regreening occurs in some citrus cultivars, such as Valencia
orange. The color of the fruit will reverse from orange(chromoplasts) to green (chloroplasts), when the fruit is left on
the tree till the spring or summer season. This process was
related to plant pigments such as carotenoids and chlorophylls
accumulation in the flavedos. In previous studies, several
researchers demonstrated that natural light (Sak et al., 1988),
blue LED light (Ma et al., 2021), nitrogen sources (Farag et al.,2014), and gibberellin (Rasmussen, 1973; Coggins and Lewis,1962) induced the regreening in citrus fruit. Gibberellins, oneof the plant hormones, not only delayed the degreening but also
induced the regreening in the flavedos of citrus fruits(Rasmussen, 1973). However, the effects of gibberellic acid(GA) treatment on gene expression associated with the pigment
accumulation during the regreening remained unknown. Thus,
this study aimed to investigate the effects of GA on theaccumulation of chlorophyll and carotenoid metabolites and on
related gene expression during the regreening in Valencia
oranges.

Fig. 1: The contents of chlorophylls (A) and carotenoids (B) of
Valencia orange flavedos treated with or without GA at 6th week.

Regarding to the expression of carotenoid and chlorophyll
metabolism genes, the expression of eleven carotenoid
biosynthetic genes, seven chlorophyll biosynthetic genes, andsix chlorophyll degradation genes were investigated. In thepresent study, the result showed that the up-regulation ofchlorophyll biosynthetic genes and down-regulation ofchlorophyll degradation and carotenoid biosynthetic genes
were observed in both treatments during regreening. In GA
treatment, the expression of carotenoid biosynthetic genes(CitPSY, CitPDS, CitZDS, CitLCYb1, CitLCYb2, and CitHYb)
were significantly lower, whereas the expression of CitLCYe
and CitHYe were significantly higher than the control treatment
at the 6th week (Fig. 2). The expression of genes involved inchlorophyll biosynthesis (CitGGDR, CitCHL27, CitPORA, andCitCAO) was significantly up-regulated (Fig. 3A), while theexpression of chlorophyll degradation genes (CitCLH1,
CitPPH, and CitRCCR) was significantly down-regulated by
the GA treatment compared with the control at the 6th week
(Fig. 3B).

MATERIALS AND METHODS
Fruits of ‘Valencia orange’ (Citrus sinensis Osbeck) from
Fujieda farm of Shizuoka University were used as plantmaterials. The fruit on the tree were separated into two groups,
the control (non-treated), and GA-treated group by plastic
sheets. 500 μM GA solution was used for spraying the fruit on
the tree every 2 weeks for 3 times from April. The fruits in eachtreatment were harvested at 0, 4, and 6 weeks. After harvest, theflavedo in the top part (the part from the stem end to the middleof the fruit) was sampled, 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-violaxanthin (T-vio), 9-cis-violaxanthin(C-vio), lutein (Lut), β-cryptoxanthin (β-Cry), and β-carotene(β-Car), 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).

RESULTSThe regreening of citrus fruit was observed in both treatments
during the experiment period, especially in GA treatment.
Evidently, the color of the flavedo changed from dark orange to

Fig. 2: The expression of genes involved in carotenoid biosynthesis
of Valencia orange flavedos treated with or without GA at 6th week.

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Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P09

biosynthesis genes led to the decrease of carotenoid contents inthe flavedo. These observations indicated that GA acted as a
crucial regulator in the regreening process of citrus fruits.

ACKNOWLEDGMENTS
This work was supported by KAKENHI Grant Numbers
JP20H02976 (to M.K.) and JP19K06015 (to L.Z.) from Japan
Society for the Promotion of Science (JSPS).

REFERENCES
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.
Farag, K.M., Essa, A.A., Nagy, N.M.N., Haikal, A.M., Attia,
S.M. (2014) Influencing of some factors on regreening of“Valencia” orange fruits. Advances in Plants & Agriculture
Research 1: 135-140.
Fujii, H., Shimada, T., Sugiyama, A., Endo, T., Nishikawa, F.,
Nakano, M., Ikoma, Y., Shimizu, T., Omura, M. (2008)
Profiling gibberellin (GA3)-responsive genes in mature
Mandarin fruit using a citrus 22K oligoarray. Scientia
Horticulturae 116 (3): 291-298.
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.
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.
Porat, R., Feng, X., Huberman, M., Galili, D., Goren, R.,
Goldschmidt, E.E. (2001) Gibberellic acid slows postharvest
degreening of Oroblanco citrus fruits. Hortscience 36, 937940.
Rasmussen, G.K. (1973) The effect of growth regulators on
degreening and regreening of Citrus fruit. Acta Horticulturae
34: 473-479.
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.
Saks, Y., Waks, Y., Weiss, B., Franck, A., Chalutz, E. (1988)
Light induced postharvest regreening of pummelo fruit.
Annals of Applied Biology 113: 375-381.

Fig. 3: The expression of genes involved in chlorophyll biosynthesis(A) and chlorophyll degradation (B) of Valencia orange flavedos
treated with or without GA at 6th week.

DISCUSSION
In general, the flavedo of citrus fruit accumulated high levelsof chlorophyll and β,ε-carotenoids (α-carotene and lutein) in
immature fruit. In the mature fruit, the flavedo of citrus fruit
turns from green to orange in winter season. The contents ofchlorophyll and β,ε-carotenoids decreased, and theaccumulation of β,β-carotenoids (β-cryptoxanthin, zeaxanthin,
all-trans-violaxanthin and 9-cis-violaxanthin) was observed(Kato et al., 2004; Ma et al., 2016; Rodrigo et al., 2013). In
Valencia orange, the ripened fruit can reverse from orange togreen, when the fruit is left on the tree till the spring or summer
season (Caprio, 1956). In the present study, the results showed
GA treatment induced the accumulation of chlorophylls,
whereas reduced the content of carotenoids. The changes inchlorophylls and carotenoid contents were closely related to theexpression levels of genes involved in chlorophyll andcarotenoid metabolism. The synthesis of chlorophyll andcarotenoid shared a common precursor, namely geranylgeranyl
diphosphate (GGPP). GGPP was converted into chlorophyll
catalyzed by the geranylgeranyl reductase (GGDR) and thetetrapyrrole biosynthetic genes. In the meanwhile, thecondensation of two molecules of GGPP forms phytoene by theaction of phytoene synthase (PSY), which was the first key stepin carotenoid biosynthesis (Alós et al., 2006; Ma et al., 2021).
During ripening fruit, the decrease in the gene expression of the
GGDR was observed, resulting in a decrease in chlorophyll
accumulation. In previous studies, exogenous GA wascommonly used to delay senescence and loss of chlorophyll invarious citrus fruits during pre-harvest or post-harvest (Porat etal., 2001; Alós et al., 2006). In citrus fruit, GA treatment
delayed degreening by up-regulation of the transcription ofmagnesium chelatase (Fujii et al., 2008) and repressed thechlorophyll degradation gene by down-regulation of the geneencoding chlorophyll-degrading pheophorbide a oxygenase(PAO) (Alós et al., 2006). In addition, GA treatments
suppressed almost all carotenoid biosynthesis genes, especiallythe expression of CitPSY, CitHYb, and carotenoid cleavage
dioxygenases in flavedo of citrus fruits (Fujii et al., 2008; Ma
et al., 2021).
In conclusion, the results presented in this study suggested
that GA treatment induced the regreening in Valencia orange
by up-regulation of chlorophyll biosynthesis genes and downregulation of chlorophyll degradation genes led to the increaseof chlorophyll content, and the down-regulation of carotenoid

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Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P11

Evaluation of physical and antioxidant properties of chitosan-based films
combing with curcumin nanoemulsion
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
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 Science, Gifu University, 1-1 Yanagido, Gifu 501-1193,
Japan.
size distributions and zeta potentials of the oil droplets were
measured on a Zetasizer Nano ZS at 25 °C.
INTRODUCTION
Table 1 formulation of curcumin nanoemulsion.
SampleOil: Aqueous
Ultrasonic timephase(minute)CurNE1
1:4
15
CurNE2
1:430
CurNE3
1:4
90
CurNE4
1:4
120
CurNE5
1:6
15
CurNE6
1:8
15
CurNE71:9
15

The critical environmental concerned because nonbiodegradable plastic wastes affected disposal issues.
Biopolymer materials can solve the waste disposal problems
because the biopolymer can break down cleanly, in a definedtime period. The sea-food processing industry produces a lot ofanimal wastes like skins and shells of organisms. Conversely,
the wastes are excellent sources of chitin. Chitosan, adeacetylated derivative of chitin, considered wide range ofapplications owing to its biocompatibility, biodegradability,
and antimicrobial properties. Nevertheless, chitosan-based
films (CH) demonstrate undesirable properties, such as low
mechanical strength and high permeability of water vapor
transmission, which limit their functions. To overcome theshortcomings and achieve additional functionalities, the studies
attempted to incorporate hydrophobic lipid compounds in CH
films (Peanparkdee et al., 2016). Curcumin (Cur) has a wide
range of beneficial properties, including antioxidant, andantimicrobial properties. However, the utilization of curcumin
is restricted due to poor solubility and rapid hydrolysis. Herein,
the study focused on the nanoemulsion (NE) technique toovercome dissolubility, stability, and bioactivity of various oilsoluble compounds owing to their small droplet size and highkinetic stability. As effective points of Cur and NE technique,they had high potential to be incorporated with CH-based films
in order to improve the films’ properties.
This work aimed to ameliorate CH films by incorporating
CurNE and determine the alteration of the modified film
through its physico-chemical properties, including the water
vapor permeability (WVP), mechanical properties, glasstransition temperature (Tg), and antioxidant activities.

Determination of the WVP of CH-based films
The water vapour transmission rate (WVTR) was evaluated,following the test method of ASTM (2007). The film samples
were coated over cups (diameter 5 cm) containing silica gel.
The film-covered cups were placed in a chamber that was set to
25 °C and 75% RH. WVTRs (gm−2d−1) of the films were
determined from the slope of the regression analysis of the
moisture weight gain (Δw) obtained through a film area (A)during a definite period (Δt) upon attaining the steady-state (Eq.
(1)). The WVTRs of the films were subsequently calculate
WVP via Eq. (2). At least five replications of each modified
film were tested (gmmm−2d−1kPa−1), x is the film thickness(mm), and Δp is the difference between the partial water vapour
pressures of the inner (p1) and outer (p2) surfaces of the film inthe chamber (kPa).
Δw

WVTR

AΔt

WVP = WVTR

MATERIALS AND METHODS

Eq. (1)xΔp

Eq. (2)

Determination of the mechanical properties of CH-based films
The mechanical properties, tensile strength (TS), andelongation at break (%E) were measured by a universal testing
instrument. Three samples (30×50 mm) were tested. The film
samples were clamped and deformed under a tensile load at a
crosshead speed of 1 mm/min and a gauge length of 30 mm
until the samples were broken. TS was calculated by dividing
the maximum load (Fmax) by the initial cross-sectional area (Ф)
of the film sample (Pa) (Eq. (3)). Further, %E was calculated as
the ratio of the extension of the film (Δl) at the point of sample
rupture to the initial length (l0) of the sample (Eq. (4)).

Chitosan films preparation
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 modifying the surface with CurNE andcurcumin oil (CO). The modified films were conditioned in achamber for 48 h at 50% relative humidity (RH).

𝑇𝑆
%𝐸



Eq. (3)

Ф

100

Eq. (4)

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. (5) and expressed as mmol Trolox/gweight of the dried film:
Eq. (5)RSA %A control -A sample ⁄A control 100

Preparation and characterization of curcumin nanoemulsion
Curcumin powder (70 mg) was dispersed in 10 mL of MCT
oil to form the oil phase. Tween 80 and distilled water weremixed at 1:8 to form the aqueous phase. Appropriate
proportions of the oil and aqueous phases and ultrasonication
times as shown in Table 1. Under the various conditions, the20Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P11

where Acontrol and Asample are the absorbance of the blank andsample.

