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ŝŶ DŽŶƚƉĞůůŝĞƌ ĨƌŽŵ ϭϲͲϭϴ DĂƌĐŚ ϮϬϭϱ͕ ŝŶ ƚŚĞ ĨƌĂŵĞ ŽĨ ƚŚĞ ^ĞƐƐŝŽŶ >ϯ͘ϭ͗ ůŝŵĂƚĞ ĂĚĂƉƚĂƚŝŽŶ ĂŶĚ
ŵŝƚŝŐĂƚŝŽŶ ƐĞƌǀŝĐĞƐ͘ dŚĞ ĂďƐƚƌĂĐƚ ǁŝƚŚ ƚŚĞ ĂƐƐŽĐŝĂƚĞĚ W& ǀĞƌƐŝŽŶ ŽĨ ƚŚĞ WŽǁĞƌWŽŝŶƚ ĂƌĞ ĂǀĂŝůĂďůĞ
ďĞůŽǁ͘
EŽƚĞ ƚŚĂƚ ƚŚĞ ŽďƐĞƌǀĞĚ ǀĂůƵĞƐ ĨŽƌ EϮK ŚĂǀĞ ďĞĞŶ ŚŝĚĚĞŶ ĨŽƌ ƚŚŝƐ ǀĞƌƐŝŽŶ ĂŶĚ ƚŚĂƚ ƚŚĞ ĨŝŐƵƌĞƐ ĨŽƌ
ƉƌŽĚƵĐƚŝŽŶ ;ŐƌĂŝŶ LJŝĞůĚ ĨŽƌ ĐƌŽƉƐ ĂŶĚ ŐƌĂƐƐůĂŶĚ ƉƌŽĚƵĐƚŝŽŶͿ ĂƌĞ ŶŽƚ ƚŚĞ ĚĞĨŝŶŝƚŝǀĞ ŽŶĞƐ ;ƌĞƐƵůƚƐ
ĞdžƚƌĂĐƚĞĚŽŶϭϯ͘Ϭϯ͘ϭϱͿƐŝŶĐĞŵŽĚĞůůŝŶŐƚĞĂŵƐŚĂǀĞƵƉĚĂƚĞĚƚŚĞŝƌƌĞƐƵůƚƐŝŶďĞƚǁĞĞŶ͕ĨŽůůŽǁŝŶŐƚŚĞ
ƐƚĞĞƌŝŶŐĐŽŵŵŝƚƚĞĞƌĞƋƵĞƐƚ͘
ZĞĨĞƌĞŶĐĞ
ŚƌŚĂƌĚƚ &͕ ^ŽƵƐƐĂŶĂ :Ͳ&͕ 'ƌĂĐĞ W͕ ZĞĐŽƵƐ ^͕ ^ŶŽǁ s͕ ĞůůŽĐĐŚŝ '͕ ĞĂƵƚƌĂŝƐ :͕ ĂƐƚĞƌ D͕ >ŝĞďŝŐ D͕
^ŵŝƚŚW͕ĞůƐŽ͕ŚĂƚŝĂ͕ƌŝůůŝ>͕ŽŶĂŶƚZ͕ĞůŝŐŝŽƐW͕ŽůƚƌĂ:͕&ĂƌŝŶĂZ͕&ŝƚƚŽŶE͕'ƌĂŶƚ͕,ĂƌƌŝƐŽŶ
D͕ <ŝƌƐĐŚďĂƵŵ D͕ <ůƵŵƉƉ <͕ >ĠŽŶĂƌĚ :͕ >ŝĞĨĨĞƌŝŶŐ D͕ DĂƌƚŝŶ Z͕ DĂƐƐĂĚ Z^͕ DĞŝĞƌ ͕ DĞƌďŽůĚ >͕
DŽŽƌĞ͕DƵůĂ>͕EĞǁƚŽŶW͕WĂƚƚĞLJ͕ZĞĞƐ͕^ŚĂƌƉ:͕^ŚĐŚĞƌďĂĐŬ/͕^ŵŝƚŚt͕dŽƉƉ<͕tƵ>͕ŚĂŶŐ
t͘ϮϬϭϱ͘ŶŝŶƚĞƌŶĂƚŝŽŶĂůŝŶƚĞƌĐŽŵƉĂƌŝƐŽŶΘďĞŶĐŚŵĂƌŬŝŶŐŽĨĐƌŽƉĂŶĚƉĂƐƚƵƌĞŵŽĚĞůƐƐŝŵƵůĂƚŝŶŐ
',' ĞŵŝƐƐŝŽŶƐ ĂŶĚ ƐĞƋƵĞƐƚƌĂƚŝŽŶ͘ /Ŷ͗ dŽǁĂƌĚƐ ůŝŵĂƚĞͲ^ŵĂƌƚ ^ŽůƵƚŝŽŶƐ >ϯ͘ϭ ůŝŵĂƚĞ͕ ĂĚĂƉƚĂƚŝŽŶ
ĂŶĚŵŝƚŝŐĂƚŝŽŶƐĞƌǀŝĐĞƐ͕ůŝŵĂƚĞͲ^ŵĂƌƚŐƌŝĐƵůƚƵƌĞ͕ϭϲͲϭϴDĂƌĐŚϮϬϭϱ͗DŽŶƚƉĞůůŝĞƌ͕Ɖ͘ϰϭ͘
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dŚĞ 'ƌĂƐƐůĂŶĚ ƐƵďŐƌŽƵƉ ŽĨ ƚŚĞ ŐD/W 'ƌĂƐƐůĂŶĚ Θ >ŝǀĞƐƚŽĐŬ ŐƌŽƵƉ ŚĂƐ ƐƚĂƌƚĞĚ ƚŽ ĂŶĂůLJnjĞ ƚŚĞ
ŐƌĂƐƐůĂŶĚ ƐĞŶƐŝƚŝǀŝƚLJ ƚŽ ĐůŝŵĂƚĞ ĐŚĂŶŐĞ ďLJ ĂĚĂƉƚŝŶŐ ƚŚĞ ϯDW ƉƌŽƚŽĐŽů ;ƐĞĞ
