To validate the model at North Platte, NE and Dodge City, KS we used county level yield data from 1952 to 1980; for Fargo, ND and San Antonio, TX wheat yield ...
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AGRICULTURAL AND FOREST METEOROLOGY Agricultural and Forest Meteorology 80 (19%) 215-230
.
Effects of changes in minimum and maximum temperature on wheat yields in the central US A simulation study Cynthia Rosenzweig, NASA-Goddard
Francesco N. Tubiello
*
Institute for Space Studies and Center for CIimate Systems Research, Columbia University, New York, NY 10025, USA
Received 10 January 1995; accepted 9 August 1995
Abstract Recent observations and general circulation models indicate that future temperature changes linked to global warming might be characterized by a marked asymmetry between daytime maxima and nighttime minima. We investigate the importance of such a pattern in determining future wheat (Triticum aestiuum) yields in the Central United States by using a dynamic crop growth model, CERES-Wheat, modified to include physiological effects of temperature and CO, on canopy photosynthesis. Simulations are run at four sites spanning a north-south transect of the Central US; four mean temperatures increases (1-4°C) are applied to baseline daily climate data (195 l- 1980). The effects of two different scenarios of temperature change (minimum and maximum temperatures equally raised; minima increased three times as much as maxima in agreement with recent observations) are analyzed under both current (330 ppm) and elevated (550 ppm) CO, concentrations. The main mechanisms controlling the simulated wheat responses are direct and indirect temperature effects on wheat phenological development. Negative effects of temperature on simulated wheat yields are reduced when minima increase more than maxima. Yield changes are consistently negative under temperature change and current CO, concentration, while they range from positive to negative under temperature change and elevated CO, concentration. Responses vary across the transect, with larger negative effects occurring at the southernmost site.
* Corresponding author. 0168-1923/96/$15.00 0 1996 Elsevier Science B.V. All rights reserved SSDI 0168- 1923(95)02299-6
216
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und Forest Merrorology
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1. Introduction If current rates of anthropogenic greenhouse gas emissions (CO,. CH,. CFCs, N,O) continue, it is projected that global mean surface temperatures will increase 1.5”C to 4.X in the coming century ’ (Intergovernmental Panel on Climate Change, 1995). Elevated CO, levels and rising surface air temperatures will affect crop growth and yields. tiith probable shifts in regions of crop production (Rosenzweig and Hillel, 1993). While elevated CO, alone should be beneficial to most crops-by enhancing photosynthetic rates and increasing water-use efficiency-the overall effect of temperature increase on crop yields is less certain (Conroy et al., 1994). Predicted higher temperatures could affect plant productivity in several ways. They could modify the incidence of winterkill and reduce vernalization (Meams et al., 1992). Higher potential evapotranspiration could increase crop water stress, further inhibiting yields. Temperature increases will tend to reduce the length of the growing period, potentially depressing overall biomass accumulation and yield (Monteith, 1981; Butterfield and Morison, 1992). Finally, warmer temperatures could modify the rates of photosynthesis and respiration, thus affecting crop growth rates (Long, 199 I>. While global mean surface air temperatures have increased by about 0.5”C in the past century (Jones et al., 19911, recent observations over a large portion of the earth’s land area suggest that minimum temperatures have increased about three times as much as the corresponding maxima over the period 19.5-1990 (Karl et al.. 1991). A recent multiple-regression analysis of corn yields in the Southern US for the last 50 years stressed the importance of minimum/maximum temperature asymmetries in determining interannual yield variations (Stooksbury and Michaels, 1994). In this study, we investigate the sensitivity of winter wheat yields to differential changes in minimum and maximum temperatures, with and without CO, increase.
