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CHAPTER-13
AI APPLICATIONS IN AGRICULTURAL SUPPLY CHAINS:
ENHANCING RURAL LIVELIHOODS AND FOOD SECURITY
D . Ashish Kuma Sha ma
Assis an P o esso (Selec ion G ade)
UPES, Deh adun
Abs ac
This pape explo es he ans o ma i e ole o A i icial In elligence (AI) in
s eng hening ag icul u al supply chains wi h a ocus on u al li elihood
enhancemen and ood secu i y. In egions whe e ag icul u e is he p ima y sou ce
o income, AI o e s da a-d i en solu ions o imp o ing p oduc i i y, educing
pos -ha es losses, op imizing logis ics, and inc easing ma ke access. Case
s udies om India including Mic oso ’s AI Sowing App, C opIn’s Sma Fa m,
and DeHaa ’s ag ibusiness pla o m demons a e ha AI applica ions can lead o
yield inc eases o up o 30%, pos -ha es loss educ ions o 25%, and income
imp o emen s o 20-25%. Howe e , widesp ead adop ion aces challenges such
as poo digi al in as uc u e, low echnological li e acy, a o dabili y cons ain s,
and e hical da a usage. The pape concludes by ecommending a policy and
ins i u ional amewo k ha suppo s inclusi e, scalable, and esponsible AI
deploymen in ag icul u e o os e sus ainable u al de elopmen and ensu e long-
e m ood secu i y.
Keywo ds: A i icial In elligence, Ag icul u al Supply Chain, Ru al Li elihood,
Food Secu i y, P ecision Fa ming
1. In oduc ion
Ag icul u e con inues o play a pi o al ole in he socio-economic ab ic o
de eloping na ions, employing mo e han 60% o he u al wo k o ce in low- and
middle-income coun ies (FAO, 2021). Howe e , despi e i s impo ance, he
ag icul u al sec o aces pe sis en challenges including low p oduc i i y, pos -
ha es losses, p ice ola ili y, ine icien ma ke linkages, and limi ed access o
ins i u ional c edi . Acco ding o he Wo ld Bank (2020), pos -ha es losses in
Sub-Saha an A ica and Sou h Asia can each up o 30-40%, signi ican ly
h ea ening bo h u al incomes and na ional ood secu i y.
Amid hese sys emic ine iciencies, A i icial In elligence (AI) has eme ged as a
ans o ma i e o ce capable o eshaping adi ional ag icul u al supply chains
in o dynamic, da a-d i en ecosys ems. AI echnologies anging om machine
lea ning and compu e ision o p edic i e analy ics and AI-in eg a ed blockchain
pla o ms a e enabling imely decision-making, imp o ing a m- o-ma ke
coo dina ion, and op imizing esou ce use ac oss he supply chain (Kamila is e
al., 2018). Fo ins ance, p edic i e yield models ained on wea he , soil, and
sa elli e da a can help a me s de e mine op imal sowing imes, while AI-powe ed
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logis ics sys ems can minimize pos -ha es spoilage by au oma ing cold chain
ou ing.
In u al a eas whe e ag icul u e is o en a li elihood o las eso , AI’s po en ial
ex ends beyond e iciency gains i se es as a ca alys o inclusi e de elopmen .
By p o iding smallholde a me s wi h access o ma ke o ecas s, c op ad iso y,
mic o-c edi eligibili y assessmen s, and p ecision a ming ools, AI can empowe
ma ginalized communi ies o inc ease p oduc i i y and incomes. A s udy by
Na ayanan e al. (2020) highligh ed ha he deploymen o AI-d i en ma ke
access pla o ms in India led o a 12–20% ise in a mga e p ices o ui s and
ege ables, la gely by elimina ing exploi a i e middlemen.
Mo eo e , in he con ex o global ood secu i y, AI's ole is c i ical. The Uni ed
Na ions p ojec s ha he wo ld popula ion will exceed 9.7 billion by 2050,
necessi a ing a 70% inc ease in ood p oduc ion (UN, 2019). Mee ing his demand
will equi e sma e , mo e sus ainable ag icul u al sys ems an objec i e whe e AI
can play a cen al ole. Applica ions such as sa elli e-based c op heal h
moni o ing, eal- ime soil analysis, and in elligen wa ehousing can educe
esou ce was age while ensu ing consis en ood supply ac oss egions.