Table 3 Tensile strength and %Elongation at break of CH
composited films
WVP
TensileSample(g mm m−2 d−1 kPa−
%Elongation
(MPa)
s
1)
CH
0.130a±0.010
8.82a±0.19
21.13bc±0.64
CH-NE
0.049d±0.001
7.96c±0.2254.07a±10.19
CH6.92d±0.1314.80c±1.74
0.087b±0.001
CNE
CH-CO
0.077c±0.001
8.46b±0.08
30.00bc±7.21

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

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, theincreasing adsorption of the concentration of the aqueous phase,the decrease in the interfacial tension, and the formation of fine
droplets. CurNE7 exhibited the highest absolute value(−15.43±0.74 mV) (p ≤ 0.05). Additionally, the ultrasonication
time is a crucial factor in preparing NE because prolonged time
could increase the temperature from the cavitation-induced
thermal effect, thus stimulating the deterioration andaggregation of neighboring droplets. Since CurNE7 exhibited aconcise ultrasonication time (15 min) and demonstrated higher
stability and uniformity, it was selected and integrated to CH
for further investigations.

Determination of the antioxidant activities of CH-based films
The CH–NE condition exhibited five times higher
antioxidant activity than CH (Table 4). Moreover, CH–NE
exhibited the highest antioxidant activity probably because of
the reduction in 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 and increased the release rate,
dispersion, and kinetically stable suspension.
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 higher to 78.82 and 83.42 °C,
respectively (Table 4). It is 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).

Table 2 Mean diameters and zeta potential of CurNEs
Mean diameter
Zeta potential
Condition(nm)(mV)
CurNE11023.00a±13.00-0.88b±0.36
CurNE2758.70b±3.68-0.69b±0.26
e
CurNE3360.67 ±2.29-0.50b±0.33
CurNE4378.20d±5.59-0.52b±0.48
CurNE5533.23c±3.68c-0.08b±0.21
de
CurNE6367.50 ±5.20-0.73b±0.38
f
CurNE7307.53 ±6.30-15.43a±0.74*Different letters in the same column indicate significantly different (p ≤ 0.05).

Table 4 WVP, antioxidant activities and Tg of CH
composited films
Samples
mmol Trolox/g film
Tg (℃)
CH
0.39±0.02d
80.7
CH-NE
2.11±0.09a
71.3
CH-CNE
0.58±0.03c
78.8
CH-CO
0.73±0.00b
83.4

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 (Table 3). The improved WVP of CH–NE might be due tothe presence 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. On
the contrary, 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).

In summary, to develop CH-based films by incorporating
CurNEs on various modifications. CH–NE exhibited good
appearance, enhanced WVP, %E and antioxidant activities.
Additionally, the thermal analysis revealed that the films
exhibited considerable miscibility of the components from CH
and CurNEs. The results initiated and promoted an interestingdevelopment as an active ingredient for producing foodpackaging materials.

Determination of the mechanical properties of CH-based films
CH exhibited the highest TS (8.82±0.19MPa), and CH–NE,
CH–CNE, and CH–CO exhibited significant decreases in TS (p≤ 0.05) (Table 3). TS of CH–NE was attributed to the role of
the NE composites as plasticizers, which affected themechanical properties (TS) of the film. Therefore, the internal
network and cohesiveness of the film were destroyed, and thisreduced the TS. The following inconsistency was observed
between TS and %E: CH–NE exhibited the highest %E(54.07±10.19), followed by CH, CH–CO, and CH–CNE (p <0.05). This might be attributed to the fact that NE in CH–NE
ensured efficient elongation and acted as a plasticizer to retain
the extensibility. Furthermore, the increase in %E might becaused by the change in the mobility of the CH molecules.

REFERENCES
Luangapai, F., Peanparkdee, M., & Iwamoto, S. (2022).
Effects of a curcumin nanoemulsion on the physico‐chemical properties of chitosan‐based films. International
Journal of Food Science & Technology, 57, 101-109.
Peanparkdee, M., Iwamoto, S., and Yamauchi, R (2016).
Microencapsulation: A review of applications in the foodand pharmaceutical industries. Reviews in Agricultural
Science 4, 56-65

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Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P12

Evaluation of the impact of climate change on river temperature
Khadiza Akter Mousumi1, Takeo Onishi2, Ken Hiramatsu2 and Toshifumi Imaizumi3
1. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. Faculty of Applied Biological Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
3. Faculty of Agriculture, Shizuoka University, 1-1 Yanagido, Gifu 501-1193, JapanINTRODUCTION

length of 166 km (103 mi), it drains an area of 1,985 square
kilometres (766 sq mi) in the Chūbu region and empties into IseBay. To evaluation climate change impacts on the study site
the dataset of the following website will be used.
http://www.miroc-gcm.jp/~pub/d4PDF/index_en.html
Present and future climate data will be generated by using a
regional climate mode through dynamic downscaling approach.
In this study, a watershed hydrology model, Soil and Water
Assessment Tool (SWAT) will be utilized to simulate flowwhich is developed by the United States Department ofAgriculture (USDA) and one of the most widely usedsimulators for hydrologic modelling (Arnold et al. 2012). It is asemi-distributed hydrological simulator used to simulate
several processes over an extended period, primarily in ruralcatchments (Arnold et al. 1998). The SWAT model has been
extensively applied to various problems such as land coverchanges, agricultural management, and climate changes. The
several input data and their sources are given in the following
table:

In aquatic ecosystems, river temperature is a crucial indicatorof biodiversity and sustainability. River discharge and watertemperature directly affect water quality (Ducharne 2008; Haagand Westrich 2002; Ozaki et al. 2003), the growth rate anddistribution of freshwater organisms (Eaton and Scheller 1996;
Ebersole et al. 2001; Mohseni et al. 2003). The dynamics andkinetics of biochemical reactions are influenced by rivertemperature, which also affects the availability, behavior, andkind of aquatic species in the ecosystem to which it belongs.
The availability and temperature of water are also economicallyimportant, for example for thermoelectric power production(Forster and Lilliestam 2011; Koch and Vo¨gele 2009; Manoha
et al. 2008), drinking water production (Ramaker et al. 2005;
Senhorst and Zwolsman 2005), fisheries (Bartholow 1991;
FAO 2008; Ficke et al. 2007) and recreation (EEA 2008b;
Webb et al. 2008). Climatic conditions have a strong influenceon water temperature. The increase in stream temperatures has
already been observed in many countries., including Austria(Webb and Nobilis 1994), China (Chen et al. 2016), Germany(Arora et al. 2016), Poland (Kedra and Wiejaczka 2018; Graf
and Wrzesinski 2020), UK (Orr et al. 2015), Switzerland(Michel et al. 2020) or USA (Kaushal et al. 2010; Seekell andPace 2011; Isaak et al. 2018). Changes to natural watertemperature regimes can result in myriad effects on aquaticorganisms, water quality, circulation patterns, recreation,industry, and utility operations. Consequently, livelihood of the
inhabitants depending on the river will also change. So, tracking
changes in water temperature is key to better understanding thepotential effects of global climate change on freshwater
ecosystems.

Table 1: Input data for SWAT and their sources

Data
DEM
Land use
Soil type
Discharge

River temperature data will be observed in the research project'水防災・農地・河川生態系・産業への複合的な気候変動
影 響と適応策の研究', which is funded by The Environment
Research and Technology Development Fund of the Ministryof Environment.

Predicting the impact of climate change on river systems isimperative for effective management of aquatic ecosystems.
This study will demonstrate the potential impact of climate onrivers temperature and how this might affect the future
ecological systems. The objectives of the study are:







Sourcehttps://asterweb.jpl.nasa.gov/
https://www.eorc.jaxa.jp/ALOS/en/dataset/lulc_e.htm
https://www.mlit.go.jp/en/
http://www1.river.go.jp/

To evaluate the processes involving climatic, hydrologic,
and their interactions that determine the stream
temperature.
To investigate the impact of air temperature andstreamflow changes on river temperature.
To investigate the effect of changing temperature on
aquatic ecosystem.
To determine the influence of changing stream
temperature on water quality, habitats of river and on thelifestyle of people.
To find the best way to mitigate climate change and toadapt with the changing situation
To develop long-term strategies and management policies
to match with climate change phenomenon.

Fig. 1: Points of observation for measuring river temperatures

EXPECTED RESULTS

MATERIALS AND METHODSThe study site for this research is Nagara river. The Nagara
River has its source in the city of Gujō, Gifu Prefecture, and its
mouth in the city of Kuwana, Mie Prefecture, Japan. With a

22

The results of the study will provide tool(s) that will
determine the climate change effects on rivertemperature.

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P12



The findings of the study will show the effects ofchanging river temperature on aquatic ecosystems and the
local’s livelihood on the river.
The results of this study can be very useful in water
resource planning, effective management of aquatic
ecosystems.
Kaushal, S.S., Likens, G.E., Jaworski, N.A., Pace, M.L., Sides,
A.M., Seekell, D., Belt, K.T., Secor, D.H. and Wingate, R.L.
(2010). Rising stream and river temperatures in the UnitedStates. Frontiers in Ecology and the Environment 8(9): 461466.
Koch, H., and Vo¨gele, S. (2009). Dynamic modelling of water
demand, water availability and adaptation strategies for
power plants to global change. Ecological Economics 68:
2031–2039.
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Arnold, J.G., et al. (2012). SWAT: model use, calibration, andvalidation. American Society of Agricultural and BiologicalEngineers 55 (4): 18.
Arora, R., Tockner, K., and Venohr, M. (2016). Changing rivertemperatures in northern Germany: trends and drivers ofchange. Hydrological Processes, 30(17): 3084-3096.
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Chen, D., Hu, M., Guo, Y., and Dahlgren, R. A. (2016).
Changes in river water temperature between 1980 and 2012
in Yongan watershed, eastern China: Magnitude, drivers, andmodels. Journal of Hydrology, 533: 191-199.
Ducharne, A. (2008). Importance of stream temperature toclimate change impact on water quality. Hydrology and Earth
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EEA, (2008b). Impacts of Europe’s Changing Climate –Indicator-based Assessment. EEA, Copenhagen.
Eaton, J. G., and Scheller, R. M. (1996). Effects of climate
warming on fish thermal habitat in streams of the UnitedStates. Limnology and oceanography 41(5): 1109-1115.
Ebersole, J. L., Liss, W. J., and Frissell, C. A. (2001).
Relationship between stream temperature, thermal refugia
and rainbow trout Oncorhynchus mykiss abundance inarid ‐ land streams in the northwestern United States.
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Ficke, A.D., Myrick, C.A., and Hansen, L.J. (2007). Potential
impacts of global climate change on freshwater fisheries.
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power generation in view of climate change. Regional
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Graf, R., and Wrzesiński, D. (2020). Detecting patterns ofchanges in river water temperature in Poland. Water 12(5).
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23

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P13

Fate of plastic mulch residues in agricultural soil and its influence on soil
properties response to soil amendment addition
Shiamita Kusuma Dewi1 and Yongfen Wei2
1. The United Graduate School of Agricultural Science, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
ton ha-1. Therefore, 0.5 g of each mulch plastic films, and 3.75
g of each soil amendment were mixed into 500 g of soil by
INTRODUCTION
stirring with spoon in a big container before transferring the
mixture into each experimental box with size of 14.8 cm
Mulching is a practice of covering the soil to make favorable(length) × 10.8 cm (width) × 10.1 cm (height). Experimentcondition for plant growth, development, and efficient crop
design and treatments were shown in Fig. 1. Each treatment has
production. This technology is important especially in region
3 replicates; therefore, totally 27 boxes were prepared.
with few precipitations since it not only has the function of
preventing water evaporation, but also has many advantages
such as reducing pesticides, preventing weeds and insect pests,
and reducing soil erosion.
Polybutylene succinate-co-adipate (PBSA) is biodegradable
polymer with high flexibility, processability, excellent impact
strength and chemical resistant (Palai et a.l, 2021). Due to its
semicrystalline characteristic this material is relatively easy tobiodegrade and is used in the production of mulching film andExperimental T reatments
packaging.
Control
PBSA
LDPE
Low density polyethylene (LDPE) mulch film is widely usedin farmland due to its ability to transmit long wave radiation,
Compost
PBSA+Compost
LDPE+Compost
good mechanical properties and ease of placement and removal.
Biochar
PBSA+Biochar
LDPE+Biochar
Plastic mulching practice has been globally applied forFig. 1 Experimental design and treatments (Soil Incubation)
instants economic benefits such as higher yields, improved fruit
quality and earlier harvest. However, it is reported that waste
plastic mulch recycling rate is low, less than 30%. Residual
Experiment boxes were kept at 27ºC throughout theplastic will accumulate in the soil and becomes smaller andincubation period. And to maintain soil moisture at 30-40 %,smaller (microplastics) due to repeated fragmentation. As the
every 6 days were sprayed with distilled water. Soil was
result, plastic pollution has become one of the most pressing
randomly collected on the 3rd, 15th, 40th, 80th, and next 120th
environmental concerns.
days for measurement.
On the other hand, farmer often apply soil amendments
Measurement – The basic physicochemical properties of soil
during the agricultural practice. The addition of organic
and amendments are summarized in Table 1. The pH and EC
material such as crop residues, compost, biochar, animal(electrical conductivity) were measured in the supernatantmanure can improve soil fertility, soil physical characteristic,
suspension of sample in water with ratio 1:5 (w/v), OM (organic
and augment microbial activities. However, with the presence
matter) was determined as the loss in ignition (LOI) at 600ºC.
of organic material induced in soil, the fate of plastic mulch
TN (total nitrogen) and OC (organic carbon) were measured byresidues is remained unknown.
using a nitrogen and carbon analyzer.
To study the fate of plastic mulch residues and to obtain
Mulch plastic film pieces were taken manually with
empirical evidence on the potential effect of mulch plastic
tweezers, washed with distilled water and the impurities wereresidue pollution on soil properties and microbial community,
removed with Fenton’s reagents for 20 min at 25ºC. Dried
particularly when using different farming practice, an indoor
samples were weighed by analytical microbalance. And surface
soil incubation experiment was designed and carried out.
morphology of mulch plastic films was visualized by scanningelectron microscopy (SEM/EDX).

