Negative Degree-days Research Articles

Climate Warming Impacts on Distributions of Scots Pine (Pinus sylvestris L.) Seed Zones and Seed Mass across Russia in the 21st Century

Research highlights: We investigated bioclimatic relationships between Scots pine seed mass and seed zones/climatypes across its range in Russia using extensive published data to predict seed zones and seed mass distributions in a changing climate and to reveal ecological and genetic components in the seed mass variation using our 40-year common garden trial data. Introduction: seed productivity issues of the major Siberian conifers in Asian Russia become especially relevant nowadays in order to compensate for significant forest losses due to various disturbances during the 20th and current centuries. Our goals were to construct bioclimatic models that predict the seed mass of major Siberian conifers (Scots pine, one of the major Siberian conifers) in a warming climate during the current century. Methods: Multi-year seed mass data were derived from the literature and were collected during field work. Climate data (January and July data and annual precipitation) were derived from published reference books on climate and climatic websites. Our multiple regression bioclimatic models were constructed based on the climatic indices of growing degree days > 5 °C, negative degree days < 0 °C, and annual moisture index, which were calculated from January and July temperatures and annual precipitation for both contemporary and future climates. The future 2080 (2070–2100) January and July temperatures and annual precipitation anomalies were derived from the ensemble of twenty CMIP5 (the Coupled Model Intercomparison Project, Phase 5) global circulation models (GCMs) and two scenarios using a mild RCP (Representative Concentration Pathway) 2.6 scenario and an extreme RCP 8.5 scenario. Results: Site climate explained about 70% of the seed mass variation across the Scots pine range. Genetic components explained 30% of the seed mass variation, as per the results from our common garden experiment in south central Siberia. Seed mass varied within 3.5 g (min) and 10.5 g (max) with the mean 6.1 g (n = 1150) across Russia. Our bioclimatic seed mass model predicted that a July temperature elevated by 1 °C increased seed mass by 0.56 g, and a January temperature elevated by 5 °C increased seed mass by 0.43 g. The seed mass would increase from 1 g to 4 g in the moderate RCP 2.6 and the extreme RCP 8.5 climates, respectively. Predicted seed zones with heavier seed would shift northwards in a warming climate. However, the permafrost border would halt this shifting due to slower permafrost thawing; thus, our predicted potential for Scots pine seed zones and seed mass would not be realized in the permafrost zone in a warmed climate. Our common garden experiment in central Siberia showed that trees of northerly origins produced lighter seeds than local trees but heavier ones than the trees at the original site. Trees of southerly origins produced heavier seeds than local trees but lighter seeds than the trees at the original site. Conclusions: The findings from this study could serve as blueprints for predicting new landscapes with climatic optima for Pinus sylvestris to produce better quality seeds to adjust to a warming climate.

Quantitative and qualitative analysis of sugarcane productivity in function of air temperature and water stress

Climate variables can influence the both productivity and quality of the commercial product of sugarcane crop. This study aimed to evaluate the influence of thermal time, negative degree-days and water deficit on productivity and industrial quality of sugarcane for the Goiás Brazilian Savanna region. The data of productivity and industrial quality were obtained in the field (Plant Centro Álcool®) and climate data in the weather station. Thermal time and the accumulation of negative degree-days in cycles showed significant correlations with the yield. The highest (340 mm) and the lowest (158 mm) accumulated water deficit blade not reflected in lower productivity and greater, respectively. Relative evapotranspiration (rET) was equal at 1.0 in more than 50% of the studied period, in which not were found significant correlations between the rET<1 with the productivity and industrial quality. We found significant correlations between rates of degree-days and negative degree-days with productivity rates; however, there was a low correlation between the water deficit rates and productivity. The total recoverable sugars rates and total soluble solids showed maximum accumulation point only in function of degree-day rates. We conclude that the thermal time has greater influence on yield decrease of sugarcane with different harvest cycles, significantly contributing to the accumulation of sugars in the stem.

Role of population genetics in guiding ecological responses to climate.

