ENSO Impacts on Crop Production in the Southeast US

Transcription

1 ENSO Impacts on Crop Production in the Southeast US J.W. Hansen*, J.W. Jones, A. Irmak, and F. Royce Presented at the ASA Symposium, Impacts of Climate Variability on Agriculture, October 1998, Baltimore, Maryland, USA. Pp in Impacts of El Niño and Climate Variability on Agriculture. ASA Special Publication no. 63. American Society of Agronomy, Madison, WI, USA J.W., Hansen, International Research Institute for Climate Prediction, Lamont-Doherty Earth Observatory, PO Box 1000, Palisades, NY ; J.W. Jones, F. Royce and A. Irmak, Dept. of Agricultural and Biological Engineering, Univ. of Florida, PO Box , Gainesville, FL Florida Agricultural Experiment Station, Journal Series No. R *Corresponding author.

2 ABSTRACT The El Niño-Southern Oscillation (ENSO) influences weather in the Southeast US in ways that impact agriculture. The predictability associated with ENSO suggests the opportunity to tailor decisions to expected climate conditions. We review the current knowledge of ENSO impacts on agriculture in the region, extend previous yield analyses to include the ten most important crops in eight states, examine yield impacts of two very strong El Niño events, and demonstrate an approach to tailoring crop management to ENSO phase. Response of ratios of historical yields to smoothed trends to preceding and subsequent ENSO phases were analyzed by ANOVA. Analyses included standardized yield ratios for all crops, and yield ratios for individual states and the regional average. Yields of cotton, tobacco, maize, tomato, hay, sugarcane and rice responded significantly to ENSO phase. Soybean, peanut and rice showed no response. Wheat, soybean, peanut and cotton yields were lower following very strong El Niño events than following other El Niño events. Optimal management strategies identified for wheat and maize in Tifton, Ga., using crop simulation models showed differences in planting date and N fertilizer use among ENSO phases. The estimated average value of optimal use of ENSO phase information was estimated at $4.41 and $7.00 ha -1 y -1 for wheat and maize. Results of this study provide benchmark levels of yield predictability and information value for comparing ENSO phases to other seasonal climate forecast systems. INTRODUCTION Agriculture has been characterized as the most weather-dependent of all human activities (Oram, 1989). It is also one of the most important sectors of the economy of the Southeast US, contributing about $33 billion in 1997 to the study region of Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina and Tennessee, with crop production (Table 1) valued at about $14 billion (USDA/NASS, 1997). The El Niño-Southern Oscillation, or ENSO, refers to shifts in sea surface temperatures (SST) in the eastern equatorial Pacific (El Niño in warm years, La Niña in cool years) and related shifts in barometric pressure gradients and wind patterns in the tropical Pacific (the Southern Oscillation) (Enfield, 1989). Two characteristics of ENSO -- its impacts and its predictability -- justify growing public interest and research investment. Although the ENSO phenomenon occurs within the tropical Pacific, its effects can be felt through much of the globe, where it can account for a substantial portion of the interannual variability of weather in many regions (e.g., Kiladis and Diaz, 1989; Ropelewski and Halpert 1986, 1987, 1996). The influence of ENSO-related weather variability on agriculture has been shown for cereal production in south Asia, Australia and the North American prairies (Garnett and Khandekar 1992); corn yields in Zimbabwe (Cane et al. 1994) and the US (Handler 1990; Carlson et al. 1996); the yields and combined value of several Australian crops (Nicholls 1985); wheat rust in the US and China (Scherm and Yang 1995); and U.S. soybean futures prices (Keppenne 1995). Recent years have seen substantial improvements in our understanding of interactions between the tropical oceans and the atmosphere, our ability to monitor both of these systems, and the speed and cost of computers. Together, these advances have made it possible to forecast ENSO events with useful skill at lead times of about one year (Barnston et al., 1994; Latif et al., 1994, 1998; Chen et al., 1995; National Research Council, 1996). Using either statistical measures of the historical association between measures of ENSO and relevant weather variables (e.g., Stone et al., 1996), or numerical atmospheric 1