2. Temperature
effects on wheat growth and yield
The productivity of wheat is strongly influenced by temperature, which determines both phenological development (Bauer et al., 1984) and growth rates (Grace, 1988). Temperature also affects cold hardening and winterkill (i.e.: plant death due to prolonged exposure to extreme cold events, see Table I; Gusta and Fowler, 1976), vernalization (Trione and Metzer, 19701, leaf appearance (Baker et al., 19801, carbohy(Wardlaw, drate fixation and respiration (Goudriaan et al., 19851, rate of grain-filling 19941, and evapotmnspiration and water stress (Ritchie, 1972). Because these processes are interconnected throughout the crop life cycle via a number of feedbacks, it is difficult to separate their overall effect on grain yield into distinct components. However, three main types of temperature-crop relationships can be roughly defined: 1. Direct relationships, e.g., those governing winterkill, vernalization, and water stress;
’ This temperature range does not take into account of anthropogenic sulfate aerosols.
the potential
for partially
counteractive
cooling effects
C. Rosen,weig, Table 1 Temperature
dependence
F.N. Tubiello/Agricultural
of CERES-Wheat Temperature
processes
range
and Forest Meteorology
(Ritchie and Otter-Nacke,
Temperature
80 (1996) 215-230
1985)
dependence
1. 2.
Photosynthesis Grain-tilling
All Tmi, < 10°C: T,,i, > 10°C:
P(T ml,“,Tmax) = 1 - 0.025 X (0.25T,,i, + 0.75T,,,, - 18j2 RGFILL = 0.065b X Tmin RGFILL = 0.65 + 0.00328 X [24-CT,,,, - T,,,)] x
3.
Potential ET
Tmax < 5°C: 5°C < T,,,, < 24°C: T,,,, > 24°C:
4.
Cold hardening
- 1°C < T,,,,, < 8°C T,,, > 10°C. TCrOW” = - 6°C. - 12°C. - 18°C
PET = A(0.6T,,, + 0.4T,,i, + 29) X exp[O. 18(T,,, + 20)] PET = A(0.6T,,,, + 0.4T,,,, + 29) x 1.1 PET = A(0.6Tm,, + 0.4Tmi, + 29) X (CT,,, - 24) x 0.05 + 1 Plants undergo three stages of progressive hardening. Dehardening proceeds. Crown temperatures below these thresholds lead to plant death. High threshold values correspond to low stages of cold hardening. Vernalization units accumulate during emergence and vegetative growth. Vernalization units accumulate at a less than optimal rate. Vernalization is reversed. Thermal units (degree days) are accumulated based on mean temperature above a minimum (0°C or 2°C) base temperature.
and Winterkill
5.
6.
Vernalization
Development rate
0°C < T,,,,,
< 7°C
7°C < T,,,,,
< 15°C
Tmax > 30°C All
217
T,,,: Daily maximum surface air temperature CC). Tmin: Daily minimum surface air temperature (“C). TCrow”’ Daily mean crown temperature, assumed to be equal to mean surface air temperature is present (“0. RGFILL: Rate of grain-filling (gday- ’). PET: Potential equilibrium evapotranspiration (mm day- ’). A: Correction factor for solar radiation and surface albedo.
except when snow
2. Phenological relationships, e.g., those governing duration of vegetative and reproductive growth stages and overall length of growing period; and 3. Physiological relationships, e.g., those governing rates of photosynthesis and respiration, as well as grain-filling. The pattern of temperature change of daytime maxima and nighttime minima will affect the dynamics of these relationships.
