Tadjie , Abdusame
Doc o al Thesis
Explo ing he ad an ages and d i e s o sus ainable
ag icul u al p ac ices in Cen al Asia
Sugges ed Ci a ion: Tadjie , Abdusame (2024) : Explo ing he ad an ages and d i e s o sus ainable
ag icul u al p ac ices in Cen al Asia, Uni e si ä s- und Landesbiblio hek Sachsen-Anhal , Halle
(Saale),
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EXPLORING THE ADVANTAGES AND DRIVERS OF
SUSTAINABLE AGRICULTURAL PRACTICES IN
CENTRAL ASIA
Disse a ion
Zu E langung des
Dok o g ades de Ag a wissenscha en (D .ag .)
de
Na u wissenscha lichen Fakul ä III
Ag a ‐ und E näh ungswissenscha en,
Geowissenscha en und In o ma ik
de Ma in‐Lu he ‐Uni e si ä Halle‐Wi enbe g
o geleg on
He n ABDUSAME TADJIEV
Gu ach e :
P o . D . Thomas He z eld
P o . D . Ma in Pe ick
D . Nodi Djanibeko
Tag de Ve eidigung:
23.09.2024
DEDICATION
This disse a ion is dedica ed o he che ished memo y o my g and a he s, TADJIEV
ABDURAHMONKHON and AKBAROV UZBEKHON, and my g andmo he s, AMIROVA
ROBIYAKHON and TURSUNOVA ZULFIYAKHON.
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ACKNOWLEDGMENTS
I am deeply g a e ul o a numbe o people whose unwa e ing suppo and in aluable
con ibu ions ha e made he comple ion o his Ph.D. disse a ion. Thei guidance,
encou agemen , and assis ance ha e p o oundly shaped my PhD jou ney and en iched he
ou come o his esea ch. Fi s o all, I would like o hank my supe iso s P o . D . Thomas
He z eld and D . Nodi Djanibeko who accep ed me as a Ph.D. s uden in he amewo k o
SUSADICA p ojec and o hei con inuous guidance and suppo h oughou he comple ion o
his hesis. A special hanks o D . Nodi Djanibeko o his in aluable encou agemen , s eady
mo al suppo , and insigh ul guidance, all o which ha e been ins umen al in enabling me o
ad ance wi h his hesis in a conduci e and s ess- ee manne . He engaged me in ac i e
pa icipa ion wi hin he SUSADICA p ojec , he eby aiding in he enhancemen o my
capabili ies. Mo eo e , Nodi was he one by my side and suppo i e on my ini ial day in Halle.
Commencing my PhD a IAMO, I ini ially planned a ield expe imen in Uzbekis an, in addi ion,
p io o my in ol emen in he SUSADICA p ojec , I had pa icipa ed in he AGRICHANGE p ojec ,
whe ein I had in ended o de end my i s PhD in my home coun y, Uzbekis an. Howe e ,
COVID-19 a el es ic ions p e en ed bo h my plans. Despi e acing signi ican challenges, wi h
suppo om Thomas and Nodi , I managed o adap my PhD plans and de ended my hesis
emo ely om IAMO, as well as emained connec ed wi h IAMO du ing he pandemic. I am
g a e ul o hem. Addi ionally, I ex end hanks o P o . D . Sha ka Hasano , who is my local
supe iso , and he eam a he "Tashken Ins i u e o I iga ion and Ag icul u al Mechaniza ion
Enginee s" Na ional Resea ch Uni e si y (TIIAME NRU) o hei suppo du ing my esea ch isi
in 2019 and hei assis ance in conduc ing he online de ense du ing he pandemic.
I g a e ully acknowledge he unding made a ailable by VolkswagenS i ung unde he Doc o al
P og am o Sus ainable Ag icul u al De elopmen in Cen al Asia (SUSADICA, G an Numbe
96264) wi hin he unding ini ia i e ‘‘Be ween Eu ope and he O ien – A Focus on Resea ch and
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Highe Educa ion in/on Cen al Asia and he Caucasus”. Fu he mo e, I would like o hank
SUSADICA p ojec membe s D . Nozilakhon Mukhamedo a, P o . D . Daniel Mülle , P o . D . Insa
Thees eld, D . Ilkhom Solie , Za a Ku bano , D . Ba chynai Kimsano a, A abek Umi beko ,
Daniela Peña Gue e o, Su ay Cha yye a, Da on Niyazme o , Hannes Kno , Jakhongi
Babadjano and Kady bek Sul akee . The insigh ul eedback ga ne ed om p ojec wo kshops
and ela ed e en s has signi ican ly con ibu ed o enhancing he quali y and dep h o his
esea ch ou pu .
My special g a i ude goes o P o . D . Sha ka Hasano o his commen s, mo al suppo ,
encou agemen , and in ellec ually s imula ing discussions, as well as o his aid du ing my ield
ip in Uzbekis an be o e pandemic. His suppo has been ins umen al in guiding me h oughou
he en i e y o his disse a ion. I also hank o P o . D . Olim Mu azae , D . Fa hod Ah o o ,
and D . Ib ohim Ganie o hei encou agemen and suppo , which played a pi o al ole in my
decision o pu sue doc o al s udies. Addi ionally, I hank o D . Dami Esenalie o his in aluable
commen s, enhancing he con en o he hi d chap e o his disse a ion. I would also like o
acknowledge D . A jola A api-Gjini o he expe ise, which p o ed ins umen al in e ining he
me hodology inco po a ed wi hin he ou h Chap e o his disse a ion.
I exp ess my g a i ude o he en i e Leibniz Ins i u e o Ag icul u al De elopmen in T ansi ion
Economies (IAMO) eam o hei ongoing and subs an ial assis ance h oughou my esea ch
jou ney. Thei suppo has ex ended o a ious ace s including adminis a i e p ocedu es,
access o li e a u e wi h all possible means, IT, ield- ips, con e ences, accommoda ion,
eimbu semen s, open access, language edi ing, and many mo e ma e s. I also hank o all o he
colleagues and iends (bo h iends om IAMO and Uzbekis an) whose di ec and indi ec
con ibu ions ha e signi ican ly con ibu ed o he success ul ou come o his wo k.
Finally, I ex end my since e g a i ude o my a he , Tadjie Abduhamid, my mo he , Akba o a
Shohis a, and my ex ended amily membe s including sis e s and b o he s. Thei unwa e ing
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lo e and suppo h oughou he passage o ime, whe he om a a o nea by, has been
immeasu able. Fu he mo e, my special hanks o my wi e and child en. Despi e being a om
me, hei pa ience, lo e, us and suppo gi e me mo al s eng h. I ex end my u mos g a i ude
o hem!
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SUMMARY
Imp o ing soil p oduc i i y and ag icul u al ou pu s pose signi ican challenges in de eloping
coun ies, including Cen al Asia. Inadequa e land use du ing he So ie imes, coupled wi h he
absence o a s uc u ed land managemen sys em du ing he pe iod, engende ed nume ous
issues including c opland deg ada ion ha exe s a subs an ial impac on ag icul u al
p oduc i i y in he Cen al Asian coun ies. The adop ion o sus ainable ag icul u al p ac ices
s ands as a c ucial emedy o add ess hese issues. Despi e he comp ehensi e co e age wi hin
global li e a u e ega ding he bene i s o sus ainable ag icul u al p ac ices, he e pe sis s a
ma ked disc epancy in hei adop ion le els by a me s. Fu he mo e, he e is a sca ci y o
empi ical in es iga ions explaining he p ima y d i e s and he impac s associa ed wi h he
adop ion o sus ainable ag icul u al p ac ices in Cen al Asia. F om his pe spec i e, he
o e a ching aim o his doc o al disse a ion is o gain deepe insigh s in o he ac o s ha
acili a e he adop ion o selec ed sus ainable ag icul u al p ac ices among a me s in a ious
se ings o Cen al Asia.
The hesis comp ises i e chap e s, inco po a ing h ee empi ical sec ions. The ini ial chap e
in oduces o he gene al p oblem backg ound pe aining o he issue o sus ainable ag icul u al
de elopmen in Cen al Asia and he key esea ch ques ions o he PhD disse a ion. Empi ical
indings a e p esen ed in he second, hi d and ou h chap e s. A gene al conclusion is gi en in
Chap e 5.
The second chap e in es iga es he d i e s o a me s’ decision o adop c op o a ion and how
i s adop ion impac s a me s’ co on yields and ne e u ns in wo con as ing se ings o Cen al
Asia by applying an endogenous swi ching eg ession model o c oss-sec ional su ey da a
collec ed om 592 co on g owe s in 2019 in Kazakhs an and Uzbekis an. Co on monocul u e
inhe i ed om he o me So ie cul i a ion sys em led o he decline o soil e ili y and educed
co on yields in i iga ed a eas o Cen al Asia. Adop ing a di e si ied c op o a ion app oach is
a iable solu ion o main ain soil quali y and long- e m economic bene i s. The chap e indings
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highligh hese wo coun ies' di e ing ins i u ional con ex s su ounding co on a ming.
Kazakhs ani a me s' decision o adop c op o a ion is posi i ely ela ed o age, pa icipa ion in
a m aining, he a me 's opinion abou he quali y o he i iga ion canal, and he sha e o
adop e s in a illage. In Uzbekis an, a me s who pe cei e g ea e land enu e secu i y a e mo e
inclined o adop c op o a ion. In Uzbekis an, employing c op o a ion leads o highe co on
yields compa ed o adi ional c op cul i a ion me hods. In Kazakhs an, co on a me s
expe ience a con as ing ou come.
Employing an endogenous swi ching eg ession model on he plo -le el panel da a o 878 o
Ky gyzs an’s smallholde s, he hi d chap e in es iga es he de e minan s o he decision o
adop ze o illage and i s e ec on smallholde s’ p oduc ion cos s. The chap e inds ha he
p obabili y o ze o illage adop ion is associa ed wi h employmen in ag icul u e, asse s,
ag icul u al shocks, e ilize use, numbe o plo s and a e age dis ances om he dwelling o
household ields and o he main oad. Fu he mo e, he chap e indica es ha ze o illage
adop ion dec eases land p epa a ion cos s by 23%, bu inc eases hi ed labo and he bicide cos s
by 13% and 15%, espec i ely compa ed o he con en ional illage me hod. Ne e heless, ze o
illage can educe o al p oduc ion cos s by 15%. The hi d chap e con i ms ha ze o illage can
be p omo ed as an op ion o esou ce-sca ce smallholde s, especially hose in emo e a eas
wi h poo access o inpu s and machine y se ices. P omo ing ze o illage adop ion as a labo -
sa ing o he bicide educing p ac ice can c ea e alse expec a ions among smallholde s.
The ou h chap e in es iga es he ques ion o how pa icipa ion in in o mal coope a ion in
wa e managemen in luences he in ensi y o he adop ion o sus ainable ag icul u al p ac ices
by using wo-yea s o 2019 and 2022 a m su ey da a o Uzbekis an and employing a ma ginal
ea men e ec s model. The esul s show ha a me s who a e likely o pa icipa e in in o mal
coope a ion in wa e managemen end o bene i mo e om he pa icipa ion in e ms o
highe adop ion in ensi y o sus ainable ag icul u al p ac ices.
Finally, he i h chap e syn hesizes he esea ch indings, summa izes he policy implica ions
along wi h esea ch limi a ions and p o ides ideas o u u e esea ch.
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TABLE OF CONTENTS
ACKNOWLEDGMENTS ........................................................................................................... i
SUMMARY ......................................................................................................................... i
TABLE OF CONTENTS........................................................................................................... i
LIST OF FIGURES ............................................................................................................... iii
LIST OF TABLES ................................................................................................................... ix
LIST OF TABLES IN THE APPENDIX ......................................................................................... x
ABBREVIATIONS ................................................................................................................. xi
1 GENERAL INTRODUCTION ................................................................................................ 1
1.1 Challenges o sus ainable ag icul u al de elopmen in Cen al Asia ................................ 1
1.2 Sus ainable ag icul u al p ac ices ....................................................................................... 2
1.3 P oblem s a emen , esea ch objec i es and s uc u e o he hesis ................................ 5
2 DETERMINANTS AND IMPACTS OF CROP ROTATION ADOPTION AMONG COTTON
GROWERS IN IRRIGATED AREAS OF KAZAKHSTAN AND UZBEKISTAN ............................... 11
2.1 Re o ms in he co on sec o and he adop ion o c op o a ion p ac ices in
Kazakhs an and Uzbekis an .............................................................................................. 11
2.2 Da a and desc ip i e analysis ............................................................................................ 13
2.3 Me hodological app oach ................................................................................................. 18
2.3.1 C op o a ion adop ion decision and a m ou come .............................................. 19
2.3.2 Endogenous swi ching eg ession .......................................................................... 22
2.3.3 A e age ea men e ec s ...................................................................................... 24
2.4 Resul s and discussion ...................................................................................................... 26
2.4.1 De e minan s o a me s’ decision o adop c op o a ion .................................... 26
2.4.2 De e minan s o co on yields and ne e u ns o co on g owe s ........................ 29
2.4.3 Co on yield and ne e u ns impac s o he adop ion o c op o a ion ................ 38
3 DOES ZERO TILLAGE SAVE OR INCREASE PRODUCTION COSTS OF SMALLHOLDERS
IN KYRGYZSTAN? ........................................................................................................... 41
3.1 Smallholde s’ challenges in he adop ion o conse a ion ag icul u e in
Ky gyzs an ......................................................................................................................... 41
3.2 Concep ual amewo k ..................................................................................................... 43
3.3 Da a and desc ip i e analysis ............................................................................................ 46
3.4 Me hodological app oach ................................................................................................. 52
3.4.1 Ze o illage adop ion decision and p oduc ion cos s ............................................. 52
3.4.2 Es ima ion o a e age ea men e ec on he ea ed ......................................... 55
3.5 Resul s and discussion ...................................................................................................... 56
3.5.1 De e minan s o ze o illage adop ion ................................................................... 56
3.5.2 Resou ce-sa ing e ec s o ze o illage ................................................................... 60
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Al hough he in ensi e e ilize applica ion has lessened he isible impac and masked he
p oblem's ull ex en , i iga ed c oplands in Cen al Asia—especially in co on-p oducing a eas—
ha e become ocal poin s o land deg ada ion. This deg ada ion esul ed in he loss o US$ 6
billion in 2001-2009, wi h dese i ica ion and ag icul u al abandonmen cos ing US$ 1 billion
each (Mi zabae e al., 2016).
Enhancing soil p oduc i i y and mi iga ing land deg ada ion s and as pi o al challenges in
Cen al Asia. Ye , he pos -independence ansi ion o ma ke economies in hese coun ies
p esen ed new challenges o mi iga ing land deg ada ion (Pom e , 2019). Land e o ms, aimed
a edis ibu ing s a e-owned lands o p i a e indi iduals, o en lacked he necessa y suppo
mechanisms o os e sus ainable land managemen p ac ices (Kienzle e al., 2012). Fa m
agmen a ion hinde ed he e icien use o esou ces and adop ion o mode n ag icul u al
echnologies, u he con ibu ing o land deg ada ion and lowe ag icul u al p oduc i i y
(Le man and Sedik, 2018). Access o knowledge, echnology, inancial esou ces, and
in as uc u e necessa y o implemen a ion o sus ainable land managemen by newly-
eme ged ag icul u al p oduce s was lacking (Ho nidge e al., 2016; Kienzle e al., 2012).
1.2 Sus ainable ag icul u al p ac ices
The adop ion o sus ainable ag icul u al p ac ices (SAPs) p esen a p omising solu ion o hese
challenges by imp o ing soil e ili y, cap u ing ca bon o add ess clima e change, and boos ing
bo h c op yields and inancial e u ns (Manda e al., 2016). The adop ion o SAPs s ands as a
p incipal s a egy di ec ed owa ds enhancing a m p oduc i i y, imp o ing ag icul u al
p o i abili y, and educing p oduc ion expenses (e.g., Lee, 2005; Manda e al., 2016; Tadjie e
al., 2023a; Zhao e al., 2020). Schola s a gue ha sus ainable ag icul u al de elopmen embodies
i e p ima y cha ac e is ics: (1) esou ce conse a ion, (2) en i onmen al p ese a ion, (3)
echnological sui abili y, (4) economic iabili y, and (5) social accep abili y (FAO, 1989;
Teklewold e al., 2013). Acco dingly, sus ainable ag icul u al p ac ices a e cha ac e ized by hei
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inhe en capaci y o yield posi i e ex e nali ies in c ucial domains such as biodi e si y, wa e
conse a ion, soil heal h, landscape p ese a ion, and clima e change mi iga ion. This
dis inguishing ea u e se s hem apa om con en ional p ac ices (Dessa e al., 2019).
SAPs encompass a spec um o a ming p ac ices ha include en i onmen al, socie al, and
economic aspec s. These p ac ices include c op o a ion, in e c opping, conse a ion illage,
biological me hods o pes con ol, esidue e en ion, imp o ed c op a ie ies, animal manu e,
soil and wa e conse a ion (e.g., Lee, 2005; Manda e al., 2016; Teklewold e al., 2013; Zeweld
e al., 2017). Insu iciency o inancial, physical, and human esou ces s ands as a p edominan
issue aced by u al households and indi idual a ms wi hin he Cen al Asian egion
(Wol g amm e al., 2010; Djanibeko e al., 2012). Mo eo e , he ising expenses associa ed wi h
p oduc ion inpu s pose signi ican challenges o a ms in Cen al Asia (Djanibeko e al., 2012).
Hence, i is impe a i e o ad oca e o he adop ion o esou ce-sa ing p ac ices ha en ail
lowe inancial esou ces among a ms. Fo example, c op o a ion, ze o illage, in e c opping is
an app oach o soil managemen ha in ol es minimal inpu , and p e e ably less o - a m
sou ces (Bake and Sax on, 2007; Tan ee e al., 2019). In a ious empi ical chap e s o my
disse a ion, I hus ocus on s udying he adop ion o c op o a ion, ze o illage, low- illage,
biological me hods o pes con ol, and in e c opping p ac ices in he con ex o Cen al Asia.
The u iliza ion o hese selec ed p ac ices o e s economic, social, and en i onmen al
ad an ages o a me s (e.g., Abdollahzadeh e al., 2015; Bake and Sax on, 2007; Glaze-Co co an
e al., 2020; Ogie iakhi and Woodwa d, 2022; Yigezu and El‐Sha e , 2021; Zhao e al., 2020). The
adop ion o hese p ac ices p o ides po en ial o mi iga ing challenges o sus ainable
ag icul u al de elopmen in Cen al Asia (FAO, 2013; Kienzle e al., 2012; Nu beko e al., 2016;
Pende e al., 2009). Fu he mo e, he abo e-lis ed SAPs ha e been success ully es ed o
easibili y in Cen al Asia (Nu beko e al., 2016; Pende e al., 2009).
Biological pes con ol e e s o he en i onmen ally conscious app oach o managing pes s by
ha nessing na u al ad e sa ies (Kuma i e al., 2022; Nigam and Muke ji, 2023). Biological pes
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con ols educe he dependency o mode n ag icul u e on pes icide applica ions and main ain
high c op yields (Schneide e al., 2015). Biological con ol is a componen o an in eg a ed pes
managemen s a egy and he adop ion o in eg a ed pes managemen will be in oduced o
manage pes popula ions and c op p oduce s’ ne e u ns will be imp o ed (Ho mann and
F odsham, 1993; McNama a e al., 1991).
C op o a ion is a me hod o cul i a ing c ops in a sys ema ic sequence on he same piece o
land, wi h he goal o p ese ing soil e ili y and ensu ing ha a me s main ain o inc ease
hei land- ela ed p o i s (Sumne , 1982; Tan ee e al., 2019). C op o a ion, pa icula ly wi h
leguminous c ops, has been shown o sus ain and enhance a m p oduc i i y and income (FAO,
2015). S udies like Manda e al. (2016) demons a ed ha maize-legume o a ion, combined
wi h imp o ed a ie ies and esidue e en ion, boos s maize yields and household income. In
China, co on-legume o a ion inc eases co on yields by nea ly a qua e (Zhao e al., 2020).
Implemen ing c op o a ion, especially wi h al al a, so ghum, o mung beans as co e c ops,
wi hin an o ganic-based ag icul u al amewo k in Uzbekis an enhances ne p esen alue and
educes expenses (F anz e al., 2009). Fa me s who p ac iced c op o a ion had highe wel a e
compa ed o non-adop e s (Ghimi e e al., 2012; Mohammad e al., 2012; Zeweld e al., 2020).
Conse a ion ag icul u e means no o minimum mechanical soil dis u bance, seeding o plan ing
di ec ly in o un illed soil, and using c op esidues and co e c ops o p o ec and eed soil li e
and his can help o imp o e soil quali y, and inc ease soil o ganic ma e (FAO, 2023; Nu beko
e al., 2016). The s udy in es iga es he adop ion o ze o illage as a o m o conse a ion
ag icul u e. Ze o illage, namely when c ops a e plan ed di ec ly in o a seedbed no illed a e
ha es ing he p e ious c op accumula es soil ca bon and inc eases soil ni ogen, hus
p omo ing soil, mois u e and nu ien s conse a ion o inc easing c op p oduc i i y (Bake and
Sax on, 2007; FAO, 2023; O s ehage and Neh ing, 2021). Ze o illage is also p o ed o be a
solu ion o a ge low inancial and esou ce capaci y o smallholde s in de eloping coun ies
(Jale a e al., 2016; Jale a e al., 2019; Mon and Luu, 2020; Musa i i e al., 2022).
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In e c opping is he simul aneous cul i a ion o wo o mo e c op species in he same ield a a
gi en ime (S omph e al., 2020; Wang e al., 2014). In e c opping minimizes he use o chemical
inpu s such as pes icides and he bicides, as well as enhances soil e ili y and yields (B ooke e
al., 2015; Ha e al., 2023; S omph e al., 2020).
1.3 P oblem s a emen , esea ch objec i es and s uc u e o he hesis
As ea lie men ioned, he global schola ly discou se ex ensi ely examines he issue on he
adop ion o sus ainable ag icul u al p ac ices and i s impac on a m pe o mance. To comba
he land deg ada ion, in he mid-1990s, he concep o conse a ion ag icul u e was p esen ed
by in e na ional agencies (Wol g amm e al., 2015) and se e al p ac ices ha e been success ully
es ed in Cen al Asia (Nu beko e al., 2016; Pende e al., 2009; Kienzle e al., 2012).
