Greenhouse Gas Fluxes and Modeling from Rice Workshop

1
2
Table o Con en s
Table o Con en s .................................................................................................................. 2
Lis o Figu es........................................................................................................................... 3
Lis o Tables............................................................................................................................ 2
I. Acknowledgemen s ....................................................................................................... 4
II. Execu i e Summa y ........................................................................................................ 5
III. In oduc ion ..................................................................................................................... 7
A. Backg ound and Con ex o he Wo kshop ........................................................ 7
B. Objec i es o he Wo kshop.................................................................................. 8
C. Ta ge Audience and Pa icipan s ....................................................................... 8
D. Wo kshop Agenda............................................................................................... 10
IV. Plena y Sessions............................................................................................................. 10
A. Opening Session Day 1 ........................................................................................ 10
B. Plena y Session Day 2 .......................................................................................... 15
C. Plena y Session Day 3 .......................................................................................... 19
V. Pa allel Sessions ............................................................................................................. 22
A. GHG Measu emen s Session ............................................................................... 22
B. Modeling O e iew Session ................................................................................ 26
VI. B eakou Sessions on P io i ies o Collabo a ion ......................................................... 31
VII. Ou comes and Recommenda ions ............................................................................ 37
A. Main Insigh s and Challenges ............................................................................. 37
B. Ac ionable Nex S eps ......................................................................................... 38
VIII. Conclusion .................................................................................................................... 39
IX. Li e a u e Ci ed ............................................................................................................. 40
X. Annexes ......................................................................................................................... 44
A. Summa y o Models ............................................................................................. 44
B. Pa icipan Lis ...................................................................................................... 49
C. Wo kshop Ma e ials ............................................................................................. 53
Lis o Tables
Table 1. Ancilla y da a measu emen s and desc ip ions o lux chambe
me hod used by majo ice g owing egions globally. ....................................... 23
Table 2. Requi emen s guide o GHG measu emen s. Requi ed is he
minimum you need o ensu e you measu emen s mee he
s anda d. P e e ed is wha you need o op imize you condi ions o
measu emen s o minimize unce ain ies. ............................................................ 24
Table 3. Fea u es, objec i es and applica ions o he di e en models. ........................ 29
Table 4. B eakou g oup esul s on da a ha moniza ion, key da a gaps, and
ele ance o li elihoods. ....................................................................................... 33
Table 5. Key ou comes om he ou B eakou G oups (BOGs), ou lining
a ailable da a, equi ed ac ions, esponsibili ies, collabo a ion
app oaches, needed esou ces, imelines, and challenges.............................. 34
3
Lis o Figu es
Fig. 1. Pa icipan s o he wo kshop, ep esen ing esea che s and scien is s
wo king on g eenhouse gas (GHG) measu emen and modeling in ice
sys ems. 9
Fig. 2. Geog aphic and ins i u ional di e si y o wo kshop pa icipa ion, wi h 40
o ganiza ions om 25 egions in ol ed in he wo kshop. 9
Fig. 3. D . Reine Wassmann p esen ing ASEAN clima e mi iga ion ini ia i es and
eme ging ca bon ma ke pa hways. 11
Fig. 4. D . Cyn hia Rosenzweig p esen ing he e olu ion o AgMIP s udies and
hei con ibu ions o clima e impac assessmen s in ag icul u e. 13
Fig. 5. D . Toshi Hasegawa p esen ing he cu en p io i ies o he AgMIP Rice
Team. 14
Fig. 6. D . Alishe Mi zbae p esen ing on na ional GHG in en o ies and p i a e
ice ca bon ini ia i es. 16
Fig. 7. D . Sonali McDe mid p esen ing on egional in eg a ed assessmen s o
mi iga ion and adap a ion co-bene i s o a me s. 16
Fig. 8. Panel discussions on s akeholde pe spec i es in na ional GHG
in en o ies, UNFCCC epo ing, and ca bon ma ke schemes. 17
Fig. 9. D . A lene Ad ien o-Bo be p esen ing GHG measu emen p o ocols, lux
chambe me hods, and emissions epo ing s anda ds. 20
Fig. 10. D . Tao Li p esen ing he ole o modeling as a ool o ad ancing
esea ch on GHG in ice sys ems. 21
Fig. 11. GHG lux measu emen s pa allel session. 26
Fig. 12. Modeling eam pa allel session. 28
Fig. 13. The 34 expe imen al si es o po en ial collabo a ion iden i ied om a
p elimina y pa icipan su ey. 31
Fig. 14. B eakou g oup discussions on collabo a ion oppo uni ies, da a gaps,
and he need o ha moniza ion ac oss si es and scales. 32
4
I. Acknowledgemen s
The o ganize s o he G eenhouse Gas Fluxes and Modeling om Rice Wo kshop g a e ully
acknowledge he gene ous suppo o he Global Me hane Hub (GMH) and he Minis y o
Ag icul u e, Fo es y and Fishe ies (MAFF) o Japan, whose con ibu ions made his e en
possible.
App ecia ion is also ex ended o he pa ne ins i u ions—In e na ional Rice Resea ch Ins i u e
(IRRI), he Ag icul u al Model In e compa ison and Imp o emen P ojec (AgMIP), and CGIAR
Clima e Ac ion— o hei collabo a ion in con ening his wo kshop and o hei sha ed
commi men o ad ancing clima e change mi iga ion in ice sys ems.
The o ganize s u he ecognize he aluable con ibu ions o mo e han 80 pa icipan s, expe s,
and ins i u ions om ac oss 25 egions who con ibu ed hei ime, knowledge, and insigh s. Thei
ac i e engagemen and spi i o collabo a ion we e essen ial in making he wo kshop a success
and in shaping he join agenda o ad ancing g eenhouse gas lux measu emen and
modeling in ice sys ems.
5
II. Execu i e Summa y
Me hane (CH4) emissions om paddy ields con ibu e signi ican ly o ag icul u al g eenhouse
gas (GHG) emissions, posing a c i ical challenge o achie ing global clima e goals. Despi e he
a ailabili y o he Tie 3 me hod—which u ilizes p ocess-based models and high- esolu ion
da ase s o cap u e a iabili y in si e-speci ic CH4 emissions, i s applica ion has been limi ed o a
ew coun ies. The e is s ill a la ge gap in es ablishing coun y and egional speci ic emissions
ac o s and an inc easing demand o measu e ac ual emissions om he ield. These
equi emen s a e no only o coun y-le el in en o ies o he Uni ed Na ions F amewo k
Con en ion on Clima e Change (UNFCCC) bu also o he eme ging ca bon ma ke in he
ag icul u al sec o . Signi ican e o s a e being in es ed in ad ancing echnologies and op ions
o a ge ing and suppo ing clima e mi iga ion ini ia i es in ice sys ems. Measu emen s and
moni o ing a e c i ical in se ing he baseline o hese ini ia i es and in e alua ing he p og ess
made. Howe e , hei implemen a ion emains challenging due o limi ed consensus in p o ocols
o measu emen s, as well as he limi ed accessibili y o ools and echnologies o
measu emen s, modeling and moni o ing o emissions.
Modeling is cen al o b idging expe imen al esea ch da a wi h ac ionable mi iga ion and
adap a ion s a egies a scale. Cu en models can eliably simula e yield, wa e balance, and
basic soil–nu ien in e ac ions, bu hey s uggle wi h ade-o s be ween me hane educ ion
and ni ous oxide emissions, o wi h in eg a ing soil ca bon dynamics.
Owing o he se ious impac s o he clima e c isis in c op p oduc ion and human heal h, global
assessmen s o GHG emissions h ough measu emen s and modeling a e u gen ly needed o
accele a e e o s in de eloping esilien ood sys ems in all ice-g owing coun ies a ound he
wo ld.
The “G eenhouse Gas Fluxes and Modeling om Rice Wo kshop” was con ened o add ess
hese challenges. The wo kshop was held om 1–5 Sep embe 2025 a he In e na ional Rice
Resea ch Ins i u e (IRRI), Los Baños, Philippines, join ly led by IRRI, AgMIP, and he Global
Me hane Hub—wi h suppo om CGIAR Clima e Ac ion and Japan’s Minis y o Ag icul u e,
Fo es y and Fishe ies (MAFF).The wo kshop ga he ed mo e han 80 pa icipan s om 40
o ganiza ions ac oss 25 egions and b ough oge he pe spec i es om esea ch, s akeholde s
engaged in na ional GHG in en o ies, UNFCCC epo ing, and he ca bon ma ke .
I was deli e ed in a hyb id o ma wi h in-pe son and online pa icipa ion. The wo kshop`s
s uc u e was composed o six plena y sessions o ganized o p o ide he gene al o e iew o he
s a e-o - he-science in GHG measu emen s and modeling in ice sys ems and wo pa allel
sessions ha we e echnical ocused on he o e iew o di e en me hodologies in
measu emen s and in modeling, espec i ely.
Pa icipan s unde sco ed he need o b ing oge he ield lux measu emen s, c op simula ions,
and socio-economic analysis o imp o e o wa d-looking impac assessmen s. Such in eg a ion
would enable e alua ion o how mi iga ion and adap a ion s a egies a ec a me s’
li elihoods, ood secu i y, and esou ce use, while quan i ying he ade-o s and syne gies
among socio-economic, biophysical, and en i onmen al ou comes.
The di e en p esen a ions highligh ed he da a gap in mi iga ion e o s’ moni o ing as cu en
na ional da a collec ion a ge s ood secu i y. Calib a ion and alida ion da a o modeling a e
sca ce, especially a egional and na ional scales. Wo kshop discussions emphasized ha while
ice models ha e ad anced signi ican ly—pa icula ly h ough mul i-model ensembles and
AgMIP’s coo dina ed in e compa ison e o s—gaps emain in simula ing ex eme e en s, ai -
based esponses, and GHG luxes beyond me hane.

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The wo kshop iden i ied se e al oppo uni ies o s eng hen ice GHG esea ch and
implemen a ion and ecommenda ions mo ing o wa d include among o he s: adop ing FAIR
(Findable, Accessible, In e ope able, Reusable) da a p inciples; pilo ing join measu emen –
modeling p ojec s ac oss di e se ice ecologies; aligning MRV equi emen s o ca bon ma ke s
wi h scien i ic p o ocol; and os e ing mul i-s akeholde pla o ms ha b idge esea che s,
go e nmen s, and p i a e-sec o ac o s.
Key ou pu s o he wo kshop include:
 Consensus on minimum p o ocols o chambe -based measu emen s (chambe design,
sampling equency, lux calcula ion, and epo ing s anda ds).
 Ag eemen on he need o ha monized da ase s and me ada a s anda ds, including
soil, wa e , c op, and wea he a iables.
 Roadmaps om ou b eakou g oups (wa e managemen , me hane modeling,
managemen in e en ions, scaling/upscaling) ou lining da a needs, p io i y
in e en ions, and collabo a i e ac ions.
 Iden i ica ion o high-po en ial inno a ions such as AWD, imp o ed e ilize
managemen , and in eg a ion o socio-economic models.
