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E ec o Ag icul u al Mechaniza ion on he G ow h o he Nige ian Economy
Kingdom Mienebimo
PhD S uden , Depa men o Economics, Nige Del a Uni e si y, Facul y o Social Sciences
Email: [email p o ec ed]
Phone: +2348066756724
Abs ac
This s udy examined he impac o ag icul u al mechaniza ion on he g ow h o he Nige ian
economy by employing an ex-pos ac o esea ch design. Seconda y da a spanning 1981 o 2023
we e ob ained om he Cen al Bank o Nige ia S a is ical Bulle in and Wo ld Bank De elopmen
Indica o s. The au o eg essi e dis ibu ed lag (ARDL) model was used o analyse he ela ionship
be ween eal g oss domes ic p oduc (RGDP) and he explana o y a iables. The empi ical esul s
indica e ha capi al in es men in ag icul u e and labou o ce in ag icul u e ha e s a is ically
insigni ican e ec s on RGDP, implying ha capi al in es men and labou o ce con ibu ions o
ag icul u al ou pu may be unde mined by s uc u al ine iciencies o inadequa e mechaniza ion.
In con as ag icul u al c edi gua an ee scheme and g oss ixed capi al o ma ion signi ican ly and
posi i ely impac RGDP, indica ing ha c edi access and in es men s in ixed capi al play pi o al
oles in enhancing economic g ow h h ough ag icul u al mechaniza ion. The s udy inds ha
while c edi and ixed capi al in es men s a e ins umen al in d i ing economic g ow h, capi al
in es men s and labou o ce con ibu ions in ag icul u e need o be op imized. Ine iciencies in
he ag icul u al sec o , such as insu icien mechaniza ion, may limi he po en ial o hese
a iables o con ibu e e ec i ely o economic g ow h. I is ecommended ha policymake s
in ensi y e o s o p omo e ag icul u al mechaniza ion h ough a ge ed unding, in as uc u e
de elopmen , and a ou able c edi policies.
Keywo ds: Ag icul u al Mechaniza ion, Economic G ow h, Inno a ions, Ag icul u al
P oduc i i y, ARDL.
1.0 In oduc ion
Ag icul u al mechaniza ion plays a c i ical d i e o p oduc i i y and economic g ow h in many
de eloping economies. In Nige ia, ag icul u e plays a i al ole in he na ion's economic
de elopmen , con ibu ing signi ican ly o employmen , ood secu i y, and u al de elopmen .
Acco ding o he Na ional Bu eau o S a is ics (NBS, 2023), ag icul u e con ibu es app oxima ely
24% o Nige ia’s G oss Domes ic P oduc (GDP) and employs o e 60% o he labou o ce,
pa icula ly in u al a eas. Ne e heless, he sec o ’s p oduc i i y has been cons ained by eliance
on adi ional a ming me hods, limi ed access o echnology, and inadequa e in as uc u e. Thus,
he adop ion o ag icul u al mechaniza ion plays a i al s a egy o imp o ing p oduc i i y and
os e ing economic g ow h (Wang, Chen, Kopi ke and Zhao (2019).
Ag icul u al mechaniza ion en ails he use o machine y and equipmen o enhance a ming
ope a ions, educe manual labo , and inc ease e iciency ac oss he ag icul u al alue chain
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(Olaniyi & Oyelami, 2021). Mechaniza ion encompasses a wide ange o ac i i ies, including land
p epa a ion, plan ing, i iga ion, ha es ing, and pos -ha es p ocessing. S udies ha e
demons a ed ha mechanized a ming can lead o highe c op yields, educed pos -ha es losses,
and imp o ed ood secu i y.
Despi e i s po en ial bene i s, he le el o mechaniza ion in Nige ia emains low. The Food and
Ag icul u e O ganiza ion (FAO, 2022) epo s ha Nige ia has one o he lowes ac o densi ies
in sub-Saha an A ica, wi h less han one ac o pe 1,000 hec a es o a able land. Fac o s such as
high cos s o machine y, limi ed access o inance, and poo in as uc u e ha e impeded he
widesp ead adop ion o mechaniza ion. Fu he mo e, smallholde a me s, who cons i u e he
majo i y o ag icul u al p oduce s in Nige ia, o en insu iciency esou ces and echnical capaci y
o in es in mechanized a ming (Jale a, Baud on, K i okapic-Skoko and E ens ein, 2019).