Ne e heless, he adop ion o sus ainable ag icul u al p ac ices is s ill low in Cen al Asia and
mos a me s a e eluc an o adop hem. The con e sion o sus ainable p ac ices o c op
cul i a ion in Cen al Asia, is challenged by he lack o ag onomic knowledge abou sus ainable
ag icul u al me hods, inadequa e supply o ex ension se ices, lack o seed a ie ies and
machine y sui able o sus ainable c op cul i a ion, as well as he absence o go e nmen
incen i es o adop ing such p ac ices (Kienzle e al., 2012; Nu beko e al., 2016).
Al hough much o he li e a u e emphasizes he main d i e s o he adop ion o SAPs and hei
impac on a m pe o mance (e.g., Knowle and B adshaw, 2007; Ruzzan e e al., 2021;
Takahashi e al., 2020), he e is a lack o empi ical s udies ha ho oughly examine he
de e minan s and impac o sus ainable ag icul u al p ac ices in he con ex o Cen al Asia. So,
he issue o how he adop ion o sus ainable ag icul u al p ac ices can be p omo ed among
a me s in Cen al Asia emains an unde s udied esea ch ques ion. This disse a ion add esses
his exis ing esea ch gap and aims o p o ide a mo e comp ehensi e and e ined unde s anding
o he subjec ma e by answe ing he esea ch ques ion “Wha ac o s de e mine adop ion o
selec ed SAPs and how his a ec s a m pe o mance?”. By doing so, he disse a ion also
~ 6 ~
con ibu es o he global discussion on adop ion and impac o SAPs in a de eloping coun y
a ming sys em.
This s udy encompasses h ee objec i es aimed a comp ehending he essen ial d i e s o
p omo ing he adop ion o sus ainable ag icul u al p ac ices by a me s and hei e ec s on a m
pe o mance in Cen al Asia:
1. o unde s and he main de e minan s o a me ’s decision o adop SAP and i s impac s on
a m ou comes;
2. o in es iga e whe he he adop ion o SAP o e s economic bene i s o a me s by educing
p oduc ion cos s;
3. o explo e de e minan s o a me s’ pa icipa ion in in o mal coope a ion in wa e
managemen and i s impac on he in ensi y o SAPs adop ion.
To achie e he esea ch goal, I s udy comme cial a ms and u al households o h ee Cen al
Asian coun ies, namely Kazakhs an, Ky gyzs an and Uzbekis an. I u ilize empi ical models as
me hodological ools o accomplish he objec i es o he s udy. As he sample is no uly
andom, bu based on p eselec ion o s udy egions, I ollow up on he p- alue wa nings when
speci ic sampling designs a e igno ed (Hi schaue e al., 2020). Ins ead o epo ing he model
esul s wi h p- alue, I use con idence in e als (CI). As he p- alue o he measu e o s a is ical
signi icance is no he ele an ou pu om an analysis, i is a gued ha epo ing he es ima ion
esul s wi h CIs is mo e p e e able (Imbens, 2021). CIs also p o ide a con enien way o
summa izing he hypo hesis es esul s o e ec sizes (G eenland e al., 2016).
This i s objec i e is add essed in Chap e 2 whe e I used c op o a ion in i iga ed co on
a ming sys ems as an example o SAP. C op o a ion, o sequen ially g owing co on wi h
leguminous c ops on he same plo , can con ibu e o sus ainabili y o co on cul i a ion by
imp o ing soil e ili y, educing land deg ada ion, and p e en ing nu ien loss in he long un
(Ball e al., 2005) and a ec ing soil mic obiology and phy o oxins (Tan ee e al., 2019). Exis ing
~ 7 ~
esea ch on Cen al Asia’s c op o a ion elies on ag onomic expe imen s (Taka a e al., 2008)
o employs emo e sensing ools (Con ad e al., 2017; Löw e al., 2017) a he han employing
economic igo o assess he ad an ages o his SAP. While he documen ed ag onomic
ad an ages o c op o a ion in co on cul i a ion schemes a e well-es ablished (Taka a e al.,
2008; Zhao e al., 2020), a me s a e in e es ed in monocul u e o eaching sho - e m p o i
maximiza ion goals. This aises ques ions abou ac o s de ining he adop ion o c op o a ion,
and whe he c op o a ion imp o es co on g owe s’ e enues and yields. Hence, I hypo hesize
ha a me s who adop c op o a ion achie e highe co on yields and ne e enues compa ed
o non-adop e s. I is he i s empi ical esea ch del ing in o he ac o s in luencing he
adop ion o c op o a ion among Cen al Asian a me s. He e, I examine wo his o ically co on-
domina ed i iga ed a eas in he egion, namely, Tu kis an p o ince in Kazakhs an and
Sama kand p o ince in Uzbekis an by using a a m su ey da a collec ed in he amewo k o he
AGRICHANGE
1
esea ch p ojec in Ma ch-Ap il 2019. The dis inc i e app oach, examining
mul iple coun ies ins ead o single-coun y esea ch ocusing solely on he p oduc i i y and
income impac s o adop ing soil-imp o emen p ac ices, p esen s addi ional insigh s on he
he e ogenei y in ins i u ional esponses and policy e ec i eness ac oss con as ing na ional
se ings. This pa o he s udy is one o he ew s udies globally o empi ically analyze c op
o a ion's e ec on co on p oduce s' pe o mance. To be e unde s and he impac o c op
o a ion on a me ’s co on yield and co on ne e u ns I employ an endogenous swi ching
eg ession (ESR) model and measu e a e age ea men e ec s. The empi ical esul s show
implica ions o wo coun ies’ con as ing ins i u ional se ings o co on cul i a ion on adop ion
o c op o a ion. Compa ed o con en ional c op cul i a ion, c op o a ion in Uzbekis an
inc eases co on yields and e enues. Howe e , an opposi e e ec is obse ed among co on
g owe s in Kazakhs an.
1
Ins i u ional Change in Land and Labou Rela ions o Cen al Asia’s I iga ed Ag icul u e (AGRICHANGE),
www.iamo.de/en/ag ichange.
~ 8 ~
The second objec i e is add essed in Chap e 3 whe e ze o illage is used as an example o an
SAP o e ing socio-economic bene i s o smallholde s h ough lowe p oduc ion cos s
(Cha e jee and Acha ya, 2021). Howe e , he e is an ongoing deba e whe he ze o illage only
educes smallholde s’ p oduc ion cos s o whe he i al e s he p oduc ion cos s uc u e. A
summa y o indings om nine empi ical s udies on he impac o conse a ion illage me hods,
including ze o illage, is p esen ed in Table A3 in he Appendix. Fo ins ance, some indings
sugges ha ze o illage can inc ease mone a y he bicide expendi u e and o al labo cos s
(Teklewold e al., 2013). While a guing ha ze o illage educes uel and labo cos , Yigezu and
El‐Sha e (2021) ound ha i s e ec on he labo equi emen and expenses a e no necessa ily
s aigh o wa d as ze o illage can inc ease manual wo k equi emen s o weeding.
Fu he mo e, while lowe ing emale and male labo equi emen s, educed and ze o- illage
me hods lead o highe applica ion doses o chemical e ilize s and he bicides (Tessema e al.,
2018). This empi ical s udy con ibu es o his deba e on whe he ze o illage sa es o inc eases
p oduc ion cos s in smallholde se ings. To achie e he esea ch objec i e, I u ilized plo -le el
panel da a om he “Li e in Ky gyzs an (LiK) su ey, which p o ides de ailed longi udinal
in o ma ion on smallholde a me s in Ky gyzs an. My in es iga ion shows ha ze o illage
adop ion inc eases hi ed labo and he bicide cos s, bu dec eases land p epa a ion and o al
p oduc ion cos s compa ed o he con en ional illage me hod among households in Ky gyzs an.
The hi d objec i e is add essed in Chap e 4. Fa me s’ pa icipa ion in in o mal coope a ion in
wa e managemen allows hem o o e come wa e dis ibu ion dispu es and o sha e
main enance cos s. In addi ion, a me s’ coope a ion in wa e managemen p o ides a pla o m
o knowledge exchange among pa icipan s, such as abou SAPs use. Pa icipa ion in collec i e
ini ia i es can imp o e soil conse a ion by acili a ing he exchange o plan ing ma e ials,
in o ma ion, and labo among a me s, o e coming household labo cons ain s, and he eby
enhancing he implemen a ion o labo -in ensi e soil conse a ion p ac ices (Willy and Holm-
Mülle , 2013). Addi ionally, communi y-based collec i e ac ion ini ia i es con ibu e o soil
~ 9 ~
conse a ion h ough collec i e lea ning and knowledge exchange. The pa icipa ion in in o mal
coope a ion is expec ed o enhance he in o ma ion sha ing among a me s, leading o an
imp o ed in ensi y o SAPs adop ion. O e he las ou decades, globally, a signi ican numbe
o s udies ha e been in es iga ing SAPs adop ion de e minan s including a m and a me
cha ac e is ics, ins i u ional and beha io al ac o s (e g., Dessa e al., 2019; D’Emden e al.,
2008; Fede e al., 1985; Ruzzan e e al., 2021). Despi e his oluminous li e a u e on SAPs
adop ion, he e is a lack o empi ical esea ch o how in o mal coope a ion among wa e use s
a ec s hei SAPs adop ion (Willy and Holm-Mülle , 2013; Xue e al., 2022). Willy and Holm-
Mülle (2013) o e a pe spec i e on he e ec s o a ious collabo a i e e o s, such as mu ual
suppo ini ia i es wi hin a communi y, he upkeep o u al access oads, and wa e
managemen , on soil conse a ion e o s o u al smallholde s. Xue e al. (2022) in es iga e he
impac o pa icipa ion in collec i e ac ion on smallholde s’ decisions o adop no- illage
echnology. Se e al s udies in es iga ed he impac o o mal coope a i e membe ship on
a me s’ SAPs adop ion decisions (e.g., Wu e al., 2023; Zhang e al., 2020). In Chap e 4 o my
hesis, I hus explo e bo h he de e minan s and e ec s o in o mal coope a ion on he in ensi y
o SAPs adop ion. Fo doing so, I use wo wa es o a m su ey da a o Uzbekis an collec ed
wi hin he amewo k o he AGRICHANGE and SUSADICA
2
p ojec s in 2019 and 2022, and
employ ma ginal ea men e ec s (MTEs) model. The analysis o he ma ginal e u ns
associa ed wi h pa icipa ion in in o mal coope a ion con ibu es o he empi ical knowledge on
SAP adop ion in de eloping coun ies. The esul s show ha a me s who a e likely o pa icipa e
in in o mal coope a ion end o bene i mo e om pa icipa ion in e ms o in ensi y o SAP
adop ion.
2
S uc u ed doc o al p og amme on Sus ainable Ag icul u al De elopmen in Cen al Asia (SUSADICA),
h ps://www.iamo.de/en/ esea ch/ esea ch-p ojec s/
~ 10 ~
The inal chap e o he disse a ion p esen s conclusions and p o ides policy ecommenda ions
aimed a enhancing he adop ion o SAPs in Cen al Asia. Fu he mo e, his concluding chap e
add esses limi a ions, as well as p oposes ideas o u u e esea ch.
~ 11 ~
2 DETERMINANTS AND IMPACTS OF CROP ROTATION ADOPTION
AMONG COTTON GROWERS IN IRRIGATED AREAS OF KAZAKHSTAN
AND UZBEKISTAN
2.1 Re o ms in he co on sec o and he adop ion o c op o a ion p ac ices in Kazakhs an
and Uzbekis an
Co on a ming in i iga ed a eas like Sou h Kazakhs an and Uzbekis an makes signi ican
con ibu ions o u al li elihoods (Sh al o na and Ho nidge, 2014), and in addi ion o a m
employmen , c ea es jobs in he ginning and ex ile sec o s (Ba es, 2005). Thus, co on
p oduc ion is di ec ly linked o u al incomes and employmen . Du ing he So ie e a co on
cul i a ion sys em in Cen al Asia, based on p oduc ion plans and s a e egula ion o
p ocu emen p ices and alue chain ac o s (Rume , 1989), combined six-yea sequences o
co on ollowed by h ee yea s o leguminous c ops (mainly al al a) and allow land (Tode ich e
al., 2007). As he So ie planne s demanded he ul ilmen o co on p oduc ion plans,
dis ega ding en i onmen al consequences, c op o a ion was o en abandoned, and a me s
elied on he in ensi e use o e ilize s and machine y (Rume , 1989). The con inuous p ac ice
o co on monocul u e esul ed in c opland deg ada ion. Along wi h his, pes s and wa e
sho ages ha e s agna ed co on yields since he 2000s (OECD/FAO, 2022). A ailable sus ainable
ag onomic p ac ices can imp o e co on yields (OECD/FAO, 2022). One example is c op o a ion,
an al e na i e o monocul u e, which in ol es g owing co on sequen ially wi h leguminous
c ops on he same plo , con ibu ing o soil e ili y, educing land deg ada ion, and p e en ing
nu ien loss (Ball e al., 2005). Di e si ying c op cul i a ion, such as wi h so ghum, ins ead o
monocul u e, o e s a solu ion o he en i onmen al challenge o soil salini y in Cen al Asia
(Bobojono e al., 2013).
Following he dissolu ion o he So ie Union, Kazakh and Uzbek go e nmen s ook con as ing
app oaches o e o m hei co on sec o s (Pom e , 2019). In Kazakhs an, an uppe middle-
income economy and he iches coun y in Cen al Asia hanks o i s oil expo s, he go e nmen
apidly e o med he co on sec o in he 1990s (Pom e , 2019). The co on sec o in
~ 18 ~
a)
b)
Figu e 2.2: Co on yields (a) and ne e enues (b) among c op o a ion adop e s and non-
adop e s in Kazakhs an and Uzbekis an
Sou ce: Based on he AGRICHANGE 2019 a m su ey da a.
2.3 Me hodological app oach
The assessmen o he echnology adop ion impac based on non-expe imen al c oss-sec ional
da a equi es he co ec ion o sel -selec ion bias, iden i ica ion o p ope coun e ac uals, and
con olling o non-obse able a m cha ac e is ics (As aw e al., 2012; Jale a e al., 2016). I
~ 19 ~
explain he empi ical models in he ollowing subsec ions and mo i a e he selec ion o he
me hodology o his sec ion.
2.3.1 C op o a ion adop ion decision and a m ou come
The iden i ica ion o a me s’ decision o adop c op o a ion is based on he measu emen o
p o i abili y and yield-inc easing e ec s. To es ima e he impac o c op o a ion on a m
ou comes, I ollowed exis ing li e a u e such as Abdulai and Hu man (2014), Amadu e al.
(2020), Jale a e al. (2016), and Issahaku and Abdulai (2020), and employed a wo-s age
es ima ion app oach. Fa me s will adop c op o a ion (𝐶1∗) i hey expec o achie e highe
yields and ne e u ns om c op o a ion compa ed o a decision wi h no o adop (𝐶0∗). He e,
expec ed yields and ne e u ns a e no obse ed, bu adop ion decision is obse ed. In his
pe spec i e, adop ion decision (𝐶𝑖) is ea ed as a dicho omous choice, namely 𝐶𝑖=1 i 𝐶1∗>
𝐶0∗ and 𝐶𝑖=0 i 𝐶0∗>𝐶1∗. Thus, a me s’ adop ion decision is ela ed o hei pe cep ion o
whe he adop ion maximizes ne e u ns o no . Based on gi en la en a iable model, in he
i s s age, de e minan s o adop ion we e analyzed by he ollowing p obi model:
𝐶𝑖∗=𝛿𝐾𝑖+𝜀𝑖 (2.1)
he e, 𝐶𝑖 is a dummy a iable indica ing whe he a me 𝑖 adop s c op o a ion o no . 𝐾𝑖 is a
ec o o de e minan s o adop ion decision (𝑛×𝑚). 𝛿 is a ec o o pa ame e s o be es ima ed
𝑚×1, 𝜀𝑖 is a ec o o e o e m (𝑛×1) no mally and independen ly dis ibu ed wi h mean 0
and a iance 𝜎2.
To connec he ela ionship be ween adop ion o c op o a ion scheme and a m ou comes, i is
assumed ha a me s maximize expec ed ne e u ns om co on p oduc ion, and he unc ion
is exp essed ollowing Dubbe (2019), and Zheng e al. (2021):
𝑚𝑎𝑥 𝜋𝑖= 𝑃𝑖𝑄𝑖(𝑅𝑖,𝑍𝑖) − 𝐼𝑖𝑅𝑖 (2.2)
~ 20 ~
whe e 𝜋 is he maximum ne e u ns o a me 𝑖 gained om co on p oduc ion, 𝑃 is co on
p ice pe kg, and 𝑄 is co on yield in kg. 𝑅 ep esen s inpu quan i ies such as e ilize , seeds,
and labo . 𝑍 ep esen s he ec o o explana o y a iables, i.e. a m/ a me cha ac e is ics. 𝐼 is
a ec o o inpu p ices. Ne e u ns (𝜋𝑖) a e exp essed as a unc ion o inpu and ou pu p ices,
a m/ a me cha ac e is ics, and adop ion o c op o a ion scheme as ollows:
𝜋𝑖=𝜋(𝑃𝑖,𝐼𝑖,𝑍𝑖,𝐶𝑖) (2.3)
Applying Ho eling’s lemma o Equa ion (2.2) yields a educed o m o he co on ou pu supply
unc ion as ollows:
𝑄𝑖=𝑄(𝑃𝑖,𝐼𝑖,𝑍𝑖,𝐶𝑖) (2.4)
Gi en he challenges in measu ing p oduc ion cos s, i is assumed ha a me s aim o maximize
bo h yield and ne e u ns. Fo la ge a ms in Sama kand, knowledge abou yields, p ices, and
e enues is a ailable, bu inpu use da a is agmen ed among a ious expe s, including
ag onomis s, machine y enginee s, and i iga ion expe s, making measu emen complex. In his
s udy, ne e u ns om co on p oduc ion a e calcula ed by deduc ing e ilize cos s (ni ogen,
phospho us, and po assium), co on seed cos s, and labo cos s (paymen s o land p epa a ion
and co on cul i a ion) om co on e enue (yield mul iplied by co on p ice). Since manual
co on-picking wages a e linked o he amoun o ha es ed c op a he han ac ual labo e o ,
his cos componen is no included. Equa ions (2.3) and (2.4) de e mine ne e u ns and co on
yield based on inpu and ou pu p ices, a m/ a me cha ac e is ics, and he adop ion o c op
o a ion.
In he second s age, o be e unde s and he impac o adop ion, I applied a simple model o
a me s’ ou comes. Co on yield and ne e u ns a e de e mined by se e al ac o s, including
land, labo , and e ilize . A Cobb-Douglas p oduc ion unc ion was used, connec ing a m
ou pu s wi h inpu s and o he ac o s:
𝑌𝑖=𝐹(𝐴,𝐿,𝑁) (2.5)
~ 21 ~
whe e 𝑌𝑖 is a ec o o ou come a iables o a me 𝑖, 𝐴 s ands o a m size (in his case, co on
a ea in ha), 𝐿 s ands o labo quan i y (in pe sons pe ha), and 𝑁 s ands o e ilize use (US$
pe ha). As men ioned abo e, amily labo domina es among Kazakh co on-g owe s, and hus
labo quan i y in pe sons is used in he model.
Taking he loga i hm o ou come a iables and p oduc ion inpu s, I de i ed co on yield (o
co on ne e u ns) unc ion as linea ly sepa able (Amadu e al., 2020). Addi ionally, I accoun ed
o o he dummy o non-loga i hmic a iables. Thus, he e ec o c op o a ion adop ion on
co on yield and ne e u ns was modelled h ough a ln(𝑌) unc ional o m ela ed o p oduc ion
inpu s and o he ac o s such as a m/ a me cha ac e is ics and ins i u ional se ings as ollows:
𝐿𝑛𝑌𝑖=𝛼0+𝛽𝑙𝑛𝐴+µ𝑙𝑛𝐿+𝜅𝑙𝑛𝑁+𝜓𝑍𝑖+𝜍𝐶𝑖+𝑢𝑖 (2.6)
I is assumed ha he ou come a iable (𝑌𝑖) is associa ed wi h p oduc ion inpu s (𝐴, 𝐿 and 𝑁), a
ec o o o he explana o y a iables (𝑍𝑖), and o a ion adop ion (𝐶𝑖) ake a alue o 1 i a a m
adop s c op o a ion and 0 o he wise. 𝛼0 is a cons an , 𝛽, µ, 𝜅, 𝜓 and 𝜍 a e ec o s o es ima ed
pa ame e s, and 𝑢𝑖 is an e o e m. The impac o he adop ion o c op o a ion on co on yield
and ne e u ns is compu ed by he es ima ion o he pa ame e 𝜍. This app oach migh c ea e
biased es ima es because i assumes ha adop ion is exogenously de e mined, while i is
po en ially endogenous (Di Falco e al., 2011). Fa me s’ decision o adop o no o adop may
be based on indi idual sel -selec ion. Fa me s who adop c op o a ion can ha e di e en
cha ac e is ics compa ed o non-adop e s. Fu he mo e, a me s can decide o adop based on
expec ed bene i s bu s uc u ally di e in hei expec a ions (As aw e al., 2012; Di Falco e al.,
2011). Conside ing ha he in e iewed a me s migh ha e sel -selec ed in o adop ing c op
o a ion schemes, selec ion bias can occu because o obse able and unobse able a ibu es
a ec ing adop ion and ou come a iables a he same ime. Hence, an O dina y Leas Squa es
(OLS) es ima o migh gene a e biased and inconsis en es ima es (Di Falco e al., 2011; Dubbe ,
2019). Following he a gumen s exp essed in ecen exis ing s udies by As aw e al. (2012), Di
~ 22 ~
Falco e al. (2011), and Jale a e al. (2016), I employed an endogenous swi ching eg ession (ESR)
model ha accoun s o bo h endogenei y and sample selec ion bias.
