Climate Change 2001: Impacts, Adaptation and Vulnerability

5.3.3.3. Impacts on Livestock

Recent research supports the major conclusions of Reilly et al. (1996) on animal husbandry. Farm animals are affected by climate directly and indirectly. Direct effects involve heat exchanges between the animal and its environment that are linked to air temperature, humidity, windspeed, and thermal radiation. These linkages influence animal performance (e.g., growth, milk and wool production, reproduction), health, and well-being. Indirect effects include climatic influences on quantity and quality of feedstuffs such as pastures, forages, and grain and the severity and distribution of livestock diseases and parasites. When the magnitudes (intensity and duration) of adverse environmental conditions exceed threshold limits with little or no opportunity for relief (recovery), animal functions can become impaired by the resulting stress, at least in the short term (Hahn and Becker, 1984; Hahn and Morrow-Tesch, 1993; Hahn, 1999). Genetic variation, life stage, and nutritional status also influence the level of vulnerability to potential environmental stresses. These relationships form the basis for developing biological response functions that can be used to estimate performance penalties associated with direct climate factors (Hahn, 1976, 1981, 1995). Earlier work (Hahn et al., 1992; Klinedinst et al., 1993) used such response functions with the Goddard Institute for Space Studies (GISS), Geophysical Fluid Dynamics Laboratory (GFDL), and United Kingdom Meteorological Office (UKMO) scenarios and found substantial reductions in dairy cow performance with climate change. For example, milk production of moderate- to high-producing shaded dairy cows in hot/hot-humid southern regions of the United States might decline an additional 5-14% beyond expected summer reductions. Conception rates of dairy cows were reduced by as much as 36% during the summer season in the southeastern United States. Short-term extreme events (e.g., summer heat waves, winter storms) can result in the death of vulnerable animals (Balling, 1982; Hahn and Mader, 1997; Hahn, 1999), which can have substantial financial impacts on livestock producers.

Table 5-4: Recent agricultural studies: a) studies with explicit global economics and/or global yields; b) studies of yield and production in developed regions, nations, and subnational regions; and c) studies of yield and production in economies-in-transition and developing regions, nations, and subnational regions.

Study	Scope	Crops	Climate Scenario^a	Yield Impact w/o Adaptation^b	Yield Impact w/ Adaptation^b	Socioeconomic Impact	Comments

a) Studies with Explicit Global Economics and/or Global Yields

Parry et al. (1999)	Global	Wheat, rice, maize, soybeans	Transient scenarios: 4 HadCM2 ensemble scenarios, 1 HadCM3 (both assume IS92a forcing)		All cereals by 2080s^c: NA (-10 to +3%); LA (-10 to 10%); WE (0 to +3%); EE (-10 to +3%); AS (-10 to +5%); AF (-10 to +3%)	By the 2080s: global cereal production (-4 to -2%), cereal prices (+13 to +45%), number of people at risk of hunger (+36 to +50%)	Farm-level adaptations (changes in plant date, varieties, irrigation, fertilizer); economic adjustments (increased investment, reallocation of resources, more land in production); no feedback between economic adjustments and yields; CO₂direct effects included

Darwin et al. (1995)	Global	13 commodities	UKMO, GISS			Agriculture prices [wheat (-10 to -3%), other grains (-6 to -4%)]; global GDP (+0.3 to +0.4%)	Adaptation through market-induced land-use change; CO₂ effect not included

Darwin (1999)	Global	Same as Darwin et al. (1995)	OSU, GFDL, GISS, UKMO			Qualitative impacts: world (positive for temperature change <2°C, negative for temperature change >2°C), regional (positive for high latitudes, negative for tropics)	Same as Darwin et al. (1995)

Darwin and Kennedy (2000)	Global	Same as Darwin et al. (1995)	CO₂ effect on yields only, no climate change	Yield changes with full CO₂ effect: wheat (7%), rice (19%), soybeans (34%), other crops (25%)		Previous studies' estimates of economic value of CO₂ fertilization effect overstated by 61-166%	Scenarios run for CO₂ effect on yields ranging from very low to full effect

Adams et al. (1998)	USA	Various	+2.5°C, +7% ppt.; +5°C, +0% ppt.			Agricultural price changes (-19 to +15%), GDP (+0 to +0.8%)	Includes direct effects of CO₂

Yates and Strzepek (1998)	Egypt	Wheat, rice, maize, soybean, fruit	GFDL and UKMO 2xCO₂ equilibrium scenarios, GISS-A transient scenario at 2xCO₂	Yield changes: wheat (-51 to -5%), rice (-27 to -5%), maize (-30 to -17%), soybean (-21 to -1%), fruit (-21 to -3%)	Yield changes: wheat (-25 to -3%), rice ( 13 to -3%), maize (-15 to -8%), soybean (-10 to 0%), fruit (-10 to -2%)	Change in selected economic indicators^d: consumer-producer surplus (-3 to +6%), calories per day (-1 to +5%), trade balance (-15 to +36%)	Includes direct effects of CO₂; adaptations (shift in plant date, increased fertilizer, new varieties)