MATERIALS AND METHODS

Table 1. Physicochemical properties of the soil andamendments
Parameter
Soil
CompostBiochar

Soil sample and amendments – Soil sample was collectedfrom vegetable field located in Gifu University. Compost andbiochar were used in the study as soil amendments. The
compost came from a local company and was made from foodwaste and cow manure, and the biochar derived from rice husk
and was obtained from TAKII SEED corporation. The soil wassieved through a 2-mm mesh sieve and homogenized for the
next stage.
Mulch Plastic – Low density polyethylene (LDPE) andPolybutylene succinate adipate (PBSA) mulch films were cut
into a square shape manually with cutter and ruler. The size was
10 mm large. The weight for PBSA was 2.1556 ± 0.11042
mg/piece and LDPE was 1.7004 ± 0.08921 mg/piece. To
minimize microbial contamination, mulch plastic film pieces
was exposed to ultraviolet in clean bench for 20 minutes.
Mulch plastic and amendments addition to soil – The soil
was mixed with each type of mulch plastic film at aconcentration of 0.1% (w/w) and soil amendment with dose 15-pH

7.34 ± 0.02

8.08 ± 0.05

10.09 ± 0.04

EC (dS m-1)

0.19 ± 0.00

6.25 ± 0.17

1.01 ± 0.01

OM content (%) 6.08 ± 0.04

67.83 ± 2.61

17.03 ± 4.31

TN ( g kg-1)

3.92 ± 0.74 33.39 ± 0.52

5.10 ± 0.97

OC (g kg-1)

16.30 ± 1.05 305.36 ± 12.53 192.07 ± 10.41

Note: values represent mean ± standard deviation (n =3), EC:
electrical conductivity; OM: organic matter; TN: total nitrogen;
OC: organic carbon.
Statistical analysis – The data are reported as the means ±standard deviation (SD). Significant differences (p < 0.05) were
tested via ANOVA using SPSS 19.0. The differences in mulch
plastic films weight and total nitrogen content were compared
between samples incubated in different days while pH values

24Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P13

significant differences were found in weight change of LDPE,
the degradation has started to occur which can be seen in Fig.
3d that shows the abrasive surface of LDPE under biochar
addition.

were compared between sample in different soil amendments(p < 0.05) via one way ANOVA in conjunction with Tukey’s
honestly significant difference (HSD).

RESULTS
2.5
PBSA Weight (mg)

Soil properties — The experimental result for soil pH andtotal nitrogen (TN) after the start of cultivation are as follows:
No significant differences found in soil pH with the addition ofsoil amendments and mulch plastic residue contamination(PBSA and LDPE) compared to control treatment (Table 2).
However, there was a significant difference in soil pH value on
day 3 of the incubation period between the addition of biochar
and compost under both mulch film contaminations.
Table 3 shows TN values under different treatments. In casesof Control, PBSA+Compost and LDPE +Biochar treatments,
TN were significantly increased on day 15 and declined on day
40. The highest TN value (7.52 g kg-1) was observed on day 15
in the case of LDPE with biochar amendment.

15 days

40 days

Control

7.35 ± 0.08 ab

7.53 ± 0.03 ab

7.35 ± 0.06 a

PBSA

7.43 ± 0.12 a

7.54 ± 0.10 ab7.40 ± 0.04 a

PBSA + Compost

7.17 ± 0.04 b

7.40 ± 0.07 b

7.34 ± 0.04 a

PBSA + Biochar

7.45 ± 0.02 a

7.59 ± 0.06 a

7.41 ± 0.06 a

Control

7.35 ± 0.08 ab

7.53 ± 0.03 a

7.35 ± 0.06 a

LDPE

7.36 ± 0.09 ab

7.56 ± 0.04 a

7.39 ± 0.05 a

LDPE+ Compost

7.23 ± 0.05 b

7.45 ± 0.12 a

7.38 ± 0.01 a

LDPE + Biochar

7.43 ± 0.01 a

7.61 ± 0.03 a

7.31 ± 0.04 a

a

a

b

b

c

day 40

ab

1.5
1
0.5

LDPE Weight (mg)0Periods of Incubation
3 daysb2

day 15

2.5

Table 2. Soil pH at different period of incubation
Treatment

day 3
a

a

2

a

a

a

a

a

a

a

a

a

1.5
1
0.5
0
soil control

soil + compost

soil + biochar

Fig. 2 Changes in weight of plastic mulch pieces withcompost and biochar addition.

Note: Value represent mean ± standard deviation (n=3).
Different letter (a-b) indicates that significant difference (p <0.05) exists among treatments within the same mulch plastic
films contamination.

a) PBSA, day 3b) PBSA, day 40c) LDPE, day 3d) LDPE, day 40

Table 3. TN of soil at different period of incubation
Periods of Incubation
3 days15 days

40 days

Control

4.61 ± 0.45 B

7.05 ± 1.27 A

3.38± 0.65 B

PBSA

4.12 ± 0.56 A

6.12± 1.58 A

3.70 ± 0.65 A

PBSA + Compost

4.11 ± 0.56 B

6.84 ± 1.21 A

3.58 ± 0.39 B

PBSA + Biochar

5.21 ± 1.00 A

5.81 ± 0.96 A

4.26 ± 0.92 A

Treatment

LDPE

3.46 ± 0.06 A

5.82 ± 1.81 A

3.76 ± 0.63 A

LDPE+ Compost

4.98 ± 0.94 A

6.76 ± 2.15 A

3.62 ± 0.53 A

LDPE + Biochar

3.70 ± 0.26 B

7.52 ± 0.71 A

3.29 ± 0.46 B

Fig. 3 Surface morphology of PBSA and LDPE on day 3 andday 40 under biochar treatment.
In this study, the fate of plastic mulch residues in agriculturalsoil and its influence on soil properties response to soil
amendment addition was carrying out. The following results
were obtained.
1. After 40 days incubation, no significant differences foundin soil pH within all treatments.
2. TN observed on day 15 shown significantly increased thendeclined on day 40 in cases of Control, PBSA+Compostand LDPE+Biochar treatments.
3. Degradation rates of PBSA films ranged from 16.38% 29.87% after 40 days.

Note: Value represent mean ± standard deviation (n=3).
Different letter (A-B) indicates that significant difference (p <0.05) exists among treatments within period of incubation.
The weight and surface morphology of mulch plastic piecesFor the weight of mulch plastic pieces, a downward trend wasobserved in all treatments. After 40 days of incubation, as
shown in Fig. 2, the weight of PBSA significantly decreased by
29.87% in control treatment, while 16.38% in compost and19.32% in biochar addition. No significance differences were
found in LDPE’ weight.
Regarding the surface morphology of LDPE and PBSA, as
shown in Fig. 3c and 3a, a smooth surface was observed on day
3 of incubation for both plastics. However, we found that at 40days PBSA pieces were fragmented into smaller pieces withcoarse surface (Fig. 3b) upon biochar addition. Even no

REFERENCE
Palai, B., Mohanty, S. and Nayak, S.K. (2021) A Comparison
on Biodegradation Behaviour of Polylactic Acid (PLA) Based
Blown Films by Incorporating Thermoplasticized Starch (TPS)And Poly (Butylene Succinate-Co-Adipate) (PBSA)
Biopolymer in Soil. Journal of Polymers and the Environment
29(9). pp. 2772–2788

25

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P14

Highly accurate estimation of swell components using ocean wave model
Shota Iguchi1, Jun Yoshino1 and Tomonao Kobayashi1
1. Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Table 1: Statistical error indices of predictions
analysis period
2019/5/15 ~ 2019/5/30

INTRODUCTION

Off the coast of

Renewable energies are currently attracting attention forrealization of a decarbonized society, Offshore wind power is in
the spotlight as one of the energies. However, the design andinstallation of offshore wind towers requires accurate predictionof waves, and wave model have difficulty predicting the swell
component of waves. This is because the swell component iseasily deformed in shallow water due to its long wavelength andpropagates over long distances.
Therefore, this study investigates a calculation setup that canaccurately estimate the swell component by changing the size ofthe calculation domain and calculation parameters using thewave model, named SWAN.

analysis point

Fukushima Prefecture（ 36.95°N, 141.20°E ）coefficient of friction
on the seafloor
resolution of wave direction

0.038m²s²
5°wind data

NCEP FNL-0.25 [3]resolution of wind data

0.25° × 0.25°

time interval of wind data

3 hours

submarine topographic data

ETOP01 [4]METHODSIn this study, we set up four calculation regions for the
analysis points and investigate which region is the best to
calculate from by examining how much the estimated values
change at each nesting stage. The effect of changing the wave
direction resolution will also be investigated.
—Model used and observation data
SWAN used in this study is one of the third-generation wave
model, and it takes into account the wave characteristics ofshallow-water areas, such as sea-bottom friction and shallowwater breaking waves [1]. It also uses a spectral method, whichallows the wave components to be broken down into wind waves
and swells, depending on the period. In SWAN, waves with aperiod of 10 seconds or longer are considered as swells. Data
provided by Nationwide Ocean Wave information network forPorts and HArbourS (NOWPHAS) [2] is used as observation data
for accuracy comparison. However, since data on the swell
component is not provided, comparisons are made based on
significant wave heights and periods in wave fields with longsignificant wave periods.

Fig. 1: Range of each calculation area set
Table 2: Nesting phase per case
case A
Domain 01〜04—Model used and observation data
The computational settings for SWAN in this study are shownin Table 1 and the computational domain for nesting set up is
shown in Figure 1. The calculation period shown in Table 1 is
one of several periods selected for eases of analysis due to thelarge changes in wave heights during the year 2019. The first
five days of the calculation period are used for spin-up. The
calculation was performed off the coast of Fukushima
Prefecture, where NOWPHAS stations are located, in the Pacific
Ocean coastal region where swells are easily transmitted from
far in the ocean. The wave direction resolution and sea-bottom
friction coefficient are the settings recommended by SWAN for
swell estimation. The computational domain shown in Figure 1
has a grid spacing (resolution) of 2° × 2° (about 220 km × 220km) for Domain 01, the largest domain, and the spacing
decreases by a factor of 4 as the domain becomes smaller(resolution increases by a factor of 4). Table 2 shows casedivisions for each stage of nesting.

case B

Domain 02〜04

case C

Domain 03〜04

case D

Domain 04

listed in Table 2. Significant wave heights in the figure seem
similar in each case, so the values can be viewed as pure wave
heights. The larger the period of a significant wave, the larger
the wave component with a longer period, and thus a larger
estimated value means that more swell components with long
periods are predicted.
The closest to the observed wave height and period to theestimated value was case B. Case A shows almost exact values
for the May 21 peak, but slightly larger values overall. On theother hand, for cases C and D, the peak values are quite close to
the observed values, but the values are basically smaller than theobserved values.
The increase in wave height on May 21 may be induced by thelow pressure cyclone in the vicinity of Japan. Figure 3 shows the
distribution of the sea surface winds at that time, and it can beseen that the location where the sea surface wind disturbancebecame larger due to the pressure configuration extends beyond
the range of Domain 03. Therefore, Figures 4 and 5, which show
the distribution of wave heights on the day, show that the swell

RESULTS AND DISCUSSIONS—Estimation results by nesting
Figure 2 shows the results of estimation for each of the cases

26

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P14

generated in case B came intermittently, while it was interrupted
in case C.

