© A. Ripamon i, 2025
1PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
The cu en PDF con ains he p esen a ion deli e ed du ing he public PhD hesis de ence o “Sus ainable
li es ock in ensi ica ion in Medi e anean ag osil opas o al sys ems”, held on May 30, 2025, a he
Depa men o Ag icul u e, Food, and En i onmen , Uni e si y o Pisa.
The PhD hesis is unde a h ee-yea emba go, as i includes unpublished da a in ended o u u e
publica ion. Howe e , his p esen a ion p ima ily ea u es da a al eady published in pee - e iewed
jou nals. Fo ci a ion pu poses, please e e o he co esponding published pape s.
Whe e unpublished da a a e p esen ed, his is clea ly indica ed on he espec i e slides.
Reuse o igu es o da a should ci e he ele an published a icles.
© A. Ripamon i, 2025
© A. Ripamon i, 2025
Sus ainable li es ock
in ensi ica ion in Medi e anean
ag osil opas o al sys ems
Alice Ripamon i
XXXVII PhD Cycle
PhD Thesis De ence
Supe iso s
P o . Ma cello Mele
D . Albe o Man ino Pisa, 30 h May 2025
Pho o c edi : J. Go acci
© A. Ripamon i, 2025
1. In oduc ion
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
P o ision o 38% o global p o ein
supply
•Ri chie and Rose (2019)
Animal ood ich in essen ial nu ien s
(B12, i amins A, D, i on, Amino Acids)
•Beal e al. (2023)
Abili y o use low-oppo uni y-cos eed
(c op esidues, ood by-p oduc s,
g assland)
•Ge be e al. (2015)
Sou ce o income, especially in u al
a ea
53% o ag icul u al GHG emissions
•FAO (2022)
Food- eed compe i ion, 40% o a able
land used o eed p oduc ion
•Van Zan en e al. (2018)
Risk o wa e eu ophica ion and
acidi ica ion
P o
Cons
4
The ole o li es ock in ood sys ems
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
5PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Technical and
economic
Social
En i onmen al
Ag icul u al and
sec o ial policies
Socie al demand:
consume s and ci izens
Global change
Be nués e al. (2011)
Con ex
Fa m
and less- a ou ed a eas in Medi e anean: syne gies and ade-o s
Pas u e-based li es ock a ming sys ems
© A. Ripamon i, 2025
6
Re iew: Make uminan s g een again
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Sus ainable
In ensi ica ion
Ag oecology
how can sus ainable in ensi ica ion and ag oecology con e ge o a
be e u u e?
Dumon e al. (2018)
Nu ien use e iciency
and echnology
Sys em e-concep ion
and ecosys em se ices
© A. Ripamon i, 2025
7
Ag oecology o adap a ion o clima e change
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
and esou ce deple ion in he Medi e anean egion. A e iew
Aguile a e al. (2020)
C op Li es ock
Biodi e si y
managemen
Inc easing soil
o ganic ma e
Renewable
ene gy Ex ensi e he ds,
di e si ica ion and
local b eeds Pas u e and o age
managemen
Ag o o es y
© A. Ripamon i, 2025
SI can be each h ough
ag icul u al sys ems edesign
a landscape le els
7 possible solu ions iden i ied
8
In ensi ica ion o edesigned
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
“The combina ion o ag icul u al p ocesses
in which p oduc ion is main ained o inc eased,
while en i onmen al ou comes a e enhanced”
P e y (2018)
and sus ainable ag icul u al sys ems
© A. Ripamon i, 2025
‘’ he p ac ices o delibe a ely in eg a ing woody ege a ion ( ees o sh ubs)
wi h c op and/o animal sys ems o bene i om he esul ing ecological and economic in e ac ion‘’
9
Ag o o es y: ‘’a new name o an old p ac ice‘’
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Nai (1991); Bu gess and Rosa i (2018)
Mic oclima e
egula ion
and shade
Ca bon seques a ion
and s o age
Ai and wa e
pu i ica ion
Food, eed
and odde
P o ec ion om
soil e osion
Tibe and
i ewood
Nu ien cycling
and soil e ili y
Pho o c edi : A. Ripamon i (modi ied om Bu gess and Rosa i 2018)
© A. Ripamon i, 2025
16
PRISMA Me hodology
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
TITLE-ABS-KEY (ag o o es y OR sil opas * OR ag osil opas *
OR ee OR shad*) AND (quali y OR nd OR “nu i i e alue” OR
p o ein OR diges ibili y OR p oduc * OR “chemical composi ion”
OR yield OR “phenological cycle”) AND ( o age OR g assland
OR g ass OR pas u e OR pas u eland)
Resea ch ques ion de ini ion
Resea ch que y de ini ion
How does ee p esence in luence
o age yield and nu i i e alue?
AND (LIMIT-TO(DOCTYPE, “a ”) OR LIMIT-TO(DOCTYPE, “ e”)
OR LIMIT-TO(DOCTYPE, “ch”) OR LIMIT-TO(DOCTYPE, “bk”))
AND (LIMIT-TO(SUB-JAREA, “AGRI”)) AND (LIMIT-
TO(LANGUAGE, “English”)) AND (LIMIT-TO(SRC-TYPE, “j”)
Au oma ic il e s
Pic u e sou ce: www.dis ille s .com Page e al. (2021)
© A. Ripamon i, 2025
131 O iginal a icles; 5 Re iews; 4 Me analysis
17
Iden i ica ion and sc eening p ocess
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
S udies included in he epo : 140
Ma e ials
and
Me hod
sc eened:
364
Abs ac
sc eened:
513
Ti les
sc eened:
6,126
Scopus esul s: 11,028
(au oma ically excluded h ough ille e s: 4,902)
S udies ha
conside o age
yields solely
we e excluded
Le el o he expe imen
Loca ion o ial si e(s)
Fo age bo anical amily
Soil cha ac e is ics
Limi ing ac o s con olled
Expe imen al design
Fo age yield and quali y
Köppen-Geige Clima e
(T opical, D y, Tempe a e, Con ine nal)
In o ma ion e ie ed
© A. Ripamon i, 2025
18
S udy le el classi ica ion and indings
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
8
110
13
Numbe o eco ds
Po Field Landscape
Sc eening o shade adap abili y and
esilience o wa e sca ci y
Unde s anding o ee compe i ion
wi h li es ock and g ass o e alua e
whole ag icul u al sys em p oduc i i y
Imp o ing o age yield and quali y
by es ing o age mix u es
and di e en ee plan ing design
© A. Ripamon i, 2025
19
Clima e and loca ion classi ica ion and indings
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
43
19
48
17
4
Numbe o eco ds
A - T opical B - A id
C- Tempe a e D - Con inen al
n.a.