Population responses to climate were assessed using 3-7years height growth data gathered for 266 populations growing in 12 common gardens established in the 1980s as part of five disparate studies of Pinus contorta var. latifolia. Responses are interpreted according to three concepts: the ecological optimum, the climate where a population is competitively exclusive and in which, therefore, it occurs naturally; the physiological optimum, the climate where a population grows best but is most often competitively excluded; and growth potential, the innate capacity for growth at the physiological optimum. Statistical analyses identified winter cold, measured by the square root of negative degree-days calculated from the daily minimum temperature (MINDD01/2 ), as the climatic effect most closely related to population growth potential; the colder the winter inhabited by a population, the lower its growth potential, a relationship presumably molded by natural selection. By splitting the data into groups based on population MINDD01/2 and using a function suited to skewed normal distributions, regressions were developed for predicting growth from the distance in climate space (MINDD01/2 ) populations had been transferred from their native location to a planting site. The regressions were skewed, showing that the ecological optimum of most populations is colder than the physiological optimum and that the discrepancy between the two increases as the ecological optimum becomes colder. Response to climate change is dependent on innate growth potential and the discrepancy between the two optima and, therefore, is population-specific, developing out of genotype-environment interactions. Response to warming in the short-term can be either positive or negative, but long term responses will be negative for all populations, with the timing of the demise dependent on the amount of skew. The results pertain to physiological modeling, species distribution models, and climate-change adaptation strategies.

Predicting Hemlock Woolly Adelgid Winter Mortality in Connecticut Forests by Climate Divisions

Abstract Hemlock Woolly Adelgid (HWA) is a devastating non-native pest of North American Tsuga canadensis (Eastern Hemlock) and Tsuga caroliniana (Carolina Hemlock). I analyzed 15 years of data collected during the period 2000–2015 to determine important winter variables influencing HWA mortality in the 3 Connecticut climatic divisions. Absolute minimum daily winter temperature, the number of subzero days (temperature drops below -17.8 °C [0 °F]), and a new interaction variable—negative degree days (NDD)—were identified as significant predictors of HWA winter mortality. The absolute minimum daily winter temperature was the most critical factor. Minimum daily winter temperatures of -24 °C, 5.5 subzero days, and -130 NDD in Division 1(Northwest); -22.4 °C, 6 subzero days, and -100 NDD in Division 2 (Central); and -21.2 °C, 2.6 subzero days, and -45 NDD in Division 3 (Coastal) resulted in 90% HWA mortality. Patterns of HWA winter mortality in coastal Division 3 were distinct from the interior and suggest col...

Statistical characterization of the sea ice extent during different winter scenarios in the Gulf of Riga (Baltic Sea) using optical remote-sensing imagery

ABSTRACTThis study focuses on the statistical characterization of ice conditions (extent, sea ice occurrence probability (SIOP), and length of ice season) in the Gulf of Riga, Baltic Sea, using remote-sensing data. The optical remote-sensing data with 250 m resolution acquired by a Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002–2011 were used for statistical characterization of sea ice. A method based on bimodal histogram analysis of remote-sensing reflectance data was developed to discriminate ice from water. In general, ice extent information obtained from MODIS data agrees with the official ice chart data (synthetic aperture radar (SAR) and in situ measurements) and multi-sensor product containing data from microwave and infrared instruments (R2 >0.83). However, in case of severe winters and extremely mild winters there are differences in the dates when maximum ice extent is registered. MODIS data can be used for detailed analysis of ice extent in specific basins of Baltic Sea. Depending on the year, the ice season length in the Gulf of Riga ranged from 68 to 146 days, and the maximum ice extent varied greatly from 329 to 15,350 km2. SIOP and number of ice days increased significantly in areas where the depth is less than 15 m. Based on negative-degree days and ice cover characteristics (SIOP and ice season length), three winter scenarios were defined: severe (2003, 2006, 2010, and 2011), medium (2004 and 2005), and mild (2007, 2008, and 2009).

Inter‐annual variability and long‐term trends in breeding success in a declining population of migratory swans

Population declines among migratory Arctic‐breeding birds are a growing concern for conservationists. To inform the conservation of these declining populations, we need to understand how demographic rates such as breeding success are influenced by combinations of extrinsic and intrinsic factors. In this study we examined inter‐annual variation and long‐term trends in two aspects of the breeding success of a migratory herbivore, the Bewick's swanCygnus columbianus bewickii, which is currently undergoing a population decline: 1) the percentage of young within the wintering population and 2) mean brood size. We used an information‐theoretic approach to test how these two measures of productivity were influenced over a 26 yr period by 12 potential explanatory variables, encompassing both environmental (e.g. temperature) and intrinsic (e.g. pair‐bond duration) factors. Swan productivity exhibited sensitivity to both types of explanatory variable. Fewer young were observed on the wintering grounds in years in which the breeding period (May to September) was colder and predator (Arctic fox) abundance was higher. The percentage of young within the wintering population also showed negative density‐dependence. Inter‐annual variance in mean swan brood size was best explained by a model comprised of the negative degree days during the swan breeding period, mean pair‐bond duration of all paired swans (i.e. mean pair duration), and an interaction between these two variables. In particular, mean pair duration had a strong positive effect on mean brood size. However, we found no long‐term directional trend in either measure of breeding success, despite the recent decline in the NW European population. Our results highlight that inter‐annual variability in breeding success is sensitive to the combined effects of both intrinsic and extrinsic factors.