CONCLUSIONS
The results of the estimation performed in this study indicate
that, in order to accurately estimate the far-field swell
component, it is necessary to establish a domain large enough toinclude most of the offshore wind disturbances within thecalculation domain. However, it was also found that calculations
from too large a region may overestimate the swell. In suchcases, it was found that increasing the wave direction resolution
can more accurately calculate the propagation of waves overlong distances and improve the accuracy of wave height andperiod estimation.

—Estimation results by changing wave height resolution
Next, we examined how much the estimated values wouldchange if the wave direction resolution was increased from 5° to
2° to make the wave direction finer. The results are shown in
Figure 6. From this figure, it can be seen that both cases E and F
are closer to the observed values than the estimated values forcases A and B.

REFRENCES[1] SWAN USER MANUAL (SWAN Cycle Ⅲ version
41.01)[2] Real Time Nowphas < https://www.mlit.go.jp/kowan/nowphas/index.html > (last visited Sep 18, 2022)[3] Wind data on a 0.25° x 0.25° grid from the U.S. National
Centers for Environmental Prediction (NCEP)[4] Digital topographic data created by the National Oceanicand Atmospheric Administration (NOAA)

Fig. 5: Wave height distribution on May 21 (case C)(Left: significant wave, Right: swell)Fig. 2: The results of estimation by SWAN (case A, B, C, D)(Upper: significant wave height, Middle: swell,
Lower: significant wave period)

Fig. 3: Wind distribution on May 21

Fig. 6: The results of estimation by SWAN (case A, B, E, F)(Upper: significant wave height, Middle: swell,
Lower: significant wave period)

Fig. 4: Wave height distribution on May 21 (case B)(Left: significant wave, Right: swell)27

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P15

High-resolution analysis of atmospheric optical fluctuations for laser
communication using WRF model
Naoya Shimpuku1, Jun Yoshino1, Ichiro Tamagawa1 and Tomonao Kobayashi1
1. Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanINTRODUCTION

Table 3.1 WRF settings

Laser-based communications perform with ultra-high speed,high capacity, high reliability, compare with the current
microwave-based ones. However, the quality of thecommunication is strongly dependent on the atmosphericconditions when they are used on the earth, because theatmospheric fructurations cause of instabitily of laser paths andsuspeneded matters reduce its intensity. In daytime, the warm
air is usually layered near the ground due to heating by thesunshine. The temperature difference in the layered air createsa density difference, which causes convection and fluctuationsin the refractive index. In communications using laser beams,
atmospheric fluctuations are risk of communication quality
such as its degradation and interruption. In order to maintain thequality of this optical communication, the distribution (1) of thestructure coefficient of refractive index fluctuation 𝐶𝑛 , which
is an index related to fluctuations, is predicted at high resolution
with a numerical weather model.

input data

computation

2019-08-02_00:00:00 UTC

periodComputational
domainMETHODOLOGYThe numerical weather forecasting model, WRF (2) is
employed for representing the meteorological condition near
the ground in this study. It’s usually used for simulating theweather in resolution from 1 km to several thoudand km.
However, we calculate the weather finer than the usual use for
evaluation of 𝐶𝑛 . With this simulation, the popularmeteorological parameters, such as the temperature, wind, andalso special ones, like turbulence, friction velocity and heattransportation.
The optical fluctuation in the atmosphere is caused bylayered thermal distributions and turbulent intensity of
atmosphere. They are expressed with Monin-Obukhov's
similarity rule (3) near the ground. It is evaluated from somemeteological parametes; e.g. friction velocity and frictuation oftemperature. These parameters are from represented weather
condition simulated with WRF.
This calculation is performed for each horizontal and verticalgrid, and the distribution of the structure coefficient ofrefractive index variation 𝐶𝑛 in the atmosphere is calculated
and output as a figure.

initial value:
Local Forecast Model GPV(LFM)（Horizontal resolution 2㎞, Every
3 hours ）NCEP GFS-0p25 forecast value（Horizontal resolution 0.25゜,
3 hour intervals）
2019-07-31_23:00:00 UTC 〜

Number ofvertical layers

Domain 1 : 3km×3km（100×100 grids）
Domain 2 : 1km×1km（100×100 grids）
Domain 3 :333m×333m(100×100 grids)
Domain 4 : 111m×111m（100×100 grids）
55 layers (ground surface〜
500hPa)

CFL condition (4) in the WRF calculation for Domain 4, so thecalculation did not run properly and was limited to 19 hours.
Therefore, the results show the distribution of the structure
coefficient 𝐶𝑛 of the refractive index variation of the verticalsection as shown in Figure 4.1 for every minute for 19 hours.

MODEL SETTINGS
The center of this study was set at 35.4635°N, 136.7472°E,
where is near Gifu University. Table 3.1 accounts for the
optional settings in the WRF for this study. The computational
domain was set up with four levels of two-way nesting (Fig.
3.1) to enable the analysis of optical path scale resolution. Only
Domain 4 was set to be calculated using LES (Large Eddy
Simulation). The calculation period was set from August 1,
2019, the day of the heat wave, to August 2, 2019 (UTC in
Table 3.1). Calculations were performed one hour in advancefor the purpose of familiarizing the calculations.

RESULTS

Figure 3.1 WRF nesting setting area
Domain1～Domain 4

It was thought that many calculations would not satisfy the28Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P15

The horizontal axis of this figure indicates the east-west
distance (m), the vertical axis indicates the elevation (m), andthe arrows indicate the wind speed and direction in the verticalplanes. The closer to the warmer color, the larger the value ofthe structure coefficient of refractive index variation 𝐶𝑛 ,indicating atmospheric instability.
Figure 4.2 shows the horizontal wind speed u (m/s) andvertical wind speed w (m/s) at the point of 777 ⅿ in altitude and2,500 ⅿ in horizontal direction (black dot in Figure 4.1) and
log10 𝐶𝑛 time variability time average (black line) andvariability component (red line), respectively.

2,500 ⅿ in horizontal direction (black dot in Figure 4.1) and
log10 𝐶𝑛 time variability time average (black line) andvariability component (red line), respectively.
5.1 Short-time horizontal variation of 𝑪𝒏𝟐
We can see wind speeds that behave like waves at the redcircle in Figure 4.1. In the same region, there is a green region
which correspond to the low 𝐶𝑛 . Since both of them seem to
be moving in the east-west direction, it is thought that air
masses with a certain structure coefficient of refractive index
variation 𝐶𝑛 are advected from the west to the east by thewind.

DISCUUSIONS

5.2 Vertical variation of 𝑪𝒏𝟐

Figure 4.2 shows the horizontal wind speed u (m/s) andvertical wind speed w (m/s) at the point of 777 ⅿ in altitude andAs shown in 4.1, we can find the vertical downward wind
and the vertical upward wind in the red circle. When there is
downward wind, the green area, that is, the area where the valueof the structure coefficient of refractive index variation 𝐶𝑛 issmaller than that of the surrounding area, can be seen in thelarge area. In the blue regions at the west side vertical
downwind can also be found. It is thought that the air in theregion with small (large) structure coefficient of refractive
index variation 𝐶𝑛 s advected downward (upward) by thevertical downward wind.
5.3 𝐥𝐨𝐠 𝟏𝟎 𝑪𝒏𝟐 Regarding time variation
Focusing on the time averages in Figure 4.2, we can see that𝐶𝑛 maintains an unstable value during the day andshows stability during the night. From this, we can assume thatlog 𝐶𝑛 will increase with the sunrise in the morning when
we could not obtain the result. In addition, when we focus
on the fluctuation components, we can see that when local
horizontal or vertical fluctuation in wind occurs, the fluctuationof log 𝐶𝑛 is also remarkable. Especially when the windspeed in the vertical direction fluctuates locally and extremely,
the fluctuation component of log 𝐶𝑛 also tends tofluctuate evidently. When the local vertical upward wind blows,
log 𝐶𝑛 is small, and when the local vertical downward
wind blows, the value of log 𝐶𝑛 seems to fluctuate to alarge value. It can be inferred that the air masses showing
respective log 𝐶𝑛 are advected both horizontally andvertically by the wind.

log

Figure 4.1 Change in the distribution of 𝐶𝑛

REFERENCES(1)N. Akiyama, T. Kobayashi, J. Yoshino, I. Tamagawa, H. Yoshida,
and Y. Takayama: Atmospheric transmission and stability for satelliteto-ground optical communication, Proc. of Space Science andTechnology 64th, 1C10, 2020
(2)WRF Model Users Page:
http://www.mmm.ucar.edu/wrf/users/(3)Monin, A.S. and A.M. Obukhov, 1954: Basic laws of turbulent
mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst.
Acad. Sci., USSR, (151), 163–187 (in Russian).
(4)Seiichi Koshizuka, "Numerical Fluid Dynamics", Kaifukan, 1997, p.
26 - p. 27

Figure 4.2 Time averaged and fluctuating componentsof vertical wind speed u and horizontal wind speed w andCn229

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P16

Antibiotic resistance genes and 16S rDNA in large Johkasou treating
residential area domestic wastewater
Haoning Su1, Shinya Okumura 1, Yasushi Ishiguro 2 and Fusheng Li 2
1.
Graduate School of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan(qPCR). Tetracycline resistance gene (tet G), a mobile genetic
element (MGE) (integrase class 1 gene, intl 1), and total
bacteria reflected by 16S rDNA were quantified by qPCR withthe utilization of SYBR® Premix Ex Taq™ (TaKaRa, Japan)
based on the manufacturers' manual.

INTRODUCTION(1) Sample collection and pre-treatment
Water and sludge samples were collected from eightsampling points (Fig. 1) in a large-scale Johkasou located in
Aichi, Japan. Sampling was performed weekly in 2 months,
corresponding to the operation time after accumulated sludge
cleaning. Samples were divided into two parts for storage: forphysicochemical parameter analysis, samples were stored at
4 °C and processed within 48 h; and for ARGs analysis, samples
were stored at −20 °C before analysis.
To classify ARGs on the remained suspended solids, sieves
with the opening of 250, 125, 75 and 25µm respectively, and amembrane filter with the pore size of 3 µm were used to filter
the treated wastewater. The filtrates were used for ARGs
quantitation.

Raw water storage
Pre-aeration(1) Abundance of ARG and MGE during treatment
16S rDNA

Gene concentration (copies/L)

METHODSRESULT AND DISCUSSION

tetG

intl1
28 days

7 days1.E+13

1.E+13

1.E+101.E+10

1.E+07

1.E+07

1.E+04

1.E+04

16S rDNA
35 days

Gene concentration (copies/L)

Antibiotic resistance genes (ARGs) are emerging
environmental contaminants and pose serious threat to publichealth. Wastewater from livestock, agriculture, industry,
hospitals, antibiotic manufacturers, and households is the main
source of ARGs. For Johkasou, a facility used to treat domestic
wastewater from residential area simply, research related to
ARGs is limited. The objectives of this study were (1) toquantify the ARGs in the wastewater of the whole treatment
process in Johkasou, (2) to investigate the existing form of
ARGs in relation to the size of suspended solids in treatedwastewater, and (3) to explore the relation between ARGs andmicrobial activity.

1.E+13

1.E+10

tetG

intl1

63 days

1.E+13

1.E+1035 days
1.E+07

1.E+07

1.E+04

1.E+04

1st & 2nd aeration
Sedimentation

Fig. 2: Concentration changes of ARGs in wastewater
during treatment (The data of 7, 28, 35 and 63 days after
cleaning were plotted here as the examples).

