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DOI: 10.15240/ ul/001/2019-4-007
In oduc ion
Owing o a special ole o ag icul u e in he
na ional economy, go e nmen s ha e become
he main supplie s as well as he main use s
o ag icul u al p edic ions ( o ecas s). They
equi e in e nal o ecas s o implemen policies
ha p o ide echnical and ma ke suppo o
he ag icul u al sec o (Hed ich, Loy, & Muelle ,
2012). Fo ecas s o ag icul u al p oduc ion
and p ices ough o be help ul no only o he
go e nmen s, bu p ima ily o a me s and he
en i e ag icul u e indus y. Thus, ag icul u e is
an a ea whe e poli icians, consume s, scien is s
and en i onmen alis s encoun e (Ri e a-Fe e,
2008). Remeikiene, Rozsa, Gaspa eniene and
Pěnčík (2018) s a e ha suppo i e poli ical
a i udes owa ds he ag icul u al sec o along
wi h employmen o p o ec ionis measu es
de e mine igno ance o he ules o supply
and demand, dis o ion o he condi ions o
ee ma ke compe i ion, closeness o he
ag icul u al sec o in compa ison o o he
economic sec o s, incomple e in e na ional
ag icul u al p ice ansmission, inconsis ence
o long- e m ma ke p ices o ag icul u al
commodi ies and exis ence o ag icul u e in
dis a ou ed a eas.
In i s Eu ope 2020 de elopmen s a egy,
he Eu opean Commission has de i ned an
ambi ious goal o imp o ing he le el o
esou ce e i ciency (Li, 2018). Analyses o
echnical e i ciency and o al ac o p oduc i i y
(TFP) wi hin he Czech ag icul u e ha e
been conduc ed by Čechu a (2012). His
objec i e consis s in iden i ying key ac o s
ha de e mine an e i ciency o inpu u iliza ion
and TFP de elopmen . He uses a i xed con ol
model o es ima e he echnical e i ciency and
TFP design o he o e all s a us in ag icul u e
and i s indi idual sec o s. The esul s show
ha echnical ine i ciency is an impo an
phenomenon o he Czech ag icul u e and i s
indi idual sec o s. The mos impo an ac o s
ha de e mine bo h he echnical e i ciency and
he TFP a e ac o s associa ed wi h ins i u ional
and economic changes, pa icula ly a d ama ic
inc ease in mea impo s and an inc ease in
subsidies (Čechu a, 2012). In addi ion, an
analysis o ag icul u al p oduc i i y ends in he
EU coun ies on he TFP basis was pe o med,
o ins ance, by Dokic, Jo ano ic and Vujanic
(2017). Thei esul s show ha he o e all EU
ag icul u al p oduc i i y g ow h has slowed
down in ecen yea s and has lagged behind
leading global compe i o s, which is mainly
due o dec easing numbe o employees in
ag icul u e. Acco ding o he au ho s, ag icul u e
de elopmen should be es ablished on scien i i c
bases. Remeikiene e al. (2018) s a e ha
his may be caused when he olumes o he
in e na ional ade in ag icul u al p oduc s a e
dec easing.