ŚƚƚƉ͗ͬͬǁǁǁ͘ĂŐŵŝƉ͘ŽƌŐͬĐϯŵƉͬͿ͘ ƉŽƐƚĞƌ ŚĂƐ ďĞĞŶ ƉƌĞƐĞŶƚĞĚ Ăƚ ƚŚĞ ^ ϭϱ ;ƐĞƐƐŝŽŶ͗ >ϯ͘ϭ ůŝŵĂƚĞ
ĂĚĂƉƚĂƚŝŽŶĂŶĚŵŝƚŝŐĂƚŝŽŶƐĞƌǀŝĐĞƐͿ͕ƉƌĞƐĞŶƚŝŶŐƚŚĞĞŶƐĞŵďůĞŽĨŵŽĚĞůƐƚŚĂƚŚĂǀĞĐŽŶƚƌŝďƵƚĞĚƚŽƚŚĞ
ƐĞŶƐŝƚŝǀŝƚLJĂŶĂůLJƐŝƐĨŽƌƚĞŵƉĞƌĂƚĞŐƌĂƐƐůĂŶĚƐLJƐƚĞŵƐ͘
ŵĞƚĂͲĞŵƵůĂƚŽƌĨŽƌƚŚĞĞƐƚŝŵĂƚŝŽŶŽĨƚŚĞƌĞůĂƚŝǀĞĐŚĂŶŐĞƐ͕ƌĞƐƉĞĐƚŝǀĞůLJŝŶŐƌĂƐƐůĂŶĚƉƌŽĚƵĐƚŝŽŶĂŶĚ
EϮK ĞŵŝƐƐŝŽŶƐ͕ ŚĂƐ ďĞĞŶ ĐĂůĐƵůĂƚĞĚ ƌĞƐƵůƚŝŶŐ ŝŶ ƐƵƌĨĂĐĞ ƌĞƐƉŽŶƐĞƐ ŝŶ ƚŚĞ dĞŵƉĞƌĂƚƵƌĞ ;ΣͿ ĂŶĚ
WƌĞĐŝƉŝƚĂƚŝŽŶ;ŵŵͿĚŝŵĞŶƐŝŽŶƐĂƚKϮĞdžƚƌĞŵĞůĞǀĞůƐŽĨƚŚĞϵϵƐĐĞŶĂƌŝŽƐ;ϯϴϬĂŶĚϵϬϬƉƉŵͿ͘
'ůŽďĂů ŵĂƉƐ ƐŚŽǁŝŶŐ ƚŚĞƌĞůĂƚŝǀĞ ĐŚĂŶŐĞƐ ŝŶ ƌĞƐƉĞĐƚŝǀĞůLJ͕ ŐƌĂƐƐůĂŶĚ ƉƌŽĚƵĐƚŝŽŶ ĂŶĚ EϮK ĞŵŝƐƐŝŽŶƐ
ƵŶĚĞƌĨƵƚƵƌĞĐůŝŵĂƚŝĐƐĐĞŶĂƌŝŽƐ;ϮϬϱϬƐZWϰ͘ϱ͕ϮϬϱϬƐZWϴ͘ϱͿĐŽŵƉĂƌĞĚƚŽĐƵƌƌĞŶƚĐůŝŵĂƚĞ;ϭϵϴϬͲ
ϮϬϬϵͿĐĂŶďĞƉƌŽĚƵĐĞĚĨƌŽŵƚŚĞƐŝƚĞͲƐƉĞĐŝĨŝĐĞŵƵůĂƚŽƌƐ͘
ZĞĨĞƌĞŶĐĞ
ĞůůŽĐĐŚŝ͕'͕͘ŚƌŚĂƌĚƚ͕&͕͘^ŽƵƐƐĂŶĂ͕:͘Ͳ&͕͘ŽŶĂŶƚ͕Z͕͘&ŝƚƚŽŶ͕E͕͘,ĂƌƌŝƐŽŶ͕D͕͘>ŝĞĨĨĞƌŝŶŐ͕D͕͘DŝŶĞƚ͕:͕͘
DĂƌƚŝŶ͕ Z͕͘ DŽŽƌĞ͕ ͕͘ DLJƌŐŝŽƚŝƐ͕ s͕͘ ZŽůŝŶƐŬŝ͕ ^͕͘ ZƵŐĞƚ͕ &͕͘ ^ŶŽǁ͕ s͕͘ tĂŶŐ͕ ,͕͘ tƵ͕ >͕͘ ϮϬϭϱ͘
^ĞŶƐŝƚŝǀŝƚLJ ĂŶĂůLJƐŝƐ ĨŽƌ ĐůŝŵĂƚĞ ĐŚĂŶŐĞ ŝŵƉĂĐƚƐ͕ ĂĚĂƉƚĂƚŝŽŶ ĂŶĚ ŵŝƚŝŐĂƚŝŽŶ ƉƌŽũĞĐƚŝŽŶ ǁŝƚŚ ƉĂƐƚƵƌĞ
ŵŽĚĞůƐ͘ /Ŷ͗ dŽǁĂƌĚƐ ůŝŵĂƚĞͲ^ŵĂƌƚ ^ŽůƵƚŝŽŶƐ >ϯ͘ϭ ůŝŵĂƚĞ͕ ĂĚĂƉƚĂƚŝŽŶ ĂŶĚ ŵŝƚŝŐĂƚŝŽŶ ƐĞƌǀŝĐĞƐ͕
ůŝŵĂƚĞͲ^ŵĂƌƚŐƌŝĐƵůƚƵƌĞ͕ϭϲͲϭϴDĂƌĐŚϮϬϭϱ͗DŽŶƚƉĞůůŝĞƌ͕Ɖ͘ϭϭϬ͘
‹–‡”ƒ–‹‘ƒŽ‹–‡”…‘’ƒ”‹•‘Ƭ„‡…Šƒ”‹‰‘ˆ…”‘’ƒ†’ƒ•–—”‡‘†‡Ž•
•‹—Žƒ–‹‰
‡‹••‹‘•ƒ†•‡“—‡•–”ƒ–‹‘
Š”Šƒ”†– ‹‘ƒ͙ǡ ‘—••ƒƒ ‡ƒǦ”ƒ­‘‹•͙ǡ ”ƒ…‡ ‡–‡”͚ǡ ‡…‘—• ›Ž˜‹‡͛ǡ ‘™ ƒŽ͜ǡ ‡ŽŽ‘……Š‹ ‹ƒ‹͝ǡ
‡ƒ—–”ƒ‹• ‘•‡ˆ͞ǡ ƒ•–‡” ƒ”͟ǡ ‹‡„‹‰ ƒ”͠ǡ ‹–Š ‡–‡͡ǡ ‡Ž•‘ ‹–ƒ͙͘ǡ Šƒ–‹ƒ ”–‹͙͙ǡ ”‹ŽŽ‹ ‘”‡œ‘͙͚ǡ ‘ƒ–