Sludge storage

Post-aeration

The abundance of ARG, MGE and 16SrDNA in thetreatment process showed tetG and intl1 existed in all
wastewater samples and more genes existed in biologicaltreatment units (Pre-aeration, 1st aeration and 2nd aeration)with more microbial diversity and interactions (Fig. 2), possibly
due to the high abundance, density, activity, and interactions ofbacteria increase the rate of gene transfer. The reduction ofgenes observed in the sedimentation tank may indicate, most of
the resistance genes that arrives with the wastewater and thatgrows and propagates through the treatment facility end up in
the settled sludge. In the operation of 7 to 63 days after sludge
cleaning, the fate and behavior of ARGs were similar, but theextent of ARGs reduction was higher for the time of 28 and 35

Re-sedimentation&Disinfectionti

Fig. 1: Schematic flow diagram of the Johkasou with thesampling points (Water sample;
Sludge sample).
(2) DNA extraction and determination of genes
DNA extraction for all samples was conducted using Power
Soil DNA Extraction Kit (MOBIO, USA) based on
manufacturer's manual. The extracted DNA was stored at−25 °C for determination of target genes by quantitative PCR30

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P16(3) Correlations between ARG and microbial activity
The large-scale Johkasou contains an activated sludge
sequencing batch reactor, and the high abundance, diversity,
activity, and interactions in the reactor may suggest an
increased rate of gene transfer, through horizontal and/or
vertical transfer of ARGs (Qin et al., 2020).
The correlation analysis of the resistance genes and microbialactivity in sludge is shown in Fig. 5. tetG and DHA showed
positive correlations in the 4 sets of data after sludge cleaningfor 14, 21, 28 and 35 days. Positive correlations may suggestthat higher microbial activity could probably lead to a higher
rate of gene dissemination. Additionally, the sludge with higher
DHA also showed better settleability (SVI ranged from 91.0 to
116.8 mL/g), so more ARGs can be concentrated in the settled
sludge.

days from the influent to the effluent: 2.6 and 2.7 log removalof tetG; 2.8 and 2.7 log removal of intl1 (Fig. 3).
4

tetG

intl1

16S rDNALog removal

3

2

1

0
7
14
21
28
35
42
49
56
Operation time after accumulated sludge cleaning (Days)

63

Absolute abundance of tetG (copies/g-d.w)

0

Fig. 3: Log removal of tetG, intl1 and 16S rDNA in theoperation time after sludge cleaning.
(2) Distribution of ARG on suspended solids
tetG and intl1 were detected in all filtrates, with theirconcentrations varying within one order of magnitude (Fig.
4A). For the absolute abundances based on the dry weight ofsuspended solids, The highest absolute abundances of tetG and
intl1 were found in filtrate with small microparticles which areless than 3 µm (Fig. 4B). Compared to larger suspended
particles, small particles are difficult to settle and can transporteasily to the receiving water body. Therefore, the potential riskof the proliferation of ARGs is high.

Abundance of genes (copies/L)

A

tetG

intl1

14 days
R² = 0.882

1.6E+10
1.4E+1021 days
R² = 0.985

1.2E+10
1E+10
8E+09
6E+09

28 days
R² = 0.965

4E+09
2E+09
0
0.E+00

4.E+084.E+082E+10
1.8E+1035 days
R² = 0.877
1.E+03

2.E+03

3.E+03

4.E+03

5.E+03

6.E+03Dehydrogenase activity, (μg-TF/g-VSS)/hour

16S rDNA

Fig. 5: Correlations between tetG and DHA in the sludge
with different cleaning time intervals.

3.E+083.E+08In this study, tetG and intl1 were found existent in the wholetreatment process of the investigated large-scale Johkasou fortreating residential area domestic wastewater, more ARGs and
16SrDNA were found in biological treatment units. Positive
correlations between tetG and DHA were revealed in thesludge. Higher abundance of ARGs is possibly due to the higher
levels in bacteria concentration and microbial activity, which
increased the rate of gene transfer. In the effluent, ARGs weredetected from suspended particles with different sizes(including those with sizes below 3 µm), fine particles with thissize are difficult to settle and exist in the treated water assuspended solids. Consequently, the high potential risk of
ARGs dissemination is substantial.
Further investigations are required in order to line out thequantification of other ARGs (sul1, sul2, qnrA and qnrS) on thesuspended particles with different sizes during the wholetreatment process

2.E+082.E+081.E+085.E+070.E+00

250
250µm
µm

125
125µm
µm

7575µmµm

2525µm
µm

3 µm
3 µm

2.E+11
1.E+11B

3.E+15

tetG

intl1

16S rDNA
3.E+15

1.E+11
2.E+15

1.E+11
8.E+102.E+15

6.E+101.E+15

4.E+105.E+142.E+100.E+00

16S rDNA (copies/g-d.w)

Absolute abundance of genes copies/g-d.w)

Openings of sieves (µm) and pore size ofmembrane (for 3 µm only)

REFERENCE
Zhou, C., Yin, J., (1996) A method for measurement of ttcdehydrogenase activity. Acta Scientiae Circumstantiae.
CNKI:SUN:HJXX.0.1996-04-003.
Ashbolt NJ, Amézquita A, Backhaus T, Borriello P, Brandt KK,
Collignon P., (2013) Human health risk assessment (HHRA)
for environmental development and transfer of antibiotic
resistance. Environ Health Perspect121(9):993–1001.
Qin, K., Wei, L., Li, J., Lai, B., Zhu, F., Yu, H., Zhao, Q., Wang,K., (2020) A review of ARGs in WWTPs: Sources, stressors
and elimination, Chinese Chemical Letters, Volume 31, Issue
10.

0.E+00125~250µm 75~125µm

25~75µm

3~25µm<3µm

Fig. 4: tetG, intl1 and 16S rDNA abundance in treatedwastewater after filtration through different sieves ormembrane (A), and the absolute abundance in treatedwastewater with different size (B).

31

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P17

Statistical analysis of solar radiation under future climate in Chubu region
Tomoki Kitaoka1, Jun Yoshino1 and Tomonao Kobayashi1
1. Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
distribution, a total of 90 member ensemble experiments wereconducted.
For the 2°C rise, we simulated a 2°C increase in global
mean temperature compared to the pre-industrial revolution(1850); as in the 4°C rise experiment. Six different SST futurechange patterns and added 9 different perturbations areprepared to each pattern for a total of 54-member ensemble
experiments.
In the domain model experiments, dynamical downscaling
was performed from the following 60 km resolution global
experiment to 20 km resolution.
・ Past experiment September 1950 - August 2011 x 50
members,
・4°C rise experiment September 2050 - August 2111 × 90
members,
・2°C rise experiment September 2030 - August 2091 x 90
members.
In this experiment, five data components, e.g. global solar
radiation, direct solar radiation, scattered solar radiation,
temperature, and wind speed from the historical experiment,
4°C rise experiment, and 2°C rise experiment of the domain
model NHRCM are extracted to estimate and analyze solar
radiation for future climate.

INTRODUCTION
Global warming is currently a major problem around theworld. As a result, demand for power generation fromrenewable energy sources that do not emit greenhouse gases isincreasing. The same as the other countries, Japan is also
moving toward a decarbonized society and is focusing on
renewable energy. Among these, solar power generation, which
can utilize unused space and has high energy generation
efficiency, is becoming increasingly popular. However, solar
power has the disadvantage of being susceptible to climatechange. If solar power generation continues to become morewidespread, the effects of climate change on solar power
generation will become more pronounced.
This study aims to estimate and analyze changes in solar
radiation in the future climate for the Chubu region. Using theweather forecast database d4PDF, we will estimate solar
radiation under the future climate.

MATERIALS
The ⅾ4PDF is an ensemble climate prediction database that
contributes to global warming countermeasures. In order tofully evaluate the uncertainties associated with infrequent
extreme weather events, the Ministry of Education, Culture,
Sports, Science and Technology and the Climate Change Risk
Information Creation Program conducted an unprecedentedly
large number of ensemble experiments (up to 100 members)
using high-resolution global atmospheric models and highresolution regional atmospheric models, and the d4PDF is adatabase of the experimental results. The calculations were
performed using the Earth Simulator under the "Earth Simulator
Special Proposal" conducted by the Japan Agency for MarineEarth Science and Technology (JAMSTEC).
The ⅾ4PDF consists of a global experiment using MRIAGCM3.2, the global atmospheric model of the MeteorologicalResearch Institute with a horizontal resolution of 60 km, and aregional experiment using NHRCM, the regional climate modelof the Meteorological Research Institute covering the Japaneseregion with a horizontal resolution of about 20 km.
The global experiment is divided into the following three typesof ensemble experiments;
・Historical experiment: 1951 - August 2011 x 100 members,
・4°C rise experiment 2051 - August 2111 x 90 members,
・2°C rise experiment 2031 - August 2091 x 54 members.
The global models use the following parameters as the input
data; the observed changes in sea surface temperature (SST),
sea ice, greenhouse gas concentrations, sulfuric aerosol
concentrations, ozone concentrations, and volcanic aerosol
concentrations in the past experiments. 100 members start theircalculations from different initial values and are also subjected
to small perturbations of sea ice and sea surface temperature(SST).
The 4°C rise experiment simulates a 4°C increase inglobal mean temperature compared to the pre-industrial
revolution (1850). Six SST future change patterns 1) CCSM4,
2) GFDL-CM4, 3) HadGEM2-AO, 4) MIROC5, 5) MPI-ESMMR, 6) MRI-CGCM3, and 15 perturbations to each patternwere added for a total of 90 patterns based on the global
atmosphere-ocean coupled model experiment. By giving the

METHODSIn this section, we discuss to compare data from d4PDF's pastexperiments to bring them closer to actual observed data, andhow to correct for this.
The data from the d4PDF's past experiments are used tocompare the observed data. This time, we extracted five datafrom d4PDF: global solar radiation, direct radiation, scatteredsolar radiation, temperature, and the U and V components ofwind speed. The data are then compared with the observation
data at Tateno, Tsukuba City in Ibaraki Prefecture at Japan. The
data period is from January 1982 to August 2011, which is theoverlap between the d4PDF past data and the observation data.
Figures 1 to 4 in the next section show the comparison. The
total solar radiation is the sum of direct and scattered solar
radiation and is therefore omitted.
In order to compare the observed data with data fromprevious d4PDF experiments, the following calculations were
performed for direct and scattered solar radiation.
Average value of observed data ∕ Average value of d4PDF(1)The values for direct and scattered solar radiation were 0.72
and 1.27, respectively. For direct irradiation, the d4PDF values
tend to be larger than the observed data. Conversely, for
scattered irradiation, the d4PDF values tend to be smaller thanthe observed data.
RESULTS AND DISCUSSION
A comparison of the corrected d4PDF data with the observed
data is shown below.
The correction method for direct and scattered solar radiation
is to approach the observed data so that the cumulative
frequency distribution of the d4PDF data fits every 10 W/m².
Next, for temperature, the following formula is applied.

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Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P17

Fig. 5: Comparison of observed data and d4PDF
data after correction for direct solar radiationon the normal surface

Fig. 1: Comparison of d4PDF and observed data for
direct normal surface solar radiation

Fig. 6: Comparison of observed data and d4PDF
after correction for scattered solar radiation
Fig.2: Comparison of d4PDF and observed data forscattered solar radiation

Fig. 7: Comparison of observed data and d4PDF
after correction for temperature
Fig.3: Comparison of d4PDF and observed data for
temperature

Fig. 8: Comparison of observed data and d4PDF
after correction for wind speed
were performed for direct and scattered solar radiation.
Fig.4: Comparison of d4PDF and observed data forwind speed

Mean value of observed data ∕ Mean value of corrected d4PDF(4)In the case of direct solar radiation, the value was 0.99. In thecase of scattered solar radiation, the accuracy was 1.00, whichis considered to be considerably better. In the case of the U and
V components of temperature and wind speed, the overall
accuracy was sufficient due to the overlap between the observed
data and the d4PDF. Therefore, this correction method is
considered to be the best one to bring the d4PDF weather
forecast data closer to the actual weather.
Therefore, there is no problem in using this correction
method for the future climate data of the 4°C rise experimentand 2°C rise experiment for the central region and d4PDF.

Finally, the following method of correction in the U and V
components of wind speed is used in the following equation.