Sou h Ame ica, ocus on: (i) ew key g ass species; (ii) p o i abili y and animal
pe o mance; (iii) low p esence o g ass-legume mix u e
© A. Ripamon i, 2025
20
Con olled ac o s classi ica ion and indings
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Conduc ing s udies in eal condi ion is
p e e able o e alua e ee-g ass
in e ac ions bu a i icial shade is cos -
e ec i e.
No ni ogen limi a ions o isola e ligh
compe i ion e ec .
In es iga e N dynamics, in ela ion o ligh
educ ion.
Explo e he ade-o s be ween educed
ligh a ailabili y and inc eased wa e use
e iciency.
Analysis o ee-g ass-animals in e ac ions
o op imize g azing managemen and
imp o e pas u e cha ac e is ics
Ligh
Nu ien s
Wa e
G azing
110 on ield16
110 on ield42
110 on ield9
110 on ield45
AFS: Ag o o es y sys ems
© A. Ripamon i, 2025
21
Bo anical amily classi ica ion and indings
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
72
7
52
Numbe o eco ds
G ass Legume Mix u e
Shade has highe de imen al e ec on legumes a he han g asses
© A. Ripamon i, 2025
On- a m ial: sampling p o ocols
and da a collec ion
G assland and g azing managemen
Animal wel a e and hea s ess
Da a collec ion o Quan i ica ion o ecosys em se ices
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
23
T ial si e desc ip ion
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Loca ion:
Ma emma a ea, Sou h o Tuscany
Clima e:
Medium a e age empe a u e 15.6°C
Medium a e age p ecipi a ion 840 mm
Su ace:
3.69 ha o empo a y g assland
3.31 ha o Tu key oak o es
Animal:
S ee s and hei e s o Ma emmana b eed
T ea men s:
Pas o al PA (only g assland)
Sil opas o al SP (g assland and o es )
Feed supplemen s:
Oa - e ch hay p oduced on a m (ad libi um)
Mixed g ain lou p oduced on a m (1% o body weigh )
Pho o c edi : Ripamon i e al., 2023
24
Animal cha ac e is ics
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Yea 2021 2022
Sys em Pas o al sys em
Sil opas o al sys em
Pas o al sys em
Sil opas o al sys em
To al head 29 21 20 20
Hei e s 16 13 12 13
S ee s 13 8 8 7
A e age age (day) 314 ± 46 331 ± 66 331 ± 34 330 ± 38
A e age weigh (kg) 298 ± 57 271 ± 55 286 ± 58 276 ± 57
S ocking a e g assland (LU ha
−1 d−1
)
3.38 2.96 2.15 2.69
S ocking a e o es (LU ha−1 d−1)-0.46 -0.50
Pho o c edi : A. Ripamon i
𝐻𝑒𝑟𝑏𝑎𝑔𝑒 𝐴𝑙𝑙𝑜𝑤𝑎𝑛𝑐𝑒 𝑔 𝐷𝑀 𝑘𝑔 𝐵𝑊−1𝑑−1
𝑃𝑟𝑒𝐻𝑀 𝑔 𝐷𝑀 𝑚−2
𝐷 𝑑 + 𝐷𝐻𝐺 𝑔 𝐷𝑀 𝑚−2𝑑−1 × 𝐴 𝑚2
BW 𝑘𝑔
𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝐻𝑒𝑟𝑏𝑎𝑔𝑒 𝐼𝑛𝑡𝑎𝑘𝑒 𝑔 𝐷𝑀 𝑑−1
𝐸𝐶𝐻𝑀 𝑔 𝑚−2 − 𝑃𝑜𝑠𝑡𝐻𝑀 𝑔 𝑚−2 ×𝐴 𝑚2
𝐷 𝑑 ×BW 𝑘𝑔
Tempe a u e Humidi y Index and
Black Globe Humidi y Index
Bu ing on e al. (1981); Made e al. (2004)
A e age daily gain,
Glucose, Se um co isol and
Hai co isol
Belhadj Slimen e al. (2016); Heimbü ge e al.