‹…Š͟ǡ ‡Ž‹‰‹‘•ƒ‘Žƒ͙͛ǡ‘Ž–”ƒ‘”†‹͙͜ǡƒ”‹ƒ‘„‡”–ƒ͙͝ǡ‹––‘—ƒŽƒ͡ǡ
”ƒ–”‹ƒ͙͞ǡƒ””‹•‘ ƒ––Š‡™͙͟ǡ
‹”•…Š„ƒ— ‹‘͙͠ǡ Ž—’’ ƒ–Œƒ͝ǡ ±‘ƒ”† ‘´Ž͙͡ǡ ‹‡ˆˆ‡”‹‰ ƒ”͞ǡ ƒ”–‹ ƒ’Šƒ´Ž͝ǡ ƒ••ƒ† ƒ‹ƒ ›Ž˜‹ƒ͚͘ǡ
‡‹‡” Ž‹œƒ„‡–Š͚͙ǡ ‡”„‘Ž† —–œ͚͚ǡ ‘‘”‡ †”‡™͚͙ǡ —Žƒ ƒ—”ƒ͙͛ǡ ‡™–‘ ƒ—Ž͚͙ǡ ƒ––‡› Ž‹œƒ„‡–Š͙͞ǡ ‡‡•
‘„͚͛ǡŠƒ”’‘ƒƒ͚͜ǡŠ…Š‡”„ƒ…—”‹‹͚ǡ‹–Šƒ”†͙͞ǡ‘’’ƒ‹”•–›͚͛ǡ—‹ƒŠƒ‹͚͝ǡŠƒ‰‡͚͞
͙ǡƒ”‹•ǡ”ƒ…‡
͚—‡‡•Žƒ†‹˜‡”•‹–›‘ˆ‡…Š‘Ž‘‰›ǡ”‹•„ƒ‡ǡ—•–”ƒŽ‹ƒ
͛ǡǡ‡‹•ǡ”ƒ…‡
͜‰‡•‡ƒ”…Šǡ‹…‘Ž‡•‡ƒ”…Š‡–”‡ǡŠ”‹•–…Š—”…Šǡ‡™‡ƒŽƒ†
͝ǡ
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͞‰‡•‡ƒ”…Š
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͟ǡ‘Ž‘”ƒ†‘–ƒ–‡‹˜‡”•‹–›ǡ‘”–‘ŽŽ‹•ǡ
͠‰”‹…—Ž–—”ƒŽ‡•‡ƒ”…Š‡”˜‹…‡ǡƒ†ƒǡ
͡•–‹–—–‡‘ˆ‹‘Ž‘‰‹…ƒŽƒ†˜‹”‘‡–ƒŽ…‹‡…‡•ǡ‹˜‡”•‹–›‘ˆ„‡”†‡‡ǡ…‘–Žƒ†ǡ‹–‡†‹‰†‘
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‰”‹Ǧ‘‘†ƒƒ†ƒǡ––ƒ™ƒǡƒƒ†ƒ
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͚͛†‹„—”‰Šƒ’—•ǡ…‘–Žƒ†ǡ‹–‡†‹‰†‘
͚͜Š‡‡™‡ƒŽƒ†•–‹–—–‡ˆ‘”Žƒ–Ƭ‘‘†‡•‡ƒ”…Šǡ‡™‡ƒŽƒ†
͚͝‡’ƒ”–‡–‘ˆ—•–ƒ‹ƒ„Ž‡‘‹Ž…‹‡…‡ƒ†
”ƒ••Žƒ†›•–‡ǡ‘–Šƒ•–‡†‡•‡ƒ”…Šǡ‹–‡†‹‰†‘
͚͞•–‹–—–‡‘ˆ–‘•’Š‡”‹…Š›•‹…•ǡŠ‹‡•‡…ƒ†‡›‘ˆ…‹‡…‡•ǡ‡‹Œ‹‰ǡŠ‹ƒ
Š‡ †‡˜‡Ž‘’‡– ‘ˆ …Ž‹ƒ–‡ ‹–‹‰ƒ–‹‘ •‡”˜‹…‡• ’ƒ”–Ž› †‡’‡†• ‘ ‘—” ƒ„‹Ž‹–› –‘ •‹—Žƒ–‡ǡ ™‹–Š …‘ˆ‹†‡…‡ǡ
ƒ‰”‹…—Ž–—”ƒŽ ’”‘†—…–‹‘ ƒ† ‰”‡‡Š‘—•‡ ‰ƒ• ȋ
Ȍ ‡‹••‹‘• •‘ ƒ• –‘ —†‡”•–ƒ† –Š‡ ‡ˆˆ‡…–‹˜‡‡•• ‘ˆ –Š‡
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ŽŽ‹ƒ…‡ ȋ
Ȍ ‘ Šƒ• ‹‹–‹ƒ–‡† ƒ ‹–‡”ƒ–‹‘ƒŽ ‘†‡Ž „‡…Šƒ”‹‰ ƒ† ‹–‡”Ǧ…‘’ƒ”‹•‘ –Šƒ– ™‹ŽŽ
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An international intercomparison and