3. Temperature
relations in CERES-Wheat
CERES-Wheat is a well-validated model for the prediction of wheat growth and yield (Otter-Nacke et al., 1986). It has also been used in crop impact assessment studies of climate change around the world (Rosenzweig et al., 1995). Climate input variables for the model are daily solar radiation (MJ me2 day- ‘1, minimum and maximum surface air temperatures (T), and precipitation (mm day- ’1. Other inputs include soil parame-
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ters, cultivar type, and management factors-plant population, row spacing, irrigation and nitrogen fertilization. The CERES-Wheat model simulates the major mechanisms of plant growth: phenological development as a function of temperature and daylength: leaf expansion and canopy light interception; biomass accumulation driven by photosynthesis and limited by temperature: soil water balance and nitrogen uptake; evapotranspiration; carbon partitioning as a function of plant age; sink strength of growing organs; and plant senescence. These calculations are performed in daily time steps. The modified version we use in this work includes the effects of CO1 on photosynthesis and stomata1 closure, the effects of temperature on photorespiration, and a more detailed simulation of canopy light interception (Peart et al., 1989; Tubiello et al., 1995). Table 1 summarizes the CERES-Wheat equations that depend on temperature. Model processes sensitive to asymmetric patterns of minimum/maximum temperature change are winterkill and vernalization, plant evapotranspiration, biomass production, and rate of grain-filling. Thermal time accumulation responds to mean temperature only. It is clear from Table 1 that, apart from grain-filling, CERES-Wheat equations are more sensitive to changes in temperature maxima than to changes in minima. For a given mean temperature increase, overall model sensitivity should be larger when mimima and maxima are raised equally, compared to when minima are raised more than maxima. It has been suggested that an important effect of asymmetric temperature changes on plant growth and yield could be a shift in plant respiratory loss, due to an increase in nighttime maintenance respiration rates (Ryan, 1991). Short-term experiments found a Q,, of about 2 for wheat (i.e., maintenance respiration doubles for every 10°C increase) (Conroy et al., 1994). On the other hand, longer-term experiments have shown much
Fig.
I. Four sites in central US transect.
C. Rosen,weig. F.N. Tubiello/Agricultural
and Forest Meteorology 80 (1996) 215-230
219
lower sensitivities to temperature, with respiration rates roughly constant in the range of 15-30°C (Gifford, 1994). We have not modified CERES-Wheat to include the effects of increased minimum temperatures on respiration rates. If these were to affect yields, then the difference between the responses to the simulated temperature scenarios would be smaller than presented here.
4. Model performance:
simulated
vs. observed
yields
We compared CERES-Wheat long-term simulations against observed yields at four sites along a North-South transect in the Central United States: Fargo, ND; North Platte, NE; Dodge City, KS; and San Antonio, TX (Fig. 1). The CERES model has already been validated with data from short-term field experiments (typically one to two years) at several sites in the Central US (Otter-Nacke et al., 1986). Some validation sites are near those used in this work (i.e.: Garden City, KS; Carrington, ND; and Temple, TX). Moulin and Beckie (1993) used longer-term records (30 years) to evaluate CERESWheat at one site in the Canadian Great Plains. To validate the model at North Platte, NE and Dodge City, KS we used county level yield data from 1952 to 1980; for Fargo, ND and San Antonio, TX wheat yield data were from 1972 to 1980 (US Department of Agriculture, 1952- 1980). County extension agents provided information on the soil types, wheat cultivars, and management practices for each site. Observed local weather data for the thirty-year period 1951-1980 were provided by Dr. Roy Jenne of NCAR. Table 2 summarizes the inputs used in the model runs. Simulations were re-started each year after harvest using the same initial soil water profile, calculated as the mean soil water content for the thirty years of weather data. Initial soil water content was about one-fourth of field capacity at all sites.
Table 2 Simulation
parameters
Parameter
Fargo, ND
North Platte, NE
Dodge City, KS
San Antonio, TX
Latitude, Longitude
46”46’ N, %“58’ W 11.4
41’08’ N, 100”41’ w 11.6
37”46’ N, 99”58’ W 14.4
29”32’ N, 98”28’ W 20.5
5 10 deep silty clay northern plains high
497 deep silty clay northern plains high
525 deep silty loam southern plains high
730 deep sandy loam Nor. King #812
rainfcd non-limiting September 11 270
rainfed non-limiting September 15 180
rainfcd non-limiting September 25 125
rainfed non-limiting November 1 240
3.2 17
4.0 18
4.4 28
3.0 18
Mean annual temperature (“0 Mean annual precipitation Generic soil type cuhivar type Vernalization requirement hrigation Fertilizer Planting date Plant population (m-‘) Sowing depth (cm) Row spacing (cm)
(mm)
IOW
220
C. Rosenmvig,
Table 3 Simulated
and observed
F.N. Tubiello/A~riculturaI
mean yields and standard
and Forest Meteorology
deviations.