(2019); Ghassemi Nejad e al. (2022)
Nu i ional alue,
He bage Allowance and
Po en ial He bage In ake
Undi e al. (2008); Man ino e al. (2021)
25
On ield da a collec ion
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Mic oclima ePas u e Animal
HM: he bage mass; DHG: daily he bage
g ow h; EC: Exclusion cage
BW: Body weigh ; A: A ea;
D: day
p- alue
He bage mass Nu i i e Value
Ne Ene gy o
g ow h
He bage
Allowance
He bage In ake
P e-
g azing
Pos
-
g azing
CP NDF
(S) 0.6492 0.658 0.0593 0.073 0.649 0.189 0.57
Pe iod (GP) 0.0193 0.006 <.0001 <.0001 <.0001 <.0001 0.002
x GP 0.8831 0.16 0.526 0.067 0.0336 0.3612 0.61
32 PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
- C ude p o ein (p < .0001)
- Neu al De e gen Fib e (p < .0001)
- P e-g azing he bage mass (p = 0.019)
- Pos -g azing he bage mass (p = 0.006)
Signi ican di e ences among g azing pe iods in:
2^ ound: lowe p e-g azed
biomass bu no pos -g azed
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•GP = G azing Pe iod – Fixed (n = 6)
•P = Paddock – Random nes ed wi hin sys em 2^ ound:
highe NDF
con en
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑮𝑷𝒋 + 𝑺 ∙ 𝑮𝑷 𝒊𝒋 + 𝑷𝒌|𝑺 + 𝜺𝒊𝒋𝒌
He bage biomass and nu i i e alue
R Co e Team; Pinhei o e al. (2021)
p- alue
He bage mass Nu i i e Value
Ne Ene gy o
g ow h
He bage
Allowance
He bage In ake
P e-
g azing
Pos
-
g azing
CP NDF
(S) 0.6492 0.658 0.0593 0.073 0.649 0.189 0.57
Pe iod (GP) 0.0193 0.006 <.0001 <.0001 <.0001 <.0001 0.002
x GP 0.8831 0.16 0.526 0.067 0.0336 0.3612 0.61
33 PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
- C ude p o ein (p < .0001)
- Neu al De e gen Fib e (p < .0001)
- P e-g azing he bage mass (p = 0.019)
- Pos -g azing he bage mass (p = 0.006)
Signi ican di e ences among g azing pe iods in:
2^ ound: lowe p e-g azed
biomass bu no pos -g azed
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•GP = G azing Pe iod – Fixed (n = 6)
•P = Paddock – Random nes ed wi hin sys em 2^ ound:
highe NDF
con en
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑮𝑷𝒋 + 𝑺 ∙ 𝑮𝑷 𝒊𝒋 + 𝑷𝒌|𝑺 + 𝜺𝒊𝒋𝒌
He bage biomass and nu i i e alue
R Co e Team; Pinhei o e al. (2021)
O e all low pas u e p oduc i i y and quali y
Po queddu e al. (2016)
D ough condi ion (-24% o measu ed ain all s. long- e m da a) accen ua e
he e ec o he low soil e ili y
Seligman and Van Keulen (1989)
Lack o p ope synch oniza ion be ween g azing and
he phenological phase
Unde sande e al. (2002)
Lack o ain all in sp ing (-24% o measu ed ain all)
Fas seasonal ad ancemen
No on e al. (2016)
34 PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Wi h supplemen s:
- He bage allowance (p = 0.18)
- Po en ial he bage in ake (p = 0.57)
No signi ican di e ences be ween sys ems in:
- He bage allowance (p = <.0001)
- Po en ial he bage in ake (p = 0.002)
Signi ican di e ences among g azing pe iods in:
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•GP = G azing Pe iod – Fixed (n = 6)
•P = Paddock – Random nes ed wi hin sys em
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑮𝑷𝒋 + 𝑺 ∙ 𝑮𝑷 𝒊𝒋 + 𝑷𝒌|𝑺 + 𝜺𝒊𝒋𝒌
(Da a no shown)
Animal he bage and eed in ake
R Co e Team; Pinhei o e al. (2021)
35 PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Wi h supplemen s:
- He bage allowance (p = 0.18)
- Po en ial he bage in ake (p = 0.57)
No signi ican di e ences be ween sys ems in:
Ca le in pas o al and sil opas o al had he
same le el o he bage in ake
- He bage allowance (p = <.0001)
- Po en ial he bage in ake (p = 0.002)
Signi ican di e ences among g azing pe iods in:
Consequence o lowe he bage biomass in
second ound
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•GP = G azing Pe iod – Fixed (n = 6)
•P = Paddock – Random nes ed wi hin sys em
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑮𝑷𝒋 + 𝑺 ∙ 𝑮𝑷 𝒊𝒋 + 𝑷𝒌|𝑺 + 𝜺𝒊𝒋𝒌
(Da a no shown)
Animal he bage and eed in ake
R Co e Team; Pinhei o e al. (2021)
36 PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Wi h supplemen s:
- He bage allowance (p = 0.18)
- Po en ial he bage in ake (p = 0.57)
No signi ican di e ences be ween sys ems in:
Ca le in pas o al and sil opas o al had he
same le el o he bage in ake
- He bage allowance (p = <.0001)
- Po en ial he bage in ake (p = 0.002)
Signi ican di e ences among g azing pe iods in:
Consequence o lowe he bage biomass in
second ound
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•GP = G azing Pe iod – Fixed (n = 6)
•P = Paddock – Random nes ed wi hin sys em
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑮𝑷𝒋 + 𝑺 ∙ 𝑮𝑷 𝒊𝒋 + 𝑷𝒌|𝑺 + 𝜺𝒊𝒋𝒌
(Da a no shown)
Po en ial he bage in ake less han 1% o he body
weigh , lowe han li e a u e