One o he basic p inciples o sus ainable
ag icul u e is he e o e o ecas ing i s u u e
de elopmen . Fo ecas s a e p edominan ly
made using con en ional econome ic me hods,
wi h ime se ies app oaches ha ing smalle
oles (Klieš ik, V bka, & Rowland, 2018). In
ela ion o ce ain dominance o ag icul u al
economis s, g ea emphasis has been pu
on explana ions, ye he p edic i e powe o
models has been o only small conce n. In
ecen yea s, se e al ag icul u al economis s
ha e been engaged in compa ing o ecas s wi h
a ious o he me hods and hei conclusions
gene ally co espond o commonly accep ed
belie s. In he case o sho - e m o ecas ing,
combina ions lead o be e and mo e accu a e
p ognoses, e en be e han he ones p oduced
by ec o au o- eg ession, which is su p isingly
one o he bes me hods. Fo example, Klepáč
and Hampel (2018) wan ed o i nd ou i i
possible o p edic bank up cy 1–3 yea s ahead
wi h he sound accu acy. Thei esul s show
PREDICTION OF INSTITUTIONAL
SECTOR DEVELOPMENT AND ANALYSIS
OF ENTERPRISES ACTIVE IN AGRICULTURE
Voj ěch S ehel, Jakub Ho ák, Ma ek Vochozka
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104 2019, XXII, 4
Business Adminis a ion and Managemen
ha models a e able o no ice co ec labels
o ac i e companies, howe e , he longe ime
pe iod o bank up cy is, he wo se he esul s
a e, o he esul s a e inconsis en o bank up ed
companies which ha e been ac i e ill now. The
au ho s o cou se also say ha bank up cy
p edic ion is cha ac e ized by he ac ha he
esul s a e ma kedly in l uenced by he da a used
and pa ly by se ing o classi i ca ion models.
Ka as and Režňáko á (2017) e i i ed whe he
bank up cy p edic o s a e speci i c in e ms o
indus y o ime and es ablished which p edic o s
can signal an imminen bank up cy mo e han
one pe iod be o e bank up cy occu s. The
esea ch con i med hei p esump ion. Au ho s
iden i i ed indica o s, which a e indus y-speci i c
and a e equen ly associa ed wi h a speci i c
pe iod o ime p eceding he bank up cy
( o manu ac u ing companies: he e u n on
asse s and he ne wo king capi al o o al asse s;
o cons uc ion companies: he ne wo king
capi al o sales and he in e es co e age). Ka as
and Režňáko á (2017) iden i i ed also indica o s
whose inclusion in he model would p obably
inc ease i s p edic ion capaci y – hese a e he
indica o s e u n on asse s, in en o y u no e and
asse composi ion. As demons a ed by Hed ich,
Loy and Muelle (2012), i is also su p ising ha
econome ic models as well as one-dimensional
me hods a e poo ly compa able and nai e
models. Howe e , i is inc easingly challenging
o p edic bank up cy isk as co po a ions ha e
become mo e global and mo e complex and as
hey ha e de eloped sophis ica ed schemes o
hide hei ac ual si ua ions unde he guise o
op imiza ion o ax au ho i ies (Klieš ik e al.,
2018).
The p ice ola ili y o commodi ies has
inc eased g ea ly in ecen yea s. In o ma ion
abou he de elopmen o ag icul u al ma ke s
has dissemina ed among ma ke pa icipan s o
di e ing deg ees. This in o ma ion asymme y
is he basis o ading p o i s on ma ke s.
Di e en o ecas ing ools, pa icula ly s a is ical
and econome ic me hods, we e de eloped in
he ag icul u al sec o in he pas , bu hey did
no achie e e y good o ecas ing accu acy,
he eby esul ing in a conside able p ice isk.
The addi ional in o ma ion held by ma ke
pa icipan s and o he people in he ag icul u al
sec o has been neglec ed by hese ools
(Hed ich, Loy, & Muelle , 2012).
Hed ich e al. (2012) u he add ha
me hods using he “wisdom o c owds” e ec
achie ed be e o equal o ecas ing accu acy in
many o ecas ing applica ions han he s anda d
app oaches applied. The “wisdom o c owds”
e ec indica es ha g oups each be e esul s
han indi iduals o expe s. P edic ion ma ke s
a e seen by he same au ho s, i.e. Hed ich e al.
(2012), as a new o ecas ing me hod. They use
a ading mechanism simila o a s ock ma ke
o achie e he “wisdom o c owds”. P edic ion
ma ke s a e o ums o ading con ac s ha
yield paymen s based on he ou come o
unce ain e en s. The e is moun ing e idence
ha such ma ke s can help o p oduce o ecas s
o e en ou comes wi h a lowe p edic ion
e o han con en ional o ecas ing me hods
(Valáško á, Klieš ik, Š ábo á, & Adamko,
2018). Fo example, p edic ion ma ke p ices
can be used o inc ease he accu acy o poll-
based o ecas s o elec ion ou comes (A ow
e al., 2008).