80 (1996) 215-230
Units are in kg ha-
’
Site
Observed
Simulated
R2
Fargo, ND North Platte, NE Dodge City, KS San Antonio, TX
2402 + 404 1863*685 1512k651 1237i409
3010~+1530b 1974a+1450b 1796af1372b 1569”f123.1b
0.41 0.40 0.49 0.72
a Simulated b Simulated
mean not significantly different from observed. variance significantly different from observed.
Observed and simulated wheat yields are compared in Table 3 and Fig. 2. Simulated and observed means were not significantly different (P > 0.05). In particular, CERESWheat was able to capture the observed gradient of wheat yields across the transect of study, predicting yields twice as high in Fargo, ND, as in San Antonio, TX, with Dodge
NORTH PLATTE, NEBRASKA 6000 5000
--
r-m-R2 = 0.40 1
DODGE CITY, KANSAS --
---6000 R2 = 0.49
Fig. 2. Comparison
of CERES-Wheat
-simulated
simulations
1
i
and county level data
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and Forest Meteorology
80 (1996) 215-230
221
City, KS, and North Platte, NE, showing intermediate values. Simulated standard deviations of wheat yield were significantly larger than observed (P < 0.001). This result was expected, as yield variations at any one location should be larger than those averaged over an entire county (i.e., local minimum and maximum yields tend to be ‘smoothed out’ by performing spatial averages). Finally, the correlations between model simulations and observed yields were in the range 0.4-0.7, with the best agreement at San Antonio, TX. The interannual variability of simulated and observed yields depended primarily on the amount of rainfall during the growing period.
5. Simulation
of wheat responses
in the central US
The effects of a baseline scenario (no temperature change) and four mean temperature increases (l-4°C) were analyzed at each site. For each mean temperature increase, we considered two temperature change scenarios: Scenario A incorporated equal increases in daytime maxima and nighttime minima; Scenario B had minima raised three times as much as the corresponding maxima. In addition, simulations were run under two atmospheric CO, concentrations: current (330 ppm), and elevated (550 ppm). The latter represents an equivalent doubling of atmospheric CO, concentration that takes into account the forcing of radiatively-active trace gases other than CO, (based on Hansen et al., 1988). Crop variables analyzed were: simulated grain yield; number and type of crop failure; above-ground biomass at harvest; length of the growing period; cumulative vemalization; average plant water stress after flowering; and average growth rate. The latter was calculated as a derived measure: total above-ground biomass divided by the length of the growing period.
6. Results 6.1. Temperature change Increasing mean temperatures by l-4°C caused yield reductions at each site (Fig. 3). Raising minima and maxima by an equal amount (Scenario A) resulted in larger reductions in grain yield compared to increasing minima three times as much as maxima (Scenario B). Yield decreases were in the range of 540% apart from San Antonio, TX, where yields dropped 20-80%. The coefficients of variation for yield increased under both temperature scenarios, with larger increases at San Antonio, TX (Fig. 4). Simulated responses to temperature at sites in the North-Central US (Fargo, ND, North Platte, NE, and Dodge City, KS) were significantly different from those at San Antonio, TX (Tables 4-7). Yield reductions at the three sites were caused primarily by the direct effects of mean temperature increase on wheat phenological development. By reducing the duration of stem and ear growth in particular, less carbohydrate reserves were available for grain-filling. Yields were thus significantly reduced, despite small positive effects of temperature on photosynthesis at North Platte, NE (Scenario A) and
222
C. Rosen,weig,
F.N. Tubielh /ApYcultural FARGO.
NORTH
and Forest Meteorology
NORTH
DAKOTA
NEBRASKA
-10
-10 -20
.z -20 t; 4 -30 e P -40 .s!
s n -30 g, p -40 E -50 5 g -60 x -70
PLATTE,
0
0
$
80 (1996) 215-230
s 0 2 t
330 ppm
-50 -60
330 ppm
-70
B
-80
-80 I
2 MEAN
4
3
TEMPERATURE
DODGE
GIN,
2
1
INCREASE
MEAN
3
TEMPERATURE
SAN
KANSAS
0
0
-10
-10
4 INCREASE
ANTONIO,
TEXAS
.E -20 t; : -30 e 9 -40 .z ,” -50 f 2 -60 t -70
330 ppm Ej
330 ppm IOSCENARlO]
1HSCENARI~ 61
-80
-80 1 MEAN
2 TEMPERATURE
3
4 INCREASE
1
2 MEAN
TEMPERATURE
3
4 INCREASE
Fig. 3. Simulated yield response to temperature change under current CO, concentration (330 ppm). Percent reduction is relative to yields simulated with baseline scenario (195 1- 1980 observed weather).