 Commi men s o de elop sha ed eposi o ies and adop common epo ing empla es.
The pa icipan s ha e ag eed o he ollowing ac ionable nex s eps o con i m hei o e all
commi men o high quali y esea ch o add ess global challenges in acing clima e change:
 Con inuing communica ion h ough sha ed pla o ms (e.g., Sha ePoin , AgMIP
amewo ks) and pe iodic ollow-up mee ings o ack p og ess.
 Co-de eloping guidelines o GHG measu emen s ac oss global ice g owing a eas ha
will suppo capaci y building o a global ne wo k o GHG esea che s and long- e m lux
expe imen al s udy.
 Es ablishing a wo king g oup o coo dina e model in e compa ison, p o ocol
s anda diza ion, and join calib a ion e o s.
 Launching pilo case s udies in Asia, A ica, and La in Ame ica o es ha monized
p o ocols and scale indings.
 Selec ing case s udies ha can be used o conduc in eg a ed assessmen s o clima e
change, adap a ion, and mi iga ion using he AgMIP’s MAC-B modeling amewo k.
 Linking wo kshop ou comes o policy p ocesses, including na ional GHG in en o ies, NDC
implemen a ion, and A icle 6 mechanisms.
Toge he , hese ou pu s and commi men s signal a s ong ounda ion o a coo dina ed global
e o o educe me hane emissions om ice sys ems, while ensu ing ha mi iga ion s a egies
align wi h adap a ion goals, a me li elihoods, and sus ainable de elopmen .
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III. In oduc ion
A. Backg ound and Con ex o he Wo kshop
GHG emission a es a e inhe en ly a iable in ime and space, o en associa ed wi h
high e o es ima es. Accu a e es ima es o GHG luxes a e inc easingly needed in
accoun ing o GHG in en o ies and C in-se s a he ield o ield and global scale
bene i s. I has been epo ed ha unce ain ies may each 20-35% o in en o ies o
an h opogenic emissions in speci ic sec o s (IPCC, 2021). Many s udies ha e
documen ed ha di e en lux calcula ion me hods can p oduce subs an ially di e en
GHG lux es ima es o a gi en se o chambe da a (Le y e al., 2011). These lux
compu a ions di e no only in hei accu acy (close o ue lux alue) bu also in hei
p ecision ( epea abili y).
C op models a e widely accep ed o be used o scale emissions da a o egional
assessmen s such as done in coun ies epo ing hei GHG emissions using he Tie 3
app oach. They can use spa ial high- esolu ion da ase s o cap u e si e-speci ic
a iabili y ha may a ec emissions and pa icula ly CH4 such as clima e, managemen
p ac ices, a ie ies and soils. The applica ion o c op models o GHG in en o y is limi ed
o a ew coun ies and his is p ima ily due o he demanding da a equi emen s o hei
use and he limi ed capabili y o ice c op g ow h models simula ing GHG emissions and
pa icula ly me hane. Up o da e, he e has been no sys ema ic in e compa ison
among ice c op models agains obse ed da a o quan i y unce ain ies associa ed
wi h CH4 emission p edic ions, lea ing gaps in unde s anding he mi iga ion po en ial o
a ious s a egies. The AgMIP Rice p ojec has a long his o y o in e compa ing c op
models o assess clima e change impac s and adap a ion s a egies. Some o hese
models simula e soil-plan in e ac ions, including soil ca bon dynamics, which enable
CH4 emission simula ions and p esen oppo uni ies o enhance GHG es ima es.
P ecise GHG emissions suppo obus iden i ica ion o s a egies o mi iga e emissions
and main ain high in eg i y o he da a o epo ing. The e o e, a comp ehensi e
assessmen o chambe me hod, ace gas lux compu a ion and GHG modeling is
c i ical o accu a ely es ima e GHG emissions and mi iga ion in e en ions co-bene i s
a scale.
In addi ion, AgMIP has ex ensi e expe ience conduc ing in eg a ed assessmen s ha
link clima e, c ops, li es ock, economic da a and models o e alua e he impac s o
clima e change and ag icul u al adap a ion s a egies. O e he pas decade, AgMIP
has implemen ed coo dina ed egional assessmen s ac oss A ica and Asia, engaging
scien is s and policymake s o co-de elop scena ios ha e lec local eali ies and
na ional p io i ies. Building on his ounda ion, AgMIP is now implemen ing he
Mi iga ion–Adap a ion Co-Bene i s (MAC-B) amewo k o assess ice managemen
p ac ices in Vie nam, Bangladesh, and India. This new amewo k combines ield da a,
c op and li es ock modeling, and economic analysis o e alua e how p ac ices such
as he Sys em o Rice In ensi ica ion (SRI) and Al e na e We ing and D ying (AWD)
pe o m unde cu en and u u e clima es—quan i ying hei po en ial o enhance
a me li elihoods, imp o e esou ce e iciency, and educe g eenhouse gas emissions.
The i e-day scien i ic wo kshop held om Sep embe 1 o 5, 2025, a he In e na ional
Rice Resea ch Ins i u e (IRRI) Headqua e s aimed o add ess hese gaps and o
le e age he di e en oppo uni ies wi h he ecen ad ances in measu emen s in GHG
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and in ice c op modeling. The documen summa izes he insigh s om he closed
in e ac ion among he di e en expe s in GHG measu emen s and modeling in
de ining p io i ies o esea ch and o collabo a ion. Indeed, he wo kshop aimed o
os e collabo a ion be ween g eenhouse gas (GHG) emissions esea che s, ice c op
modele s, ag onomis s, spa ial science and emo e sensing expe s, and o con ene in
add essing challenges in CH₄ da a a ailabili y and collec ion, as well as mi iga ion
in e en ions assessmen a di e en scales. The ini ia i e was join ly led by he
In e na ional Rice Resea ch Ins i u e (IRRI), he Ag icul u al Model In e compa ison and
Imp o emen P ojec (AgMIP), and he Global Me hane Hub (GMH), in pa ne ship wi h
CGIAR Clima e Ac ion and he Minis y o Ag icul u e, Fo es y and Fishe ies (MAFF) o
Japan.
B. Objec i es o he Wo kshop
The wo kshop se ed as a pla o m o knowledge exchange, capaci y building, and
collabo a ion de elopmen . The aim was o ad ance bes p ac ices in chambe
me hod applica ions, GHG lux analysis, and as well o a emp he de elopmen o
simpli ied CH4 simula ion me hods o enable b oade pa icipa ion o c op models o
Tie 3 app oach o GHG in ice sys ems. This la e will also o e an oppo uni y o he
egional in eg a ion o clima e change adap a ion conside a ions in o ice modeling
and in eg a ed assessmen s o be e unde s and he adeo s be ween mi iga ion and
adap a ion s a egies.
The speci ic objec i es we e o:
 Fos e collabo a ion be ween ice c op g ow h modele s, expe imen alis s, and
emo e sensing scien is s.
 Add ess challenges in CH4 da a a ailabili y and collec ion
 Enhance da a sha ing and in eg a ion be ween expe imen alis s, modele s,
and emo e sensing expe s.
 Fos e join lea ning on bes p ac ices o CH4 measu emen and modeling.
 De elop a oadmap o in eg a ing expe imen al da a wi h modeling e o s o
imp o e me hane emission p edic ions and mi iga ion assessmen s.
C. Ta ge Audience and Pa icipan s
The wo kshop was in ended o esea che s and p ac i ione s wo king on g eenhouse
gas (GHG) measu emen and modeling in ice sys ems (Fig. 1). The p ima y audience
included:
 GHG emissions esea che s wi h expe ise in ield measu emen s and lux analysis.
 C op modele s ocusing on CH₄ simula ion and clima e- ice in e ac ions.
 Ag onomis s and soil scien is s engaged in sus ainable ice cul i a ion and
mi iga ion p ac ices.
 Economis s modele s conduc ing ex-an e impac assessmen s and in eg a ed
assessmen s
 Spa ial science and emo e sensing expe s applying geospa ial ools o la ge-
scale moni o ing and sys ems mapping.
9
 Policy pa ne s and ins i u ional ep esen a i es suppo ing e idence-based
decision-making in clima e-sma ag icul u e.
 Ea ly-ca ee esea che s and echnical s a seeking capaci y building in GHG
measu emen and modeling app oaches.
O e all, he e we e 40 o ganiza ions om 25 egions in ol ed (Fig. 2).
Fig. 1. Pa icipan s o he wo kshop, ep esen ing esea che s and scien is s wo king on
g eenhouse gas (GHG) measu emen and modeling in ice sys ems.
Fig. 2. Geog aphic and ins i u ional di e si y o wo kshop pa icipa ion, wi h 40 o ganiza ions
om 25 egions in ol ed in he wo kshop.
16
Fig. 6. D . Alishe Mi zbae p esen ing on na ional GHG in en o ies and p i a e ice ca bon
ini ia i es.
Fig. 7. D . Sonali McDe mid p esen ing on egional in eg a ed assessmen s o mi iga ion and
adap a ion co-bene i s o a me s.

17
Fig. 8. Panel discussions on s akeholde pe spec i es in na ional GHG in en o ies, UNFCCC
epo ing, and ca bon ma ke schemes.
Na ional GHG in en o y sys ems and UNFCCC epo ing p ocesses cu en ly ely on
limi ed, non- a ge ed da a collec ion wi h mos e o s ocused on ood secu i y a he
han mi iga ion moni o ing. Da a o model calib a ion and alida ion is a ely a ailable,
especially a egional and na ional scales. The concep o baseline in ice p oduc ion
wi h con inuous looding ield can be challenged as unin en ional AWD p ac ices a e
widesp ead. Fu he mo e, s aw managemen is la gely excluded, despi e i s majo
in luence on GHG ou comes.
Signi ican da a gaps emain in ice sys ems e alua ion such as:
 I iga ed ice a eas quan i ica ion and mapping.
 Adop ion a es o AWD.
 The disc epancies in indica o s o ex ension bulle in AWD de ini ions and
a me s’ p ac ices.
 Soil o ganic ca bon dynamics unde AWD.
 S aw managemen a iabili y in ice sys ems.
18
The e a e howe e some iden i ied oppo uni ies ha can be explo ed mo ing o wa d
such as:
 Es ablish sys ema ic g ound- u hing o alida ion o upscaling assessmen s.
 Le e age emo e sensing (RS) and AI ools o scalable, cos -e ec i e
moni o ing.
To be e ec i e, his would equi e a da a-sha ing pla o m as well as long- e m s udies
combined wi h modeling. An example sha ed was IRRI’s e o s o es ablish a amewo k
linking a m su ey da a wi h dis ic -le el RS in o ma ion, which is hen in eg a ed in o
GHG calcula o s and models o egional assessmen s. Cu en ly, da a collec ion lacks
incen i es, and he e a e no s anda dized p o ocols o c oss-checking hese da a.
F om he p i a e sec o and ca bon ma ke pe spec i e, main challenges discussed
include:
 The need o s a i ica ion, each equi ing i s own emission ac o (EF).