Business analysis is used o collec ing
necessa y da a and hei iden i i ca ion,
including o mula ing he need o change(s) in
unc ioning o o ganiza ions and acili a ing he
change(s). In e ms o e i cien use o business
analyses, an in i ni e amoun o da a may be
p o ided (Vochozka, Rowland, & V bka, 2016).
Andekina and Rakhme o a (2013) desc ibes
business analysis as a disciplined app oach
o implemen ing and managing changes wi hin
o ganiza ions, whe he hey a e bene i cial
businesses, go e nmen s o non-p o i
o ganiza ions.
A p esen , a i i cial in elligence can be
de i ni ely ecognized as a use ul ool o business
analyses and o ecas ing. This esea ch may
be da ed back o he 1940s, and o e he las
h ee decades, i has been ex ended up o
he poin o sol ing ag icul u al p oblems as
well (Rowland, & V bka, 2016). A i i cial neu al
ne wo ks a e l exible in hei own use, and a e
able o analyse highly complex pa e ns e y
quickly (San in, 2008). These ne wo ks can be
used o classi y, app oxima e unc ions, and
p edominan ly o p edic ime se ies (Al un,
Bilgil, & Fidan, 2007). E en in he ag icul u al
sec o , a i i cial in elligence has helped o
de elop machine lea ning algo i hms om
a i i cial neu ons and a i i cial neu al ne wo ks
ha mimic he human b ain wi h syn he ic
neu ons (Vochozka, 2017). The disad an age
o hese ne wo ks is he need o la ge sample
da a, since many es obse a ions a e needed
o p oduce such da a, howe e his is e y
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Business Adminis a ion and Managemen
complica ed o use s (Hossain, Chao, Ismail,
No oozi, & Khoo, 2017).
Fo da a p ocessing and business analyses,
he so-called Kohonen ne wo k can be applied.
I consis s o an inpu laye ha is comple ely
in e connec ed wi h an ou pu laye and is sel -
o ganizing, i.e. has he abili y o lea n wi hou
“a eache ”. This ne wo k has a e y wide
ange o uses, since i is an al e na i e ne wo k
applicable o mos neu al ne wo k calcula ions
(Vochozka & Macho á, 2018). I is used mainly
o audio edi ing, speech p ocessing, pho os,
ideos, secu i y applica ions, and allows o he
p ojec ion o high dimensional da a in o lowe
dimension da a (Konečný & T enz, 2010). When
using his a ac i e model o g oup da a se s
in o di e en g oups, he da a a e g ouped by
a sys em whe e eco ds wi hin a g oup end o
be simila o each o he and eco ds in di e en
g oups a e di e en . Many expe imen al esul s
show ha Kohonen ne wo ks a e e y e ec i e
no only o assessing ag icul u al companies
(Han & Wang, 2008).
The objec i e o he con ibu ion is an
analysis o companies ac i e in ag icul u e o
he Czech Republic using Kohonen ne wo k and
he subsequen p edic ion o hei de elopmen .
1. Da a and Me hods
The esea ch ques ion was se : Is i possible
o i nd ou he s a us o companies ope a ing in
ag icul u e in he Czech Republic on he basis
o clus e analysis using a i i cial in elligence?
Fo he pu poses o his con ibu ion, a da a
se will be c ea ed, which will include comple e
da a om i nancial s a emen s o 4,201
companies ac i e in ag icul u e o he Czech
Republic in 2016. These a e he en i ies whose
co e ac i i ies a e classi i ed in Sec ion A o he
economic ac i i ies classi i ca ion CZ-NACE.
The se o companies will be gene a ed om
he Bisnode Albe ina da abase.
The da a will be lis ed in an Excel able.