Signi ican ly a ec ed by low he bage allowance
Doughe y e al. (1989); Wilkinson e al. (2019)
P e e ences o concen a e eed o e he bage
He bage biomass and nu i i e alue no su icien o
sa is y animal equi emen s
Animal he bage and eed in ake
R Co e Team; Pinhei o e al. (2021)
37
Animal weigh gain and hai co isol
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑻𝒋 + 𝑺 ∙ 𝑻 𝒊𝒋 + 𝑨𝒌|𝑺 + 𝜺𝒊𝒋𝒌
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•T = Time – Fixed (n = 3)
•A = Animal – Random nes ed wi hin sys em
Signi ican di e ences be ween sys ems in:
Signi ican in e ac ion o sys em and ime in:
- Body Weigh (p = 0.001)
- Hai co isol accumula ion (p = 0.04)
- Se um co isol (p = 0.18)
No signi ican di e ences be ween sys ems o among ime in:
R Co e Team; Pinhei o e al. (2021)
38
Animal weigh gain and hai co isol
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑻𝒋 + 𝑺 ∙ 𝑻 𝒊𝒋 + 𝑨𝒌|𝑺 + 𝜺𝒊𝒋𝒌
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•T = Time – Fixed (n = 3)
•A = Animal – Random nes ed wi hin sys em
Signi ican di e ences be ween sys ems in:
Signi ican in e ac ion o sys em and ime in:
- Body Weigh (p = 0.001)
- Hai co isol accumula ion (p = 0.04)
- Se um co isol (p = 0.18)
Ca le s a ed wi h any weigh di e ence bu
ended wi h a signi ican di e ence
No signi ican di e ences be ween sys ems o among ime in:
R Co e Team; Pinhei o e al. (2021)
39
Animal weigh gain and hai co isol
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑻𝒋 + 𝑺 ∙ 𝑻 𝒊𝒋 + 𝑨𝒌|𝑺 + 𝜺𝒊𝒋𝒌
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•T = Time – Fixed (n = 3)
•A = Animal – Random nes ed wi hin sys em
Signi ican di e ences be ween sys ems in:
Signi ican in e ac ion o sys em and ime in:
- Body Weigh (p = 0.001)
- Hai co isol accumula ion (p = 0.04)
- Se um co isol (p = 0.18)
Ca le s a ed wi h any weigh di e ence bu
ended wi h a signi ican di e ence
No signi ican di e ences be ween sys ems o among ime in:
R Co e Team; Pinhei o e al. (2021)
Lowe and slowe g ow h in sil opas o al sys em
(SP: 1.02 kg d-1 s. PA: 1.20 kg d-1)
Due o possible highe ene gy expendi u e o walking
ac i i y in o es a ea
Vande meulen e al. (2018b); de Oli ei a e al (2021)
Possible educ ion o economic income
(slowe g ow h, highe eed in ake)
No e idence o animal hea s ess bu possible
handling s ess
Fo long ch onic s ess p e e able using hai co isol
B is ow and Holmes (2007); Ghassemi Nejad e al. (2019) (2020)
40
E ec on soil co e
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅 −𝑅𝑒𝑑
𝑁𝐼𝑅 +𝑅𝑒𝑑
•NDVI = No malized
Di e ence Vege a ion
Index
•NIR = Nea In a ed
Image analysis:
densi y cha o pixels
subdi ided in deciles
analysis
41
E ec on soil co e
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅 −𝑅𝑒𝑑
𝑁𝐼𝑅 +𝑅𝑒𝑑
•NDVI = No malized
Di e ence Vege a ion
Index
•NIR = Nea In a ed
Image analysis:
densi y cha o pixels
subdi ided in deciles
analysis
© A. Ripamon i, 2025
48
Po en ial hea s ess classi ica ion
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
BGHI Scale
> 79 - Eme gency
75 - 79 - Ale
< 75 - Minimal isk
𝑩𝑮𝑯𝑰 = 𝑩𝑮𝑻 + 𝟎.𝟑𝟔 ∗ 𝑫𝑷𝑻 + 𝟒𝟏.𝟓
Whe e:
•BGHI = Black Globe
Humidi y Index
•BGT = Black Globe
Tempe a u e (°T)
•DPT = Dew Poin
Tempe a u e (°T)
Fo es has lowe BGHI alues in 2021 and 2022
(Signi ican acco ding o he Chi-squa e es )
Highe po en ial hea s ess in June and July 2022
Lemes e al. (2021)
T ees mi iga e po en ial o hea s ess un il a ce ain
h eshold
Fo es migh limi wind ci cula ion
Bu ing on e al. (1981)
© A. Ripamon i, 2025
49
Sp ing he bage in ake
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑮𝑷𝒋 + 𝑺 ∙ 𝑮𝑷 𝒊𝒋 + 𝑷𝒌|𝑺 + 𝜺𝒊𝒋𝒌
- He bage allowance (p = <.0001)
- Po en ial he bage in ake (p = 0.07)
Signi ican di e ences be ween
sys ems in 2022:
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•GP = G azing Pe iod – Fixed (n = 6)
•P = Paddock – Random nes ed wi hin
sys em
R Co e Team; Pinhei o e al. (2021)
In gene al, highe he bage allowance
compa ed o 2022
Ca le in pas o al had highe he bage
allowance in 2022 compa ed o
sil opas o al
© A. Ripamon i, 2025
50
Sp ing he bage in ake
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒚𝒊𝒋𝒌 = 𝝁 + 𝑺𝒊 + 𝑮𝑷𝒋 + 𝑺 ∙ 𝑮𝑷 𝒊𝒋 + 𝑷𝒌|𝑺 + 𝜺𝒊𝒋𝒌
- He bage allowance (p = <.0001)
- Po en ial he bage in ake (p = 0.07)
Signi ican di e ences be ween
sys ems in 2022:
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•GP = G azing Pe iod – Fixed (n = 6)
•P = Paddock – Random nes ed wi hin
sys em
R Co e Team; Pinhei o e al. (2021)
In gene al, highe he bage allowance
compa ed o 2022
Ca le in pas o al had highe he bage
allowance in 2022 compa ed o
sil opas o al
G azing ial delayed and o bene icial ain alls be o e
he s a da e