2.3.2 Endogenous swi ching eg ession
To examine he in luence o c op o a ion on a m ou comes, I applied he A e age T ea men
E ec on he T ea ed (ATT). The ATT es ima es a e age di e ences in ou come a iables
be ween adop e s who ac ually adop ed c op o a ion (obse ed) and hose who would no
ha e adop ed i (coun e ac ual). Al hough he P opensi y Sco e Ma ching (PSM) me hod can
also calcula e ATT, i does no accoun o unobse able ac o s ha simul aneously in luence
a me s’ adop ion decisions and ou come a iables (Jale a e al. 2016). Fo ins ance, Abdulai and
Hu man (2014), As aw e al. (2012), Jale a e al. (2016) and Issahaku and Abdulai (2020) applied
he ESR model app oach o analyze he impac o sus ainable ag icul u al p ac ices on ou come
a iables in he bina y egime o adop e s and non-adop e s. Following hese s udies, in he
second s age, he ela ionship be ween ou come a iables and adop ion decisions including
o he explana o y a iables, can be o mula ed in wo egimes wi h an OLS eg ession model.
Consequen ly, Equa ion 2.6 is exp essed as ollows:
Regime 1 (c op o a ion adop e s): 𝑦1𝑖 =𝑋𝑖1𝛽1+𝜔1𝑖 i 𝐶=1 (2.7a)
Regime 2 (c op o a ion non-adop e s): 𝑦2𝑖 =𝑋𝑖2𝛽2+𝜔2𝑖 i 𝐶=0 (2.7b)
whe e 𝑦1𝑖 and 𝑦2𝑖 a e ou come a iables o adop e s and non-adop e s. 𝑋𝑖1 and 𝑋𝑖2 a e ec o s
o de e minan s o he ou come a iables. 𝛽1 and 𝛽2 a e ec o s o pa ame e s o be es ima ed.
𝜔1𝑖 and 𝜔2𝑖 a e e o e ms.
The p obi model in Equa ion 1 supplies essen ial in o ma ion o examine and co ec he
po en ially esul ing bias (Maddala, (1983, 223); Pe ick, (2004, 151)). To es selec ion bias,
acco ding o Heckman (1979) he In e se Mills Ra io (IMR) can be calcula ed om he esul s o
a p obi es ima ion as ollows:
~ 23 ~
𝜆1𝑖 = 𝜑(𝛿𝐾𝑖)
𝛷(𝛿𝐾𝑖) 𝜆2𝑖 =−𝜑(𝛿𝐾𝑖)
1−𝛷(𝛿𝐾𝑖) (2.8)
whe e 𝜑(.) and 𝛷(.) indica e p obabili y densi y unc ion and cumula i e densi y unc ion o
he s anda d no mal dis ibu ion, espec i ely. 𝜆1𝑖 and 𝜆2𝑖 ep esen IMR. Equa ions 2.7a and
2.7b a e used o co ec selec ion bias. Thus, he ou come equa ions in wo egimes s and o :
Regime 1 (c op o a ion adop e s): 𝑦1𝑖 =𝑋𝑖1𝛽1+𝜎1𝜀𝜆1𝑖 +𝜂1𝑖 i 𝐶=1 (2.9a)
Regime 2 (c op o a ion non-adop e s): 𝑦2𝑖 =𝑋𝑖2𝛽2+𝜎2𝜀𝜆2𝑖 +𝜂2𝑖 i 𝐶=0 (2.9b)
whe e 𝜎1𝜀 and 𝜎2𝜀 a e pa ame e s o be es ima ed, 𝜂1𝑖 and 𝜂2𝑖 a e no mally dis ibu ed e o
e ms wi h mean ze o and cons an a iance.
Exis ing s udies explain ha o a mo e obus iden i ica ion, i is impo an o selec
ins umen al a iables (IV) ha a ec 𝐶𝑖 in Equa ion 2.1 and do no appea in explana o y
a iables o ou come equa ion. Technology adop ion s udies employ in o ma ion sou ces, such
as o he a me s, neighbo s, and ela i es, as alid IVs (As aw e al., 2012; Di Falco e al., 2011;
Manda e al., 2016). P e ious indings show ha adop e s o maize-legume o a ion o imp o ed
echnologies ha e be e access o ele an in o ma ion on applica ion and associa ed bene i s
(Manda e al., 2016). Based on hese a gumen s, I used a iables “in o ma ion om o he
a me s and neighbo s”, and “in o ma ion om media, in e ne and adio abou echnologies
and ag onomy” as IVs o measu ing he impac o c op o a ion in bo h s udy egions. Fo
Kazakhs an, I also used “ illage sha e o c op o a ion adop e s” as an IV. Thus, hese a iables
we e excluded om Equa ions 2.9a and 2.9b.
I explo ed accep abili y o ins umen s h ough a simple alsi ica ion es o de e mine whe he
he selec ed a iables we e easonable and hus a ec a me ’s adop ion decision, bu no
ou come a iables (Di Falco e al., 2011; Jale a e al., 2016). The esul s o he alsi ica ion es
show ha selec ed ins umen s a e join ly s a is ically signi ican in he adop ion decision ( o
adop ion decision χ2=7.81, p- alue=0.05 o Kazakhs an; χ2=6.93, p- alue=0.03 o Uzbekis an),
~ 24 ~
bu s a is ically insigni ican in he ou come equa ion o non-adop e s ( o ne e u ns and
co on yield espec i ely F-s a =0.55 and 0.78, p alue=0.65 and 0.51 o Kazakhs an; F-s a =0.22
and 0.18, p alue=0.80 and 0.83 o Uzbekis an) (See Table A1 in Appendix A). Consequen ly, he
selec ed ins umen s can be conside ed as plausible.
2.3.3 A e age ea men e ec s
The impac o c op o a ion on a me s’ ou come can be es ed h ough he compa ison o
expec ed ou comes o adop e s and non-adop e s in ac ual and coun e ac ual si ua ions. Fo
his, he A e age T ea men E ec on he T ea ed (ATT) and he T ea men E ec on he
Un ea ed (ATU) we e compu ed wi hin he ESR model. To do his, I calcula ed he expec ed
ou come o adop e s and non-adop e s in ac ual and coun e ac ual scena ios based on
Equa ions 2.9a and 2.9b as ollows:
𝐸(𝑦1𝑖|𝑋, 𝐶𝑖=1)=𝑋1𝑖𝛽1+𝜎1𝜀𝜆1𝑖 (2.10a)
𝐸(𝑦2𝑖|𝑋,𝐶𝑖=0)=𝑋2𝑖𝛽2+𝜎2𝜀𝜆2𝑖 (2.10b)
𝐸(𝑦2𝑖|𝑋, 𝐶𝑖=1)=𝑋1𝑖𝛽2+𝜎2𝜀𝜆1𝑖 (2.10c)
𝐸(𝑦1𝑖|𝑋. 𝐶𝑖=0)=𝑋2𝑖𝛽1+𝜎1𝜀𝜆2𝑖 (2.10d)
He e, Equa ion 2.10a is o adop e s (𝐶=1) , and Equa ion 2.10b is o non-adop e s (𝐶=0),
bo h obse ed in he sample. In con as , wo o he equa ions conside coun e ac uals, such as
Equa ion 2.10c is o adop e s who would ha e decided no o adop , and Equa ion 2.10d is o
non-adop e s who would ha e decided o adop . The di e ences be ween Equa ions 2.10a and
2.10c can be o mula ed as Equa ion 2.11 which explains he compa isons o he expec ed
ou comes (ne e u ns in US$/ha, and co on yield in /ha), and allows o he calcula ion o he
a e age ea men e ec on he ea ed (ATT) as ollows:
𝐴𝑇𝑇=(2.10𝑎)−(2.10𝑐)=𝐸(𝑦1𝑖|𝑋,𝐶𝑖=1)−𝐸(𝑦2𝑖|𝑋, 𝐶𝑖=1)=𝑋1𝑖 (𝛽1− 𝛽2)+
𝜆1𝑖(𝜎1𝜀−𝜎2𝜀) (2.11)
~ 25 ~
The di e ences be ween Equa ions 2.10b and 2.10d can be o mula ed as Equa ion 2.12 which
is he a e age ea men e ec on he un ea ed (ATU):
𝐴𝑇𝑈=(2.10𝑏)−(2.10𝑑)=𝐸(𝑦2𝑖|𝑋, 𝐶𝑖=0)−𝐸(𝑦1𝑖|𝑋, 𝐶𝑖=0)=𝑋2𝑖 (𝛽1− 𝛽2)+
𝜆2𝑖(𝜎1𝜀−𝜎2𝜀) (2.12)
Thus, he he e ogenei y e ec is measu ed by u ilizing Equa ions 2.11 and 2.12. Acco ding o
As aw e al. (2012), Di Falco e al. (2011), and Jale a e al. (2016) he e ec o base he e ogenei y
(BH) o adop e s can be calcula ed as he di e ence be ween Equa ions 2.10a and 2.10d, and
o non-adop e s as he di e ence be ween Equa ions 2.10c and 2.10b (see Table 2.2).
Addi ionally, he ou comes o wo g oups (c op o a ion adop e s and non-adop e s) may di e
because o unobse ed ac o s, as each g oup may eac di e en ly o changing condi ions o e
ime. This a ia ion is e e ed o as “ ansi ional he e ogenei y” (TH), indica ing ha he e ec s
o adop ing c op o a ion can a y ac oss g oups.
Table 2.2: Expec ed condi ional, a e age ea men and he e ogenei y e ec s
Subsamples
Decision s age
T ea men
e ec s
To adop CR
No o adop CR
Adop e s
(a) 𝐸(𝑦1𝑖|𝑋, 𝐶𝑖=1)
(c) 𝐸(𝑦2𝑖|𝑋, 𝐶𝑖=1)
ATT
Non-adop e s
(d) 𝐸(𝑦1𝑖|𝑋, 𝐶𝑖=0)
(b) 𝐸(𝑦2𝑖|𝑋, 𝐶𝑖=0)
ATU
He e ogenei y
e ec s
BH1
BH2
TH
No es: (a) and (b) ep esen obse ed expec ed a m ou come (co on ne e u ns (US$/ha), and
c op yield ( /ha)). (c) and (d) ep esen coun e ac ual expec ed a m ou come (co on ne
e u ns (US$/ha), and c op yield ( /ha)).
C = 1 i a me 𝑖 adop ed c op o a ion. C = 0 i a me 𝑖 did no adop c op o a ion.
y1i = a m ou come i a me s ea ed wi h c op o a ion adop ion; y2i = a m ou come i a me s
ea ed wi h c op o a ion non-adop ion.
ATT = a e age ea men e ec on ea ed.
ATU = a e age ea men e ec on un ea ed.
BH1 = he e ec o base he e ogenei y o c op o a ion adop e s.
BH2 = he e ec o base he e ogenei y o c op o a ion non-adop e s.
TH = ansi ional he e ogenei y (ATT-ATU).
Sou ce: Au ho s based on Jale a e al. (2016).
~ 26 ~
2.4 Resul s and discussion
2.4.1 De e minan s o a me s’ decision o adop c op o a ion
This sec ion p esen s and discusses he esul s om he p obi model. Figu e 2.3 shows he
es ima ed a e age ma ginal e ec coe icien s and 90% CIs o each explana o y a iable. Table
A2 in Appendix A p o ides mo e de ails o he model esul s. The econome ic models we e
es ima ed in STATA 17 so wa e.
A c oss-coun y compa ison o he model esul s highligh s he in luence o di e gen
ins i u ional se ings in he co on sec o s o he wo coun ies. Kazakhs an's app oach in ol ed
dis ibu ing land o o me membe s o s a e and collec i e a ms who we e ac i ely in ol ed in
c op cul i a ion (Pe ick e al., 2017). In con as , Uzbekis an alloca ed land h ough auc ions o
esiden s, bo h u al and u ban, wi h s ong en ep eneu ial skills and capi al, some imes wi h
limi ed ag icul u al expe ience (Djanibeko e al., 2012). The es ima ion esul s e eal ha in
Kazakhs an, a me s' age posi i ely co ela es wi h he likelihood o adop ing c op o a ion,
indica ing ha olde a me s wi h mo e expe ience a e mo e likely o adop his p ac ice.
Howe e , his is no he case o co on g owe s in Uzbekis an.
Fu he mo e, a m es uc u ing implied con as ing le els o land enu e secu i y in wo
coun ies. In Kazakhs an, indi iduals ecei ed a mland o p i a e use (Pe ick e al., 2017). In
Uzbekis an, a mland emained s a e-owned and could be e oked a any ime, as i has been
done h ough se e al a m consolida ion campaigns (Djanibeko e al. 2012). Schola s
con inuously no e ha Uzbek land enu e insecu i y hinde s he wide adop ion o sus ainable
ag icul u al p ac ices equi ing a longe li espan o gene a ing a m bene i s (Hamido e al.,
2022; Kienzle e al., 2012). The model esul s con i m he e ec o land enu e secu i y o
Uzbekis an, whe e co on g owe s pe cei ing highe land enu e secu i y a e mo e likely o
adop c op o a ion. This ela ionship is no obse ed o Kazakhs an, whe e esponden s
gene ally el mo e op imis ic abou hei land enu e secu i y.
~ 27 ~
Ano he con as ing esul is ela ed o he o ganiza ion o ag icul u al c edi s o co on
g owe s. In Kazakhs an, p i a e ginne ies p o ide inancing h ough con ac a ming, o a me s
seek comme cial o subsidized c edi s (Pe ick e al., 2017). Con e sely, Uzbekis an's igh ly
con olled co on sec o elies on pa as a al ag icul u al banks o sho - e m c edi s. The model
esul s e lec his di e ence. In Kazakhs an, c edi a ioning does no impac c op o a ion
adop ion, as a me s ob ain inance and inpu s h ough ginne ies in con ac a ming
a angemen s. In Uzbekis an, a me s acing c edi a ioning a e mo e likely o adop c op
o a ion, which makes sense since c op o a ion wi h legumes o al al a equi es ewe inancial
esou ces and inpu s. This aligns wi h Mon and Luu’s (2020) indings o a posi i e ela ionship
be ween c edi a ioning and he adop ion o cos -e ec i e c op managemen p ac ices. C edi -
cons ained a me s op o mo e a o dable o a ion wi h legumes and al al a ins ead o cos ly
high- alue c ops. Consequen ly, he esul s sugges ha access o ag icul u al inance makes
Uzbek co on g owe s less inclined o employ c op o a ion.
Bo h s udy a eas ely on i iga ion, impac ing a me s’ c op choices h ough a iables like
p oximi y o he main i iga ion canal and a me s' pe cep ion o i s condi ion. Inhe i ed So ie
i iga ion sys ems a o a me s a he canal's head, leading o dec eased co on c op o a ion
nea canals due o he lowe wa e equi emen s o legumes compa ed o high- alue c ops like
ice, po a oes, ege ables, and melons. Fa me s wi h be e wa e access a e mo e likely o
o a e co on wi h wa e -in ensi e c ops. This e ec is mo e p onounced in Uzbekis an, whe e
limi ed wa e supply p essu es co on cul i a ion and c op o a ion becomes a s a egy.
Addi ionally, a me s' opinions abou canal condi ions in luence hei choices, wi h imp o ed
condi ions encou aging sus ainable c op o a ion and de ining in es men isks in mul i-yea
land-imp o ing p ac ices (Hamido e al., 2022).
Fa m aining and knowledge deli e y me hods a y be ween Kazakhs an and Uzbekis an. In
Kazakhs an, a m aining pa icipa ion is olun a y and based on a me s' eques s (Sh al o na
and Ho nidge, 2014), whe eas in Uzbekis an, he go e nmen manda es co on- ocused aining
~ 34 ~
Table 2.3: Second s age endogenous swi ching eg ession es ima es o ne e u ns om co on
Kazakhs an
Uzbekis an
Adop e s
Non-adop e s
Adop e s
Non-adop e s
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Log o o al co on
e ilize cos (US$/ha)
-0.132
(0.085)
-0.275
0.011
-0.025
(0.059)
-0.123
0.073
-0.330
(0.448)
-1.082
0.421
0.020
(0.142)
-0.214
0.254
Log o o al labo in a
a m (pe sons /ha)
0.012
(0.062)
-0.091
0.116
0.024
(0.035)
-0.034
0.081
0.021
(0.123)
-0.186
0.228
0.083
(0.056)
-0.010
0.176
Log o co on a ea (ha)
0.238
(0.080)
0.103
0.371
-0.014
(0.048)
-0.093
0.065
-0.012
(0.118)
-0.210
0.186
0.168
(0.082)
0.033
0.304
Fa me ’s age (yea )
0.001
(0.007)
-0.010
0.013
-0.008
(0.003)
-0.012
-0.003
-0.006
(0.006)
-0.015
0.004
-0.003
(0.004)
-0.009
0.004
Fa me has educa ion in
ag icul u e (1/0)
-0.070
(0.173)
-0.361
0.221
-0.052
(0.098)
-0.214
0.110
0.148
(0.153)
-0.109
0.405
0.251
(0.067)
0.140
0.362
Fa me pe cei es canal
condi ion as good (1/0)
0.242
(0.130)
0.024
0.461
0.219
(0.090)
0.070
0.369
-0.036
(0.144)
-0.278
0.206
0.248
(0.097)
0.088
0.409
C edi - a ioned a me
(1/0)
0.017
(0.144)
-0.224
0.259
0.068
(0.079)
-0.063
0.199
-0.545
(0.275)
-1.005
-0.084
0.036
(0.096)
-0.124
0.195
Fa me pa icipa es in
a m ainings (1/0)
0.228
(0.374)
-0.399
0.855
0.091
(0.263)
-0.343
0.525
0.155
(0.158)
-0.109
0.420
-0.020
(0.101)
-0.187
0.148
Sha e o land wi h good
e ili y (0-1)
0.187
(0.114)
-0.004
0.378
0.080
(0.075)
-0.044
0.204
0.065
(0.248)
-0.351
0.482
-0.016
(0.098)
-0.179
0.146
Dis ance o he dis ic
cen e (km)
-0.021
(0.006)
-0.030
-0.011
-0.006
(0.003)
-0.011
-0.001
0.014
(0.014)
-0.010
0.038
-0.011
(0.006)
-0.021
-0.001
Fa m ields loca ed a
i iga ion canal head
(1/0)
0.303
(0.136)
0.074
0.532
0.098
(0.089)
-0.049
0.245
0.543
(0.287)
0.061
1.025
0.043
(0.104)
-0.129
0.215
Fa me pe cei es land
enu e as secu e (1/0)
x
x
x
x
x
x
-0.721
(0.464)
-1.498
0.057
0.168
(0.126)
-0.040
0.376
~ 35 ~
Table 2.3 con .
Kazakhs an
Uzbekis an
Adop e s
Non-adop e s
Adop e s
Non-adop e s
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Fa me pa icipa es in
con ac a ming
(1/0)
-0.243
(0.154)
-0.501
0.015
0.215
(0.080)
0.083
0.348
x
x
x
x
x
x
Fa m loca ed in
Sha da a (1/0)
1.375
(0.390)
0.720
2.029
0.327
(0.157)
0.068
0.586
x
x
x
x
x
x
Fa m loca ed in
Pas da gom (1/0)
x
x
x
x
x
x
0.191
(0.173)
-0.100
0.481
-0.121
(0.067)
-0.231
-0.010
mills1
0.204
(0.364)
-0.406
0.814
x
x
x
-1.616
-2.624
-0.609
x
x
x
mills2
x
x
x
-0.101
-0.737
0.535
x
x
x
0.183
-0.388
0.754
_cons
6.332
(0.999)
4.658
8.007
7.057
(0.324)
6.521
7.593
10.658
(3.165)
5.352
15.964
5.924
(0.713)
4.747
7.102
N
64
214
64
243
R-squa ed
0.423
0.163
0.419
0.163
No e: S anda d e o in pa en hesis. Ne e u ns om co on is gi en in US$/ha (ln).
Sou ce: Based on he AGRICHANGE 2019 a m su ey da a.
~ 36 ~
Table 2.4: Second s age endogenous swi ching eg ession es ima es o co on yield
Kazakhs an
Uzbekis an
Adop e s
Non-adop e s
Adop e s
Non-adop e s
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Coe .
[90%
con idence
in e al]
Log o o al co on e ilize
cos (US$/ha)
0.050
(0.057)
-0.046
0.147
0.104
(0.039)
0.039
0.169
0.096
(0.205)
-0.247
0.440
0.159
(0.078)
0.031
0.288
Log o o al labo in a a m
(pe sons /ha)
0.049
(0.040)
-0.019
0.116
0.042
(0.026)
-0.001
0.085
0.051
(0.061)
-0.052
0.153
-0.007
(0.030)
-0.056
0.043
Log o co on a ea (ha)
0.184
(0.055)
0.093
0.276
-0.009
(0.036)
-0.069
0.050
-0.044
(0.061)
-0.146
0.057
-0.002
(0.036)
-0.061
0.057
Fa me ’s age (yea )
0.001
(0.004)
-0.006
0.009
-0.004
(0.002)
-0.008
0.000
-0.001
(0.003)
-0.006
0.004
-0.002
(0.002)
-0.005
0.001
Fa me has educa ion in
ag icul u e (1/0)
-0.127
(0.111)
-0.312
0.059
0.001
(0.069)
-0.113
0.115
0.005
(0.074)
-0.121
0.130
0.077
(0.036)
0.018
0.135
Fa me pe cei es canal
condi ion as good (1/0)
0.118
(0.095)
-0.041
0.278
0.120
(0.064)
0.014
0.225
-0.007
(0.079)
-0.139
0.126
0.037
(0.059)
-0.048
0.120
C edi - a ioned a me (1/0)
-0.010
(0.97)
-0.173
0.153
0.014
(0.066)
-0.095
0.122
-0.279
(0.127)
-0.491
-0.066
0.019
(0.048)
-0.060
0.099
Fa me pa icipa es in a m
ainings (1/0)
0.160
(0.239)
-0.241
0.560
0.033
(0.181)
-0.267
0.332
0.001
(0.077)
-0.129
0.131
0.001
(0.048)
-0.077
0.080
Sha e o land wi h good
e ili y (0-1)
0.185
(0.083)
0.045
0.324
0.096
(0.053)
0.009
0.183
0.066
(0.117)
-0.131
0.262
0.062
(0.043)
-0.010
0.133
Dis ance o he dis ic cen e
(km)
-0.012
(0.004)
-0.018
-0.005
-0.004
(0.002)
-0.007
-0.001
0.012
(0.007)
0.001
0.024
-0.0004
(0.003)
-0.006
0.005
Fa m ields loca ed a
i iga ion canal head (1/0)
0.164
(0.105)
-0.012
0.339
0.055
(0.063)
-0.049
0.159
0.246
(0.141)
0.009
0.483
0.046
(0.051)
-0.038
0.131
Fa me pe cei es land enu e
as secu e (1/0)
x
x
x
x
x
x
-0.333
(0.208)
-0.682
0.016
0.037
(0.059)
-0.061
0.135
~ 37 ~
Table 2.4 con .