Rosenz-weig and Iglesias (1998)	global (same sites as Parry et al., 1999)	Wheat, rice, soybean, maize	Sensitivity analysis (+2, +4°C)	+2°C^e [+8% (maize) to +16% (soybean)]; +4°C^e [-8% (rice) to -2% (wheat)]	Adaptation more successful at high and mid-latitudes than at low latitudes		Includes direct effects of CO₂; transient yield response highly nonlinear
			GISS-A transient scenario, GISS 2xCO₂ scenario	Wheat^f [2050 (-18 to +25%), 2xCO₂ (-32 to +27%)]; maizef [2050 (-26 to +13%), 2xCO₂ (-35 to +23%)]; soybeanf [2050 (+23 to +24%), 2xCO₂ (+13 to +17%)]

Winters et al. (1999)	Africa, Asia, Latin Americ	Maize, rice, wheat, coarse grains, soybean, "cash crops"	GISS, GFDL, UKMO		Africa [maize (-29 to -23%), rice (0%), wheat (-20 to -15%), coarse grains (-30 to -25%), soybean (-2 to +10%), cash crops (-10 to -4%)]; Asia [maize (-34 to -20%), rice (-12 to -3%), wheat (-54 to -8%), coarse grains (-34 to -22%), soybean (-9 to +10%), cash crops (-13 to +2%)]; Latin America [maize (-26 to -18%), rice (-26 to -9%), wheat (-34 to -24%), coarse grains (-27 to -19%), soybean (-8 to +12%), cash crops (-20 to -5%)]	Africa [total agricultural production (-13 to -9%), GDP per capita (-10 to -7%), agricultural prices (-9 to +56%)]; Asia [total agricultural production (-6 to 0%), GDP per capita (-3 to 0%), agricultural prices (-17 to +48%)]; Latin America [total agricultural production (-15 to -6%), GDP per capita (-6 to -2%), agricultural prices (-8 to +46%)]	Yield impacts based on Rosenzweig and Parry (1994) values for "level 1" (farm-level) adaptations and CO₂ direct effects; yield impacts are weighted (by production) average of country-level yield changes; values for total agricultural production and per capita GDP include both yield and price impacts; range for agricultural prices is across food and cash crops, and GCMs

b) Studies of Yield and Production in Developed Regions, Nations, and Subnational Regions

Hulme et al. (1999)	Europe	Wheat	HadCM2—moderate (1% yr^-1) and low forcing (0.5% yr^-1) simulations for 2050	+9 to +39%^g (note that climate change impacts are indistinguishable from climate variability for 4 of 10 countries)			Includes direct effects of CO₂

Antle et al. (1999b), Paustian et al. (1999)	Montana, USA	Winter wheat, spring wheat, barley	CCC	Climate change only (-50 to -70 %); CO₂ fertilization only (+17 to +55%); climate change + CO₂ (-30 to +30%)		With adaptation [mean returns (-11 to +6%), variability of returns (+7 to +25%)]; without adaptation [mean returns (-8 to -31%), variability of returns (+25 to +83%)]	Scenarios include climate change plus CO₂ fertilization; yield impacts from Century model; adaptation modeled as change in crop rotation and management

Barrow and Semenov (1995)	1 site in UK; 1 site in Spain	Wheat	Sensitivity analysis (+2,+4°C); downscaled UKMO high-resolution transient run (UKTR)	UK site only [+2°C (-7%), +4°C (-10%)]; both sites [+3°C (-14 to -5%), UKTR (-5 to +1%)]			Direct effects of CO₂ not considered

Dhakhwa et al. (1997)	North Carolina, USA (1 site)	Maize	GFDL, UKMO with equal and unequal day/night warming	-28 to -2%			Includes direct effects of CO₂

Tung and Haith (1998)	New York, Indiana, and Oklahoma (1 site each)	Corn	GFDL	-24 to -15%^h	-19 to -9%^h		Direct effects of CO₂ not considered; water supply also modeled; adaptations (change in variety, plant date, irrigation amount); assumes management practices currently optimal

Howden et al. (1999a)	Australia	Wheat	CSIRO 1996	9 to 37%	13 to 46%	Gross margins (28 to 95%)	Assumes prices unchanged

Brown and Rosen- berg (1999)	USA corn and wheat regions	Corn, wheat	Three GCM-based 2xCO₂ scenarios distributed over timeⁱ (GISS, UKTR, BMRC)	Corn^j [+1°C (-6 to +7%), +3°C (-17 to +4%), +5°C (-34 to -3%)]; wheat^j [+1°C (-8 to +47%), +3°C (-20 to +37%), +5°C (-70 to -11%)]	Change in production: Corn^j [+1°C (-10 to +10%), +3°C (-20 to +5%), +5°C (-35 to -5%)]; wheat^j [+1°C (-10 to +55%), +3°C (-25 to +45%), +5°C (-75 to -8%)]		CO₂ level corresponds to temperature change (365-750 ppm); dryland cropping only; planting date and growing season length allowed to vary in response to climate

c) Studies of Yield and Production in Economies-in-Transition and Developing Regions, Nations, and Sub-National Regions

