Academic Edi o : Jian Zhang
Recei ed: 15 Oc obe 2025
Re ised: 12 No embe 2025
Accep ed: 12 No embe 2025
Published: 18 No embe 2025
Ci a ion: Magazin, N.; Vuji´c, S.;
Lali´c, B.; Koˇci, V.; Benka, P.; ´
Ci i´c, V.;
Sedla , A.; ´
Cupina, B.; Bi akou, E.;
Nychas, K.; e al. Whea P oduc ion
T ansi ion Towa ds Digi al Ag icul u e
Technologies: A Re iew. Ag onomy
2025,15, 2640. h ps://doi.o g/
10.3390/ag onomy15112640
Copy igh : © 2025 by he au ho s.
Licensee MDPI, Basel, Swi ze land.
This a icle is an open access a icle
dis ibu ed unde he e ms and
condi ions o he C ea i e Commons
A ibu ion (CC BY) license
(h ps://c ea i ecommons.o g/
licenses/by/4.0/).
Re iew
Whea P oduc ion T ansi ion Towa ds Digi al Ag icul u e
Technologies: A Re iew
Nenad Magazin 1, S e lana Vuji´c 1,* , B anisla a Lali´c 1, Vladimi Koˇci 2, Pa el Benka 1, Vladimi ´
Ci i´c 1,
Aleksanda Sedla 1, B anko ´
Cupina 1, E osyni Bi akou 3, Kons an inos Nychas 4, Vasilis Psi oukis 4,
Ma ianna I. Ko zabasaki 5and Kons an inos Demes ichas 3
1Facul y o Ag icul u e, Uni e si y o No i Sad, T g Dosi eja Ob ado i´ca 8, 21000 No i Sad, Se bia;
[email p o ec ed] (N.M.); [email p o ec ed] (B.L.); [email p o ec ed] (P.B.);
ladimi [email p o ec ed] (V. ´
C.); aleksanda [email p o ec ed] (A.S.);
[email p o ec ed] (B. ´
C.)
2Clima e Sma Solu ions, 21000 No i Sad, Se bia; ladimi [email p o ec ed]
3In o ma ics Labo a o y, Depa men o Ag icul u al Economics and Ru al De elopmen , Ag icul u al
Uni e si y o A hens, Ie a Odos 75, 11855 A hens, G eece; [email p o ec ed] (E.B.); [email p o ec ed] (K.D.)
4Labo a o y o Fa m Machine Sys ems, Depa men o Na u al Resou ces De elopmen and Ag icul u al
Enginee ing, Ag icul u al Uni e si y o A hens, Ie a Odos 75, 11855 A hens, G eece;
[email p o ec ed] (K.N.); [email p o ec ed] (V.P.)
5
Labo a o y o Fa m S uc u es, Depa men o Na u al Resou ces De elopmen and Ag icul u al Enginee ing,
Ag icul u al Uni e si y o A hens, Ie a Odos 75, 11855 A hens, G eece; [email p o ec ed]
*Co espondence: [email p o ec ed]
Abs ac
Digi al ag icul u e echnologies p o ide po en ial o inc eased yield and quali y o whea
g ain wi h an op imized inpu use ela ed o si e-speci ic condi ions. This e iew aims
o p esen he global dis ibu ion o digi aliza ion in whea p oduc ion, o iden i y he
co e digi al echnologies applied in whea managemen , and o add ess challenges and
u u e di ec ions o ensu ing he secu i y o p oducing his s aple ood. Fo his pu pose,
a sys ema ic li e a u e e iew based on he PRISMA 2020 guidelines was conduc ed, and
113 pee - e iewed pape s wi hin he pe iod o 2015–2025 we e selec ed and examined. The
highes numbe o esea ch pape s e e s o Asia (37.4%), ollowed by Eu ope (17.4%) and
No h Ame ica (15.7%). The majo i y o he pape s ela ed o he ield o emo e sensing,
mo e speci ically, in 40.2% o he pape s, sa elli es a e lis ed as a pla o m, ollowed by
UAVs (in 33.0% o s udies). The e iew e eals une en global dis ibu ion o digi aliza ion,
wi h a signi ican need o imp o emen in less de eloped coun ies o add ess ood sa e y
in a mo e balanced way. This comp ehensi e analysis p oposes in eg a ion o he cu en
s a e o digi alizing whea p oduc ion wi h u u e oppo uni ies o la ge, bu mo eo e ,
o small and medium a me s, along wi h s ong suppo o he policies.