No e: S anda d e o in pa en hesis. Co on yield is gi en in kg/ha (ln).
Sou ce: Based on he AGRICHANGE 2019 a m su ey da a.
Kazakhs an
Uzbekis an
Adop e s
Non-adop e s
Adop e s
Non-adop e s
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Coe .
[90% con idence
in e al]
Fa me pa icipa es in
con ac a ming (1/0)
-0.240
(0.114)
-0.430
-0.049
0.115
(0.058)
0.020
0.211
x
x
x
x
x
x
Fa m loca ed in Sha da a
(1/0)
0.835
(0.272)
0.378
1.291
0.233
(0.095)
0.076
0.390
x
x
x
x
x
x
Fa m loca ed in Pas da gom
(1/0)
x
x
x
x
x
x
0.018
(0.075)
-0.107
0.143
-0.128
(0.032)
-0.181
-0.075
mills1
0.156
(0.224)
-0.219
0.531
x
x
x
-0.832
(0.267)
-1.279
-0.384
x
x
x
mills2
x
x
x
-0.065
(0.286)
-0.538
0.407
x
x
x
0.001
(0.161)
-0.265
0.266
_cons
6.697
(0.636)
5.631
7.763
7.325
(0.230)
6.945
7.706
8.630
(1.458)
6.186
11.075
7.006
(0.369)
6.396
7.616
N
64
214
64
243
R-squa ed
0.425
0.177
0.475
0.151
~ 38 ~
2.4.3 Co on yield and ne e u ns impac s o he adop ion o c op o a ion
As desc ibed ea lie , he impac o he adop ion o c op o a ion on a me s’ expec ed ou comes
unde ac ual and coun e ac ual condi ions is measu ed by he a e age ea men e ec on he
ea ed (ATT) and he a e age ea men e ec on he un ea ed (ATU) es ima ed by he ESR
model. Table 2.5 p esen s he esul s om he ESR ea men e ec model o Kazakhs an and
Uzbekis an. The i h column o Table 2.5 p o ides he ea men e ec s o he adop ion o c op
o a ion. The ob ained esul s e eal ha he impac o he adop ion o c op o a ion on ne
e u ns and co on yields di e s be ween Kazakh and Uzbek a me s.
The adop ion o c op o a ion has a nega i e impac on he ou comes o Kazakh a me s. This
means ha in Kazakhs an, he ea men e ec o he adop ion o c op o a ion on ne e u ns
and co on yield pe ha is -0.168 and -0.139, espec i ely. In o he wo ds, in e iewed Kazakh
adop e s o c op o a ion would ha e ecei ed 15% highe ne e u ns and 12% highe co on
yields had hey no adop ed c op o a ion. Based on a me a- eg ession analysis, Ogunda i and
Bola inwa (2018) show ha c op o a ion among o he na u al esou ce managemen s a egies
has a subs an ial e ec on p oduc ion and social measu es bu no on economic ou comes. Such
an unexpec ed impac o c op o a ion on he pe o mance o co on g owe s in Kazakhs an can
be explained by he ac ha he exis ing ins i u ional en i onmen and in as uc u e in
Kazakhs an a e he co on sec o e o m ac ually p o ide an ad an age o a me s who
p ac ice “con en ional co on” monocul u e. In he sample o Kazakh esponden s, co on is
cul i a ed on 85% o a me s’ sown a ea (Table 2.1). This can be explained by condi ions imposed
by con ac a ming a angemen s wi h p i a e ginne ies, a o ing co on monocul u e p ac ices
o ensu e a supply o aw co on (Pe ick e al., 2017). Thus, Kazakh a me s who choose
sus ainable c op o a ion a e likely o lose imely access o he ginne y’s p o ision o inpu s like
seeds, e ilize s, pes icides, and machine y se ices. This sugges s ha o p omo e sus ainable
ag icul u al p ac ices in Kazakhs an’s co on g owing a eas, economic incen i es o bo h sides
~ 39 ~
o he con ac ual a angemen should be conside ed, no only o co on g owe s bu also o
p ocesso s.
In con as , he adop ion o c op o a ion has a posi i e impac on bo h ou come a iables o
Uzbek co on g owe s. The es ima ion esul s e eal ha he adop ion o c op o a ion inc eases
co on yields and ne e enues by 19% and 5% espec i ely. In o he wo ds, in e iewed Uzbek
a me s who ac ually adop ed c op o a ion would ha e ob ained 19% less ne e u ns o 5%
less co on yield had hey no adop ed c op o a ion. These indings a e con i med by o he
s udies. Fo ins ance, Zhao e al. (2020) ound ha in China c op o a ion inc eased co on yields
on a e age by 20% compa ed wi h “con en ional co on” monocul u e. Löw e al. (2017) ound
ha highe co on yields in Uzbekis an a e likely o be a eas wi h highe sha e o c op o a ion
a ea.
Fo bo h coun ies, he model esul s show ha co on g owe s who ac ually did no adop c op
o a ion would ha e lowe ne e u ns and co on yields i hey had adop ed his p ac ice.
Al hough, he ATU on he ou come a iable is nega i e, he esul p esen s posi i e TH e ec s
o ne e u ns and co on yields among Uzbek co on g owe s indica ing ha ne e u ns and
co on yields a e highe among adop e s o c op o a ion. Howe e , nega i e TH e ec s a e
obse ed o Kazakh co on g owe s e ealing ha co on yields and ne e u ns a e lowe
among adop e s o c op o a ion.
Fu he mo e, coun e ac ual adop e s o c op o a ion ha e highe co on yield and ne e u ns
han ac ual non-adop e s in bo h coun ies (Table 2.5). The esul shows ha he e a e se e al
impo an sou ces o he e ogenei y ha make adop e s o c op o a ion be e co on p oduce s
han non-adop e s.
~ 40 ~
Table 2.5: A e age expec ed ne e u ns and co on yield o adop e s and non-adop e s o c op
o a ion in Kazakhs an and Uzbekis an
Ca ego y
To adop
No o adop
T ea men e ec
1
2
3
4
5
6
Co on ne
e u ns
(US$/ha)
(ln)
Decisions in Kazakhs an
[90% con idence
in e al]
ATT
(a) 6.587
(0.039)
(c) 6.755
(0.025)
-0.168
(0.048)
-0.247
-0.088
ATU
(d) 6.639
(0.031)
(b) 6.723
(0.015)
-0.084
(0.034)
-0.140
-0.027
HE
BH1= -0.052
BH2= 0.03
TH = -0.084
Decisions in Uzbekis an
ATT
(a) 6.591
(0.042)
(c) 6.415
(0.028)
0.176
(0.051)
0.091
0.260
ATU
(d) 6.263
(0.031)
(b) 6.363
(0.015)
-0.100
(0.034)
-0.156
-0.043
HE
BH1= 0.328
BH2= 0.052
TH = 0.276
Co on yield
(kg/ha) (ln)
Decisions in Kazakhs an
ATT
(a) 7.561
(0.028)
(c) 7.700
(0.017)
-0.139
(0.032)
-0.193
-0.084
ATU
(d) 7.578
(0.021)
(b) 7.668
(0.011)
- 0.090
(0.024)
-0.129
-0.051
HE
BH1= -0.017
BH2= 0.032
TH =-0.049
Decisions in Uzbekis an
ATT
(a) 7.802
(0.022)
(c) 7.757
(0.012)
0.045
(0.025)
0.002
0.086
ATU
(d) 7.594
(0.018)
(b) 7.712
(0.007)
-0.118
(0.019)
-0.150
-0.086
HE
BH1=0.208
BH2=0.045
TH =0.163
No e: S anda d e o s a e in pa en hesis. Fo calcula ion o he pe cen di e ences o ea men
e ec , 100*(eATT -1) equa ion is used ollowing As aw e al. (2012).
Sou ce: Based on he AGRICHANGE 2019 a m su ey da a.
~ 41 ~
3 DOES ZERO TILLAGE SAVE OR INCREASE PRODUCTION COSTS OF SMALLHOLDERS IN
KYRGYZSTAN?
3
3.1 Smallholde s’ challenges in he adop ion o conse a ion ag icul u e in Ky gyzs an
Ky gyzs an is a land-locked low-income ood-de ici coun y wi h a popula ion o abou 6 million,
o which almos wo- hi ds li e in u al a eas (FAO, 2020). In 2021, GDP pe capi a was US$ 1,123
(in cons an 2015 US$). Despi e he p og ess in po e y educ ion, one- ou h o he popula ion
li es below he po e y line (Wo ld Bank, 2023). Ru al a eas, whe e wo- hi ds o he popula ion
li e in po e y, a e s ill lagging behind hese igu es (FAO, 2020). Al hough ag icul u e's
con ibu ion o he coun y’s g oss domes ic p oduc (GDP) has been s eadily declining, i s ill
plays a cen al ole in he u al economy. In 2021, ag icul u e accoun ed o almos 15% o GDP
(Wo ld Bank, 2023). As o 2019, abou 20% o employmen was in ag icul u e (Wo ld Bank,
2023).
Ky gyzs an's la e-1990s land e o m d o e he swi ch om planned socialis ag icul u e o
smallholde ma ke -o ien ed ag icul u e (Le man and Sedik, 2018). Th ough he ecogni ion o
p i a e land owne ship in 1996-1999 he go e nmen edis ibu ed o e 80% o a able land
among u al amilies, c ea ing a smallholde -based a ming sys em (FAO, 2020). The majo i y o
smallholde s a e cha ac e ized by in e c opped and mixed c op-li es ock sys ems wi h
p oduc ion mos ly o hei own consump ion (Jalilo a e al., 2019). In 2016, he o icial s a is ics
epo ed abou 1,150,000 u al households and peasan a ms wi h an a e age size o abou
0.87 ha (FAO, 2020). This includes 727,000 u al households wi h an a e age land size o abou
0.1 ha, and 415,000 peasan a ms wi h an a e age size o 2.2 ha (FAO, 2020).
Al hough he smallholde s ha e been impo an in ood secu i y and po e y alle ia ion, he
agmen ed na u e o he a ming sys em is p one o he p oblems o ‘smallness’. Fo ins ance,
3
Chap e 3 was published ollowing open-access a icle: Tadjie , A., Djanibeko , N., He z eld, T. (2023)
Does ze o illage sa e o inc ease p oduc ion cos s? E idence om smallholde s in Ky gyzs an.
In e na ional Jou nal o Ag icul u al Sus ainabili y 21 (1), 2270191.
h ps://doi.o g/10.1080/14735903.2023.2270191. This chap e builds upon ha a icle.
~ 42 ~
in agmen ed ag icul u al se ings o Ky gyzs an, limi ed physical, inancial, and human
esou ces aise conce ns abou he u u e o ag icul u al ood p oduc ion and he sus ainabili y
o a able lands (Wol g amm e al., 2010). Among he easons is ha u al households ha e o
cope wi h he inc easing cos s o ag icul u al inpu s. Mos public inance and ag icul u al
subsidies do no each u al households and a e cap u ed by la ge comme cial a ms (Le man
and Sedik, 2018). The go e nmen does no ha e a su icien budge o p o ide adequa e
suppo o smallholde s o co e ield ope a ion cos s. The sca ci y o ag icul u al machine y has
been imposing high machine y se ice cos s o land p epa a ion among smallholde s, making i
55% mo e expensi e han in neighbo ing sou he n Kazakhs an, and has hinde ed ag icul u al
p oduc i i y in Ky gyzs an (Guadagni and Fileccia, 2009). Fa me s migh be acing a mix o p ice,
isk and quan i y a ioning as he numbe o c edi s a a o dable a es is limi ed (Kuhn and
Bobojono 2021). The high a es and ansac ion cos s o comme cial c edi s may be
unaccep able o smallholde s he majo i y o whom canno access limi ed subsidized c edi s.
The lack o access o new echnologies and o knowledge o conse a ion illage p ac ices limi s
he wide adop ion o ze o illage among smallholde s in Ky gyzs an. Ky gyzs an’s i iga ed
ag icul u e is among he mos ulne able in Eas e n Eu ope and Cen al Asia o clima e change
(Fay e al., 2010). A modeling s udy by Bobojono and Aw-Hasan (2014) sugges s ha unde a
wa e sho age scena io, p edic ed a m incomes in he semia id pa s o Ky gyzs an migh
decline by 15% ha ming smallholde s’ p o i s and long- e m sus ainabili y. In ligh o he
impo ance o ag icul u e in u al incomes and ood secu i y, he in ensi y and sp ead o land
deg ada ion and inc easing p essu e om wa e sca ci y will a ec ag icul u al p oduc i i y and
h ea en ag icul u al li elihoods.
Cos -sa ing p ac ices like ze o illage can be an op ion o smallholde s ha su e om low
c edi access, unde in es men and a e p one o wa e s ess. In 2016, he ull echnical
po en ial adop ion le el o conse a ion ag icul u e in Ky gyzs an, including educed and ze o-
illage and c op o a ion, was es ima ed a 1.2 million ha o cul i a ed a ea unde ce eals, oil and
~ 43 ~
leguminous c ops (Polo e al., 2022). The esul s o he inancial analysis p esen ed by Polo e al.
(2022) show ha conse a ion ag icul u e sco es mode a ely wi h an in es men e u n a e o
13% and a payback pe iod o se en yea s. I was es ima ed ha conse a ion ag icul u e can
inc ease ag icul u al p oduc ion ia long- e m imp o ed soil nu ien managemen and wa e
e en ion. Fo ins ance, aised-bed and no- illage plan ing can inc ease whea yield by 25–38%
compa ed o he con en ional cul i a ion me hod (Nu beko e al., 2016). The economic alue
o he annual addi ional p oduc ion due o he adop ion o conse a ion ag icul u e in
Ky gyzs an was es ima ed a o e US$ 35 million o 9% o g oss ag icul u al alue (Polo e al.,
2022). Howe e , despi e hese ad an ages, he gap be ween p esen and po en ial up ake has
emained subs an ial wi h li le change (Polo e al., 2022).
3.2 Concep ual amewo k
Nume ous s udies ha e no ed h ee pa adigms such as “ he inno a ion-di usion”, “ he
adop ion pe cep ion” and “economic cons ain s” o de ine a me s’ adop ion o conse a ion
p ac ices (Cha e jee and Acha ya, 2021; Ruzzan e e al., 2021). Each pa adigm assumes se e al
ac o s in luencing he adop ion decision (Figu e 3.1). Fo example, o illus a e adop ion
beha io , he economic pa adigm assumes he maximiza ion o he a me ’s p o i and conside s
economic cons ain s such as access o na u al esou ces, access o capi al, in es men cos s
and isk a i ude. The inno a ion-di usion pa adigm assumes ha access o in o ma ion is he
main pa ame e o imp o e adop ion decisions. The adop ion pe cep ion pa adigm pos ula es
ha a a me ’s adop ion beha io depends on pe cei ed a ibu es o inno a ion, access o
in o ma ion, and indi idual ac o s such as he a me ’s expe ience and educa ion, as well as
ins i u ional ac o s ha can a ec hei pe cep ions (Ruzzan e e al., 2021).
I concep ualize ha a household aces he decision o adop ze o illage on a speci ic plo e sus
con en ional illage me hods in c op cul i a ion. F om his pe spec i e, he economic pa adigm
s ipula es ha he adop ion decision occu s unde he a me ’s objec i e o p o i maximiza ion.
~ 50 ~
Table 3.1 con .
Va iables
2016
2019
Full sample
Mean
Sd
Mean
Sd
Mean
Sd
Household a m cha ac e is ics
Numbe o household membe s
ha can wo k in ag icul u e
(abo e 10 and unde 65 yea s
old)
4.407
1.781
4.570
1.927
4.490
1.859
Asse index
0.400
0.139
0.354
0.168
0.376
0.156
Household owns a ac o (1/0)
0.051
0.226
0.035
0.188
0.043
0.207
Numbe o li es ock uni s owned
by household
3.364
4.260
2.377
4.268
2.859
4.292
Household ecei ed emi ances
las yea (1/0)
0.145
0.353
0.226
0.418
0.187
0.390
Household applied chemical
e ilize s las yea (1/0)
0.252
0.434
0.246
0.431
0.249
0.432
Household expe ienced a
wea he shock las yea (1/0)
0.629
0.483
0.161
0.367
0.390
0.488
Household expe ienced an
ag icul u al shock las yea (1/0)
0.364
0.481
0.088
0.283
0.223
0.416
Plo unde g ains and legumes
(1/0)
0.318
0.466
0.353
0.478
0.336
0.472
Plo unde ege ables (1/0)
0.400
0.490
0.270
0.444
0.334
0.472
Plo unde a mix o c ops (g ain,
legumes and ege ables) (1/0)
0.073
0.260
0.022
0.146
0.047
0.211
Loca ion cha ac e is ics
Dis ance o main oad om
dwelling (km)
0.521
0.738
0.777
0.891
0.652
0.830
Dis ance om dwelling o plo
(km)
1.470
2.840
1.174
2.536
1.319
2.693
Numbe o land plo s owned by
household
1.966
0.627
1.980
0.702
1.973
0.666
Plo size (ha)
0.694
1.340
0.794
1.898
0.745
1.649
Ins i u ional se ings
Amoun o c edi ecei ed by
household las yea (US$)
210.154
775.394
339.183
1387.313
276.103
1131.974
P o inces
Issyk Kul (1/0)
0.160
0.367
0.179
0.383
0.170
0.375
Djalal Abad (1/0)
0.213
0.409
0.201
0.401
0.207
0.405
Na yn (1/0)
0.073
0.260
0.048
0.213
0.060
0.237
Ba ken (1/0)
0.125
0.331
0.122
0.328
0.123
0.329
Osh (1/0)
0.260
0.439
0.293
0.455
0.277
0.448
Talas (1/0)
0.079
0.270
0.070
0.256
0.075
0.263
Chuy (1/0)
0.160
0.367
0.179
0.383
0.170
0.375
No e: N=1363 o 2016, N=1425 o 2019 and N=2788 o he ull sample. Because o missing
alues o he bicide cos s, he numbe o obse a ions o 2016, 2019 and he ull sample a e
1342, 1396 and 2738, espec i ely.
Sou ce: Tadjie e al. (2023a).
~ 51 ~
As a p oxy o household weal h, I calcula ed he asse index using he p incipal componen
analysis (PCA) as sugges ed in Filme and P i che (2001). I used bina y in o ma ion ega ding
owne ship o 35 asse s based on he s anda dized PCA sco es, and he min-max no maliza ion
( ea u e scaling) me hod was used o con e he scaled da a o a ange (0–1).
The numbe o o al li es ock uni s (TLU) is an addi ional household weal h indica o . I calcula ed
TLU based on li es ock uni coe icien s
5
.. Fi s , I mul iplied each ype o li es ock by LU
coe icien s, and hen summa ized he esul by households. The summa y s a is ics sugges ha
he numbe o li es ock uni s owned by a household was on a e age 3 in 2016 and 2 in 2019.
In he s udy, I also conside ed he numbe o plo s owned by households. Table 3.1 indica es
ha households ha e on a e age 2 plo s in bo h yea s. Remi ances and mig a ion ha e been
among he main income sou ces in u al a eas o many de eloping coun ies and pa icula ly o
Ky gyzs an whe e emi ances a ec households’ decisions in ag icul u e (A amano and Van
den Be g, 2012). Following he a gumen by Mon and Luu (2020) ha success ul conse a ion
ag icul u e p ac ice equi es app op ia e managemen o ex e nal inpu s such as e ilize s, I
added a household’s applica ion o e ilize s as an explana o y dummy a iable in he models.
Fu he mo e, I conside ed he opinion o household heads abou whe he hei households
expe ienced ag icul u al shocks o e he las yea such as pes in es a ions, c op and li es ock
diseases, insu icien i iga ion wa e supply, he o li es ock, o inabili y o sell ag icul u al
p oduc s as well as wea he shocks such as d ough , lood, hea y ain o ex emely cold win e
empe a u es. I assumed ha such ag icul u al and wea he shocks can a ec a household’s
decision o adop ze o illage p ac ices by ha ming he household’s ag icul u al ou pu s and
asse s.
5
To al li es ock uni s (TLU) is calcula ed based on li es ock uni (LU) coe icien s acco ding o he ollowing
sou ces:
(1) h ps://ec.eu opa.eu/eu os a /s a is ics-explained/index.php? i le=Glossa y:Li es ock_uni _(LSU)
and (2) h p://adlib.e e ysi e.co.uk/adlib/de a/con en .aspx?id=000il3890w.198awldohj69 3#nix .
~ 52 ~
Smallholde s o en cul i a e a mix o c ops on a single plo . I agg ega ed all cos s o a ious c op
ypes o con ol hei e ec on ze o illage adop ion decision. I gene a ed h ee dummy a iables
which explain ha he plo was cul i a ed (1) pu ely by g ain and legume c ops, (2) by
ege ables, and (3) by a mix o g ains, legumes and ege ables.
3.4 Me hodological app oach
The ollowing subsec ions explain he empi ical models and mo i a e he selec ion o he
es ima ion s a egy. The assessmen o he economic e ec s o echnology adop ion om non-
expe imen al su ey da a equi es he co ec ion o sel -selec ion bias, iden i ica ion o p ope
coun e ac uals and con ol o non-obse able a m cha ac e is ics (As aw e al., 2012; Jale a
e al., 2016). I based he iden i ica ion o a a me ’s decision o adop ze o illage on he
measu emen o p o i abili y h ough i s p oduc ion cos educing e ec s. To es ima e he
impac o ze o illage on p oduc ion cos s, I ollowed he exis ing li e a u e such as Abdulai and
Hu man (2014), Jale a e al. (2016), Keil e al. (2020), Khonje e al. (2018), and Mon and Luu
(2020) and employed a wo-s age es ima ion app oach. I assessed di e en models o
in es iga e he ela ionships be ween ze o illage adop ion and paymen s o hi ed labo ,
machine y cos s o land p epa a ion and seeding, weeding, and he bicide cos s as well as o al
cos s. The Mundlak de ice (Mundlak, 1978) was employed o es ima e ime-in a ian
endogenei y. Fu he mo e, I used he endogenous swi ching eg ession (ESR) model o accoun
o selec ion bias. To es ima e he associa ion be ween ze o illage adop ion and each
p oduc ion cos conside ed abo e, I used he coun e ac ual amewo k ha measu es a e age
ea men e ec s on he ea ed (ATT).