3 models coupled with ocean models (e.g., Barnett et al., 1994), ENSO-based forecasts can now provide useful climate information in many regions with useful skill at lead times of several months. Many critical agricultural decisions from farm to policy level have complex interactions with weather conditions, but must be made several months or seasons before the impacts of weather are realized. The emerging capacity to predict ENSO events and their influence on regional climates at seasonal lead times suggests an unprecedented opportunity to tailor agricultural decisions to anticipated weather conditions. Where influence of ENSO on agricultural production can be demonstrated, the ENSO prediction may be used to either mitigate the impacts of adverse conditions or take advantage of favorable conditions. This study has three objectives. First, we review what is already known about agricultural responses to ENSO-related weather variability in the Southeast US. Our second objective is to extend the spatial scope and range of crops included in previous statistical analyses of historical agricultural data (Hansen et al., 1998a). These analyses provide an objective basis for identifying the enterprises and locations that are most vulnerable to ENSO-related weather variability, and therefore most likely to benefit from ENSO-based climate prediction. We also examine impacts of the very strong and El Niño events on agriculture in the region. Finally, we present a case study that illustrates the potential for tailoring management to each ENSO phase. WHAT WE ALREADY KNOW Influence on Weather In the Southeast US, El Niño events are characterized by lower winter temperatures (Green et al., 1997). Precipitation tends to be higher in the Gulf Coast states in the winter, and throughout the region by spring. In the summer, climate impacts of El Niño events are more localized, including drier conditions along the Atlantic Coast and from North Texas to northern Alabama. Evidence indicates that regional climate anomalies associated with strong El Niño events (e.g., ) are not simply amplifications of normal El Niño conditions (Rosenberg et al., 1997). With some exceptions, La Niña events show the reverse of the climate anomalies associated with El Niño events. They include above-average temperatures east of the Mississippi River in the winter months, and in Georgia, northern Florida, and South Carolina in the spring. Positive precipitation anomalies associated with La Niña events occur in a band from northern Mississippi to Southwest Pennsylvania in the winter, and in the Gulf Coast region in the spring. Effects in the subsequent summer are weaker and spatially more variable, and include reduced temperatures in the extreme South and reduced precipitation in parts of Louisiana. Shifts in rainfall patterns associated with ENSO have secondary effects that may be relevant to agriculture. For example, increased rainfall in the Florida peninsula during the fall (OND) and winter (JFM) of El Niño events is associated with reduced solar irradiance (Hansen et al., 1999) the energy source for photosynthesis. The demonstrated lagged effect of ENSO-related precipitation anomalies on streamflow in the region (Kahya and Dracup, 1993; Zorn and Waylen, 1996) could have implications for allocation of limited water resources for irrigation. Examination of shifts in mean climatic conditions can overlook possible influences of ENSO on the vulnerability of agriculture in the Southeast region to extreme meteorological events such as freezes and hurricanes. The vast majority of hurricanes reaching land in the US do so in the southeastern states considered in this study. El Niño conditions significantly reduce Atlantic hurricane landfall frequency in the US whereas La Niña conditions do not appear to influence Atlantic hurricane frequency (Gray, 1984; 2

4 O'Brien et al, 1996). Florida's highly profitable citrus and fresh winter vegetable industries are particularly vulnerable to low temperature extremes. Analyses of historical data have not shown a significant association between ENSO and low temperature extremes in Florida (Hansen et al., 1999) or the Southeast (Gershunov and Barnett, 1998), or agriculturally-important freeze events in Florida (Downton and Miller, 1993). Influence on Agriculture Awareness of the timing of ENSO events relative to crop growing seasons is important for understanding published analyses of ENSO's influence on agriculture. Although the timing of ENSO events can vary among years, sea surface temperature anomalies associated with El Niño or La Niña conditions typically peak between October and December (Fig. 1). Their influence on the climate of the Southeast US is usually greatest from October to March. However, the summer crop growing season in the Southeast US roughly spans March to October. This suggests that the summer field crops that account for most of the value of crop production in the region could be influenced by either the preceding or the subsequent ENSO phase; both must be considered. Handler (1990) examined linear correlation between ENSO-related SST anomalies in the eastern Pacific at varying lag times and state average maize yields throughout the continental US. Yields in much of the Southeast (Florida, South Carolina, Georgia, Alabama and Tennessee) and northward to the Great Lakes were negatively correlated (r < -0.35) with SSTs for the winter (DJF) preceding the growing season. Sea surface temperatures for the subsequent autumn (SON) were positively correlated (r > 0.35) with maize yields only in Florida and the corn belt states of the Upper Midwest. To identify which areas and crops are most likely to benefit from application of ENSO-based climate forecasts, we examined the historical ( ) response of total value and its components (yield, area harvested and price) to ENSO phases and ENSO-related sea surface temperature anomalies for six crops (peanut, tomato, cotton, tobacco, maize and soybean) in four states (Alabama, Florida, Georgia and South Carolina) (Hansen et al., 1998a). We evaluated and used a harmonic smoothing technique to account for trends in technology, then analyzed anomalies relative to the trend using ANOVA and canonical correlation analysis. Results showed that ENSO has significantly influenced maize and tobacco yields, areas of soybean and cotton harvested, and total values of corn, soybean, peanut and tobacco in Alabama, Florida, Georgia and South Carolina. Yields of these crops tended to be higher than the trend in the summer growing season after La Niña winters. ENSO phases explained an average shift of $212 million, or 26% of the long-term average, inflation-adjusted value of field corn production, and $133 million, or 18% for soybean. Using similar techniques, we conducted a more thorough assessment of the vulnerability of Florida crops to ENSO-related weather variability (Hansen et al., 1998b, 1999). Average winter harvest season yields of Florida tomato, bell pepper, sweet corn, and snap beans have been lower in El Niño than in neutral or La Niña years. The average yield decrease was 28% of the long-term average for tomato, 31% for bell pepper, 27% for sweet corn, and 18% for snap bean. Prices averaged 31% higher for bell pepper and 21% higher for snap beans in El Niño than in neutral or La Niña winter harvest seasons. The total value of Florida tomatoes averaged $26 million (adjusted for inflation to a 1995 basis), or 22%, higher in La Niña than in neutral or El Niño winters. Sugarcane ( ) yield anomalies averaged 5.5 Mg of cane ha -1, or 7.4% of the long-term mean, higher in the harvest periods following La Niña than following neutral or El Niño years. This may be due in part to the beneficial effects of drier conditions in La Niña winters on plant stand at the time of establishment of either a planted or ratoon crop. As with sugarcane, 3