Keywo ds: whea p oduc ion; digi al ag icul u e echnologies; e iew; PRISMA 2020
1. In oduc ion
Whea (T i icum aes i um L.) is one o he s aple oods wo ldwide [
1
,
2
], and i makes
up he majo i y o he human die and p o ides a signi ican amoun o he daily equi ed
ene gy and nu ien s. The his o y o whea u iliza ion and domes ica ion passes om he
human e o s o con ol ood supply and p e en s a a ion [
3
] h ough o he e olu ion o
ag icul u al p oduc ion, which inc eased yield and made whea he s a egic ade good.
Ag onomy 2025,15, 2640 h ps://doi.o g/10.3390/ag onomy15112640
Ag onomy 2025,15, 2640 2 o 22
The ea ly 1960s we e he yea s when he G een Re olu ion made a signi ican change
in ag icul u e wi h he in oduc ion o semi-dwa whea and ice a ie ies o he ields and
wi h he applica ion o mine al e ilize s, pes icides, moldboa d plowing, and i iga ion [
4
].
This u no e esul ed in highe yields and educed hunge , making many coun ies sel -
su icien , and e en p o iding ex a p o i . Howe e , hese ini ial bene i s o he e olu ion
made se ious changes o he soil, wa e , and ai biodi e si y and o he en i onmen in
gene al. The G een Re olu ion inno a ions las ed un il he 1990s, when yield in many
egions s a ed o decline [
4
]. The e o e, he pa hway om “g een e olu ion o g een
ag icul u e” is he esul o decades o p ac ices ha ini ially had a signi ican impac on
ood p oduc ion bu h ough uncon olled use ha e led o he de e io a ion o he quali y
o na u al esou ces. The digi aliza ion o ag icul u e is one way o educe he ha m ul
e ec s o in ensi e ag icul u e in oduced by he G een Re olu ion, while s ill achie ing
adequa e yields.
Acco ding o he da a analysis p esen ed by [
5
], whea was he mos equen ly g own
c op in he wo ld by 2018, wi h an es ima ed 217 million (M) hec a es, ollowed by maize
wi h nea ly 200 M ha and ice wi h 165 M ha. I is g own in di e se egions om la i udes
60
◦
N o 44
◦
S and a 3000 m abo e sea le el [
6
], which is a wide cul i a ion a ea compa ed
o ice and maize. Globally, Asia is he egion ha p oduces he mos whea (44%, TE2018),
ollowed by Eu ope (34%, TE) [
5
], while in e ms o whea ade in 2020 TE, Eu ope had he
highes expo s (110 M ), while Asia egis e ed he highes impo s (78 M ) [
7
]. All ac s
and igu es emphasized he impo ance o he con inuous p oduc ion o whea o ood,
bu also o he economic bene i s.
The la e 1990s we e he yea s when digi al ag icul u e was in oduced in ag icul u al
p ac ice as a way o inc ease ag icul u al p oduc i i y and p o i abili y h ough he use o
in o ma ion and GIS echnology [
8
]. Digi aliza ion in ag icul u e e e s o he use o digi al
echnologies o moni o and ga he in o ma ion o he op imiza ion o a ming p ac ices
and o educe he use o esou ces (soil, wa e ) and inpu s ( e ilize s, pes icides) [
9
]. Today,
he ag icul u e and ag i- ood sec o is signi ican ly impac ed by DA echnologies, such
as big da a, In e ne o Things (IoT), obo ics, senso s, a i icial in elligence (AI), machine
lea ning, digi al wins, and he blockchain [10–13], o collec ing pas da a, o moni o he
p esen and o p edic he u u e, and o make accu a e imely decisions and ac ions [
14
–
16
].
Te ms like “digi al ag icul u e”, “p ecision ag icul u e”, and “sma a ming” e e o he
use o basic applica ions, like a mobile phone, o he use o obo s and sa elli es o suppo
decision making and o educe esou ce exploi a ion while ob aining an adequa e quan i y
and quali y o p oduc s [17,18].