Alex- androv (1999)	Bulgaria (2 sites)	Winter wheat, maize	GISS, GFDL R-30, CCC, OSU, UK89, HCGG, and HCGS equilibrium scenarios;	Maize (-35 to -1%), wheat (+8 to +20%)	Maize (-24 to -10%)^k	Net return with adaptation: maize (-29 to -12%)^k	Includes CO₂ direct effects; adaptation (change in planting date)
			GFDL-T transient scenario at 2060s	Maize (-22%), wheat (+14%)	Maize (-21%)^l	Maize (-26%)^l

Cuculeanu et al. (1999)	Romania (5 sites)	Winter wheat, maize	CCC, GISS	Wheat (+15 to +21%), dry maize (+43 to +84%), irrigated maize (-12 to +4%)	Irrigated maize (-18 to +8%)^m		Includes CO₂ direct effects; adaptation (new cultivars and changes in plant date, crop density, fertilizer amount)

Matthews et al. (1997)	Asia	Rice	Sensitivity analysis (+1, +2, +4°C);	+1°C (-7 to +26%)ⁿ, +4°C (-31 to -7%)ⁿ			Includes CO₂ direct effects; adaptation (single to double cropping system, planting date shift, change in variety)
			GFDL, GISS, UKMO	-8 to +5%^o	+14 to +27%^o (with change in variety)^o	Change in production: China with change in cropping system (+37 to +44%), region with change in variety (+13 to +25%)^o

Smith et al. (1996a)	The Gambia	Maize, millet-early, millet-late, groundnuts	CCC, GFDL, GISS	-26 to -15% -44 to -29% -21 to -14% +40 to +52%			CO₂ direct effects considered in all cases but Mongolia; adaptation in Mongolia consists of earlier seeding
	Zimbabwe	Maize	CCC, GFDL	-14 to -12%
	Kazakhstan	Spring wheat, winter wheat	CCC, GFDL^p, incremental scenarios	-70 to -25% -35 to +17%
	Mongolia	Spring wheat	GFDL, GISS	-74 to +32%	-67 to -5%^q
	Czech Republic	Winter wheat	Incremental scenarios	-3 to +16%

Singh and Mayaar (1998)	Trinidad	Sugar cane	4 synthetic scenarios, 1 GCM- based (CCC equilibrium)	-42 to -18%			Direct effects of CO₂ not considered

Magrin et al. (1997)	Argentina (pampas region)	Wheat, maize, soybean	GISS scenario for 2050	Wheat (-15 to +15%), maize (-30 to -5%), soybean (+10 to +70%)		Change in production: wheat (+4%), maize (-16%), soybean (+21%)	Includes direct effects of CO₂ (550 ppm)

Amien et al. (1996)	Indonesia	Rice	GISS transient	2050 (-14 to -9%)^r			Includes direct effects of CO₂

Saseendran et al. (2000)	India (5 sites)	Rice	Synthetic (+1.5°C, +2 mm day^-1 precipitation)	-15 to -3%^s			Includes direct effects of CO₂ (460 ppm)

Lal et al. (1999)	India	Soybean	Sensitivity analysis (+2,+4°C; ±20, ±40% precipitation)	-22 to +18%			Includes direct effects of CO₂

Buan et al. (1996)	Philippines (6 sites)	Rice, corn	CCC, GFDL, GISS, UKMO	Rice (-13 to +9%), corn (-14 to -8%)			Includes direct effects of CO₂

Karim et al. (1996)	Bangladesh	Rice, wheat	CCC, GFDL	Rice (-17 to -10%), wheat (-61 to -20%)			CO₂ direct effects not considered

Jinghua and Erda (1996)	China	Maize	GFDL, UKMO, MPI	-19 to +5%^t		Change in production: -6 to -3%	CO₂ direct effects not considered

^a GCMs are 2xCO₂, unless otherwise noted. ^b Unless otherwise noted, range is across GCM scenarios and site values are averaged; for sensitivity analyses, yield range is across sites. ^c Range across countries and GCM scenarios; NA = North America, LA = Latin America, WE = Western Europe, EE = Eastern Europe, AS = Asia, AF = Africa. ^d Range across three GCM scenarios, two levels of adaptation, and two baseline scenarios for 2060 (optimistic and pessimistic). ^e World yields (weighted by production); range across crops. ^f Range across countries in study. ^g Range across countries and GCM scenarios. ^h Range across sites. ⁱ Temporal distribution accomplished using SCENGEN scenario generator. ^j Change in global mean temperature (used as surrogate for time); regional temperature change may be higher or lower. ^k One site only; range across three 2xCO₂ equilibrium GCM scenarios (two sites and seven GCMs considered in without adaptation values). ^l One site (two sites considered in without adaptation values). ^m Range across two climate scenarios and degree of adjustment (e.g., different planting date options). ⁿ Range across two crop models and CO₂ levels (1, 1.5, and 2xCO₂). ^o Range across crop model and GCM scenarios. ^p Transient scenario. ^q Adaptation not applied when climate change increases yields in the no-adaptation case. ^r Average of "normal" and El Niño years; range across two sites. ^s Range across sites. ^t Range across sites and GCM scenarios.

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