E o s by he Nige ian go e nmen o p omo e ag icul u al mechaniza ion ha e included policies
such as he Ag icul u al T ans o ma ion Agenda (ATA) and he Ag icul u al P omo ion Policy
(APP), which aim o enhance access o c edi , subsidize equipmen , and de elop u al
in as uc u e (Fede al Minis y o Ag icul u e and Ru al De elopmen , 2023). Ne e heless, he
impac o hese ini ia i es has been mixed, wi h many a me s s ill acing signi ican ba ie s o
mechaniza ion. P i a e sec o in ol emen and public-p i a e pa ne ships ha e also been explo ed
as po en ial solu ions o add ess he mechaniza ion gap.
Al hough, ag icul u e emains he co ne s one o Nige ia’s economy, con ibu ing signi ican ly o
employmen , ood secu i y, and na ional income. Ne e heless, he sec o 's po en ial has been
limi ed by eliance on subsis ence a ming p ac ices cha ac e ized by low p oduc i i y and
ine iciency. In Nige ia, whe e he popula ion exceeds 200 million, he need o a ib an
ag icul u al sec o is c i ical o sus aining economic g ow h and educing po e y. Despi e a ious
policy in e en ions, ag icul u al p oduc i i y has no me expec a ions, la gely due o inadequa e
mechaniza ion.
The unde pe o mance o Nige ia's ag icul u al sec o has been a longs anding conce n o
policymake s and s akeholde s. Low p oduc i i y, ine iciencies in he alue chain, and high pos -
ha es losses pe sis o unde mine he sec o ’s po en ial o d i e economic g ow h and ensu e ood
secu i y. One o he c i ical challenges acing he sec o is he limi ed adop ion o mechanized
a ming p ac ices. T adi ional a ming me hods, cha ac e ized by he use o basic ools and eliance
on manual labo , domina e ag icul u al p oduc ion in Nige ia. These p ac ices a e labo -in ensi e,
ime-consuming, and o en yield subop imal esul s (Leo, 2019).
The impac o low mechaniza ion ex ends beyond p oduc i i y o a ec he b oade economy.
Ag icul u e is a key sec o o economic di e si ica ion in Nige ia, pa icula ly in he con ex o
luc ua ing oil p ices and he need o non-oil e enue sou ces. The insu iciency o mechaniza ion
limi s he sec o 's abili y o con ibu e meaning ully o economic g ow h, expo ea nings, and job
c ea ion. In addi ion, ood insecu i y and ising impo bills o ag icul u al p oduc s highligh he
need o inc eased domes ic p oduc ion and compe i i eness (Ndubuisi, 2019).
Se e al s udies ha e highligh ed he posi i e ela ionship be ween ag icul u al mechaniza ion and
economic g ow h. Fo ins ance, Ogundele and Ojo (2023) ound ha mechanized a ming
signi ican ly boos s ag icul u al ou pu and educes u al po e y in Nige ia. Ne e heless, he
insu iciency o comp ehensi e and coo dina ed e o s o scale up mechaniza ion emains a majo
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bo leneck. The high cos o machine y, inadequa e inancing op ions, and poo main enance
cul u e a e some o he ac o s impeding p og ess. Addi ionally, in as uc u al de ici s, such as
poo oad ne wo ks and limi ed access o elec ici y, u he cons ain mechaniza ion e o s.
Gi en he impo ance o ag icul u e o Nige ia's economy and he p essing need o enhance
p oduc i i y, unde s anding he impac o ag icul u al mechaniza ion on economic g ow h is
c ucial. This s udy seeks o explo e he e ec o mechaniza ion on ag icul u al ou pu , employmen
gene a ion, and o e all economic pe o mance. By examining he challenges and oppo uni ies
associa ed wi h mechaniza ion, he s udy aims o p o ide insigh s and ecommenda ions o
policymake s, s akeholde s, and in es o s in e es ed in p omo ing sus ainable ag icul u al
de elopmen in Nige ia by de e mining he ela ionship be ween capi al in es men in ag icul u e
and economic g ow h in Nige ia and o examine he ela ionship be ween labou o ce in
ag icul u e and economic g ow h in Nige ia. The s udy also e alua es he ela ionship be ween
ag icul u al c edi gua an ee scheme und and economic g ow h in Nige ia and de e mine he
ela ionship be ween g oss ixed capi al o ma ion on ag icul u e and economic g ow h in Nige ia.