In 2021 he highe s ocking a e used in pas o al sys em
allows o equalize he di e ences be ween sys em
paddocks
© A. Ripamon i, 2025
51
Animal p oduc i i y: a e age daily gain
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒅𝒂𝒊𝒍𝒚 𝒈𝒂𝒊𝒏𝒊𝒋𝒌 = 𝝁+𝑻𝒊+𝑺𝒋+ 𝑻∙𝑺 𝒊𝒋 +𝑨𝒌ቚ𝑺 +𝒔𝒆𝒙 + 𝜺𝒊𝒋𝒌
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•T = Time – Fixed (n = 3)
•A = Animal – Random nes ed
wi h sys em
Signi ican in e ac ion o
sys em and ime in:
-2021 (p = 0.0001)
-2022 (p = <.0001)
G ow h peak a he end o sp ing,
bigge o pas o al sys em
Lowe educ ion o a e age daily
gain in summe mon hs in
sil opas o al sys em
R Co e Team; Pinhei o e al. (2021)
© A. Ripamon i, 2025
52
Animal p oduc i i y: a e age daily gain
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒅𝒂𝒊𝒍𝒚 𝒈𝒂𝒊𝒏𝒊𝒋𝒌 = 𝝁+𝑻𝒊+𝑺𝒋+ 𝑻∙𝑺 𝒊𝒋 +𝑨𝒌ቚ𝑺 +𝒔𝒆𝒙 + 𝜺𝒊𝒋𝒌
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•T = Time – Fixed (n = 3)
•A = Animal – Random nes ed
wi h sys em
Signi ican in e ac ion o
sys em and ime in:
-2021 (p = 0.0001)
-2022 (p = <.0001)
G ow h peak a he end o sp ing,
bigge o pas o al sys em
Lowe educ ion o a e age daily
gain in summe mon hs in
sil opas o al sys em
Con ined o a ional g azing educed ene gy
expendi u e wi hou po en ial hea s ess
Posi i e e ec o ees on animal g ow h esponse o
hea s ess un il a ce ain h eshold
R Co e Team; Pinhei o e al. (2021)
© A. Ripamon i, 2025
53
Animal wel a e: hai co isol accumula ion
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒄𝒐𝒓𝒕𝒊𝒔𝒐𝒍𝒊𝒋𝒌 = 𝝁+𝑻𝒊+𝑺𝒋+ 𝑻∙𝑺 𝒊𝒋 +𝑨𝒌ቚ𝑺 + 𝒂𝒅𝒈+ 𝜺𝒊𝒋𝒌
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•T = Time – Fixed (n = 3)
•A = Animal – Random nes ed
Signi ican in e ac ion o
sys em and ime in:
-2021 (p = 0.04)
-2022 (p = <0.001)
Highe absolu e alues in 2022
R Co e Team; Pinhei o e al. (2021)
ADG: A e age daily gain
© A. Ripamon i, 2025
54
Animal wel a e: hai co isol accumula ion
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
𝒄𝒐𝒓𝒕𝒊𝒔𝒐𝒍𝒊𝒋𝒌 = 𝝁+𝑻𝒊+𝑺𝒋+ 𝑻∙𝑺 𝒊𝒋 +𝑨𝒌ቚ𝑺 + 𝒂𝒅𝒈+ 𝜺𝒊𝒋𝒌
Linea Mixed-E ec Model:
•S = Sys em – Fixed (n = 2)
•T = Time – Fixed (n = 3)
•A = Animal – Random nes ed
Signi ican in e ac ion o
sys em and ime in:
-2021 (p = 0.04)
-2022 (p = <0.001)
Highe absolu e alues in 2022
In 2022: because o highe BGHI, highe co isol le els
o bo h sys ems
Lowe hai co isol accumula ion in sil opas o al sys em
Inc eased esilience o li es ock o hea s ess and
inc eased animal wel a e
B oom e al. (2013); Lemes e al. (2021)
R Co e Team; Pinhei o e al. (2021)
ADG: A e age daily gain
© A. Ripamon i, 2025
5. The use o Yield-SAFE o model
animal hea indices on pas o al and
o es land unde wo u u e clima e
scena io
Quan i ica ion o ecosys em se ices
Which app oaches can be used o quan i y ecosys em se ices in
ag o o es y sys em?
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
56
Backg ound
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Cha ac e is ics o
Yield-SAFE:
Biophysical: ‘simula ion o a biological sys em using
ma hema ical o maliza ions o he physical p ope ies’
Dynamic: ‘p edic how sys em un olds wi h he passage o ime’
Aim o Yield-SAFE: Simula e he de elopmen , g ow h and p oduc i i y o he ee
and c op o e he leng h o a ee o a ion
Ou pu s o Yield-
SAFE:
P edic ions o ee and c op yield. Used o inancial and
economic analyses and ecosys em se ices
Yield-SAFE da a
equi emen :
Daily empe a u e, adia ion and p ecipi a ion, plan ing
densi ies, ini ial biomasses o ee and c op species, and soil
pa ame e s G a es e al. (2010)
© A. Ripamon i, 2025
Clima e simula ions we e conduc ed using he
Regional A mosphe ic Clima e Model (RACMO)
Jacob e al. (2014); Vau a d e al. (2021)
The model gene a ed me eo ological a iables o
bo h his o ical and u u e pe iods:
-Baseline scena io: 1953–2023
-Fu u e scena ios: 2030–2100
-RCP4.5 (mode a e emissions scena io)
-RCP8.5 (high emissions scena io)
The simula ed da a we e co ec ed using case-s udy
his o ical da a o imp o e accu acy.
Cannon e al. (2015)
57
De elopmen o u u e clima e scena ios
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
64
T ee densi y and s ocking a e: esul s and limi s
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Need o use a BGT equa ion and BGHI equa ion mo e sensi i e o changes in ee
densi y, sola adia ion and empe a u e a ia ions
Need o imp o e he
es ima ion o s ocking
a e conside ing
seasonal changes in
he bage quali y and
g assland eg ow h o
simula e he e ec o
g azing.
© A. Ripamon i, 2025
6. Unlocking he po en ial o in a ed
he mog aphy: challenges and
oppo uni ies in moni o ing animal
empe a u e in ex ensi e sys ems
Applica ion o echnology
A e he e echnologies applicable in ex ensi e li es ock sys em o moni o
animal beha iou and wel a e?