 While ice a eas may be mapped and iden i ied using ALU so wa e, he cen al
challenge— he “million-dolla ques ion”— emains how o scale measu emen s.
The e a e conce ns abou whe he measu emen s can be s anda dized and
made compa able.
 Exis ing policies o model e alua ion a e less accessible and may no be i o
pu pose. A p oposal was aised on whe he a di e en cos o ca bon could be
applied based on he obus ness o es ima ion app oaches.
Lessons om JCM implemen a ion by G een Ca bon highligh ed di icul ies in applying
me hodologies consis en ly.
While da a collec ion ini ia i es a e pe o med by di e en ice ca bon de elope s he
challenge is making hese da a use ul o esea ch. Sugges ions included c ea ing
bene i -sha ing mechanisms o co e he cos s o da a collec ion, ansi ion, and
implemen a ion, wi h pa icula a en ion o ensu ing local go e nmen s also bene i . In
addi ion, se e al c i ical ques ions we e aised, such as:
 Can he measu emen s be s anda dized and made compa able ac oss egions
and con ex s?
 How o balance cos and accu acy in model-based es ima es o ca bon
p icing?
The p esen a ion sha ed by D . Sonali Shukla McDe mid on in eg a ed assessmen s
linking clima e, c op, and economic models p o ided a di e en pe spec i e on he
alua ion o mi iga ion e o s beyond he clima e a ge s and he ca bon c edi s
es ima e. She sha ed he wo k on RIA ha builds on AgMIP’s his o y o in e compa isons
and expands owa d add essing g eenhouse gases, a me adop ion, and egional
scaling ele an o na ional clima e commi men s (NDCs).
The AgMIP Mi iga ion and Adap a ion Mi iga ion Co-Bene i s (MAC-B) amewo k was
de eloped o s anda dize e alua ion o mul i-model unce ain y and egional
he e ogenei y o clima e change impac s. The amewo k:
 Links clima e models, mul iple c op models, and he economic ade-o analysis
model (TOA-MD).
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 Cap u es unce ain y ac oss scena ios and si es, calib a ed/ alida ed wi h ield
da a.
A ecen case s udy ocusing on combined mi iga ion + adap a ion in ice was sha ed
using he coupled c op–soil models (ORYZA + DNDC) applied in Bangladesh (3 dis ic s,
6 s a a, ~432 a ms) and e alua ing AWD and SRI.
Key indings sugges ed ha in eg a ed modeling amewo ks enable assessmen o
bo h biophysical and socioeconomic ade-o s. AWD and SRI inc eased yields sligh ly,
educed me hane and imp o ed wa e -use e iciency. SRI deli e ed g ea e ne a m
e u n gains han AWD alone. D Sonali has emphasized he need o comp ehensi e
and consis en da a collec ion on yields, GHGs, soils, and socioeconomic condi ions.
The RIA models equi e mul i- ac o sensi i i y es s ( empe a u e + CO2 + p ecipi a ion)
o cap u e unce ain y, and esul s a e sensi i e o geno ype x en i onmen x
managemen in e ac ions.
She emphasized he app oach in RIA needs o be co-de eloped wi h s akeholde s o
ensu e alignmen o s a egies e alua ed wi h a me incen i es, biodi e si y, and policy
goals. Go e nance p io i ies di e (local wa e sca ci y s na ional GHG a ge s) and
he eme ging ca bon ma ke s and incen i es may al e adop ion beha io .
Fu he mo e, add essing unce ain y and he e ogenei y is essen ial be o e scaling
ecommenda ions. The eam is cu en ly conduc ing a p ojec on RIA o a case s udy in
India (Tamil Nadu) whe e a new su ey da a has been conduc ed. An incoming pape
wi h esul s om Vie nam will be soon published.
Ques ions we e aised on he challenges in de ining s a a o he s udy and he
conside a ion o he biodi e si y ade-o s (e.g., amphibian mo ali y inc ease wi h
AWD) wi h p ac ice change. S a i ica ion based on a m size may be imp o ed by
cap u ing he e ogenei y ac oss a ms and p ac ices
C. Plena y Session Day 3
The Day 3 session cen e ed on echnical p esen a ions ha highligh ed and e iewed
he key ools o ocus o he wo kshop, deli e ed by D . A lene Ad ien o-Bo be and D .
Tao Li.
D . Ad ien o-Bo be deli e ed a c ash cou se on he his o y o GHG measu emen s and
he cu en p o ocols in use. She sha ed as well he lea ning om he pa allel sessions
conduc ed du ing Days 1, 2 and 3 including he gene al p inciple o he lux chambe
me hod, he GHG lux compu a ions and emissions epo ing aiming o consensus on
minimum equi emen s and s anda diza ion (Fig. 9).
20
Fig. 9. D . A lene Ad ien o-Bo be p esen ing GHG measu emen p o ocols, lux chambe
me hods, and emissions epo ing s anda ds.
D . Ad ien o-Bo be acknowledged he global collabo a ion na u e o he wo kshop
wi h pa icipan s om majo ice egions: U.S., Colombia, Spain, China, Japan,
Indonesia, Vie nam, India, A ica, and Taiwan which o med a ne wo k o esea che s
buil on ea lie collabo a ions (e.g., IRRI–NARS collabo a ion, 1993–1998) in assessing ice
as a sou ce o GHG and a sink o CO₂. She p o ided a b ie his o y and he s a us o
mi iga ion s udy in ice in he US ice sec o da ed back om 1980s– o p esen wi h
ocus on majo d i e s o emissions, mi iga ion s a egies, upscaling models.
She ei e a ed he challenges in GHG measu emen s such as he
 GHG p oduc ion mechanism in he soil ha has mul iple p ocesses, highly
a iable in ime & space.
 The labo -in ensi e na u e o GHG measu emen s ( equi es many people pe
ield campaign)
 The lack o ha monized p o ocols ac oss egions.
The pa allel sessions on GHG luxes discussed he di e se measu emen p ac ices ac oss
he di e en egions and es ablished a classi ica ion o equi ed and p e e ed
app oaches o uni y p o ocol o GHG measu emen s. These pa allel sessions discussed
he compa abili y o he measu emen s and as well as how o allow c oss alida ion o
e i ica ion. The ag eed p o ocol emphasizes manda o y equi emen s o p oducing
obus es ima es, and sugges ions we e made o educe unce ain ies and ha monize
p ac ices ac oss ice s udies (Table 2).
D . Ad ien o-Bo be ended he p esen a ion wi h ecommenda ions well aligned wi h
he wo kshop objec i es which a e:
 Es ablish s anda dized chambe p o ocols globally.
 Consolida e da ase s o de ine global emission anges.
21
 Imp o e epo ing consis ency (SI uni s, GWP, yield-scale).
 Enhance da a collec ion e iciency (au oma ion, coo dina ed campaigns).
 In eg a e GHG wi h c op & soil models o be e p edic ion.
 B oaden in e na ional collabo a ion o policy- ele an epo ing.
D . Tao Li highligh ed he ole o modeling as a esea ch ool ha can be used o
 Ex apola e expe imen al esul s.
 In e p e ield s udies.
 Gene a e insigh s o op imize sys ems and designs o adap a ion, mi iga ion,
p oduc i i y, and yield. (Fig. 10)
Fig. 10. D . Tao Li p esen ing he ole o modeling as a ool o ad ancing esea ch on GHG in
ice sys ems.
Cu en models used o clima e change adap a ion can p edic yields while ewe can
di ec ly quan i y GHG emissions. He iden i ied Gaps and Challenges and oppo uni ies
o ice modeling as below:
Gaps and challenges
 Inconsis en GHG esponses o ele a ed CO₂ ac oss s udies and imescales.
 Model unce ain ies s em om bo h s uc u e and inpu da a quali y.
 Lack o modules o ce ain mi iga ion op ions (e.g., biocha , inhibi o s).
 GHG measu emen s a e o en localized, limi ing scalabili y.
 Da a- ela ed issues a e he ha des o esol e and signi ican ly a ec
quan i ica ion.

22
Oppo uni ies o Imp o emen
 Collabo a ion and da a sha ing ac oss modeling g oups.
 S anda diza ion o p o ocols, modules, and MRV alignmen wi h NDCs.
 De elopmen o sha ed calib a ion p o ocols o a oid o e - o unde -
calib a ion.
Discussion poin s a e his p esen a ion we e c i ical including:
 Wa e managemen ep esen a ion -The e is a conce n in models capabili y o
adequa ely cap u e posi i e yield e ec s o AWD and d ainage e en s. Mos
models can simula e wa e balance (e apo anspi a ion, d ainage, uno ), bu
accu acy may a y. Roo g ow h and soil physical change e ec s a e pa ially
handled by some models; he e a e a eas o imp o emen o conside in ice
modeling.
 Model imp o emen and da a sha ing - Fo imp o ed modeling o wa e dep h
e ec s on me hane, ni ous oxide emissions, lodging impac s, and mi iga ion
oppo uni ies like mid-season d ainage, s anda dized calib a ion p o ocols a e
key o a oid o e i ing and imp o e model compa abili y. The adop ion o AI-
eady da a o s anda diza ion and he sha ing o p o ocols o model
calib a ion and he alida ed model pos -calib a ion may o e oppo uni y o
con inuous collabo a ion be ween modele s and expe imen alis s.
Modeling o e s oppo uni ies o s anda dize GHG measu emen s da ase s, making
hem Findable, Accessible, In e ope able and Reusable bu as well o cap u e he
a iabili y o ice sys ems and add ess challenges o he e ogenei y in GHG emissions
due o di e ences in en i onmen s, managemen , and a ie ies.
The discussion on da a o modeling iden i ied he po en ial o build an in en o y o
da ase s linked o 1) sea chable me ada a, and 2) s uc u ed in o po able, ha monized
inpu s usable no only o modeling bu also o b oade applica ions and analyses.
The implemen a ion o hese equi ed, howe e , an open discussion on da a sha ing
and owne ship, and acknowledgemen in co-au ho ship. I was sugges ed o conside
he AgMIP’s concep o FAIRER da a p inciples: Findable, Accessible, In e ope able,
Reusable, E hical, and Responsible use. FAIRER could se e as a guiding amewo k,
going beyond he s anda d FAIR p inciples o he onwa ds collabo a ion among he
wo kshop pa icipan s.
V. Pa allel Sessions
A. GHG Measu emen s Session
Following in ensi e e iew o li e a u e on GHG emissions in paddy ice sys ems, he
o ganizing eam o he wo kshop chose scien is s and esea che s who a e ac i ely
conduc ing GHG emissions s udies in majo ice egions o he wo ld (Table 1). The eam
in i ed nine scien is s/p o esso s o speak abou chambe me hods o GHG lux
measu emen s om eigh (8) egions ep esen ing Asia, Ame icas, Eu ope, and A ica
(Fig. 11). Addi ionally, he eam in i ed wo scien is s o speak on analy ics and ools
ha imp o e compu a ion o GHG emissions om chambe me hod. Pa icipan s who
a e in ol ed in GHG emission da a assessmen , in en o ies and sus ainabili y p og am
23
we e also in i ed o sha e hei knowledge and expe iences in using GHG da a o hei
applica ions. Annex B. Table 2 summa izes he in o ma ion abou speake s and
pa icipan s o he mee ing and he able below shows he speci ica ions o he
di e en GHG measu emen s app oaches sha ed du ing he sessions.