Ne e heless, he deploymen o AI in u al ag icul u al supply chains is augh
wi h challenges, including inadequa e digi al in as uc u e, low digi al li e acy,
a o dabili y issues, and he isk o algo i hmic bias. These limi a ions necessi a e
an ecosys em app oach one ha combines echnological inno a ion wi h obus
policies, ins i u ional suppo , and inclusi e go e nance amewo ks.
This chap e explo es he mul iple dimensions o AI in eg a ion in o ag icul u al
supply chains. I in es iga es how AI can enhance u al li elihoods, p omo e
sus ainable ag icul u e, and ensu e ood secu i y h ough a ge ed in e en ions
ac oss he alue chain. Real-wo ld case s udies, impac da a, and policy
sugges ions a e p esen ed o o e a comp ehensi e unde s anding o he
oppo uni ies and cons ain s in deploying AI o ag icul u al ans o ma ion.
2. AI Applica ions in Ag icul u al Supply Chains
The ag icul u al supply chain is inhe en ly complex, in ol ing mul iple s ages
om inpu p ocu emen , c op p oduc ion, and ha es ing o pos -ha es handling,
p ocessing, anspo a ion, s o age, and ma ke ing. A i icial In elligence (AI) is
inc easingly being deployed ac oss hese s ages o enhance e iciency, educe
was e, and p omo e equi able access o ma ke s. AI echnologies such as machine
lea ning, compu e ision, na u al language p ocessing (NLP), and au onomous
sys ems ha e shown signi ican po en ial in ackling long-s anding ine iciencies
and dispa i ies in u al ag icul u al sys ems.
• P ecision Fa ming and C op Moni o ing: P ecision ag icul u e is one o he
mos p ominen applica ions o AI in a ming, pa icula ly o op imizing
inpu use and moni o ing c op heal h. AI-d i en sys ems use da a om IoT
senso s, d ones, and sa elli e image y o assess plan heal h, soil nu ien s,
mois u e le els, and pes in es a ions. These da a a e analyzed using machine
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lea ning algo i hms ha ecommend si e-speci ic ac ions, such as i iga ion
schedules, pes icide applica ion, and e ilize dosage. Fo example,
Mic oso ’s AI Sowing App deployed in Andh a P adesh, India, used machine
lea ning o ad ise a me s on op imal sowing da es based on his o ical wea he
and soil da a. The in e en ion epo edly led o a 30% inc ease in c op yield
and a 15% educ ion in inpu use (Wo ld Bank, 2020). Simila ly, s a ups like
Fasal and DeHaa p o ide AI-powe ed c op ad iso y se ices o Indian
a me s, using eal- ime wea he and soil da a o p e en pes ou b eaks and
disease sp ead.
• Yield Fo ecas ing and Ea ly Wa ning Sys ems: AI is also being used
ex ensi ely o yield p edic ion and disas e o ecas ing. Machine lea ning
models ained on wea he pa e ns, sa elli e da a, and his o ical yields can
p o ide accu a e yield es ima es well in ad ance o he ha es . These
p edic ions help a me s make in o med decisions abou c op planning,
esou ce alloca ion, and insu ance. Fo ins ance, he Food and Ag icul u e
O ganiza ion (FAO) and NASA de eloped he Ag icul u al S ess Index
Sys em (ASIS), which uses AI o analyze sa elli e image y and p edic
d ough s, loods, o o he clima e- ela ed s esses. The sys em is now used
ac oss se e al A ican and Asian coun ies o suppo ea ly wa ning and
eme gency esponse planning. Acco ding o FAO (2021), hese ools ha e
educed ood c isis esponse ime by 30-50%, enhancing na ional ood
secu i y and esilience.