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
66
Backg ound
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
†C ea
Global
posi ioning
sys ems
T i-axis
accele ome e
Remo e
sensing
Unnamed
ae ial ehicles
Decision
suppo sys ems
Vi ual
encing
In a ed
he mog aphy
Main limi s o echnology sp ead
Cos s
Ba e y du a ion
Da a analysis
Fa me s’ age
Odin so Vain ub e al. (2021); Aquilani e al. (2022)
Pho o c ea ed wi h Cha GPT
© A. Ripamon i, 2025
1. Ins umen : In a ed came a FLIR se ies E75
2. Da a collec ion: h ee consecu i e days pe mon h (June, July,
Augus and Sep embe ). Twice a day: 9.30-11.30; 16.00-
18.00
3. Dis ance om animal: be ween 5 and 10 me e s
4. Numbe o animals in ol ed: a leas 20 animal
5. Iden i ica ion o animal: wi h ID ea ag, animal ID associa ed
wi h pho o ID
6. Pa o he body o poin : le lank, i possible, pho o o he
en i e body. I possible bigge images o he ace ( o ehead
and eye)
7. Posi ion o he ins umen : i possible in a ed came a
posi ioned pe pendicula o he le an ime o he animal.
8. In a ed came a pa ame e s: p e-se pa ame e s du ing da a
collec ion
oEn i onmen al empe a u e: 20%
oRela i e Humidi y: 50%
oRe lec ed empe a u e: 20°C
oEmissi i y: 0.95
67
P o ocols used on bee cows in summe 2022
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
1. Ins umen : In a ed came a FLIR se ies E75
2. Da a collec ion: h ee consecu i e days pe mon h (June, July,
Augus and Sep embe ). Twice a day: 9.30-11.30; 16.00-
18.00
3. Dis ance om animal: be ween 5 and 10 me e s
4. Numbe o animals in ol ed: a leas 20 animal
5. Iden i ica ion o animal: wi h ID ea ag, animal ID associa ed
wi h pho o ID
6. Pa o he body o poin : le lank, i possible, pho o o he
en i e body. I possible bigge images o he ace ( o ehead
and eye)
7. Posi ion o he ins umen : i possible in a ed came a
posi ioned pe pendicula o he le an ime o he animal.
8. In a ed came a pa ame e s: p e-se pa ame e s du ing da a
collec ion
oEn i onmen al empe a u e: 20%
oRela i e Humidi y: 50%
oRe lec ed empe a u e: 20°C
oEmissi i y: 0.95
68
P o ocols used on bee cows in summe 2022
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Missing animal measu emen (i.e. ec al empe a u e,
pain ing sco e)
Ope a o / esea che ’s p esence migh in luence animal
na u al beha iou
O e hea ing o he IRT came a, especially du ing
summe
© A. Ripamon i, 2025
69
Images and da a analysis
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
1. So wa e: FLIR Resea ch S udio (2024 © Teledyne FLIR
LLC)
2. Pic u e p ocessing: eigh body a eas we e de ined
manually (Ko ba e al. 2007): (i) neck, (ii) dewlap, (iii)
unk, (i ) body o epa , ( ) ba el, ( i) body hind pa ,
( ii) o elimb, and ( iii) ea limb
3. Fo each egion, he ollowing pa ame e s we e
calcula ed: (i) minimum, (ii) mean, and (iii) maximum
empe a u e; (i ) s anda d de ia ion; ( ) numbe o pixels.
4. Each image was associa ed wi h: (i) mon h, (ii) day, and
(iii) hou in which he pic u e was aken; (i ) subjec ed
animal and ( ) dis ance; measu ed ( i) ai empe a u e,
( ii) ela i e humidi y, ( iii) empe a u e o dew poin , (ix)
black globe empe a u e; and las ly he mal indices: (x)
THI, and (xi) BGHI
5. Du ing image p ocessing: (i) ai empe a u e, (ii) ela i e
humidi y, (iii) dis ance om he animal, and (i ) emissi i y
(se as 0.98) we e adjus ed o each image
© A. Ripamon i, 2025
70
Images and da a analysis
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
1. So wa e: FLIR Resea ch S udio (2024 © Teledyne FLIR
LLC)
2. Pic u e p ocessing: eigh body a eas we e de ined
manually (Ko ba e al. 2007): (i) neck, (ii) dewlap, (iii)
unk, (i ) body o epa , ( ) ba el, ( i) body hind pa ,
( ii) o elimb, and ( iii) ea limb
3. Fo each egion, he ollowing pa ame e s we e
calcula ed: (i) minimum, (ii) mean, and (iii) maximum
empe a u e; (i ) s anda d de ia ion; ( ) numbe o pixels.