2.0 Li e a u e Re iew
Mechaniza ion spans a ious s ages o ag icul u e, including land p epa a ion, plan ing,
cul i a ion, ha es ing, and pos -ha es p ocessing, and is a c i ical d i e o ag icul u al
p oduc i i y, u al de elopmen , and economic g ow h. This amewo k emphasizes
mechaniza ion as an in eg a i e p ocess ha enhances e iciency, educes d udge y, and os e s
sus ainabili y wi hin ag icul u al sys ems (Bako, Jacob, Bi us and Yakubu, 2018).
Mechaniza ion includes he use o ac o s o ploughing, seed d ills o plan ing, i iga ion sys ems
o wa e managemen , and ha es e s o e icien c op collec ion. Pos -ha es mechaniza ion,
in ol ing milling, h eshing, and s o age echnologies, is equally impo an o educing losses and
enhancing alue addi ion. Mechaniza ion is also inc easingly inco po a ing enewable ene gy
sou ces, such as sola -powe ed i iga ion sys ems and biogas-powe ed equipmen , e lec ing a
g owing emphasis on sus ainabili y (Sims, Kienzle, & Hilmi, 2022).
The low mechaniza ion in ensi y in Nige ia is a c i ical conce n. The coun y has ewe han 1.5
ac o s pe 1,000 hec a es o a able land, a below he Food and Ag icul u e O ganiza ion's
ecommended minimum o 4 ac o s pe 1,000 hec a es (FAO, 2022). This lack o mechaniza ion
is a key ac o in he low p oduc i i y le els expe ienced by Nige ian a me s, wi h c op yields
o en signi ican ly below global a e ages. Fo example, maize yields in Nige ia a e abou 1.8 ons
pe hec a e, compa ed o a global a e age o 5.5 ons (Olukoya e al., 2022).
The ele ance o mechaniza ion o Nige ia’s ag icul u al and economic de elopmen is
mul i ace ed. Mechaniza ion has he po en ial o add ess labou sho ages caused by u al-u ban
mig a ion, imp o e p oduc i i y, and educe he d udge y associa ed wi h manual a ming.
Fu he mo e, i enhances he imeliness and p ecision o ag icul u al ope a ions, enabling a me s
o cul i a e la ge a eas and achie e highe yields. Mechaniza ion also suppo s he de elopmen
o ag o-indus ies by p o iding a s eady supply o aw ma e ials and educing pos -ha es losses.
This con ibu es o alue addi ion and he c ea ion o employmen oppo uni ies in ela ed sec o s
such as machine y manu ac u ing, epai , and logis ics.
Wi h Nige ia’s popula ion p ojec ed o exceed 250 million by 2050, mechaniza ion is essen ial o
mee ing he ising ood demand and educing he eliance on ood impo s (Zhai e al., 2022). The
adop ion o mechaniza ion also p omo es u al de elopmen by inc easing a m incomes, educing
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po e y, and encou aging in es men in u al in as uc u e. Mechanized a ms a e mo e likely o
a ac in es men s in oads, s o age acili ies, and ma ke s, u he s imula ing economic ac i i ies
in u al a eas.
To add ess hese issues, he Nige ian go e nmen has implemen ed ini ia i es such as he Na ional
Ag icul u al Mechaniza ion P og am (NAMP) and pa ne ships wi h p i a e sec o en i ies o
es ablish ac o leasing cen es. These p og ams aim o inc ease he a ailabili y and a o dabili y
o machine y o smallholde a me s. Addi ionally, in e na ional collabo a ions, such as hose
wi h China and India, ha e acili a ed he impo a ion o low-cos machine y sui able o Nige ia’s
a ming condi ions.