Table 1. Ancilla y da a measu emen s and desc ip ions o lux chambe me hod used by majo
ice g owing egions globally.
Coun y
Chambe
design
Gas sampling
Gas s o age
Gas analyses
Flux
calcula ion
Ancilla y da a
Japan
 Rec angula -
ansplan ed
 Base
 Wa e sealing
 Mid-mo ning
 20-30 min
 3-4 gas
samples
 >3 eps
 P e-e acua ed
glass con aine
 GC
 Ex e nal
calib a ion
 Linea
Tempe a u e
Indonesia
 Rec angula –
ansplan ed
 Base
 Wa e sealing
 Mid-mo ning
 20-40 min
 4 gas samples
 3 eps
 P e-e acua ed
glass con aine
 GC
 Ex e nal
calib a ion
 Linea
Eh, pH, plan
heigh , ille
numbe , g ain
yield, weigh o
biomass
India
 Rec angula –
ansplan ed
 Base
 Wa e sealing
 Mid-mo ning
 20-60 min
 4 gas samples
 3 eps
 N2 lushed, p e-
e acua ed
glass con aine
 GC
 Ex e nal
calib a ion
 Linea
 Non-linea
Tempe a u e,
wa e dep h,
MC, WFPS,
g ain yield, LAI,
Eh, pH, EC, N
up ake
China
 Rec angula –
ansplan ed
 Round – CO2
 Base
 Wa e sealing
 Mid-mo ning
 30 min
 4 gas samples
 au oma ed
 P e-e acua ed
glass con aine
 GC
 Linea
Tempe a u e
Taiwan
 Rec angula –
ansplan ed
 Base
 Wa e sealing
 Mid-mo ning
 5 min
 4 gas samples
 Sma chambe
 No applicable
 LICOR-8710
 LICOR-8720
 Linea
Tempe a u e,
wa e dep h,
Eh, soil ex u e,
g ain yield
Vie nam
 Rec angula –
ansplan ed
 Base
 Wa e sealing
 Wa e holes
 Mid-mo ning
 30 min
 4 gas samples
 P e-e acua ed
glass con aine
 GC
 LICOR-8710
 Linea
Tempe a u e,
loodwa e
dep h, MC,
g ain yield,
nu ien up ake,
Eh, pH, OC, N
US
 Round – d ill
seeded
 Base
 Silicon band
 Wa e holes
 Mid-mo ning
 60 min
 4-5 gas
samples
 3-10 eps
 P e-e acua ed
Exe aine glass
ial
 Double Si seal
 GC
 Ex e nal
calib a ion
 3 le els
 Linea
 Non-linea
(HMR)
Tempe a u e,
wa e dep h,
MC, yield, g ain
quali y, soil
p ope ies,
nu ien s
Colombia
 Round – d ill
seeded
 Base
 Silicon band
 Wa e holes
 Mid-mo ning
 60 min
 4-5 gas
samples
 P e-e acua ed
glass con aine
 GC
 Ex e nal
calib a ion
 5 le els
 Linea
Tempe a u e,
loodwa e ,
MC, yield, g ain
quali y, EC, pH,
soil ex u e, N
Spain
 Rec angula –
d ill seeded
 Base
 Wa e sealing
 10:00 – 15:00
 10-30 min
 4-5 gas
samples
 3 eps
 P e-e acua ed
glass con aine
 GC
 Pho oacous ic gas
analyze
 Linea
Tempe a u e,
Eh, EC, pH,
OC, NPK, soil
ex u e, yield,
C:N a io
GC= gas ch oma og aph, LAI = lea a ea index, WFPS = wa e illed po e spaces, MC = mois u e con en , OC =
o ganic C, N = ni ogen.
24
Gene ally, he chambe me hod in ol es he ins alla ion o a chambe base
( ec angula o cylind ical) o e he soil su ace, which is closed wi h a chambe co e .
Many o he s udies on soil-a mosphe e exchange o soil ace gases om paddy ice
ha e used chambe me hod because i is common and he mos sui able o he s udy
o he e ec s o di e en ac o s. I is as well less expensi e o ield ins alla ion and
ope a ions, compa ed o mic ome eo ological echniques (e.g. eddy co a iance o
g adien echniques). Many esea che s ha e long acknowledged he occu ence o
he “chambe e ec ” due o he dis up ion o na u al condi ions and he di usion o
gas a e chambe deploymen in case o ina en ion, esul ing in an o e - o unde -
es ima ion o he ac ual ace gas lux. S udies ha e demons a ed ha chambe da a
can su e om nega i ely biased lux es ima es when add essing chambe e ec s.
Addi ionally, he e is wide a ia ion in he applica ion o chambe ypes and echniques
(Table 1), so he magni ude o lux es ima ion is also expec ed o a y widely ac oss
s udies.
Gi en his unce ain y and a ia ion, he s anda diza ion o imp o e he chambe
me hod and compu a ion o GHG luxes may equi e an inno a i e app oach han a
simple a emp o uni ica ion. Also, he sensi i i y o he chambe e ec s o a ia ions o
clima e, soil and c op p ope ies which in luence he in e p e a ion o ea men e ec s
wi hin a gi en s udy is minimized when da ase s a e p ocessed and analyzed using a
simila app oach ac oss ice c opping egions. S anda dizing he chambe me hod and
lux compu a ion when easible will widen he implemen a ion o he obus chambe
me hod p o ocol in ice s udies.
Following an exhaus i e e iew o he many chambe me hods ac oss ice egions he
g oup eached a s ong consensus on minimum equi ed me ics, sampling p inciples,
lux calcula ion me hods, and epo ing s anda ds (Table 2). These ag eemen s a e
in ended o s eng hen da a in eg i y, educe a iabili y ac oss s udies, and make esul s
mo e use ul o bo h scien i ic modeling and clima e policy applica ions.
Table 2. Requi emen s guide o GHG measu emen s. Requi ed is he minimum you need o
ensu e you measu emen s mee he s anda d. P e e ed is wha you need o op imize you
condi ions o measu emen s o minimize unce ain ies.
Pa ame e
Requi ed
P e e ed
Key conside a ions
Chambe size a ea
(24-250 L)
Yes
Volume o su ace a ea a io should be su icien o a oid dilu ion e ec , minimize
wa ming, adequa e ai mixing and made o non-pe meable and ine ma e ial. (Pa kin and
Ven e ea, 2010, Healy e al., 1996)
Chambe base
(0.03 o 0.15 m soil dep h
inse ion)
Yes
Assu e good chambe - o-soil seal, minimize dis u bance o oo sys em, shel e e ec s
( ain, e ilize , amendmen s), accoun o he po osi y o he opsoil and minimize
mic oen i onmen pe u ba ions. (Pa elka e al. 2018). Ins alled base a leas 24 h p io
o sampling (Bahn e al., 2009)
Fan
Yes
Be e mixing o he enclosed ai , a oid dead space in co ne s o ec angula (Li ings on
and Hu chinson, 1995, Li ings on e al., 2006, Maie e al. 2022, Pa elka e al., 2018)
Boa dwalk
Yes
A oid soil dis u bance, pumping e ec , plan damages, and compac s soil (Maie e al.,
2022)
Glass sample con aine
Yes
Use o Exe aine
ials
Abili y o hold acuum and p essu e and p ese e gas concen a ion >1 week (Gla zel and
Well, 2008, Roche e and E iksen-Hamel, 2008, Laughlin and S e ens, 2003, Roche e
and Be and, 2003)
Gas o e p essu e o
anspo and s o age
Yes
The use o bu yl ubbe s oppe and Exe aine ials a e ecommended o long- e m
s o age and anspo (Gla zel and Well, 2008, Roche e and E iksen-Hamel, 2008)
Sy inge needle size
Yes
22- 25-gauge needle p o ides minimal p essu e losses (Pa kin and Ven e ea, 2010)
Time 0
Yes
Measu ed gas concen a ion ep esen s p e-deploymen soil-chambe e ical GHG p o ile
(Roche e, 2011)
O e head ambien gas
sampling
Yes
Minimize ele a ed ambien gas concen a ions du ing gas sampling (Roche e, 2011,
Pa king and Ven e ea, 2010)
25
Time o gas sampling
Yes
Diu nal GHG emission measu emen s p o ide daily emission es ima es yielding he
smalles a e age bias (Wu e al., 2021). I is ecommended o do measu emen s e e y 4 h
wi h minimum 4 measu emen s du ing he day (Pa elka e al., 2018, Da eno a e al.,
2014)
Sepa a e chambe o N2O
lux
This p ac ice is implemen ed in Indonesia o low ange o N2O emissions (IAERI)
Numbe o chambe s pe
plo
≥3 o depends on
p ojec
Numbe o chambe s needed adequa ely es ima e he mean and a iance o gas luxes
wi hin a si e (Da idson e al., 2002, Li ings on and Hu chinson, 1995)
Ven ubes and dimensions
Yes
P ope en ube dimensions ansmi changes in ex e nal p essu e o chambe headspace,
hus minimizing supp ession by deploymen on gas luxes (Hu chingson and Li ings on,
2001, Pa kin and Ven e ea, 2010, Xu e al., 2006, Da idson e al., 2002). Dimensions o
en ubes o selec ed wind speeds and enclosu e olumes a e desc ibed by Pa kin and
Ven e ea, 2010)
Du a ion o chambe
closu e
Depending on he
p ojec
Du a ion should be based on expec ed lux and p ecision o gas analyses. 20-60 min o
GC is ecommended (Maie e al., 2022, Pa kin and Ven e ea, 2010, Roche e and
Hu chinson, 2015, de Klein and Ha ey, 2012)
Numbe o gas samples
≥4 (min o 3 i
linea )
Gas samples wi hd awn a egula in e als du ing closu e o <60 min. A leas 3 ime
poin s a e equi ed o lux calcula ion. Use o i ing models and unce ain y in dC/d
equi es >2 samples (Pa kin and Ven e ea, 2010, Pede sen e al., 2010)
End o he season lux
Depending on
esea ch o ield
condi ion and
managemen
Sho -li ed CH4 peaks occu ed a e d y e en due o champagne e ec o physical
elease o en apped gas du ing soil d ying be o e ha es . Can con ibu e o 10-15% o
o al g owing season emissions (Ad ien o-Bo be e al., 2015, an de Gon and Neue,
1995)
Gas sampling du ing d ain
e en s (AWD, Fu ow)
Yes
Sho -li ed CH4 peaks occu ed a e d y e en due o champagne e ec o physical
elease o en apped gas du ing d y cycle (Ad ien o-Bo be e al., 2015, an de Gon and
Neue, 1995)
F equency o esponse
cu e o GC calib a ion
Depending on
s abili y o GC
Quali y con ol needed in sepa a ion o N2O and CO2 wi h simila e en ion imes, check
non-linea i y o ECD a high N2O concen a ions using mul ipoin calib a ion. Include
check s anda d e e y 10 samples o accoun o ins umen d i , check calib a ion cu e
using con ol alues and CV o 1 o 3% (Ha ey e al., 2020, de Klein & Ha ey, 2012,
Pa elka e al., 2018).
