Working Group II: Impacts, Adaptation and Vulnerability

Other reports in this collection 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 Scenarioa Yield Impact w/o Adaptationb Yield Impact w/ Adaptationb Socioeconomic Impact Comments
a) Studies with Explicit Global Economics and/or Global Yields
Parry et al. (1999) Global Wheat,
Transient scenarios:
4 HadCM2 ensemble scenarios, 1 HadCM3 (both assume IS92a forcing)
All cereals by 2080sc:
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; CO2 direct 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.;
+0% ppt.
    Agricultural price changes (-19 to +15%), GDP (+0 to +0.8%) Includes direct effects of CO2
Yates and Strzepek (1998) Egypt Wheat,
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 indicatorsd: 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,
Sensitivity analysis
(+2, +4°C)

+2°Ce [+8% (maize) to +16% (soybean)]; +4°Ce [-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 Wheatf [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,
Latin Americ
coarse grains,
"cash crops"
[maize (-29 to -23%), rice (0%), wheat (-20 to -15%), coarse grains (-30 to -25%), soybean (-2 to +10%), cash crops (-10 to -4%)];
[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%)]
[total agricultural production (-13 to -9%), GDP per capita (-10 to -7%), agricultural prices (-9 to +56%)];
[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
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)

-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) Cornj
[+1°C (-6 to +7%),
+3°C (-17 to +4%),
+5°C (-34 to -3%)];
[+1°C (-8 to +47%),
+3°C (-20 to +37%),
+5°C (-70 to -11%)]
Change in production: Cornj
[+1°C (-10 to +10%),
+3°C (-20 to +5%),
+5°C (-35 to -5%)];
[+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

GISS, GFDL R-30, CCC, OSU, UK89, HCGG, and HCGS equilibrium scenarios;

(-35 to -1%),
(+8 to +20%)


(-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 (-26%)l  

Cuculeanu et al. (1999)

(5 sites)
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,
(-31 to -7%)n

    Includes CO2 direct effects; adaptation (single to double cropping system, planting date shift, change in variety)
-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,
-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,
-14 to -12%    
  Kazakhstan Spring wheat,
winter wheat
GFDLp, 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)
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)
(-13 to +9%),
(-14 to -8%)
    Includes direct effects of CO2
Karim et al. (1996) Bangladesh Rice,
(-17 to -10%),
(-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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