Pasture, grazing, and meat production in Kazakhstan

Hanke son, B e R.
Doc o al Thesis
Pas u e, g azing, and mea p oduc ion in Kazakhs an
Sugges ed Ci a ion: Hanke son, B e R. (2025) : Pas u e, g azing, and mea p oduc ion in
Kazakhs an, Humbold -Uni e si ä zu Be lin, Be lin,
h ps://doi.o g/10.18452/34649 ,
h p://edoc.hu-be lin.de/18452/35289
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/328055
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Humbold -Uni e si ä zu Be lin – Geog aphisches Ins i u
Pas u e, g azing, and mea p oduc ion in
Kazakhs an – cu en s a e and u u e
oppo uni ies
DISSERTATION
zu E langung des akademischen G ades
Doc o e um na u alium (D . e . na .)
im Fach Geog aphie
einge ich e an de
Ma hema isch-Na u wissenscha lichen Fakul ä
de Humbold -Uni e si ä zu Be lin
on
B e Robe Hanke son, M.Sc.
P äsiden in de Humbold -Uni e si ä zu Be lin
P o . D . Julia on Blumen hal
Dekan de Ma hema isch-Na u wissenscha lichen Fakul ä
P o . D . Emil Lis -K a och il
Gu ach e :
P o . D . Pa ick Hos e
P o . D . D . h.c. No be Hölzel
P o . D . F iedhelm Taube
Einge eich am: 25. Mä z 2025
Da um de P omo ion: 8. Sep embe 2025
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iii
Acknowledgmen s
I ha e had he pleasu e and he p i ilege o wo king wi h an as ic people a he Leibniz Ins i u e o
Ag icul u al De elopmen in T ansi ion Economies (IAMO). My supe iso , Daniel Mülle , ga e me he
oppo uni y o check all he boxes o my g adua e esea ch goals: s udy he geog aphy o ag icul u e using
emo e sensing in a o me eas e n bloc coun y a a op Eu opean uni e si y. Daniel p o ided me wi h he
ools I needed o succeed, and he eedom o de elop and adap my esea ch o he mos ele an and
in e es ing opics. I am o e e g a e ul o he many ield ips ha allowed me o di e deep in o Kazakh
ag icul u e and unde s and on he g ound le el he ques ions I was ying o answe wi h sa elli es. Thank
you, Daniel, o s icking wi h me h ough he lean yea s, when i was no a all clea i I would e e inish.
Al ons Balmann, I hank you o he oppo uni ies I ecei ed h ough my schola ship a IAMO and he
eedom you g an ed me in pu suing my esea ch in e es s, i was a g ea pleasu e o be a pa o he
Depa men o S uc u al Change. Flo ian Schie ho n, o en my a el companion and co-conspi a o in
Kazakh ag icul u al esea ch, hank you o you many con ibu ions and hough s ha helped shape bo h
my esea ch ques ions and hei answe s. Simila ly, Sasha P ishchepo , hank you o helping me in my
ea ly days, honing my scien i ic ocus and enhancing ou ield ips o Kazakhs an wi h you local knowledge
and con ac s. In Kazakhs an, I wan o hank Dau en Oshakbae , o en he o ganize o ou ac i i ies,
esea ch pa ne , ansla o , and ou guide all olled in o one ou s anding colleague. Also om Kazakhs an,
bu who quickly became a ellow s uden and as iend, I wan o hank And ey Da a, i s o you help
and en husiasm on ou ield ips, and la e o you iendship and collabo a ion as we de eloped ou
disse a ions a ound e y simila opics.
Fellow coau ho s: I wan o hank Changxing Dong, wi hou whose coding expe ise I wouldn’ ha e been
able o de elop he inal model o ou i s pape . Also om ha pape , I wan o hank Ch is ina Eis elde
o doing all he ha d wo k in de e mining g assland p oduc i i y in Kazakhs an. F om my second pape , I
would’ e been los wi hou he na u al p ocesses knowledge o Tobias Kümme le and he local g assland
expe ise o Johannes Kamp, hank you.
I also wan o hank my i s s uden assis an , Alina D okina, whose i eless e o s digi izing handw i en
s a is ics and compiling annual bulle ins allowed he b ead h and dep h o Kazakh da a u ilized in my
esea ch. Also ins umen al in da a collec ion, I wan o hank Max Ho mann o you expe ise in R and
simila esea ch in e es s, helping o keep me in es ed du ing he lean yea s. I managed o keep he IT
depa men p e y busy wi h my nume ous and occasionally impuden eques s, hank you especially o
Tim Illne o always keeping me connec ed and upda ed.
My ime in Halle was a wonde ul pe iod o my li e. Besides he inc edible a el oppo uni ies ha my
esea ch a o ded me, I was also blessed wi h he oppo uni y o a el o my new iends’ home coun ies.
I will ne e o ge hose ips, and hose I ook hem wi h. I wasn’ all jus a eling, ei he : in Halle li elong
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iends we e made du ing weekend g ill-ou s, game nigh s, mo ie nigh s, Supe Bowl pa ies, Thanksgi ing
dinne s, and all manne o social ge - oge he s. I I begin naming e e yone now, I won’ know whe e o
s op, and so I’m going o do a e y annoying hing and say: you know who you a e, hank you! I do need o
name Lena Kuhn, Vasyl K a iuk, and Bo is Rajko ić, excep ional iends who ha e g aciously len me hei
couches while I inished his disse a ion.
The mos ecen acknowledgmen s belong o Pa ick Hos e , No be Hölzel, and F iedhelm Taube, who
ha e kindly ag eed o e iew his disse a ion, hank you.

Abs ac
The d ama ic inc ease o demand mea and animal p oduc s has placed a s ain on land esou ces and
c ea ed a li es ock sec o ha is esponsible o one i h o all an h opogenic g eenhouse gas emissions
and is associa ed wi h dese i ica ion, de o es a ion, wa e con amina ion, and he loss o wild g aze s,
biodi e si y, and ecosys em se ices. Howe e , quan i ying his s ain on land esou ces emains di icul ,
as li es ock a e mobile and g azing is a spec um. This disse a ion ocuses on a coun y wi h a long his o y
o pas o alism ha has unde gone d ama ic changes in he las cen u y. Kazakhs an has one o he wo ld’s
la ges g assland a eas and suppo s a la ge li es ock popula ion ha has luc ua ed g ea ly om ea ly
anshuman nomadism, o seden a y pas u ing o he So ie E a, o a pa ial e u n o mig a o y pas u ing
pos -So ie . Li es ock numbe s peaked nea he end o he So ie pe iod, plumme ed in he 1990s, bu
ha e seen s eady inc ease since. Ample e idence o o e g azing du ing he So ie E a exis s, howe e wi h
li es ock mo emen se e ely es ic ed, many dis an pas u es ell ou o use, and he ac ual ca ying
capaci y o Kazakh g asslands is poo ly unde s ood. Wi h he cu en expansion o he li es ock indus y—
especially bee —i is c ucial ha ( e)u iliza ion o Kazakhs an’s immense g assland ese es is sus ainable. A
key ac o o main ain biodi e si y and ecological unc ions on g asslands is ecu en i e e en s. G azing is
an impo an de e minan o i e egimes, bu he ela i e magni ude in compa ison o clima ic ac o s is
poo ly unde s ood. The his o ical con ex o li es ock in Kazakhs an p o ides an excellen se ing o s udy
he e ec o g azing on i e occu ence o e ime.
In he i s pa o his disse a ion, I p esen a spa ial model de eloped o assess he ex en and he
in ensi y o g azing demand by li es ock. In Kazakhs an 2015, abou hal o a ailable pas u e was u ilized,
bu mos a low in ensi ies. A 31% inc ease in bee p oduc ion could be possible wi hou imp o ed
in as uc u e o in ensi ica ion p ac ices. In he second pa , I co ela ed he annual change in g azing
in ensi y o he g asslands u ilized by li es ock wi h annual bu ned a ea o e he pe iod 2001-2019,
accoun ing o changes in empe a u e, p ecipi a ion, g owing deg ee days, and ela i e humidi y. The e
was a clea and nega i e ela ionship be ween g azing demand and bu ned a ea. G azing demand also had
mo e explana o y powe in bu ned a ea han any o he clima e a iables es ed. In he hi d pa , I
explo ed he po en ial o ho semea as an al e na i e o bee . Nu i ionally, ho semea is he bes ed mea
in almos e e y way, wi h a simila as e and ex u e o bee , and compa es a o ably o he majo whi e
mea s (po k and chicken). Addi ionally, aising ho ses ins ead o ca le o mea p oduc ion would
d as ically educe he amoun o me hane eleased pe kilog am o mea p oduced. Howe e , ho ses
equi e mo e land a ea, and a ull con e sion would likely coincide wi h a educ ion in mea consump ion.
In summa ion, Kazakhs an has a la ge po en ial o pas u e-based li es ock expansion, a coo dina ed
expansion o g azing li es ock could be an excellen s a egy o mi iga ing i e isk, and he mos
app op ia e li es ock species o ul ill bo h oles a e ho ses, due o hei mobili y, ha diness, mea
cha ac e is ics, and en i onmen al oo p in .
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Zusammen assung
De d ama ische Ans ieg de Nach age nach Fleisch und ie ischen P oduk en ha den D uck au die
Land essou cen e s ä k und dazu beige agen, dass die Tie p oduk ion ü ein Fün el alle
an h opogenen T eibhausgasemissionen e an wo lich is und zu Wüs enbildung, Abholzung,
Wasse e schmu zung, und dem Ve lus on Wildweide ie en, A en iel al und
Ökosys emdiens leis ungen beige agen ha . Alle dings is es wei e hin schwie ig, den Fußabd uck de
Tie p oduk ion zu quan i izie en, da Weide ie e meis mobil sind und in un e schiedliche In ensi ä
beweiden. Diese Disse a ion konzen ie sich au Kasachs an, ein Land mi eine langen Geschich e des
Pas o alismus, das im le z en Jah hunde d ama ische Ve ände ungen du chgemach ha . Kasachs an
e üg übe eine de g öß en G ünland lächen de Wel und behe be g einen g oßen Viehbes and, de
sich om ühen T anshuman -Nomaden um übe die sessha e Weidehal ung in de Sowje zei bis hin zu
eine eilweisen Rückkeh de wande nden Weidewi scha nach de Sowje zei wandel e. De
Viehbes and e eich e gegen Ende de Sowje zei seinen Höhepunk , ging in den 1990e Jah e s a k zu ück
und s ieg sei dem wiede an. Es gib zahl eiche Beweise ü Übe weidung wäh end de Sowje zei , abe da
die Bewegung des Viehs s a k eingesch änk wa , wu den wei e en e n e Weiden nich genu z .
Auße dem is die a sächliche T ag ähigkei des kasachischen G aslandes kaum bekann . Angesich s de
de zei igen Expansion de Viehwi scha in Kasachs an – insbesonde e des Rind leischsek o s – is eine
nachhal ige (Wiede -)Nu zung de immensen G ünland ese en Kasachs ans on en scheidende
Bedeu ung. Ein Schlüssel ak o ü die E hal ung de A en iel al und de ökologischen Funk ionen on
G ünland sind wiede keh ende B ände. Die Beweidung is eine wich ige De e minan e des B and egimes,
abe das ela i e Ausmaß im Ve gleich zu klima ischen Fak o en is nu unzu eichend e s anden. De
his o ische Kon ex de Viehwi scha in Kasachs an e möglichen es die Auswi kungen de Beweidung au
das Au e en on B änden im Lau e de Zei zu un e suchen.
Im e s en Teil diese Disse a ion s elle ich ein äumliches Modell o , das en wickel wu de, um das
Ausmaß und die In ensi ä de Weidenach age du ch das Vieh zu e assen. In Kasachs an wu de 2015 e wa
die Häl e de e ügba en Weide lächen genu z , meis abe mi ge inge In ensi ä . Eine 31% S eige ung
de Rind leischp oduk ion wä e ohne e besse e In as uk u ode In ensi ie ungsp ak iken möglich. Im
zwei en Teil ko elie e ich die jäh liche Ve ände ung de Weidein ensi ä de on Tie en genu z en
G ünland lächen im Zei aum 2001-2019 mi de jäh lichen e b ann en Fläche un e Be ücksich igung on
Ände ungen in Tempe a u , Niede schlag, Wachs umsg ad age und ela i e Lu euch igkei . Es bes and
ein kla e und nega i e Zusammenhang zwischen de Weidenach age und de e b ann en Fläche. Ich
s ell e auße dem es , dass die Nach age nach Weide lächen eine g öße e E klä ungsk a ü das Ausmaß
de e b ann en Fläche ha als jede de ge es e en Klima a iablen. Im d i en Teil un e such e ich das
Po enzial on P e de leisch als Al e na i e zu Rind leisch un e such . Aus e näh ungsphysiologische Sich
is P e de leisch in as jede Hinsich das bes e o e Fleisch, es ha einen ähnlichen Geschmack und eine
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ähnliche Tex u wie Rind leisch und schneide im Ve gleich zu den gängigen weißen Fleischso en (Schwein
und Huhn) gu ab. Da übe hinaus wü de die Hal ung on P e den ans elle on Rinde n zu
Fleischp oduk ion die Menge an Me han, die p o Kilog amm p oduzie em Fleisch eigese z wi d,
d as isch eduzie en. P e de benö igen jedoch meh Land läche, und eine olls ändige Ums ellung wü de
wah scheinlich mi eine Reduzie ung des Fleischkonsums einhe gehen.
Zusammen assend läss sich sagen, dass Kasachs an übe ein g oßes Po enzial ü die Auswei ung de
Weide iehhal ung e üg . Eine koo dinie e Auswei ung de Weide iehhal ung könn e eine he o agende
S a egie zu Minde ung des B and isikos sein. Die am bes en geeigne e Nu z ie a zu E üllung diese
beiden Au gaben sind P e de au g und ih e hohen Mobili ä und Wide s ands ähigkei , de
ausgezeichne en Fleischeigenscha en und ih es ge ingen ökologischen Fußabd ucks.
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Con en s
ACKNOWLEDGMENTS .......................................................................................................................................................... III
ABSTRACT .......................................................................................................................................................................... V
ZUSAMMENFASSUNG ........................................................................................................................................................... VI
CONTENTS ....................................................................................................................................................................... VIII
LIST OF FIGURES .................................................................................................................................................................. X
LIST OF TABLES .................................................................................................................................................................... X
LIST OF SUPPLEMENTARY EQUATIONS ..................................................................................................................................... XI
LIST OF SUPPLEMENTARY FIGURES .......................................................................................................................................... XI
LIST OF SUPPLEMENTARY TABLES ........................................................................................................................................... XI
CHAPTER 1: INTRODUCTION ....................................................................................................................................... 1
1.1 SCIENTIFIC BACKGROUND ................................................................................................................................................ 2
1.1.1 G asslands and pas u e .................................................................................................................................... 2
1.1.2 Li es ock ........................................................................................................................................................... 3
1.1.3 S udy egion ...................................................................................................................................................... 5
1.1.4 Li es ock in Kazakhs an .................................................................................................................................... 7
1.1.5 G azing and g assland i es .............................................................................................................................. 8
1.2 CONCEPTUAL FRAMEWORK ............................................................................................................................................. 8
1.2.1 Resea ch ques ions and objec i es ................................................................................................................... 9
1.2.1.1 Ques ion I: Whe e, and o wha ex en , could inc eases in Kazakh li es ock p oduc ion ake place?......................... 9
1.2.1.2 Ques ion II: To wha ex en a e li es ock in Kazakhs an ac o s in i e egimes? ......................................................... 9
1.2.1.3 Ques ion III: is he e a pa h o sus ainable li es ock de elopmen in Kazakhs an? ................................................... 10
1.3 STRUCTURE OF THE THESIS ............................................................................................................................................ 10
CHAPTER 2: MODELING THE SPATIAL DISTRIBUTION OF GRAZING INTENSITY IN KAZAKHSTAN ................................ 11
ABSTRACT ....................................................................................................................................................................... 12
2.1 INTRODUCTION ........................................................................................................................................................... 13
2.2 MATERIALS AND METHODS ........................................................................................................................................... 15
2.2.1 S udy a ea ....................................................................................................................................................... 15
2.2.2 Kazakh a m s uc u e .................................................................................................................................... 16
2.2.3 Model de elopmen ........................................................................................................................................ 16
2.2.4 G azing supply ................................................................................................................................................ 18
2.2.5 G azing demand ............................................................................................................................................. 19
2.2.6 P oduc ion po en ials o mea and milk ......................................................................................................... 22
2.3 RESULTS .................................................................................................................................................................... 23
2.3.1 G azing gap and demand dis ibu ion ............................................................................................................ 23
2.3.2 O - ake a e ................................................................................................................................................... 24
2.3.3 G azing dis ances and pas u e ex en ............................................................................................................ 25
2.3.4 P oduc ion po en ials o mea and milk ......................................................................................................... 27
2.4 DISCUSSION ............................................................................................................................................................... 29
2.4.1 Compa ison o global p oduc s ....................................................................................................................... 29
2.4.2 Valida ion ....................................................................................................................................................... 31
2.4.3 Ta ge ing a eas o li es ock expansion ......................................................................................................... 31
2.4.4 No es on model inpu s and assump ions ........................................................................................................ 32
2.4.5 Li es ock p oduc i i y on p i a e a ms .......................................................................................................... 34
2.5 CONCLUSION .............................................................................................................................................................. 34
2.6 ACKNOWLEDGMENTS ................................................................................................................................................... 35
2.7 SUPPLEMENTARY MATERIAL .......................................................................................................................................... 37
CHAPTER 3: CHANGES IN LIVESTOCK SYSTEMS EXPLAIN POST-SOVIET FIRE TRENDS ON THE EURASIAN STEPPE
BETTER THAN CLIMATE ............................................................................................................................................. 45
ABSTRACT ....................................................................................................................................................................... 46
3.1 INTRODUCTION ........................................................................................................................................................... 47
3.2 MATERIALS AND METHODS ........................................................................................................................................... 50
3.2.1 S udy a ea ....................................................................................................................................................... 50
3.2.2 Li es ock g azing demand .............................................................................................................................. 50
3.2.3 Clima e indica o s ........................................................................................................................................... 51
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conside ed g azed (Say e e al., 2017). G assland is conside ed a land co e , while pas u e- and g azing land
a e land uses. Rangeland is ypically conside ed a land co e whe e g azing could occu , bu is also used as
a e m o g azed na i e g asslands o di e en ia e om sown pas u eland. Unlike g ass- and pas u eland,
ange- and g azing land can encompass non-g asslands—sh ubland, we land, woodland, und a, and semi-
dese (Allen e al., 2011). Rega dless o he e minology used, de e mining he ex en and in ensi y o he
u iliza ion o land by li es ock is di icul (Ramanku y e al., 2008).