• AI o Pos -Ha es Supply Chain Managemen : Pos -ha es losses due
o poo logis ics, s o age issues, and delays in anspo a ion a e a majo
challenge in ag icul u al supply chains. AI is inc easingly used o op imize
logis ics and wa ehousing h ough ou e op imiza ion algo i hms, cold chain
moni o ing, and in elligen demand o ecas ing. Pla o ms like AgNex and
Ag ibolo in India employ compu e ision and deep lea ning models o assess
p oduce quali y in eal- ime a a m ga es, he eby helping o educe ejec ion
a es and ensu ing ai p ices o a me s. Addi ionally, AI-based wa ehouse
managemen sys ems op imize space u iliza ion and au oma e so ing and
g ading. Acco ding o a s udy by Na ayanan e al. (2020), AI-based logis ics
and g ading pla o ms ha e educed pos -ha es losses by 15–25% in pilo
egions o Maha ash a and U a P adesh.
• Ma ke In elligence and P ice Fo ecas ing: AI is e olu ionizing how
a me s access ma ke in o ma ion, including p ice o ecas ing, demand
p edic ion, and ma ke ma ching. Machine lea ning models ained on
commodi y ends, ma ke a i als, seasonal demand, and mac oeconomic
indica o s a e helping a me s make be e ma ke ing decisions. Fo example,
RML AgTech in India p o ides a me s wi h AI-d i en daily p ice upda es
and loca ion-speci ic ma ke ecommenda ions h ough mobile apps in
egional languages. This has helped a me s in emo e illages ea n 10-20%
mo e by iming hei ma ke en y and choosing op imal ma ke places
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(Kamila is e al., 2018). Fu he mo e, pla o ms like e-NAM a e in eg a ing
AI o p edic demand-supply misma ches and op imize auc ion p ocesses a
Ag icul u al P oduce Ma ke Commi ees (APMCs).
• Financial Inclusion h ough AI-based C edi Sco ing: Access o c edi
emains a c i ical cons ain o smallholde a me s who o en lack o mal
documen a ion o c edi his o y. AI algo i hms now help inancial ins i u ions
c ea e c edi sco es using non- adi ional da a such as mobile usage pa e ns,
c op cycles, land owne ship eco ds, wea he exposu e, and supply chain
ansac ions. Pla o ms like S ellapps and C opIn ha e pa ne ed wi h banks
and NBFCs in India o p o ide AI-gene a ed c edi sco es o a me s and
ag i-coope a i es. This has led o a 40% inc ease in c edi disbu semen in
u al dis ic s wi h his o ically low inancial inclusion (Wo ld Bank, 2020).
Mo eo e , he digi al aceabili y o a m inpu s and ou pu s acili a ed by AI
ensu es ha loans a e used p oduc i ely, imp o ing eco e y a es and
educing inancial isks o bo h a me s and lende s.
3. Case S udies o India
India, wi h i s as and di e se ag icul u al landscape, has eme ged as a e ile
g ound o he applica ion o A i icial In elligence (AI) in ans o ming
ag icul u al supply chains. These AI in e en ions span a ious aspec s om
p oduc ion and p ocu emen o ma ke linkage and u al inancing demons a ing
measu able impac s on u al li elihoods, p oduc i i y, and ood secu i y. Below
a e ou key case s udies ha showcase success ul AI implemen a ions in he
Indian ag icul u al con ex .
• Case S udy 1: Mic oso AI Sowing App in Andh a P adesh
In collabo a ion wi h he In e na ional C ops Resea ch Ins i u e o he Semi-
A id T opics (ICRISAT), Mic oso de eloped he AI Sowing App, a cloud-
based solu ion o assis a me s wi h da a-d i en decisions. Launched in 2016
in Anan apu dis ic , a d ough -p one egion in Andh a P adesh, he app
u ilized machine lea ning models ained on his o ical wea he da a, soil heal h
eco ds, and c op pa e ns. Fa me s ecei ed ad iso ies h ough SMS in
Telugu abou op imal sowing da es, e ilize applica ion, and wea he ale s.
The pilo p og am led o a 30% inc ease in a e age c op yield and helped
a me s educe seed was age and inpu cos s (Wo ld Bank, 2020). The success
o he pilo led o i s ex ension o o e 3,000 a me s ac oss mul iple dis ic s
in Andh a P adesh.