5 the influence of ENSO on citrus is seen in the harvest a year after the ENSO event. Average yields of all grapefruit, and seeded varieties in particular, and tangerines have been higher following El Niño than following neutral or La Niña years; limes showed the opposite response (i.e., low yields following El Niño events). Oranges did not show a statistically significant yield response to ENSO. Statistical analyses showed inconclusive evidence that average yields of Florida field corn planted in El Niño years have tended to be lower than when planted in neutral or La Niña years. Florida peanut and soybean did not show a significant response to ENSO. Our analyses suggest that ENSO does not influence the probability of agriculturally-important freezes in Florida in a consistent or predictable manner. CROP RESPONSE TO ENSO Approach The current study extends previous studies of crop yield response to ENSO phase in three ways. First, we expand the geographical scope to include an eight-state region (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina and Tennessee). Second, we extend the set of crops to include the ten most valuable crops in the region (Table 1) other than Florida oranges, which have been studied previously (Hansen et al., 1998b). Our third enhancement is to examine the hypothesis that very strong El Niño events affect yields differently than weak-to-moderate events. Data ENSO. This study used the Japan Meteorological Agency s classification of ENSO phases (Sittel, 1994; Trenberth, 1997) based on five-month running means of sea surface temperature (SST) anomalies averaged over the tropical pacific (4 N to 4 S and 150 to 90 W) (D. Legler, personal communication, 1998). A year (previous October to current September) is classified as El Niño (La Niña) if SST anomalies are at least 0.5 C (-0.5 C) for at least six consecutive months starting before October and extending through December. The period from 1965 to 1999 includes 9 El Niño (1966, 1970, 1973, 1977, 1983, 1987, 1988, 1992 and 1998), and 8 La Niña events (1965, 1968, 1971, 1972, 1974, 1976, 1989 and 1999). Crops. The USDA/NASS provided historical state-average crop yields through 1997 and 1 August estimates of 1998 yields of field crops. Regional average yields were calculated from production and harvested area totals among the states. The period of our analyses to 1997 plus 1998 estimates where available -- is a compromise between the greater confidence in results that include more ENSO events in a longer series, and the greater relevance of results for the more recent period. Changes in the interannual variability of yields of several crops in the 1960s (maize, wheat and tobacco) and 1970s (cotton, soybean, hay and peanut) (Fig. 2) suggest possible changes in technology that could limit the relevance of earlier results. Higher-frequency anomalies attributed primarily to weather variability were separated from lower-frequency trends attributed to changes in technology and management. The harmonic smoothing technique used (Press et al. 1989) applies a low-pass filter covering a specified period to detrended, Fourier-transformed data. Each crop data series was smoothed with a filtering period of 7.0 years (i.e., frequency of yr 1 ) (Hansen et al. 1998a,b, 1999). For most of the crops, the variability about the smoothed trends tended to increase with increasing yields (Fig. 2). We therefore analyzed ratios of observed to smoothed values rather than anomalies to avoid giving excessive weight to periods of higher variability. Analyses 4