Pa ame e s o GC
calib a ion and check
Yes
Ins umen me ics o GC calib a ion depend on GC b and, his includes span calib a ion,
column cleaning, pneuma ic line checks, sample loop. Bes sensi i i y is achie ed in P5
gas o ECD de ec ion (N2O (Maie e al., 2022, Pa elka e al., 2018)
Regula s anda d check pe
samples analyzed
Yes
To a oid bias in analy ical esponse, include s anda d checks e e y 10 andomized
samples; ), abou 20% o analyzed samples a e adequa e numbe o s anda d gases o
calib a ing ins umen o cope wi h empo al d i (Pa kin and Ven e ea, 2010, Pa elka e
al., 2018).
Linea lux equa ion
Yes
GHG luxes a e compu ed om a e o change o gas concen a ion o e ime o closu e,
when a e o change is cons an , linea eg ession is app op ia e (Lundega dh, 1927,
Ven e ea e al., 2009). Dasig model is so wa e o compu e linea inc ease o gas con en
o e ime (Ad ien o-Bo be e al., 2025)
Non-linea lux equa ion
Yes
GHG luxes a e compu ed om a e o change o gas concen a ion o e ime o closu e,
when a e o change is no cons an , non-linea eg ession is app op ia e. HMR model
p o ides non-linea lux es ima es and Dasig so wa e inco po a ed he HMR equa ion
(Pede sen e al., 2010, Ad ien o-Bo be e al., 2025).
Pa ame e s o linea i y
Yes
Use R2 alue ha close o 1, a iance associa ed wi h slope is conside ed, use a leas
h ee da apoin s, and implemen signi ican es o slope and minimum de ec ion limi o
ambien ai (Pa kin and Ven e ea, 2010, Maie e al., 2022)
Ze o lux
Yes
Conside ze o lux when gas concen a ions a e abo e minimum de ec ion limi o
chambe and gas analyze s (Ad ien o-Bo be e al., 2025, Pa kin e al., 2012).
Nega i e lux
Yes
Conside nega i e lux when R2 is high, gas concen a ions a e abo e minimum de ec ion
limi (Ad ien o-Bo be e al., 2025).
Linea in e pola ion o
missing days o samples
Depending on
p ojec objec i es
Daily GHG emissions a e compu ed using apezoidal ime in eg a ion ei he seasonal o
annual es ima es (Ad ien o-Bo be e al., 2025).
Fallow emissions
Depending on
p ojec objec i e
To al GHG emissions du ing he non-g owing season a e ele an in he assessmen o
annual emissions and impac s o s aw, illage and c opping p ac ice on GHG emissions
om nex c opping (Fi zge ald e al., 2000, Ad ien o-Bo be e al., 2013)
IPCC con e sions using
100-y and 20-y ime
ho izon
Yes
Emission me ic ha is c ucial ool in se ing e ec i e exchange a es be ween non-CO2
gases wi h a bi a y choice o ime ho izon. (Abe ne hy and Jackson, 2022, Allen e al.,
2016)
Da a epo ing
Depending on
p ojec objec i es
Conside uni as elemen al o compound o m o GHG emissions o modeling and lux
es ima es i.e. CH4/m2/h ; CH4 kg/ha/d o CH4-C/m2/h ; CH4-C kg/ha/d. Yield-scaled and
a ea-scaled GWP uni s exp ess bo h yield and emission po en ial o mi iga ing p ac ice
(Ad ien o-Bo be e al, 2013)
Ancilla y da a
Yes
Include measu emen o g ain yield exp essed a 14% mois u e con en and milling
quali y.
The nex s eps ag eed du ing he pa allel sessions we e as ollow:
 Technical epo de elopmen - Summa ize chambe commonali ies and
me hodological app oaches; p o ide e idence-based ecommenda ions.
 P epa e a s ep-by-s ep da a handling p o ocol o lux p ocessing o be
included in he echnical epo as handou se ies.
 De elop annex compa ing DASIG, GET, and HMR models, including hei
compliance wi h he MIRSA guidelines, ad an ages and limi a ions.
32
Fig. 14. B eakou g oup discussions on collabo a ion oppo uni ies, da a gaps, and he need o
ha moniza ion ac oss si es and scales.

33
Table 4. B eakou g oup esul s on da a ha moniza ion, key da a gaps, and ele ance o li elihoods.
BOG
Da a Ha moniza ion
Key Da a Gaps
Rele ance o Li elihoods
BOG 1 – Simula ing Rice
P oduc i i y unde Wa e
Managemen Scena ios
S anda dize AWD de ini ions and
h esholds; align p o ocols o
wa e , yield, and GHG
simula ions ac oss coun ies;
in eg a e i iga ion and g ain
quali y pa ame e s.
Lack o consis en da a on
i iga ion olumes, soil wa e
con en , ain all, soil ype, and
g ain quali y; a iable AWD
h esholds complica e modeling.
Be e wa e managemen (e.g.,
AWD) can educe wa e use,
main ain yields, lowe GHGs, and
enable access o ca bon c edi s,
suppo ing a me esilience and
income.
BOG 2 – Modeling and
Measu ing CH₄ Emissions
om Paddy Fields
Es ablish common e alua ion
me ics (R², RMSE); ha monize
minimum da ase s o soil, wa e ,
c op, esidues; use sha ed
eposi o ies wi h me ada a and
quali y s anda ds.
Missing soil da a (bulk densi y,
o ganic ma e , C/N con en ),
inconsis en wa e and wea he
da a, limi ed esidue
managemen da a, lack o
s anda dized AWD de ini ions.
Imp o ed me hane models
suppo c edible MRV amewo ks,
enabling pa icipa ion in ca bon
ma ke s and clima e policies ha
could bene i a me s inancially.
BOG 3 – Managemen
In e en ions and Thei
Impac on Emissions
S anda dize moni o ing sys ems
and model calib a ion o
in e en ions (wa e , e ilize ,
a ie ies); compile in en o y o
models and hei applica ions.
Da a misma ches be ween
in e en ions and model
equi emen s; limi ed da a o
c op a ie ies, o ganic
amendmen s, and soil
mic obiomes; lack o egional lux
ne wo ks.
Adop ion o imp o ed
managemen p ac ices (AWD,
biocha , e icien e ilize s, be e
a ie ies) can inc ease yield,
educe emissions, and imp o e
g ain quali y, enhancing a me
li elihoods.
BOG 4 – Scaling om
Field o Region: Da a
In eg a ion and
Upscaling
P omo e s anda dized da a
o ma s/uni s; use AI/ML o
in eg a e emo e sensing,
clima e, soil, and
socioeconomic da ase s; link
GHG luxes o managemen
p ac ices.
Spa ial and empo al misma ches
( ield s. na ional da a, high-
equency needs); coa se soil
da a; limi ed lux ne wo k
co e age; inconsis en
managemen da ase s.
Scaling in eg a ed assessmen s
helps design egion-speci ic
policies ha balance p oduc i i y,
p o i abili y, and sus ainabili y,
ensu ing ha a me s bene i om
bo h yield gains and clima e
incen i es.
34
Table 5. Key ou comes om he ou B eakou G oups (BOGs), ou lining a ailable da a, equi ed ac ions, esponsibili ies, collabo a ion
app oaches, needed esou ces, imelines, and challenges.
BOG
Wha da a we
ha e?
Wha needs
o be done?
Who will do
i ?
How we can
wo k oge he ?
Requi ed
esou ces/suppo
Timelines
An icipa ed
challenges
BOG 1 –
Rice
P oduc i i y
unde
Wa e
Managem
en
Ponding heigh ,
wa e able, daily
wea he , i iga ion
equency (bu
o en missing
olumes), soil
ex u e (0–15 cm),
NPK da a, yields,
biomass,
phenology.
De elop
s anda dized
wa e
managemen
da ase s (AWD
h esholds,
i iga ion, g ain
quali y);
ha monize
p o ocols;
in eg a e
i iga ion use
in o models.
Modele s
(AgMIP)
p epa e
empla es;
da a p o ide s
supply
da ase s;
appo eu
ensu es quali y
check.
Sha ed eposi o ies;
di ec
communica ion
be ween modele s
and p o ide s; co-
au ho ship and
c edi ag eemen s.
Da a managemen
ools, eposi o y
hos ing, aining in
da a use and
models.
Sho - e m: d a
e ms o
use/ empla es;
medium- e m:
i e a i e
ha moniza ion.
Missing/incomple e
da a; labo -
in ensi e empla es;
ins i u ional
es ic ions; da a
secu i y isks.
BOG 2 –
Modeling
and
Measu ing
CH₄
Emissions
Flux
measu emen s
(chambe s, eddy
co a iance); soil
p ope y da a
(inconsis en );
limi ed
wa e /wea he
moni o ing;
esidue
managemen
eco ds a some
si es.
Ha monize
AWD
de ini ions;
c ea e
minimum
da ase (soil,
wa e ,
wea he , c op,
esidues); c oss-
alida e
models; build
sha ed
eposi o y.
Flux g oups,
modele s
(DSSAT, DNDC),
egional eams
o da a
in en o ies.
Open da a
exchange wi h
me ada a;
egional
pa ne ships;
modeling g oup
in eg a ion; public–
p i a e
collabo a ion.
Sus ained unding;
coo dina ion s a ; IT
suppo o
eposi o y and
quali y con ol.
Sho - e m:
cu a e/sha e
da ase s; Medium-
e m: c oss-
alida ion and
in eg a ion pilo s;
Long- e m: sha ed
eposi o ies and
AWD
s anda diza ion.
Soil he e ogenei y;
inconsis en
p o ocols;
ins i u ional
es ic ions;
unce ain y in
mic obial impac s.
BOG 3 –
Managem
en
In e en io
ns &
Emissions
Da a s onge o
wa e and e ilize
in e en ions;
limi ed o c op
a ie ies, o ganic
amendmen s,
mic obiomes;
une en lux
ne wo k
co e age.
In en o y
models/da ase
s; me a-
analysis o
in e en ion
impac s;
s anda dize
moni o ing;
benchma k
model
Regional
leade s
assigned; Toshi
o de elop
me ada a
empla e;
pa icipan s
p o ide
eedback and
da a.
Me ada a-d i en
sha ing; open-da a
jou nals; phased
ocus
(wa e / e ilize i s ,
b oade la e ).
Ins i u ional suppo
o da a access;
unding o
coo dina ion;
jou nal access o
e iews.
2-week me ada a
eedback; phased
benchma king and
in e en ion ocus.
Ins i u ional
es ic ions;
agmen ed
da ase s;
ha monizing
uni s/de ini ions;
egional
coo dina ion
challenges.
35
pe o mance.