3.4.1 Ze o illage adop ion decision and p oduc ion cos s
The decision o adop ze o illage and he selec ion o plo s unde his me hod a e made by a
household head and o he household membe s, and hus a e no andom. Such a sel -selec ion
p oblem implies a po en ial bias in he e ec o ze o- illage adop ion on p oduc ion cos s. In
~ 53 ~
eali y, households migh apply ze o illage on plo s wi h highe p oduc ion cos s. As a esul ,
he e ec o ze o illage on p oduc ion cos s can be o e es ima ed. As commonly done in o he
s udies (e.g., Jale a e al., 2016; Khonje e al., 2018; Keil e al., 2020; Mon and Luu, 2020), o
co ec o selec ion bias, I employed a wo s age ESR model. In he i s s age, I es ima ed he
main de e minan s o ze o illage adop ion. The p obabili y o ze o illage adop ion o an
indi idual can be w i en as ollows:
𝑃𝑟 (𝑧𝑡𝑗𝑖𝑡)=𝑓(𝑋𝑗𝑖𝑡) (3.1)
whe e, 𝑃𝑟 (𝑧𝑡𝑗𝑖𝑡) is he p obabili y o ze o illage adop ion o 𝑖′s household in 𝑗′s plo a ime 𝑡.
𝑋 is a ec o o explana o y a iables desc ibing household and plo cha ac e is ics, pe sonal
cha ac e is ics, loca ion se ings, e c.
I used he Mundlak app oach whe e he means o obse able ime- a ian a iables we e added
o he model. The Mundlak app oach is applied o panel ixed-e ec s in cases o a ia ion wi hin
uni s o e ime and when ime-in a ian obse ables a ec bo h adop ion decision and
ou comes (Khonje e al., 2018; Mon and Luu, 2020; Mundlak, 1978). This app oach also
educes he p oblem o unobse ed he e ogenei y. The undamen al assump ion o using he
Mundlak app oach is o conside unobse ed ime-in a ian componen s by calcula ing and
employing he mean o ime- a ian a iables as a p oxy (Mon and Luu, 2020; Mundlak, 1978).
I compu ed he means o all ime- a ian a iables (𝑥𝑖) and added hem o a p obi eg ession
model o measu e he p obabili y o ze o illage adop ion. Fu he mo e, I included p o ince
dummies (𝑅𝑝, he e, Issyk Kul is he e e ence p o ince) and a ime dummy (𝑌𝑡, he e, 2016 is he
e e ence yea ) o all models o accoun o he p o ince-le el and yea di e ences. The
egional dummies allow o accoun ing o o he c oss- egional di e ences ha can be
associa ed wi h adop ion decisions such as cos s o machine y, labo and o he inpu s. Thus,
om Equa ion (3.1), a household 𝑖′s likelihood o adop ing ze o illage in hei 𝑗′s plo a ime
can be o mula ed as:
~ 54 ~
𝑃𝑟 (𝑧𝑡𝑗𝑖𝑡 =1|𝑋𝑖,𝑅𝑝,𝑋𝑖
,𝑌𝑡)=𝛷(𝑎𝑖+𝛽′𝑥𝑗𝑖𝑡 +𝛿′𝑥𝑖𝑡 +𝑅𝑝+𝑌𝑡) (3.2)
whe e, 𝛽, 𝛿 and 𝛾 a e he pa ame e s o be es ima ed. 𝑥𝑗𝑖𝑡 con ains obse ables a he plo
le el. 𝑥𝑖𝑡 con ains obse ables a he household le el. 𝑥𝑖 is he mean o ime- a ying a iables
ha ollow he Mundlak app oach.
In he second s age, an OLS model was applied unde wo egimes, namely, unde non-adop ion
and adop ion o ze o illage. He e, he model es ima es he ela ionship o ou come a iables
o ze o illage adop e s and non-adop e s as ollows:
{𝑦1𝑗𝑖𝑡 =𝐾𝑗𝑖𝑡1𝛽1+𝑘
𝑖1𝜈1+𝑅𝑝+𝑌𝑡+𝜂1𝑗𝑖𝑡 , i 𝑍𝑇=1
𝑦0𝑗𝑖𝑡 =𝐾𝑗𝑖𝑡0𝛽0+𝑘
𝑖0𝜈0+𝑅𝑝+𝑌𝑡+𝜂0𝑗𝑖𝑡 , i 𝑍𝑇=0 (3.3)
whe e 𝑦𝑗𝑖𝑡 is an ou come a iables such as machine y cos s o land p epa a ion, machine y
cos s o weeding, paymen o hi ed labo , and he bicide cos s, on plo 𝑗 o 𝑖’s household a
ime 𝑡. 𝐾𝑗𝑖𝑡 is a se o explana o y a iables ha ela e o he ou comes. 𝑘
𝑖 is he mean o ime-
a ying a iables. As men ioned be o e, 𝑅𝑝 and 𝑌𝑡 a e he p o ince and ime dummies. Some
households epo ed ela i ely high cos s pe plo and high amoun s o c edi . The e o e, he
na u al loga i hm was used o hese a iables. Howe e , he e a e some obse a ions wi h “0”
alues. Hence, o a oid missing alues, I added “+1” o hese a iables be o e ans o ming o
he na u al loga i hm.
The p obi model supplies essen ial in o ma ion o examine and co ec he po en ially esul ing
bias (Maddala, 1983: 223; Pe ick, 2004: 151). To es selec ion bias, I ollowed Heckman (1979)
and used he In e se Mills Ra io (IMR) calcula ed om he esul s o a p obi es ima ion as
ollows:
𝜆1𝑗𝑖𝑡 = 𝜑(𝛿𝑥𝑗𝑖𝑡)/𝜙(𝛿𝑥𝑗𝑖𝑡); 𝜆0𝑗𝑖𝑡 = −𝜑(𝛿𝑥𝑗𝑖𝑡)/[1−𝜙(𝛿𝑥𝑗𝑖𝑡)] (3.4)
whe e 𝜑(.) and 𝛷(.) indica e he densi y and cumula i e densi y unc ion o he s anda d
no mal dis ibu ion, espec i ely. 𝜆0𝑖𝑡𝑗 and 𝜆1𝑖𝑡𝑗 ep esen he IMR. The calcula ed IMR was
~ 55 ~
added o he second s age model o co ec selec ion bias and esul ed in he ollowing
equa ion:
{𝑦1𝑗𝑖𝑡 =𝐾𝑗𝑖𝑡1𝛽1+𝑘
𝑖1𝜈1+𝑅𝑝+𝑌𝑡+𝜆1𝑗𝑖𝑡𝜎1+ 𝑌𝑡∗𝜆1𝑗𝑖𝑡𝜏1+𝜂1𝑗𝑖𝑡 ,i 𝑍𝑇=1
𝑦0𝑗𝑖𝑡 =𝐾𝑗𝑖𝑡0𝛽0+𝑘
𝑖0𝜈0+𝑅𝑝+𝑌𝑡+𝜆0𝑗𝑖𝑡𝜎0+ 𝑌𝑡∗𝜆0𝑗𝑖𝑡𝜏0+𝜂0𝑗𝑖𝑡 ,i 𝑍𝑇=0 (3.5)
Fu he mo e, o conside changes in he selec ion e ec o e ime, he IMR was in e ac ed wi h
he ime dummy ( 𝑌𝑡∗𝜆𝑗𝑖𝑡) ollowing Mon and Luu (2020).
Se e al s udies emphasize he selec ion o alid ins umen s ha in luence adop ion decisions
bu do no a ec ou come a iables. I assume households nea he main oad will ha e mo e
con enience in using con en ional illage me hods due o easy access o machine y se ices and,
hus, hus, a e less likely o adop ze o illage han households loca ed u he away om he
oad. A alsi ica ion es shows ha “dis ance o he main oad” ela es o ze o illage adop ion
decision bu does no a ec he ou come a iables (see Table A5 in he Appendix).
3.4.2 Es ima ion o a e age ea men e ec on he ea ed
The a e age ea men e ec was es ima ed wi hin he ESR amewo k me hod o es he
impac o ze o illage adop ion on ou come a iables. Fi s , he expec ed ou comes o ze o
illage adop e s and non-adop e s we e compa ed in ac ual and coun e ac ual si ua ions. The
expec ed (ac ual) ou come o ze o- illage adop e s can be exp essed as ollows:
𝐸(𝑦1𝑗𝑖𝑡|𝑧𝑒𝑟𝑜 𝑡𝑖𝑙𝑙𝑎𝑔𝑒=1)=𝐾𝑗𝑖𝑡1𝛽1+𝑘
𝑖1𝜈1+𝑅𝑝+𝑌𝑡+𝜆1𝑗𝑖𝑡𝜎1+ 𝑌𝑡∗𝜆1𝑗𝑖𝑡𝜏1 (3.6)
The expec ed ou come o adop e s had hey no adop ed ze o illage (coun e ac ual) can, hus,
be exp essed as ollows:
𝐸(𝑦0𝑗𝑖𝑡|𝑧𝑒𝑟𝑜 𝑡𝑖𝑙𝑙𝑎𝑔𝑒=1)=𝐾𝑗𝑖𝑡1𝛽0+𝑘
𝑖1𝜈0+𝑅𝑝+𝑌𝑡+𝜆1𝑗𝑖𝑡𝜎0+ 𝑌𝑡∗𝜆1𝑗𝑖𝑡𝜏0 (3.7)
Second, he di e ences be ween he ac ual and coun e ac ual expec ed ou comes, which
explain he ATT a e es ima ed as ollows:
𝐴𝑇𝑇=𝐸(𝑦1𝑗𝑖𝑡|𝑧𝑒𝑟𝑜 𝑡𝑖𝑙𝑙𝑎𝑔𝑒=1)−𝐸(𝑦0𝑗𝑖𝑡|𝑧𝑒𝑟𝑜 𝑡𝑖𝑙𝑙𝑎𝑔𝑒=1) (3.8)
~ 56 ~
3.5 Resul s and discussion
3.5.1 De e minan s o ze o illage adop ion
This sec ion b ie ly discusses he esul s om a p obi adop ion model since he p ima y in e es
o his chap e is o s udy he esou ce-sa ing impac o ze o illage. The a e age ma ginal e ec s
a e assessed om he p obi model (Equa ion 3.2). The model esul s a e gi en in he i s
column o Table 3.2. The s a is ical signi icance o he Wald es shows ha all coe icien s o
explana o y a iables a e no simul aneously equal o ze o. The alsi ica ion es shows a
signi ican co ela ion be ween he ins umen al a iable and ze o illage adop ion decision, bu
no wi h p oduc ion cos s (Table A5 in he Appendix). Hence, he selec ed ins umen is
plausible. The econome ic models we e es ima ed in STATA 17 so wa e.
In summa y, he esul s show ha ze o illage is a o ed by poo e households whose heads a e
employed in ag icul u e, ha e ewe plo s, a e loca ed in emo e a eas and do no apply
chemical e ilize s. Mo e speci ically, household heads wi h ag icul u al employmen a e mo e
likely o use ze o illage because hey a e exposed o knowledge abou sus ainable p ac ices.
Secondly, ag icul u al wages a e lowe han in o he sec o s (A amano and Van den Be g, 2012)
and hus such households a e mo e likely o op o ze o illage a he han apply con en ional
illage.
The ela ionship be ween he asse index o households and he adop ion o ze o illage
p ac ices is nega i e. This indica es ha households wi h mo e asse s, i.e., weal hie households,
a e likely o adop con en ional ag icul u al p ac ices ha depend on mechanized ac o
se ices. This esul is consis en wi h Ngoma (2018), who ound ha household asse s educe
he likelihood o minimum illage adop ion. Fu he mo e, he model esul s show ha
households wi h mo e plo s a e less likely o adop ze o illage. Applying chemical e ilize can
also be ela ed o smallholde s’ weal h s a us, whe e poo smallholde s ha e mo e challenges
~ 57 ~
accessing his inpu and o en canno a o d i . The model esul shows ha households who
apply chemical e ilize s a e less likely o adop ze o illage.
Households loca ed u he away om hei land plo s and main oads a e likely o adop ze o
illage. This is no su p ising since i is expec ed ha households loca ed u he away om hei
lands and oad a e likely o ha e highe cos s o accessing p oduc ion inpu s and machine y
se ices and, hus, likely o swi ch o inpu -sa ing ze o illage. This esul is in line wi h he
indings o Jale a e al. (2016) and Tessema e al. (2018), who ound a posi i e ela ionship
be ween plo dis ance and minimum illage adop ion. A emo e loca ion in a u al a ea can be
associa ed wi h lowe weal h s a us.
Households ha expe ienced ag icul u al shocks a e less likely o adop ze o illage. O he
s udies ha conside ed ag icul u al shocks, e.g., wa e logging s ess by Teklewold e al. (2013),
d ough s and loods equencies by Wainaina e al. (2016), did no ind an associa ion wi h he
adop ion o conse a ion illage.
Finally, he es ima ion esul s show ha he dummy a iable o “g ain and legume p oduc ion”
is posi i e bu s a is ically insigni ican in ela ion o ze o- illage adop ion. In con as , he e is a
nega i e and s a is ically signi ican ela ionship be ween ege able p oduc ion and ze o illage
adop ion decision. I can be explained ha some ege able c ops may no be plan ed using ze o-
illage me hod.
The nega i e alues o egional dummy a iables show ha he adop ion o ze o illage among
smallholde s in Djalal Abad, Osh and Talas egions is less likely han among smallholde s in he
Issyk Kul p o ince. A he same ime, he e is no signi ican di e ence in likelihood o ze o- illage
adop ion be ween smallholde s in he Issyk Kul p o ince and in i s neighbo ing Na yn and Chuy
egions and he Ba ken p o ince. Va ious unobse ed egion-speci ic cha ac e is ics can explain
hese c oss- egional di e ences in he adop ion o ze o- illage. Fo ins ance, highe popula ion
densi y and limi ed a ailabili y o land in Osh and Djalal-Abad p o inces (Zhunuso a and
~ 58 ~
He mann, 2018) can educe smallholde s’ cos s o hi ed labo in illage ope a ions and as a
esul lowe he adop ion a e o ze o- illage in hese wo p o inces. Fu he mo e, ag o-
ecological zoning o he egions o Ky gyzs an can accoun o di e ences in c op po olio,
p oduc ion specializa ion and illage me hods (Jalilo a e al., 2019). Chuy, Talas and Issyk Kul
egions a e close ag o-ecologically o each o he ep esen ing he no he n egions. Djalal
Abad, Osh and Ba ken ep esen sou he n ag o-ecological egions encompassing he Fe gana
alley. Na yn egion ep esen s he cen al zone wi h as alpine a eas o moun ains and alleys
sui able o win e g azing and c op cul i a ion.
~ 59 ~
Table 3.2: De e minan s o ze o illage adop ion decision
Ma ginal
e ec
S anda d
e o
[90% con idence
in e al]
Age o household head (yea s)
0.0003
0.002
-0.002
0.003
Educa ion le el o household head
(ca ego ical, 1=illi e a e…7=uni e si y)
0.002
0.005
-0.007
0.011
Female household head (1/0)
-0.008
0.016
-0.033
0.018
Household head employed in ag icul u e (1/0)
0.048
0.025
0.007
0.089
Numbe o household membe s ha can wo k
in ag icul u e (abo e 10 and unde 65 yea s
old)
-0.003
0.009
-0.018
0.011
Household head’s e hnici y (1/0)
0.022
0.017
-0.005
0.050
Asse index
-0.245
0.068
-0.357
-0.132
Household owns a ac o (1/0)
0.089
0.051
-0.001
0.167
Household ecei ed emi ances las yea
(1/0)
-0.042
0.028
-0.088
0.003
Household expe ienced a wea he shock las
yea (1/0)
0.034
0.022
-0.002
0.069
Household expe ienced an ag icul u al shock
las yea (1/0)
-0.043
0.024
-0.082
-0.004
Amoun o c edi s ecei ed by household las
yea (loga i hm, US$)
0.002
0.003
-0.004
0.008
Numbe o land plo s owned by household
-0.021
0.010
-0.038
-0.004
Numbe o li es ock uni s owned by
households
0.002
0.003
-0.002
0.007
Dis ance om dwelling o plo (km)
0.004
0.002
0.001
0.008
Household applied chemical e ilize s las
yea (1/0)
-0.051
0.019
-0.083
-0.020
Plo size (ha)
0.001
0.004
-0.005
0.006
Plo unde g ains and legumes (1/0)
0.009
0.017
-0.018
0.036
Plo unde ege ables (1/0)
-0.062
0.017
-0.089
-0.035
Plo unde a mix o c ops (g ain, legumes and
ege ables) (1/0)
-0.026
0.034
-0.082
0.030
Djalal Abad (1/0)
-0.096
0.022
-0.131
-0.061
Na yn (1/0)
-0.042
0.028
-0.088
0.005
Ba ken (1/0)
0.012
0.022
-0.024
0.048
Osh (1/0)
-0.127
0.024
-0.166
-0.088
Talas (1/0)
-0.221
0.040
-0.286
-0.155
Chuy (1/0)
0.048
0.023
0.009
0.086
Su ey yea (2019=1)
0.133
0.015
0.108
0.158
Dis ance om dwelling o he main oad (km)
0.019
0.007
0.008
0.031
Pseudo R2
0.168
N
2788
Sou ce: Tadjie e al. (2023a).
~ 66 ~
In Uzbekis an, coope a ion occu s wi hin na ow social g oups al eady in equen in e ac ion,
such as neighbo ing a me s (Radni z e al., 2009). In his ega d, in o mal coope a ion helps
pa icipa ing a me s o o e come o ganiza ional p oblems caused by esou ce sho ages and
educe ansac ion cos s (Djanibeko e al., 2015). Fo ins ance, when i iga ion wa e se ice
p o ision becomes un eliable, a me s ex end hei eliance on mu ual sel -help by con ibu ing
labo o inancial means o epai a commonly owned i iga ion pump o canal (O’Ha a, 2000).
Beyond managing wa e esou ces, coope a ion plays a c ucial ole in knowledge sha ing among
Uzbekis an's a ming communi y. The p ima y challenges lie in he absence o ex ension se ices
acili a ing knowledge exchange o in o ma ion sha ing in ag icul u e (Kazbeko and Qu eshi,
2011). Due o he poo ex ension sys em, a me s o en seek o he sou ces o in o ma ion, such
as exchanging knowledge wi h hei pee s (Ku bano e al., 2022), o example, h ough he
pa icipa ion in in o mal coope a ion ac i i ies, like i iga ion canal cleaning and he epai o
common i iga ion pump. Fu he mo e, a me s pa icipa ing in in o mal coope a ion ac i ely
engage in social media g oups, acili a ing he dissemina ion o in o ma ion and he exchange o
knowledge (Tadjie e al., 2023b).
4.2 Concep ual amewo k
I concep ualize ha a me s who pa icipa e in in o mal coope a ion in i iga ion wa e
managemen a e mo e p edisposed o adop ing SAPs. This asse ion de i es om he no ion
ha pa icipa ion in in o mal coope a ion acili a es he dissemina ion o knowledge and he
exchange o in o ma ion ela ed o SAPs among pa icipan s, he eby os e ing highe
awa eness and comp ehension o hese p ac ices (Olawuyi and Mushunje, 2020; Willy and
Holm-Mülle , 2013). Thus, I assume ha pa icipa ion in in o mal coope a ion can se e as a
ca alys o g ea e SAPs adop ion among a me s, po en ially leading o a b oade
implemen a ion o such p ac ices wi hin he ag icul u al communi y. Mo eo e , he adop ion o
~ 67 ~
new ag icul u al p ac ices in oduces unce ain y, whe ein a me s may possess inadequa e
knowledge conce ning he a ibu es o SAPs (Cha as and Nauges, 2020; Roge s, 2003). This
includes unde s anding he sui abili y o new SAPs unde speci ic soil condi ions, as well as
unde s anding how bes o use he SAPs especially when combined wi h o he p oduc ion
ac o s such as e ilize s (Cha as and Nauges, 2020). Hence, ga he ing in o ma ion om ea ly
adop e - a me s h ough pa icipa ion in in o mal coope a ion in wa e managemen may
educe a me s’ esis ance o he adop ion o SAPs.
Se e al ac o s exhibi associa ions wi h bo h pa icipa ion in in o mal coope a ion and he
decision o adop SAPs ( igu e 4.1). They can be di ided in o a m and a me cha ac e is ics,
a m biophysical cha ac e is ics, ins i u ional ac o s, and loca ional se ings (e g., Dessa e al.,
2019; D’Emden e al., 2008; Fede e al., 1985; Ruzzan e e al., 2021). The di e se a ay o ac o s
exhibi s a ying e ec s on bo h he in ensi y o SAP adop ion and a me s’ decision o
pa icipa e in in o mal coope a ion. Figu e 4.1 shows hypo hesized signs o he ela ionship
be ween explana o y a iables and dependen a iables.
Figu e 4.1 Concep ual ela ionship be ween SAP adop ion and in o mal coope a ion, and
explana o y a iables
+/‐
+/‐
+/‐
+/‐
+/‐
In ensi y o
SAPs
adop ion
Pa icipa ion in in o mal
coope a ion
Fa m and a me
cha ac e is ics (age,
educa ion,...)
Fa m biophysical
cha ac e is ics (land
size, soil e ili y,.)
Ins i u ional ac o s
(land enu e, pa icipa ion
in ainings, decision
making au onomy)
Loca ional se ings
(nea / u he in an
i iga ion canal,
dis ance…)
+/‐
?
+/‐
~ 68 ~
I es whe he pa icipa ion in in o mal coope a ion in wa e managemen inc eases o
dec eases he in ensi y o SAP adop ion. Social lea ning e e s o he p ocess h ough which
a me s acqui e new knowledge, skills, a i udes, o beha io s by in e ac ing wi h o he s in hei
social en i onmen . I in ol es he sha ing and exchange o in o ma ion, expe iences, and
p ac ices among a me s o imp o e a ming me hods, and enhance p oduc i i y and e enues.