6 For each crop, we applied analysis of variance (ANOVA) to hypothesized influences of ENSO phases on yield averages for the states that reported production. Dividing the series of yield ratios among the three ENSO phases results in a completely randomized sampling design within individual states or the region average, and a completely randomized factorial design for the set of states. We tested the influence of both the preceding and subsequent ENSO phase on yields of summer field crops because the summer growing season overlaps the possible period of influence of both (Fig. 1). The first step was to analyze effects of ENSO phase and state on yield ratios standardized within each state that reported yields. Because standardized values have zero mean and unit standard deviation, a state main effect would have no meaning; only the ENSO phase effect and its interaction with state were considered. If ANOVA showed either a significant ENSO phase effect or significant interaction with state, we examined the influence of ENSO phase for each individual state and for yields averaged across the Southeast region. When response to ENSO phase was significant, Duncan s multiple range test identified which ENSO phases differed significantly (P < 0.05) in their influence. Serious departures from normality and heterogeneity of variances can distort ANOVA results, particularly if standard deviations are correlated with means among ENSO phases. Because such violations of the assumptions of ANOVA can occur in historical crop yield records, we supplemented ANOVA of ENSO phase effects for particular states with two nonparametric tests. The Kruskal-Wallis (1952) method applies ANOVA to test the hypothesis that the ranks, rather than the yield ratios themselves, are influenced by ENSO phase. The Mann-Whitney (1947) U test is the most sensitive nonparametric test of differences between a pair of independent samples. It tests the hypothesis that mean ranks of yield ratios differ between El Niño and La Niña events. Subdividing yield ratios for El Niño years between weak-to-moderate events and the very strong and events allows us to test the hypothesis that very strong El Niño conditions affect yields differently than weak or moderate conditions. We applied a t-test and the Mann-Whitney U test for differences in response of region average crop yield to El Niño type. Sensitivity to differences is low due to the small numbers of El Niño events (seven and two) of each type. We also applied an ANOVA of El Niño type for the set of states in a factorial sampling design. Replication among states increases the sensitivity of ANOVA. Response to ENSO Phases Results and Discussion Table 2 summarizes responses of standardized yield ratios of each crop to preceding and subsequent ENSO phase for the set of states in the study region that reported yields. Tomato, tobacco and grass hay responded significantly to the preceding ENSO phase, cotton and sugarcane to the subsequent phase, and maize and wheat to both the preceding and subsequent ENSO phases. Only the grain legumes (soybean and peanut) and rice showed no evidence of response to ENSO. Yield responses did not differ enough among states for ANOVA to detect any ENSO phase by state interaction. Of the crops included in this study, maize showed the strongest response to ENSO. Statistical tests showed some evidence that the preceding ENSO phase influenced yields in five of the states and average yields across the region (Table 3). However, in four of those states, only the Mann-Whitney U test showed significant ENSO influence. Mann-Whitney showed greater sensitivity than the other tests because it compared only the El Niño and La Niña extremes. In all cases, yields tended to be high following La Niña and low following El Niño events. Yields showed the opposite response to the 5

7 subsequent ENSO phase: low before La Niña and high before El Niño events. However, statistical tests did not identify significant yield responses to the subsequent ENSO phase in any particular state or in the series of regional averages even though the set of standardized state-level maize yield ratios responded significantly to the subsequent ENSO phase (Table 2). For southern Georgia, Hansen et al. (1998a) attributed maize yield response to ENSO primarily to enhanced June precipitation in La Niña years, coinciding with tasseling when maize is most susceptible to water stress. ENSO has little discernable influence on total summer precipitation in the area of southern Georgia and Alabama and northern Florida. However, the onset and peak of summer precipitation shifts to earlier in the season, reducing the probability of damaging water stress at tasseling and early grain fill (Fig. 5 and J.W. Hansen, unpublished data, 1999). Winter wheat is grown during the period of strong ENSO activity, and showed a significant response to both the current and subsequent phase (Table 2). Regional average yield ratios and yield ratios in Mississippi responded significantly to the current ENSO phase (Table 4). The regional average and each state series showed a tendency for higher yields during El Niño years, and little difference between neutral and El Niño years. The response of the set of standardized state-level yield ratios to the subsequent ENSO phase is surprising considering that wheat is harvested several months before shifts in Pacific SSTs associated with ENSO events are normally detectable. Sea surface temperatures in the eastern tropical Pacific exhibit a negative one-year-lag autocorrelation. The significant but opposite direction of response of wheat to the subsequent ENSO phase probably reflects the resulting tendency for El Niño years to be followed by La Niña years. For example, five of the 10 El Niño events from 1964 to 1998 were followed by La Niña events. This compares to an expected frequency of 2.0 if the ENSO events are independent. Although the set of standardized tomato yield ratios for all eight states responded significantly to the preceding ENSO phase (Table 2), the only individual states that showed significant yield responses were South Carolina and Tennessee (Table 5). Yields tended to be lower following El Niño events, while the ranking of response to neutral and La Niña events was not consistent. Previous work showed that seasonal differences can mask the strong influence of ENSO on Florida tomato yields in the winter harvest season (Hansen et al., 1999). Cotton yields responded to the subsequent rather than the preceding ENSO phase. Yields tended to be higher before El Niño events. Losses likely result from cotton s sensitivity to excessive water late in the growing season. Only the Mann-Whitney U test, which does not consider neutral years, identified a significant cotton yield response in Florida (Table 6). Climate variability can potentially influence sugarcane either during the preceding ENSO period when the planted or ratooned crop is established, or during the subsequent ENSO period when ripening and harvest occur. ANOVA identified effects of only the subsequent ENSO phase on yields (Table 2). Only two states in the region report sugarcane production. Statistical tests identified yield responses to the subsequent ENSO phase in the regional average and in Florida (Table 7). Florida accounts for the majority of production and, therefore, for the response of regional average yield to ENSO. Although standardized state-level yield ratios of tobacco and hay responded to the preceding ENSO phase (Table 2), neither showed significant yield responses to ENSO phase in individual states. Their tendencies toward decreased yields following El Niño events (Tables 8, 9) were consistent with those of maize (Table 3) and tomato (Table 5). 6