BOG 4 –
Scaling
Field o
Region
Remo e sensing
(MODIS, Landsa ,
Sen inel-2),
clima e (MERRA-2,
ERA5, NCEP), soil
(SoilG ids, WoSIS),
c op models
(DNDC, ORYZA,
Daycen ,
RiceG ow).
Regional da a in
India, Bangladesh,
Vie nam.
Conduc
egional
in eg a ed
assessmen s;
selec sen inel
si es; link
managemen
da a wi h GHG
luxes; in eg a e
economic and
biophysical
models.
Si e leade s
(India,
Vie nam,
Bangladesh);
acili a o s o
clima e, c op,
socioeconomi
c modeling.
Collabo a i e
p oposals; sen inel
si e ca ego iza ion;
phased scaling
(pilo → mul i-
model → emo e
sensing).
AgMIP IT e i al;
capaci y building;
unding o
su eys/wo kshops/s
p in s; high-
pe o mance
compu ing.
S a wi h India pilo ;
expand o
Vie nam/Banglades
h; la e include
o he egions.
Da a
he e ogenei y; high
moni o ing cos ;
ins i u ional
ag eemen s;
balancing
esolu ion s
easibili y.
36
All BOGs highligh ed missing o inconsis en da ase s (soil, wa e , c op, managemen ,
and socio-economic da a). The e was consensus on he u gen need o s anda dized
da a o ma s, me ada a, and ha monized p o ocols ac oss egions.
The pa icipan s emphasized he impo ance o benchma king models, in eg a ing
mul iple modeling ools, and aligning simula ions wi h a ailable da ase s o educe
unce ain ies and imp o e eliabili y.
Fo mi iga ion in e en ions, Al e na e We ing and D ying (AWD) eme ged as a ocal
p ac ice, equi ing clea e de ini ions, h esholds, and s anda diza ion o bo h scien i ic
use and o p ac ical implemen a ion such as in ca bon c edi me hodology.
Es ablishing du able da a-sha ing communi ies, suppo ed by p inciples o ecip oci y,
co-au ho ship, and anspa ency, was p io i ized. Expe iences om ne wo ks such as
Fluxne and AgMIP we e ci ed as models.
The eam has hen iden i ied he ollowing i ems as p io i ies:
 De elop a global benchma k da abase o ice sys ems ha combines
biophysical, managemen , and socioeconomic da ase s.
 S anda dize p o ocols o AWD implemen a ion, GHG measu emen , and da a-
sha ing amewo ks.
 Mobilize unding o sus ained da a collec ion, da a managemen
coo dina o s, and collabo a i e model in e compa isons.
 Es ablish me ada a-d i en mapping o da ase s o iden i y egional s eng hs,
gaps, and p io i ies.
 Pilo egional in eg a ed assessmen s (s a ing wi h India) o demons a e
scalable app oaches, ollowed by expansion o o he coun ies.
 S eng hen c oss-disciplina y collabo a ion be ween ag onomis s, modele s, soil
scien is s, and socioeconomic esea che s o ensu e bo h scien i ic igo and
policy ele ance.
A he end o he sessions, he s ee ing commi ee o he wo kshop has de ined he
below plan o sus ain he wo k accomplished du ing he i e days:
 The wo kshop will gene a e a echnical documen compiling he di e en
eam’s p ac ices ha will se e as e e ences o con ex dependen bes
p ac ices in GHG measu emen s in ice.
 The wo kshop pa icipan s ag eed o es ablish a communi y o p ac ices in GHG
measu emen s and modeling ha will ha e egula / mon hly mee ing. This will be
an ini ial pla o m o knowledge sha ing un il a mo e o mal pla o m is
es ablished. The eam will explo e he es ablishmen as well o an AgMIP
emissions da a subg oup.
 Wi h he planned posi ion pape s on policy, GHG measu emen s and modeling,
he o ganizing commi ee o he wo kshop will de elop an in eg a ed p ojec
p oposal ha will aim o le e age he wo kshop pa icipan s ne wo k and
expe ise o capaci y building in measu emen s and modeling and o he
es ablishmen o a amewo k o da a, modeling and egional in eg a ed
assessmen o imp o e baseline es ima e in ice g owing a eas globally and o
37
mi iga ion in e en ions a ge ing guided by obus es ima e o gains and co
bene i s a scale.
VII. Ou comes and Recommenda ions
The plena y and pa allel sessions o he wo kshop ha e con i med he imeliness o he
ini ia i e o add ess he challenges in mi iga ion esea ch and e o s in ice sys ems. The
di e en sessions ha e a icula ed he challenges and he exis ing oppo uni ies o
educing me hane emissions in ice om he s anda ds o measu emen s a ield le el o
he moni o ing and assessmen s o co-bene i s a global scale o sys em sus ainabili y
and esilience o clima e change.
A. Main Insigh s and Challenges
The planned wo kshop ou comes include documen a ion on he bes p ac ices in
chambe me hod applica ions and GHG lux analysis, ice model in e compa ison and
imp o emen , and policy conside a ions. These documen s will a icula e he main
insigh s and challenges sha ed du ing he wo kshop ha will se e as e e ence o
esea che s and p ac i ione s in ice-g owing egions ac oss he Uni ed S a es and La in
Ame ica, Eu ope, Sou heas Asia, Sou h Asia, Eas Asia, and Sub-Saha an A ica.
These insigh s and challenges can be ca ego ized as below:
Policy and Mi iga ion Con ex
 Rice is ecognized as a signi ican con ibu o o GHG emissions, ye no ASEAN
coun y has se speci ic mi iga ion a ge s o ice sys ems; exis ing commi men s
a e usually condi ional on in e na ional inancing.
 Achie ing he Global Me hane Pledge (30% educ ion by 2030) would equi e
la ge-scale adop ion o mi iga ion in e en ions such as o AWD es ima ed
oughly up o 60% o ice a ea.
 Ca bon ma ke s a e eme ging wi h mul iple schemes (compliance, olun a y,
bila e al, ODA, inancial ins i u ions, scope 3), bu epo on success o hese
emains e y limi ed.
 I is essen ial o quan i y he impac s o GHG mi iga ion s a egies on a me s’
li elihoods and b oade policy- ele an ou comes such as po e y and ood
secu i y, while assessing he ade-o s among socio-economic, biophysical,
and en i onmen al dimensions.
Scien i ic App oaches o GHG Measu emen s and Modeling
 Measu emen p ac ices emain di e se, c ea ing inconsis encies.
 Ag eemen was eached on he need o a uni ied p o ocol o ensu e obus
es ima es and educe unce ain ies.
 Rice models a e gene ally eady o clima e change applica ions bu equi e
u he de elopmen o emission modules and s anda dized calib a ion.
 Modeling o e s pa hways o s anda dize and ha monize da ase s, imp o ing
usabili y and compa abili y.
 Wa e managemen p ocesses need be e ep esen a ion, pa icula ly o AWD
and d ainage e ec s on yield and GHG emissions.

38
 AgMIP and i s ice eam p o ide ools o in eg a e clima e, socio-economic,
and c op models o adap a ion and mi iga ion co-bene i s.
 Modeling suppo s unce ain y analysis, ex apola ion o expe imen al esul s,
and scaling om ield o egional and global le els.
 The e is s ong po en ial o s anda dized p o ocols, ensemble modeling, and
in eg a ion o eme ging echnologies (ML/AI, digi al wins, genomics).
Da a Gaps
 Cu en da a collec ion is designed o ood secu i y, no mi iga ion.
 The baseline concep is lawed: unin en ional AWD adop ion and s aw
managemen a iabili y a e no accoun ed o .
 Na ional in en o ies lack sys ema ic, eliable, and alida ed da a o
calib a ion/ alida ion.
 Da a a ailabili y and quali y emains he la ges ba ie - sha ing, s anda diza ion,
and coo dina ed calib a ion/ alida ion e o s a e c ucial.
 Examples like IRRI’s a m su ey + RS amewo k show pa hways o link a m-le el
da a wi h dis ic / egional assessmen s.
 Da a sha ing emains a sensi i e issue due o owne ship and access conce ns.
 AgMIP’s FAIRER p inciples (Findable, Accessible, In e ope able, Reusable, E hical,
Responsible euse) p o ide a s onge amewo k han he basic FAIR app oach.
Scaling assessmen
 The limi ed unde s anding and quan i ica ion o a iabili y o ice ecosys ems
and he he e ogenei y o managemen p ac ices, and a ie ies ha in oduce
wide a iabili y in emissions and mi iga ion po en ial. This has impac on he
Baseline alidi y in ice ha is cu en ly unde mined by widesp ead un epo ed
AWD and s aw managemen p ac ices.
 Limi ed obus ness o Moni o ing and Ve i ica ion ool: Tie 1 and 2 app oaches
ca y la ge unce ain ies, equi ing anspa en epo ing o hese and sha ed
unde s anding o hei limi s.
 Da a sca ci y as he e is no clea incen i es, p o ocols, o bene i -sha ing
mechanisms o da a collec ion and sha ing among s akeholde s ei he o
na ional ac o s o p i a e en i ies.
 Knowledge gaps in esea ch and in modeling such o ins ance ep esen a ion
o a oon c ops, g ain quali y (e.g., a senic, s a ch, and chalkiness), SOC
a iabili y, and pes /disease in e ac ions.
B. Ac ionable Nex S eps
The di e en discussions aised om he di e en sessions ha e sugges ed he below
ecommenda ions o he eam o le e age and o add ess mo ing o wa ds:
 Implemen a uni ied GHG measu emen p o ocol ac oss eams o educe
inconsis encies. These can con ibu e o add essing e i ica ion challenges by
s anda dizing emission ac o s and s a i ica ion me hods.
 C ea e da a-sha ing pla o ms wi h clea bene i -sha ing mechanisms and
oppo uni ies o go e nmen s, esea che s, and local s akeholde s. Use o such
pla o m can be used o upda e baselines as hese need o be ede ined o
39
e lec ac ual p ac ices (e.g., unin en ional AWD, s aw managemen ). I is
impo an o mo e beyond FAIR o FAIRER o his pla o m embedding e hical
and esponsible euse in o all da a ini ia i es. And as da a became a ailable,
oppo uni ies o le e age ad anced echnologies in RS, AI/ML, genomics and
o he will o e oppo uni ies o scale mi iga ion and adap a ion in e en ions
wi h con idence and sus ainable impac .
 P omo e long- e m s udies combining measu emen s and modeling ha will
ensu e close in eg a ion be ween modele s and expe imen alis s which can
imp o e bo h ield expe imen design and model pe o mance.
 Expand capaci y building in GHG lux measu emen , c op modeling, and
in eg a ed assessmen app oaches. A s uc u ed aining and men o ship
p og am will help ha monize me hods ac oss egions, s eng hen echnical
expe ise, and suppo he nex gene a ion o esea che s in applying hese
ools o policy- ele an ques ions.
 These ini ia i es can encou age coope a ion ac oss egions, disciplines and
sec o s ha will ensu e da a access, a ailabili y and us wi h u u e e o s
ocusing da a-d i en model in e compa ison, and MRV/NDC alignmen o
b oade , policy- ele an applica ions.