benchmarking of crop and pasture models
simulating GHG emissions and C sequestration
ŚƌŚĂƌĚƚ &ŝŽŶĂϭ͕ ^ŽƵƐƐĂŶĂ :ĞĂŶͲ&ƌĂŶĕŽŝƐϭ͕ 'ƌĂĐĞ WĞƚĞƌϮ͕ ZĞĐŽƵƐ ^LJůǀŝĞϯ͕ ^ŶŽǁ sĂůϰ͕ ĞůůŽĐĐŚŝ
'ŝĂŶŶŝϱ͕ ĞĂƵƚƌĂŝƐ :ŽƐĞĨϲ͕ ĂƐƚĞƌ DĂƌŬϳ͕ >ŝĞďŝŐ DĂƌŬϴ͕ ^ŵŝƚŚ WĞƚĞϵ͕ ĞůƐŽ ŝƚĂϭϬ͕ ŚĂƚŝĂ ƌƚŝϭϭ͕
ƌŝůůŝ >ŽƌĞŶnjŽϭϮ͕ ŽŶĂŶƚ ZŝĐŚϳ͕ ĞůŝŐŝŽƐ WĂŽůĂϭϯ͕ ŽůƚƌĂ :ŽƌĚŝϭϰ͕ &ĂƌŝŶĂ ZŽďĞƌƚĂϭϱ͕ &ŝƚƚŽŶ EƵĂůĂϵ͕
'ƌĂŶƚ ƌŝĂŶϭϲ͕ ,ĂƌƌŝƐŽŶ DĂƚƚŚĞǁϭϳ͕ <ŝƌƐĐŚďĂƵŵ DŝŬŽϭϴ͕ <ůƵŵƉƉ <ĂƚũĂϱ͕ >ĠŽŶĂƌĚ :Žģůϭϵ͕
>ŝĞĨĨĞƌŝŶŐ DĂƌŬϲ͕ DĂƌƚŝŶ ZĂƉŚĂģůϱ͕ DĂƐƐĂĚ ZĂŝĂ ^LJůǀŝĂϮϬ͕ DĞŝĞƌ ůŝnjĂďĞƚŚϮϭ͕ DĞƌďŽůĚ >ƵƚnjϮϮ͕
DŽŽƌĞ ŶĚƌĞǁϮϭ͕DƵůĂ >ĂƵƌĂϭϯ͕ EĞǁƚŽŶ WĂƵůϮϭ͕ WĂƚƚĞLJ ůŝnjĂďĞƚŚϭϲ͕ ZĞĞƐ ŽďϮϯ͕ ^ŚĂƌƉ :ŽĂŶŶĂϮϰ͕
^ŚĐŚĞƌďĂĐŬ /ƵƌŝŝϮ͕ ^ŵŝƚŚ tĂƌĚϭϲ͕ dŽƉƉ <ĂŝƌƐƚLJϮϯ͕ tƵ >ŝĂŶŚĂŝϮϱ͕ ŚĂŶŐ tĞŶϮϲ
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^ƚĂƚĞ hŶŝǀĞƌƐŝƚLJ͕ h^͖ h^ ŐƌŝĐƵůƚƵƌĂů ZĞƐĞĂƌĐŚ ^ĞƌǀŝĐĞ͕ h^͖ hŶŝǀĞƌƐŝƚLJ ŽĨ ďĞƌĚĞĞŶ͕ h<͖ &ĞĚĞƌĂů hŶŝǀĞƌƐŝƚLJ ŽĨ ^ĂŶƚĂ DĂƌŝĂ͕
ƌĂnjŝů͖ /ŶĚŝĂŶ ŐƌŝĐƵůƚƵƌĂů ZĞƐĞĂƌĐŚ /ŶƐƚŝƚƵƚĞ͕ /ŶĚŝĂ͖ hŶŝǀĞƌƐŝƚLJ ŽĨ &ůŽƌĞŶĐĞ͕ /ƚĂůLJ͖ hŶŝǀĞƌƐŝƚLJ ŽĨ ^ĂƐƐĂƌŝ͕ /ƚĂůLJ͖ ĂŶƚĂďƌŝĂ ŐƌŝĐƵůƚƵƌĂů
ZĞƐĞĂƌĐŚ ĂŶĚ dƌĂŝŶŝŶŐ ĞŶƚƌĞ͕ ^ƉĂŝŶ͖ ZĞƐĞĂƌĐŚ ĞŶƚƌĞ ĨŽƌ ƚŚĞ ^ŽŝůͲWůĂŶƚ ^LJƐƚĞŵ͕ /ƚĂůŝĂ͖ ŐƌŝĐƵůƚƵƌĞ ĂŶĚ ŐƌŝͲ&ŽŽĚ ĂŶĂĚĂ͕ ĂŶĂĚĂ͖
dĂƐŵĂŶŝĂŶ ŝŶƐƚŝƚƵƚĞ ŽĨ ŐƌŝĐƵůƚƵƌĞ͕ ƵƐƚƌĂůŝĂ͖ >ĂŶĚĐĂƌĞ ZĞƐĞĂƌĐŚ͕ EĞǁ ĞĂůĂŶĚ͖ ^/ZK͕ ƵƐƚƌĂůŝĂ͖ d, ƵƌŝĐŚ͕ ^ǁŝƚnjĞƌůĂŶĚ͖ ^Zh
ĚŝŶďƵƌŐŚ ĂŵƉƵƐ͕ h<͖ EĞǁ ĞĂůĂŶĚ /ŶƐƚŝƚƵƚĞ ĨŽƌ WůĂŶƚ Θ &ŽŽĚ ZĞƐĞĂƌĐŚ͕ EĞǁ ĞĂůĂŶĚ͖ ĞƉĂƌƚŵĞŶƚ ŽĨ ^ƵƐƚĂŝŶĂďůĞ ^Žŝů ^ĐŝĞŶĐĞ