Digi aliza ion is also p esen in whea p oduc ion wo ldwide (Figu e 1). Globally,
whea a me s a e acing many challenges. I he geopoli ical issues a e excluded, e en wi h
he e y signi ican impac on he global p oduc ion and ade, he mos no able challenges
in whea p oduc ion is he g owing need o ood, na u al esou ce deg ada ion [
19
]—
mainly soil deg ada ion— he ising labo cos s, lowe ing ou ca bon oo p in , he e ec s
o clima e change [
20
], and he equi emen o cu inpu s in many a eas [
21
]. Fo ins ance,
soil o ganic ma e con en , which is c ucial o soil heal h and quali y in e ms o e ili y,
s uc u e, ac i i y o mic oo ganisms, and nu ien cycling [
22
,
23
], signi ican ly declined in
ag icul u al soils o e he yea s as a esul o managemen p ac ices and en i onmen al
condi ions [
24
,
25
]. In addi ion, un a o able abio ic and bio ic condi ions ha e signi ican ly
hinde ed p oduc ion and inc eased unce ain y [
26
–
28
]. In he analyses o Pinke and
Lo ei [29] in Hunga y, o a 30-yea pe iod (1981–2010), i was de e mined ha o whea ,
a 1
◦
C empe a u e inc ease caused a yield loss o a ound 10%. In F ance in 2015, ex emely
high empe a u es in la e au umn s imula ed he de elopmen o aphids and lea hoppe s,
which con ibu ed o a 25% dec ease in win e whea yield ha es ed in 2016 [
30
]. La ge-
Ag onomy 2025,15, 2640 3 o 22
scale ield su eillance, moni o ing changes in mic oclima e condi ions, and he de ec ion
o pes s and diseases is ime-consuming and equi es mo e labo , which is no always in
acco dance wi h he labo cos s and a ailable wo ke s [
31
–
33
]. Joshi e al. [
34
] poin ed
ou ha digi al echnologies, such as unmanned ae ial ehicles (UAVs) o image cap u e,
compu e ision, and machine lea ning algo i hms, can be used o as disease de ec ion
and he imely implemen a ion o adequa e measu es.
Figu e 1. Visualiza ion o he whea managemen e olu ion; DA—Digi al Ag icul u e.
Ad ancemen s in blockchain echnology o e p ospec s o i s applica ion in he
supply chain sys em o whea c ops, he eby enhancing aceabili y, anspa ency, and
secu i y. Fa ooq e al. [
35
] de eloped a anspa en and e icien amewo k o he whea
c op supply chain u ilizing blockchain echnology. The p oposed blockchain ne wo k
u ilizes a decen alized sys em o moni o whea ansac ions among a me s, supplie s,
and ade s h ough c yp og aphically secu e ledge s and sma con ac s, ini ia ed wi h
okens like “whea coin” (WC). Addi ionally, he in e plane a y ile sys em (IPFS) has been
de eloped o he secu e s o age o con iden ial ansac ion da a. The p oposed model
o he whea supply chain is an icipa ed o ans o m he alue chains o whea c ops
ega ding e iciency and sus ainabili y in ag icul u al supply sys ems wo ldwide.
Mo eo e , digi al win (DT) echnology o whea g ow h has been used in se e al
ecen esea ch pape s. Xu e al. [
36
] p oposed a DT model o win e whea by combining
a DSSAT amewo k wi h he SUBPLEX op imiza ion algo i hm, ob aining a coe icien o
de e mina ion (R
2
) alue o 0.98 o simula ing bo h he lea a ea index (LAI) and abo e-
g ound biomass (AGB). Simila ly, ano he esea ch pape by Skobele e al. [
37
] p oposed
a mul i-agen cybe -physical sys em simula ing a DT model o whea o p ecision ag icul-
u al pu poses. All he abo e-men ioned s udies demons a e DT echnology’s e iciency in
enhancing whea g ow h [38,39].