Each line will con ain da a om i nancial
s a emen o one conc e e company, which
will be iden i i ed by i s name and iden i i ca ion
numbe . The da a se will no con ain he da a
o he en e p ises ha did no pe o m hei
co e ac i i ies in he en i e moni o ed pe iod,
ha is he companies ha we e closed in his
pe iod (and he e o did no ha e any signi i can
in l uence on he di ec ion o na ional economy),
and companies ha s a ed hei ac i i ies
(and did no ha e a signi i can in l uence on
he di ec ion o he ag icul u e sec o in he
CR ei he ). The companies ha s a ed hei
ac i i ies on 1 Janua y 2016 o hose ha
i nished hei business on 31 Decembe 2016
could ha e been conside ed; howe e , his
would no ha e big in l uence on achie ing he
objec i e o he con ibu ion.
Mo eo e , he columns o da a showing no
a iance we e also excluded.
The da a se will be subsequen ly subjec ed
o clus e analysis using Kohonen ne wo k.
Fo clus e analysis, Dell’s S a is ica so wa e,
e sion 12 will be used. The e will be used
he Da a mining module and as a speci i c
ool, neu al ne wo ks. Neu al ne wo k wi hou
a eache (Kohonen ne wo k) will be used. The
da a o analysis will be chosen – Excel able
wi h da a se . In all cases hese a e con inuous
p edic o s. The se will be di ided in o h ee
pa s:
1. T aining da a se : ep esen s 70% o he
en e p ises om he da a se . Fo his se ,
Kohonen ne wo k will be c ea ed.
2. Tes ing da a se : i includes 15% o
companies om he o iginal da a se . This
se will be used o e i ying he pa ame e s
o c ea ed Kohonen ne wo k.
3. Valida ion da a se : his se will also con ain
15% o he companies om he da a se .
This da a se will be es ed he applicabili y
o inapplicabili y o he c ea ed Kohonen
ne wo k.
Topological leng h and wid h o Kohonen
ne wo k will be se a 10. The numbe o
i e a ions will be se a 10,000. Howe e , i
shall be no ed ha e o le el is a decisi e
ac o . I he Kohonen ne wo k pa ame e s a e
no imp o ed wi h each ei e a ion, he aining
will be i nished be o e he 10,000 h i e a ion is
comple ed. In case ha he ne wo k pa ame e s
do no imp o e wi h he 10,000 h ei e a ions,
he en i e p ocess mus be epea ed, and
a highe numbe o equi ed i e a ions shall be
se in o de o ge he bes esul possible. The
lea ning speed will be se a 0.1 a he beginning
and 0.02 a he end.
The esul s, i.e. he di ision o he indi idual
companies in o 100 clus e s will be en e ed
in Excel able. Subsequen ly, he indi idual
clus e s will be subjec ed o analysis o absolu e
and selec ed indica o s (o mo e p ecisely, hei
mean alues – a i hme ic a e age) and he
esul s will be in e p e ed.
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106 2019, XXII, 4
Business Adminis a ion and Managemen
2. Resul s
Based on he me hods applied, clus e s we e
c ea ed. The numbe o companies in each
clus e o he Kohonen ne wo k is shown in
Fig. 1.
Fig. 1 shows a 3-D ep esen a ion o he
Kohonen ne wo k c ea ed and he numbe
o companies in he indi idual clus e s. The
i gu e shows ha he highes ep esen a ion o
companies is in clus e (1, 8), and clus e (3, 9).
The hi d place is occupied by clus e (3, 10).
Mo eo e , he clus e wi h a highe ep esen a ion
o companies a e clus e s (4, 10) and (5, 10).
Fo he emaining clus e s, he ep esen a ion o
companies is signi i can ly lowe . I shall also be
no ed ha h ee o he Kohonen ne wo k clus e s
a e no occupied. Mo e pa icula ly, hese a e
clus e s (2, 3), (3, 1) and (3, 2).