VIII. Conclusion
The 5-day wo kshop achie ed i s main objec i e: es ablishing a communi y o p ac ice
ha can be used as a pla o m o knowledge exchange, capaci y building, and
collabo a ion de elopmen . By b inging oge he GHG measu emen expe s and
modele s, he wo kshop has c ea ed an oppo uni y o dialogue be ween hese wo
disciplines and es ablished he key gaps ha may o e oppo uni ies o e ec i e
collabo a ion, such as he de elopmen o a common on ology and he es ablishmen
o minimum da a equi emen s. As a i s example, a da a empla e was de eloped o
e alua e da a a ailabili y o model applica ions ha will be used o c ea e a
eposi o y o me ada a o a ailable da a ha he pa icipan s o he wo kshop will be
eady o sha e o c op model e alua ions. The oadmap below was iden i ied o he
in eg a ion o bo h e o s in measu emen s and in modeling GHG quan i ica ion o
baseline, p edic ions, and mi iga ion om ield o egional assessmen s.
Ag eed Ac ions
 Technical epo on na a i es o commonal ies among p ac ices
 Posi ion pape s on Measu emen s, Modeling, and Impac and Policy
 Regula mon hly mee ing o sus aining impac
 Ac i i ies plan in a yea ’s ime - Eas Asia AgMIP No 2026, Global ea ly 2027,
IRC 2027
GMH Commi men
 Suppo o a co e g oup o keep and b ing people oge he
 Ca alyze exis ing oppo uni ies and unding
 Seed und o p oposal de elopmen
Roles and Responsibili ies
40
 D . Hasegawa o he modeling - Modeling di e en pa hways o low emissions
ice
 Robe o o he RIA - Co- oas ing unding- Building he scien is ne wo k owa ds
sha ed goals - Google docs o p oposal de elopmen and seed unding o
p oposal de elopmen
 Ando o he GHG da a - Robus GHG da a, sha ing da a and capaci y
building sus aining he discussion
P io i y Objec i es
 Publica ion and sha ing o he wo kshop ou pu s
 Capaci y building o GHG measu emen s and modeling
 P oac i e engagemen in ini ia i es o suppo na ional capaci y o mi iga ion
da a collec ion, ha moniza ion and di e en coun ies GHG in en o y o
UNFCCC epo ing aiming o obus baseline upda e ac oss ice g owing
egions and a ge ed in e en ions o mi iga ion and adap a ion co bene i s
IX. Li e a u e Ci ed
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Sheppa d LJ (2011). Quan i ica ion o unce ain y in ace gas luxes measu ed by
he s a ic chambe me hod. Eu opean Jou nal o Soil Science 62(6):811-821. DOI:
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G oup I o he Six h Assessmen Repo o he In e go e nmen al Panel on Clima e
Change[Masson-Delmo e, V., P. Zhai, A. Pi ani, S.L. Conno s, C. Péan, S. Be ge , N.
Caud, Y. Chen, L. Gold a b, M.I. Gomis, M. Huang, K. Lei zell, E. Lonnoy, J.B.R.
Ma hews, T.K. Maycock, T. Wa e ield, O. Yelekçi, R. Yu, and B. Zhou (eds.)].
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o g eenhouse gas emission me ics. En i on. Res. Le ., 17, 024019. DOI:10.1088/1748-
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2015. Me hane and ni ous oxide emissions om looded ice sys ems ollowing he end-o -
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Ad ien o-Bo be, M.A.A., Pi elkow, C.M., Ande s, M., an Kessel, C., Hill, J.E., McClung, A.M., Six,
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Ma sico, T., Teague, T.G. 2025. DASIG model: A da a au oma ion p og am o he
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Allen, M.R., Fugles ed , J.S., Shine, K.P., Reisnge , A., Pie ehumbe , T., Fo s e , P.M. 2016. New
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pollu an s. Na u e Clima e Change, 6, 773-776.
Bahn M., Ku sch W.L., Heinemeye A., and Janssens I.A., 2009. Towa ds a s anda dized p o ocol
o he measu emen o soil CO2 e lux. In: Soil Ca bon Dynamics: An In eg a ed
Me hodology (Eds W.L. Ku sch, M. Bahn, A. Heinemeye ). Camb idge Uni e si y P ess, 272-
281.
Da eno a E., Pa elka M., and Acos a M., 2014. Diu nal de ia ions in he ela ionship be ween
CO2 e lux and empe a u e: A case s udy. Ca ena, 123, 263-269.
Da idson E.A., Sa age K., Ve cho L.V., and Na a o R., 2002. Minimizing a i ac s and biases in
chambe -based measu emen s o soil espi a ion. Ag ic. Fo . Me eo ol., 113(1), 21-37.
De Klein, C.A.M., Al a o, M.A., Gil ap, D., Topp, C.F.E., Simon, P.L., Noble, A.D.L., an de
Wee den, T.J. 2020. Global Resea ch Alliance N2O chambe me hodology guidelines:
S a is ical conside a ions, emission ac o calcula ion and da a epo ing. J En i on. Qual.,
49, 1156-1167. DOI: 10.1002/jeq2.20127.
de Klein, C. A., & Ha ey, M. J. 2012. Ni ous oxide chambe me hodology guidelines(1s ed.).
Global Resea ch Alliance on Ag icul u al G eenhouse Gases. Minis y o P ima y Indus ies.
Denie an de Gon, H., N. an B eemen, H.U. Neue, R.S. Lan in, J.B. Aduna, M.C.R. Albe o, and
R. Wassmann. 1996. Release o en apped me hane om we land ice ields upon soil
d ying. Global Biogeochem. Cycles 10:1–7. doi:10.1029/95GB03460
Fi zge ald, G.J., K.M. Scow, and J.E. Hill. 2000. Fallow season s aw and wa e managemen
e ec s on me hane emissions in Cali o nia ice. Global Biogeochem. Cycles 14:767–776.
doi:10.1029/2000GB001259.
Gla zel, S. and R. Well. 2008. E alua ion o sep um-capped ials o s o age o gas samples
du ing ai anspo . En i on Moni . Assess. 2008, 136:307–311.
Ha ey, M. J., Spe lich, P., Clough, T. J., Kellihe , F. M., McGeough, K. L., Ma in, R. J., & Moss, R.
2020. Global Resea ch Alliance N2O chambe me hodology guidelines: Recommenda ions
o ai sample collec ion, s o age, and analysis. Jou nal o En i onmen al Quali y, 49(5),
1110–1125
Healy, R. W., R. G. S iegl, T. F. Rusell, G. L. Hu chinson, and G. P. Li ings on, 1996. Nume ical
e alua ion o s a ic- chambe measu emen s o soil -a mosphe e gas exchange:
Iden i ica ion o physical p ocesses, Soil Sci. Soc. Am. J. 60:740-747.
Hu chinson, G.L., Li ings on, G.P., 2001. Ven s and seals in non-s eady-s a e chambe s used o
measu ing gas exchange be ween soil and he a mosphe e. Eu . J. Soil Sci. 52, 675–682.
Hu chinson, G.L.,Mosie , A.R. 1981. Imp o ed soil co e me hod o ield measu emen o ni ous
oxide luxes. Soil Sci. Soc. Am. J. 45:311-316.
Laughlin, R. J., S e ens, R. J. 2003. Changes in composi ion o ni ogen-15-labeled gases du ing
s o age in sep um-capped ials. Soil Science Socie y o Ame ica Jou nal, 67, 540–543.
Li ings on, G.P., and G.L. Hu chinson. 1995. Enclosu e-based measu emen o ace gas
exchange: applica ions and sou ces o e o . In. P.A. Ma son and R.C. Ha iss (eds.)
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Blackwell Science Camb idge Uni e si y P ess. pp 14-51.
Li ings on, G.P., G.L. Hu chinson and K. Spa alian. 2006 T ace gas emission in chambe s: A non-
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www.a s.usda.go / esea ch/GRACEne
Pa kin, T.B., Ven e ea, R.T., Ha g ea es, S.K., 2012. Calcula ing he de ec ion limi s o chambe -
based soil g eenhouse gas lux measu emen s. J. En i on. Qual. 41, 705–715.
Pa elka, M., Acos a, M., Kiese, R., Al imi , N., B umme , C., C ill, P., Da eno a, E., FuB, R., Gielen,
B., G a , A., Klemed sson, L., Lohila, A., Longdoz, B., Lind o h, A., Nilsson, M., Jimenez, S.,
Me bold, L., Mon agnani, L., Peich, M., Phila ie, M., Pumpanen, J., O iz, P., Sil ennoinen, H.,
48
RiceG ow
Liujun
Xiao
liujunxiao@nja
u.edu.cn
Nanjing
Ag icul u al
Uni e si y
h ps://doi.o g/10.101
6/j.njas.2009.12.003;
10.1016/j.ag o me .
2025.110452
Yield p edic ion;Clima e
change impac
assessmen ;Adap a ion
s a egy
assessmen ;Cul i a /geno
ype compa ison;Nu ien
managemen
simula ion;Wa e
mana emen
simula ion;G eenhouse
gas (GHG) eimissions
es ima ion ;Nu ien cycling
modeling;Ca bon
seques a ion modeling
Phenological
de elopmen ;Lea and
canopy
pho osyn hesis;Respi a io
n;Biomass accumula ion
and
pa i ioning;A chi ec u e o
below-g ound
o gans;A chi ec u e o
abo e-g ound
o gans;Nu ien up ake
and pa i ioning;Yield
o ma ion;P oduc
quali y;T anspi a ion
CO2;H2O;CH
4;N2O
Soil wa e
mo emen ;Nu ien
mine aliza ion ;Nu ien
immobiliza ion;Nu ien
leaching;O ganic ma e
decomposi ion;CH4
p oduc ion ;CH4
oxida ion;Deni i ica ion
and ni i ica ion
RicePSM
Lloyd T.
(Ted)
Wilson
l -
wilson@aes g.
amu.edu
Texas A&M
Uni e si y
h ps://doi.o g/10.101
6/S0308-
521X(97)00070-X
Yield p edic ion;Clima e
change impac
assessmen ;Adap a ion
s a egy
assessmen ;Cul i a /geno
ype compa ison;Pes and
disease dynamics;
Phenological
de elopmen ;Lea and
canopy
pho osyn hesis;Respi a io
n;Biomass accumula ion
and
pa i ioning;A chi ec u e o
below-g ound
o gans;A chi ec u e o
abo e-g ound
o gans;Nu ien up ake
and pa i ioning;Yield
o ma ion;P oduc
quali y;T anspi a ion;
CO2;
Soil wa e mo emen ;
RiceSM
Xianguan
Chen
1069335668@
qq.com
Fujian
Ag icul u e
and Fo es y
Uni e si y
Scheduled o
publica ion his yea .