Digi al moni o ing echnologies o e essen ial da a s eams o iden i ying and eac -
ing o disas e s caused by clima e change, which can a ec whea g ow h [
40
–
43
]. Remo e
sensing echnology, such as sa elli es and UAS, allows o immedia e iden i ica ion o
d ough , looding, and hea s ess [
36
,
44
–
48
]. En i onmen al senso s measu e soil mois u e,
empe a u e, and ela ed g ow h indica o s o c ops in eal- ime [
49
]. A i icial in elligence
(AI) models ely on hese da a s eams o ea ly wa ning ale s on in es a ions wi h pes s
and ou b eaks o plan diseases ueled by clima e change [
36
,
50
–
54
]. Disas e esponse
s a egies modeled by digi al da a include managing i iga ion egimes o whea c ops,
e ilizing sec ions wi h imp o ed esis ance o clima ic ins abili y, and in oducing clima e-
esilien a ie ies o imp o ed whea g ow h. An all-encompassing digi al sys em o
disas e esponse is mo e accu a e han adi ional assessmen s a egies o es ima ing
disas e damage, pa icula ly o i egula ly i iga ed c ops like whea ha a e a ec ed by
apidly luc ua ing clima ic condi ions on ag icul u al land [29,30,55,56].
Ag onomy 2025,15, 2640 4 o 22
This e iew aims o p o ide a comp ehensi e syn hesis o echnologies and analy ical
me hods used in di e en aspec s o whea p oduc ion. I will ocus on a scien i ic app oach
wi hin he DA applica ion in whea p oduc ion, as well as poin ou challenges and u u e
esea ch in whea p oduc ion o s eng hen he implemen a ion o digi al echnologies
along he pa hway om sowing and managemen o he ha es . Ha ing emphasized
his, he speci ic objec i es o his s udy a e as ollows: (i) wo ld spa ial dis ibu ion o
he esea ch on DA in whea p oduc ion; (ii) o iden i y and ca ego ize DA and analy ical
me hods in whea echnology; and (iii) o highligh u u e esea ch di ec ions o b oade
DA implemen a ion in whea g owing and g ain managemen .
2. Ma e ials and Me hods
2.1. Sea ch Que ies and S a egy
To ensu e anspa ency and ep oducibili y, we ha e conduc ed his sys ema ic e iew
in acco dance wi h he PRISMA 2020 guidelines [
57
]. Ou comp ehensi e li e a u e sea ch
o wo espec ed scien i ic da abases—Scopus and Web o Science—was pe o med o
iden i y all he ele an pee - e iewed esea ch s udies ega ding he applica ion o DA
echnologies o whea p oduc ion.
The e o e, we designed a de ailed sea ch que y ha was applied o bo h da abases’
sea ch engines, as shown in Table 1.
Table 1. Sea ch engines and que ies.
Sea ch Engine Websi e Sea ch Que y
Scopus h p://www.scopus.com/
(“UAV” OR “UAS” OR “D one” OR “RPAS” OR “Mul ispec al
came a” OR “Hype spec al came a” OR “UGV” OR “RGB
Came a” OR “Image analysis” OR “Robo ” o “Robo ic” OR
“Remo e sensing”) AND (“Machine Lea ning” OR “A i icial
In elligence”) AND (“whea ” OR “ i icum aes i um”) AND
(“Disease de ec ion” OR “weed de ec ion” OR “pes de ec ion” OR
“Yield P edic ion” OR “Yield Es ima ion” OR “Yield Fo ecas ” OR
“Damage De ec ion”)
Web o Science
h p:
//www.webo science.com/
(accessed da e 14 Oc obe
2025)
(“UAV” OR “UAS” OR “D one” OR “RPAS” OR “Mul ispec al
came a” OR “Hype spec al came a” OR “UGV” OR “RGB
Came a” OR “Image analysis” OR “Robo ” o “Robo ic” OR
“Remo e sensing”) AND (“Machine Lea ning” OR “A i icial
In elligence”) AND (“whea ” OR “ i icum aes i um”) AND
(“Disease de ec ion” OR “weed de ec ion” OR “pes de ec ion” OR
“Yield P edic ion” OR “Yield Es ima ion” OR “Yield Fo ecas ” OR
“Damage De ec ion”)
The key s a egy o ensu ing he mos e icien and ep esen a i e sea ch included
he use o Boolean e ms in addi ion o accu a e keywo ds ela ing o digi al echnology
p ac ices pe o med in whea ag icul u e. Thus, by inco po a ing he Boolean e ms (AND,
OR), we ensu ed a b oad and accu a e analysis o he li e a u e.
Ano he impo an aspec o ou s udy was o ensu e he ele ance o he esea ch
pape s ob ained. Hence, we ocused on bo h esea ch a icles and e iew a icles in he
English language ha we e eleased in he pe iod om Janua y 2015 o Ap il 2025.