To ge a de ailed iew on ep esen a ion o
companies in he indi idual clus e s, Fig. 1 is
complemen ed by conc e e alues shown in
Tab. 1.
Tab. 1 shows ha only he clus e s ma ked
in g ey include mo e han 100 companies. O he
clus e s include less han 100 companies,
wi h on (2, 7) clus e includes 90 companies.
The ollowing analysis deals only wi h he
highligh ed clus e s. They con ain da a on
1,919 companies, which accoun s o 45% o
all he companies in he se , al hough hey a e
in 5 clus e s ou o he 100 exis ing (and 97
occupied ones). Mo eo e , one o he clus e s,
speci i cally (1, 8), includes 990 companies,
i.e. almos 24% o he companies in he se .
I can he e o e be assumed, ha he analysis
pe o med will ha e a su i cien explana o y
powe and will be able o ep esen he po en ial
o he en i e Czech ag icul u e. The mean
alues o he se as a whole, and selec ed
clus e s a e gi en in Tab. 2.
Tab. 2 clea ly shows he di e ence o
alues o ag icul u e as a whole and o
indi idual clus e s. The mean alues o he
whole ag icul u e sec o a e signi i can ly highe
Fig. 1: Numbe o companies in each clus e o Kohonen ne wo k
Sou ce: own
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Neu on 1 2 3 4 5 6 7 8 9 10
1 1 3 9 2 4 17 16 990 26 4
2 2 7 0 20 44 42 90 30 29 11
3 0 0181448328682427249
4 131315194645372264150
5 9 14 26 22 27 22 47 15 72 103
6 10212413193721395556
7 6 17 28 17 25 36 23 17 37 48
8 6 10 12 28 17 16 7 40 23 30
9 2 51931181928231818
10 1 2 12 24 31 26 21 34 14 26
Sou ce: own
I em In o al (1, 8) (3, 9) (3, 10) (4, 10) (5, 10)
To al asse s 74,413.5 1,297.1 2,914.8 5,281.9 7,782.5 13,113.9
Fixed asse s 50,237.8 694.7 1,164.8 2,651.2 4,589.1 7,737.8
In angible i xed asse s 139.5 5.1 12.0 4.4 8.3 82.0
Tangible i xed asse s 46,922.1 524.8 1,123.8 2,340.4 4,170.6 6,410.0
Land 20,010.9 57.3 56.9 189.6 468.8 1,010.0
Cons uc ions 15,733.1 110.5 171.5 285.6 1,182.2 1,424.7
Sepa a e mo able hings and se s
o mo able hings 6,748.5 53.2 201.0 366.4 693.5 1,112.6
Long- e m i nancial asse s 3,176.2 164.8 29.0 306.7 410.2 1,245.8
Cu en asse s 23,623.8 589.0 1,705.8 2,555.4 3,035.1 4,961.8
In en o ies 9,576.0 68.4 284.4 643.5 802.6 1,580.9
Ma e ial 954.6 6.7 23.4 38.5 66.5 41.1
Un i nished and semi- i nished p oduc s 1,985.7 6.9 31.6 57.5 82.5 308.5
Long e m ecei ables 487.9 19.1 35.0 17.3 169.2 101.0
Sho e m ecei ables 7,784.0 182.1 661.8 1,130.4 1,354.3 2,221.3
T ade ecei ables (sho - e m) 4,260.9 43.0 198.8 354.8 487.2 700.4
S a – ax asse s 691.7 10.2 31.6 68.6 116.6 279.6
O he ad ances p o ided 165.1 4.0 9.9 21.3 80.9 32.9
Sho - e m i nancial asse s 5,789.6 320.6 724.7 764.8 712.3 1,058.6
Cash 141.8 49.4 54.8 109.0 94.4 185.1
Bank accoun s 4,466.3 96.9 236.0 302.8 279.9 384.6
Acc uals 513.9 10.5 42.5 73.5 150.2 389.3
Sou ce: own
No e: The amoun s a e gi en in housands CZK.