8 Response to Very Strong El Niño Events ANOVA showed significant differences in standardized yield ratios for the set of states included in the study due to the type of El Niño for seven of the nine crops (Table 10). ANOVA of regional average yields showed significant differences due to El Niño type for five crops. Wheat showed a strong yield enhancement during weak-to-moderate El Niño events, but yields similar to the neutral-year median during very strong El Niño events (Fig. 3a). Wheat normally benefits from enhanced winter precipitation during El Niño. However, the strong events, particularly , brought excessive winter precipitation. The three summer crops that responded to the preceding El Niño type showed suppressed yields following very strong El Niño events (Fig. 3b-d). A drought and heat wave in the Gulf Coast states during late spring and early summer of 1998 apparently accounts for much of the observed negative response of these crops to very strong El Niño events. Tobacco showed enhanced yields the summers before strong El Niño events (Fig. 3e). Analysis of three ENSO phases overlooks the qualitatively-different effect of very strong El Niño events. Attempts at applying ENSO information to predictions and improved management should distinguish between normal and very strong El Niño events. However, the two events considered in this analysis may not be sufficient to make confident predictions about impacts of future strong El Niño events. TAILORING CROP MANAGEMENT TO ENSO PHASES A Crop Management Optimizer Although the combined use of crop models and optimization algorithms to identify optimal management has been considered for some time (Dent and Blackie, 1979), practical difficulties have limited such use. The multidimensional response surfaces of dynamic simulation models to management variables can have discontinuities and multiple local optima that often lead gradient and direct search algorithms to incorrect results (Grimm et al., 1993; Mayer et al., 1996). Varying dates of planting and fertilizer applications in particular can generate many local yield optima. Simulated annealing is a class of stochastic optimization algorithms that have proven reliable in identifying global optima on a variety of poorly-behaved nonlinear systems (Goffe et al., 1994; Press et al., 1989; Ingber 1996), including agricultural simulation models (Mayer et al., 1996, 1998). We have linked adaptive simulated annealing (Ingber 1996) with the DSSAT family of crop models (Jones et al., 1998) to identify the combination of management variables that maximizes either the mean or expected utility of net returns for weather data grouped by ENSO phase. The optimization algorithm handles categorical, discrete and continuous decision variables, and supports constraints and variable step sizes for cardinal decision variables. The resulting optimizer can simultaneously optimize up to ten management variables: cultivar, planting date, the amount and timing of three nitrogen fertilizer applications, row spacing and plant density. Although the use of common data standards and formats allows the optimizer to work with all of the DSSAT crop models, it has been tested extensively primarily with the CERES-Maize model for locations in the Pampas region of Argentina (Royce et al., 1998). Farmers in this region are currently testing optimal maize management strategies identified for each ENSO phase on their farms. The CERES model for wheat and maize was used for the present study. Once optimal crop management is identified for each category of years at a given location, the potential value of optimal use of ENSO information can be evaluated as the mean difference between returns to management optimized for each ENSO phase in a time series and returns to management optimized for 7