1.1.2 Li es ock
While de ining and delinea ing pas u e is di icul , keeping ack o he numbe o animals u ilizing pas u e
is ela i ely easie (Figu e 1-1). Ca le, goa s, and, on smalle scales, bu alo and camels, ha e all seen
s eady inc eases in numbe s o e he las 60 yea s. Sheep numbe s ha e luc ua ed, bu a e now highe
han e e . O he g azing li es ock included in FAO s a is ics, only ho ses and ho se c osses (mules and
hinnies) ha e dec eased in numbe since 1961. This can mos ly be a ibu ed o he dec ease in demand o
d a animals (Pea son, 1998).
Figu e 1-1. Global popula ion o li es ock species ha ob ain ene gy p ima ily h ough o age and odde , 1961-2023 (FAO, 2025).
(a) The h ee main g azing species. (b) Mino g azing li es ock species. O he camelids include llamas and alpacas. No e he scale.
As he global human popula ion has inc eased, he dis ibu ion o weal h has shi ed, wi h some de eloping
coun ies becoming mo e a luen and demanding a la ge p opo ion o mea in hei die s (FAO, 2023b).
This has d i en he inc ease in mea p oduc ion o e he las 60 yea s (Figu e 1-2). In he 1960s and 1970s,
bee and po k we e p oduced a nea ly equal quan i ies a he global scale. Bee p oduc ion inc eases
began o ail o in he 1980s, wi h po k aking he clea lead in p oduc ion quan i y un il he mid-2010s,
when A ican Swine Fe e de as a ed popula ions in Asia and Eu ope (Pen i h, 2020). O e he las 60
yea s, chicken has seen he mos d ama ic upswing in p oduc ion, and ecen ly o e ook po k as he la ges
mea p oduc ion sec o in he wo ld. Duck, u key, and goose a e o en combined wi h chicken as he

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poul y sec o . F equen ly combined as closely ela ed cap ines, sheep and goa o en co-occu . Ca le and
bu alo a e closely ela ed bo ines ha can also be combined, hough hey do no o en co-occu . Though
e y impo an in he ew egions whe e hey a e aised o mea , equids and camelids (mainly ho ses and
camels, espec i ely) a e no majo playe s a he global scale. Though no exp essly de ined, game mea
likely includes a high p opo ion o g azing animals, as many dee species a e popula hun ing a ge s and
a e e en a med as a special y mea in many egions (Se ano e al., 2019).
Figu e 1-2. Global p oduc ion o mea , 1961-2023 (FAO, 2025). (a) The h ee main sou ces o mea . (b) Seconda y sou ces o mea .
(c) Mino sou ces o mea . O he mea includes oden and snail. Equid includes ho se, ass, and mule. Camelid includes camel, llama,
and alpaca. O he bi ds include bu a e no limi ed o pa o s, pheasan s, and pigeons. No e he scale.
Despi e alling o a dis an hi d, s eady g ow h in he bee sec o has kep bee p oduc ion a he o e on
o sus ainabili y awa eness. In a mul i ude o me ics, bee is he mos ine icien o he majo mea s:
poul y, po k, bee , and mu on/goa mea . Ca le a e much less e icien han poul y o pigs a u ning
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eed ene gy in o mea (Hou e al., 2016), and accoun o h ee ou hs o all non-CO2 g eenhouse gas
emissions om li es ock p oduc ion, mainly due o me hane om hei uminan diges i e sys em (Dunkley
& Dunkley, 2013; He e o, Ha lík, e al., 2013). Howe e , ca le and o he g aze s ha e one majo
ad an age o e poul y and pigs: hey ob ain he majo i y o hei ene gy om o age, which can be g own
on land ha is o he wise unsui able o ag icul u e. Thus, g azing li es ock p o ide human-edible ood om
land o he wise incapable o being a ood o ene gy sou ce ( an Zan en e al., 2018).
1.1.3 S udy egion
S e ching 8000 km om cen al Eu ope o no heas China, he Eu asian s eppe is a as g assland biome
ha is i al o he ag icul u al economies o he coun ies i spans. D yland ag icul u e suppo s p ima ily
small g ains and g azing li es ock. The s eppe has been home o anshuman pas o alism o millennia,
wi h signi ican and es ablished he ds o sheep, goa s, ca le, ho ses, camels, and yaks. Wild g aze s a e
also a majo ea u e, wi h impo an popula ions o P zewalski’s ho se (Equus e us p zewalskii), wild ass
(Equus hemionus), saiga an elope (Saiga a a ica), goi e ed gazelle (Gazella subgu u osa), Mongolian
gazelle (P ocap a gu u osa), wild camel (Camelus bac ianus), and mou lon (O is o ien alis) (Wesche e al.,
2016). Many eco egions subdi ide he Eu asian s eppe, wi h one o he la ges alling mos ly wi hin he
bo de s o Kazakhs an and, p edic ably, is called he Kazakh s eppe, whe e se e al ac o s make i a e y
in e es ing egion o s udy. Fi s , he Kazakh s eppe is huge and la gely unin e up ed, co e ing 808,000
km2, which places i among h ee la ges empe a e g assland eco egions in he wo ld along wi h he
Mongolian-Manchu ian g assland (889,000 km2) and he Pon ic s eppe (997,000 km2)—bo h also membe s
o he Eu asian s eppe (Dine s ein e al., 2017). Second, in ecen his o y li es ock popula ions wi hin
Kazakhs an ha e luc ua ed g ea ly. While pa o he So ie Union, Kazakhs an unde wen subs an ial land-
use con e sion o c op cul i a ion, and li es ock p oduc ion became la gely seden a y. Du ing his pe iod
li es ock numbe s sky ocke ed, and o e g azing caused deg ada ion and dese i ica ion o g asslands
(Robinson e al., 2003). The b eakup o he So ie Union in 1991 p ecipi a ed a collapse in he Kazakh
li es ock sec o , wi h li es ock numbe s dec easing by 70% wi hin a decade (KazS a , 2019). The e has been
s eady g ow h in li es ock numbe s since hen, p o iding a na u al expe imen o huge swings in li es ock
numbe s and dynamic shi s in g azing p ac ices o e a ela i ely sho ime pe iod. Gi en he subs an ial
land esou ces, and he go e nmen al ocus on li es ock de elopmen (The Wo ld Bank, 2020), i is an ideal
ime o iden i y a eas o in e es and gaps in he knowledge eco d.
The eas e n and sou heas e n bo de o Kazakhs an uns along he edge o he Al ai (a he con e gence o
China, Kazakhs an, Mongolia, and Russia) and Tien Shan ( unning h ough Ky gyzs an and in o China)
moun ain anges (Figu e 1-3). Ecosys ems along hese bo de s di e g ea ly om he majo i y o
Kazakhs an, ecei ing he highes annual p ecipi a ion amoun s ha suppo o es s and alpine meadows
(Figu e 1-4). In he sou h, he clima e quickly u ns d y a eling wes . The Chu, Sy Da ya, and Amu Da ya
i e s suppo ex ensi e i iga ed ag icul u e p oducing a a ie y o ui s, ege ables, and nu s. Howe e ,
6
while Alma y and Shymken ecei e upwa ds o 600 mm/yea p ecipi a ion, nea by Ta az and Tu kis an
ecei e only 350 mm/yea and 200 mm/yea , espec i ely, and he mon ane clima e quickly ansi ions o
semi-dese and e en ually dese o he wes and sou hwes , wi h less han 100 mm/yea in he Kyzylkum
Dese , loca ed be ween he Amu Da ya and Sy Da ya. Mo ing no h, p ecipi a ion inc eases g adually
ac oss he coun y (excep ing he a o emen ioned eas e n and sou heas e n bo de ) o o e 350 mm/yea
in Pe opa l (Clima e Resea ch Uni , 2019). No hcen al Kazakhs an is home o con en ional ain ed
ag icul u e, p edomina ely whea and odde c ops. Li es ock a e ound h oughou he coun y (FAO
classi ies nea ly 70% o he coun y as “pe manen meadows and pas u es”) (FAO, 2024), wi h he highes
concen a ion in he e ile oo hills o he Tien Shan. Ca le, sheep, goa s, and ho ses a e all ubiqui ous and
g own o mea , dai y, and o he p oduc s. Camels a e also an impo an ood sou ce in he d ie egions o
he sou h and sou hwes (KazS a , 2019).
Figu e 1-3. Topog aphy o Kazakhs an in Cen al Asia wi h majo wa e ea u es and ci ies wi h o e 100,000 esiden s (Eu opean
Space Agency & Sine gise, 2021; GADM, 2022; Geo ab ik, 2024; KazS a , 2011).
7
Figu e 1-4. A e age annual p ecipi a ion o Kazakhs an (de Pauw, 2008; GADM, 2022).
1.1.4 Li es ock in Kazakhs an
While ca le, sheep, goa s, and ho ses a e ound in nea ly e e y dis ic o Kazakhs an, and o en sha e
pas u e, he e a e dis inc niches ha he di e en species occupy a mo e local scales. Ca le a e he
b eadwinne s ha ecei e he choices g ass, ei he nea human se lemen s o shephe ded o seasonal
pas u es. Ca le almos exclusi ely g aze on g ass, bu a e ela i ely unconce ned abou he quali y o g ass.
They use hei ongues o ip easily accessibly g asses, o en lea ing behind sho , young, mo e nu i ious
g ass. Sheep and goa s a e no as mobile as ca le, bu a e su e- oo ed and occupy mo e ma ginal pas u es,
ei he deg aded lands su ounding se lemen s o mo e mon ane pas u es inaccessible o ca le. Sheep a e
picky g aze s in he sense ha hey seek ou highly nu i ious g asses and can nibble hem o he oo s.
When an a ea is o e g azed by sheep his can be de as a ing, bu in a heal hy o a ion sheep a e able o
u ilize quali y g ass ha ca le lea e behind. Goa s a e he leas picky g aze s, willing o munch on jus
abou any ype o plan . Thus, hey can be des uc i e when le in a p i a e ya d o ga den, bu in a
pas u e, goa s a e able o u ilize oliage ha is snubbed by he o he g aze s. Ho ses a e he mos mobile
and o en ange g ea dis ances om hei poin o o igin, accessing a a ie y o pas u es beyond he ange
o ca le, sheep, and goa s. Like sheep, ho ses a e picky g aze s ha seek high-quali y g asses and can chew
g asses down o hei oo s. Unlike sheep, ho ses a e highly mobile, so unless hey a e con ined o e g azing
is less o a h ea (Goodwin, 2007; Koshkina e al., 2022; Oso o e al., 2015).
The di e en species also occupy dis inc economical niches. P oduc s om ca le a e conside ed he mos
aluable, bo h bee and dai y, and ecei e he majo i y o go e nmen al s imuli (Robinson e al., 2021).
Sheep meanwhile a e physically smalle , ela i ely cheape , and a e he mos nume ous o Kazakhs an’s
8
li es ock. Lamb is a common mea , and mu on is also ound om he u al illages o he ci y
supe ma ke s. Sheep milk is d unk in he coun yside and hei cheese is popula e e ywhe e. Sheep also
ha e he added alue o wool p oduc ion. The main economic bene i o goa s is ha hey can p oduce
human ood ou o plan s eschewed by o he g aze s. Goa mea is no pa icula ly p ized, hough i can be
ound in mos mea ma ke s. Goa milk and especially goa cheese, howe e , is qui e popula . Ho ses a e
ea ed somewha like goa s in he economic sense, hey a e able o u n o he wise inaccessible biomass
in o human ood (Taylo e al., 2020). Howe e , ho ses ha e he added bene i o being a cul u ally
signi ican and his o ically adi ional ood sou ce (Malaca ne e al., 2002). Ma e milk is e men ed in o he
adi ional d ink kumys, and ho semea is ound in e e y bu che shop and in mos es au an s.
1.1.5 G azing and g assland i es
Fi e is a na u al phenomenon ha is in eg al o he heal h and unc ioning o g asslands (Bhagwa e al.,
2023). I can also be de as a ing o li es ock, consuming en i e he ds i he e a e no means o escape o
shel e . Kazakhs an has expe ienced se e al pe iods o unusually high i e occu ence since he b eakup o
he So ie Union. Clima e a iables a e always an enabling ac o wi h na u ally-occu ing i es, and clima e
change has led o an inc ease in ex eme wea he e en s wo ldwide (Newman & Noy, 2023), bu he
sudden and epea ed inc eases in equency and ex en in Kazakhs an wa an in es iga ion.
The biophysical cha ac e is ics o g azing—biomass emo al, plan damage, soil compac ion, i e bank
e osion, manu e e iliza ion—change he communi y s uc u e o a g assland ecosys em (Milchunas e al.,
1988). Li es ock g azing is gene ally unde s ood o be a mi iga ing ac o in i e occu ence— hei
consump ion o uel o e shadows any po en ially exace ba ing ea u e o animal husband y. Howe e , he
deg ee o which li es ock g azing con ibu es o i e mi iga ion has his o ically been di icul o de e mine,
pa ially due o he ac ha in o de o de e mine he magni ude, a e y la ge, in ac g azing a ea ha
expe iences egula i es would ha e o be subjec ed o inc eases and dec eases in li es ock numbe s ha
a y o e space and ime. The la ge-scale, widesp ead, and mos impo an ly, well-documen ed changes in
li es ock numbe s and g azing p ac ices in Kazakhs an o e he las se e al decades— he i s a such a
scale—p o ide ample da a and oppo uni y o explo e he ex en o which li es ock g azing can p omo e o
s i le i e occu ence and bu ned a ea ex en .
1.2 Concep ual amewo k
The o e a ching goal o his disse a ion is o ad ance he knowledge and unde s anding o land use,
especially pas u eland use, in Kazakhs an. Ex en , g azing in ensi y, p oduc i i y, accessibili y, and species
dis ibu ion a e all i al aspec s o de e mining he cu en and u u e p ospec s o li es ock p oduc ion
and g azing in Kazakhs an. These aspec s a e explo ed by 1) de eloping a ine-scale, spa io empo al model
o li es ock dis ibu ion and 2) assessing he en i onmen al ex e nali ies o li es ock p oduc ion wi h
ega d o changes in g azing in ensi y and species dis ibu ion.

9
1.2.1 Resea ch ques ions and objec i es
1.2.1.1 Ques ion I: Whe e, and o wha ex en , could inc eases in Kazakh li es ock p oduc ion ake
place?
De e mining he land use oo p in o li es ock is his o ically di icul . Sa elli e image y has been used o
decades o accu a ely map c opland use, howe e pas u es lack he clea ly de ined bounda ies and changes
in spec al e lec ance ha make c opland easy o de ec (Sheles o e al., 2017; Yan & Roy, 2014).
Addi ionally, “use” is a ma e o deg ee much mo e in pas u eland han in c opland, as equency, leng h,
and in ensi y can all e y g ea ly on land classi ied as pas u e (Kuemme le e al., 2013). Mo eo e , o
alloca e li es ock on pas u e p o ides an addi ional quanda y, as li es ock may be housed in eedlo s,
enced pas u es, o nomadically g azed ac oss hund eds o squa e kilome e s. In Kazakhs an i is ypical o
all h ee s yles o g azing p ac ices o exis in he same a ea.
1.2.1.1.1 Objec i e I: de elop a spa ial model using ine-scale inpu s o alloca e li es ock in Kazakhs an based
on he p edomina ing local g azing p ac ices.
Be o e he spa ial dis ibu ion o li es ock in Kazakhs an can ake place, se e al inpu s a e needed.
Li es ock numbe s, he d dynamics, ene gy equi emen s, and ene gy p o ided by eed and odde we e
used o de e mine g azing demand. Land co e , ne p ima y p oduc i i y, and plan species ene gy con en
we e used o de e mine g azing supply. Se lemen loca ions we e used as a s a ing poin in a sea ch
algo i hm designed wi h knowledge o he local g azing p ac ices o es ima e he ex en and o - ake a e o
pas u e in Kazakhs an.