Tab. 1: Numbe o companies in indi idual clus e s o Kohonen ne wo k
Tab. 2: Mean alues o selec ed a iables o mos ep esen ed clus e s
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108 2019, XXII, 4
Business Adminis a ion and Managemen
han he mean alues o he clus e s wi h he
highes numbe o companies. This could mean
ha he selec ed clus e s conside ed also he
size o companies as one o he pa ame e s
o clus e ing ( his does no necessa ily mean
i was a decisi e ac o ; i could ha e happened
inad e en ly). The mos impo an alues o
asse s a e gi en in g aphs below.
Fig. 2 compa es he mean alues o asse s
o ag icul u e in o al and o indi idual clus e s.
Fo he sec o o ag icul u e, a e age
balance shee , ha is, he alue o asse s is
almos CZK 75 million pe company. Clus e
(1, 8) achie es he a e age asse s alue o CZK
1.2 million. Only one clus e , speci i cally (5, 10)
achie es he a e age alue o o al asse s o
mo e han CZK 10 million. In his clus e wi h
he alue o mo e han CZK 13 million he
companies ha e mo e han CZK 5 million a
hei disposal compa ed o he companies
Fig. 2: Mean alues o asse s o ag icul u e and o selec ed clus e s
Sou ce: own
Fig. 3: Mean alues o i xed asse s o ag icul u e sec o and o selec ed clus e s
Sou ce: own
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Business Adminis a ion and Managemen
in he second mos success ul clus e (4, 10).
These a e ollowed by clus e s (3, 10) and (3, 9)
wi h he asse s a he amoun o almos CZK
5.3 million, o almos CZK 3 million.
An indica o o equal impo ance, which
di e s he companies in he indi idual clus e s
is he amoun o i xed asse s (Fig. 3).
Ag icul u e is a e y speci i c sec o . I is
a s a egic ins i u ional sec o o he na ional
economy. I s esul s a e highly dependen on
wea he and ex e nal in l uence s. The ou pu s
o ag icul u al p oduc ion a e seasonal in
cha ac e . Due o his ac , i egula cash l ows
a e ypical o companies ac i e in ag icul u e.
In ag icul u e is also e o o highe le el o
p oduc ion mechaniza ion and au oma ion.
Fixed asse s hus indica e a possible ma ginal
a e o eplacemen o capi al o wo k. The
mo e machines he companies ha e, he ewe
wo ke s a e needed. In ag icul u e, he amoun
o i xed asse s pe one company is abo e CZK
50 million. The companies in clus e s examined
use i xed asse s signi i can ly less. The bes
posi ion has he clus e (5, 10), achie ing
he alue o mo e han CZK 7.2 million. The
clus e wi h he highes numbe o companies,
clus e (1, 8), achie es he alue o almos CZK
0.7 million pe company. The second mos
ep esen ed clus e (3, 9) achie es he amoun
o CZK 1.16 million pe company. The alue o
he wo emaining clus e s is CZK 2.6 million
(3, 10), o 4.5 million o clus e (4, 10).
Assuming ha using i xed asse s is g owing
wi h he size o company, i may be concluded
ha he clus e s examined do no include
la ge o ex emely la ge companies ac i e in
ag icul u e o he Czech Republic. Ano he
a iable examined is cu en asse s. Fo mo e
de ails, see Fig. 4.
Cu en asse s (al hough no equal o
wo king capi al) a e an impo an pa o each
company, in pa icula o an ag icul u al
company. I includes in en o ies, ecei ables
and i nancial asse s. Small animals, eed and
ools cos s a e ela i ely high in ag icul u e
companies. Simila ly, he paymen cul u e in
he Czech Republic is no op imal; he e o e he
le el o ecei ables is a he high. The highes
mean alues o cu en asse s a e in ag icul u e
as a whole (mo e han CZK 23.6 million).
The companies in he clus e (1, 8) ha e he
a e age cu en asse s alue o CZK 0.6 million.