Th ough his p ocess, pa icipa ion in in o mal coope a ion is an icipa ed o in luence he ex en
o SAP adop ion. This model elies on pee - o-pee lea ning and sha ed expe iences o o e come
ba ie s o adop ing new ag icul u al echnologies (Fos e and Rosenzweig, 1995). On one hand,
in o mal coope a ion among a me s, acili a ed by sha ed wa e managemen and knowledge
exchange, is seen as a way o enhance SAP adop ion, pa icula ly whe e o mal ex ension
se ices a e weak. In con as , in con ex s whe e low-le el awa eness ega ding SAPs is high in
he a ming communi y, his e y in o mal in e ac ion migh ein o ce adi ional, inpu -
in ensi e cul i a ion p ac ices, educing he adop ion in ensi y o SAPs (Bakke e al., 2021;
Wagne e al., 2016).
Facili a ing access o ag icul u al echnology in o ma ion among a me s enhances coope a ion
and signi ican ly educes he ansac ion cos s linked o echnology adop ion decisions
(Ugochukwu and Phillips, 2018). Du ing communal ac i i ies, such as hasha o wa e
in as uc u e main enance, a me s engage in open discussions abou ag onomy, echnology
p ocesses, and he economic implica ions o adop ing new ag icul u al p ac ices. This
coope a i e en i onmen os e s a obus pla o m o capaci y building and he exchange o
c i ical in o ma ion ela ed o SAPs wi hin he u al ag icul u al se ing (Olawuyi and Mushunje,
2020). Mo eo e , he in luence o neighbo hood social dynamics plays a c ucial ole in os e ing
social lea ning, he eby accele a ing he up ake o inno a i e c op cul i a ion me hods (Willy
and Holm-Mülle , 2013). The in e play be ween in o ma ion sha ing, social lea ning, and social
capi al is iden i ied as ins umen al in he adop ion o ag icul u al echnologies among local
a ming communi ies (Dessa e al., 2019; Ma a e al., 2003).
~ 69 ~
4.3 Da a and desc ip i e analysis
The p esen s udy u ilizes a m su ey da a de i ed om wo dis inc wa es conduc ed wi hin
he amewo k o he AGRICHANGE and SUSADICA
7
p ojec s om he Tu kis an (Kazakhs an)
and Sama kand (Uzbekis an) p o inces. In he s udy, I ocus on Uzbekis an, hence Kazakhs an
subsample is excluded. The ini ial wa e o he su ey was collec ed in 2019, ollowed by a
subsequen wa e in 2022. In he i s wa e o 2019, 460 a me s ac i ely pa icipa ed om he
Sama kand p o ince. In he 2022 su ey, 450 a me s we e su eyed in he Sama kand p o ince.
A o al o 309 a me s pa icipa ed in bo h su ey wa es, he eby acili a ing a longi udinal
analysis o ag icul u al dynamics.
A mul is age andom sampling p ocedu e was used o selec a me s o in e iews. Fo his, I
selec ed h ee dis ic s acco ding o hei c op specializa ion. Pas da gom and Paya ik dis ic s
in Sama kand a e mo e specialized in co on cul i a ion, while a me s in Jomboy dis ic in
Sama kand ha e di e si ied om co on o o he high- alue c ops such as ege ables and
melons. Iden i ied a me s answe ed a de ailed ques ionnai e on indi idual socio-demog aphic
da a, indi idual beha io al pe cep ion, as well as a m, ield, and loca ion cha ac e is ics, and
he adop ion o SAPs.
I applied se e al condi ions o he da ase o i he esea ch objec i es. I pooled he yea s 2019
and 2022, because mo e han 30% o a me s pa icipa ed in only one wa e. Addi ionally,
a ia ions among a m managemen we e no ed be ween he wo yea s, whe e a m business
was un by di e en amily membe s in 2019 and 2022, u he jus i ying he decision o
agg ega e he da ase s o a comp ehensi e and ep esen a i e analysis.
Following he de ini ion by Piñei o e al. (2020), se e al a ming p ac ices we e co e ed in he
su eys, such as c op o a ion, biological pes con ol me hods, lase le elling o ields, low
7
S uc u ed doc o al p og amme on Sus ainable Ag icul u al De elopmen in Cen al Asia (SUSADICA),
h ps://www.iamo.de/en/ esea ch/ esea ch-p ojec s/
~ 70 ~
illage o land, di ec plan ing wi hou illage, in e c opping, d ip and sp inkle i iga ion. Fo
each ype o p ac ice, a me s had he op ion o choose one o he ollowing h ee answe s,
hus, (1) “yes and s ill use i ”, (2) “yes, bu I s opped using i ” and (3) “no, ne e used i ”. I ea ed
he esponses “yes and s ill use i ” as adop ion, and he o he wo esponses we e agg ega ed
in o a non-adop ion. By doing so, a bina y a iable was gene a ed o each ype o SAP. Figu e
4.2 p esen s he adop ion le el o each p ac ice.
Figu e 4 2: SAPs adop ion le el in Sama kand egion in 2018 and 2021 (pooled)
Sou ce: Au ho s.
The p ima y objec i e o his s udy is o sc u inize he adop ion pa e ns o low-cos ag icul u al
p ac ices. Consequen ly, I excluded he assessmen o adop ion pe aining o lase le eling, d ip
i iga ion, and sp inkle i iga ion p ac ices. The me hodology in ol es he consolida ion o he
u ilized SAPs by indi idual a me s, which is subsequen ly no malized by Simpson’s (Simpson,
1949) di e si y index (e.g., Con ad e al., 2017; Lyson and Welsh, 1993). The e o e, I use he
ollowing o mula o calcula e he in ensi y o adop ion o di e en SAPs ela i e o a m size as
p esen ed in equa ion 4.1:
𝑆=∑𝑎2
𝐴2 (4.1)
whe e, 𝑆 is he in ensi y o SAP adop ion, 𝑎 is he a ea unde a pa icula SAP ou o o al land
a ea, 𝐴 is a a m’s o al land a ea. This index exp esses he in ensi y o SAP adop ion o each
~ 71 ~
a me and anges om ze o o 2.4. An index alue o 0 signi ies he absence o SAP applica ion,
while a highe nume ical alue indica es a g ea e implemen a ion o di e si ied SAPs ac oss a
la ge sha e o he a me ’s land. In he analysis, i is shown ha a a me adop s maximum h ee
SAPs ou o i e. The heo e ically obse able maximum alue o S=2.4 would imply ha mo e
SAPs will be used on a a m’s o al a ea. This index o SAP se es as he p incipal ou come
a iable in he ollowing analysis.
The su ey p o ides in o ma ion on which o mal and in o mal o m o coope a ion a me s
pa icipa ed in ega ding i iga ion wa e managemen . A bina y dummy a iable was c ea ed,
ep esen ing he ea men a iable in he esea ch, he eby ca ego izing a me s in o wo
dis inc g oups: "pa icipan s in in o mal coope a ion" and "non-pa icipan s in in o mal
coope a ion". I use his bina y a iable measu ing a me s’ engagemen in in o mal coope a ion
in “i iga ion o ields and con ol o wa e dis ibu ion”, “ epai and cleaning o i iga ion and
d ainage canals”, and “join main enance, u iliza ion, cons uc ion and epai o i iga ion
equipmen and in as uc u e”. I a me s esponded ha hey pa icipa ed in in o mal
coope a ion in i iga ion wa e managemen - ha is, i a me s made in o mal ag eemen s wi h
o he a me s o i hey pa icipa ed in hasha o i iga ion ac i i ies he alues o he a iable
o in e es we e coded as one. The da ase o 909 obse a ions included 51 esponden s who
disclosed pa icipa ion in wa e managemen coope a ion pu ely based on o mal ag eemen s.
Gi en he ela i ely small numbe o esponden s in ol ed exclusi ely in o mal coope a ion, I
excluded hese cases om he es ima ion model o main ain ocus on in o mal coope a ion's
impac . Fa me s no pa icipa ing in any o m o coope a ion, i.e. 351 esponses, we e assigned
a alue o ze o. In e es ingly, Table 4.1 e eals ha 32 a me s we e in ol ed in bo h o mal and
in o mal coope a ion. These indi iduals, alongside ano he 475 who we e engaged exclusi ely
in in o mal coope a ion, we e coded as one in he analysis, highligh ing he p edominan ole o
in o mal mechanisms in i iga ion wa e managemen . Thus, I used 858 obse a ions in he
es ima ions, including 507 a me s pa icipa ing in in o mal coope a ion.
~ 72 ~
Table 4.1: C oss abula ion o o ms o coope a ion in wa e managemen
Type o coope a ion
In o mal
pa icipan
non-pa icipan
Fo mal
pa icipan
32
51
non-pa icipan
475
351
To al
507
402
Sou ce: Au ho s.
Table 4.2 p o ides in o ma ion abou he summa y s a is ics o a iables ac oss he ea men
a iable used in he s udy. The a iables a e di ided in o “ou come a iable”, which is he
in ensi y o SAP adop ion, and “explana o y a iables”.
Table 4.2: Desc ip i e s a is ics o a iables ac oss a me s ha pa icipa ed and non-
pa icipa ed in in o mal coope a ion (pooled 2019 and 2022)
Va iables
Desc ip ion
Pa icipan
(N=507)
Non-
pa icipan
(N=351)
Mean di
Mean
Mean
Ou come a iable
In ensi y o SAPs
adop ion
In ensi y o SAP adop ion o a m size (0-
a a m does no use any SAPs, a highe
index alue deno es he adop ion o a
leas one SAP)
0.340
(0.490)
0.386
(0.480)
-0.046
Explana o y a iables
Age
Age o a m manage (yea s)
44.846
(10.224)
44.934
(10.252)
-0.088
Educa ion
1 i a me has special educa ion in
ag icul u e, 0 o he wise
0.444
(0.497)
0.387
(0.488)
0.056
Fa m size
To al a ailable land o a m (ha)
74.198
(53.805)
79.998
(49.413)
-5.800
Ag onomy
1 i a me has own knowledge on
ag onomy, 0 o he wise
0.462
(0.499)
0.416
(0.494)
0.046
T ac o
1 i a me owns a ac o , 0 o he wise
0.844
(0.363)
0.823
(0.382)
0.021
Ca ing opinion
A a me ca es abou opinion o
neighbo s, ela i es, and o he a me s
(index o 1-5, whe e 1- doesn’ ca e and 5
= e y much ca es)
3.274
(0.753)
3.198
(0.768)
0.076
T aining
1 i a me pa icipa es in ainings
ela ed o SAPs, 0 o he wise
0.359
(0.480)
0.262
(0.440)
0.097***
F ee decision
A a me is ee o decide wha c op o
cul i a e and whe e o sell ha es (index
o 1-5, whe e 1-no ee and 5= ully ee)
2.398
(1.165)
1.870
(1.200)
0.528***
~ 73 ~
Table 4.2 con .
Va iables
Desc ip ion
Pa icipan
(N=507)
Non-
pa icipan
(N=351)
Mean di
Mean
Mean
Land enu e
1 i a me pe cei es no losing land
igh s in he nex 3 yea s, 0 o he wise
0.469
(0.499)
0.595
(0.492)
-0.126***
Soil e ili y
Index o pe cei ed soil e ili y (0-low
e ili y … 1-high e ili y)
0.669
(0.406)
0.615
(0.382)
0.054*
WUA supplies
wa e
1 i i iga ion wa e o a m ield supplied
mos ly by local wa e use associa ion, 0
o he wise
0.712
(0.453)
0.442
(0.497)
0.270***
Canal condi ion
1 i a me sa is ied abou condi ion o
i iga ion and d ainage canals, 0
o he wise
0.921
(0.270)
0.806
(0.396)
0.115***
Plo loca ion
1 i a m ield is loca ed a he head o
he wa e sou ce, 0 o he wise
0.233
(0.423)
0.182
(0.387)
0.050*
Dis ance o house
Dis ance o he house om a m ield
(km)
4.403
(5.041)
4.098
(5.014)
0.305
Dis ance o local
ma ke
Dis ance o he local ma ke om a m
ield (km)
12.301
(6.183)
14.610
(6.791)
-2.309***
Yea
1 i obse a ions belong o 2022, 0
o he wise
0.562
(0.497)
0.330
(0.471)
0.232***
No e: S anda d de ia ion a e epo ed in pa en heses; ***, ** and * a e signi ican a p<0.01,
p<0.05 and p<0.1 le el, espec i ely.
Sou ce: Au ho s.
In he ques ionnai e, a me s we e asked abou he signi icance hey a ibu e o he opinions
o neighbo s, ela i es, and a m colleagues. Responden s we e equi ed o selec esponses on
a ca ego ical scale anging om 1 o 5, whe e "1" deno ed "no a all" and "5" signi ied " e y
much". The wo ca ego ical ques ions, namely "how much hey ca e abou he opinion o
neighbo s and ela i es" and "how much hey ca e abou he opinion o a m colleagues," we e
used o compu e he a e age, he eby gene a ing a new con ol a iable called " a me s’ ca ing
opinion o neighbo s, ela i es, and a m colleagues".
In addi ion, he ques ionnai e also p o ides in o ma ion on “ o wha ex en a me s a e ee in
c op cul i a ion and c op o a ion o use” and “how ee a me s a e in deciding whe e o sell
hei main ha es ed c ops.”. The esponses o hese ca ego ical ques ions anged om "1= I
canno decide mysel " o "5= I is ully my decision." The con ol a iable " eedom o decide
~ 74 ~
c op cul i a ion and selling" ep esen s he a i hme ic a e age o a me s’ esponses o each o
hese ques ions.
As indica ed be o e, in he s udy, main ou come a iable is he in ensi y o SAP use, calcula ed
as in Equa ion 4.1. The summa y s a is ics show ha he in ensi y o SAP use is sligh ly highe
o non-pa icipan a me s han pa icipan s, bu he di e ence is ela i ely small. The da a
p esen ed in Table 4.2 also show signi ican di e ences in pa icipa ion in ainings, ee decision
o c op cul i a ion and selling, land enu e secu i y, soil e ili y, condi ion o i iga ion and
d ainage, a m ield loca ion ela i e o wa e esou ces and dis ance o he local ma ke om
a m ield be ween pa icipan and nonpa icipan a me s. Ne e heless, a simplis ic
compa ison o mean di e ences be ween pa icipan and nonpa icipan a me s ails o
conside po en ial con ounding ac o s con ibu ing o hese dispa i ies. Consequen ly, I employ
a s a e-o - he-a econome ic me hod, namely he ma ginal ea men e ec model, o
disen angle biases s emming om sel -selec ion in o pa icipa ion in in o mal coope a ion, and,
in u n, o analyze i s in luence on he in ensi y o SAP adop ion.
4.4 Me hodological app oach
4.4.1 Me hodological app oach o he de e minan s o pa icipa ion in in o mal
coope a ion
Following exis ing li e a u e such as Addai e al. (2023), And esen (2018), Dubbe e al. (2023),
his s udy employs he ma ginal ea men e ec (MTE) app oach. Thus, I assume ha
pa icipa ion in he in o mal coope a ion o a a m 𝑖 is a bina y a iable indica ed by 𝐺𝑖. The
assump ion is ha a a me pa icipa es in in o mal coope a ion in wa e managemen wi h
o he a me s, whe e hey sha e ag icul u al knowledge and in o ma ion ha imp o es he SAP
adop ion le el. Pa icipa ion in in o mal coope a ion can be exp essed as a unc ion o
obse able and unobse able elemen s in he ollowing la en a iable model:
𝐺𝑖∗=𝛽𝐺(𝑍)−𝑉𝑖 (4.2)
~ 75 ~
wi h 𝐺𝑖=1 i 𝐺𝑖∗≥0 and 𝐺𝑖=0 o he wise, whe e 𝐺𝑖 is a bina y indica o ha equals 1 i a a m
pa icipa es in in o mal coope a ion, and ze o o he wise. 𝑍=(𝑋𝑖,𝑍
𝑖) s and o a ec o o
obse able a iables gi en in igu e 4.1 as 𝑋𝑖 ha in luence he ou come equa ion o he
in ensi y o SAP adop ion, and an ins umen o iden i ica ion, 𝑍
𝑖, excluded om he ou come
equa ion. In ou case, 𝑍
𝑖, is he dis ance o a local ma ke om a m ield. 𝛽𝐺 is a ec o o
pa ame e s o be es ima ed. 𝑉𝑖 is he unobse ed esis ance o ea men o pa icipa ion in
in o mal coope a ion, i.e., he e o e m. The nega i e sign associa ed wi h he e o e m in
he selec ion equa ion signi ies he unobse ed cha ac e is ics ha migh dec ease he
likelihood o an indi idual a me engaging in in o mal coope a ion. In he MTE li e a u e, i is
commonly known as he “unobse ed esis ance" o he ea men (And esen, 2018; Dubbe
e al., 2023). Fa me s wi h high alues o 𝑉 a e less likely o pa icipa e (high esis ance o
pa icipa e) in in o mal coope a ion, compa ed o a me s wi h low 𝑉 alues who a e mo e likely
o pa icipa e (low esis ance o pa icipa e) in in o mal coope a ion.
4.4.2 Me hodological app oach o he impac o pa icipa ion in in o mal coope a ion on
adop ion le el o sus ainable ag icul u al p ac ices
I se ou he model o he ela ionship be ween pa icipa ion in in o mal coope a ion and SAP
adop ion le el by ollowing an app oach p esen ed by Dubbe e al. (2023). 𝑆1𝑖 deno es he
in ensi y o SAP adop ion o a me 𝑖 unde he assumed condi ion whe e he a me is ea ed,
ha is, pa icipa es in in o mal coope a ion. 𝑆0𝑖 ep esen s he in ensi y o SAP adop ion unde
he assump ion ha he a me 𝑖 is no ea ed, and does no pa icipa e in in o mal
coope a ion. This ela ionship be ween SAP adop ion in ensi y 𝑆𝑗𝑖 and pa icipa ion in in o mal
coope a ion can be modelled as ollows:
𝑆𝑗𝑖 =𝛽𝑗𝑋𝑖+𝑈𝑗𝑖 𝑗=0,1 (4.3)
whe e 𝑋𝑖 s ands o a ec o o obse able a iables as in Equa ion 4.2. 𝛽𝑗 ec o o pa ame e s
o be es ima ed. 𝑈𝑗𝑖 is he e o e m ep esen ing unobse ed cha ac e is ics ha a ec SAP
~ 82 ~
poin s. In con as , in he pa icipa ion s a e a me s’ owne ship o a ac o dec eases SAP
adop ion in ensi y by 37.3 pe cen age poin s. Schola s emphasize ha adop ion o SAPs, such
as conse a ion ag icul u e, o any new echnology equi e su icien inancial well-being (e.g.,
Knowle and B adshaw, 2007). Fa me s wi h hei own ac o a e conside ed weal hie and may
be mo e likely adop SAPs. Such a me s ha e also g ea e access o c edi as hey a e able o
use hei ac o s as colla e al (Ruzzan e e al., 2021). Howe e , in he pa icipa ion s a e a me s
may coope a e wi h hei pee s on sha ing ac o s and less likely o adop echnologies such as
ze o illage (Ngoma, 2018; Tadjie e al., 2023a).
Soil e ili y also ends o p oduce di e en ial e ec s on ea ed and un ea ed a me s. The
es ima ed esul s demons a e ha SAP adop ion in ensi y may dec ease by 21 pe cen age
poin s o a me s wi h be e soil e ili y in he non-pa icipa ion s a e. This phenomenon may
be a ibu ed o cases whe e a ms wi h e ile soil migh neglec he implemen a ion o soil
managemen me hods, such as c op o a ion o in e c opping. Knowle and B adshaw (2007)
showed an in e se co ela ion be ween highly p oduc i e soil and he adop ion o conse a ion
ag icul u al p ac ices. Howe e , when a me s wi h be e soil e ili y pa icipa e in in o mal
coope a ion hei in ensi y o SAP adop ion inc eases by 36.2 pe cen age poin s.
In he un ea ed s a e, i a local WUA is p ima y supplie o i iga ion wa e o a me s, in ensi y
o SAP adop ion will dec ease by 68.5 pe cen age poin s. Howe e , i a me s pa icipa e in
in o mal coope a ion hei in ensi y o SAP adop ion will inc ease by 64.7 pe cen age poin s
when he i iga ion wa e is mainly supplied by a local WUA. This highligh s he impo ance o a
WUA in encou aging a me s o pa icipa e in in o mal coope a ion ha can inc ease he
in ensi y o SAPs adop ion
The coe icien o land enu e secu e in Table 4.4 is posi i e in he non-pa icipa ion s a e,
indica ing ha a me s wi h mo e land enu e secu i y will ha e mo e in ensi y o SAP adop ion.
Howe e , he esul is nega i e bu no s a is ically signi ican in he pa icipa ion s a e.
~ 83 ~
Mo eo e , in he non-pa icipa ion s a e, he in ensi y o SAP adop ion will dec ease by 1.6
pe cen age poin s o a me s who ha e a m ields in dis ance om home dwellings.
In ou model, we also con ol yea dummy (he e 2019 is he e e ence yea ) a iable o be e
unde s and yea di e ences o he in ensi y o SAP adop ion in bo h he non-pa icipa ion and
pa icipa ion s a e. The esul shows ha in he non-pa icipa ion s a e, he in ensi y o SAP
adop ion is highe by 113 pe cen age poin s in 2022 compa ing o 2019, in con as , in he
pa icipa ion s a e, he in ensi y o SAP adop ion is lowe by 83.3 pe cen age poin s in 2022
compa ing o 2019.
~ 84 ~
Table 4.4: Ou come equa ions
Va iables
(1)
(2)
Ou come (𝛽0)
Ou come (𝛽1−𝛽0)
Coe .
S d.e
[90% con idence in e al]
Coe .