1.2.1.2 Ques ion II: To wha ex en a e li es ock in Kazakhs an ac o s in i e egimes?
Fi e is an in eg al pa o g assland ecosys ems, and human ac i i ies ha e al e ed he na u al i e egimes
wi h di e se and di e ging e ec s (Bowman e al., 2011). In Kazakhs an, i e occu ence is widesp ead, bu
spa ially and empo ally a ied, and does no appea o be well-explained by clima e pa e ns (Y. Xu e al.,
2021). Addi ionally, li es ock numbe s ha e luc ua ed g ea ly o e ime and space, p o iding a unique
na u al expe imen o s udy he links be ween i e, g azing, and clima e a iables.
1.2.1.2.1 Objec i e II: analyze he co ela ion be ween li es ock g azing demand and bu ned a ea ex en
compa ed o he adi ional clima e a iables o p ecipi a ion, empe a u e, ela i e humidi y, and g owing
deg ee days.
A binomial gene alized linea mixed-e ec s model was used o eg ess he p edic o s agains he obse ed
alues o bu ned a ea (Bolke e al., 2009; Wa on & Hui, 2011). Akaike In o ma ion C i e ion was
calcula ed o de e mine model s eng h, as well as condi ional and ma ginal R2. Since none o he clima e
o li es ock a iables occu in a acuum, in e ela ions we e explo ed as well, namely wi h addi i e,
in e ac i e, and quad a ic models.
10
1.2.1.3 Ques ion III: is he e a pa h o sus ainable li es ock de elopmen in Kazakhs an?
In Kazakhs an, like mos coun ies, bee is king. Despi e bee cu en ly coming in a dis an hi d in global
consump ion o chicken and po k, ca le a e he s anda d o weal h o li es ock owne s in de eloping
coun ies (He e o, G ace, e al., 2013). The mea i sel is also a d i ing o ce behind bee ’s p oli e a ion,
none o he o he majo mea s can easily eplace bee ’s as e and ex u e. Unlike chicken and po k, bee
can be p oduced on land o he wise unsui able o human ood p oduc ion, bu by many me ics bee is an
ex ao dina ily ine icien sou ce o p o ein (Hou e al., 2016). Wha is needed is a mo e en i onmen ally-
iendly sou ce o mea ha can s ill be p oduced by g azing he ea h’s as g assland esou ces.
1.2.1.3.1 Objec i e III: explo e he po en ial o he e u n o a his o ically popula and gas onomically simila
mea in o de o a leas pa ially eplace bee as he ed mea o choice.
Ho ses we e one o mankind’s ea lies known sou ces o mea (Taylo , 2024). The cu en aboo
su ounding hippophagy needs o be ee alua ed. The en i onmen al ex e nali ies o g eenhouse gas
emissions and land use we e e alua ed, especially in compa ison o bee p oduc ion. Howe e , all he
en i onmen al a gumen s mean no hing i ho semea is no desi able as a dish, hus he nu i ional
cha ac e is ics we e also e alua ed in compa ison o bee and o he ed mea s.
1.3 S uc u e o he hesis
This hesis consis s o i e chap e s. Chap e 1 in oduces he co e h ee Chap e s 2-4, which add ess he
a o emen ioned h ee objec i es, and Chap e 5 syn hesizes he esul s and conclusions p esen ed and
looks owa d he u u e landscape o li es ock, mea p oduc ion, land use, and g assland ecosys ems.
Chap e s 2-4 we e w i en as independen manusc ip s o pee - e iewed jou nals, and as such ha e only
mino o ma ing al e a ions om hei published o submi ed e sions:
Chap e 2 Hanke son, B e R., Flo ian Schie ho n, Alexande V. P ishchepo , Changxing Dong, Ch is ina
Eis elde , Daniel Mülle (2019). Modeling he spa ial dis ibu ion o g azing in ensi y in
Kazakhs an. PLOS ONE, 14 (1), e0210051.
Chap e 3 Hanke son, B e R., Flo ian Schie ho n, Johannes Kamp, Tobias Kuemme le, Daniel Mülle (in
e iew). Changes in li es ock sys ems explain pos -So ie i e ends on he Eu asian s eppe
be e han clima e. Regional En i onmen al Change.
Chap e 4 Hanke son, B e R., Daniel Mülle (in e iew). The o he ed mea : en i onmen al and
nu i ional ad an ages o ho semea o e bee and o he ed mea s. Na u e Food.
11
Chap e 2:
Modeling he spa ial dis ibu ion o g azing
in ensi y in Kazakhs an
PLOS ONE, 2019, Volume 14, Issue 1
B e R. Hanke son, Flo ian Schie ho n, Alexande V. P ishchepo , Changxing Dong, Ch is ina Eis elde ,
Daniel Mülle
12
Abs ac
Wi h inc easing a luence in many de eloping coun ies, he demand o li es ock p oduc s is ising and he
inc easing eed equi emen con ibu es o p essu e on land esou ces o ood and ene gy p oduc ion.
Howe e , he e is cu en ly a knowledge gap in ou abili y o assess he ex en and in ensi y o he
u iliza ion o land by li es ock, which is he single la ges land use in he wo ld. We de eloped a spa ial
model ha combines ine-scale li es ock numbe s wi h hei associa ed ene gy equi emen s o dis ibu e
li es ock g azing demand on o a map o ene gy supply, wi h he aim o es ima ing whe e and o wha
deg ee pas u e is being u ilized. We applied ou model o Kazakhs an, which con ains la ge g assland a eas
ha his o ically ha e been used o ex ensi e li es ock p oduc ion bu o which he cu en ex en , and
hus he po en ial o inc easing li es ock p oduc ion, is unknown. We measu ed he g azing demand o
Kazakh li es ock in 2015 a 286 Pe ajoules, which was 25% o he es ima ed maximum sus ainable ene gy
supply ha is a ailable o li es ock o g azing. The model esul ed in a g azed a ea o 1.22 million km2, o
48% o he a ea heo e ically a ailable o g azing in Kazakhs an, wi h mos u ilized land g azed a low
in ensi ies (a e age o - ake a e was 13% o o al biomass ene gy p oduc ion). Unde a conse a i e
scena io, ou es ima ions showed a p oduc ion po en ial o 0.13 million ons o bee addi ional o 2015
p oduc ion (31% inc ease), and much mo e wi h u iliza ion o dis an pas u es. This model is an impo an
s ep o wa d in e alua ing pas u e use and a ailable land esou ces, and can be adap ed a any spa ial scale
o any egion in he wo ld.
19
p oduc ion (g ams d y ma e /squa e me e ) using he con e sion coe icien o 0.47 g ams ca bon/g ams
d y ma e (IPCC, 2006). On he g assland map, we con e ed biomass o a ailable ene gy using a alue o
8.6 Megajoules (MJ) pe kilog am d y ma e (kgDM), based on li e a u e om simila egions and clima es
(Figu e 2-) (G ebenniko & Shipilo , 2012; Nasie e al., 2014; Sa in e al., 2011). The o al ene gy a ailable
om g asslands was calcula ed o be 3537 PJ. To es ima e he biomass a ailable om he o aging o c op
esidues, we applied a ha es index o 0.48 (using whea as he base e e ence) o he annual NPP (Chen e
al., 2014; Somme e al., 2013). The ha es index is he mass a io o c op yield (g ain) o he c op’s o al
abo eg ound biomass (Smil, 1999). Whea is he dominan c op g own in Kazakhs an and hus was used o
he calcula ion o c op esidues (KazS a , 2016). A ha es , a ound 90% o whea biomass is abo eg ound
(Ba e e al., 1992). The ene gy con ained in c op esidues is gene ally less han ha o pas u e, and a alue
o 6 MJ/kgDM (using whea as he base e e ence) was used o c oplands (Ø sko e al., 1988; Smil, 1999).
The o al ene gy a ailable o g azing om c oplands was calcula ed o be 293 PJ. The g assland map and
c opland map we e hen me ged o p oduce a map o g azing supply (Figu e 2-2).
Figu e 2-2: Map o annual a ailable g azing supply (MJ/m2) de i ed in his s udy. Based on o al NPP measu ed by Eis elde e al.
(2014). The land-co e classi ica ion used o masking is om Klein e al. (2012), and he p o ec ed a ea mask is om Kamp e al.
(2015). Low a ailable NPP o o aging on c oplands can easily be seen in he no hcen al. Whi e a eas a e una ailable o g azing.
2.2.5 G azing demand
To calcula e g azing demand, we ga he ed da a on li es ock numbe s, odde yield and odde consump ion
a he dis ic le el (2nd le el adminis a i e di ision) o 2015 om he Kazakh Na ional S a is ics Agency
(Figu e 2-3) (KazS a , 2016). In Kazakhs an, he e a e 200 dis ic -le el uni s, consis ing o dis ic s ( ayons)
and ci y adminis a i e uni s (go odskie adminis a sii). Li es ock nu i i e equi emen s we e aken om
ecommended alues published in a li es ock nu i ion handbook by KazAg oInno a ion (2008), a

20
subo dina e o he Kazakh Minis y o Ag icul u e. These alues a e speci ic o Kazakh li es ock a di e en
s ages o g ow h and o di e en animal unc ions (e.g., bee hei e s o b eeding s. bee hei e s o
inishing), as well as o di e en desi ed g ow h a es. Because sheep a ou numbe goa s in Kazakhs an,
and because o hei simila g azing cha ac e is ics and ene gy equi emen s, all goa s we e ea ed as
sheep.
Figu e 2-3: Li es ock densi y (head/km2) a he dis ic le el o he h ee a m ypes in Kazakhs an o he yea 2015 (KazS a ,
2016). Li es ock in Kazakhs an a e no dis ibu ed e enly ac oss space, no ac oss he h ee a m ypes.
To calcula e he o al nu i i e demand (Figu e 2-), we used in o ma ion on he age g oup and animal
unc ion o he Kazakh li es ock he ds. The p opo ions o he di e en age g oups and animal unc ions
we e a ailable a he p o ince (oblas ) le el o he di e en li es ock ypes om he 2006 ag icul u al
census (KazS a , 2008). As no newe o mo e de ailed da a exis , hese p opo ions we e applied o he
2015 numbe s in ou disagg ega ion equa ion (Equa ion 2-S2). Animal p oduc i i y di e s depending on
li ing condi ions, and li ing condi ions in Kazakhs an can b oadly be de ined based on he a m ype. The
di e ences in animal p oduc i i y, and hus ene gy demand, on he di e en a m ypes was es ima ed
using di e en animal g ow h a es (g/day) as indica ed in he handbook (Table 2-S2) (Zhazylbeko e al.,
2008). A desc ip ion o how he handbook alues we e used o calcula e he di e en animal age and
unc ion g oups can be ound in Table 2-S3.
21
The ac ion o o al ene gy demand ha is no me by odde and he e o e mus be me by g azing (i.e.,
g azing demand di ided by o al demand) is called he g azing gap, and was ob ained by sub ac ing he
amoun o ene gy ha is consumed as odde om he o al demand (Fe zel, Ha lík, He e o, Kaplan, e al.,
2017). The es ima ion o odde consump ion was no s aigh o wa d, as such s a is ics a e epo ed
consis en ly only o ag icul u al en e p ises. Ins ead, g oss yield o ha es ed odde c ops a he dis ic
le el was used (KazS a , 2016), and odde consump ion s a is ics we e used in an equa ion (Equa ion 2-S3)
o alloca e odde o he di e en li es ock ypes using hei ela i e p opo ions (i.e., p opo ion o o al
odde consump ion alloca ed o ca le, sheep, goa s, pigs, poul y, ho ses, and camels). This was he only
possible way o es ima e he o al amoun o odde consumed by g azing li es ock in Kazakhs an. Fodde
c op yields we e con e ed o ene gy alues using he So ie sys em o “ odde uni s” (Fo age On-Line,
2009), due o i s easy con e sion o MJ and i s widesp ead use in Kazakh ag icul u al li e a u e and
s a is ical epo ing (Table 2-S4 shows he con e sion a es used).
Table 2-1 de ails he inpu s used o dis ibu e he demand on o he supply as shown in Figu e 2-. When
dis ibu ing he supply, i is impo an o acknowledge ha only a ac ion o he o al NPP can be
consumed by li es ock. The Eis elde e al. (2014) map is a measu emen o o al NPP, which includes he
po ion ha is belowg ound and una ailable o he li es ock. We used he wo k o P opas in e al. (2011),
who ound abo eg ound NPP in cen al Kazakhs an o be on a e age 77% o o al NPP. In addi ion, a
conside able po ion o he abo eg ound NPP mus be le o allow eg ow h. Published alues o
ecommended s ocking a es and pas u e u iliza ion in simila clima ic condi ions sugges a maximum o -
ake a e o 40% o abo eg ound NPP (Hall e al., 1998; Holechek, 1988; Wi senius, 2000), and hus he
maximum sus ainable o - ake a e was es ima ed a 40% o 77%, o 30% o o al NPP.
Table 2-1: Summa y o pa ame e s used. Ne p ima y p oduc ion was aken as an a e age o he annual p oduc s om 2003 o
2011.
Inpu pa ame e (uni s in pa en heses)
Spa ial esolu ion
Re e ence
pe iod
Sou ce
Li es ock numbe s
Dis ic
2015
KazS a (2016)
Fodde p oduc ion (Joules)
Dis ic
2015
KazS a (2016)
Fodde consump ion (Joules)
Dis ic /P o ince
2015
KazS a (2016)
Nu i i e equi emen s (Joules)
2008
KazAg oInno a ion
(2008)
He d age s uc u e
P o ince
2006
KazS a (2008)
Human popula ion
Se lemen
2009
KazS a (2011)
Se lemen loca ion
Se lemen
2016
Geo ab ik (2016)
Dis ic and municipal a eas
Dis ic
2015
GADM (2015), GIS-Lab
(2013)
Ne p ima y p oduc ion (gC/m2)
1 km
2003-2011
Eis elde e al. (2014)
Land co e
250 m
2009
Klein e al. (2012)
Ob iously, no all pas u e is g azed a he maximum sus ainable o - ake a e. Mapping ac ual g azing
in ensi y equi es es ima ing he a ia ion in o - ake a e on a spa ial scale. To map a ia ion in o - ake
a e using ou model, we i s an he model unde a ange o ele en di e en o - ake a e assump ions
(5% inc emen s om 10%-60%). We hen calcula ed he maximum dis ance om each se lemen ha each
22
li es ock ype needed o ul ill hei g azing demand unde each o - ake a e. To de e mine an accu a e
o - ake a e o each se lemen , we used he maximum g azing dis ances o ca le in households as he
de ining a iable, as hey, along wi h sheep and goa s (which a e dis ibu ed be o e ca le), a e he mos
es ic ed by dis ance om se lemen . We chose 10 km as he maximum dis ance o ca le in households
based on he indings o Kamp e al. (2012). We g ouped se lemen s in o hei dis ic s, and o each
dis ic , we selec ed he lowes o - ake a e ha co esponded o he median o maximum g azing
dis ances o ca le in households being less han 10 km. In cases whe e dis ic s had no epo ed ca le in
households, sheep and goa s in households ins ead we e used (dis ic s wi hou ei he o hese had no
g azing li es ock o any kind). The model was hen un again, wi h se lemen s main aining hei
de e mined o - ake a e.
2.2.6 P oduc ion po en ials o mea and milk
We es ima ed he po en ial o inc ease p oduc ion o mea and milk in Kazakhs an based on he e iciency
wi h which pas u e is being used. To es ima e pas u e use e iciency, we ook mea and milk p oduc ion in
2015 om he na ional s a is ics (KazS a , 2016). We calcula ed pas u e equi emen om he model
esul s and es ima ed he yield o mea and milk ( ons pe km2 u ilized) o he di e en li es ock ypes.
Ou model esul s do no di e en ia e be ween bee and dai y ca le, so we made an adjus men based on
he ela i e p opo ions o bee and dai y ca le. The ac ion o ca le classi ied as dai y in 2015 o
ag icul u al en e p ises, p i a e a ms, and households was 0.40, 0.50, and 0.85, espec i ely (KazS a ,
2016). We mul iplied he land equi emen by his ac ion as a ough es ima e o he a ea used by dai y
ca le. We hen di ided milk p oduc ion by he adjus ed land equi emen o es ima e milk p oduc i i y.
We used he calcula ed land use e iciencies o es ima e p oduc ion po en ial. Fi s , we made a
conse a i e assump ion ha all land wi hin 10 km o a se lemen could cu en ly be u ilized. The e o e,
unu ilized land wi hin 10 km o a se lemen was conside ed o po en ial expansion. The modeled o - ake
a es we e used o calcula e he numbe o addi ional li es ock ha could be suppo ed. Second, we
p oposed a scena io whe e pas u e was g azed a i s maximum sus ainable in ensi y (30% o - ake a e),
and calcula ed he esul ing unu ilized a ea wi hin 10 km o a se lemen . Using a less conse a i e
assump ion ha all land wi hin 20 km o a se lemen could be u ilized, we epea ed he p e ious wo
calcula ions. Po en ial inc ease in bee p oduc ion was calcula ed wi h he assump ion ha all addi ional
li es ock we e bee ca le. Simila ly, o po en ial inc ease in milk p oduc ion, we assumed ha all
addi ional li es ock we e dai y ca le. The e o e, he esul s p esen ed a e “ei he -o ”, and he eali y likely
alls somewhe e in be ween.