Ag icul u al mechaniza ion in Nige ia also highligh s he impo ance o capaci y building. Many
a me s lack he echnical skills needed o ope a e and main ain mode n equipmen . Ex ension
se ices and aining p og ams a e he e o e c i ical o ensu ing he e ec i e use o mechanized
ools and p omo ing hei widesp ead adop ion. Ag icul u al mechaniza ion is a i al componen
o Nige ia’s s a egy o ans o ming i s ag icul u al sec o and achie ing sus ainable economic
g ow h. While signi ican p og ess has been made in ecognizing i s impo ance, he ull po en ial
o mechaniza ion emains un ealized due o pe sis en challenges. Add essing hese challenges
equi es a holis ic app oach ha combines policy suppo , in as uc u e de elopmen , inancial
incen i es, and capaci y building.
2.2 Theo e ical F amewo k
The p ima y heo y unde pinning his esea ch is he Solow–Swan g ow h model due o i s
emphasis on echnological p og ess, capi al accumula ion, and long- un equilib ium analysis, all
o which align closely wi h he esea ch objec i es. The Solow–Swan G ow h (SSG) model,
o mula ed by Robe Solow and T e o Swan du ing he 1950s and 1960s, s ands as a undamen al
concep in economic heo y. I ex ends he Ha od–Doma model by inco po a ing labou as a
ac o o p oduc ion and allowing o a iable capi al-ou pu a ios. The model aims o elucida e
he ac o s d i ing long- e m economic g ow h, emphasizing he con ibu ions o capi al
accumula ion and echnological p og ess (Solow 1956). A i s co e, he SSG model posi s ha
economic g ow h is a unc ion o inc eases in capi al (bo h physical and human) and echnological
ad ancemen s. Ma hema ically, his can be exp essed as ollows:
𝑌 = 𝑓(𝐾, 𝐻, 𝐴, 𝐿)
whe e Y ep esen s ou pu o GDP, K deno es he s ock o physical capi al, H ep esen s he s ock
o human capi al, A signi ies echnological p og ess o o al ac o p oduc i i y (TFP), and L
deno es he labou o ce. In he absence o echnological b eak h oughs, he model p edic s ha
economies will e en ually each a s eady s a e whe e u he accumula ion o capi al ceases o
signi ican ly impac g ow h. Ma hema ically, his s eady s a e can be ep esen ed by he ollowing
condi ion:
𝑠𝑌 =(𝑛 + 𝛿)𝐾
whe e s deno es he sa ings a e, n ep esen s he a e o popula ion g ow h, and δ signi ies he
dep ecia ion a e o capi al.
Technological p og ess, conside ed an ex e nal o ce, becomes impe a i e o b eaking ou o his
equilib ium and achie ing sus ained economic g ow h (F ey 2017). Endogenous g ow h heo y
u he ex ends he Solow model by emphasizing he ole o knowledge and human capi al (Rome
1990). I posi s ha echnological ad ancemen s a e endogenously de e mined by ac o s such as
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esea ch and de elopmen (R&D) in es men s, and human capi al is conside ed a c i ical d i e o
g ow h.
The ele ance o his model o he s udy o echnological inno a ion’s impac on ag icul u al
p oduc i i y in Nige ia lies in i s ecogni ion o he ans o ma i e po en ial o echnological
p og ess. While ini ial in es men s in capi al, such as mode n machine y and in as uc u e, may
yield sho - e m gains in ag icul u al p oduc i i y, he model sugges s ha he sus ained impac
comes om exogenous echnological inno a ion (Olayide e al. 2016; Tabe-Ojong e al. 2023).
This implies ha o long- e m and sus ained g ow h in he ag icul u al sec o in Nige ia, he
adop ion o cu ing-edge echnologies like p ecision a ming, gene ic modi ica ions, and da a-
d i en decision-making is pa amoun .
In summa y, he SSG model o e s a solid heo e ical ounda ion o unde s anding he in e play
be ween echnological p og ess, capi al accumula ion, and economic g ow h. Applied o
ag icul u e, i highligh s he c ucial ole o echnological inno a ion in enhancing p oduc i i y and
achie ing sus ainable ag icul u al p ac ices, making i a pe inen guide o examining he impac
o echnological inno a ion on ag icul u al p oduc i i y in Nige ia.