9 all weather data in the series: where N is the number of years of weather data, n i is the number of years in phase i, A * j,i is net return in the jth year of phase i given optimal management for phase i, and A * k,a is net return in the kth year of the series given optimal management for the set of all years. Approach To illustrate one potential application of information about ENSO impacts on crops, we simulated yields and identified optimal management of maize and winter wheat in Tifton, Georgia for the set of all years and for each ENSO phase from 1923 to Although our analyses suggest that the difference between normal and very strong El Niño events might be important from a management perspective, we optimized for all El Niño events because of the small number of very strong El Niño events in the record. The climate and agricultural systems of Tifton are representative of much of the study region. We selected wheat and maize to contrast ENSO signals in the winter and summer growing seasons, and because the preceding analyses of historical data showed evidence of regional ENSO influence on these two crops. Data and assumptions Management. The management variables optimized included planting date, the amount of N applied at planting, and the amount and date of a second N application (Table 11). Two regionally-adapted cultivars were considered for maize: 'Pioneer 3382' and the longer-season 'McCurdy 84aa.' Recent changes to CERES-Wheat require recalculation of genetic coefficients. Because U.S. wheat cultivars had not been recalibrated at the time of analysis, we used a cultivar, 'Oasis,' that is adapted to similar latitudes and management in Argentina, and that has been calibrated based for use with CERES-Wheat based on field experiments (M. Travasso, INTA, 1998, personal communication). The initial values in Table 11 and the cultivars Oasis (wheat) and 'Pioneer 3382' (maize) represent typical management scenarios and were also used as a basis for simulations used to test hypothesized influence of ENSO on simulated yields of the two crops. All simulations assumed rainfed production, and planting densities of 400 m -2 at 15 cm row spacing for wheat and 7.1 m -2 at 50 cm row spacing for maize. Weather and climate. Daily temperature and precipitation data were obtained from the NCDC Summary of the Day data base (EarthInfo, 1996) for 1922 to 1990, and from the Georgia Automated Environmental Monitoring Network for 1991 to Solar irradiance for was used to parameterize a stochastic weather generator (Hansen, 1999) that generated daily solar irradiances for the earlier period conditioned on the observed temperatures and occurrence of precipitation. The classification of ENSO years was extended to include the period prior to the start of the Japan Meteorological Agency SST records in 1949 (D. Legler, personal communication, 1998) from spatially averaged monthly mean Pacific SST fields reconstructed from available data ( ) using an orthogonal projection technique (Meyers et al., 1999). The period from 1922 to 1997 includes 15 El Niño (1926, 1930, 1931, 1941, 1952, 1958, 1964, 1966, 1970, 1973, 1977, 1983, 1987, 1988 and 1992) 8

10 and 16 La Niña events (1923, 1925, 1939, 1943, 1945, 1950, 1955, 1956, 1957, 1965, 1968, 1971, 1972, 1974, 1976 and 1989). Soil. The soil used for the simulations was a Faceville sandy loam (Clayey, kaolinitic thermic, Typic Paleudults) with a 130 cm deep root zone. Simulation runs were initialized with 30% relative water content on 20 October for wheat, and 40% relative water content on 1 January for maize. Costs and prices. The estimated variable costs of production, excluding N fertilizer, were $304 ha -1 for wheat (Lee et al., 1996) and $365 ha -1 for maize (1996 estimate, USDA/NASS, unpublished data, 1998, We used a 1997 cost of $0.63 kg -1 for urea N and 1997 grain prices of $118 Mg -1 for wheat and $114 Mg -1 for maize (USDA/NASS, unpublished data, 1998). Results and Discussion Simulated yields of wheat and maize responded significantly to ENSO phase (Fig. 4). Under typical management scenarios, ANOVA identified significant response to the current ENSO phase for simulated winter wheat yields (P < 0.05) and the preceding phase for maize (P < 0.01). The direction of simulated yield response is consistent with observed responses (Tables 3 and 4). The optimal strategies identified for wheat includes later planting, less total N fertilizer, and a higher proportion of N applied at planting in La Niña years, and earlier planting in El Niño years relative to all years (Table 12). The optimal strategy for neutral years was similar to the strategy optimized for all years. Reduced precipitation during grain fill, but enhanced rainfall near harvest (May) tend to reduce wheat yields, thereby reducing optimal N amounts. In contrast to wheat, the optimal strategy for maize following La Niña events included earlier planting. The optimal planting date for El Niño years fell between the optimal values for La Niña and neutral years, and matched the optimal values identified for all years. Optimal maize strategies always included 'Pioneer 3382' rather than the longer-season 'McCurdy 84aa.' The earlier planting date for maize following La Niña events can be explained by enhanced precipitation from May to July, and reduced precipitation in August (Fig. 5). The optimal planting dates result in tasseling in mid June for the La Niña phase and early to mid July for the other groups of years. Differences among ENSO phases in optimal N amounts were small for maize. Although the estimated average value of optimal use of ENSO phase information for wheat production was modest, the estimated value was much higher for La Niña years (Table 13). Much of the benefit during La Niña events was apparently due to the reduced cost of N fertilizer. Maize also showed most of the financial benefit of optimal use of ENSO phase information associated with La Niña years, when the value was quite high ($26 ha -1 ). Simulated yields and net returns were lower for maize following neutral than following either La Niña or El Niño events. The higher potential information value for maize than for wheat can be attributed largely to its higher yields. IMPLICATIONS ENSO phases have measurable effects on yields of seven of the 10 most important crops (excluding Florida oranges) to the economy of the Southeast US. These effects explain and, by extrapolation, predict large impacts on the economy of the region. For example, with prices and areas harvested held constant, the 8.1% mean increase in regional average maize yields following La Niña events (Table 3) would have been worth $83.5 million in The 9.0% yield reduction following El Niño events would cost $92.7 million under these assumptions. For wheat, the regional average yield responses to 9