Dodge City, KS (Scenarios A and B), as indicated by the increase in average growth rates (Fig. 5). Although the length of the growing period was the same under both temperature scenarios, yields predicted for the three sites under Scenario B were 4-25% higher than those predicted under Scenario A. This was due to higher grain growth rates, which in CERES-Wheat are more sensitive to changes in temperature minima than maxima. Water stress increased only slightly at the three sites for the l-2°C temperature increase scenarios, and by about 25% (North Platte, NE) under the highest scenario. In Dodge City, KS, zero crop failures occurred in the base case as well as in all temperature change scenarios. Higher mean temperatures reduced the risk of crop failure due to winterkill in North Platte, NE. In Fargo, ND, mean temperature increases in the range of I-2°C slightly increased the risk of winterkill: warmer winter temperatures caused dehardening during the vegetative stage, thus lowering the wheat crop defenses against subsequent extreme cold events (see Table 1). Reductions in simulated yield in San Antonio, TX, were primarily caused by temperature effects on vernalization. This occurred despite the low vernalization require-
C. Rosenzweig.
F.N. Tubiello/Agricultural FARGO,
1
2
MEAN
2
MEAN
DAKOTA
3
TEMPERATURE
DODGE
1
NORTH
TEMPERATURE
and Forest Meteorology
NORTH
PLATTE,
NEBRASW
4
INCREASE
CITY,
223
80 (1996) 215-230
MEAN
TEMPERATURE
KANSAS
3
SAN
4
INCREASE
Fig. 4. Percent change in coefficient of variation current CO, concentration (330 ppm).
1
2
MEAN
of simulated
TEMPERATURE
INCREASE
ANTONIO,
3
TEXAS
4
INCREASE
yield as a function of temperaturechange under
ments of the simulated wheat cultivar, corresponding to the locally grown Nor. King #812 (see Table 2). Reduction of cumulative vernalization days caused, a longer duration of the vegetative growth phase from emergence to terminal spikelet formation (Table 7). This delay reduced the time available for subsequent grain-filling, resulting in high biomass production and low yields, as observed during warmer years in the San Antonio area (T. Miller, person1 communication, 1995). Under the largest mean temperature increases applied (3-4”C), the vegetative stage became so long (> 200 days) that in some years grain growth did not occur at all. As a result, the number of simulated crop failures increased significantly with temperature. The risk of crop failure was reduced when minimum temperatures were increased more than maxima, because wheat crops simulated under Scenario B were better vernalized than those grown under Scenario A due to lower maximum temperatures, and reached terminal spikelet formation earlier. Mean temperature increases depressed photosynthesis, as indicated by the decreases in the simulated growth rates, although total biomass production went up due to longer growing periods (Fig. 5, Table 7). Water stress at San Antonio did not change significantly under the l-2°C temperature increase scenarios, and rose only slightly under the 3-4°C scenarios.
224
C. Rosenrweig,
Table 4 Simulation Run Base + 1; + I; +2; i2; +3; +3; f4; +4;
A B A B A B A B
F.N. Tuhiell~~/A~riculturul
und Forest Meteorology
80 (1996) 215-230
results for Fargo, ND Yield (kgha-‘)
crop failures a
Biomass (grn-*)
E-Mb (days)
Vem ’
SWD * (%)
Growth rate ’ (gm-‘day-‘)
3010* 1530 2557 k 1508 2647k 1516 2100* 1279 2445 + 1377 1773*1130 2203+ 1315 1606k 1105 1977f 1260
0
1054+313 1007+353 1026i-353 921*357 loll+342 883~318 1015+316 844 + 306 944 * 337
306+8 303*s 303i8 299k8 299+8 29558 295k8 291 +8 291 k8
yes yes
52+31 52*31 54*31 55+29 52530 57&27 52+29 58527 51+30
3.4+ 3.4* 3.5+ .?.I* 3.4& 3.0 * 3.4+ 2.9 i 3.2*
yes yes yes yes yes
yes yes
1.0 I.1 1.1 I.2 1.1
I. I 1.1 1.O 1.1
a Due lo winterkill. b Length of growing period, emergence to maturity. ’ The required period for full vernalization (50 cumulative vernalization days) was always achieved. d Average water stress after flowering, defined as the ratio of actual versus potential transpiration. e Average growth rate, calculated as the ratio between biomass and length of growing period. Note: Average growth period length, vernalization, and growth rates refer to years without crop failures.