• Case S udy 2: C opIn Technology’s Sma Fa m Pla o m
C opIn, a Bengalu u-based ag i- ech s a up, de eloped Sma Fa m, a digi al
AI pla o m ha enables emo e moni o ing o a m ac i i ies using sa elli e
image y, machine lea ning, and ield da a. The sys em p o ides eal- ime
insigh s in o c op g ow h, disease p edic ion, and ha es eadiness. Deployed
in pa ne ship wi h ag ibusinesses and s a e go e nmen s, Sma Fa m has
helped digi ize o e 16 million ac es and bene i ed o e 7 million a me s in
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India as o 2022. No ably, in Maha ash a, he sys em was used by ag icul u al
coope a i es o ack suga cane heal h and op imize i iga ion. The pla o m’s
p edic i e capabili ies educed wa e usage by 20% and inc eased suga cane
yields by 15-18%, demons a ing he alue o AI o clima e- esilien
ag icul u e (C opIn, 2022).
• Case S udy 3: AgNex and AI-based Quali y Assessmen in Punjab
AgNex , a Chandiga h-based s a up, has in oduced AI-powe ed quali y
assessmen sys ems ha use image ecogni ion and compu e ision o
e alua e he quali y o p oduce such as spices, g ains, and milk a p ocu emen
cen e s. In pa ne ship wi h he Punjab Mandi Boa d, AgNex deployed i s
echnology in mandi ya ds o ensu e anspa en and objec i e quali y g ading.
P e iously, quali y assessmen was manual and o en manipula ed, leading o
a me exploi a ion. The AI-based sys em sped up he g ading p ocess by 70%
and imp o ed p ice ealiza ion o a me s by 10-15%, especially in u me ic
and whea p ocu emen (Na ayanan e al., 2020). These imp o emen s
encou aged us in p ocu emen sys ems and educed con lic s be ween
a me s and middlemen.
• Case S udy 4: DeHaa ’s AI-Powe ed Ag ibusiness Pla o m in Biha and
U a P adesh
DeHaa , ounded in 2012, is an AI-enabled pla o m ha p o ides end- o-end
ag icul u al se ices including c op ad iso y, inpu supply, wea he o ecas s,
soil es ing, and ma ke linkage. I uses machine lea ning o deli e
cus omized c op ecommenda ions and connec s a me s o o e 200
co po a e buye s. Ope a ing p ima ily in Biha , UP, and Odisha, DeHaa has
on boa ded o e 1.5 million a me s and c ea ed digi al c edi p o iles using
a ming and ansac ion da a. Acco ding o company da a, a me s using
DeHaa ’s se ices epo ed a 20-25% inc ease in income and 25% educ ion
in c op ailu e isk (DeHaa , 2022). The pla o m’s success has a ac ed
unding om global in es o s and is now scaling ac oss no he n and eas e n
India.
Table 1: AI in Ag icul u e Case S udies (India)
Case S udy
AI Applica ion
Key Impac
Bene icia ies
Mic oso AI
SowingApp
(Andh a
P adesh)
Machinelea ning-
based c op ad iso y &
sowing
ecommenda ions
30%yield inc ease,
15% inpu cos
educ ion
3,000+ a me s
inAndh a
P adesh
C opIn
Sma Fa m
(Pan-India)
Remo ec op
moni o ing,disease
p edic ion using ML
& sa elli e da a
20% wa e use
educ ion,
15–18% yield
gain
7million+
a me s;16
millionac es
digi ized

150
AgNex AI
Quali y
Assessmen
(Punjab)
Compu e ision o
eal- imequali y
g ading o c ops
70% as e g ading,
10–15% be e
p ice ealiza ion
Tu me ic&
whea a me s
in Punjab
mandi ya ds
DeHaa
Ag ibusiness
Pla o m
(Biha & UP)
ML-powe edc op
ad iso y,ma ke
linkage & digi al
c edi p o iling
20–25% income
inc ease, 25%
educ ion in c op
ailu e
1.5 million+
a me s ac oss
no he n India
Figu e 1: Impac o AI Applica ions on Indian Ag icul u al Supply Chains
The g ouped ba cha illus a es he compa a i e pe o mance o ou AI-powe ed
ag icul u al ini ia i es in India, showcasing hei impac ac oss h ee key
dimensions: yield/income inc ease, cos o c op ailu e educ ion, and p ice
ealiza ion gain, all exp essed in pe cen age e ms.
a. Mic oso AI Sowing App (Andh a P adesh)
• Yield/Income Inc ease: The app led o a 30% inc ease in yield, p ima ily
by ecommending op imal sowing da es using his o ical wea he and soil
da a.