4. Each image was associa ed wi h: (i) mon h, (ii) day, and
(iii) hou in which he pic u e was aken; (i ) subjec ed
animal and ( ) dis ance; measu ed ( i) ai empe a u e,
( ii) ela i e humidi y, ( iii) empe a u e o dew poin , (ix)
black globe empe a u e; and las ly he mal indices: (x)
THI, and (xi) BGHI
5. Du ing image p ocessing: (i) ai empe a u e, (ii) ela i e
humidi y, (iii) dis ance om he animal, and (i ) emissi i y
(se as 0.98) we e adjus ed o each image
Me hodology as a s a ing poin o s anda dize
p o ocols o imp o e consis ency o he mal da a
collec ion
Image and da a p ocessing s ill manual: high wo kload
and ime consuming
© A. Ripamon i, 2025
6. Conclusions
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
Pay a en ion o he ag o o es y sys em design
Mic oclima e imp o emen s inc ease he
esilience o clima e change
S a egic managemen o long- e m
sus ainabili y including he use o odde ees
To e alua e animal wel a e in eg a ed
app oaches a e needed
Ag o o es y modelling equi e imp o emen s o
adap o ag osil opas o al sys ems
The use o echnology is easible bu s ill limi ed
o esea ch pu poses
72
In a nu shell
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
Di e si ica ion
Local b eeds
G azing managemen
73
Winning ag oecological s a egies used
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
Sus ainable
In ensi ica ion
Ag oecology
Ag o o es y
Con ibu e o inc ease
sys em esilience and
sus ainabili y
Pho o c edi : J. Go acci
© A. Ripamon i, 2025
Cannon AJ, Sobie SR and Mu dock TQ (2015) Bias co ec ion o simula ed p ecipi a ion by quan ile mapping: How well do me hods p ese e ela i e changes in quan iles
and ex emes? Jou nal o Clima e, 28:6938-6959. h ps://doi:10.1175/JCLI-D-14-00754.1
de Oli ei a CC, de Almeida RG, Ka a e Junio N, e al (2021) Day ime inges i e beha iou o g azing hei e s unde opical sil opas o al sys ems: Responses o shade
and g az-ing managemen . Appl Anim Beha Sci 240:105360. h ps://doi.o g/10.1016/j.appla-nim.2021.105360
Doughe y C, Lau iaul L, Co nelius P e al (1989) He bage allowance and in ake o ca le. J Ag ic Sci 112(3):395–401. h ps://doi.o g/10.1017/S0021859600085841
Dumon B, G oo JCJ, Tichi M (2018) Re iew: Make uminan s g een again - how can sus- ainable in ensi ica ion and ag oecology con e ge o a be e u u e?. Animal
12:S210–S219. h ps://doi.o g/10.1017/S1751731118001350
FAO (2022) FAOSTAT - Emissions o als. A ailable online a : h ps://www. ao.o g/ ao-s a /en/#da a/GT
Foley JA, DeF ies R, Asne GP, e al (2005) Global consequences o land use. Science (80- ) 309:570–574. h ps://doi.o g/10.1126/science.1111772
Ge be PJ, Mo e A, Opio CI, e al (2015) En i onmen al impac s o bee p oduc ion: Re iew o challenges and pe spec i es o du abili y. Mea Sci 109:2–12.
h ps://doi.o g/10.1016/j.mea sci.2015.05.013
Ghassemi Nejad J, Gha a i MH, A aallahi M, e al (2022) S ess Concep s and Applica ions in Va ious Ma ices wi h a Focus on Hai Co isol and Analy ical Me hods.
Animals 12:3096. h ps://doi:10.3390/ani12223096
Ghassemi Nejad J, Lee B-H, Kim J-Y e al (2019) Compa ing hai co isol concen a ions om a ious body si es and se um co isol in Hols ein lac a ing cows and hei e s
du ing he mal com o zone. J Ve Beha 30:92–95. h ps://doi.o g/10.1016/j.j eb.2018.12. 007
Ghassemi Nejad J, Pa k K-H, Fo ghani F e al (2020) Measu ing hai and blood co isol in sheep and dai y ca le using RIA and ELISA assay: a compa ison. Biol Rhy hm
Res 51:887–897. h ps://doi.o g/10.1080/09291016.2019.16113 35
G a es AR, Bu gess PJ, Palma J, e al (2010) Implemen a ion and calib a ion o he pa ame e -spa se Yield-SAFE model o p edic p oduc ion and land equi alen a io in
mixed ee and c op sys ems unde wo con as ing p oduc ion si ua ions in Eu ope. Ecol Modell 221:1744–1756. h ps://doi.o g/10.1016/j.ecolmodel.2010.03.008
Heimbü ge S, Kani z E, O en W (2019) The use o hai co isol o he assessmen o s ess in animals. Gen Comp Endoc inol 270:10–17.
h ps://doi.o g/10.1016/j.ygcen.2018.09.016
Heimbu ge S, Kani z E, Tuchsche e A, O en W (2020) Wi hin a hai ’s b ead h— ac o s in lu-encing hai co isol le els in pigs and ca le. Gen Comp Endoc inol
288:113359. h ps://doi.o g/10.1016/j.ygcen.2019.113359
80
Re e ences
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
Ho mann I, F om T, Boe ma D (2014) Ecosys em se ices p o ided by li es ock species and b eeds, wi h special conside a ion o he con ibu ions o small-scale li es ock
keep-e s and pas o alis s. Comm Gene Resou Food Ag ic 158. A ailable online a : h ps://openknowledge. ao.o g/se e /api/co e/bi s eams/d24e 49d-0d 2-465a-
8be -a0acd0 00236/con en
Jacob D, Pe e sen J, Egge B, Alias A, Ch is ensen OB, Bouwe LM, B aun A, e al. (2014) EURO-CORDEX: New high- esolu ion clima e change p ojec ions o Eu opean im-
pac esea ch. Regional En i onmen al Change 14(2):563-578.
jian V, Bha a R, Gaughan JB, e al (2018) Re iew: Adap a ion o animals o hea s ess. An-imal 12:S431–S444. h ps://doi.o g/10.1017/S1751731118001945
Ko ba, R., Knížko á, I., Kunc, P., & Ba oš, L. (2007). Compa ison be ween he coa em-pe a u e o he eland and dai y ca le by in a ed he mog aphy. Jou nal o
The mal Biology, 32(6), 355-359. h ps://doi.o g/10.1016/j.j he bio.2007.05.006
Lemai e G, F anzluebbe s A, de Faccio Ca alho PC, Dedieu B (2014) In eg a ed c op-li e-s ock sys ems: s a egies o achie e syne gy be ween ag icul u al p oduc ion
and en- i onmen al quali y. Ag ic. Ecosys . En i on. 190:4–8. h ps://doi.o g/10.1016/j.agee.2013.08.009
Made TL, Da is MS, B own-B andl T (2006) En i onmen al ac o s in luencing hea s ess in eedlo ca le. J Anim Sci 84:712–719.