23
2.3 Resul s
2.3.1 G azing gap and demand dis ibu ion
The g azing demand was calcula ed o each animal and a m ype combina ion (Figu e 2-4). The o al
ene gy demand by all li es ock ypes in 2015 ( he sum o all ba s in Figu e 2-4) was 368 Pe ajoules (PJ). The
g azing gap is displayed abo e each ba as he ac ion o o al ene gy demand ob ained h ough g azing. O
he h ee li es ock ypes, he g azing gap is lowes o ca le, and o he h ee a m ypes, he g azing gap is
lowes o ag icul u al en e p ises, wi h he lowes being ca le on ag icul u al en e p ises. This is due o
ca le on ag icul u al en e p ises ecei ing mo e and highe -quali y odde han o he li es ock and on
o he a m ypes. The o al amoun o ene gy supplied by odde was 82 PJ (sum o all da ke po ions o
he ba s), lea ing 286 PJ o be ob ained h ough g azing.
Figu e 2-4: Ene gy balance o Kazakh g azing li es ock in 2015. The da ke bo om po ion o each ba is he odde supply, and he
ligh e op po ion is he emaining demand ha mus be acqui ed om g azing. The o al demand is ep esen ed by he ull ba
heigh . F ac ions abo e each ba show he g azing gap (g azing demand di ided by o al demand). Nu i i e demand in o ma ion is
om KazAg oInno a ion (2008) and supply s a is ics om KazS a (2016).
Figu e 2-5 shows he o al g azing demand o all li es ock ypes o each se lemen in Kazakhs an. G azing
demand is no dis ibu ed e enly ac oss he coun y. In he no h, he demand is la ge, bu dispe sed ac oss
many se lemen s, whe eas in he sou h i is also la ge, bu concen a ed in ela i ely ewe se lemen s.
The cen e and sou hwes ha e bo h ew se lemen s and li le g azing demand.
24
Figu e 2-5: G azing demand (in Te ajoules, TJ) in 2015 disagg ega ed o se lemen s (all a m ypes combined).
2.3.2 O - ake a e
O - ake a e is no uni o m ac oss Kazakhs an. The g azing demand o 2015 was 7.5% o he o al biomass
supply (when con e ed o ene gy)—i.e., i he o - ake a e we e 7.5%, all a ailable land would be u ilized.
We es ed he sensi i i y o o - ake a e by unning he model wi h ele en di e en o - ake a es, om
10% o 60% (5% inc emen s) (Figu e 2-6). As he o - ake a e dec eases, he a ea equi ed o g azing
inc eases exponen ially (Figu e 2-7). In ou esul s, all o - ake alues used a e as a pe cen o o al a ailable
NPP.

25
Figu e 2-6: G azing ex en by all li es ock unde a ying o - ake a es. The image is a supe imposi ion o he ele en model uns. The
map o each indi idual o - ake a e includes he a ea o all highe o - ake a es.
Figu e 2-7: A ea equi ed by g azing li es ock depending on he pe cen o biomass o - ake.
2.3.3 G azing dis ances and pas u e ex en
The maximum dis ances a eled by household ca le unde each o - ake a e assump ion we e analyzed
a he dis ic le el o de e mine he a e age o - ake a e in each dis ic . The model was e- un wi h
a iable o - ake a es o de i e he maximum g azing dis ances. Figu e 2-8 shows he median and qua iles
o hese dis ances by animal and a m ype. A smalle qua ile ange on he le -hand side o he median o
e e y li es ock ype is a esul o he dis ances being skewed by ela i ely ew se lemen s wi h a la ge
li es ock popula ion loca ed close o one ano he , mos no ably in sou hcen al Kazakhs an (Figu e 2-5).
Mos se lemen s had much sho e maximum g azing dis ances, wi hin 6 km o ca le, sheep, and goa s in
households, and wi hin 15 km o ca le, sheep, and goa s on p i a e a ms. Despi e being dis ibu ed la e ,
ca le on ag icul u al en e p ises we e ound o ha e lowe maximum g azing dis ances han sheep and
goa s on ag icul u al en e p ises. This was due o ag icul u al en e p ises specializing in ca le p oduc ion
26
being loca ed mainly in he no h in small se lemen s, whe eas ag icul u al en e p ises wi h sheep and
goa s we e loca ed mainly in he sou h and sou heas in o nea la ge se lemen s.
Figu e 2-8: Maximum g azing dis ances o he a iable o - ake a e map. Box shows 50% o se lemen s (wi h median), whiske s
a e ½ in e -qua ile ange. AE: ag icul u al en e p ises, PF: p i a e a ms, HH: households.
Figu e 2-9 shows he land-use oo p in o g azing li es ock in 2015, using a iable o - ake a es a he
dis ic le el. The a ea equi ed was 1.22 million km2, 48% o he a ea heo e ically a ailable o g azing.
While he o - ake a e was de e mined a he dis ic le el, he esul shows ha o - ake a es did no
s ic ly adhe e o dis ic bounda ies, as indi idual se lemen s a e no obliged o g aze wi hin dis ic
bounda ies. In he no h, a ela i ely highe numbe o li es ock a e kep in p i a e a ms and ag icul u al
en e p ises, which a e no as es ic ed as household li es ock o he immedia e icini y o se lemen s.
Thus, hey can u ilize dis an pas u es a lowe o - ake a es, and almos all o he no h and no heas was
u ilized o some ex en . The sou h and sou heas showed less land being u ilized, howe e a a much highe
o - ake a e. In he eas , high NPP allows o lowe o - ake a es, and high numbe s o p i a e a m
li es ock can sea ch ou dis an pas u es. Two ipa ian pas u e egions a e clea ly isible due o hei cou se
unning h ough o he wise a id and semi-a id egions: he U al in he a wes and he Sy Da ya lowing
no hwes ou o he sou he n ip. The Chu Ri e ( o he eas o he Sy Da ya) is a his o ically impo an
i e ha used o low in o he Sy Da ya, bu o many yea s has been di e ed o i iga ion and now
disappea s be o e eaching he Sy Da ya. The Ili Ri e in he sou heas lows om he moun ains o Tian
Shan in o Lake Balkhash, whe e i o ms a la ge del a, p o iding g azing oppo uni ies in an o he wise a id
landscape.
27
Figu e 2-9: Dis ibu ion o g azing in ensi y in Kazakhs an o he yea 2015. O - ake a e is he pe cen o o al a ailable biomass
ha is consumed. I.e. on c oplands, i is he pe cen age consumed o he biomass ha emained a e ha es . Majo i e s a e
shown in blue wi h names.
2.3.4 P oduc ion po en ials o mea and milk
The summa iza ion o u ilized pas u e made i possible o es ima e he associa ed p oduc i i y o li es ock
p oduc ion wi h ega d o pas u e use. Table 2-2 shows he a ea o pas u e u ilized and he espec i e
p oduc i i y o mea and milk (p oduc ion pe km2 u ilized). Mea p oduc i i y is highes o ca le on
ag icul u al en e p ises, bu no by a lo . Wi h he much smalle g azing gap o ca le on ag icul u al
en e p ises (Figu e 2-4), one would expec he mea p oduc i i y (which doesn’ accoun o odde ) o be
much highe . This is no he case because mos ca le on ag icul u al en e p ises a e in no hcen al
Kazakhs an (Figu e 2-3), whe e he o - ake is low (Figu e 2-9) and a lo o g azing on c opland (Figu e 2-2)
occu s. Bo h ac o s inc ease he land u ilized by ca le on ag icul u al en e p ises compa ed o o he
li es ock and on o he a m ypes, and hus dec ease he ela i e mea p oduc i i y. To al bee p oduc ion
in 2015 was 417 housand ons (k ), and o al dai y milk p oduc ion was 5.1 million ons (M ).
Table 2-2: Pas u e use, p oduc ion, and p oduc i i y o mea and milk in 2015. Mea and milk p oduc ion s a is ics om KazS a
(2016). These numbe s do no accoun o land used o odde p oduc ion. *Adjus ed o ela i e p opo ion o ca le in dai y
p oduc ion. AE: ag icul u al en e p ises, PF: p i a e a ms, HH: households.
Li es ock
ype
Fa m
ype
Pas u e
u ilized
(mil. km2)
Mea
p oduc ion
(k )
Mea
p oduc i i y
( /km2)
Milk
p oduc ion
(k )
Milk
p oduc i i y
( /km2)
Ca le
AE
0.30
28.44
96.09
263.01
2213.05*
PF
1.47
77.80
52.90
777.55
1048.46*
HH
4.44
310.57
69.98
4101.06
1091.96*
Sheep
AE
0.10
3.16
32.52
0.04
0.38
and goa s
PF
1.14
38.74
34.11
0.33
0.29
HH
1.82
123.19
67.60
1.25
0.68
28
Ho ses
AE
0.17
2.17
12.94
0.69
4.14
PF
1.35
25.01
18.47
9.76
7.21
HH
1.44
74.26
51.44
15.42
10.68
Fo sheep, goa s, and ho ses, mea p oduc i i y is highes in households. Fo sheep and goa s, his is due o
sheep on ag icul u al en e p ises and p i a e a ms p ima ily being aised o wool, wi h mea only a
byp oduc . Simila ly, o ho ses, mos mea p oduc ion is done a he household le el. Rega ding milk
p oduc ion, ca le on ag icul u al en e p ises a e clea ly he mos land p oduc i e (when adjus ed o he
p opo ion o dai y p oduc ion). Milk p oduc i i y is e y low o sheep and goa s, wi h almos all
p oduc ion coming om he e y ew goa s in he coun y. Ho se milk p oduc i i y is somewha highe ,
because o he demand o he adi ional ho se-milk d ink kumys, which is p oduced mainly a he
household le el.
We p oduced a conse a i e es ima e o inc eased p oduc ion po en ial by implemen ing he scena io
whe e all land wi hin 10 km o a se lemen is u ilized. Assuming he es ima ed o - ake a es shown in
Figu e 2-9 as business-as-usual (BAU), he addi ional pas u e u ilized was 0.14 million km2, wi h an
associa ed ene gy o 29.9 PJ. Assuming a p opo ional inc ease in odde p oduc ion (i.e., ha he g azing
gap emains he same), i he addi ional 0.14 million km2 o pas u e was used en i ely o ca le on
ag icul u al en e p ises, bee p oduc ion could be inc eased by 0.13 M , an inc ease o 31% (Figu e 2-10).
Con e sely, i all expansion was used o dai y on ag icul u al en e p ises, dai y milk p oduc ion could be
inc eased by 3.11 M (abo e 2015 le el) unde he business-as-usual scena io, an inc ease o 60%. These
a e conse a i e es ima es, as o - ake a es we e e y low o mos se lemen s (Figu e 2-9). I all land
wi hin 10 km was used a i s maximum sus ainable o - ake a e (30%), and i all addi ional li es ock on
pas u e wi hin 10 km we e ca le on ag icul u al en e p ises, bee p oduc ion could be inc eased 1.91 M
(abo e 2015 le el), an inc ease o 457%. By compa ison, B azilian bee expo s in 2013 o aled 1.25 M
(Schie ho n e al., 2016). Hence, Kazakhs an has he po en ial o become one o he leading bee expo e s
in he wo ld. I he adius o land a ound a se lemen ha can be u ilized was inc eased o 20 km, wi h
business-as-usual o - ake a es, and i all addi ional pas u e was u ilized by ca le on ag icul u al
en e p ises, bee p oduc ion could be inc eased by 0.41 M (98%). Assuming maximum sus ainable o - ake
a es wi hin 20 km, his es ima e inc eases o 3.96 M .
35
The model enabled a g idded es ima e o he u ilized pas u e in Kazakhs an, which is a p ime example o a
coun y well sui ed o g azing li es ock p oduc ion. Kazakhs an’s d y con inen al clima e also educes he
sui abili y o li es ock p oduc ion’s main compe i o o land, c op p oduc ion, making i a sui able a ge
a ea o de elopmen o ange-based li es ock p oduc ion. Ou esul s show ha despi e ela i ely low
na u al p oduc i i y, ample capaci y exis s o inc ease li es ock p oduc ion in Kazakhs an because la ge
a eas a e cha ac e ized by low pas u e u iliza ion and o - ake a e, and a ailable biomass esou ces could
suppo many mo e g azing animals, especially in he eas and he no hwes (Figu e 2-9). Unde
conse a i e es ima es o g azing ange cons ain s and wi h 2015 p oduc i i y le els, bee p oduc ion
could be inc eased by 0.13 M (31%) o milk p oduc ion by 3.11 M (60%), o some combina ion. Howe e ,
ha nessing e en a ac ion o hese po en ials would necessi a e in as uc u e de elopmen measu es,
such as mo e, imp o ed p ocessing acili ies and imp o ed oad ne wo ks and ma ke access. Repai ed
wells and ou pos s would allow he eju ena ion o old mig a ion pa e ns and would open up dis an
pas u es o e en mo e po en ial p oduc ion inc eases.
This esea ch is an impo an s ep o wa d in he ield o li es ock mapping. Ou model uses much ine -
scale inpu s han o he global-scale p oduc s, and a di ec measu emen o biomass p oduc ion, enabling
us o make a g idded es ima e o pas u e dis ibu ion based on he ene gy demand o he li es ock. The
sea ch algo i hm we c ea ed is easily ans e able o o he egions whe e li es ock a e es ic ed o a
cen al poin , bu can be adap ed o any egion whe e g azing pa e ns can be de ined. The esul o ou
esea ch can be used o ind pa e ns in li es ock dis ibu ion, and o a ge a eas whe e he supply is
unde u ilized. Mo eo e , ou esul s help he spa ial a ge ing o possible in es men s o expanding he
p oduc ion o g azing li es ock, including assessing he adeo s o p oduc ion expansion wi h g eenhouse
gas emissions and biodi e si y conse a ion.
2.6 Acknowledgmen s
We wan o hank se e al colleagues who p o ided in aluable assis ance and ad ice. We hank Anne
Jungand eas o help wi h coding he ea ly e sions o he sea ch algo i hm, Alina D okina o compiling he
ag icul u al s a is ics om KazS a , Johannes Kamp o his expe ise in g azing pa e ns, No be Hölzel o
his expe ise in plan species dis ibu ion, Ma hias Baumann o his insigh s in o land co e dynamics,
Ma in Pe ick o his insigh s in o household socioeconomics, And ey Da a, Na alya Tsychuye a, and
Alyona Chukha ina o hei guidance and expe ise du ing ield ips, and Ye lan Syzdyko o his e o s in
da a p ocu emen and his insigh s on la ge a m managemen in Kazakhs an. We a e pa icula ly g a e ul o
Dau en Oshakbae o his assis ance, expe ise, and coo dina i e e o s, and wi hou whom his esea ch
could no ha e been comple ed.
Addi ionally, we a e hank ul o Ruslan U azaliye , Albe Salemga eye , and he colleagues wo king o he
Al yn Dala Conse a ion Ini ia i e o hei knowledge and in o ma ion on wild g aze s in Kazakhs an. We

36
a e also g a e ul o Tama a Fe zel o sha ing he map o global g azing in ensi y, allowing us o pe o m a
quali a i e compa ison o simila esea ch.
37
2.7 Supplemen a y ma e ial
Equa ion 2-S1: The piecewise linea unc ion used o es ima e he numbe o li es ock owne s in a se lemen based on he o al
se lemen popula ion.
𝑓(𝑃)=
{
0.95∗𝑃, 𝑃≤1000
(0.95−0.15
5000−1000∗(𝑃−1000))∗𝑃, 1000<𝑃≤5000
(0.8− 0.5
10000−5000∗(𝑃−5000))∗𝑃, 5000<𝑃≤10000
(0.3− 0.2
50000−10000∗(𝑃−10000))∗𝑃, 10000<𝑃≤50000
(0.1− 0.095
1000000−50000∗(𝑃−50000))∗𝑃, 50000<𝑃≤1000000
0.005∗𝑃, 𝑃>1000000
whe e:
𝑃=𝑠𝑒𝑡𝑡𝑙𝑒𝑚𝑒𝑛𝑡 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 (2009 𝑐𝑒𝑛𝑠𝑢𝑠)
𝑓(𝑃)=𝑠𝑒𝑡𝑡𝑙𝑒𝑚𝑒𝑛𝑡 𝑙𝑖𝑣𝑒𝑠𝑡𝑜𝑐𝑘 𝑜𝑤𝑛𝑒𝑟𝑠
Figu e 2-S1: Piecewise unc ion o he dis ibu ion o li es ock o se lemen s based on human popula ion. The s eps a e 95% om
0-1,000, 80% a 5,000, 30% a 10,000, 10% a 50,000, and 0.5% a 1 million esiden s (no shown), wi h linea in e pola ion. Values
we e de e mined om expe opinion and pe sonal obse a ion. No e ha his does no a ec he o al numbe o li es ock, only
hei dis ibu ion wi hin a dis ic .
Table 2-S1: Assump ions made in he dis ibu ion model.