• Cos Reduc ion: Fa me s expe ienced a 15% educ ion in inpu cos s, as
he app p e en ed unnecessa y seed usage and e ilize applica ion.
• P ice Gain: This ini ia i e does no di ec ly add ess ma ke p icing, hence
no signi ican impac in his ca ego y.
b. C opIn Sma Fa m (Pan-India)
• Yield/Income Inc ease: Fa me s using Sma Fa m epo ed an a e age
18% inc ease in c op yield, due o AI-powe ed disease and i iga ion
managemen .
• Cos Reduc ion: The e was a 20% educ ion in wa e and e ilize usage,
showing i s s ong ole in esou ce op imiza ion.
• P ice Gain: C opIn ocuses mo e on p oduc ion e iciency han ma ke
access, so p ice gain emains un epo ed.
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c. AgNex Quali y Assessmen (Punjab)
• Yield/Income Inc ease: No di ec impac on yield was eco ded.
• Cos Reduc ion: No applicable, as his sys em mainly s eamlines quali y
g ading.
• P ice Gain: Fa me s expe ienced a 15% inc ease in p ice ealiza ion,
hanks o AI-based objec i e g ading ha educed dispu es and imp o ed
p ocu emen anspa ency.
d. DeHaa Ag ibusiness Pla o m (Biha & UP)
• Yield/Income Inc ease: Fa me s using DeHaa ’s AI se ices epo ed a
25% income imp o emen , d i en by be e c op ad iso y and inpu
access.
• Cos Reduc ion: A 25% educ ion in c op ailu e isk was obse ed due
o ea ly wa nings and a ge ed ad iso y.
• P ice Gain: Fa me s gained 20% mo e h ough di ec ma ke linkage,
elimina ing exploi a i e in e media ies.
4. Challenges and Limi a ions
Despi e he p omising po en ial o A i icial In elligence (AI) o e olu ionize
ag icul u al supply chains and upli u al li elihoods, he implemen a ion o AI
in de eloping coun ies like India aces se e al s uc u al and con ex ual
challenges. These limi a ions a e especially p onounced in u al egions whe e
echnological eadiness, in as uc u e, and human capi al emain
unde de eloped.
♦ Digi al In as uc u e De ici : A undamen al challenge is he lack o obus
digi al in as uc u e in many u al and semi- u al a eas. Acco ding o TRAI
(2021), only 37% o u al India had access o mobile in e ne se ices wi h
adequa e bandwid h, which is essen ial o eal- ime da a ansmission and
cloud-based AI applica ions. Wi hou consis en in e ne connec i i y and
digi al ha dwa e (e.g., sma phones, senso s, GPS), AI models canno be
e ec i ely deployed o scaled. This digi al di ide c ea es an asymme y whe e
bene i s o AI low p ima ily o be e -connec ed egions, lea ing
ma ginalized a me s u he behind.
♦ Low Digi al Li e acy and Technological Awa eness: Ano he majo ba ie
is limi ed digi al li e acy among a me s, many o whom ha e li le o no
expe ience wi h sma phones, apps, o digi al pla o ms. Acco ding o he
Minis y o Ru al De elopmen (2020), nea ly 60% o smallholde a me s in
India we e un amilia wi h digi al a ming ools. E en when AI-based
ad iso ies o pla o ms a e made a ailable, adop ion emains low due o a lack
o us o inabili y o in e p e he insigh s p o ided. This necessi a es long-
e m in es men in capaci y building, digi al aining, and localized use
in e aces.
♦ High Ini ial Cos s and A o dabili y Issues: AI-d i en solu ions o en
in ol e signi ican up on in es men s in in as uc u e such as d ones,
senso s, and emo e sensing sys ems which a e ou o each o indi idual
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a me s o e en coope a i es wi hou ex e nal suppo . Al hough se e al ag i-
ech s a ups o e sha ed se ices o subsc ip ion-based models, hese oo
may be una o dable o small and ma ginal a me s who li e on unce ain
incomes. A epo by NITI Aayog (2021) emphasized ha mo e han 85% o
Indian a me s all in o he smallholde ca ego y, possessing less han 2
hec a es o land and limi ed in es men capaci y.