In order to compare the corrected d4PDF data from previous
experiments with the observed data, the following calculations

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Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P17

Diagnosis of soil fertility by remote sensing data and GIS
Jieli Zhou1 and Yongfen Wei2
1. Graduate school of Natural Science and Technology, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Sentinel-2 provides global 10-meter resolution multispectral
images every 5 days. Besides, since it has a red edge band, which
is more sensitive to vegetation information, therefore, it is
widely used for mapping changes in land cover and monitoring
the world's forests. In this study, Sentinel-2 satellite dataobtained on the Copernicus Open Access Hub website were used.

INTRODUCTION
Soil fertility is a measurement of the soil’s ability to provide
various nutrients needed for crop growth. Faced with thegrowing global demand for food due to the world population
increase from 7.6 billion in 2020 to 9.6 billion in 2050, how toeffectively assess soil fertility to maximize crop yields on limited
arable land is a critical issue. The evaluation of arable land’s soil
fertility has mainly based on soil’s physical and chemical
properties, so far, in most cases, the evaluation is a small-scalebased approach and lacks quantitative and visualized strategies.
The main purpose of this study is to use remote sensing data,
GIS, field investigation data and laboratory analysis results ofsoil samples to comprehensively quantify and visualize the soil
fertility of agricultural soil.

Soil sampling
Field investigations were conducted after wheat harvest to
avoid the effect of fertilization on soil fertility during wheat
growth. And soil sampling was conducted from 5 blocks shownin Fig. 2 (No. 1- 5). In block No.1, four 10×10 m plots wereselected for soil sampling, and three 10 × 10 m plots wereselected in block No. 2- 5. For each plot, five samples werecollected. Totally, 80 samples were collected from surface soil
depths of 0-15 cm. The location of each soil sampling point wasrecorded using a handheld GPS.

Study area
Kaizu City as shown in Fig. 1, located in the southernmost ofGifu Prefecture, was used as study area in this research. It has anarea of about 112 square kilometers. The arable land accounts
for about 37% of its total area. The main crops of rice, wheat andsoybeans are cultivated in rotation. In this study, wheat was usedas the research object to analyze soil fertility.

No. 1
No. 2
No. 3

No. 4

No. 5
10-meter square plot
and 5 sampling points

Fig. 1 Study area (Satellite image on April 15, 2022)

Fig. 2 Five blocks for soil sampling

Research flowchart
This study was carried out according to the following
flowchart.

—Method
Image-based wheat area extraction
Decision tree classification method was used to extract wheatarea based on NDVI (Normalized Difference Vegetation Index)
value and texture of the image. NDVI images of each month in
the growing period of wheat were produced to improve theextraction accuracy (Fig. 3).
2021 year

2022 year

DATA AND METHODS— DataThe following data were used in this study.
Satellite data

Fig. 3 Variation trend of NDVI in wheat growth period

34Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P17

2 and No. 4 is almost at a medium level.

The distribution map obtained was shown in Fig.4. According
to the field investigation results, extraction accuracy of wheatarea was 98%.

Fig. 4 Extracted wheat area
Sample measurement
For freeze-drying, 20g of soil was taken from each sample,
and then stored in a -20°C freezer. The freeze-dried soil was usedfor DNA extraction and enzyme activity detection. The
remaining soil is naturally air-dried and stored at room
temperature for measuring soil physical and chemical properties
affecting soil fertility, such as soil pH, soil organic matter (SOM),
electrical conductivity (EC), organic carbon (OC), total nitrogenand so on. So far, parameters of soil moisture, soil pH, EC, DNA
extraction, and dehydrogenase activity (DHA) have beenmeasured.
Soil fertility index
Soil fertility index (SFI) proposed by Tülay Tunçay et al.
(2021) was used to estimate agricultural soil fertility (Eq. (1).
SFI = [Rmax ∗

×

× ……

]

(1)

In this equation, A, B …X mean rating values for different soil
parameters of soil physical and chemical properties, which wasgraded into 5 different levels from 100 to 10. Rmax is a ratio of(A + B+ ……+X) to total numbers of soil parameter used.
SFI was reported to be effective for quantifying the spatial
distribution of soil fertility. However, biological parameters were
not considered. This study also took into account biologicalinfluence factors such as DNA and DHA.

RESULTS AND DISCUSSION
Fig. 5 indicated the distribution of SOM, EC, DNA (16S
rDNA), NDVI, DHA and soil moisture. These maps produced
according to Kriging interpolation, a geostatistical interpolation
method that considers both the distance and the degree ofvariation between known data points when estimating values in
unknown areas. Observing Fig. 5, we can find that theseparameters were relatively high in the central part of Kaizu City,but relatively low at the lower ends of Kaizu City. Especially inthe vicinity of the third block, the values of these parameters
were generally the highest.
NDVI is an index that reflects the growth and health ofvegetation. From both two figures of SOM and NDVI, it can beseen that the distribution of high and low values is roughly thesame, indicating that SOM and NDVI are highly correlated.
From the distribution map of dehydrogenase activity (DHA)
and moisture, it can be seen that the dehydrogenase activity andthe moisture are most active near the third block. Based on thesemaps, it can be roughly estimated that the soil fertility in the third
block is the highest, while in the southernmost part of Kaizu City
has the lowest soil fertility, and the soil fertility in the bloke No.

Fig. 5 Distribution map of soil parameters
The final objective of this study is to quantify and visualize
soil fertility in agricultural soil using remote sensing data, GIS,
field survey data and laboratory analysis results of soil samples.
So far, the extraction of wheat has been completed, and the
distribution of some soil parameters also clarified. However, torealize the quantitative and visual evaluation of soil fertility, themeasurement results of other soil parameters are still needed. At
the same time, finding the relationship between soil fertility andcrop yield is also of great importance for validating andimproving soil fertility.

REFERENCES
Tülay Tunçay et al, Assessing soil fertility index based on remotesensing and GIS techniques with field validation in a semiarid
agricultural ecosystem. Journal of Arid Environments. Volume
190, 2021.

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Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P19

Potential hosts and transfer of As, Cr, and Pb-induced resistance genes insoil under coal mining disturbanceYajie Wang1 and Fusheng Li1,2
1. Graduate School of Engineering, 1-1 Yanagido, Gifu 501-1193, Japan
2. River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
were examined by spearman correlation analysis using “psych”
packages in R (version 4.0.3) and redundancy analysis (RDA)INTRODUCTION
using “vegan” packages in R (version 4.0.3). The potential hostbacteria at species level were obtained through the coCoal continues to be a major global primary energy (Arratiaoccurrence analysis drawn using Gephi 0.9.2 based on R
Solar, 2019), and the concerns about the impacts of coallanguage correlation analysis (p < 0.01).
production on the environment have been intensifying due tothe considerable growth in coal consumption in recent years.
Heavy metals including chromium (Cr), cadmium (Cd), nickel(Ni), copper (Cu), zinc (Zn), arsenic (As), and lead (Pb) are
possible to be released during coal production with dust andwastewater. Heavy metal contaminations in soil seriously
threaten the safety of soil ecology due to their high toxicity,
non-biodegradability, and accumulative property. Bacteria as
the most abundant organism in soil are sensitive to the contentsof heavy metals. Long-term stress from these elements
promotes the soil bacterial species with strong resistance togrow as dominating ones after adapting to their toxicity. The
metal resistance genes (MRGs), arsB, chrB, and pbrT, are thegenetic basis for bacteria adaption under stress from As, Cr andPb, respectively. The investigation on the potential host of arsB,
chrB, and pbrT and their transfer will provide information for
an in-depth understanding of the mechanism behind theadaption of bacteria to the stress from Cr, As, and Pb. The aim
was to identify the potential host bacteria of As, Cr, and Pbinduced resistance genes and clarify the transfer of the genes insoil under the disturbance of coal mining for better managementof coal production.

RESULTSThe concentration of heavy metals in soil of different coalmines showed variations. The statistical analysis results basedon the concentrations of heavy metals and basic physiochemical
properties of the soils reflected by pH, EC, moisture, total
phosphorus (TP), total nitrogen (TN), and organic matter (OM)
will be shown in the presentation. The potential ecological riskof single heavy metal was shown in Table 1, which varied in
the range of 0.3-82.5. Cd was the major contaminant.
In total, 963 bacterial species were identified from soil
samples. The top 20 species displayed according to themagnitude of the relative abundance were chosen for thehistogram analysis (Fig. 1). Pseudomonas_sp_108Z1
belonging to Proteobacteria dominates in M1, and thecorresponding relative abundance was significantly higher thanthat in other mines.
The absolute abundance of MRGs, ARGs, intl1 and 16S
rDNA in soil of the six coal mines was shown in Fig. 2. The
abundance of MRGs in M2-M5 was higher than that of ARGs.
arsB was the most abundant metal resistance gene (Fig. 3).

MATERIALS AND METHODS
Six coal mines in Luliang, Shanxi Province of China withdifferent scale (M1: 0.6, M2: 1.2, M3: 1.5, M4: 1.8, M5:3.0,
M6: 8.0 × 106 t) were selected for study. In each mine, surface
soil (1-5cm) was collected from three different sites (0, 1, and
3km apart from center of the mine). Organic matter (OM), total
nitrogen (TN), total phosphorus (TP), and seven heavy metalelements (Cr, Ni, Cu, Zn, As, Cd, and Pb) were measured.
The potential ecological risk index (RI) was used to assess
the ecological risk of heavy metals in coal mine soil. The
formulas are as follows:

RI =∑𝐸𝑟,𝑖 = ∑𝑇 , x𝐶 ,Fig. 1 Bacterial community for the top 20 dominant taxa at
species level in soil of the coal mines (M1-M6).

𝐶 , = 𝐶 , /𝐶 ,where, 𝑛 is the number of heavy metals, Er is the potential
ecological risk of single heavy metal, Tr is the heavy metal toxic
response factor (Xiang et al., 2022), Cf is the single heavy metal
pollution index, CD is the measured heavy metal concentration,
and CR is the geochemical background value.
The abundance of MRGs (arsB, pbrT, and chrB), Mobile
Gene (intl1), and total bacterial (16S rDNA) were measured
through quantitative real-time PCR (qPCR). For comparation,
the abundance of antibiotic resistance gene (AGRs: tetG, tetM,and sul1) were also measured. The bacterial community
structure was analyzed through high-throughput sequencing
technology. The relations between the potential ecological riskof heavy metals and the abundance of arsB, chrB, and pbrT

Fig. 2 Absolute abundance of MRGs, ARGs, intl 1 and 16S
rDNA in soil of the coal mines (M1-M6).

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Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P19

Based on the data of the six coal mines, eight potential hostbacteria of arsB and pbrT, two poential host bacteria of chrB
were identified (Fig. 4). The potential host bacteria of MRGs in
M1 were different from M2-M5. The potential host bacteria of
MRGs in the soil 0, 1, and 3km apart from the center of themine showed variations. The relevant results will be shown in
the presentation. The MRGs (arsB, pbrT, chrB) and ARGs
(tetG, tetM, sul1) were categorized into different classes.

Fig. 5 Effect of potential ecological risk of heavy metals on
the distribution of MRGs, 16S rDNA and intl1 in soil of thecoal mines. P: total phosphorus; W: water content; N: total
nitrogen; OM: organic matter; 16S: 16S rDNA. As, Cr, Pb,
Ni, Cu, Zn, and Cd represent the corresponding potential
ecological risk index.

Fig. 3 Relative abundance of MRGs (arsB, chrB and pbrT)
in soil of the coal mines (M1-M6).

CONCLUSION
This study identified the potential host bacteria and exploredthe transfer of MRGs (arsB, chrB, and pbrT) in coal mine soils.
The potential host bacteria in M1 were different from M2-M5.
The potential host bacteria showed variations in the soil 0, 1,
and 3km apart from the center of the mines. The variations ofpotential host bacteria may be due to the different bacterial
compositions. The abundance of MRGs was closely related tothat of the total bacteria (16S rDNA), and mobile gene (intl1),
which is negatively associated with heavy metals. Heavy metals
may control the spreading of MRGs by inhibiting their verticaland horizontal transfer.

Fig. 4 Potential host bacteria of MRGs (arsB, pbrT, andchrB), ARGs (tetG, tetM, and sul1), and Mobile Gene (intl1)
in soil of the coal mines.

【Reference】
Arratia-Solar, A., 2019. Help curtail new coal mines. Nature
567, 175.
Xiang, Q., Yu, H., Chu, H., Hu, M., Xu, T., Xu, X., He, Z.,
2022. The potential ecological risk assessment of soil
heavy metals using self-organizing map. Science of The
Total Environment, 843, 156978.