2.3 Empi ical Li e a u es
Edwin (2024) examined accele a ing Nige ia’s Economy in he 21s Cen u y Th ough Mechanized
P oduc ion: A Thema ic App oach. The esea ch adop ed seconda y sou ces o da a collec ion such
as ex books, jou nals, go e nmen documen s e c and subsequen ly analyzed he da a h ough he
use o desc ip i e me hod o analysis also known as con en analysis. The pape ound ou ha
a ming sys ems cu en ly p ac ised in some pa s o Nige ia do no encou age mechanized
p oduc ion. Majo i y o oads especially in he Sou h Eas e n pa o Nige ia a e no accessible.
The pape ecommends among o he s ha Nige ian go e nmen should gi e u mos p io i y o
oad econs uc ion in o he o make hem accessible o ag icul u al machines. The s udy concludes
by asse ing ha i Nige ia ails o enhance he economy h ough mechanized, pe haps, she is
announcing he economic doom.
Joel, Adel, Dominic e al, (2024) in es iga e echnological Inno a ion and Ag icul u al
P oduc i i y in Nige ia Amids Oil T ansi ion: ARDL Analysis. Using he ARDL es ima ion
echnique, ou indings e eal a signi ican nega i e in luence o immedia e lagged ag icul u al
p oduc i i y (AGTFP(−1)), indica ing echnological cons ain s. Technological inno a ion,
p oxied by TFP, shows a subs an ial impac on ag icul u al p oduc i i y, wi h a nega i e long- e m
e ec (−90.71) bu a posi i e, hough insigni ican , impac on ag icul u al ou pu (0.0034). The
compa a i e analysis unde sco es ha he ag icul u al sec o ends o bene i mo e om
echnological inno a ion han he oil sec o . This highligh s he c i ical need o p io i ize
echnological ad ancemen s in ag icul u e o d i e sus ainable g ow h and economic esilience in
Nige ia.
Simila ly, Agbaje and Hassan (2023) analyzed he mac oeconomic impac o mechaniza ion on
Nige ia’s GDP using an e o co ec ion model (ECM). Thei esul s demons a ed a signi ican
posi i e ela ionship be ween he numbe o ac o s pe hec a e and ag icul u al GDP,
unde sco ing he impo ance o mechanized ools in enhancing ag icul u al e iciency and
con ibu ing o na ional economic g ow h. The s udy ecommended inc eased in es men in
subsidized mechaniza ion p og ams o boos smallholde a me s' access o machine y.
Olukoya e al. (2022) in es iga ed he ela ionship be ween mechanized a ming and c op yields
in Nige ia using ime-se ies da a om 1990 o 2020. Thei indings e ealed ha a ms u ilizing
mechanized ools achie ed a 35% highe ou pu compa ed o hose elian on adi ional me hods.
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The s udy concluded ha mechaniza ion is c i ical o closing Nige ia’s yield gap, pa icula ly in
s aple c ops like maize and ice.
Howe e , challenges pe sis . Omo ayo and Bola inwa (2022) no ed ha he high cos o
machine y, coupled wi h limi ed access o c edi and agmen ed land holdings, has cons ained
he widesp ead adop ion o mechaniza ion in Nige ia. Thei indings emphasized he need o
policies ha suppo coope a i e a ming and p o ide inancial incen i es o lowe he cos o
mechanized ools o smallholde a me s.
Kehinde, Oye ola, Oladunni. and A. T. (2022) analyzed he e ec o Ag icul u al Mechaniza ion
on P oduc ion and Fa me s Economy in Nige ia: A Case S udy o Lagos S a e. The in es iga i e
esea ch app oach me hod was employed o e ie e in o ma ion om a me s h ough a s uc u ed
ques ionnai e. A i e- a ing scale ques ionnai e was u ilized o he esponden s o show hei le el
o ag eemen o disag eemen . The pe cen age was used o analyze he esponden s' bio-da a. A
he same ime, he mean was employed o answe he esea ch ques ions. The null hypo heses we e
es ed using Chi-squa e s a is ics a 0.05 signi ican le els. The esul s e ealed ha ag icul u al
mechaniza ion inc eased he cul i a ed land, c op yields, and a me s’ income. The s udy showed
ha ag icul u al mechaniza ion had a signi ican in luence on c op p oduc ion and a me s’
income. The e o e, he e is a need o imp o e he a ailable echnologies and o mula e and
implemen policies o make ag icul u al mechaniza ion accessible and sus ainable.