6.2. Elevated
CO, simulations
We investigated
possible
interactions
of temperature
effects
and CO,
fertilization
by
concentration. Table 8 shows our simulation results. The effects of elevated CO, alone (no temperature change) increased wheat yields 25 to 35% in the three North-Central sites, and about repeating
Table 5 Simulation
the previous
runs
under
elevated
atmospheric
CO,
(5.50 ppm)
results for North Platte, NE
Run
Yield (kgha-‘)
Base + 1; A +l;B +2; A +2; B +3;A +3; B +4; A +4; B
1974* 1450 18355 1427 1899* 1473 1631 Jo 1326 1883* 1490 1347* 1180 1671 f 1392 1058 f 1042 13865 1346
Crop failures a
Biomass (pm-*)
E-Mb (days)
695f371 673 f 354 710*367 639 Ifr322 722*371 586f300 728 f 347 559f 278 723 f 334
285f 13 281 f 12 281 f 12 277* 11 277rtll 273fll 273rtll 2681tll 268511
Vem ’
SWD d (%)
Growth rate e (gm-* day-‘)
41*31 44k28 43&29 47&29 45+29 48f31 47*31 511t29 50+30
2.5+ 2.4+ 2.5* 2.3 * 2.6* 2.2* 2.7i 2.1+ 2.7 f
1.3 1.3 1.3
1.2 1.4 1.1 1.3 1.1 1.3
a Due to winterkill. b Length of growing period, emergence to maturity. ’ The required period for full vernalization (50 cumulative vernalization days) was always achieved. d Average water stress after flowering, defined as the ratio of actual versus potential transpiration. ’ Average growth rate, calculated as the ratio between biomass and length of growing period. Note: Average growth period length, vernalization, and growth rates refer to years without crop failures.
C. Rosenzweig, Table 6 Simulation Run
Base + 1; A + 1; B +2; A +2; B +3;A +3; B f4; A +4; B
FN. Tubiello/Agricultural
and Forest Meteorology
80 (1996) 215-230
225
results for Dodge City, KS Yield (kg ha-
’1
1796k 1372 1684+ 1319 17s9* 1341 1438f 1137 1717+ 1338 1285tll50 154Ot1251 1222!c 1188 1465k 1320
E-Mb
crop failures a
Biomass (gmm2)
(days)
0 1 0 0 0 0 0 0 0
685 f 378 687+371 718k369 679 f 352 746k381 667 & 359 759 + 376 669 rt 373 771 f 396
251+9 246*9 2461t9 242f 10 2421t 10 238k 10 238 * 10 233 f 10 233f 10
Vem ’
yes yes yes yes yes yes yes yes yes
(%I
SWD ’
Growth rate ’ (gm-‘day‘)
54k28 57+26 56,26 60*26 57&26 62+28 61 t27 62&-29 60+29
2.75 2.s* 2.9& 2.8* 3.1* 2.8 k 3.25 2.85 3.3*
1.5 1.5 1.4 1.4 1.5 1.4 1.5 1.5 1.6
’ Due to winterkill. b Length of growing period, emergence to maturity. ’ The required period for full vernalization (50 cumulative vernalization days) was always achieved. d Average water stress after flowering, defined as the ratio of actual versus potential transpiration. ’ Average growth rate, calculated as the ratio between biomass and length of growing period. Note: Average growth period length, vernalization, and growth rates refer to years without crop failures.