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DŽŶƚƉĞůůŝĞƌDĂƌĐŚϭϲͲϭϴ͕ϮϬϭϱ
Session L3.1 Climate adaptation and mitigation services
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ŐƌŽƵƉŽĨƚŚĞ'ůŽďĂůZĞƐĞĂƌĐŚůůŝĂŶĐĞ
1st workshop ‘Model intercomparison’
Paris, France – March 2014
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DŽĚĞůƐϰWĂƐƚƵƌĞƐ͕
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D''Ed
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• хϰϬ ƉĞŽƉůĞ ŝŶǀŽůǀĞĚ͗ ŵŽĚĞůĞƌƐ͕ ƐŝƚĞ
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ƐƚĂƚŝƐƚŝĐŝĂŶƐ͕ ƉƌŽũĞĐƚ ŚŽůĚĞƌƐ
2nd workshop ‘Model intercomparison’
Fort Collins, USA – March 2015
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Sensitivityanalysisforclimatechangeimpacts,adaptationandmitigationprojectionwith
pasturemodels
BellocchiGianni1,EhrhardtFiona2,SoussanaJeanǦFrançois2,ConantRich3,FittonNuala4,Harrison
Matthew5,LiefferingMark6,MinetJulien7,MartinRaphaël1,MooreAndrew8,MyrgiotisVasileios9,
RolinskiSusanne10,RugetFrançoise11,SnowVal12,WangHong13,WuLianhai14
1
INRA,GrasslandEcosystemResearch(UR874),ClermontFerrand,France
2
INRA,Paris,France
3
NREL,ColoradoStateUniversity,FortCollins,USA
4
InstituteofBiologicalandEnvironmentalSciences,UniversityofAberdeen,Scotland,UnitedKingdom
5
TasmanianinstituteofAgriculture,Burnie,Australia
6
AgResearchGrasslands,PalmerstonNorth,NewZealand
7