In ano he s udy, Olo un emi and Adekunle (2021) explo ed he impac o pos -ha es
mechaniza ion on Nige ia's ood secu i y and ag o-indus ial de elopmen . The esea che s ound
ha mechanized p ocessing and s o age acili ies signi ican ly educed pos -ha es losses by up
o 40%, he eby inc easing he a ailabili y o aw ma e ials o ag o-indus ies. The s udy
highligh ed he indi ec bene i s o mechaniza ion in s imula ing ag o-indus ial g ow h and
c ea ing employmen oppo uni ies in u al a eas.
Hi oyuki, Pa ick and Hyacin h (2020) explo e he e ec s o ag icul u al mechaniza ion on
economies o scope in c op p oduc ion in Nige ia. Using panel da a om a m households and
c op-speci ic p oduc ion cos s in Nige ia, we es ima e how he adop ions o animal ac ion o
ac o s a ec he economies o scope (EOS) o ice, non- ice g ains, and legumes/seeds, which
a e he c op g oups ha a e mos widely g own wi h animal ac ion o ac o s in Nige ia, wi h
espec o o he non- ice c ops. The in e se-p obabili y-weigh ing me hod is used o add ess he
po en ial endogenei y o mechaniza ion adop ion and is combined wi h p imal- and dual-models
o EOS es ima ion. The esul s show ha he adop ion o hese mechaniza ion echnologies is
associa ed wi h g ea e EOS be ween ice and non- ice c ops bu lowe EOS among non- ice c ops
(i.e., be ween non- ice g ains, legumes/seeds, and o he non- ice c ops). Mechanical echnologies
may aise EOS be ween c ops ha a e g own in mo e he e ogeneous en i onmen s, e en hough
i may lowe EOS be ween c ops ha a e g own unde ela i ely simila ag oecological condi ions.
To he bes o ou knowledge, his is he i s pape ha shows he e ec s o mechanical
echnologies on EOS in ag icul u e in de eloping coun ies
These empi ical li e a u es hus emphasize he ans o ma i e po en ial o ag icul u al
mechaniza ion o Nige ia’s economy, while also highligh ing he s uc u al challenges ha mus
be add essed o maximize i s bene i s.
2.4 Li e a u e Gap
Ag icul u al mechaniza ion has been widely s udied as a d i e o economic g ow h, wi h esea ch
ocusing on i s ole in imp o ing p oduc i i y, educing labou in ensi y, and enhancing he
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Rese ed.
e iciency o ag icul u al ope a ions. Howe e , exis ing s udies o en exhibi se e al limi a ions
ha his esea ch aims o add ess.
Fi s , many s udies on ag icul u al mechaniza ion in Nige ia end o ocus p ima ily on i s di ec
impac on ag icul u al p oduc i i y wi hou adequa ely explo ing i s b oade economic
implica ions, such as i s in luence on GDP g ow h, employmen gene a ion, and u al-u ban
mig a ion. This s udy di e ges by in es iga ing no jus he p oduc i i y gains bu also he
mac oeconomic ipple e ec s o mechaniza ion on Nige ia's economic g ow h. This s udy also
in oduces a egionally disagg ega ed app oach, p o iding a nuanced unde s anding o how
mechaniza ion impac s g ow h di e en ly ac oss Nige ia’s geopoli ical zones.
Finally, p io esea ch o en elies on olde da a se s o employs ou da ed me hodologies ha may
no e lec ecen ad ancemen s in ag icul u al echnology o policy changes. This s udy uses
upda ed da a (1981–2023) and ad anced econome ic models o cap u e con empo a y ends and
p o ide mo e eliable insigh s in o he ela ionship be ween ag icul u al mechaniza ion and
economic g ow h in Nige ia.