♦ Da a P i acy and E hical Conce ns: AI applica ions o en ely on sensi i e
da a such as land owne ship, inancial ansac ions, and biome ic inpu s.
Howe e , da a p o ec ion egula ions o a me s emain ague o poo ly
en o ced. The e is a g owing conce n ha ag ibusiness co po a ions and
digi al se ice p o ide s may exploi his da a asymme y, ein o cing
monopolies and ma ginalizing a me s. The lack o clea owne ship, consen ,
and usage igh s o a m-le el da a aises e hical conce ns, especially when
AI models a e p op ie a y and decisions a e non- anspa en (Na ayanan e al.,
2020).
♦ Bias and Inaccu acy in AI Models: The quali y and ep esen a i eness o
da a used o ain AI models signi ican ly a ec hei accu acy. In many cases,
AI algo i hms a e ained on da ase s om speci ic egions o seasons, making
hem unsui able o gene al applica ion ac oss di e se ag o-clima ic zones.
This can lead o inaccu a e p edic ions o lawed ad iso ies. Fo example, a
pes p edic ion model ained on whea ields in Punjab may no be applicable
o whea in Biha due o di e ences in clima e and soil. Such limi a ions e ode
use us and educe he c edibili y o AI-based se ices.
♦ F agmen ed Policy and Lack o Ins i u ional Suppo : Finally, he absence
o an in eg a ed policy amewo k o digi al ag icul u e poses a se ious
limi a ion. While mul iple go e nmen ini ia i es like Digi al India, PM-
KISAN, and e-NAM exis , hey o en unc ion in silos wi hou c oss-sec o al
coo dina ion. Fu he mo e, he e is no na ional guideline o ce i ica ion o
AI applica ions in ag icul u e, leading o ma ke en y o sub-s anda d o
un es ed solu ions. A cohesi e digi al ag icul u e policy ha aligns inno a ion
wi h a me igh s, sus ainabili y, and da a go e nance is u gen ly needed
(FAO, 2021).
5. Conclusion
A i icial In elligence (AI) is playing a ans o ma i e ole in mode nizing
ag icul u al supply chains, pa icula ly in enhancing u al li elihoods and ood
secu i y in de eloping na ions like India. By enabling p ecision a ming, c op
moni o ing, p edic i e analy ics, ma ke access, and inancial inclusion, AI has
helped inc ease yields by up o 30%, educe pos -ha es losses by 20–25%, and
imp o e a me s’ income by 20–25% in a ious case s udies. Ini ia i es such as
Mic oso ’s AI Sowing App, C opIn’s Sma Fa m, and DeHaa ’s digi al
ag ibusiness pla o m demons a e how AI in e en ions can empowe
smallholde a me s wi h imely, da a-d i en decisions. Howe e , challenges such
as digi al illi e acy, high implemen a ion cos s, da a p i acy conce ns, and
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in as uc u al de ici s pe sis . To unlock AI’s ull po en ial, a coo dina ed e o
in ol ing policy suppo , digi al in as uc u e, capaci y-building, and e hical da a
go e nance is essen ial. Wi h he igh ecosys em, AI can se e no jus as a
echnological ool bu as a co ne s one o sus ainable, inclusi e ag icul u al
de elopmen .
Re e ences
1. C opIn. (2022). Sma Fa m: Digi al T ans o ma ion in Ag icul u e.
2. DeHaa . (2022). Ag iTech o Smallholde Fa me s in India.
3. FAO. (2021). Digi al Ag icul u e: Fa me s in he Age o A i icial
In elligence. Food and Ag icul u e O ganiza ion o he Uni ed Na ions.
4. FAO. (2021). The S a e o Food and Ag icul u e: Making Ag i ood Sys ems
Mo e Resilien o Shocks and S esses. Food and Ag icul u e O ganiza ion o
he Uni ed Na ions.
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