The potential ecological risk of heavy metals in coal minesoil showed negative correlations with the abundance of MRGs
(Fig. 5). The abundance of MRGs positively correlated withthat of the total bacteria (16S rDNA), and mobile gene (intl1).
Cd, Cr and Zn significantly and negatively correlated with theabundance of 16S rDNA and intl1 (relevant results will beshown in presentation).

Table 1 The mean values of potential ecological risk of heavy metals in soil of the coal mines (M1-M6).

As

3.73

3.23

3.51

4.50

5.57

3.28

Average
M1-M6
3.97

Cr

1.17

1.02

1.42

1.36

1.56

1.10

1.27

Coal mine

M1

M2

M3

M4

M5

M6

Averagecontribution (%)
7.1
2.3

Pb

3.24

2.67

3.88

3.35

3.46

3.00

3.26

5.9

Ni

4.28

3.63

4.23

5.06

4.66

3.97

4.31

7.7

Cu

3.31

3.41

3.70

3.44

3.77

3.02

3.44

6.2

Zn

0.52

0.44

0.66

0.51

0.58

0.52

0.54

0.9

Cd
Overall risk
(RI)

36.72

32.01

53.56

38.47

37.58

35.40

38.95

69.9

52.98

46.41

70.96

56.68

57.17

50.28

55.75

-

37

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P20

Can activated carbon allow antibiotic resistance genes to access into its
pores
Sri Anggreini1 and Fusheng Li2
1. Graduate School of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
Amplicon solutions containing target genes – The ampliconsolution of tetG was prepared by referring to the TA cloning
method (Takara, Japan). The E. coli DH5α containing plasmid
pMD20 which carries target genes (tetG) was used in this study.
After TA cloning, the confirmed colonies containing the desired
plasmid were cultured overnight with LB medium andampicillin. Plasmid pMD20 was extracted from E. coli DH5αusing a plasmid extraction kit (MACHEREY-NAGEL,
Germany). Non-quantitative PCR (LifePro, China) was
performed to amplify the target gene. A designed primer wasused to amplify tetG with the amplification products at 133 bp(45.22 nm). The amplification product was isolated by gelelectrophoresis and purified using a Gel and PCR Clean-up kit.
The purified amplification product was diluted with sterile
deionized water to obtain the amplicon solution.

INTRODUCTION
The emergence and proliferation of antibiotic resistancegenes (ARGs) in the environment have become a global healthissue. Nowadays, antibiotic resistance causes 700,000 deaths
globally each year (O’Neill. 2016). The existence of ARGs has
been confirmed in drinking water sources and tap water, which
threaten human health. Activated carbon (AC) adsorption isused in drinking water treatment for removal of trace organic
compounds. ARG may enter the pore of AC depending on thepore size and surface properties of AC. However, little is known
about the access possibility of ARG into the pores of AC.
Previous studies reported that granular AC adsorbers increased
the abundance of ARGs due to the enrichment of ARGs in thebiofilm (Su et al. 2018). Detailed information on the fate andbehavior of ARG during contact with AC is still limited. The
objective of this study was to investigate the possible access of
ARGs into the pore of AC. For this purpose, the tetracycline
resistance gene (tetG) was selected as a target gene and ten typesof commercially available ACs with different pore size
distributions were used in this study.

Contact experiments of ARG with AC – Contact experiments
were conducted in 5 mL vials at room temperature. The
amplicon solution containing only tetG was applied as the blankcontrol. The AC dose was set as 1 g/L. After 180 minutes ofshaking, centrifugation was conducted for 10 minutes at 12,000g
to separate AC particles. The obtained supernatant was
subjected to ARG quantification. The concentration of ARGs
before and after contact with AC was determined by qPCR. The
pseudo-first and pseudo-second-order kinetic models described
below were used to analyze the data from the experiments.

MATERIALS AND METHODS
Activated carbon – Ten commercially available ACs wereused, namely carbon A, B, C, D, E, F, G, H, I, and J. The
physicochemical properties of ten types of AC are displayed inTable 1. The surface morphology and elemental composition of
AC were observed by scanning electron microscopy (SEM) andenergy dispersive X-ray analyzer (EDX). The charge
characteristics of AC were evaluated by the pH of point of zerocharges (pHPZC). The BET pore distribution was determinedfrom the nitrogen adsorption technique using the Micromeritics
3Flex instrument. The functional groups present on the surfaceof ACs were investigated by Fourier transform infrared
spectroscopy (FTIR).

Coal-based

745

0.28

6.4

C

1322

0.66

7.2

D

Coconut shellbased
Coal-based

820

0.44

6.5

E

Coal-based

856

0.55

6.8

F

Wood-based

1,290

1.64

5.1

G

Wood-based

866

0.37

7.6

H

1238

0.59

7.5

I

Coconut shellbased
Coal-based

775

0.48

7.8

J

Wood-based

1,053

0.62

8.45

(1)

𝑑𝑞
= 𝑘 (𝑞 − 𝑞 )𝑑𝑡(2)where qt (copies/mg) is ARG adsorbed onto AC at time t, qe(copies/mg) is adsorption capacity at equilibrium, k1 and k2(copies.min/mg) are the pseudo-first and pseudo-second-order
rate constant, respectively.

Table 1. Physicochemical properties of different types of AC
Total pore
pHpzc
Type Origin
Surfacevolumearea(m2/g)(cm3/g)A
Coal-based
956
0.69
6.3
B

𝑑𝑞
= 𝑘 (𝑞 − 𝑞 )𝑑𝑡

RESULTS AND DISCUSSION
The residual concentrations of tetG during contact withvarious types of AC are shown in Fig. 1. Most ACs showed low
residual concentrations except carbon-B. The lower residual
concentration of tetG can be attributed to the pore size, porevolume, and surface chemistry of AC. The size of tetG is 2.2–
2.6 nm in width and 45.22 nm in length. TetG probably can enter
the pore of AC by stretching into an elongated form and fixed
into the active site of AC. However, carbon-B possessed morepores in the size region between 0-1.5 nm as displayed in Fig 2.
The lowest reduction of tetG was obtained from carbon-B (0.75
log reduction). The size exclusion effect probably prevented
tetG from entering the pores of carbon-B which has more poreswith sizes less than 1.5 nm.
Model analysis indicated that the pseudo-second-orderkinetic model fitted all experimental data much better than thepseudo-first-order ones. The average correlation coefficient (R2)of pseudo-first and pseudo-second-order were 0.5722 and38

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P20

0.9975, respectively. The estimated value of qe as shown in Fig.
3 agreed well with the experimental ones. The qe values
followed the order F > I > J > G > C > B > H > A > E > D,indicating wood-based ACs had higher capability to accept
ARG than coal-based ACs.

1.0

0.8

0.6

A

B

C

D

E

F

G

H

I

J

Estimated data

2.50E+10
2.00E+10
1.50E+10
1.00E+10
5.00E+09
0.00E+00

0.4

A

0.2

B

C

D
E
F
G
Activated carbon

H

I

J

Fig. 3 Adsorption capacity of tetG on different types of AC.
Table 2. Correlation of model-estimated qe with the porevolume of specific size regions, surface area, total porevolume, and pHPZC of ACs.
Linear correlation coefficient
Pore size (nm)10
5 Coal-based
5 Woodbased ACs
ACs
ACs
0-1.5
-0.666-0.052-0.977

0.0
0

50

100
Time (min)

150

200

Fig. 1 Residual concentrations of tetG during contact withdifferent types of AC.
Pore size of AC (nm)0-1.5
3.0-5.00-3.0
3.0-101.5-3.0
3.0-24

1.5-105.0-101.5-24
5.0-240-3.0-0.7430.307-0.989100%

5.00

1.5-3.0

0.417

0.342

0.917

90%

4.50

1.5-10

0.572

0.122

0.966

80%

4.00

1.5-24

0.666

0.052

0.977

70%

3.50

3.0-5.0

0.574-0.136

0.981

60%

3.00

3.0-100.652-0.2340.973

3.0-240.251-0.3070.142

Log C0/C

Pore volume (%)

Experimental data

3.00E+10

Type of ACs

C0 = 6.39 × 1012 copies/L

C/C0

3.50E+10

qe (copies/mg)

1.2

4.00E+10

50%

2.50

40%

2.00

30%

1.50

5.0-10-0.007-0.376-0.20220%

1.00

5.0-240.790-0.448

0.990

10%

0.50

0.380

-0.480

0.217

0%

0.00

Surface area
Total porevolume
pHPZC-0.213-0.1890.911-0.2900.837-0.744

A

B

C

D

E

F

G

H

I

J

Activated carbon

Fig. 2 Relationship between pore size distribution of ACwith reduction of tetG after contact at 180 minutes.

CONCLUSIONSThe correlation coefficients of qe with the properties of AC
are summarized in Table 2. The correlation analysis using 10
types of AC indicated that qe has a good correlation with porevolume in the specific size region of AC. When ACs wereclassified based on their raw materials, the R2 of qe become
bigger in wood-based ACs. It was indicated that qe wassignificantly influenced by the raw material of ACs. This result
is consistent with the adsorption capacity changes mentioned
previously. A good positive correlation of qe from ten types ofAC and wood-based ACs is noticed with the pore size regionsof 3-5 and 3-10 nm. The negative correlation of qe was found in
the size region between 5-10 nm. This is related to the attraction
force between tetG and active site of AC. TetG that has entered
the external pores of AC will diffuse through the big and smallinternal pores. Smaller internal pores have a greater attraction
force compared to bigger ones. Therefore, the pore size region
between 3-5 nm was more effective for admission of tetG.

The access possibility of ARG into different types of AC wasinvestigated. AC has a big capacity to admit ARG to enter thepore and fixed inside the pores. TetG may enter the pore of AC
by extending into an elongated shape. Wood-based ACs had ahigher capability to accept ARG than coal-based ACs.

REFERENCES
O'Neill, J., 2016. Tackling drug-resistant infections globally: final
report and recommendations, The Review on Antimicrobial
Resistance (London, UK).
Su, H.C., Liu, Y., Pan, C., Chen, J., He, L., Ying, G. 2018. Persistenceof antibiotic resistance genes and bacterial community changes indrinking water treatment system: From drinking water source to tap
water. Science of the Total Environment. 616–617, 453–46.

39

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P21

Improvement of membrane filtration performance by pre-coating withpowdered activated carbon
Wenqing Li 1 and Fusheng Li 2
1. Graduate School of Natural Science and Technology, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. River Basin Research Center, Gifu University, 1-1 Yanagido, Gifu 501-1193, JapanINTRODUCTION

The images of Scanning Electron Microscope (SEM)
for non-pre-coated and pre-coated membrane are showed
in Fig. 1. The extent of the fouling of membrane isevaluated by the flux over the filtration process and theMembrane filtration is a new technology being
increasingly considered as an alternative to conventional
sand filtration for drinking water production. Membrane
fouling is an important factor that limits its application tomost surface water sources (including rivers, lakes andreservoirs). Membrane fouling refers especially tobiofouling and fouling by dissolved organic substances,with the latter being occurring in almost all cases where
membrane filtration is applied to treat surface water
which contains, to different extents, naturally occurringorganic matter (NOM), a mixture of organic components
with different physicochemical properties. Powdered
activated carbon (PAC) can remove dissolved organic
matter through adsorption and is thus consideredeffective to alleviate membrane fouling when used as apretreatment prior to membrane filtration. However, thismethod has the shortages: one is related to the adsorption
life of PAC and the other one is related to the filtration
resistance from the mixed cake layer formed onmembrane surface by the added PAC and the originallyexited suspended particles in water.
In this study, membrane filtration using membranepre-coated with PAC is investigated. Pre-coating with
PAC is considered to enable the formation of a thinbarrier layer for contact adsorption of NOM and forinhibiting the direct contact of suspended particles inwater with the membrane surface. The investigation is
focused on evaluation of the effects on membrane foulingand dissolved organic matter removal, for which,membrane filtration experiments are conducted using
membranes with different pore sizes and PAC withdifferent particle sizes and coated thicknesses.

images of SEM. The filtration flux, J (m3·m−2·h−1), is computed
based on the following equation,
J=

∆V
A·∆T

(1)

where, ∆V is the accumulative volume of water
filtered within the filtration time length of ∆T, A is themembrane surface area. The adsorption performance ofthe pre-coated
PAC is evaluated based on the measurements fordissolver organic carbon (DOC), UV absorbance at 260
nm (UV260), fluorescence excitation-emission matrix(EEM) and molecular weight distribution (MW) forwater before and after treatment.