Assump ion
Sou ce
Impac
Li es ock a e loca ed in
se lemen s
(Alimae &
Behnke, 2008;
Coughenou e
al., 2008; Ellis
& Lee, 2003;
Kamp e al.,
2012;
Nomadism is p ecluded, as a e po en ial ou pos s and wells
ha a e loca ed a away om any se lemen . Thus he
a eas u hes om se lemen s a e he leas likely o be
dis ibu ed in his model
38
Robinson,
2000)
Li es ock seek pas u es
wi h he highes
p oduc i i y
(Deli e al.,
2005; Ke en
e al., 2016)
“Op imal o age ” ac i i y is se e ely unca ed due o he
in oduc ion o sea ch adii. A ec s he dis ibu ion o
indi idual li es ock/ a m ypes. The o e all dis ibu ion is
a ec ed only a he edge o each se lemen ’s g azing a ea,
whe e dis ibu ed a ea is clus e ed due o he p io i y o
high-NPP alues
Li es ock g aze on
c oplands
(Coughenou
e al., 2008)
The o al ene gy a ailable o g azing inc eased by 293 PJ, o
8.3%. Mos o his ene gy was loca ed in he no hcen al,
whe e c opland domina es.
Figu e 2-S2: A magni ied iew showing he esul o he dis ibu ion model a he se lemen le el. Each unique colo ep esen s he
land dis ibu ed o a unique se lemen (black do s). This illus a es he ope a ion o he sea ch algo i hm when se lemen s a e in
close p oximi y and hei pas u e equi emen s con lic .
Equa ion 2-S2: The equa ion used o es ima e he numbe o li es ock in each age g oup in each dis ic in 2015 using he mo e
de ailed 2006 ag icul u al census. 𝐿𝑚𝑟𝑙𝑎 =𝐿𝑚𝑟𝑙 ∗𝐿𝑜𝑎
𝐶
𝐿𝑜
𝐶
whe e:
𝐿=𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑙𝑖𝑣𝑒𝑠𝑡𝑜𝑐𝑘
𝑚=𝑓𝑎𝑟𝑚 𝑡𝑦𝑝𝑒
𝑟=𝑑𝑖𝑠𝑡𝑟𝑖𝑐𝑡
𝑙=𝑙𝑖𝑣𝑒𝑠𝑡𝑜𝑐𝑘 𝑡𝑦𝑝𝑒
𝑎=𝑎𝑔𝑒 𝑔𝑟𝑜𝑢𝑝
𝑜=𝑟𝑒𝑔𝑖𝑜𝑛 𝑤ℎ𝑒𝑟𝑒 𝑜∋𝑟
𝐿𝐶=𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑙𝑖𝑣𝑒𝑠𝑡𝑜𝑐𝑘 𝑖𝑛 2006 𝑐𝑒𝑛𝑠𝑢𝑠
Table 2-S2: Values used in he calcula ion o o al ene gy demand (Zhazylbeko e al., 2008). The numbe o each li es ock species
a e eco ded a he dis ic le el o each a m ype. Li es ock numbe s o each age g oup we e eco ded in he 2006 ag icul u al
39
census a he egional le el (in which ca le we e u he di ided in o bee and dai y) (KazS a , 2008). The numbe o li es ock in
each age g oup o each li es ock species in each dis ic was es ima ed by mul iplying he numbe o each li es ock species by he
numbe in each species’ age g oup (in 2006) and di iding by he numbe o each li es ock species (in 2006). Sheep and goa
numbe s we e combined and sheep nu i i e equi emen s we e used, due o he low numbe o goa s and hei simila nu i i e
equi emen s.
Type
B eed
Age/Sex
Pe iod
Mass
(kg)
Gain
(g/d)
Milk
(kg/d)
ME
(MJ/d)
Ca le
Bee
Cows
2 mos p e cal ing
400
91
Ca le
Bee
Cows
2 mos p e cal ing
450
98
Ca le
Bee
Cows
2 mos p e cal ing
500
105
Ca le
Bee
Cows
2 mos p e cal ing
550
112
Ca le
Bee
Cows
3-4 mos pos cal ing
400
113
Ca le
Bee
Cows
3-4 mos pos cal ing
450
117
Ca le
Bee
Cows
3-4 mos pos cal ing
500
122
Ca le
Bee
Cows
3-4 mos pos cal ing
550
127
Ca le
Bee
Cows
2nd hal lac a ion
400
92
Ca le
Bee
Cows
2nd hal lac a ion
450
101
Ca le
Bee
Cows
2nd hal lac a ion
500
109
Ca le
Bee
Cows
2nd hal lac a ion
550
117
Ca le
Bee
Bulls
No coupling
800
83.2
Ca le
Bee
Bulls
A g load
800
90
Ca le
Bee
Bulls
Inc load
800
110
Ca le
Bee
Hei e s
9-10 mos
244
575
69
Ca le
Bee
Hei e s
9-10 mos
260
675
78
Ca le
Bee
Hei e s
11-12 mos
277
575
76
Ca le
Bee
Hei e s
11-12 mos
300
675
85
Ca le
Bee
Hei e s
13-14 mos
311
575
83
Ca le
Bee
Hei e s
13-14 mos
340
675
94
Ca le
Bee
Hei e s
15-16 mos
345
575
90
Ca le
Bee
Hei e s
15-16 mos
380
675
104
Ca le
Bee
Hei e s
17-20 mos
413
575
105
Ca le
Bee
Hei e s
17-20 mos
420
675
112
Ca le
Bee
Bull cal es
9-10 mos
279
825
72
Ca le
Bee
Bull cal es
9-10 mos
285
925
79
Ca le
Bee
Bull cal es
11-12 mos
330
825
80
Ca le
Bee
Bull cal es
11-12 mos
340
925
88
Ca le
Bee
Bull cal es
13-14 mos
379
825
86
Ca le
Bee
Bull cal es
13-14 mos
396
925
94
Ca le
Bee
Bull cal es
15-16 mos
428
825
92
Ca le
Bee
Bull cal es
15-16 mos
451
925
102
Ca le
Bee
Cal es
0-6 mos
32
Ca le
Bee
Cal es
6-9 mos
54.6
Ca le
Bee
Cal es
10-12 mos
78.5
Ca le
Bee
Cal es
13-15 mos
93.5
Ca le
Dai y
Cows
Milking 1s hal
400
6
72.5
Ca le
Dai y
Cows
Milking 1s hal
400
10
106
Ca le
Dai y
Cows
Milking 1s hal
400
14
127.5
Ca le
Dai y
Cows
Milking 1s hal
400
18
149
Ca le
Dai y
Cows
Milking 1s hal
400
22
172.5
Ca le
Dai y
Cows
Milking 1s hal
400
26
197.5
Ca le
Dai y
Cows
Milking 1s hal
400
30
227.5
Ca le
Dai y
Cows
Milking 2nd hal
400
5
61
Ca le
Dai y
Cows
Milking 2nd hal
400
7
83.5
40
Ca le
Dai y
Cows
Milking 2nd hal
400
9
100.5
Ca le
Dai y
Cows
Milking 1s hal
500
6
79.5
Ca le
Dai y
Cows
Milking 1s hal
500
10
115
Ca le
Dai y
Cows
Milking 1s hal
500
14
137
Ca le
Dai y
Cows
Milking 1s hal
500
18
158
Ca le
Dai y
Cows
Milking 1s hal
500
22
180.5
Ca le
Dai y
Cows
Milking 1s hal
500
26
205.5
Ca le
Dai y
Cows
Milking 1s hal
500
30
230.5
Ca le
Dai y
Cows
Milking 2nd hal
500
5
65
Ca le
Dai y
Cows
Milking 2nd hal
500
7
94.5
Ca le
Dai y
Cows
Milking 2nd hal
500
9
109.5
Ca le
Dai y
Cows
D y o s e ile
400
0
66
Ca le
Dai y
Cows
D y o s e ile
500
0
78
Ca le
Dai y
Cows
D y o s e ile
400
0
78
Ca le
Dai y
Cows
D y o s e ile
500
0
86
Ca le
Dai y
Bulls
No coupling
600
70
Ca le
Dai y
Bulls
No coupling
700
78
Ca le
Dai y
Bulls
No coupling
800
84
Ca le
Dai y
Bulls
No coupling
900
91
Ca le
Dai y
Bulls
No coupling
1000
97
Ca le
Dai y
Bulls
No coupling
1100
102
Ca le
Dai y
Bulls
No coupling
1200
108
Ca le
Dai y
Bulls
A g load
600
76
Ca le
Dai y
Bulls
A g load
700
83
Ca le
Dai y
Bulls
A g load
800
90
Ca le
Dai y
Bulls
A g load
900
97
Ca le
Dai y
Bulls
A g load
1000
104
Ca le
Dai y
Bulls
A g load
1100
110
Ca le
Dai y
Bulls
A g load
1200
117
Ca le
Dai y
Bulls
Inc load
600
92
Ca le
Dai y
Bulls
Inc load
700
102
Ca le
Dai y
Bulls
Inc load
800
110
Ca le
Dai y
Bulls
Inc load
900
119
Ca le
Dai y
Bulls
Inc load
1000
127
Ca le
Dai y
Bulls
Inc load
1100
134
Ca le
Dai y
Bulls
Inc load
1200
141
Sheep
Wool
Ewes
P eg 1s hal
50
12.5
Sheep
Wool
Ewes
P eg 1s hal
60
13.5
Sheep
Wool
Ewes
P eg 2nd hal
50
14.5
Sheep
Wool
Ewes
P eg 2nd hal
60
16.5
Sheep
Mea /wool
Ewes
P eg 1s hal
60
12.1
Sheep
Mea /wool
Ewes
P eg 1s hal
70
13
Sheep
Mea /wool
Ewes
P eg 2nd hal
60
16
Sheep
Mea /wool
Ewes
P eg 2nd hal
70
17.2
Sheep
Mea / allow
Ewes
P eg 1s hal
60
13.5
Sheep
Mea / allow
Ewes
P eg 1s hal
70
14.5
Sheep
Mea / allow
Ewes
P eg 2nd hal
60
17.5
Sheep
Mea / allow
Ewes
P eg 2nd hal
70
18.5
Sheep
Wool
Ewes
Suckling 1s hal
50
20
Sheep
Wool
Ewes
Suckling 1s hal
60
23
Sheep
Wool
Ewes
Suckling 2nd hal
50
15.5
Sheep
Wool
Ewes
Suckling 2nd hal
60
17

41
Sheep
Mea /wool
Ewes
Suckling 1s hal
60
22
Sheep
Mea /wool
Ewes
Suckling 1s hal
70
23
Sheep
Mea /wool
Ewes
Suckling 2nd hal
60
18.4
Sheep
Mea /wool
Ewes
Suckling 2nd hal
70
19.2
Sheep
Mea / allow
Ewes
Suckling 1s hal
60
21
Sheep
Mea / allow
Ewes
Suckling 1s hal
70
22
Sheep
Mea / allow
Ewes
Suckling 2nd hal
60
18.5
Sheep
Mea / allow
Ewes
Suckling 2nd hal
70
19.5
Sheep
Wool & mea /wool
Rams
No coupling
90
19
Sheep
Wool & mea /wool
Rams
No coupling
100
20
Sheep
Wool & mea /wool
Rams
No coupling
110
21
Sheep
Mea / allow
Rams
No coupling
80
19
Sheep
Mea / allow
Rams
No coupling
90
20
Sheep
Mea / allow
Rams
No coupling
100
21
Sheep
Wool & mea /wool
Rams
Coupling
90
24
Sheep
Wool & mea /wool
Rams
Coupling
100
25
Sheep
Wool & mea /wool
Rams
Coupling
110
26
Sheep
Mea / allow
Rams
Coupling
80
24
Sheep
Mea / allow
Rams
Coupling
90
25
Sheep
Mea / allow
Rams
Coupling
100
26
Sheep
Wool
Ewe lambs
4-6 mos
27.5
8.525
Sheep
Wool
Ewe lambs
6-8 mos
33
9.625
Sheep
Wool
Ewe lambs
8-10 mos
38
10.725
Sheep
Wool
Ewe lambs
10-12 mos
41
11
Sheep
Wool
Ewe lambs
12-18 mos
46
11.275
Sheep
Mea /wool
Ewe lambs
4-6 mos
38.5
11
Sheep
Mea /wool
Ewe lambs
6-8 mos
38.5
12.1
Sheep
Mea /wool
Ewe lambs
8-10 mos
43
13.2
Sheep
Mea /wool
Ewe lambs
10-12 mos
47.5
14.025
Sheep
Mea /wool
Ewe lambs
12-18 mos
51.5
14.025
Sheep
Wool
Ram lambs
4-6 mos
32
11.275
Sheep
Wool
Ram lambs
6-8 mos
39.5
12.43
Sheep
Wool
Ram lambs
8-10 mos
45
13.75
Sheep
Wool
Ram lambs
10-12 mos
49.5
15.18
Sheep
Wool
Ram lambs
12-18 mos
61.5
15.4
Sheep
Mea /wool
Ram lambs
4-6 mos
36.5
13.2
Sheep
Mea /wool
Ram lambs
6-8 mos
44.5
14.3
Sheep
Mea /wool
Ram lambs
8-10 mos
52.5
15.4
Sheep
Mea /wool
Ram lambs
10-12 mos
60
16.775
Sheep
Mea /wool
Ram lambs
12-18 mos
70
17.325
Ho ses
Mea
S allions
350
1000
93.2
Ho ses
Mea
S allions
400
1000
97.4
Ho ses
Mea
S allions
450
1000
101
Ho ses
Mea
S allions
500
1000
108.8
Ho ses
Mea
S allions
550
1000
112
Ho ses
Mea
S allions
600
1000
122.4
Ho ses
Mea
Foals
0-1 mos
70
1300
42
Ho ses
Mea
Foals
1-2 mos
105
1000
45
Ho ses
Mea
Foals
2-3 mos
133.5
900
47
Ho ses
Mea
Foals
3-4 mos
160.5
900
52
Ho ses
Mea
Foals
4-5 mos
187.5
900
56
Ho ses
Mea
Foals
5-6 mos
214.5
900
60
42
Ho ses
Mea
Foals
6-7 mos
235.5
900
60
Ho ses
Mea
Foals
7-8 mos
258
1000
71
Ho ses
Mea
Foals
8-9 mos
291
1300
88
Ho ses
Milk
Ma es
400
10
84.8
Ho ses
Milk
Ma es
400
12
92.1
Ho ses
Milk
Ma es
400
14
98.4
Ho ses
Milk
Ma es
500
14
105.7
Ho ses
Milk
Ma es
500
16
113.1
Ho ses
Milk
Ma es
500
18
120.4
Ho ses
Milk
Ma es
500
20
126.5
Table 2-S3: Age g oups used in he es ima ion o li es ock ene gy demand. Res ic ions a e no ed when he age g oup is a subse o
he Age/Sex in Table 2-S2. Nomencla u e o he age g oup is as ollows (squa e b acke s enclose s ing a iables):
[*Bee /Dai y*][Func ion]_[*age*]_[* ep oduc ion s age*]. * – i applicable. Func ion – common name conside ing age, sex, and
cas a ion.
Age g oup
Age/Sex in Table 2-S2
Pe iod/weigh es ic ion
Bee Cows
Bee Cows
Bee Bulls_b eeding
Bee Bulls
Bee Hei e s
Bee Hei e s
Bee Hei e s_1 o2y s
Bee Hei e s
13-16 mos.
Bee Hei e s_1 o2y s_insemina ed
Bee Hei e s
17-20 mos.
Bee Hei e s_2y s
Bee Hei e s
17-20 mos.
Bee Cal es_hei e s_0 o1y
Bee Cal es
0-12 mos.
Bee Cal es_bulls_0 o1y
Bee Cal es
0-12 mos.
Bee Cal es_bulls_1y
Bee Bull cal es
13-16 mos.
Bee S ee s
Bee Bull cal es
Bee Oxen
Bee Bulls
No coupling
Bee Ca le_ inishing
Bee Bull cal es
15-16 mos.
Bee Bu aloes
Bee Bulls
No coupling
Dai yCows
Dai y Cows
Dai yBulls_b eeding
Dai y Bulls
Dai yHei e s
Bee Hei e s
Dai yHei e s_1 o2y s
Bee Hei e s
13-16 mos.
Dai yHei e s_1 o2y s_insemina ed
Bee Hei e s
17-20 mos.
Dai yHei e s_2y s
Bee Hei e s
17-20 mos.
Dai yCal es_hei e s_0 o1y
Bee Cal es
0-12 mos.
Dai yCal es_bulls_0 o1y
Bee Cal es
0-12 mos.
Dai yCal es_bulls_1y
Bee Bull cal es
13-16 mos.
Dai yS ee s
Bee Bull cal es
Dai yOxen
Bee Bulls
No coupling
Dai yCa le_ inishing
Bee Bull cal es
15-16 mos.
Dai yBu aloes
Bee Bulls
No coupling
EweDoe_1y
Ewes
RamBuck_b eeding
Rams
LambKid_0 o1y
Lambs
We he s_1y
Rams
No coupling
Ma es_3y s
Ma es
S allions_b eeding
S allions
Foals_0 o1y
Foals
Fillies_1 o3y s
S allions
350 kg, 400 kg, 450 kg
Col s_1 o3y s
S allions
350 kg, 400 kg, 450 kg
Col s_3y s
S allions
500 kg, 550 kg, 600 kg
Geldings
S allions
Equa ion 2-S3: The equa ion used o es ima e he amoun o ene gy supplied by odde o each li es ock species in each dis ic in
2015.