2.2. Me hodology and Fil e ing S eps
Ha ing applied he ad anced que y sea ch, we ended up ob aining one hund ed
and se en y pape s om he Scopus da abase (n = 170), in addi ion o wo hund ed and
wen y- wo pape s om he Web o Science da abase (n = 222). The me a-analysis o he
Ag onomy 2025,15, 2640 5 o 22
acqui ed pape s was pe o med in Excel. We ex ac ed me ada a on he pape s, in line wi h
he PRISMA 2020 guidelines. A e ha , we sc eened o duplica es, also using Excel. Ou
o he h ee hund ed and nine y- wo pape s (n = 392), one hund ed and wen y- i e we e
duplica es (n = 125).
Following he duplica e exclusion, a sepa a e g oup o e iewe s pe o med a sc een-
ing and epo ed on he a icles wi hou he ones ha we e excluded o i le, abs ac , o
keywo ds i ele ance. A e his p ocess, a o al o one hund ed and i y- wo pape s we e
excluded (n = 152). In he subsequen s ep, we included he inal selec ion o one hund ed
and hi een pape s (n = 113) (Table 2and Figu e 2).
Table 2. Inclusion and exclusion c i e ia.
Inclusion C i e ia Exclusion C i e ia
The pape mus ha e been published
be ween Janua y 2015 and Ap il 2025 A icles published be o e Janua y 2015
The a icle mus be a jou nal a icle Non-pee - e iewed pape s (such as book
chap e s, heses, e c.)
The a icle mus be w i en in English A icle was no w i en in English
Mus no be a duplica e
Appea s in a sea ch in a di e en da abase
Figu e 2. The PRISMA low diag am o he li e a u e e iew sea ch o his s udy.
2.3. C i e ia o Analysis (Yea o Publica ion, Impac Fac o , Type o Publica ion, Publishe ,
and Jou nal)
2.3.1. Yea o Publica ion
An analysis o he empo al dis ibu ion o a icle publica ions was pe o med o
obse e ends o e ime. Hi he o, we ha e only conside ed a icles published be ween
Janua y 2015 and Ap il 2025 (Figu e 3).
Ag onomy 2025,15, 2640 6 o 22
Figu e 3. Numbe o s udies by yea .
2.3.2. Impac Fac o
We acked he impac ac o a ings, based on he la es Cla i a e Jou nal Ci a ion
Repo s, as an indica o o he schola ly in luence o a publica ion. This was an impo an
s ep in he isible and accu a e ep esen a ion o he quali y o jou nals whe e he pape s
we e being published (Figu e 4).
Figu e 4. Jou nal impac ac o in he pe iod om Janua y 2015 o Ap il 2025.
2.3.3. Type o Publica ion
To ensu e he inclusion o pape s ha ma ch igo ous scien i ic c i e ia, we exclusi ely
included esea ch a icles and e iew pape s published in pee - e iewed jou nals (Figu e 5).
Ag onomy 2025,15, 2640 7 o 22
Figu e 5. Type o epo ed s udies.
2.3.4. Publishe and Jou nal
In addi ion o he impac ac o , we me iculously acked jou nal and publishe names
o inspec he ones ha mos o en ecu ed. This allowed o a be e unde s anding o
ends in ag icul u al publishing and p o ided dedica ed a en ion o he mos popula
publishe s and jou nals. This analysis is p esen ed in Figu e 6.
Figu e 6. Sp ead o publica ions wi h di e en publishe s.
3. Resul s
On he basis o he se c i e ia p esen ed in he scien i ic esea ch me hod, wo ks ha
sa is ied he se c i e ia we e singled ou . A e ha , hese pape s we e e iewed and
analyzed. A o al o 113 pape s we e e iewed. A o al o 18 o hese wo ks we e e iews,
which is 15.9%, while he emaining 95 pape s we e esea ch a icles (84.1%).
Ag onomy 2025,15, 2640 8 o 22
3.1. Geog aphic Co e age o Resea ch
In addi ion o he ype o published ad ice, he esea ch a ea o each published
a icle in ou s udy is analyzed. Pape s ha we e ype e iews had global co e age.