Feed water

(a)

(b)

Feed water(c)

MATERIALS AND METHODS
Nagara River water filtered through 0.2 μm membrane
was used as the water for study. A series of batch
membrane filtration experiments are planned by filtering
the water with 0.1 μm hydrophilic PVDF membrane(effective filtration area of 10.7cm2). Three PAC types
are compared: W-PAC (wood-based), S-PAC (woodbased) and CB-PAC (coconut shell-based); and, each
PAC type is categorized into three fractions withdifferent particle size ranges through sieving: 22-44 μm,
44-63 μm and 63-88 μm. The thickness of pre-coated
PAC on the membrane surface is varied by the total
weight of PAC loaded through filtration of designatedamounts of PAC solution prepared using pure water
through the membrane: 0 mg/10.7cm2, 6.45 mg/10.7cm2,
12.9 mg/10.7cm2, 25.8 mg/10.7cm2.

(d)Fig. 1. SEM images of membrane: a and b are thecut section and surface of membrane before precoating; c and d are the cut section and surface of
membrane after pre-coating of PAC(25.8mg/10.7cm2)

RESULTS AND DISSCUTION
The results on the changes of filtration flux for themembrane filtration experiment conducted with precoated PAC with the size of 22-44 μm for the PAC typeof W-PAC are shown in Fig. 2. It can be seen from thefigure that compared to the experiment without PAC pre-40

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P21

coating, the experiments with PAC pre-coating resulted
in flux decline, with the extent of decline being moresignificant for the two experiments with the smaller
thickness of PAC (6.45 mg/10.7cm2 and 12.9
mg/10.7cm2).
The concentrations of dissolved organic matter in thewater after treatment reflected by UV260 are shown in
Fig. 3. UV260 decreases significantly at the beginning,
and then increase gradually with the increases of thefiltered water volume. The larger the thickness of precoated PAC, the lower the value of UV260. This may
indicate that pre-coating PAC can improve the filteredwater quality, therefore reduce the existence of humic
substances that could cause membrane fouling, but at thesame time the carbon layer itself causes membrane
filtration resistance.
The results of dissolved organic matter reflected by
TOC are shown in Fig. 4. It can be seen from the figurethat with the increase of the thickness of PAC pre-coated,
TOC in the filtered water decreases more obviously, andthen similar to the profile of UV260, TOC increases with
the increases of filtered water volume. This also indicatesthe effect of PAC pre-coating on the filtered water quality
and membrane filtration resistance development.

0.8

TOC (mg/L)

0.7
0.6
0.5
Pre-coated PAC(mg/10.7cm2)

0.4
0.3

0
6.45
12.9
25.8

0.2
0

200

400
600
Volume filtered (mL)

800

1000

Fig. 4. Changes in TOC with the increase of
cumulative filtration volume.
SEM images of membrane with precoated PAC(25.8mg/10.7cm2) after filtration process are showed inFig. 5.
Feed water

Filtration flux (m3·m-2·h-1)

2.4
2.2

(a)

Pre-coated PAC(mg/10.7cm2)

2

Fig. 5. SEM images of membrane with precoated
PAC (25.8mg/10.7cm2) after filtration process: a isthe cut section of membrane; b is the surface of
membrane

0
6.45
12.9
25.8

1.8
1.6
1.4
0

200

400
600
Volume filtered (mL)

800

CONCLUSION

1000

The pre-coating PAC can reduce the existence of
humic substances that could cause membrane foulingthrough adsorption, and improve the filtered water
quality. However, the carbon layer itself caused
membrane filtration resistance. Of the three tested layer
thickness, the largest one revealed the smallest
membrane filtration resistance, suggesting the existenceof optimal pre-coating condition, which will be
investigated.

UV260 (m-1)

Fig. 2. Changes in filtration flux with the increase of
cumulative filtration volume.
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0

Pre-coated PAC(mg/10.7cm2)

REFERENCES

0
6.45

Amaral, Pauline, et al. “Superfine Powdered Activated Carbon(S-PAC) Coatings on Microfiltration Membranes: Effects ofMilling Time on Contaminant Removal and Flux.” Water
Research, vol. 100, pp. 429–438, 2016,
Zhang Li, Graham Nigel, et al. “Targeting membrane foulingwith low dose oxidant in drinking water treatment: Beneficial
effect and biological mechanism.” Water Research, Vol.209,
pp.73-78, 2022 Light in a Stratified Water Column.
American Naturalist 174:190-203.

12.9
25.8
0

200

400
600
Volume filtered (mL)

800

(b)

1000

Fig. 3. Changes in UV260 with the increase of
cumulative filtration volume.

41

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P22

Effect of bacteria on Uroglena sp. growth in surface water
Ramayandi1, Sarkar Kanika1, Nurul Syuhada Abdul Latif2 and Fusheng Li3
1. Graduate School of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
2. Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
3. River Basin Research Centre, 1-1 Yanagido, Gifu 501-1193, JapanINTRODUCTION

The remaining cultured sample was filtered through acellulose acetate membrane with a pore size of 0.2 µm for thesubsequent analysis of DOC, DN, DP, and UV260.

Uroglena sp. (golden algae) is a free-swimming, colonial
microalgae species whose individual cells are connected by asystem of dichotomously branched structures (cytoplasmic
threads or gelatinous stalks) radiating from the center of thecolony. The presence of Uroglena sp. leads to a fishy odor in
the water source, and the specific compounds that cause thefishy odor in Uroglena have been identified as 2,4heptandienal, 2,4-decadienal, and 2,4,7-decatrienal.
The heaviest blooms of Uroglena sp. occur in spring (April
to May). Uroglena sp. blooms are unpredictable because of theinteraction of many different factors, including watertemperature, salinity, flushing rate, turbulence, light, predation,and interspecies competitions and inhibitions.
Bacteria and microalgae are ubiquitous and abundantmicroorganisms in aquatic environments. Many studies haveshown their influence on each other, both stimulative andinhibitory. Knowing the stimulative and inhibitory effects ofbacteria for Uroglena sp. is very important in monitoring the
growth of this microalgae. Therefore, the objective of thisresearch is to investigate the effect of bacteria on Uroglena sp.
growth in surface water.

RESULTS AND DISCUSSIONS
Growth of Uroglena sp. in water samples of May and
September
The initial number of Uroglena sp. in the May and September
samples were 146 cells/mL and 5 cells/mL, respectively. The
number of Uroglena sp. then increased after incubation. The
peak growth of Uroglena sp. in both samples were different at
each temperature where the highest growth was recorded at thetemperature of 5 °C. The increase in temperature reduced thegrowth of Uroglena sp. and caused it to lyse faster.
Figure 1 shows the Uroglena sp. number during the
incubation time for the May, and September samples
respectively. For both samples, at the beginning of incubation,
Uroglena sp. growth rose higher at temperatures of 10-20°C. At
5°C of incubation, Uroglena sp. in the May sample survived up
until the 24th day then disappear completely. This is different
with the September sample where it grew up until 1 month with
the number still being high on the last day of incubation.
However, at 30°C, Uroglena sp. did not grow at all during
incubation for May sample and grew only for 3 days for the
September sample.
The results of this study indicate that Uroglena sp. in the
September sample can grow longer than Uroglena sp. in the
May sample. This is because the ratio of the number ofUroglena sp. and other microalgae in the May sample is higher,
causing faster nutrient uptake compared to the September
sample.

MATERIALS AND METHODS
Surface water
The surface water sample was collected from a natural
reservoir-a water source for drinking water treatment. It wascollected in May and September, using a Kemmerer sampler
from the surface of the pond (up to 1 meter in depth).
Incubation experiment
The water sample was divided and placed into 1 L Borosilicate
glass reactors, and incubated at temperatures of 5, 10, 15, 20,
and 30 °C for 30 days, respectively. The incubation was
duplicated for all temperatures. Incubators (FCI-280GHS) withwhite fluorescent lamps were used. Samples (each for about 100mL) during incubation were collected from each reactor atdesignated time points within the incubation period.

aAnalytical methods
The parameters measured include pH, electrical conductivity(EC), dissolved oxygen (DO), oxidation-reduction potential(ORP), microalgae, total bacteria (16SrDNA), dissolved
nitrogen (DN), dissolved phosphorus (DP), dissolved organiccarbon (DOC), and UV260.
Total bacteria were measured by filtering 50 ml of theculturing sample using a 0.2 µm polytetrafluoroethylene(PTFE). Filters containing bacteria were isolated for theirmicrobial genomic DNA using the DNaeasy PowerSoil Pro kit.
All the extracted DNA samples were analyzed by Quantitative
PCR (qPCR).
Furthermore, the number of microalgae was calculated under
an inverted microscope of brand OLYMPUS BX53 by placing
a sample (1 ml) into a Sedgewick rafter cell chamber.

bFig. 1: Uroglena sp. number during incubation for water
sample of a) May and b) September.

42

Proceedings of UGSAS-GU & BWEL Joint Poster Session on Agricultural and Basin Water Environmental Sciences 2022: P22

Interrelation of Uroglena sp. with other parameters

The initial bacterial count in the September sample washigher than the May sample at 7.5x109 copies/L and 8.7x108
copies/L, respectively. The number of bacteria continued toincrease in the May sample, while the number of bacteria in the
September sample decreased slightly at low temperatures, onlyat high temperatures did the number of bacteria increase.
Higher temperatures can promote the growth of bacteria that
can affect the growth of Uroglena sp. Although Uroglena sp. isa photo-phagotrophic organism that ingests bacteria fornutritional purposes. However, the results of this study show an
excess number of bacteria (>1x1010 copies/L) at hightemperatures can inhibit the growth of Uroglena sp. This might
be due to the inability of Uroglena sp. to compete with bacteria
for nutrient uptake.

PCA was conducted with the aim of showing parameters thathave positive and negative relationships with Uroglena sp.
The PCA results in the May sample demonstrate thesimilarity through clustering among Uroglena sp., several
microalgae (Peridinium sp., Chlamydomonas sp., andLepocinclis sp.), pH, and nutrients such as DN and DP.
However, other parameters such as microalgae (Sphaerocystissp., Gelonkinia sp., Scenedesmus sp., and Nitzchia sp.), total
bacteria (16SrDNA), UV260, DOC, ORP, and DO were
divergent from Uroglena sp.
As for the September results Uroglena sp. samples were in
the same clustering with several microalgae species(Microcystis sp., Lepocinclis sp., and Mallomonas sp.), DP,
DO, and UV260. While total bacteria and other parameters
diverged from Uroglena sp.
The results of this analysis show that several parameters havea consistent relationship with Uroglena sp. in both incubation
samples, both positive relationships such as DP and microalgae(Lepocinclis sp.) and negative relationships such as total
bacteria, microalgae (Sphaerocystis sp., Scenedesmus sp., andNitzchia sp.).

a

a

b

b

Fig. 3: The relationship of Uroglena sp. with bacteria(16SrDNA) during incubation for samples of a) May and b)
September

CONCLUSION
The growth of Uroglena sp. was the highest at 5 °C in May
and September incubation samples, but Uroglena sp. grew
longer in the September sample. The growth of Uroglena sp.
has a negative relationship with bacteria. Excessive amounts ofbacteria may inhibit the growth of Uroglena sp.

REFERENCES
Kimura Bon. 1989. Studies on Chemical and Biological Factors
Influencing in the Growth of Uroglena Americana, a Red
Tide Chrysophyceae in Lake Biwa. The Journal of
Shimonoseki of Fisheries. 38, 23-70.
Green W. R., Hufhines B. 2017. A rare Uroglena bloom in
Beaver Lake, Arkansas, spring 2015. Lake Reserve
Management. 33, 8-1

Fig. 2: Principal Component Analysis of interrelation
Uroglena sp. and other parameters for water sample of a)
May and b) September
Relationship between Uroglena sp. and bacteria (16SrDNA)
In this study, total bacteria had a negative relationship with
Uroglena sp.; this result was consistent in both incubation
samples.

43

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