43
𝐶𝑚𝑟𝑓𝑙 =𝑃𝑚𝑟𝑓 ∗𝐶𝑜𝑓𝑙
𝐶𝑜𝑓
whe e:
𝐶=𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝑀𝐽)
𝑃=𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 (𝑀𝐽)
𝑚=𝑓𝑎𝑟𝑚 𝑡𝑦𝑝𝑒
𝑟=𝑑𝑖𝑠𝑡𝑟𝑖𝑐𝑡
𝑓=𝑓𝑜𝑑𝑑𝑒𝑟 𝑡𝑦𝑝𝑒
𝑙=𝑙𝑖𝑣𝑒𝑠𝑡𝑜𝑐𝑘 𝑡𝑦𝑝𝑒
𝑜=𝑟𝑒𝑔𝑖𝑜𝑛 𝑤ℎ𝑒𝑟𝑒 𝑜∋𝑟
Table 2-S4: Con e sion a ios used o con e kg o MJ (Fo age On-Line, 2009). P oduc ion o each odde ype is eco ded a he
dis ic le el o each a m ype. Consump ion o each odde ype by each li es ock species is eco ded a he egional le el (all a m
ypes combined). The consump ion o each odde ype by each li es ock species a he dis ic le el o each a m ype was
es ima ed by mul iplying he p oduc ion o each odde ype by he consump ion o each odde ype by each li es ock species and
di iding by he o al consump ion o each odde ype by each li es ock species.
Feed class
ME (MJ/kg)
FeedG ain
12.98
FeedLegume
13.2
SilageNonCo n
1.98
FeedRoo
1.65
FeedMelon
0.88
FeedCo n
2.2
Co nG eenFodde
2.2
Co nSilage
2.2
Hay
5.72
HayPas u e
5.72
HayPas u eCul i a ed
5.72
HayPas u eSeeded
5.72
HayPas u eNa u al
5.72
HayAnnualG ass
5.61
HayPe ennialG ass
5.61
G assFodde
2.75
G assFodde Seeded
2.53
G assFodde Na u al
2.86
G assFodde Annual
1.87
G assFodde Pe ennial
2.75
SeedG assAnnual
3.41
SeedG assPe ennial
3.41
G azingG assAnnual
2.53
G azingG assPe ennial
2.75
44
Figu e 2-S3: Side-by-side compa ison wi h he esul s o a) Fe zel e al. (2017) and b) ou model. No e ha he uni s a e no di ec ly
con e ible. Howe e , using e y di e en me hods and inpu s, bo h maps show simila dis ibu ions o ela i e g azing ac i i y.
Whi e a eas in bo h maps ep esen unu ilized a ea. a) Rep oduced wi h pe mission o he au ho s.
51
he s udy egion. We ob ained annual li es ock numbe s a he dis ic le el om he Kazakh Na ional
S a is ics Agency (KazS a , 2019). To de e mine he ene gy demand o he li es ock, we used he 2006
ag icul u al census o di ide he he ds in o age and unc ion g oups (e.g. bee hei e s) (KazS a , 2008). We
hen applied ecommended ene gy in ake alues om he Minis y o Ag icul u e (Zhazylbeko e al.,
2008). We summed hese alues o c ea e a o al li es ock ene gy equi emen o each dis ic . We used
dis ic -le el g oss yields (KazS a , 2019) o de e mine annual odde p oduc ion, which we con e ed o
ene gy and hen sub ac ed om o al li es ock ene gy demand, esul ing in he o al ene gy demand o
li es ock om g azing. We hen con e ed he annual g azing demand o uni s o ene gy pe a ea g assland
o each dis ic . We in oduced a lag o one yea o accoun o he “end o yea ” alue o g azing demand
(Table 3-).
3.2.3 Clima e indica o s
Mon hly p ecipi a ion and a e age empe a u e alues we e ob ained om he Clima e Resea ch Uni
(CRU) (Ha is e al., 2014). Annual g owing deg ee days (base 5°C) (Wou e s, 2021) and mon hly ela i e
humidi y (He sbach e al., 2018) we e aken om ERA5 eanalyses. The g idded da a we e a e aged o
p oduce a single alue o each dis ic . He e we we e in e es ed in annual and seasonal alues—sp ing
(Ma ch-May), summe (June-Augus ), all (Sep embe -No embe ), and win e (Decembe -Feb ua y), as well
as i e season (May-Oc obe ), and i e o -season (No embe -Ap il). Fo simplici y we e e o hese
a iables as clima e, o bo h annual and long- e m analyses. Fo p ecipi a ion, he sum o he gi en pe iod
was calcula ed. Fo empe a u e and ela i e humidi y, he mean o he gi en pe iod was calcula ed. The e
we e 124 dis ic s and 19 yea s, o which we used 18 a e excluding he lag yea , hence 2,232 obse a ions
o each a iable. Lagging he clima ic a iables by one yea did no imp o e he model, he e o e he
cu en -yea clima ic a iables we e combined wi h he p e ious yea ’s g azing demand.
3.2.4 Fi e indic o s
We used he Te a and Aqua combined MCD64A1 6 Bu ned A ea p oduc o es ima e he annual a ea
bu n in he s udy a ea (Giglio e al., 2015). We s acked he mon hly MODIS 500m esolu ion images o
c ea e an annual bina y ime se ies o bu ned o unbu ned a eas. We used he land-co e map o Klein and
o he s (2012) o mask cul i a ed a eas, a i icial su aces, o es s, ba e a eas, ice and snow, and
wa e bodies. The esul an g assland map, along wi h an adminis a i e dis ic map (GADM, 2018),
allowed us o calcula e he p opo ion o g assland bu n in each dis ic o each yea .
3.2.5 S a is ical analysis
We used a binomial gene alized linea mixed-e ec s model (GLMM) o eg ess he p edic o s agains he
obse ed alues o bu ned a ea (Bolke e al., 2009; Wa on & Hui, 2011). Fixed-e ec s a iables in ou
models we e annual g azing demand (MJ/km2), annual cumula i e g owing deg ee days, and se en
agg ega ions o o al p ecipi a ion (mm), a e age empe a u e (°C), and ela i e humidi y (%): annual, i e

52
season, o -season, sp ing, summe , all, and win e . We included adminis a i e dis ic iden i ie s as
andom e ec s o accoun o he non-independence o epea ed sampling o bu n a eas wi hin he same
dis ic in di e en yea s. We assume opog aphy doesn’ change o e he s udy pe iod, and hus slope—
an impo an d i e o i e—is accoun ed o a he dis ic le el as a andom e ec . All a iables we e
no malized o compa e di e en uni s and di e en scales o magni ude. The binomial eg ession ollowed
he o m 𝑝= 𝑒𝛽0+𝛽1𝑥+𝛽2𝑥2
1+𝑒𝛽0+𝛽1𝑥+𝛽2𝑥2 whe e 𝑝=𝑝𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 𝑏𝑢𝑟𝑛𝑡 and 𝛽0+𝛽1𝑥+𝛽2𝑥2 is he quad a ic (o linea
i educed o 𝛽0+𝛽1𝑥) eg ession exp ession (C awley, 2013). We de e mined he goodness-o - i o he
a o emen ioned combina ions o a iables using condi ional and ma ginal R2. Ma ginal R2 measu es
a iance explained by ixed ac o s, while condi ional R2 measu es a iance explained by bo h ixed and
andom ac o s (Nakagawa & Schielze h, 2013). Akaike In o ma ion C i e ion (AIC) was calcula ed o
de e mine model s eng h (Maze olle, 2006). The calcula ions we e pe o med using he “a m” and “lme4”
packages wi hin R (Gelman e al., 2022). In addi ion o single- a iable models, we explo ed mul i- a iable
models o de e mine i combining a iables p oduced be e models.
3.3 Resul s
3.3.1 Tempo al ends in bu ned a ea
Be ween 2001 and 2019, he annual bu ned a ea exceeded 5% (abou 74,000 km2) o he o al g assland
a ea in se en yea s (Figu e 3-2a), o which six yea s occu ed be ween 2001 and 2010. The Kazakh s eppe
has expe ienced a gene al downwa d end in bu ned a ea o 3,670 km2 pe yea o e hese 19 yea s,
hough wi h high a ia ion be ween yea s. P ecipi a ion expe ienced a gene al dec ease du ing he 2000s,
ollowed by la ge in e -annual a ia ion h oughou he 2010s (Figu e 3-2b). Tempe a u e was ela i ely
s able and ela i ely high du ing he 2000s, be o e also en e ing a pe iod o la ge in e -annual a iabili y in
he 2010s. G azing demand has isen s eadily (mi o ing he ebounding li es ock numbe s since ci ca 2000)
(Figu e 3-2c). The obse able b eak in 2011 is an a i ac o a e e endum in s a is ical coun ing
(Naza baye , 2010), and is accoun ed o by he GLMM.
53
Figu e 3-2. (a) Annual bu ned a ea ex en on g assland in ou s udy egion (Giglio e al., 2015). The linea endline is o
isualiza ion pu poses only. (b) A e age annual p ecipi a ion (blue) and a e age annual ela i e humidi y (g een) ac oss he s udy
egion (Ha is e al., 2014; He sbach e al., 2018). (c) Annual g azing demand o g azing li es ock in he s udy egion. The ene gy
equi alen was calcula ed using annual na ional s a is ics bulle ins (KazS a , 2019), he 2006 ag icul u al census (KazS a , 2008),
and ecommended in ake alues (Zhazylbeko e al., 2008). See Hanke son and o he s (2019) o calcula ions. (d) A e age annual
empe a u e and a e age annual g owing deg ee days (Ha is e al., 2014; Wou e s, 2021).
3.3.2 Spa ial ends in bu ned a ea
Ac oss he Kazakh s eppe, he mos equen ly bu ned a eas occu ed ac oss he middle and no heas o
he s udy a ea, bo de ing he p edomina ely c op-p oducing egion in he no hcen al (Figu e 3-3). O he
g assland in he s udy a ea o e he 19-yea s udy pe iod, 42% bu ned a leas once, 21% a leas wice,
10% a leas h ee imes, and 4% ou o mo e imes.
54
Figu e 3-3. F equency o i es (measu ed as he numbe o yea s wi h bu ned a ea) ac oss he s udy a ea om 2001-2019. Only
bu ns on g asslands a e shown. G ay a ea shows he es o Kazakhs an, wi h neighbo ing coun ies (GADM, 2018).
3.3.3 Analyses o de e minan s in i e pa e ns and ends
We i s es ed ou assump ion o es ic ing he a ea o in e es o g asslands a ailable o pas u e. We
es ed h ee di e en scena ios, i s whe e g azing demand can be sp ead ac oss all land ypes, second
whe e g azing demand can only be sp ead ac oss g asslands and c oplands (g azing is known o occu on
c oplands (Coughenou e al., 2008)), and hi d whe e g azing demand can only be sp ead ac oss
g asslands. Using g asslands only o he dis ibu ion o g azing demand had he highes R2 alues (Table 3-
1. Ma ginal and condi ional R2 alues o model esul s o h ee di e en land use masks wi h and wi hou
a one-yea lag o bu ned a ea wi h espec o g azing demand (Nakagawa & Schielze h, 2013).). We hen
examined he lag e ec o he a iables, o de e mine whe he bu ned a ea is mo e a esul o he
condi ions in he cu en yea o in he p e ious yea . Applying a one-yea lag o bu ned a ea wi h espec
o p ecipi a ion, empe a u e, ela i e humidi y, g owing deg ee days o combina ions o hem did no
inc ease he models’ R2 alue. Howe e , applying a one-yea lag o bu ned a ea wi h espec o g azing
demand inc eased he R2 alue by app oxima ely 0.2. This makes sense conside ing ha he li es ock
s a is ics we e epo ed “a end o yea ”. The e o e, we chose o use he clima ic a iables o he cu en
yea in combina ion wi h he p e ious yea ’s g azing demand, which has p eceden in simila egions
(Dubinin e al., 2011).
Table 3-1. Ma ginal and condi ional R2 alues o model esul s o h ee di e en land use masks wi h and wi hou a one-yea lag o
bu ned a ea wi h espec o g azing demand (Nakagawa & Schielze h, 2013).
Se o a iables
Ma ginal R2
Condi ional R2
G azing on all land ypes
0.157
0.369
G azing on g asslands and c oplands
0.215
0.424
G azing on g asslands
0.295
0.522
G azing on all land ypes wi h lag
0.327
0.526
G azing on g asslands and c oplands wi h lag
0.404
0.612
G azing on g asslands wi h lag
0.505
0.749
55
We hen i ed he GLMMs wi h all possible combina ions o g azing demand and clima e a iable
combina ions as p edic o s, and he p opo ion bu ned a ea pe g id cell as he esponse a iable (o e
16000 models we e analyzed, a comple e lis can be p o ided upon eques ). An analysis o R2 alues
p oduced he highes alues when bu ned a ea was a unc ion o he p e ious yea ’s g azing demand.
Figu e 3-4 plo s he no malized alues o p ecipi a ion, empe a u e, ela i e humidi y, g owing deg ee
days, and g azing demand (p e ious yea ) agains he p opo ion bu ned in a gi en dis ic and yea . Fo
each se o clima e a iables, we chose he bes -pe o ming single- a iable model acco ding o AIC.
In e es ingly, longe ime pe iods explained bu ned a ea be e , ei he he annual a e age o empe a u e,
o he i e season alues o p ecipi a ion and ela i e humidi y.
Figu e 3-4. Bu ned a ea o a dis ic -yea wi h i ed binomial eg ession o no malized alues o (a) i e season p ecipi a ion (P), (b)
a e age all ela i e humidi y (RH), (c) a e age all empe a u e (T), (d) annual g owing deg ee days (DD), and (e) annual g azing
demand (G).
We ound ha any model ha included g azing demand g ea ly inc eased bo h ma ginal and condi ional R2
alues compa ed o models wi hou ha a iable (Figu e 3-5). The bes combina ion o solely clima ic
a iables was he in e ac ion be ween summe p ecipi a ion and all ela i e humidi y combined wi h
annual empe a u e in e ac ed wi h g owing deg ee days (Figu e 3-5k). Howe e , whene e g azing
demand was combined wi h a leas wo o he a iables, he R2 alues we e ou inely be e han any
model wi h solely clima ic a iables.
56

57
Figu e 3-5. Ma ginal and condi ional R2 o inc easingly complex models. As e isks a e applied o he bes model o i s ype o each
clima e and g azing a iable. Fo example (h) has only wo g aphs, he i s is he bes -pe o ming ou - a iable addi i e model ha
has ela i e humidi y in i , he second g aph is sha ed among e e y o he a iable as hei bes -pe o ming ou - a iable addi i e
model. The o e all bes -pe o ming model (by AIC alue) is he i e- a iable model including in e ac ing summe p ecipi a ion & all
ela i e humidi y, in e ac ing annual empe a u e & g owing deg ee days, and g azing demand (k). ann=annual, ssn= i e season,
o = i e o -season, sp =sp ing, sm =summe , al= all, win=win e .
3.3.4 Regional analyses
We e alua ed each o he eigh eco egions in he s udy a ea sepa a ely o de e mine possible a ia ions in
bu ned a ea de e minan s ( wo eco egions, he Jungga Basin semi-dese and he Emin Valley s eppe,
encompass only a single dis ic , and we e he e o e no analyzed). Pe eco egion, bo h he bes se o
p edic i e a iables (de e mined by AIC) and he model wi h he highes condi ional R2 was de e mined
(Table 3-2).
The R2 alues a ied subs an ially be ween eco egions (c . Figu e 3-). The Kazakh s eppe eco egion has he
la ges numbe o dis ic s, bu only mode a e R2 alues, wi h a quad a ic model p oduced he highes R2
alues. The Kazakh upland s eppe co e s only h ee dis ic s and encompasses p edomina ely c op-
p oducing egions, whe e he bu ned a ea is small. He e an in e ac i e model p oduced he highes R2
alues, hough condi ional R2 is he lowes o any eco egion. The Kazakh semi-dese is he second-la ges
eco egion and exhibi ed a high deg ee o a ia ion and unce ain y. Howe e , accoun ing o spa ial
dis ibu ion h ough inclusion o andom e ec s (dis ic s) g ea ly inc eased he condi ional R2 wi h a
quad a ic model. The Pon ic s eppe was he only eco egion wi hou empe a u e in i s bes R2 model. The
Caspian lowland dese was he only eco egion wi hou p ecipi a ion in i s bes R2 model, and had he
58
highes R2 alues o any eco egion. The Al ai s eppe and semi-dese is he second smalles eco egion and
did no ha e g azing demand in i s bes R2 model. Rela i e humidi y was he only a iable ound in e e y
eco egion’s bes R2 model. No ably, he bes p edic i e powe ( alua ed by AIC) was ob ained by including
all a iables in all eco egions, wi h he wo mos well- ep esen ed eco egions (Kazakh s eppe and Kazakh
semi-dese ) ha ing he bes associa ed R2 alues.