O he o he wo ks, he la ges pa ela es o esea ch in he e i o y o China, a o al o
28 pape s, o 24.3% o all a icles. In second place is he USA, which is p ocessed in a o al o
17 a icles (14.8%). A e ha , i was de e mined ha a o al o nine pape s we e w i en o
esea ch in Aus alia (7.8%). The ollowing h ee coun ies could also be singled ou he e:
Ge many, India, and Pakis an, all o which appea in i e esea ch pape s, ha is, 4.3% o
each coun y. Ano he ou coun ies (Denma k, Hunga y, Poland, and Spain) appea in
wo e iewed a icles. The e is a o al o 30 coun ies in he co e age o e iew, bu he
o he 20 a e lis ed only in one pape . The numbe o published s udies is shown in he map
(Figu e 7) and diag am (Figu e 8).
Figu e 7. Numbe o published esea ch pape s by coun y, p esen ed on a wo ld map.
Figu e 8. Numbe o published esea ch pape s by coun y.
When compa ing he numbe o published esea ch pape s by con inen , he la ges
numbe o esea ch pape s e e s o Asia (37.4%), ollowed by Eu ope (17.4%) and No h
Ame ica (15.7%), while A ica (3.5%) and Sou h Ame ica (2.6%) a e he leas ep esen ed
(Figu e 9). F om he esul s shown, he expec ed end can be seen in ha he mos
Ag onomy 2025,15, 2640 9 o 22
ep esen ed coun ies a e he ones wi h a la ge scope o he applica ion o digi al ools
in ag icul u e.
Figu e 9. Numbe o published pape s by con inen .
Digi al ag icul u al echnologies ha e been e olu ionizing whea a ming ac oss he
wo ld. Howe e , dispa i ies in policies a ec hese echnologies ac oss na ions. China’s
policy on digi al ag icul u al echnology in ol es public in es men and implemen a ion
on a na ional scale, which in ol es inc easing adop ion and big da a in eg a ion [58]. The
policy in he USA in ol es public in es men wi h a ocus on olun a y p ecision a ming
by ag icul u al s akeholde s, which is conduc ed by ag icul u al ex ension p og ams [
59
].
Membe na ions ha comp ise Eu ope’s EU ha e adop ed policy p og ams o digi al
ag icul u al echnology [
60
]. These p og ams in ol e egula ions on sus ainabili y and
inclusi i y in digi al ag icul u al echnology access, especially o small-scale a me s.
These policy app oaches ha e esul ed in di e ing le els o ma u i y in digi al ag icul u al
echnology adap a ion o whea a ming.
3.2. Keywo ds and Subjec A ea
A o al o 377 keywo ds we e de e mined by e iewing selec ed pape s. The e m
machine lea ning occu s he mos and appea s as a keywo d in 43 a icles, ollowed by
emo e sensing (in 34 a icles) and deep lea ning (in 29 a icles), as well as he e m andom
o es (in 11 a icles). The e m UAV (independen ly o as pa o he keywo d) was
men ioned in 20 pape s; he e m p ecision ag icul u e was men ioned in 7 pape s, and
c op yield (independen ly o as pa o he keywo d) was men ioned in 17 pape s; a i icial
in elligence (independen ly o as pa o he keywo d) was men ioned in eigh pape s. The
equency o occu ence o ce ain keywo ds is shown in Figu e 10.
A o al o 81 di e en e ms ou o a o al o 275 e ms a e iden i ied in he analysis
o he e iewed a icles as a subjec a ea. Remo e sensing (in 58 pape s), hen p ecision
ag icul u e (in 48 pape s), and machine lea ning (in 28 pape s), as well as ag onomy
(20 pape s), ag icul u e (18 pape s), o ag icul u al science (in 16 pape s), we e he sub-
jec a eas o he mos e iewed pape s. The equency o occu ence o ce ain e ms
ep esen ing he subjec a ea is shown in Figu e 11.
Ag onomy 2025,15, 2640 16 o 22
Con lic s o In e es : Au ho Vladimi Koˇci was employed by he company Clima e Sma Solu ions.
The emaining au ho s decla e ha he esea ch was conduc ed in he absence o any comme cial o
inancial ela ionships ha could be cons ued as a po en ial con lic o in e es .
Abb e ia ions
The ollowing abb e ia ions a e used in his manusc ip :
DA Digi al ag icul u e
UAV Unmanned ae ial ehicles
Re e ences
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