11 ENSO (Table 4) would translate to a gain of $21.7 million during El Niño and a loss of $17.3 million during La Niña events. However, the economic impact of ENSO is not this simple. ENSO can influence the price of a crop by influencing aggregate production in the various regions that produce that crop (Keppene, 1995; Hansen et al., 1999). Price expectations can, in turn, influence the use of production inputs and the intensity of management. Indirect influences of ENSO on prices could therefore potentially either offset or enhance its effects on yields. Simulation analyses suggest that farmers can improve their income by tailoring crop management to ENSO phases. If the results (Table 13) could be extended to the entire 1,292,000 ha of maize and 822,000 ha of wheat harvested in the Southeast region in 1997, management optimized for each ENSO phase would be worth an average of $9.0 million y -1 for maize and $3.6 million y -1 for wheat. Further research is needed before strategies derived from simulation and optimization can be recommended and adopted with confidence. Such research should include both traditional and farmer-managed field trials of model-based strategies conducted for multiple years. Farmers might also benefit from adjusting land allocation among crops based on ENSO phase (Messina et al., 1999). ENSO phases are perhaps the simplest and most widely-available form of seasonal climate forecast. Current efforts and future expectations focus on dynamic coupled ocean-atmospheric models. The yield response and management optimization results presented here can serve as benchmarks for comparing other climate prediction models. A climate prediction system cannot be recommended for routine use in a particular region unless hindcast analysis can show higher predictability of yields and value of optimal use of information than those associated with ENSO phases at adequate lead times. REFERENCES Barnett, T.P., L. Beingtsson, K. Arpe, M. Flügel, N. Graham, M. Latif, J. Ritchie, E. Roeckner, U. Schlese, U. Schulzweida, and M. Tyree Forecasting global ENSO-related climate anomalies. Tellus 46A: Barnston, A.G., H.M. van den Dool, S.E. Zebiak, T.P. Barnett, M. Ji, D.R. Rodenhuis, M.A. Cane, A. Leetmaa, N.E. Graham, C.R. Ropelewski, V.E. Kousky, E.A. O'Lenic, and R.E. Livezey Long-lead seasonal forecasts -- where do we stand? Bull. Am. Meteor. Soc. 75: Cane, M.A., G. Eshel, and R.W. Buckland Forecasting Zimbabwean maize yield using eastern equatorial Pacific sea surface temperature. Nature 370: Carlson, R.E., D.P. Todey, and S.E. Taylor Midwestern corn yield and weather in relation to extremes of the southern oscillation. J. Prod. Agric. 9: Chen, D., S.E. Zebiak, A.J. Busalacchi, and M.A. Cane An improved procedure for El Niño forecasting: Implications for predictability. Science 269: Dent, J.B. and M.J. Blackie Systems simulation in agriculture. Applied Science Publ., London. Downton, M.W. and K.A. Miller The freeze risk to Florida citrus. Part II: Temperature variability and circulation patterns. J. Climate 6: EarthInfo Database guide for EarthInfo CD 2 NCDC Summary of the Day. EarthInfo, Inc., Boulder, CO. Enfield, D.B El Niño, past and present. Rev. Geophysics 27: Garnett, E.R. and M.L. Khandekar The impact of large-scale atmospheric circulations and anomalies on Indian monsoon droughts and floods and on world grain yields a statistical analysis. Agric. For. Meteorol. 61:

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14 Table 1. Value of crop production ($million) in the Southeast US, Crop AL FL GA LA MS NC SC TN SE %US Field crops Cotton , Tobacco , , Soybean , Maize , Hay Sugarcane Peanut Rice Wheat Sorghum ALL ,610 1,467 1,493 2, ,225 10, Fruit and nuts Orange 0.0 1, , Grapefruit Pecan Tangerine Peach ALL , , Vegetables Tomato Bell pepper Strawberry Sweet corn Watermelon Snap bean Cucumber ALL , , ALL CROPS 576 3,793 2,051 1,490 1,498 2, ,260 13,