3.0 Me hodology
The s udy u ilized seconda y da a, which was ob ained om Cen al Bank o Nige ia S a is ical
bulle in o e a pe iod o 43 yea s om 1981 o 2023. The s udy applied ex-pos ac o esea ch
design. The s udy used he Au o eg essi e Dis ibu ed Lag (ARDL) model. The ARDL model is
suppo ed by desc ip i e s a is ics, s a iona i y es and coin eg a ion es o long un.
Model Speci ica ion
Following he e iew o li e a u es and pa icula ly he Solow Swan G ow h model, which se es
as a ehicle o economic g ow h o any coun y. The model o his s udy was s a ed:
RGDP_ = (CIA_ , LFA_ , ACGS_ , GFCF_ ) (3.1)
The econome ic o m o he model is exp essed as:
RGDP_ = β_0 + β_1 CIA_ + β_2 LFA_ + β_3 ACGS_ + β_4 GFCF_ + μ (3.2)
Whe e;
RGDP is Real G oss Domes ic P oduc p oxy as economic g ow h; CIA is Capi al in es men in
Ag icul u e; LFA is Labou o ce in Ag icul u e; ACGS is Ag icul u al c edi gua an ee scheme
and GFCF is G oss Fixed Capi al Fo ma ion. β_0 is Cons an e m; β_1-4 is he Pa ame e s and
μ is he e o e m.
4.0 Resul s and Discussion o Findings
Desc ip i e Analysis
The desc ip i e s a is ic echnique on he da a was conduc ed using measu es o cen al endency,
measu es o dispe sion, and da a no mali y measu e. The esul s ob ained om he desc ip i e
analysis a e p esen ed in Table 4.1.
Table 4.1 Desc ip i e S a is ics o he da a
RGDP
CIA
LFA
ACGS
GFCF
Mean
39902.54
22.89659
47218889
3208744.
8743.094
Maximum
77936.10
36.96508
75721345
12061412
15789.67
Minimum
16211.49
12.24041
32844703
9853.900
5668.870
S d. De .
21651.62
4.480943
13370148
3756299.
1992.304
Obse a ions
43
43
43
43
43
Sou ce: Au ho ’s own compu a ion using E iew 10.
52
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The desc ip i e s a is ics p o ide an o e iew o he da a used in he s udy, summa izing he
cen al endency, dispe sion, and ange o alues o each a iable. The eal g oss domes ic p oduc
(RGDP) has a mean alue o 39,902.54, e lec ing he a e age economic ou pu o e he obse ed
pe iod. The s anda d de ia ion o 21,651.62 indica es conside able a iabili y in economic ou pu
ac oss he pe iod, wi h alues anging om a minimum o 16,211.49 o a maximum o 77,936.10.
Capi al in es men in ag icul u al ou pu s (CIA) has an a e age alue o 22.90, e lec ing a
mode a e le el o inancial inpu in o ag icul u al ac i i ies. The s anda d de ia ion o 4.48
demons a es some a iabili y, indica ing ha in es men le els we e no consis en h oughou
he pe iod.
The labou o ce in ag icul u e (LFA) has a mean o 47,218,889, ep esen ing he a e age numbe
o indi iduals engaged in ag icul u al ac i i ies. The s anda d de ia ion o 13,370,148 e eals
signi ican a iabili y, sugges ing subs an ial di e ences in labou o ce size o e ime.
The ag icul u al c edi gua an ee scheme (ACGS) eco ds an a e age o 3,208,744, which
highligh s he mean le el o gua an eed c edi o suppo ag icul u al ac i i ies. Wi h a s anda d
de ia ion o 3,756,299, he e is no iceable luc ua ion, e lec ing di e ences in he scheme's
applica ion ac oss he pe iod.
G oss ixed capi al o ma ion (GFCF), ep esen ing in as uc u e in es men , has a mean o
8,743.09. The s anda d de ia ion o 1,992.30 indica es mode a e a iabili y, sugges ing ha
in es men le els we e ela i ely s able compa ed o o he a iables.