UniversitédeLiège,Arlon,Belgium
8
CSIRO,Australia
9
SRUCEdinburghCampus,Scotland,UnitedKingdom
10
PotsdamInstituteforClimateImpactResearch,Germany
11
INRA,UMREMMAH,Avignon,France
12
AgResearch,LincolnResearchCentre,Christchurch,NewZealand
13
AgricultureandAgriǦFoodCanada,Saskatoon,Canada
14
DepartmentofSustainableSoilScienceandGrasslandSystem,RothamstedResearch,United
Kingdom
Thedevelopmentofclimateadaptationservicesrequiresanimprovedaccuracyinmodel
projectionsforclimatechangeimpactsonpastures.Moreover,changesingrasslandmanagement
needtobetestedintermsoftheiradaptationandmitigationpotential.WithinAgMIP(Agricultural
ModelIntercomparisonandImprovementProject),basedontheC3MPprotocolforcrops,we
exploreclimatechangeimpactsonfuturegreenhousegasemissionsandremovalsintemperate
grasslandsystems.Sitecalibratedmodelsareusedtoprovideprojectionsunderprobabilistic
climatechangescenarios,whicharedefinedbyacombinationofairtemperature,precipitationand
atmosphericCO2changes.Thisdesignprovidesatestofyield,greenhousegasemissions(N2Oand
CH4)andCsequestrationsensitivitytoclimatechangedrivers.Moreover,changesinanimal
stockingdensityandingrazingvs.cuttingareexploredtotestpotentialmitigationandadaptation
options.Thisintegratedapproachhasbeentestedfor12modelsappliedto19grasslandsitesover
threecontinentsandisseenasapreǦrequisitefortheuseofmodelsinthedevelopmentofclimate
adaptationandmitigationservicesforgrazinglivestock.