S d.e
[90% con idence in e al]
Age
-0.001
0.005
-0.009
0.006
0.004
0.008
-0.009
0.016
Educa ion
0.024
0.108
-0.153
0.201
-0.004
0.169
-0.282
0.275
Fa m size
-0.004
0.001
-0.006
-0.002
0.008
0.002
0.004
0.011
Ag onomy
-0.021
0.088
-0.166
0.125
0.056
0.148
-0.188
0.299
T ac o
0.223
0.118
0.028
0.418
-0.373
0.200
-0.702
-0.044
Ca ing opinion
-0.027
0.059
-0.124
0.070
-0.076
0.095
-0.233
0.080
T aining
0.021
0.106
-0.153
0.196
0.274
0.162
0.008
0.540
F ee decision
-0.097
0.072
-0.215
0.021
0.080
0.112
-0.103
0.264
Land enu e
0.250
0.114
0.063
0.437
-0.222
0.208
-0.565
0.121
Soil e ili y
-0.210
0.116
-0.400
-0.019
0.362
0.193
0.044
0.680
WUA supplies wa e
-0.685
0.179
-0.979
-0.390
0.647
0.315
0.128
1.167
Canal condi ion
-0.020
0.123
-0.223
0.182
-0.100
0.303
-0.598
0.399
Plo loca ion
0.013
0.118
-0.180
0.207
-0.038
0.180
-0.335
0.259
Dis ance o house
-0.016
0.008
-0.030
-0.002
0.009
0.013
-0.013
0.031
Yea
1.132
0.197
0.808
1.456
-0.833
0.301
-1.329
-0.338
Cons an
0.232
0.249
-0.178
0.643
-0.196
0.560
-1.118
0.726
Tes o obse ed he e ogenei y,
p- alue
0.005
Tes o essen ial he e ogenei y,
p- alue
0.049
Numbe o obse a ions
858
No e: Columns 1 and 2 o e he es ima es o he in ensi y o SAPs adop ion equa ion in he non-pa icipa ion and no pa icipa ion in in o mal coope a ion
s a es ( he di e ence be ween pa icipa ion and non-pa icipa ion), espec i ely. The epo ed es he e ogenei y shows whe he he ea men e ec
(𝛽1−𝛽0) a ies ac oss he obse ed co a ia es (Addai e al., 2023; And esen, 2018; Dubbe e al., 2023).
Sou ce: Au ho s.
~ 85 ~
4.5.3 A e age and ma ginal ea men e ec s es ima es
The main goal o his s udy is o be e unde s and how a me s’ pa icipa ion in in o mal
coope a ion in wa e managemen ends o impac he in ensi y o SAP adop ion. This sec ion
helps in asce aining whe he a me s bene i om pa icipa ion in in o mal coope a ion and
how hese e ec s di e wi h ega d o hei unobse ed cha ac e is ics.
The MTE cu e in Figu e 4.4 illus a es he dis ibu ion o ma ginal e u ns o ea men o e
a ying le els o unobse ed esis ance o ea men ( e e ed o as 𝑈𝐺), speci ically he
esis ance o pa icipa ion in in o mal coope a ion among a me s. I shows a downwa d slope,
wi h ela i ely high ea men e ec s abo e 2 a he beginning o 𝑈𝐺 dis ibu ion and e en ually
declining o nega i e e ec s below -2 a he igh end o he dis ibu ion, sugges ing ha he
e ec o pa icipa ion in in o mal coope a ion on he in ensi y o SAP adop ion a ies wi h le els
o unobse ed cha ac e is ics.
In Figu e 4.4, he ATE line s ays a a ound 0.33, and he downwa d sloping pa e n implies
posi i e selec ion on unobse able pa e ns. This inding hus ells us ha , gi en he unobse ed
cha ac e is ics, a me s who a e mo e likely o pa icipa e in in o mal coope a ion in wa e
managemen ha e highe in ensi y o SAP adop ion om pa icipa ion. This pa e n o
unobse ed he e ogenei y in e u ns o pa icipa ion is s a is ically signi ican a he 5% le el
( he p- alues o he es o unobse ed he e ogenei y is gi en in Table 4.4) o he in ensi y o
SAP adop ion. Consequen ly, a lowe le el o unobse ed esis ance o pa icipa ion (high
p opensi y o pa icipa e in in o mal coope a ion) is linked wi h a highe in ensi y o SAP
adop ion, bu he in ensi y o SAP adop ion ends o dec ease as he unobse ed esis ance o
pa icipa ion inc eases.
~ 86 ~
Figu e 4.4: MTE cu e o he in ensi y o SAPs adop ion
Sou ce: Au ho s.
Table 4.5 pu s o wa d a summa y o he ea men e ec s in e ms o SAP adop ion om he
pa icipa ion in in o mal coope a ion. The esul o he ATE shows ha pa icipa ion in in o mal
coope a ion signi ican ly inc eases he in ensi y o SAPs adop ion o he a e age a me . The
ATE es ima ion o SAPs adop ion is 0.328, which indica es ha andomly selec ing a me s om
he popula ion and ha ing hem pa icipa e in in o mal coope a ion inc eases he in ensi y o
SAP adop ion by 32.8 pe cen age poin s.
The indings o he ATT, which pu mo e weigh on a me s wi h high p opensi y sco es o
pa icipa ion, sugges ha pa icipa ion in in o mal coope a ion signi ican ly – in his case, by
98.3 pe cen age poin s - inc eases SAPs adop ion in ensi y o he a e age a me who
pa icipa es in in o mal coope a ion. On he o he hand, he ATUT es ima es p esen s ha
pa icipa ion in in o mal coope a ion would dec ease he in ensi y o SAP adop ion o he
a e age un ea ed a me , bu he hypo hesis ha ATUT equal o ze o canno be ejec ed.
A gene al pic u e o he es ima es shows ha he coe icien o ATT is g ea e han he ATE,
which is also g ea e han he ATUT: ATT (0.983)>ATE (0.328)>ATUT (−0.616). This
~ 87 ~
anking o h ee e ec measu emen s indica es posi i e selec ion on gains, whe e a me s who
a e p obable o pa icipa e in in o mal coope a ion end o bene i mo e om pa icipa ion in
e ms o he in ensi y o SAP adop ion. The inding con i ms he s udy by Willy and Holm-Mülle
(2013), who ound ha pa icipa ion in collec i e ac ion enhances soil conse a ion e o s.
Table 4.5 also p o ides he esul o an es ima ion o he local a e age ea men e ec (LATE).
The LATE es ima e o SAP adop ion is 0.664. This indica es ha a me s who pa icipa e in
in o mal coope a ion due o close loca ion o he local ma ke inc ease he in ensi y o SAP
adop ion by 66.4 pe cen age poin s.
Table 4.5: A e age ea men e ec s
The in ensi y o SAP adop ion
Coe .
S d.e .
[90% con idence
in e al]
ATE
0.328
0.183
0.026
0.629
ATT
0.983
0.466
0.216
1.750
ATUT
-0.616
0.514
-1.462
0.230
LATE
0.664
0.170
0.383
0.944
Tes o essen ial he e ogenei y, p- alue
0.049
Sou ce: Au ho s.
~ 88 ~
5 GENERAL CONCLUSIONS AND POLICY IMPLICATIONS
The p esen sec ion s a s by summa izing he esea ch indings o h ee empi ical chap e s o
he disse a ion. Following his, i de i es impo an policy messages based on he esea ch
indings. Finally, i p esen s esea ch limi a ions and ou look o u he esea ch.
5.1 Syn hesis o esea ch indings
O e all, he h ee empi ical chap e s o he disse a ion p o ide a comp ehensi e analysis o
SAPs in Cen al Asia and emphasize hei di e se and con ex -speci ic na u e, highligh ing he
po en ial o p ac ices like c op o a ion and ze o illage in imp o ing a m ou comes, and he
impo ance o in o mal coope a ion in wa e managemen o expansion o sus ainable
ag icul u e.
Chap e 2 del es in o c op o a ion in Kazakhs an and Uzbekis an, highligh ing i s a iable impac
on co on yields and ne e enues, and how ac o s such as a me age, educa ional aining,
and pe cep ions o land enu e secu i y play a ole. Chap e 3's ocus on ze o illage in Ky gyzs an
sheds ligh on i s economic ade-o s o smallholde s, balancing inpu cos sa ings agains
inc eased labo and he bicide expenses. Chap e 4’s explo a ion o in o mal coope a ion in
wa e managemen in Uzbekis an e eals i s in luence on he adop ion o sus ainable p ac ices,
unde sco ed by a iables like a me s’ ag onomy knowledge and he quali y o ag icul u al
in as uc u e. These chap e s unde sco e he di e se and con ex -speci ic na u e o adop ion
o sus ainable ag icul u e in Cen al Asia.
Chap e 2 examined ac o s in luencing he adop ion o c op o a ion and i s impac on co on
yields and ne e enues o co on g owe s by applying pa ame ic and nonpa ame ic empi ical
me hods on c oss-sec ional a m su ey da a om Cen al Asia’s wo co on-g owing a eas. The
esul s demons a ed ha sample selec ion bias could ha e occu ed i he impac o c op
o a ion was es ima ed wi hou conside ing obse able and unobse able ac o s in he
adop ion decision. Thus, o con ol o he selec ion bias issues a ising om obse able and
~ 89 ~
unobse able ac o s, he ESR model is employed ha es ima es di e en ial impac s o adop ion
o c op o a ion on con inuous ou come a iables like co on yields and ne e enues. The model
esul s sugges ed he p esence o selec ion bias. A e con olling o he bias, he es ima ion
esul s showed ha c op o a ion inc eases co on yields by 5% and ne e enue by 19% in
Uzbekis an. Howe e , an opposi e (nega i e) impac is e ealed o Kazakhs an. Such
unexpec ed impac o c op o a ion on pe o mance o co on g owe s in Kazakhs an is
explained by he ac ha he exis ing ins i u ional en i onmen and in as uc u e in Kazakhs an
a e he co on sec o e o m ac ually p o ide ad an age o a me s who p ac ice con en ional
co on monocul u e. The la ge ole he e can be assigned o con ac ual a angemen s wi h
p i a e gins who a o a me s cul i a ing co on each yea , i.e. co on monocul u e (Pe ick e
al., 2017). As a esul , Kazakh a me s who op o he soil-imp o ing c op o a ion scheme a e
mos likely o end up ou side such con ac ual a angemen s and lose imely access o ex e nal
inpu s like co on seeds, e ilize , pes icides and machine y supplied by a p i a e ginne y
h ough con ac a ming.
Fu he mo e, he esul s p o ided insigh s in o ac o s a ec ing a me s’ decision o adop c op
o a ion as well as i s impac on co on yields and ne e u ns. In Kazakhs an, c op o a ion
adop ion posi i ely associa es wi h a me s’ age, pa icipa ion in a m ainings, pe cep ion
abou i iga ion canal condi ion, and illage sha e o adop e s. The esul s also sugges ed ha
Kazakh a me s who lea ned abou new echnologies and ag onomy om pee s and neighbo s
we e less likely o use c op o a ion. In Uzbekis an, he p obabili y o adop ion o c op o a ion
is highe among c edi - a ioned a me s and hose who pe cei e land enu e as secu e. Uzbek
a me s in emo e a eas, a i iga ion canal heads, and who ecei e ag icul u al in o ma ion om
he in e ne , media, and adio a e less likely o adop c op o a ion. The esul s showed ha
p oduc ion inpu s we e impo an de e minan s o co on cul i a ion in bo h s udy a eas. In
Kazakhs an, he in ensi y o e ilize use a ec s co on yields o non-adop e s, while size o
co on sown a ea is an impo an ac o o adop e s. In Uzbekis an, co on sown a ea is
~ 90 ~
posi i ely ela ed wi h ne e u ns o non-adop e s, and e ilize use posi i ely a ec s co on
yields o non-adop e s. In Kazakhs an, con ac a ming wi h p i a e ginne ies aises ou comes
in ‘con en ional co on’ monocul u e, bu pu s adop e s o c op o a ion in disad an age.
Fu he mo e, in bo h coun ies, c op o a ion allows be e use o he ad an age o close
loca ion o i iga ion canals.
Chap e 3 measu ed he adop ion de e minan s and esou ce-sa ing e ec s o ze o illage
among smallholde s in Ky gyzs an by using pa ame ic and nonpa ame ic empi ical me hods
on wo wa es o longi udinal da a. The indings sugges ha ze o illage can be an a ac i e
op ion o esou ce-poo smallholde s loca ed in emo e a eas. The p obabili y o ze o illage
adop ion is posi i ely associa ed wi h household head’s employmen in ag icul u e, and dis ance
o household dwellings o household ields and main oad. Fu he mo e, he p obabili y o ze o
illage adop ion is nega i ely ela ed o household weal h measu ed in asse index and numbe
o household plo s as well as e ilize applica ion. The indings sugges ha ze o illage can
gene a e angible bene i s o smallholde s in e ms o educing inpu cos s by 15%. A he same
ime, ze o illage adop ion a ec s he s uc u e o p oduc ion cos s. As expec ed i educes
machine y cos s o land p epa a ion and seeding by almos 23%. As a esul , o subs i u ing he
machine y se ices wi h ex e nal wo ke s, ze o illage can inc ease hi ed labo cos s by 13%.
Fu he mo e, ze o illage inc eases he bicide cos s by 15%.
Chap e 4 p o ides i s - ime es ima ion o ma ginal e ec s o pa icipa ion in in o mal
coope a ion in wa e managemen on he adop ion in ensi y o sus ainable ag icul u al
p ac ices in a de eloping coun y se ing. To do so, i uses wo yea s 2019 and 2022 su ey da a
o a me s in Uzbekis an and employed empi ical me hods. The MTE model was used o accoun
o selec ion bias, and obse able and unobse able he e ogenei y among a me s.
Fu he mo e, I in es iga e main de e minan s o a me s’ pa icipa ion in in o mal coope a ion.
The indings showed ha he p obabili y o pa icipa ion in in o mal coope a ion is posi i ely
ela ed o a me s’ knowledge on ag onomy, ac o owne ship, pa icipa ion in SAP ainings,
~ 91 ~
decision making eedom, sou ces o i iga ion wa e supply, and quali y o i iga ion and
d ainage in as uc u e. Fu he mo e, he p obabili y o pa icipa ion in in o mal coope a ion is
nega i ely associa ed wi h land enu e secu i y and dis ance o he local ma ke . The indings
indica e ha pa icipa ion in in o mal coope a ion eme ges as a a o able p ospec o a me s
who possess educa ional backg ound in ag icul u e and ha e imp o ed condi ion o i iga ion
and d ainage in as uc u e. The empi ical esul s showed signi ican he e ogenei y in he
in luence o in o mal coope a ion on he in ensi y o SAP adop ion. No ably, he esul s
pe aining o obse able cha ac e is ics sugges ha a me s possessing own ac o s end o
mo e likely pa icipa e in in o mal coope a ion. Howe e , i s ea men e ec s e eal ha he
in ensi y o SAP adop ion le el becomes lowe compa ed o nonpa icipan a me s.
Addi ionally, pa icipa ion in in o mal coope a ion exhibi s a p opensi y o posi i ely in luence
he in ensi y o SAP adop ion, pa icula ly among a me s endowed wi h la ge land holdings
and be e soil e ili y. Finally, he esul s on ea men e ec s e ealed ha a me s who a e
likely o pa icipa e in in o mal coope a ion end o bene i mo e om he pa icipa ion in e ms
o highe in ensi y o SAP adop ion.
The h ee empi ical chap e s unde sco e he c ucial ole o SAPs in enhancing a m ou comes as
exempli ied in di e en se ings o Cen al Asia. The analyzed p ac ices, including c op o a ion
and ze o illage, demons a e signi ican po en ial o imp o ing c op yields and a m e enues.
Fu he mo e, he s udies highligh he pi o al ole o in o mal coope a ion in wa e
managemen . This coope a ion is key o enhancing he adop ion in ensi y o SAP, e lec ing he
impo ance o communi y-le el engagemen in wa e esou ce managemen o os e ing
ag icul u al sus ainabili y in he egion.
5.2 Policy ecommenda ions
Empi ical indings p esen ed in his disse a ion allow o de i e policy ecommenda ions ela ed
o he p omo ion o he adop ion o sus ainable ag icul u al p ac ices among a me s and
~ 98 ~
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APPENDIX
Table A1: Falsi ica ion es o ins umen al a iables
Kazakhs an
Uzbekis an
Join signi icance
es
p- alue
Join
signi icance es
p- alue
C op o a ion adop ion
(p obi model eg ession)
χ2 (3)=7.81
0.05
χ2 (3)= 6.93
0.03
Ne e u ns om co on o
non-adop e s
F(3, 197) = 0.55
0.65
F(2, 227) =0.22
0.80
Co on yield o non-
adop e s
F(3, 197) = 0.78
0.51
F(2, 227) =0.18
0.83
No e: IV a e In o ma ion sou ce abou new echnologies and ag onomy (i.e., in o ma ion om
o he a ms and communi y, in o ma ion om media, in e ne and adio), and he illage sha e
o adop e s o c op o a ion (only o Kazakhs an o es ima ing he c op o a ion impac ).
Sou ce: Based on AGRICHANGE 2019 a m su ey da a.
~ 118 ~
Table A2 P obi es ima ion on de e minan s o adop ion decision o c op o a ion among co on
g owe s
Kazakhs an
Uzbekis an
Ma ginal
e ec
90% Con idence
In e al
Ma ginal
e ec
[90% Con idence
In e al]
Fa me ’s age (yea ) [age]
0.003
(0.002)
0.001
0.006
0.001
(0.002)
-0.003
0.004
Fa me has educa ion in
ag icul u e (1/0) [a edu]
-0.091
(0.057)
-0.185
0.002
-0.022
(0.044)
-0.094
0.050
Fa me pe cei es canal
condi ion as good (1/0)
[cancon]
0.081
(0.049)
0.001
0.161
0.075
(0.051)
-0.009
0.159
C edi - a ioned a me (1/0)
[c ed a 1]
-0.080
(0.056)
-0.173
0.013
0.109
(0.043)
0.039
0.180
Fa me pa icipa es in a m
ainings (1/0) [pa aining]
0.319
(0.051)
0.235
0.403
-0.063
(0.052)
-0.149
0.022
Sha e o land wi h good e ili y
(0-1) [good e ]
0.014
(0.052)
-0.072
0.101
0.033
(0.051)
-0.051
0.117
Dis ance o he dis ic cen e
(km) [discen e ]
-0.001
(0.002)
-0.003
0.002
-0.009
(0.003)
-0.014
-0.004
Fa m ields loca ed i iga ion
canal head (1/0) [locw ]
0.052
(0.052)
-0.034
0.138
-0.117
(0.057)
-0.212
-0.022
Fa me ecei es in o ma ion
abou new echnologies and
ag onomy om o he a ms
and neighbo s (1/0)
[in s echag ]
-0.083
(0.046)
-0.160
-0.007
-0.018
(0.045)
-0.092
0.056
Fa me ecei es in o ma ion
abou new echnologies and
ag onomy om media, in e ne
o adio (1/0) [media_1]
0.058
(0.058)
-0.037
0.153
-0.117
(0.043)
-0.188
-0.047
Village sha e o adop e s o
c op o a ion (Kazakhs an)
(%)[c sha e_adop _mah]
0.782
(0.396)
0.132
1.433
x
x
x
Fa me pe cei es land enu e
as secu e (1/0) [ n _secu ]
x
x
x
0.210
(0.042)
0.141
0.279
Fa me pa icipa es in con ac
a ming (1/0) [con c m]
-0.033
(0.057)
-0.127
0.062
x
x
x
Fa me loca ed in Sha da a
(1/0)
-0.044
(0.086)
-0.184
0.097
x
x
x
Fa me loca ed in Pas da gom
(1/0)
x
x
x
-0.014
(0.043)
-0.085
0.056
N
278
307
Pseudo R2
0.153
0.172
Wald χ2 (13)/(12)
47.66
46.10
Log likelihood
-126.98
-130.08
No e: S anda d e o in pa en hesis.
Sou ce: Based on AGRICHANGE 2019 a m su ey da a.
~ 119 ~
Table A3: Re iew o empi ical s udies on impac o ze o- illage adop ion on c op p oduc ion cos s
Au ho (s)
Coun y
Da a
Me hod
Main indings
Teklewold e
al. (2013)
E hiopia
Fa m household
su ey
Mul inomial ESR
Adop ion o conse a ion illage signi ican ly inc eased pes icide
applica ion and labo demands.
El‐Sha e e al.
(2016)
Sy ia
Fa m su ey o 621
whea a me s
PSM and ESR
Nega i e ela ionship ound be ween quan i y o e ilize use, quan i y
o labo use and ze o illage adop ion. The majo bene i s o he
adop ion o ZT come om illage and labo cos sa ings.
Jale a e al.
(2016)
E hiopia
Su ey o 12 peasan
associa ions
P obi and ESR models
Minimum illage educes o al labo use, and d a powe use (oxen-
days/ha) o land p epa a ion.
Tessema e al.
(2018)
E hiopia
Households su ey
2 s ep es ima ion
p ocedu e, p obi
model, OLS
Conse a ion illage inc eases he bicide use bu educes emale and
male labo equi emen s.
Keil e al.
(2020)
India
Panel da ase om
961 a m households
ESR
Adop ion o ze o illage leads o educ ion o o al a iable pe -uni
p oduc ion cos
Mon and Luu
(2020)
Eas e n and
Sou he n
A ica
Longi udinal a m
da a o 3,617
households
Mul inomial logi and
ERS models
Conse a ion illage inc eases labo equi emen s in households.
Howe e , minimum illage sa es wo king ime du ing land p epa a ion
and weed con ol.
Yigezu and El‐
Sha e (2021)
Mo occo
Su ey o 995
households
ESR model
Ze o illage has no e ec on he o al amoun o ag icul u al labo use.
E ens ein e al.
(2008)
India &
Pakis an
Su ey o 400
households in India
and 458 households in
Pakis an
Desc ip i e analysis
Resou ce-sa ing e ec s in diesel, ac o ime and cos sa ings o whea
cul i a ion. Wa e sa ings a e less p onounced han expec ed om on-
a m ial da a.