20% in San Antonio, TX. As mean temperature increased, the beneficial effects of CO, progressively diminished until the negative effects of temperature became dominant in most cases (Fig. 6). As found in the simulations with current CO, levels, increasing temperature minima three times as much as maxima produced higher yields. In three specific cases (North Platte, NE, 3°C increase; Dodge City, KS, 3-4°C increase), Scenarios A and B differed in the direction of predicted yield changes.
Table 7 Simulation Run
Base + 1; A + 1; B +2; A +2; B +3; A +3; B +4; A +4; B
results for San Antonio, Yield (kgha-‘) 1569f 1233 1274k 1058 1306f 1067 958 f 846 1120+915 723 f 807 8011t781 376 f 576 552 f 742
Crop failures ’
TX E-Mb (pm-*)
(days)
923*448 9121t446 921 f452 933 f 621 932 * 524 941 f 942 987 f 933 1124+ 1095 1030*975
177* 10 179f 12 178* 12 187&11 186* 11 190+11 187*11 199* 11 195* 11
Vem ’
SWD d (%I
Growth rate e (gm -* day- ‘)
47.5 f 3.7 44.9 It 5.5 45.0 f 5.4 42.1 k6.1 42.5 f 5.9 39.7f5.5 40.3 f 5.6 35.7 f 3.9 36.4 zt 3.7
51*31 51*30 51*30 53+30 521t30 54*30 52zt30 5s+31 53f31
5.2 f 2.5 5.1 +2.5 5.2 f 2.5 4.8 f 2.5 5.0+ 2.6 4.45 2.6 4.6 f 2.6 3.9* 2.6 4.3 f 2.5
a Due to inability to reach terminal spikelet formation in < 200 days. b Length of growing period, emergence to maturity. ’ Cumulative vernalization days (Optimum vernalization is 50). d Average water stress after flowering, defmed as the ratio of actual versus potential transpiration. ’ Average growth rate, calculated as the ratio between biomass and length of growing period. Note: Average growth period length, vernalization, and growth rates refer to years without crop failures.
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NORTH PLATTE, NEBRASKA
FARGO, NORTH DAKOTA
=4.: z 9
80 (1996) 215-230
.K] 4-
330 ppm
1
1
BASE
1
2
3
MEAN TEMPERATURE
I
11
4
BABE
I 1
2
MEAN TEMPERATURE
INCREASE
DODGE CITY, KANSAS
$ 9
4.: 4-
4
INCREASE
SAN ANTONIO,
6 ‘5:
3
TEXAS
5.5 .
._I
330 ppm
BABE
1
1
2
MEAN TEMPERATURE
Fig. 5. Simulated
average
3
4
BABE
INCREASE
1
2
3
MEAN TEMPERATURE
growth rate as a function
of temperature
4
INCREASE
change under current CO,
concentration
(330 ppm).
Table 8 Simulated yields under elevated CO, (550 ppm) and temperature (330 ppm) runs are given for comparison
change
scenarios.
Results of current
Run
Fargo, ND (kgha-‘)
North Platte, NE (kgha-‘)
Dodge City, KS (kgha- ‘)
San Antonio, TX (kgha- ’)
Base (330 ppm) Base (550 ppm) +l; A +l;B +2; A +2; B +3; A +3;B +4; A +4; B
3014* 3687 f 3099 * 3225 * 2539k 2997k 21435 2648 f 1933f 2385 f
1974+ 1450 2541& 1744 2374F 1707 2436k 1745 2110+1643 2383 f 1779 1763f 1512 2089+ 1685 1371 f 1350 1712& 1641
1796* 2441& 2243 f 2327f 1887& 2167_t 1643 * 1896k 1535 f 1810f
1569k 1315 1874k 1505 1503+ 1277 1529+ 1304 1075*957 1267* 1103 801 f 785 888 i 850 442*613 65lk722
1530 1786 1784 1822 1544 1608 1357 1540 1332 1493
Note: Crop failures at each site were identical
to those simulated
1372 1763 1664 1695 1437 1632 1395 1533 1435 1606
under 330 ppm CO,.