Yield p edic ion;Clima e
change impac
assessmen ;Adap a ion
s a egy
assessmen ;Nu ien
managemen
simula ion;G eenhouse
gas (GHG) eimissions
es ima ion ;Nu ien cycling
modeling;Ca bon
seques a ion modeling
Phenological
de elopmen ;Lea and
canopy
pho osyn hesis;Respi a io
n;Biomass accumula ion
and
pa i ioning;A chi ec u e o
below-g ound
o gans;A chi ec u e o
abo e-g ound
o gans;Nu ien up ake
and pa i ioning;Yield
o ma ion;T anspi a ion
CO2;N2O
Soil wa e
mo emen ;Soil
empe a u e
dynamics;Nu ien
mine aliza ion ;Nu ien
immobiliza ion;Nu ien
leaching;O ganic ma e
decomposi ion;Deni i ic
a ion and
ni i ica ion;Nu ien
ola iliza ion

49
B. Lis o Pa icipan s
Table 2. Comple e lis o speake s and pa icipan s o he wo kshop.
No
Fullname
Email Add ess
A ilia ion
1
Agnes Pad e
agnes i olpad [email protected]
Global Me hane Hub, Philippines
2
Aimee E angelis a
e angelis aa@clima e.go .ph
Clima e Change Commission O ice Philippines
3
Alishe Mi zabae
A.Mi zabae @cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
4
Anaida Fe e
a. e e @cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
5
Ando Radanielson
A.Radanielson@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
6
And iamananja a And y
nja aand [email protected]
Uni e si y o An anana i o, Madagasca
7
Angelica A iel Mawili-Ca pio
[email protected]
Uni e si y o he Philippines Los Baños, Philippines
8
Angg i He ani
angg ihe [email protected]
Minis y o Ag icul u e, Indonesia
9
Anki a Paul
anki a.paul@cgia .o g
In e na ional Rice Resea ch Ins i u e, India
10
An on U els
a.u els@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
11
A i Bha ia
a ibha ia@ica .o g.in;
a ibha ia.ia [email protected]
ICAR-Indian Ag icul u al Resea ch Ins i u e, India
12
Aung Zaw Oo
aungz0290@ji cas.go.jp
Japan In e na ional Resea ch Cen e o
Ag icul u al Sciences, Japan
13
Aus in Pea ce
apea ce@ ield oma ke .o g
Field To Ma ke : The Alliance o Sus ainable
Ag icul u e, USA
14
Benjamin Runkle
b unkle@ua k.edu
The Uni e si y o A kansas, USA
15
Bjoe n Ole Sande
b.sande @cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
16
Caesa A loo Cen eno
c.cen eno@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
17
Ca alina T ujillo
C.T [email protected]
Alliance o Bio e si y In e na ional / CIAT,
Colombia
18
Chen Xianguan
chenxianguan@ a u.edu.cn
Fujian Ag icul u e and Fo es y Uni e si y, China
19
Chia-Kang Huang
ckhuang@ a i.go . w
Taiwan Ag icul u al Resea che Ins i u e, Taiwan
20
Chi-Chieh Hu
[email protected] . w
Kaohsiung Dis ic Ag icul u al Resea ch and
Ex ension S a ion, Taiwan
21
C is hian Delgado
delc 974@s uden .o ago.ac.nz
Uni e si y o O ago, New Zealand
22
Dalmo Viei a
dalmo. iei [email protected]
A kansas S a e Uni e si y, USA
23
Emmali Manalo
e.manalo@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
24
E ik Mencos Con e as
e [email protected]
Columbia Uni e si y, USA
25
E angeline Sibayan
e.sibayan@c ea u a.com.ph
C ea u a Company L d, Philippines
50
26
Feng Zhou
zhou @pku.edu.cn
Peking Uni e si y, China
27
F ancis Rubianes
. ubianes@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
28
Fulu Tao
ao l@igsn .ac.cn
Chinese Academy o Sciences, China
29
Hannah Jose
h.jose@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
30
Jean Ma ial Johnson
J.Johnson@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
31
Ka he ine Nelson
K.Nelson@cgia .o g
In e na ional Rice Resea ch Ins i u e, Vie nam
32
Kazuki Sai o
K.Sai o@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
33
Kazuno i Minamikawa
minakazu@ji cas.go.jp
Japan In e na ional Resea ch Cen e o
Ag icul u al Sciences, Japan
34
Kel in Mashisia Shikuku
K.M.Shikuku@cgia .o g
In e na ional Li es ock Resea ch Ins i u e, Kenya
35
Ke in Ka l
ke in.ka [email protected]
Columbia Uni e si y / NASA-GISS, USA
36
Ko i Konadu Boa eng
ko i.boa eng@globalme hanehub.o g
Global Me hane Hub, Ghana
37
K is ine Pascual
[email protected] ice.go .ph
Philippine Rice Resea ch Ins i u e, Philippines
38
Lau a Na alia A enas Calle
la397@co nell.edu
Co nell Uni e si y, USA
39
Linh Huynh
L.Huynh@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
40
Liping Feng
[email protected]
BeiDaHuang G oup, China
41
Liujun Xiao
[email protected]
Nanjing Ag icul u al Uni e si y, China
42
Lixin Qiu
[email protected]
BeiDaHuang G oup, China
43
Li-Yu Daisy Liu
lyliu@n u.edu. w
Na ional Taiwan Uni e si y, Taiwan
44
Loli a Ad iano
l.ad iano@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
45
Longlong Xia
[email protected]
Chinese Academy o Sciences, China
46
Maduabuchi Paul Iboko
m.iboko@cgia .o g
A icaRice, Co e d'I oi e
47
Mai Văn T ịnh
mai an [email protected]
Ins i u e Fo Ag icul u al En i onmen , Vie nam
48
Mai e Ma ínez Eixa ch
Mai e.Ma inezEixa ch@i a.ca
Ins i u e o Ag i ood Resea ch and Technology,
Spain
49
Ma ia An hea Fal ado
m. al ado@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
50
Ma ia Camila Rebolledo
m.c. ebolledo@cgia .o g
In e na ional Cen e o T opical Ag icul u e,
Colombia
51
Ma y Ann Bu ac
m.bu ac@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
52
Meijian Yang
[email protected]
Columbia Uni e si y, USA
53
My iam Adam
my iam.adam@ci ad.
Cen e de Coope a ion In e na ional en
Reche ché Ag onomique pou le De elopmen ,
Cambodia
51
54
Naoya Takeda
n3. akeda@qu .edu.au
Queensland Uni e si y o Technology, Aus alia
55
Na a aja Subash Pillai
nsubashpd s @gmail.com
ICAR-Indian Ag icul u al Resea ch Ins i u e, India
56
Oli yn Angeles
o.angeles@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
57
Pauline Chi enge
p.chi enge@cgia .o g
In e na ional Rice Resea ch Ins i u e, Tanzania
58
Reine Wassmann
[email protected]
In e na ional Rice Resea ch Ins i u e, Philippines
59
Re i Ranniku
anniku@ua k.edu
Uni e si y o A kansas, USA
60
Robe o Valdi ia
Robe o.Valdi ia@o egons a e.edu
O egon S a e Uni e si y / AgMIP, USA
61
Rubeni o Lampayan
[email protected]
Uni e si y o he Philippines Los Baños, Philippines
62
Rus y Bau is a
bau is [email protected]
RiceTec, Inc., USA
63
Ryan Romasan a
. omasan a@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
64
S.M. Mo ijul Islam
mislamb [email protected]
Soil Science Di ision, Bangladesh
65
Saz Hamsha
s.hamsha@g een-ca bon.inc
G een Ca bon Inc., Philippines
66
Sonam Rinchen She pa
s.she pa@cgia .o g
Cen o In e nacional de Mejo amien o de Maíz y
T igo, Nepal
67
Soo a Na esh Kuma
na eshkuma .soo [email protected]
Indian Ag icul u e Resea ch Ins i u e, India
68
Sukamal Sa ka
sukamal.sa ka @gm. km u.ac.in
Ramak ishna Mission Vi ekananda Educa ional
and Resea ch Ins i u e, India
69
Swamikannu Neduma an
s.neduma an@cgia .o g
In e na ional C ops Resea ch Ins i u e o he Semi-
A id T opics, India
70
Tao Li
.li@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
71
Toshihi o Hasegawa
hasegawa. oshihi o633@na o.go.jp
Na ional Ag icul u e and Food Resea ch
O ganiza ion, Japan
72
Tsai-Wei Chiang
d09621201@n u.edu. w
Na ional Taiwan Uni e si y, Taiwan
73
Upend a Singh
usingh@i dc.o g
In e na ional Fe ilize De elopmen Cen e , USA
74
Vi ende Kuma
i ende .kuma @cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
75
Vo Thi Bach Thuong
T.Vo@cgia .o g
In e na ional Rice Resea ch Ins i u e, Philippines
76
William Salas
bill@ eg ow.ag
Reg ow Ag icul u e, USA
77
Winnie Jelaga Kimu a
[email protected]
Kenya a Uni e si y, Kenya
78
Xiaobo Qin
[email protected]
Chinese Academy o Ag icul u al Sciences, China
79
Xuhui Wang
[email protected]
Peking Uni e si y, China
80
Yujing Gao
[email protected]
The Uni e si y o Ma yland Cen e o
En i onmen al Science, USA
52
Vi ual Pa icipan s
No
Fullname
Email Add ess
A ilia ion
1
Achim Dobe mann
adobe mann@ e ilize .o g
In e na ional Fe ilize Associa ion, F ance
2
Anna McClung
[email protected]
Global Me hane Hub
3
Chloe Lai
[email protected]
Uni e si y o Sou he n Queensland, Aus alia
4
Ch is ophe P adal
ch is ophe.p adal@ci ad.
Cen e de Coope a ion In e na ional en
Reche ché Ag onomique pou le De elopmen ,
F ance
5
Cyn hia Rosenzweig
c [email protected]
Columbia Uni e si y / NASA-GISS, USA
6
Hao Liang
[email protected];
[email protected]
Hohai Uni e si y, China
7
Hi oe Yoshida
yoshida.hi oe457@na o.go.jp
Na ional Ag icul u e and Food Resea ch
O ganiza ion, Japan
8
Mi an i A iani
mi an i_a [email protected]
Indonesian Ag icul u al En i onmen
S anda diza ion Ins i u e, Indonesia
9
Sonali Mcde mid
[email protected]
New Yo k Uni e si y, USA
10
So en O. Pede sen
sop@ag o.au.dk
Uni e si y o Aa hus, Denma k
11
Tamon Fumo o
umo o. amon877@na o.go.jp
Na ional Ag icul u e and Food Resea ch
O ganiza ion, Japan
12
Tanh Nguyen
n n [email protected]. n
An Giang Uni e si y, Vie nam
13
Ted Wilson
l -wilson@aes g. amu.edu
Texas A&M Uni e si y, USA
14
Xue ei Li
xue [email protected] g
Uni ed Na ions En i onmen P og ame
15
Yan Zhu
[email protected]
Nanjing Ag icul u al Uni e si y, China
16
Yubin Yang
yyang@aes g. amu.edu
Texas A&M Uni e si y, USA
53
C. Wo kshop Ma e ials
P og am and Powe Poin P esen a ions (LINK)