The alue o cu en asse s o clus e (3, 9) is
less han CZK 2 million. The emaining h ee
clus e s (3, 10), (4, 10) and (5, 10) include he
companies wi h cu en asse s mean alues
highe han CZK 2 million. Fig. 5 shows he
amoun s o cash in he con ex .
Fig. 5 shows ha an a e age ag icul u al
company has cash a he amoun abo e CZK
141 housand. All companies in he examined
clus e s ha e a ela i e highe amoun o money.
Fig. 4: Mean alues o cu en asse s o ag icul u e and o selec ed clus e s
Sou ce: own
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In clus e (5, 10), he amoun o cash is abo e
he a e age (mo e han CZK 185 housand).
No su p isingly, equally in e es ing a e
he alues o unding sou ces – capi al. The
selec ed liabili ies i ems and compa ison o
hei mean alues a e shown in Tab. 3.
A en ion should be also paid o he
s uc u e o i nancing ag icul u e companies.
The e o e, Fig. 6 shows he mean alues o
equi y and bo owed capi al o each clus e
and o ag icul u e.
Fig. 6 shows ha in he ag icul u al sec o , he
sha e o bo owed capi al is 33% on a e age. In
Fig. 5: Mean alues o cash in ag icul u e and in selec ed clus e s
Sou ce: own
Fig. 6: Mean alues o equi y and bo owed capi al o ag icul u e
and o selec ed clus e s
Sou ce: own
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Business Adminis a ion and Managemen
he case o he en i e se , he amoun o equi y
and bo owed capi al is signi i can ly highe
han CZK 70 million. The highes capi alized
clus e in he examined se is clus e (5, 10).
The e is a sligh p edominance o bo owed
capi al. Bo h unding componen achie e he
amoun highe han CZK 12 million, whe e
he equi y accoun s o 25%. In he emaining
clus e s, he e is p edominan ly bo owed
capi al. The only excep ion is clus e (1, 8). An
in e es ing compa ison can be seen in Tab. 4,
which shows selec ed i ems o p o i and loss
accoun .
Ag icul u e as an ins i u ional sec o o
na ional economy iden i i es a ce ain deg ee o
sel -su i ciency and independence o na ional
economy on o he economies. The ag icul u e
pe o mance is less sensi i e o l uc ua ions
in economic cycle. Howe e , i is s ill possible
o judge he le el o economy acco ding o
he ag icul u e pe o mance sha e o he o al
GDP. I he absolu e alue is high and he
ela i e alue is low, he economy is e alua ed
as de eloped. This means ha no only he
sales olume bu also pe o mance and alue
added o ag icul u e companies is conside ed.
I em In o al (1, 8) (3, 9) (3, 10) (4, 10) (5, 10)
To al liabili ies 74,432.0 1,297.2 2,914.8 5,281.9 7,782.5 13,113.9
Equi y 51,225.2 1,062.6 1,213.0 1,420.2 1,334.9 3,311.0
Regis e ed capi al 15,768.1 746.6 522.5 588.5 1,545.3 3,201.6
Regis e ed capi al 2 15,226.3 455.1 167.6 266.3 1,321.8 2,913.8
Rese e unds, indi isible und and
o he e enue ese es 8,460.5 106.2 102.8 268.9 229.9 496.5
Legal ese e / Indi isible und 3,898.4 32.5 19.0 177.2 128.3 143.9
S a u o y and o he unds 4,296.3 9.7 53.0 77.9 90.8 119.2
P o i / loss om p e ious yea s 8,590.4 -46.3 161.3 136.5 -1,029.5 -1,019.5
Re ained ea nings om p e ious yea s 10,195.7 179.1 339.5 494.9 446.0 1,951.1
Loss o he p e ious yea s be o e
co e age -2,130.4 -311.6 -148.0 -464.4 -1,182.4 -2,832.6
P o i / loss o he cu en i nancial yea 3,340.1 10.5 131.9 130.4 8.0 91.3