Bellocchi, G., Ehrhardt, F., Soussana, J.-F., Conant, R., Fitton, N., Harrison, M., Lieffering, M., Minet, J., Martin, R.,
Moore, A., Myrgiotis, V., Rolinski, S., Ruget, F., Snow, V., Wang, H., Wu, L., 2015. Sensitivity analysis for
climate change impacts, adaptation and mitigation projection with pasture models. In Towards ClimateSmart Solutions L3.1 Climate, adaptation and mitigation services
(http://csa2015.cirad.fr/var/csa2015/storage/fckeditor/file/L3%20Towards%20Climatesmart%20Solutions(1).pdf, accessed 25 March 2015), Climate-Smart Agriculture, 16-18 March 2015:
Montpellier, p. 110.
Session: L3.1 Climate adaptation and mitigation services
Sensitivity analysis for climate change impacts, adaptation & mitigation projection with pasture models.
Bellocchi Gianni1, Ehrhardt Fiona2, Conant Rich3, Fitton Nuala4, Harrison Matthew5, Lieffering Mark6, Minet Julien7, Martin Raphaël1,Moore Andrew8, Myrgiotis Vasileios9,
Rolinski Susanne10, Ruget Françoise11, Snow Val12, Wang Hong13, Wu Lianhai14, Ruane Alex15, Soussana Jean-François2.
1 INRA, Grassland Ecosystem Research (UR874), Clermont Ferrand, France; 2 INRA, Paris, France; 3 NREL, Colorado State University, Fort Collins, USA; 4 Institute of Biological and Environmental
Sciences, University of Aberdeen, Scotland, UK; 5 Tasmanian institute of Agriculture, Burnie, Australia; 6 AgResearch Grasslands, Palmerston North, New Zealand; 7 Université de Liège, Arlon,
Belgium; 8 CSIRO, Australia; 9 SRUC Edinburgh Campus, Scotland, UK; 10 Potsdam Institute for Climate Impact Research, Germany; 11 INRA, UMR EMMAH, Avignon, France; 12 AgResearch, Lincoln
Research Centre, Christchurch, New Zealand; 13 Agriculture and Agri-Food Canada, Saskatoon, Canada; 14 Department of Sustainable Soil Science and Grassland System, Rothamsted Research, UK.
15NASA Goddard Institute for Space Studies, Climate, Impacts Group, New York, NY, United States of America.
Context - A study for temperate grassland systems
The development of climate adaptation services requires an improved accuracy in model projections for climate change impacts on pastures. Moreover, changes in
grassland management need to be tested in terms of their adaptation and mitigation potential. Within AgMIP (Agricultural Model Intercomparison and Improvement
Project), based on the C3MP protocol for crops, we explore climate change impacts on grassland production, future greenhouse gas emissions and removals in temperate
grassland systems. Key words: Climate change, Pasture, Carbon, Greenhouse gas, Soil, Livestock.
1000
800
Fig.c
Mean annual precipitation (mm)
Yield
Mean annual precipitation (mm)
1200
600
1200
1000
800
600
Fig.a
400
6
8
10
Fig.b
400
12
14
16
18
20
22
24
6
8
10
1600
1600
1400
1400
1200
1000
800
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
600
Fig.c
400
6
8
10
12
14
16
18
Mean annual temperature (°C)
12
14
16
18
20
22
20
22
24
800
600
Fig.d
8
10
1800
1600
1400
1200
1000
800
600
400
200
800
700
P (m
m)
2
600
500
CO (ppm
2
)
400
5
0
-5
1400
1200
1000
26
24
22
20
18
16
14
12
10
P (mm
800
)
600
T2 vs CO2 vs Yield
-5
0
5
10
15
20
15
-1
-1
.yr )
20
10
5
26
24
22
20
18
16
14
12
0
T(°
C)
4
10
Yield (t DM.ha
8
15
-5
800
700
600
500
CO (ppm
2
)
400
Relative Yield (2050s RCP 4.5/actual)
Relative Yield (2050s RCP 8.5/actual)
Relative N2O emissions (2050s RCP 4.5/actual)
1000
6
10
T2 vs P2 vs Yield
-5
0
5
10
15
20
20
Mean surface response of temperate
grasslands N2O emissions to climatic drivers
(n=891 ; r2 = 0,296, P<0.001).
Production changes were calculated relative to
climate conditions leading to maximum N2O
emissions, under 380 ppm (Fig.c) and 900 ppm
(Fig.d).