The eco egions can be spli oughly in o wo g oups, he “we ” g oup—Kazakh s eppe, Kazakh upland
s eppe, and Al ai s eppe and semi-dese —and he “d y” g oup—Kazakh semi-dese , Pon ic s eppe, and
Caspian lowland dese . The “d y” g oup a e si ua ed in he sou h and wes o he s udy a ea, and sha e a
numbe o cha ac e is ics, including many o he lowes p ecipi a ion and highes empe a u e alues. In
hese, bu ned a ea shows a s onge ela ionship wi h sp ing, summe , and i e season clima e a iables.
The “we ” g oup a e in he no h and eas o he s udy a ea and ha e he highes p ecipi a ion and lowes
empe a u e alues. In hese, win e and sp ing clima e a iables a e mo e common in he bes -pe o ming
R2 models. These g oups also sha e he ype o p edic i e model (AIC), wi h he “we ” g oup p e e ing
in e ac i e models and he “d y” g oup p e e ing quad a ic. Pe haps mos no ably, when analyzing on he
scale o eco egions, he seasonali y o clima e a iables a e e ealed as many mo e o he sho e ime
pe iods (sp ing, summe , all, and win e ) appea in he bes -pe o ming models.
Table 3-2. Models wi h he lowes AIC alue and he highes condi ional R2 o each eco egion.
Eco egion
№ dis ic s
Value
Bes -pe o ming model
R2m, R2c
Kazakh s eppe
73
AIC
R2c
Pssn*RH al, To *DDann, Gann
Pwin2, Tsp 2, RHsm 2, DDann, Gann
0.39, 0.60
0.54, 0.75
Kazakh upland s eppe
3
AIC
R2c
Pwin*RHssn, Tann*DDann, Gann
Pwin*DDann, Tsm *RH al, Gann
0.42, 0.46
0.47, 0.64
Al ai s eppe & semi-dese
6
AIC
R2c
Psp *DDann, Twin*RHo , Gann
Psp 2, Twin2, RHann
0.28, 0.37
0.47, 0.81
Kazakh semi-dese
24
AIC
R2c
Pssn2, To 2, RHssn2, DDann2, Gann
Pssn2, Tann, RHssn2, Gann
0.23, 0.67
0.26, 0.74
Pon ic s eppe
8
AIC
R2c
Psm 2, Tsm 2, RH al2, DDann2, Gann
Psm *RHsp , Gann
0.22, 0.52
0.60, 0.80
Caspian lowland dese
8
AIC
R2c
Psm 2, To 2, RH al2, DDann2, Gann
Tsm 2, RHsp 2, DDann, Gann
0.26, 0.53
0.50, 0.97
3.4 Discussion
Clima e ac o s and g azing bo h ha e been shown o be impo an de e minan s o i e pa e ns and
ends, bu hei ela i e impo ance has o en been elusi e. Unde s anding hei oles is key as clima e
change p og esses. Making use o he na u al expe imen o apid, widesp ead, and spa ially
he e ogeneous declines in li es ock numbe s in he s eppes o pos -So ie Kazakhs an, a global i e ho spo
(A chibald e al., 2013; Han son e al., 2013), we assessed he ela i e impo ance o changes in g azing
demand and clima e a iables on bu ned a ea a unp eceden ed spa ial and empo al de ail. Ou analyses
yield h ee majo insigh s. Fi s , i e egimes changed ma kedly, wi h excep ionally high equency and
ex en o i es in he 2000s ollowed by a decline in he 2010s, co obo a ing ea lie indings o smalle
59
sub egions and om o he pa s o he pos -So ie sphe e (Da a, Baumann, Hölzel, e al., 2020; Dubinin e
al., 2010; Hao e al., 2021). Second, he e was a s ong nega i e associa ion be ween g azing demand and
bu ned a ea, sugges ing ha es o ing g azing egimes—ei he wi h domes ic li es ock o na u al g aze s—
can supp ess bu ning in Eu asian s eppes. Thi d, changing li es ock g azing demand a e he b eakdown o
he So ie Union explained i e dynamics be e han bo h b oad-scale clima e pa e ns and in e -annual
clima e a ia ion, highligh ing he impo ance o g azing and, mo e gene ally, he need o be e da a on
g azing p essu e in g asslands.
Answe ing ou i s esea ch ques ion, we ound ha bu ned a ea ex en a ied g ea ly om yea o yea
(Figu e 3-2a). Linea eg ession showed an a e age dec ease in bu ned a ea o 3,670 km2/yea om 2001-
2019. As pe cen change (abou -5.4%), his is well abo e he global end o abou -1.6% o e he same
ime pe iod (Zheng e al., 2021). We also ound ha he mos equen ly bu ned a eas a e loca ed
p edominan ly along he bo de be ween he Kazakh s eppe and he Kazakh semi-dese , as well as in he
Pon ic s eppe (c . Figu e 3- & Figu e 3-3). This ag ees wi h he median i e e u n in e al o his egion
ound by A chibald and o he s (2013), which ex ends wes wa d ac oss he Sou he n Fede al Dis ic o
Russia (Dubinin e al., 2010).
The nega i e associa ion be ween g azing and i e in ou s udy a ea is in line wi h Da a and o he s (2020),
who ound i e occu ence o be co ela ed wi h li es ock p esence in a subse o ou s udy a ea. Simila ly
Dubinin and o he s (2011) ound li es ock popula ion o be nega i ely associa ed wi h bu ned a ea in a
egion adjacen o ou s udy a ea. We also ind ag eemen wi h Hao and o he s (2021), who looked a a
simila ime pe iod ac oss a la ge a ea in no he n Eu asia and ound g azing demand o be s ongly
co ela ed wi h declining bu ned a ea. Impo an ly, we go beyond p io wo k in ha he ela ionship
be ween i e and g azing has nei he been s udied ac oss a b oad ange o en i onmen al condi ions and
na u al ege a ion zones while using high- esolu ion da a on g azing demand and i e ac i i ies, no ac oss
such a leng h o ime while using sub-annual da a.
Ou mos impo an inding was ha i e was mo e s ongly ela ed o g azing han clima e ac o s. We
ha e shown ha g azing demand is s ongly, and nega i ely, co ela ed wi h bu ned a ea (Figu e 3-4), and
can be used alone o in coho wi h clima e a iables o p oduce a obus model (Figu e 3-5). Indeed, as
g azing demand has s eadily inc eased o e he pe iod 2001-2019, bu ned a ea has become less a iable
and gene ally smalle . While we did no measu e o age quan i y o quali y, ou indings co obo a e he
linkages be ween la ge g aze s and i e occu ence (Reid e al., 2022).
We iden i ied ade-o s be ween conse a ion, i e supp ession, and li es ock p oduc ion, which can
in o m policy make s and local go e nmen s looking o op imize he alloca ion o esou ces assigned o i e
p e en ion and con ol. The esul s co obo a e ha li es ock g azing can be used o mi iga e i e
occu ence on empe a e g asslands, which mo e b oadly can suppo mi iga ion o g eenhouse gas
60
emissions om i e and p omo e p oduc ion om g azing li es ock in egions wi h la ge g assland
esou ces a lowe en i onmen al cos s. This is imely o Kazakhs an and he o me So ie Union in
gene al as hey s a o elease ca bon ha had been seques e ed in he yea s immedia ely ollowing he
dissolu ion o he So ie Union (Schie ho n e al., 2019).
A ew limi a ions need men ioning. Fi s , he e we e se e al a iables ha may ha e added explana o y
alue. Topog aphy, wind pa e ns, uel con inui y, and access o igni ion sou ces a e all unaccoun ed- o
a iables ha also de e mine i e equency and ex en (IAFC & NFPA, 2019). Ou in en was no o c ea e a
de ini i e model o anking i e d i e s, bu a he o in es iga e he ank o g azing demand in compa ison
o some o he mos impo an clima ic a iables (Oli ei a e al., 2012). Second, he MODIS bu ned a ea
p oduc may no cap u e all bu ned a eas, especially small bu ns. Howe e , small bu ns a e no
cha ac e is ic o ou s udy a ea, and indeed, empe a e g asslands and xe ic sh ublands ( he biomes o ou
s udy a ea) a e among he mos highly accu a e biomes in e ms o bu ned a ea es ima es (Bosche i e al.,
2019). Thi d, despi e ou use o dis ic -le el s a is ics, ou spa ial ep esen a ion o g azing demand
emains a coa se agg ega ion, wi h no way o de ini i ely alloca e li es ock wi hin he dis ic (Hanke son e
al., 2019). Finally, small sample sizes educed he goodness-o - i o some eco egions, e en when including
all a iables and quad a ic e ms (Table 3-2). Some eco egions, especially in eas e n Kazakhs an (c . Figu e
3-), a e small, isola ed, and unique, no lending hemsel es well o analyses a he dis ic le el. La ge
eco egions, howe e , p oduced obus esul s.
Ou wo k also has majo policy implica ions. As much o he s eppe is oo a id o p o i able c op
p oduc ion, li es ock g azing has li le compe i ion om o he land uses o Kazakhs an’s g assland
esou ces (Fe zel, Ha lík, He e o, & E b, 2017), a bias which may s eng hen unde con inuing clima e
change (Weindl e al., 2015). Addi ionally, Kazakhs an is no conside ed a p io i y a ea o clima e change
mi iga ion and adap a ion (Bonilla-Ced ez e al., 2023), sugges ing ha i is a sui able candida e o
li es ock expansion. As a p edomina ely pas o alis socie y, he mi iga ion o i e occu ence on he Kazakh
s eppe could a leas in pa be achie ed h ough he managemen o g azing p ac ices, p o iding ha
li es ock numbe s con inue o g ow in o de o la ge-scale educ ion o uel loads, possibly wi h he help
o a ge ed go e nmen al p og ams. Any p og am implemen ed by he go e nmen would ha e o ake
in o accoun he as di e ences in g azing p ac ices be ween la ge ag icul u al en e p ises wi h ens o
hund eds o housands o animals, medium-sized p i a e a ms—which a e he as es g owing a m ype—
and small households wi h a hand ul o animals, which is whe e he majo i y o g azing li es ock in
Kazakhs an a e s ill aised, bu whom a e a ely able o ake ad an age o any o he go e nmen ’s cu en
suppo p og ams (Ke en e al., 2021).
Despi e a po en ial e i al o li es ock husband y in he s eppes o Kazakhs an, ine i ably he e will also be
la ge a eas whe e li es ock g azing emains in easible (e.g., oo emo e, oo ma ginal) o p ohibi ed (e.g.,
inside p o ec ed a eas) (Da a, Baumann, F ei ag, e al., 2020; Kamp e al., 2016; Ke en e al., 2016). These
67
Figu e 4-2: Selec ed p ope ies o bee and i s less popula subs i u es. All da a om Palea i e al. (2003) excep lamb, om Palea i
e al. (2006). (a) Physical composi ion o he mea . (b) Fa y acids composi ion, SFA—sa u a ed a y acids, MUFA—
monounsa u a ed a y acids, PUFA—polyunsa u a ed a y acids. (c) Essen ial amino acids (no calcula ed o lamb) and choles e ol
le els, wi h s anda d e o . Mea s we e cu ed, e men ed, and d ied in a igo ously consis en manne ollowing he adi ional
p epa a ion o ‘b esaola’, an I alian d ied-mea p oduc .
4.3 Land use
Feed-use e iciency is an impo an indica o o he en i onmen al impac o mea p oduc ion. A numbe o
indica o s can be used, which highligh di e en oo p in s. Feed con e sion a io measu es all eed in ake
(as d y ma e ) o mass o p oduc (mea ), ene gy con e sion a io measu es ene gy in ake o ene gy in
p oduc , p o ein con e sion a io measu es p o ein in ake o p o ein in p oduc , and ni ogen use e iciency
measu es he pe cen o ni ogen in ake e ained in he p oduc (Hou e al., 2016). Fo con e sion a ios,
lowe is be e , while o use e iciencies, highe is be e . In he ela i ely in ensi e p oduc ion sys ems o
he EU, non-dai y ca le (i.e. bee ca le) ha e con e sion a ios 4-7 imes highe han po k o chicken, while
ha ing a ni ogen use e iciency 3-4 imes less (Hou e al., 2016). Simila di e ences in eed con e sion
a ios we e ound in he US (Mekonnen e al., 2019). Howe e , bee ca le ob ain he majo i y o hei
in ake equi emen h ough g azing and o age (a ound 80% in he EU & US), ene gy sou ces ha a e no in
di ec compe i ion wi h human-edible eed, whe eas po k and chicken ecei e almos exclusi ely human-
edible eed (Hou e al., 2016; Mekonnen e al., 2019). Thus, he e is a niche ha bee ills, p o iding human-

68
edible ood (mea ) om land o he wise no capable o p oducing human-edible ood (o a economically
in easible le els) ( an Zan en e al., 2018).
Figu e 4-3 shows he 2015 dis ibu ion o ca le and ho ses, along wi h he shaded ou line o ou biomes
ha a e commonly g azed (Dine s ein e al., 2017): Tempe a e b oadlea and mixed o es s (eas e n US,
Eu ope, and Eas Asia), opical and sub opical g asslands, sa annas, and sh ublands (B azil, A ica, and
no he n Aus alia), empe a e g asslands, sa annas, and sh ublands (cen al US, A gen ina and ac oss
Eu asia), and mon ane g asslands and sh ublands ( he Andes, Eas A ica and he Tibe an Pla eau).
Ob iously, his is no a pe ec ep esen a ion o a eas o a ge o expansion, o example he e a e
hea ily wooded a eas in he empe a e b oadlea and mixed o es s no sui ed o g azing, and mon ane
g asslands ange om he lush E hiopian g asslands o he igid and ba en eas e n Himalayan alpine
sh ub. Likewise, some eligible egions a e excluded om his map: The A a alli wes ho n sc ub o es s in
Pakis an a e e y hea ily g azed, bu many o he egions in he dese s and xe ic sh ublands biome a e
de oid o li es ock. The shaded a ea co e s nea ly 50 million km2, o abou one hi d o he land su ace. I
is es ima ed ha li es ock p oduc ion cu en ly u ilizes abou 33 million km2 o pas u e (X. Xu e al., 2021).
69
Figu e 4-3: The G idded Li es ock o he Wo ld o ca le and ho ses (Gilbe e al., 2022a, 2022b). Shaded a eas a e b oad,
a iega ed biomes ha may ha e desi able cha ac e is ics o ho se g azing (Dine s ein e al., 2017).
Some o he po en ial g azing land in Figu e 4-3 is cu en ly being used as c opland. Li es ock p oduc ion
uses abou 20% o he 16 million km2 o global c oplands o eed (FAO, 2024; X. Xu e al., 2021).
Addi ionally, eed p oduc ion consumes abou 20% o ag icul u al blue wa e use (mainly i iga ion) (Heinke
e al., 2020). Ho ses can g aze on he same land ha ca le do, as well as mo e ma ginal and dis an
pas u es due o hei g ea e mobili y, s u diness, and he highe p opo ion o g ass in hei die
(Cymbaluk, 1994; Na ional Resea ch Council, 1981; Willekes, 2013). Mo eo e , s udies ha e shown ha
in oducing human-edible ce eals du ing inishing can e en be was e ul economically and ha m ul o
ho ses’ wel a e (Raspa e al., 2021). Thei abili y o dig h ough snow and b eak ice wi h hei hoo es a e
skills ha ca le lack and allow ho ses o g aze yea - ound a la i udes and al i udes whe e ca le need o be
shel e ed and ed en i ely wi h eed and odde (Gudmundsson & Dy mundsson, 1994).
70
4.4 G eenhouse gas emissions
Li es ock p oduce app oxima ely 21% o human-induced ca bon dioxide-equi alen (CO2e) GHG emissions
(X. Xu e al., 2021). Me hane (CH4) is by a he la ges sou ce o li es ock GHG emissions, and ca le a e by
a he la ges con ibu o s o me hane p oduc ion, accoun ing o 77% o all non-CO2 emissions om
li es ock p oduc ion. The amoun o me hane eleased o p oduce a kilog am o bee a ies g ea ly
depending on he p oduc ion sys em and egion, wi h in ensi e sys ems in de eloped na ions p oducing
he ewes GHGs pe kg p o ein and ex ensi e sys ems in de eloping coun ies p oducing he mos ,
especially in a id egions (He e o, Ha lík, e al., 2013). Dai y bee emi s abou 25% less CO2e GHG han
adi ional bee cow/cal sys ems (Nguyen e al., 2010).
G eenhouse gas emissions om li es ock depend on managemen p ac ices. Ho ses a e also hindgu
e men e s, meaning ha hey p oduce subs an ially less me hane han o egu e men e s (such as ca le,
sheep, and goa s) (H is o e al., 2013). Ho ses emi an es ima ed 18 kg me hane pe head pe yea h ough
en e ic e men a ion (C u zen e al., 1986). Ca le a e ea ed o mo e egionalized p ecision, wi h dai y
ca le anging om 46 kg/head/yea in A ica and he Middle Eas o 128 kg/head/yea in No h Ame ica.
O he ca le (mainly bee ca le) ange om 27 kg/head/yea in he Indian Subcon inen o 60 kg/head/yea
in Oceania (Dong e al., 2006). In 2021, a e age global me hane emissions (en e ic e men a ion plus
manu e managemen ) om dai y ca le we e 74.7 kg/head/yea , o he ca le 45.4 kg/head/yea , and
ho ses 19.5 kg/head/yea (FAO, 2023a).