Co ela ion Ma ix o Mul icolinea i y
The essence o he co ela ion ma ix is o es he p esence o mul icollinea i y in he model. The
esul is p esen ed in able 4.2
Table 4.2 Co ela ion Ma ix
CIA
LFA
ACGS
GFCF
CIA
1.000000
LFA
0.132807
1.000000
ACGS
-0.009014
0.221848
1.000000
GFCF
-0.353972
0.461247
0.376666
1.000000
Sou ce: Au ho ’s own compu a ion using E iew 10.
The co ela ion ma ix e eals he ela ionships among he independen a iables, helping o assess
he p esence o mul icollinea i y. Capi al in es men in ag icul u al ou pu s (CIA) shows weak
co ela ions wi h he o he a iables, wi h alues o 0.13 o labou o ce in ag icul u e (LFA), -
0.009 o he ag icul u al c edi gua an ee scheme (ACGS), and -0.35 o g oss ixed capi al
o ma ion (GFCF). Simila ly, LFA exhibi s weak o mode a e posi i e co ela ions wi h ACGS
(0.22) and GFCF (0.46). ACGS and GFCF show a mode a e posi i e co ela ion o 0.38. O e all,
he co ela ions a e below 0.8, indica ing he absence o signi ican mul icollinea i y among he
a iables.
Tes o s a iona i y using ADF uni oo es
Table 4.3: Uni Roo Tes using ADF
ADF @ Le el
Va iables
ADF es
C i ical alue
P ob.
I (0)
Decision
S a is ics
@ 5%
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Log(RGDP)
-1.500608
-3.526609
0.8128
I (0)
Non-S a iona y
Log(CIA)
-2.807484
-3.526609
0.2031
I (0)
Non-S a iona y
Log(LFA)
-3.359733
-3.526609
0.0718
I (0)
Non-S a iona y
Log(ACGS)
-1.511085
-3.526609
0.8095
I (0)
Non-S a iona y
Log(GFCF)
-5.883203
-3.523623
0.0001
I (1)
S a iona y
ADF @ 1s Di e ence
Va iables
ADF es
C i ical alue
P ob.
I (0)
Decision
S a is ics
@ 5%
Log(RGDP)
-4.010653
-3.523623
0.0160
I (1)
S a iona y
Log(CIA)
-6.959123
-3.523623
0.0000
I (1)
S a iona y
Log(LFA)
-4.082763
-3.523623
0.0134
I (1)
S a iona y
Log(ACGS)
-4.237968
-3.523623
0.0090
I (1)
S a iona y
Log(GFCF)
-
-
-
I (1)
S a iona y
Sou ce: Au ho ’s own compu a ion using E iew 10.
The ADF es in able 1 and 2 clea ly e ealed ha , all he a iables we e no s a iona i y a 1(0)
excep g oss ixed capi al o ma ion which was s a iona y a le el. Howe e , hey became
s a iona i y a hei i s di e ence 1(1). Hence, he s udy used o Au o eg essi e Dis ibu ed Lag
(ARDL) model and Bound es o es o he long un ela ionship be ween he independen and
dependen a iable.
Coin eg a ion Analysis
The coin eg a ion es was conduc ed o de e mine he exis ence o a long- un ela ionship among
he a iables in each o he models ea lie speci ied. The summa ies o he esul s om he es s
a e p esen ed in Table 4.3.
Table 4.4: Bound es o long un ela ionship
F-Bounds Tes
Null Hypo hesis: No le els ela ionship
Tes S a is ic
Value
Signi .
I(0)
I(1)
Asymp o ic:
n=1000
F-s a is ic
7.141539
10%
2.2
3.09
K
4
5%
2.56
3.49
2.5%
2.88
3.87
1%
3.29
4.37
Sou ce: Au ho ’s own compu a ion using E iew 12.
Table 4.4 indica es ha he e exis s long- un ela ionship among he a iables o he s udy. This
because he F-s a is ics (7.141539) is g ea e han 5% uppe and lowe bound (3.49 and 2.56).
The e o e, he esea che s concluded ha economic g ow h a iables ha e long- un ela ionship
wi h me chandized ag icul u al measu es in Nige ia.
Long un ARDL Resul
The ARDL esul o he long un p esen ed in able 4.5 below.
Table 4.5: ARDL long un esul o he model.