1200
400
12
P2 vs CO2 vs Yield
2
4
6
8
10
12
14
16
18
20
22
24
Mean annual temperature (°C)
Mean annual precipitation (mm)
N2O
Mean annual precipitation (mm)
Mean annual temperature (°C)
14
Mean surface response of temperate
grasslands annual production to climatic
drivers (n=1089 ; r2 = 0.64, P<0.001).
Production changes were calculated relative to
climate conditions leading to maximum
production, under 380 ppm (Fig.a) and 900
ppm (Fig.b).
1400
-0.2
0.0
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
16
3/Projection of the surface equation in {T, P, CO2}
dimensions
1600
1400
18
2/Determination of the final equation for temperate
grassland yield estimation by using a backward
stepwise regression
900 ppm
1600
20
6
Y (CO2, T, P) = a + b(T) + c(T)² + d(P) + e(P)²+
f(CO2) + g(CO2)² + h(T*P)+ i(T*CO2)+
j(P*CO2)+ k(T*P*CO2)
Sensitivity of temperate grasslands production & N2O emissions to climatic
drivers from an ensemble of models
380 ppm
22
3/ P*CO2 *Yield ;
local mean T (°C)
2/ T* CO2 *Yield ;
local mean P (mm)
C)
1/Determination of a global equation for yield by
using a regression and {T, P, CO2} terms
(= emulator):
1/ T*P*Yield ;
CO2 = 380 ppm
T (°
Calculation of an emulator for yield &
N2O emissions
-1
P
1024
1134
756
491
750
1150
1150
1071
1071
784
879
648
910
714
1383
1343
1343
378
378
-1
T
13,7
11,7
12,9
16,5
13,5
10,3
10,3
6,7
6,7
12,0
9,7
13,9
13,4
11,1
8,8
9,8
9,8
8,4
8,4
.yr )
COUNTRY
Australia
Australia
Australia
Australia
Australia
Switzerland
Switzerland
France
France
France
France
France
New Zealand
New Zealand
United Kingdom
United Kingdom
United Kingdom
United States
United States
Each dot represents an individual run. The mesh curve
was fitted with the emulator calculated as described
(n=99 ; r2 =0.995, P<0.001).
-1
MODEL
DairyMod v5.3.0
DairyMod v5.3.0
APSIM-GRAZPLAN v7.5
APSIM-GRAZPLAN v7.5
DairyMod v5.3.0
PaSim
LPJmL
PaSim
LPJmL
STICS v8.3.1
STICS v8.3.1
STICS v8.3.1
APSIM v7.6
APSIM-SWIM
Daily Daycent
Daily Daycent
Daily Daycent
Daycent
Daycent
Site specific emulator calculated with 99 scenarios.
Temperature changes from -1°C to +8°C, precipitation
changes from -50% to +50%, CO2 concentration from 380
ppm to 900 ppm.
Yield (t DM.ha
Combinations site/model : 16 sites – 8 models
Ellinbank Dairy research institute, Australia, DairyMod).
.yr )
• 16 contrasted grassland sites were used covering a large climate gradient over 3 continents (mean annual
temperature T from 7 to 14°C; mean annual precipitation P from 380 to 1380 mm)
• Model simulations of grassland systems using 99 combinations of climate drivers {Temperature, Precipitation,
CO2} to explore yield and GHG responses to climate change.
• Calibrated models on specific sites & global models on a set of common sites.
• Grassland management considered for simulation: no N-limitation, no irrigation, cutting events.
• Target outputs : Yields, GPP, NPP, net above-ground primary production, net herbage accumulation, SOC stock,
SON stock, N2O emissions
Results – Site specific sensitivity of
grasslands annual production to
climatic drivers (Illustrative example for site
-1
Modeling Project), AgMIP
Yield (t DM.ha
Methodology - An exercise adapted from C3MP (Coordinated Climate-Crop
12
14
16
18
20
22
24
Mean annual temperature (°C)
Relative N2O emissions (2050s RCP 8.5/actual)
Extension to global maps for grassland systems
Relative changes in grassland production and N2O emissions under future climatic scenarios (2050s RCP 4.5, 2050s RCP
8.5) compared to current climate (1980-2009) were projected from the emulator for all sites. Results show that without N
limitation, production and N2O emissions mostly increases by the 2050’s. Elevated CO2 (ca. 600 ppm in both RCPs) plays a
large role in this increase and this questions the extent to which models may overestimate this CO2 effect (e.g. for P limited
grasslands). The use of an emulator provides a fast-track to upscale simulations for global grasslands and test a large range
of climate scenarios, as well as adaptation and mitigation options. By extending the methodology to tropical grassland
systems and rangelands, global maps for the variation of production, carrying capacity and GHG emissions could be
generated for the grassland biome.
10