15 Table 2. Regional response of standardized crop yields to preceding and subsequent ENSO phase by ANOVA, , ENSO phase state factorial design. Crop Preceding ENSO phase Phase state Subsequent ENSO phase Phase state Cotton n.s. n.s. * n.s. Tobacco * n.s. n.s. n.s. Soybean n.s. n.s. n.s. n.s. Maize *** n.s. *** n.s. Tomato ** n.s. n.s. n.s. Grass hay * n.s. n.s. n.s. Sugarcane n.s. n.s. ** n.s. Peanut n.s. n.s. n.s. n.s. Rice n.s. n.s. n.s. n.s. Wheat *** n.s. ** n.s. *, **, *** Significant at the 0.05, 0.01, probability levels. 14

16 Table 3. Maize yield response to ENSO phase, ENSO phase effect Mean percent yield increase State A. V. K-W M-W La Niña Neutral El Niño Preceding ENSO Phase SE * * * 8.1 a 1.4 ab -9.0 b AL n.s. n.s. * 10.2 a 1.3 ab b FL ** * ** 9.8 a 1.1 a -9.8 b GA n.s. n.s. * 11.3 a 0.7 ab b LA n.s. n.s. n.s MS n.s. n.s. n.s NC n.s. n.s. * 6.3 a 2.4 ab b SC n.s. n.s. * 11.8 a 2.6 ab b TN n.s. n.s. n.s Subsequent ENSO Phase SE n.s. n.s. n.s AL n.s. n.s. n.s FL n.s. n.s. n.s GA n.s. n.s. n.s LA n.s. n.s. n.s MS n.s. n.s. n.s NC n.s. n.s. n.s SC n.s. n.s. n.s TN n.s. n.s. n.s *, **, *** Significant at the 0.05, 0.01, probability levels. Analysis of variance (ANOVA). Kruskal-Wallis nonparametric ANOVA. Mann-Whitney U test. Means of ENSO phases with no common letters differ significantly at the 0.05 probability level. 15

17 Table 4. Wheat yield response to ENSO phase, ENSO phase effect Mean percent yield increase State A. V. K-W M-W La Niña Neutral El Niño Current ENSO Phase SE ** ** ** -5.5 a -1.3 a 6.9 b AL n.s. n.s. n.s GA n.s. n.s. n.s LA n.s. n.s. n.s MS * * n.s a -4.3 a 10.4 b NC n.s. n.s. n.s SC n.s. n.s. n.s TN n.s. n.s. n.s Subsequent ENSO Phase SE n.s. n.s. n.s AL n.s. n.s. n.s GA n.s. n.s. n.s LA n.s. n.s. n.s MS n.s. n.s. n.s NC n.s. n.s. n.s SC n.s. n.s. n.s TN n.s. n.s. n.s *, **, *** Significant at the 0.05, 0.01, probability levels. Analysis of variance (ANOVA). Kruskal-Wallis nonparametric ANOVA. Mann-Whitney U test. Means of ENSO phases with no common letters differ significantly at the 0.05 probability level. 16

18 Table 5. Tomato yield response to preceding ENSO phase, ENSO phase effect Mean percent yield increase State A. V. K-W M-W La Niña Neutral El Niño SE n.s. n.s. n.s AL n.s. n.s. n.s FL n.s. n.s. n.s GA n.s. n.s. n.s NC n.s. n.s. n.s SC * * * 6.1 a 1.3 ab -8.3 b TN n.s. * * 1.9 ab 3.6 a -9.8 b *, **, *** Significant at the 0.05, 0.01, probability levels. Analysis of variance (ANOVA). Kruskal-Wallis nonparametric ANOVA. Mann-Whitney U test. Means of ENSO phases with no common letters differ significantly at the 0.05 probability level. Table 6. Cotton yield response to subsequent ENSO phase, ENSO phase effect Mean percent yield increase State A. V. K-W M-W La Niña Neutral El Niño SE n.s. n.s. n.s AL n.s. n.s. n.s FL n.s. n.s. * -7.3 a -0.3 ab 6.3 b GA n.s. n.s. n.s LA n.s. n.s. n.s MS n.s. n.s. n.s NC n.s. n.s. n.s SC n.s. n.s. n.s TN n.s. n.s. n.s *, **, *** Significant at the 0.05, 0.01, probability levels. Analysis of variance (ANOVA). Kruskal-Wallis nonparametric ANOVA. Mann-Whitney U test. Means of ENSO phases with no common letters differ significantly at the 0.05 probability level. 17