CO,
C. Rosenzweig, F.N. Tubiello/Agricultural
and Forest Meteorology
FARGO, NORTH DAKOTA
20
20
t 5 0 5 % -20 ‘S z al -40 K = -60
z!l s 0 5 ?j -20 x E 0 40 e “p -60
550 ppm
227
215-230
NORTH PLATTE, NEBRASKA
40
40
80 (1996)
kiZ?Z] -80
-80 0
I
2
3
3
2
4
MEAN TEMPERATURE INCREASE
MEAN TEMPERATURE INCREASE
SAN ANTONIO, TEXAS
DODGE CITY, KANSAS
40
40
I
20
20 : c” O ” .a, -20 ” h E 0 -40 e x -60
1
0
4
$ 2
0
,” z -20 z 0 -40 e 8 -60
550 ppm
550 ppm I
-80 0
1
2
3
I 2
3
4
MEAN TEMPERATURE INCREASE
MEAN TEMPERATURE INCREASE
Fig. 6. Simulated yield response to temperature Percent reduction is relative to baseline simulated
1
0
4
change yields
and elevated
CO,
concentration
(550 ppm CO,).
(1951- 1980 observed weather, 330 ppm CO, ).
In the three North-Central sites, positive CO, effects offset mean temperature increases up to about 2°C. In Dodge City, KS, CO,-fertilization resulted in higher than baseline yields for all temperature increases under Scenario B. In San Antonio, TX, the negative effects of temperature consistently overcame the positive effects of CO, fertilization. This was due to the temperature effects on vernalization and development, as discussed above. At this site, yield reductions of 40% and higher occurred above 2”C, regardless of temperature increase scenario and CO, fertilization.
7. Discussion and conclusions In this study the main mechanisms controlling the simulated wheat responses to higher temperature were direct and indirect effects on wheat phenological development. At Fargo, ND, North Platte, NE, and Dodge City, KS, higher mean temperatures
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shortened the growth cycle; in San Antonio, TX, they increased the length of the vegetative phase through reduced vernalization, delaying the onset of the reproductive stage. The time available for grain-filling was thus decreased at all sites, limiting potential yield. Simulated grain production under climate change conditions was characterized by larger year-to-year variations than under the present climate. Within each mean temperature change, increasing minima three times as much as maxima led, at all sites, to higher yields compared to increasing minima and maxima equally. Some temperature effects on yield not included in our simulations could alter the observed differences between the two temperature scenarios considered. An increase in nighttime respiration rates could depress biomass production. In addition, higher minimum temperatures could extend the overwintering range of some insect pests. while favoring growth of some weeds (Patterson and Flint, 1990). Both of these effects could further limit yields in the scenarios with higher minimum temperature increase. Simulations with elevated CO, indicate that temperature change at the lower end of the IPCC range (l-2°C) could result in positive wheat yield changes over the US Great Plains. At the upper end of the IPCC range (3-4”C), the negative effects of temperature increase may offset the beneficial effects of elevated CO, concentrations, depending on location and temperature change scenario. These results are consistent with other studies investigating potential effects of climate change on the Great Plains (e.g., Rosenzweig, 1990; Easterling et al., 1992). It should be noted that future patterns of temperature change are uncertain. A recent climate modeling study suggests that asymmetric temperature changes of the magnitude currently observed could characterize the initial climate response to anthropogenic forcing; however, the magnitude of the temperature asymmetry is projected to diminish (Hansen et al., 1995). In this regard, the two temperature scenarios we considered for this study could be taken to represent upper (Scenario A) and lower (Scenario B) bounds to possible temperature effects on wheat yields in the Central US plains.
Acknowledgements We thank the two anonymous reviewers for helpful comments on the original manuscript. We thank Dr. Tom Karl of NOAA and Dr. George Kukla of Lamont-Doherty Earth Observatory for organizing the Workshop on the Current Changes of Minimum and Maximum Daily Temperatures, and Dr. Roy Jenne of NCAR for providing the weather data. This work was funded in part by NOAA, National Climatic Data Center.
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