Bo owed capi al 22,970.2 227.4 1,656.2 3,829.2 6,420.4 9,661.8
Long- e m liabili ies 5,422.9 53.4 350.6 1,018.3 1,826.7 2,948.5
Sho - e m liabili ies 7,765.0 145.8 1,064.1 2,124.2 3,396.4 4,708.6
T ade liabili ies (sho - e m) 4,286.0 11.4 170.3 431.7 1,029.8 1,667.6
Liabili ies o pa ne s, membe s o
assoc. and pa icipan s in associa ion
(sho - e m)
505.7 21.3 132.5 369.4 363.1 696.6
Liabili ies o employees 339.5 4.8 18.7 21.2 23.0 42.6
Liabili ies om social secu i y and
heal h insu ance 202.9 2.1 9.6 20.5 37.2 29.0
S a e – ax liabili ies and subsidies 184.2 6.4 22.6 -24.8 79.8 65.9
Bank loans and bailou s 8,785.4 25.2 222.5 654.5 1,194.4 1,992.4
Bank loans – long- e m 6,478.1 6.4 50.6 199.3 462.7 835.0
Bank loans – sho - e m 1,756.2 1.1 15.7 90.8 94.7 362.7
Sou ce: own
No e: The alues in he able a e gi en in housands CZK.
Tab. 3: Mean alues o liabili ies o se examined and selec ed clus e s
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Abs ac
PREDICTION OF INSTITUTIONAL SECTOR DEVELOPMENT AND ANALYSIS OF
ENTERPRISES ACTIVE IN AGRICULTURE
Voj ěch S ehel, Jakub Ho ák, Ma ek Vochozka
The o e all EU ag icul u al p oduc i i y g ow h has slowed down in ecen yea s and has lagged
behind leading global compe i o s, which is mainly due o dec easing numbe o employees in
ag icul u e. Technical ine i ciency is hen an impo an phenomenon o he Czech ag icul u e and
i s indi idual sec o s. Ag icul u e de elopmen should be es ablished on scien i i c bases. One
o he basic p inciples o sus ainable ag icul u e is he e o e o ecas ing i s u u e de elopmen .
In ecen yea s, se e al ag icul u al economis s ha e been engaged in compa ing o ecas s
wi h a ious o he me hods and hei conclusions gene ally co espond o commonly accep ed
belie s. A p esen , a i i cial in elligence can be de i ni ely ecognized as a use ul ool o business
analyses and o ecas ing. The objec i e o he con ibu ion is an analysis o companies ac i e in
ag icul u e o he Czech Republic using Kohonen ne wo k and he subsequen p edic ion o hei
de elopmen . A da a se is c ea ed, which includes comple e da a om i nancial s a emen s o
4,201 companies ac i e in ag icul u e o he Czech Republic in 2016. The se o companies is
gene a ed om he Bisnode Albe ina da abase. The da a se is subsequen ly subjec ed o clus e
analysis using Kohonen ne wo k. Fo clus e analysis, Dell´s S a is ica so wa e, e sion 12 is used.
The se is di ided in o h ee pa s: aining da a se , es ing da a se , alida ion da a se . Topological
leng h and wid h o Kohonen ne wo k a e se a 10. The numbe o i e a ions is se a 10 000.
Subsequen ly, he indi idual clus e s a e subjec ed o analysis o absolu e and selec ed indica o s
(o mo e p ecisely, hei mean alues – a i hme ic a e age) and he esul s a e in e p e ed. I can be
s a ed ha he ag icul u e companies show e y a o able alues – op imal asse s le el, accep able
i nancing s uc u e and adequa e economic esul . I can be e en s a ed ha he indica o s show
abo e-a e age alues compa ed o o he in es men op ions.
Keywo ds: Ag icul u e en e p ises, ins i u ional sec o de elopmen , p edic ion, a i i cial neu al
ne wo ks, alue o he business.
JEL Classi i ca ion: C45, O13.
DOI: 10.15240/ ul/001/2019-4-007.
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