h ps://doi.o g/10.2527/2006.843712x
Man ino A, Cappucci A, Annecchini F e al (2021) An on- a m o a ional g azing ial: es ic -ing access ime o pas u e did no a ec he p oduc i i y o a dai y sheep
lock in sp ing. I al J Ag on 16:1. h ps://doi.o g/10.4081/ija.2020.1711
mes AP, Ga cia AR, Pezzopane JRM, e al (2021) Sil opas o al sys em is an al e na i e o imp o e animal wel a e and p oduc i e pe o mance in mea p oduc ion
sys ems. Sci Rep 11:14092. h ps://doi.o g/10.1038/s41598-021-93609-7
Millenium Ecosys em Assessmen (2005) Ecosys em and Human Well-being: Syn hesis. Island P ess, Washing on DC. A ailable online a : h ps://www.millenniumassess-
men .o g/documen s/documen .356.aspx.pd
Nai PKR (1991) S a e-o - he-a o ag o o es y sys ems. Fo . Ecol. Manag. 45:5-29. h ps://doi.o g/10.1016/0378-1127(91)90203-8
No on MR, Malinowski DP, Volai e F (2016) Plan d ough su i al unde clima e change and s a egies o imp o e pe ennial g asses. A e iew. Ag on Sus ain De .
h ps://doi.o g/10.1007/s13593-016-0362-1
Odin so Vain ub M, Le i H, Chinca ini M, e al (2021) Re iew: P ecision li es ock a ming, au oma s and new echnologies: possible applica ions in ex ensi e dai y sheep
a m-ing. Animal 15:100143. h ps://doi.o g/10.1016/j.animal.2020.100143
81
Re e ences
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
Page MJ, McKenzie JE, Bossuy PM, e al (2021) The PRISMA 2020 s a emen : An upda ed guideline o epo ing sys ema ic e iews. BMJ 29:372:n71.
h ps://doi.o g/10.1136/bmj.n71
Po queddu C, A es S, Louhaichi M e al (2016) G asslands in “Old Wo ld” and “New Wo ld” Medi e anean-clima e zones: pas ends, cu en s a us and u u e esea ch
p io i- ies. G ass Fo age Sci 71:1–35. h ps://doi.o g/10.1111/g s.12212
P e y J (2018) In ensi ica ion o edesigned and sus ainable ag icul u al sys ems. Science (80- ) 362:6417. h ps://doi.o g/10.1126/science.aa 0294
Pulina G, Accia o M, A zo i AS, e al (2021) Animal boa d in i ed e iew – Bee o u u e: echnologies o a sus ainable and p o i able bee indus y. Animal
15:100358. h ps://doi.o g/10.1016/j.animal.2021.100358
Rao I, Pe e s M, Cas o A, e al (2015) Li es ockPlus – he sus ainable in ensi ica ion o o age-based ag icul u al sys ems o imp o e li elihoods and ecosys em se ices in
he opics. T op G assl 3:59–82. h ps://doi.o g/10.17138/TGFT(3)59-82
Ri chie H, Rose M (2019) Hal o he wo ld’s habi able land is used o ag icul u e. In: Ou Wo ld Da a. h ps://ou wo ldinda a.o g/global-land- o -ag icul u e
Sande man J, Reseigh J, Wu s M, Young M (2015) Impac s o o a ional g azing on soil ca bon in na i e g ass-based pas u es in Sou he n Aus alia. PLoS ONE.
h ps://doi.o g/10. 4225/08/53B0EC7D91D4B
Seligman NG, Van Keulen H (1989) He bage p oduc ion o a Medi e anean g assland in e-la ion o soil dep h, ain all and ni ogen nu i ion: a simula ion s udy. Ecol
Model 47:303–311. h ps://doi.o g/10.1016/0304-3800(89)90008-2
Unde sande DJ, Albe B, Cosg o e D, e al (2002) Pas u es o p o i : a guide o o a ional g azing (A3529), p 1–38
Undi M, Wilson C, Ominski KH, Wi enbe g KM (2008) Compa ison o echniques o es ima- ion o o age d y ma e in ake by g azing bee ca le. Can J Anim Sci
88:693–701. h ps://doi.o g/10.4141/CJAS08041
Van Zan en HHE, He e o M, Van Hal O, e al (2018) De ining a land bounda y o sus ainable li es ock consump ion. Glob Chang Biol 24:4185–4194.
h ps://doi.o g/10.1111/gcb.14321
Vande meulen S, Ramí ez-Res epo CA, Becke s Y, e al (2018) Ag o o es y o uminan s: A e iew o ees and sh ubs as odde in sil opas o al empe a e and opical
p oduc- ion sys ems. Anim P od Sci 58:767–777. h ps://doi.o g/10.1071/AN16434 Bake 2023
Vande meulen S, Rami ez-Res epo CA, Ma che C e al (2018) Beha iou and b owse species selec i i y o hei e s g azing in a empe a e sil opas o al sys em. Ag o o
Sys 92:705–716. h ps://doi.o g/10.1007/s10457-016-0041-x
82
Re e ences
PhD Thesis De ence – Alice Ripamon i – 30 h May 2025
© A. Ripamon i, 2025
Vau a d R, Kadyg o N, Iles C, Bobe g F, Buonomo E, Bülow, K, e al. (2021) E alua ion o he la ge EURO-CORDEX egional clima e model ensemble. Jou nal o
Geophysical Resea ch: A mosphe es, 126, e2019JD032344. h ps://doi.o g/10.1029/2019JD032344
Wilkinson JM, Lee MRF, Ri e o MJ, Chambe lain AT (2020) Some challenges and oppo uni- ies o g azing dai y cows on empe a e pas u es. G ass Fo age Sci 75:1–17.
h ps://doi.o g/10.1111/g s.12458
PUBLISHED PAPERS
•Chap e 2: Ripamon i A, Finocchi M, Pulina A e al. Ag o o es y Sys ems 99:110 (2025)
h ps://doi.o g/10.1007/s10457-025-01214-8
•Chap e 3: Ripamon i A, Man ino A, Annecchini F e al. Ag o- o es y Sys ems 97:1071–1086
(2023). h ps://doi.o g/10.1007/s10457-023-00848-w
•Chap e 4: Ripamon i A, Foggi G, Man ino A, e al. Animal. 19(3):101425 (2025).
h ps://doi.o g/10.1016/j.animal.2025.101425
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