While bo h a e p edomina ely g azing species, ca le and ho ses di e in impo an mea -p oducing aspec s
such as a e age daily gain (ADG), li e weigh a slaugh e (LWS), and d essing pe cen age (DP%), which is a
measu e o mea yield as a pe cen o LWS. Cal es up o 16 mon hs ypically ha e highe ADG han oals
(López e al., 2019). A e 16 mon hs, howe e , ho ses o e ake ca le in ADG, and by h ee yea s a e mo e
massi e han ca le (Sa iés & Be iain, 2005). In addi ion o he inc eased LWS, DP% is also highe o
ho ses, a ound 65%, s. a ound 55% o ca le (Coyne e al., 2019; He e al., 2005). Mo eo e , DP% in
ho ses inc eases wi h age, while o ca le i dec eases (Janiak e al., 2017; Man o ani e al., 2014). This
leads he di e ence in emissions pe kg mea o be e en g ea e han pe head. In Kazakhs an, ho ses and
ca le aised o mea a e slaugh e ed a simila ages, wi h ho ses sligh ly ou weighing ca le. In 2022, he
a e age ca le LWS was 339 kg, while he a e age ho se was 347 kg (KazS a , 2023). Wi h an a e age age o
18 mon hs, me hane emissions pe kg mea we e almos h ee imes highe o bee (0.37 kg/kg) compa ed
o ho semea (0.13 kg/kg).
4.5 Seconda y sou ces o ho semea
The p ac ice o slaugh e ing ho ses ha ha e eached he end o hei use ul li e in a sec o o he han
mea p oduc ion a e o en un i o human consump ion. Raceho ses, especially, ha e been gi en many
subs ances ha a e illegal o adminis e o animals mean o human consump ion (Ande son, 2015).
71
Ho ses a e classi ied as companion animals by he Ame ican Fede al D ug Adminis a ion, meaning all d ugs
app o ed o pe s can be adminis e ed. Se e al o hese a e banned o ood-p oducing animals, and poo
o e sigh o he ho se slaugh e indus y has led o many ho ses en e ing he human ood ma ke ha
shouldn’ ha e (Webe e al., 2023). Ne e heless, he ma ke o ho ses om he non- ood sec o could be
s uc u ed and expanded such ha p ope ly documen ed ho semea he e om could sa ely en e he
human ood chain (Saas amoinen, 2015).
4.6 O he ho se p oduc s
A discussion o ho se p oduc ion would no be comple e wi hou men ioning kumys (ai ag in Mongolian), a
d ink made om e men ed ma e milk. His o ically p oduced om Hunga y o China, kumys emains a
popula dai y p oduc in Mongolia, Cen al Asia, and neighbo ing Russian p o inces (Langlois, 2011). F esh
ma e milk is simila o human milk and is an op ion o in an s alle gic o cow milk (Malaca ne e al., 2002).
Howe e , i s e y simila i y o human milk, low a con en , and inabili y o make cheese se e ely limi s
ma e milk’s abili y o eplace cow milk in he gene al public (Langlois, 2011). Kumys, on he o he hand, is
o en p oduced concomi an ly wi h ho semea , and compe es wi h ke i ( e men ed cow, goa , o sheep
milk) in he egions whe e i is popula . Conside ed an “acqui ed as e”, kumys ne e heless has been
gaining in popula i y ou side i s adi ional home due o i s pu po ed heal h bene i s (Singh & Shah, 2017),
and could accompany expanding ho semea p oduc ion (Aska o e al., 2020).
4.7 Ou look
Besides o e coming he emendous aboo su ounding ho semea consump ion, he e emains ano he ,
mo e angible hu dle. Ho ses equi e mo e g azing land pe kg mea because hey a e less e icien
diges e s han ca le, especially o highe - ibe die s (Chenos e al., 1985). While access o mo e pas u es
o longe pe iods helps o mi iga e he addi ional land demand, he ansi ion om bee o ho semea
would necessa ily coincide wi h a dec ease in ed mea consump ion. This is a end ha is al eady being
ealized and is encou aged om bo h a heal h and an en i onmen al s andpoin (Smil, 2014). Indeed,
educ ion in mea —pa icula ly bo ine mea —consump ion is use ul and impo an o a oid c ossing
se e al plane a y bounda ies (Pa lasca & Qaim, 2022). C opland eed om ca le eed p oduc ion can be
used o u he mi iga e he addi ional land demand h ough high-quali y hay and pas u e, and u he land
spa ing could occu h ough upcycling o ag icul u al byp oduc s in animal eed (Go oni e al., 2023).
The e is cu en ly a pauci y o scien i ic li e a u e conce ning ho ses aised o mea . Se e al knowledge
gaps need o be explo ed and gi en he same igo ous ea men ha bee p oduc ion has ecei ed o e
he las cen u y. How o op imize ho ses’ die o mea p oduc ion—wha , i a all, would eedlo
p oduc ion look like? How o eo ganize c opland p oduc ion, as a shi o ho semea p oduc ion would
ee up a lo o land p e iously used o ca le eed—should we use i o eed ho ses, o he li es ock, g ow
ood, bio uel, o inc ease na u al conse a ion? How di e en p oduc ion sys ems a ec GHG emissions—
72
could ho se emissions be educed e en u he wi h highe -ene gy g ain supplemen s? How o conse e
and p ese e biodi e si y and ecosys em se ices on ma ginal pas u es—wha ac ion o Figu e 4-3 is
accep able o expansion? Animal wel a e has been an inc easingly impo an aspec o li es ock
p oduc ion, wi h ca le, poul y, and po k ecei ing he lion’s sha e o a en ion. Fo example, cus ome s
a e willing o pay p emiums o “cage- ee” eggs o “g ass- ed” bee . Ho ses aised o mea ha e ecen ly
also been ea ed wi h s udies on wel a e indica o s (Raspa e al., 2020).
While we ha e elabo a ed on he en i onmen al bene i s o con e ing bee p oduc ion o ho semea , o
many consume s, he ba le will be ough on he dinne able. Ho semea is subs an ially mo e heal hy
han bee , wi h a as e and ex u e simila , i no e en supe io o bee (Jaska i e al., 2015). On op o
ex olling he heal h bene i s, p emium-quali y ho semea could also imp o e i s ma ke sha e by be e
p esen a ion and ad e ising. Besides he well-known sausages and d ied mea s in hippophagic egions,
aw ho semea could be g aded by cu in he same way ha bee is, inc easing he alue and desi abili y o
be e cu s. Res au an s ha in oduce and p epa e ho semea as gou me could do much o help
o e come he nega i e s igma ha ho semea has in many coun ies.
We ha e explo ed he po en ials and ex e nali ies o expanding ho semea p oduc ion while educing bee
p oduc ion. In a no el app oach, we ha e combined social aspec s (public pe cep ion, eligious aboos),
en i onmen al aspec s (land and wa e use, GHG emissions), p oduc ion aspec s ( eed equi emen s,
g azing pa e ns), nu i ional aspec s, and animal wel a e. Ou goal was o c ea e a holis ic and ealis ic
analysis o ho semea p oduc ion, o de ine he s a e o he a and p o ide a possible, pa ial solu ion o
he p oblem o li es ock’s long shadow (S ein eld e al., 2006).

73
Chap e 5:
Syn hesis
74
5.1 Summa y
The o e a ching goal o his hesis was o ad ance he knowledge and unde s anding o land use—
especially pas u eland use—and li es ock p oduc ion in Kazakhs an. Se e al ace s we e explo ed ha
con ibu e o knowledge gaps in he ela ionship be ween li es ock p oduc i i y, g azing pa e ns,
ecological unc ions o g asslands, and en i onmen al oo p in s o mea p oduc ion. Kazakhs an was
chosen as he s udy a ea o a numbe o easons: 1) i con ains huge ac s o unin e up ed g azing land;
2) i has signi ican he ds o ou di e en g azing li es ock species; 3) hose he ds ha e seen d ama ic ises
and alls in popula ions o e a sho and ecen ime pe iod; 4) is a isk o dese i ica ion and o he clima e
change- ela ed e ec s; and 5) has po en ial o inc ease mea p oduc ion wi hou aking land away om
c op p oduc ion.
5.1.1 Resea ch ques ions and answe s
5.1.1.1 Ques ion I: Whe e, and o wha ex en , could inc eases in Kazakh li es ock p oduc ion ake
place?
Chap e 2 de eloped a model o dis ibu e g azing demand ac oss Kazakhs an’s as g assland esou ces.
Much o Kazakhs an was ound o be ung azed o g azed a e y low in ensi ies. Howe e , he e is ample
e idence o localized o e g azing a ound se lemen s ac oss he coun y, and ex ensi e, p oli e an
o e g azing likely occu s in he sou h and sou heas , co esponding o he highes li es ock densi ies and
despi e high g assland p oduc i i y. In he a eas o low-in ensi y g azing, la ge inc eases in li es ock
p oduc ion could ake place wi hou changing he cu en s a e o pas o alism. Wi h epai ed and imp o ed
in as uc u e, howe e , Kazakhs an could inc ease mea and dai y p oduc ion by an o de o magni ude.
Imp o emen p ojec s a e well unde way in he no hcen al, whe e odde c op p oduc ion suppo s la ge
ag icul u al en e p ises and eedlo -s yle bee and especially dai y. An a ea o in e es o po en ial
expansion ha has no begun o be apped is in he no hwes , Wes Kazakhs an and Ak obe Oblas s. Bee
and dai y ca le unde s andably a ac he mos a en ion, bu Kazakhs an has he capaci y and pa hways
o u he i alize he sec o s o o he g aze s: sheep, goa s, ho ses, and camels a e all economically
signi ican and each could s ep up i global demand o ca le p oduc s we e o wane due o conce ns o e
he en i onmen al oo p in o ca le.
5.1.1.2 Ques ion II: To wha ex en a e li es ock in Kazakhs an ac o s in i e egimes?
Chap e 3 u ilized binomial gene alized linea mixed-e ec s models o analyze he co ela ion ha g azing
demand has on bu ned a ea ex en . The magni ude o his co ela ion was hen compa ed o o he
common p edic o s o bu ned a ea ex en : p ecipi a ion, humidi y, empe a u e, and g owing deg ee days.
The clima e a iables we e u he s a i ied in o seasonal alues, as i e is epheme al and o en d i en by
empo a y, ex eme condi ions, and all possible combina ions o hese a iables we e analyzed o
de e mine a bes i . Addi i e, in e ac i e, and quad a ic models we e conside ed, howe e he bes
75
pe o ming models always included g azing demand. G azing demand is s ongly, and nega i ely, co ela ed
o bu ned a ea ex en . While his is logical and no in i sel ea h-sha e ing, he magni ude o co ela ion
was su p ising, a small inc ease in g azing demand co esponded o a la ge dec ease in bu ned a ea ex en .
Besides he ob ious conclusion ha inc easing li es ock numbe s can mi iga e i e occu ence, his esea ch
sugges s he e icacy o o a ional g azing as a way o d as ically educe he isk o wild i e wi hou needing
o inc ease he d size. Imp o ed in as uc u e and incen i izing p og ams could be used in local mi iga ion
schemes. As a as a ge ing li es ock o expansion, ho ses a e mos mobile and would be bes -sui ed o
o ming a eling i e-p e en ion bands.
5.1.1.3 Ques ion III: Is he e a pa h o sus ainable li es ock de elopmen in Kazakhs an?
Chap e 4 conce ns he la ge, g owing, and de imen al impac bee and dai y ca le ha e on he
en i onmen . The biological p ocesses ha allow ca le o be ene gy-e icien diges e s o g asses also emi
mo e g eenhouse gases (GHGs) pe kilog am o p oduc han any o he mains eam mea o dai y sou ce.
The po en ial o one o mankind’s e y i s domes ica ed mea sou ces ha has only ela i ely ecen ly
allen ou o a o was explo ed as an al e na i e o bee : ho semea . Nu i ionally, ho semea is supe io
o bee in e e y measu eable way: mo e p o ein, less a , be e a , and mo e essen ial amino acids.
Tex u ally, ho semea is mo e simila o bee han any o he ed mea , and when aised o mea is swee e
and ende e . En i onmen ally, ho semea p oduc ion is mo e o a mixed bag compa ed o bee
p oduc ion. Ho ses a e hindgu e men e s, and he e o e by na u e emi ewe GHGs pe kilog am o
p oduc han ca le, bu hey a e less e icien diges e s, equi ing a mo e ene gy inpu in he o m o
g ass han ca le do, which means a la ge land use oo p in . On he posi i e side, his land equi emen
can be sa is ied using ma ginal g asslands no sui able o c op p oduc ion, bu he mo e ma ginal he land
he less p oduc i e i is and he mo e land is equi ed. Simply based on he land equi emen , ho semea is
unable o eplace bee kilog am- o -kilog am. A educ ion in mea consump ion o a shi o o he mea s is
also equi ed.
5.2 Insigh s
Toge he , Chap e s 2-4 o e se e al c oss-cu ing insigh s in o he ecen his o y and mode n s a e o
li es ock p oduc ion in Kazakhs an, en i onmen al in e ac ions, and po en ial o u he de elopmen .
Fi s , as disco e ed in Chap e 2, much o Kazakhs an is ung azed o g azed a low in ensi ies. Li es ock
numbe s plumme ed a e he So ie Union dissol ed in he ea ly 1990s, lea ing dis an pas u es and
s a ions abandoned. Since he launch o he MODIS sa elli es in 1999 and 2002 he e has been a con inuous
eco d o bu ned a ea ex en , and Chap e 3 shows a s ong co ela ion be ween li es ock g azing and
bu ned a ea ex en . The high a iabili y o bu ned a ea in Kazakhs an in he 2000s coincides wi h he
dec ease in li es ock numbe s, sugges ing ha dec eased g azing allowed a ia ion in clima ic ac o s o
play a la ge ole in i e occu ence. Gi en he cu en low g azing in ensi ies in he ho bed o Kazakhs an’s
76
g assland i es, a ge ed inc eases in pas u e use, ei he by inc easing li es ock numbe s o by scheduled
o a ional g azing, can d as ically educe he h ea o wild i e by emo ing a ela i ely small amoun o
uel.
Second, based on he g azing map om Chap e 2, much g assland nea human popula ions is a isk o
o e g azing. Mo eo e , he e a e huge swa hs o un- o unde u ilized pas u e a om se lemen s,
especially in wes e n and cen al Kazakhs an. U ilizing his pas u e is a di icul p oposi ion unde he
cu en ci cums ances o decaying So ie in as uc u e: c umbling oads, dilapida ed li es ock s a ions,
and ailed wells. Howe e , his ba ie o expansion has made one assump ion ha need no be ue: ha
ca le a e he species o p opaga e. Ca le a e dependen on human in as uc u e, sho dis ances o ood
and wa e , and a e la gely de enseless agains apex p eda o s and he bi e cold o Kazakh win e s.
In oduced in Chap e 4, he idea o expanding ho semea p oduc ion has many posi i e a ibu es. In
ela ion o Chap e 2, ho ses could be an immedia e answe o u ilizing dis an pas u es, as hey can ange
much u he han ca le, can de end hemsel es om p eda o s, and a e ha dy enough o su i e e en he
dzhu s— a ious ex eme wea he condi ions causing mass die-o s— ha epea edly h ea en g aze s on
he Eu asian s eppe.
Thi d, in a g and c oss-cu o Chap e s 2, 3, and 4, employing a highly mobile, ha dy species ha equi es
e y li le cu -and-ca y odde o oam he i e-p one g asslands o he Kazakh s eppe, d ama ically
educing he isk o uncon olled wild i e while u ilizing g assland o he wise inaccessible o human ood
p oduc ion is a powe ul a gumen o he expansion o ho se husband y. The e is s ong emo ion in many
coun ies agains he ea ing o ho ses, and in many o he s, ho se is iewed as a lesse mea . Howe e ,
Chap e 4 e eals ha ho semea is in ac supe io o bee in almos e e y way: nu i ionally,
en i onmen ally, and—pe haps mos impo an ly—gas onomically. The po en ial o ho semea
ope a ions o educe o e en supplan bee as he ed mea o choice elies on he abili y o (a) e icien ly
and sus ainably u ilize ma ginal g asslands and (b) change he majo i y o mindse s wi h ende , as y slabs
o high-quali y “I can’ belie e i ’s no bee ”.
5.3 Implica ions
The e a e wo o e a ching, uni e sal ques ions associa ed wi h changes in li es ock p oduc ion, ag icul u al
p oduc ion in gene al and indeed, wi h any kind o p oduc ion: 1) how will he economy be a ec ed, and 2)
how will he en i onmen be a ec ed? Economic impac s o ag icul u al policies a e ex ensi ely s udied
and o ecas ed be o e a policy is implemen ed, con inuously analyzed du ing i s e ec i e pe iod, and
ho oughly e alua ed a e i s conclusion. Comp ehensi e assessmen always emains elusi e, howe e , as
eal-wo ld policy ne e occu s in a acuum, and he e is no “con ol a iable”. Ne e heless, analysis has
come a long way in he las hal -cen u y, as he amewo k o assessing ag icul u al policy de eloped
(Josling, 1974), as compu a ional powe inc eased d ama ically (Jus & An le, 1990), as s a is ical da abases
83
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