K ishna &
Vee il (2014)
India
Su ey o 180
households
P oduc ion unc ion
and semi-pa ame ic
echnical e iciency
es ima ion me hods
Whea p oduc i i y inc eases by 5%, paid-ou a iable cos educes by
14%, while he esou ce use e iciency indi ec ly inc eases by 1%
~ 120 ~
Table A4: Desc ip i e s a is ics o a iables by adop e s and non-adop e s o ze o illage me hods
Va iables
2016 (N=1363)
2019 (N=1425)
Pooled 2016 and 2019 (N=2788)
ZT plo s
(N=93)
non-ZT
plo s
(N=1270)
mean
di e
ZT plo s
(N=290)
non-ZT
plo s
(N=1135)
mean di e
ZT plo s
(N=383)
non-ZT
plo s
(N=2405)
mean di e
Ou come a iables
To al paymen o hi ed labo
(US$/ha)
5.199
10.584
-5.385
8.891
6.199
2.692
7.994
8.514
-0.520
Machine y cos s o land
p epa a ion and seeding (US$/ha)
11.272
36.898
-25.626***
31.281
39.426
-8.146
26.422
38.091
-11.670***
Machine y cos s o weeding
(US$/ha)
3.714
7.395
-3.681
13.935
9.919
4.017
11.453
8.586
2.867
He bicide cos s (US$/ha)
7.940
7.213
0.727
31.644
19.497
12.147***
25.750
12.956
12.794***
To al machine y, labo and
he bicide cos s (US$/ha)
28.124
62.089
-33.955***
84.192
75.283
8.909
70.250
68.257
1.993
Household head cha ac e is ics
Age o household head (yea s)
54.516
55.887
-1.371
56.928
56.001
0.927
56.342
55.941
0.401
Educa ion le el o household head
(ca ego ical,
1=illi e a e…7=uni e si y)
4.677
4.308
0.370***
4.245
4.210
0.035
4.350
4.262
0.088
Female household head (dummy,
1= emale)
0.247
0.195
0.052
0.197
0.267
-0.070**
0.209
0.229
-0.020
Household head employmen in
ag icul u e (dummy, 1 =
occupa ion in ag icul u e)
0.559
0.324
0.235***
0.431
0.313
0.118***
0.462
0.319
0.143***
Household head’s e hnici y
(dummy, 1 = Ky gyz)
0.893
0.779
0.114***
0.790
0.766
0.024
0.815
0.772
0.042*
~ 121 ~
Table A4 con .
Va iables
2016 (N=1363)
2019 (N=1425)
Pooled 2016 and 2019 (N=2788)
ZT plo s
(N=93)
non-ZT
plo s
(N=1270)
mean
di e
ZT plo s
(N=290)
non-ZT
plo s
(N=1135)
mean di e
ZT plo s
(N=383)
non-ZT
plo s
(N=2405)
mean di e
Household a m cha ac e is ics
Numbe o household membe s
ha can wo k in ag icul u e (abo e
10 and unde 65 yea s old)
4.032
4.435
-0.403**
4.766
4.520
0.246*
4.587
4.475
0.113
Asse index
0.425
0.398
0.027*
0.356
0.353
0.003
0.373
0.377
-0.004
Household owns a ac o (dummy,
1=yes)
0.032
0.050
-0.018
0.052
0.030
0.022*
0.047
0.041
0.006
Numbe o li es ock uni s owned
by household
4.750
3.262
1.487***
2.766
2.278
0.488*
3.247
2.798
0.450*
Household ecei ed emi ance las
yea (dummy, 1=yes)
0.065
0.151
-0.087**
0.217
0.228
-0.011
0.180
0.188
-0.007
Household applied chemical
e ilize las yea (dummy,
1=applied)
0.129
0.261
-0.132***
0.221
0.253
-0.032
0.198
0.257
-0.059**
Household expe ienced a wea he
shock las yea (dummy, 1=yes)
0.849
0.613
0.237***
0.186
0.154
0.032
0.347
0.396
-0.049*
Household expe ienced an
ag icul u al shock las yea
(dummy, 1=yes)
0.204
0.376
-0.171***
0.155
0.070
0.085***
0.167
0.232
-0.065***
Plo unde g ains and legumes
(dummy, 1=yes)
0.538
0.302
0.235***
0.338
0.357
-0.019
0.386
0.328
0.058**
Plo unde ege ables (dummy,
1=yes)
0.043
0.426
-0.383***
0.245
0.277
-0.032
0.196
0.356
-0.160***
Plo unde a mix o c ops (g ain,
legumes and ege ables) (dummy,
1=yes)
0.097
0.071
0.026
0.007
0.026
-0.019*
0.029
0.050
-0.021
~ 122 ~
Table A4 con .
Va iables
2016 (N=1363)
2019 (N=1425)
Pooled 2016 and 2019 (N=2788)
ZT plo s
(N=93)
non-ZT
plo s
(N=1270)
mean
di e
ZT plo s
(N=290)
non-ZT
plo s
(N=1135)
mean di e
ZT plo s
(N=383)
non-ZT
plo s
(N=2405)
mean di e
Loca ion cha ac e is ics
Dis ance o main oad om
dwelling (km)
0.381
0.531
-0.150*
0.945
0.734
0.211***
0.808
0.627
0.181***
Dis ance om dwelling o plo (km)
2.085
1.425
0.660**
1.398
1.117
0.281*
1.565
1.280
0.285*
Numbe o land plo s owned by
household
2.323
1.939
0.383***
1.776
2.033
-0.257***
1.909
1.983
-0.075***
Plo size (ha)
1.090
0.665
0.425***
1.007
0.739
0.268**
1.027
0.700
0.327***
Ins i u ional se ings
Amoun o c edi ecei ed by
household las yea (US$)
441.183
193.236
247.9***
624.287
266.337
357.950***
579.826
227.735
352.091***
P o inces
Issyk Kul
0.419
0.141
0.278***
0.241
0.163
0.078***
0.285
0.151
0.133***
Djalal Abad
0.118
0.220
-0.101**
0.155
0.212
-0.057**
0.146
0.216
-0.070***
Na yn
0.161
0.066
0.095***
0.041
0.049
-0.008
0.070
0.058
0.012
Ba ken
0.172
0.121
0.051
0.217
0.098
0.119***
0.206
0.110
0.096***
Osh
0.075
0.274
-0.199***
0.152
0.329
-0.177***
0.133
0.300
-0.167***
Talas
0.011
0.084
-0.073**
0.017
0.084
-0.066***
0.016
0.084
-0.068***
Chuy
0.043
0.094
-0.051
0.176
0.065
0.111***
0.144
0.080
0.063***
No e: ***, ** and * a e signi ican a 1%, 5% and 10% le el, espec i ely.
Sou ce: Based on 2016 and 2019 wa es o he LiK da a.
~ 123 ~
Table A5: Falsi ica ion es o ins umen al a iable
Va iables
Tes
p- alue
Ze o illage adop ion (p obi model eg ession)
χ2 (1)=7.68
0.006
To al paymen o hi ed labo (US$/ha) (ln)
F(1, 2376) = 0.23
0.631
Machine y cos s o land p epa a ion and seeding
(US$/ha) (ln)
F(1, 2376) = 0.53
0.466
Machine y cos s o weeding (US$/ha) (ln)
F(1, 2376) = 0.00
0.946
He bicide cos s (US$/ha) (ln)
F(1, 2356) = 0.00
0.966
To al machine y, labo and he bicide cos s
(US$/ha) (ln)
F(1, 2356) = 0.01
0.934
No e: He e, “dis ance o he main oad” is ins umen a iable.
Sou ce: Based on 2016 and 2019 wa es o he LiK da a.
~ 130 ~
Table A6 con . ( o o al cos )
To al machine y, labo and he bicide cos s (US$/ha) (ln)
ZT plo s
nZT plo s
Coe .
[90% con idence in e al]
Coe .
[90% con idence in e al]
Age o household head (yea s)
-0.003
-0.036
0.030
-0.009
-0.022
0.003
Educa ion le el o household head (ca ego ical,
1=illi e a e…7=uni e si y)
0.025
-0.110
0.161
-0.028
-0.086
0.031
Female household head (dummy, 1= emale)
-0.388
-0.864
0.089
0.169
-0.007
0.345
Household head employed in ag icul u e (dummy, 1 = occupa ion as
ag icul u e)
1.066
0.201
1.931
0.442
0.127
0.756
Numbe o household membe s ha can wo k in ag icul u e (abo e
10 and unde 65 yea s old)
-0.020
-0.311
0.271
0.044
-0.046
0.135
Household head’s e hnici y (dummy, 1 = Ky gyz)
-0.026
-0.617
0.565
0.174
-0.012
0.359
Asse s index
-0.471
-3.284
2.342
0.408
-0.512
1.328
Household owns a ac o (dummy, 1=yes)
1.059
-0.413
2.531
0.361
-0.262
0.985
Plo size, (ha)
-0.013
-0.095
0.070
-0.020
-0.057
0.018
Household ecei ed emi ances las yea (dummy, 1=yes)
-0.539
-1.375
0.298
0.115
-0.155
0.384
Household expe ienced a wea he shock las yea (dummy, 1=yes)
0.734
-0.009
1.478
0.551
0.300
0.803
Household expe ienced an ag icul u al shock las yea (dummy,
1=yes)
0.239
-0.546
1.024
-0.155
-0.430
0.121
Dis ance om dwelling o plo (km)
0.066
-0.009
0.140
0.119
0.089
0.149
Household applied chemical e ilize s las yea (dummy, 1=applied)
1.059
0.202
1.916
1.233
1.006
1.459
Numbe o land plo s owned by household
-0.363
-0.733
0.008
-0.005
-0.126
0.117
Numbe o li es ock uni s owned by households
-0.051
-0.146
0.044
0.004
-0.026
0.034
Amoun o c edi s ecei ed by household las yea (loga i hm, US$)
0.071
-0.041
0.184
-0.021
-0.062
0.020
Plo unde g ains and legumes (dummy, 1=yes)
1.250
0.780
1.719
1.565
1.366
1.764
Plo unde ege ables (dummy, 1=yes)
0.511
-0.349
1.371
0.346
0.131
0.561
Plo unde a mix o c ops (g ain, legumes and ege ables) (dummy,
1=yes)
1.301
0.274
2.328
1.489
1.166
1.812
~ 131 ~
Table A6 con . ( o o al cos )
To al machine y, labo and he bicide cos s (US$/ha) (ln)
ZT plo s
nZT plo s
Coe .
[90% con idence in e al]
Coe .
[90% con idence in e al]
Djalal-Abad
-1.850
-2.889
-0.810
-1.451
-1.779
-1.122
Na yn
-1.249
-2.159
-0.338
-1.181
-1.536
-0.825
Ba ken
0.624
-0.094
1.342
-0.974
-1.271
-0.677
Osh
0.236
-1.228
1.699
-0.740
-1.123
-0.358
Talas
-2.240
-5.034
0.553
-0.975
-1.476
-0.473
Chuy
-0.270
-1.290
0.750
-0.776
-1.119
-0.433
Su ey yea (2019=1)
1.611
-0.604
3.825
-0.022
-0.310
0.265
Mean o age o household head
0.030
-0.008
0.069
0.007
-0.007
0.021
Mean o household head employed in ag icul u e
-0.562
-1.453
0.329
0.075
-0.280
0.430
Mean numbe o household membe s ha can wo k in ag icul u e
0.188
-0.132
0.507
-0.018
-0.117
0.081
Mean o asse s index
1.985
-1.849
5.818
-0.504
-1.705
0.697
Mean o household owns a ac o
-2.306
-4.185
-0.426
-0.090
-0.922
0.742
Mean o household ecei ed emi ances las yea
-0.302
-1.385
0.781
-0.199
-0.550
0.152
Mean o household expe ienced a wea he shock las yea
-0.422
-1.459
0.614
-0.412
-0.756
-0.067
Mean o household expe ienced an ag icul u al shock las yea
-0.712
-1.614
0.191
0.353
-0.033
0.740
Mean o amoun o c edi ecei ed by household las yea (ln)
0.028
-0.118
0.175
0.088
0.034
0.141
Mean o household applied chemical e ilize s las yea
0.972
-0.332
2.276
0.657
0.283
1.032
Mean o numbe o li es ock uni s owned by household
0.092
-0.017
0.201
0.019
-0.017
0.054
yea (2019)*mills1
0.424
-0.745
1.593
x
x
x
mills1
1.887
-0.380
4.153
x
x
x
yea (2019)*mills2
x
x
x
-3.460
-4.762
-2.158
mills2
x
x
x
4.490
2.428
6.553
_cons
-5.434
-10.869
0.002
1.883
1.222
2.544
R2
0.351
0.293
N
373
2365
Sou ce: Based on 2016 and 2019 wa es o he LiK da a.
~ 132 ~
Table A7: P opensi y sco e ma ching (PSM) es ima ion (ATT) esul s (impac o ze o illage
adop ion)
Ou come a iable
ATT (a e age ea men e ec on he ea ed)
Coe icien
S anda d
e o
[90% con idence
In e al]
To al paymen o hi ed labo (USD
$/ha) (ln)
0.170
0.071
0.053
0.287
Machine y cos s o land p epa a ion
and seeding (USD $/ha) (ln)
-0.292
0.159
-0.554
-0.031
Machine y cos s o weeding (USD
$/ha) (ln)
0.120
0.110
-0.062
0.301
He bicide cos (USD $/ha) (ln)
0.128
0.126
-0.080
0.335
To al cos (USD $/ha) (ln)
-0.311
0.167
-0.587
-0.036
Sou ce: Based on 2016 and 2019 wa es o he LiK da a.
Table A8: Falsi ica ion es o check alidi y o ins umen
Fi s -S age
Second-S age
Ins umen
Pa icipa ion in in o mal
coope a ion
In ensi y o Sus ainable Ag icul u al
P ac ices adop ion o Non-pa icipan
Fa me s
Dis ance o he local
ma ke om a m ield
(km)
- 0.012 (0.002)
χ2 = 22.23
p- alue = 0.000
-0.004 (0.003)
F-S a .=1.46
p- alue = 0.228
Sample Size
858
351
No e: S anda d e o s a e epo ed in pa en heses.
Sou ce: Based on AGRICHANGE and SUSADICA a m su ey da a.
~ 133 ~
CURRICULUM VITAE
ABDUSAME TADJIEV
Doc o al esea che
Depa men o Ex e nal En i onmen o Ag icul u e and Policy Analysis
Leibniz Ins i u e o Ag icul u al De elopmen in T ansi ion Economies (IAMO)
Theodo -Liese -S . 2, 06120 Halle (Saale), Ge many
Tel: +49345-2928120; Email: adjie[email p o ec ed]e
Na ionali y: Uzbekis an Da e o bi h: 01/09/1983
P esen Posi ion
Janua y 2019 – p esen Doc o al esea che a he Depa men o Ex e nal En i onmen o
Ag icul u e and Policy Analysis, Leibniz Ins i u e o Ag icul u al De elopmen in T ansi ion
Economies (IAMO). Disse a ion i le: Explo ing he ad an ages and d i e s o sus ainable
ag icul u al p ac ices in Cen al Asia
P o essional expe ience
Sep embe 2008 – Decembe 2018: Assis an P o esso and Resea che a Sama kand
Ins i u e o Ve e ina y Medicine ( o me Sama kand Ag icul u al Uni e si y), Uzbekis an.
Responsibili ies included unde g adua e classes and semina s, ag icul u al economics
esea ch, supe ision o bachelo and mas e s uden s.
Academic Quali ica ions
Janua y 2019 - Sep embe 2024: Ph.D. in Ag icul u al economics, a Leibniz
Ins i u e o Ag icul u al De elopmen in T ansi ion Economies (IAMO), Halle,
Ge many. In he amewo k “SUSADICA” p ojec , I success ully de ended my second
doc o al hesis en i led “Explo ing he ad an ages and d i e s o sus ainable
ag icul u al p ac ices in Cen al Asia” a he acul y o “Na u al Sciences III” o he
Ma in Lu he Uni e si y Halle-Wi enbe g. 23 d Sep embe , 2024.
Janua y 2019 - Sep embe 2020: Ph.D. in Ag icul u al economics (Candida e o Sciences), a
he Tashken Ins i u e o I iga ion and Ag icul u al Mechaniza ion Enginee s (TIIAME) in
Uzbekis an. I success ully de ended my i s doc o al hesis en i led “E alua ion o land and
wa e e o ms and coope a ion among a me s: The case o Sama kand p o ince”
Sep embe 2006 – July 2008: MSc. in Sec o al Economics, Sama kand Ins i u e o Economics
and Se ice, Uzbekis an
Sep embe 2002 – July 2006: BSc. in Ag icul u al Economics, Sama kand Ag icul u al
Uni e si y, Uzbekis an
Pa icipa ion in Resea ch p ojec s
Since 2023 – BMBF- unded p ojec led by IAMO “UzFa mBa ome e Be e unde s anding o
he adop ion o sus ainable ag icul u al p ac ices in Uzbekis an”
2019-2023 – VolkswagenS i ung- unded p ojec led by IAMO “S uc u ed doc o al
p og amme on Sus ainable Ag icul u al De elopmen in Cen al Asia (SUSADICA)”
~ 134 ~
2015-2019 – VolkswagenS i ung- unded p ojec led by IAMO “Ins i u ional change in land
and labou ela ions o Cen al Asia’s i iga ed ag icul u e (AGRICHANGE)”
Rele an Academic T ainings
25/06/2023 – 30/06/2023 – “Panel Da a Analysis”, Ba celona School o Economics, Spain
24/10/2018 – 28/10/2018 – “Capaci y Building o Young Resea che s om Cen al Asia and
A ghanis an in Wa e o Policy S udies”, Dushanbe o ice o OECE, Tajikis an
03/06/2018 – 14/06/2018 – “Regional aining cou se on Applied Econome ic analysis”,
Wes mins e In e na ional Uni e si y in Tashken and IFPRI, Tashken , Uzbekis an
19/02/2017 – 23/03/2017 – “T aining cou se on Ag icul u al economics”, Leibniz Ins i u e o
Ag icul u al De elopmen in T ansi ion Economies (IAMO), Halle (Saale), Ge many
Selec ed publica ions
Tadjie , A., Djanibeko , N., He z eld, T. (2023) Does ze o illage sa e o inc ease p oduc ion
cos s? E idence om smallholde s in Ky gyzs an. In e na ional Jou nal o Ag icul u al
Sus ainabili y 21 (1)
Ku bano , Z., Tadjie , A., Djanibeko , N. (2022) Adop ion o sus ainable ag icul u al p ac ices
and in es men s in p oduc i e asse s in i iga ed a eas o Cen al Asia: Fa m-su ey
e idence om Kazakhs an and Uzbekis an. IAMO Annual 24, pp. 69-79.
Tadjie , A., Ku bano , Z., Djanibeko , N., Go ind, A., Ak amkhano , A. (2023) De e minan s
and impac o a me s' pa icipa ion in social media g oups: E idence om i iga ed a eas
o Kazakhs an and Uzbekis an. IAMO Discussion Pape No. 201, Halle (Saale), IAMO.
Tadjie , A., Djanibeko , N., So iadan, M. K., & He z eld, T. (2025) Pa icipa ion in in o mal
coope a ion in wa e managemen and adop ion o sus ainable ag icul u al p ac ices:
Empi ical e idence om Uzbekis an. Aus alian Jou nal o Ag icul u al and Resou ce
Economics
Ne wo king Ac i i ies and Ou each
Science and policy b ie s
Djanibeko , N., Ku bano , Z., Tadjie , A., Go ind, A., Ak amkhano , A. (2023) Fa me s' social
media g oups o be e ex ension and ad iso y se ices. IAMO Policy B ie No. 46, Halle
(Saale).
Tadjie , A. (2023) Impac s o c op o a ion on he pe o mance o co on g owing a me s in
Cen al Asia. Science B ie 4, SUSADICA p ojec .
O ganiza ion o con e ence session
2021: "Unde s anding Fa me s’ Decisions Towa ds Sus ainable Ag icul u e in I iga ed A eas
o Cen al Asia" a 31 h Vi ual In e na ional Con e ence o Ag icul u al Economis s (ICAE).
Selec ed con e ence p esen a ions
Tadjie , A. (2023) Does adop ion o ze o illage sa e o in ensi y p oduc ion cos s? E idence
om Ky gyzs an. XVII Cong ess o EAAE “Ag i- ood sys ems in a changing wo ld”, Rennes,
F ance.
~ 135 ~
Tadjie , A. (2023) Pa icipa ion in in o mal coope a ion and adop ion o sus ainable
ag icul u al p ac ices. SUSADICA inal symposium, Tashken , Uzbekis an.
Tadjie , A., Djanibeko , N., Sanae , G. (2021) Adop ion o sus ainable ag icul u al p ac ices
in i iga ed a eas o Cen al Asia. XVI Online Cong ess o EAAE.
Tadjie , A. (2021) Impac o sus ainable ag icul u al p ac ices adop ion on ou pu o co on
p oducing a ms. 31s Vi ual ICAE 2021, Neu Delhi /Online, India.
Tadjie , A., Djanibeko , N., Sanae , G. (2020) De e minan s o sus ainable ag icul u al
p ac ices in Cen al Asia. 6 h Annual ‘Li e in Ky gyzs an’ Con e ence, Online, Ge many.
Teaching
Ad hoc lec u es on An econome ic analysis in S a a: Impac e alua ion o sus ainable
ag icul u al p ac ices, 13–14 Ma ch 2023, IAMO, Halle (Saale), Ge many.
Ad hoc lec u es on “Econome ic analysis o a me s’ adop ion decisions o sus ainable
ag icul u al p ac ices” Pa II, 7–11 Ap il 2025, Wes mins e In e na ional Uni e si y in
Tashken (WIUT), Uzbekis an.
Ad hoc lec u es on “Econome ic analysis o a me s’ adop ion decisions o sus ainable
ag icul u al p ac ices”, 7–11 Oc obe 2024, Wes mins e In e na ional Uni e si y in Tashken
(WIUT), Uzbekis an.
P o essional Ac i i ies and Membe ships
In e na ional Associa ion o Ag icul u al Economis s (IAAE),
Eu opean Associa ion o Ag icul u al Economis s (EAAE)
Halle (Saale), 2025
~ 136 ~
Eidess a liche E klä ung / Decla a ion unde Oa h
Ich e klä e an Eides s a , dass ich die A bei selbs s ändig und ohne emde Hil e e ass ,
keine ande en als die on mi angegebenen Quellen und Hil smi el benu z und die den
benu z en We ken wö lich ode inhal lich en nommenen S ellen als solche kenn lich
gemach habe.
I decla e unde penal y o pe ju y ha his hesis is my own wo k en i ely and has been w i en
wi hou any help om o he people. I used only he sou ces men ioned and included all he
ci a ions co ec ly bo h in wo d o con en .
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Da um / Da e Un e sch i des An ags elle s / Signa u e o he applican