Impacts of freezing and thawing dynamics on foliar litter carbon release in alpine/subalpine forests along an altitudinal gradient in the eastern Tibetan Plateau

Abstract. Carbon (C) release from foliar litter is a primary component in C exchange among the atmosphere, vegetation, soil and water from respiration and leaching, but little information is currently related to the effects of freezing and thawing dynamics on C release of foliar litter in cold regions. A 2-year field litter decomposition experiment was conducted along an altitudinal gradient (~ 2700 to ~ 3600 m) to mimic temperature increases in the eastern Tibetan Plateau. C release was investigated for fresh foliar litter of spruce, fir and birch. The onset of the frozen stage, deep frozen stage and thawing stage was partitioned according to changes in the freezing and thawing dynamics of each winter. More rapid 2-year C released from fresh foliar litter at upper elevations compared to lower elevations in the alpine/subalpine region. However, high C release was observed at low altitudes during winter stages, but high altitudes exhibited high C release during growing season stages. The deep frozen stage showed higher rates of C release than other stages in the second year of decomposition. Negative-degree days showing freezing degrees were correlated to C release rates for the deep frozen stages in both years, and this relationship continued for the duration of the experiment, indicating that changes in freezing can directly modify C release from foliar litter. The results suggested that the changed freezing and thawing dynamics could delay the onset of C release in fresh litter in this cold region in the scenario of climate warming.

Models for Predicting Potential Yield Loss of Wheat Caused by Stripe Rust in the U.S. Pacific Northwest

Climatic variation in the U.S. Pacific Northwest (PNW) affects epidemics of wheat stripe rust caused by Puccinia striiformis f. sp. tritici. Previous models only estimated disease severity at the flowering stage, which may not predict the actual yield loss. To identify weather factors correlated to stripe rust epidemics and develop models for predicting potential yield loss, correlation and regression analyses were conducted using weather parameters and historical yield loss data from 1993 to 2007 for winter wheat and 1995 to 2007 for spring wheat. Among 1,376 weather variables, 54 were correlated to yield loss of winter wheat and 18 to yield loss of spring wheat. Among the seasons, winter temperature variables were more highly correlated to wheat yield loss than the other seasons. The sum of daily temperatures and accumulated negative degree days of February were more highly correlated to winter wheat yield loss than the other monthly winter variables. In addition, the number of winter rainfall days was found correlated with yield loss. Six yield loss models were selected for each of winter and spring wheats based on their better correlation coefficients, time of weather data availability during the crop season, and better performance in validation tests. Compared with previous models, the new system of using a series of the selected models has advantages that should make it more suitable for forecasting and managing stripe rust in the major wheat growing areas in the U.S. PNW, where the weather conditions have become more favorable to stripe rust.

QUANTIFYING THE ABUNDANCE OF CO-OCCURRING CONIFERS ALONG INLAND NORTHWEST (USA) CLIMATE GRADIENTS

The occurrence and abundance of conifers along climate gradients in the Inland Northwest (USA) was assessed using data from 5082 field plots, 81% of which were forested. Analyses using the Random Forests classification tree revealed that the sequential distribution of species along an altitudinal gradient could be predicted with reasonable accuracy from a single climate variable, a growing-season dryness index, calculated from the ratio of degree-days >5 degrees C that accumulate in the frost-free season to the summer precipitation. While the appearance and departure of species in an ascending altitudinal sequence were closely related to the dryness index, the departure was most easily visualized in relation to negative degree-days (degree-days < 0 degrees C). The results were in close agreement with the works of descriptive ecologists. A Weibull response function was used to predict from climate variables the abundance and occurrence probabilities of each species, using binned data. The fit of the models was excellent, generally accounting for >90% of the variance among 100 classes.

Freezing of lakes on the Swiss plateau in the period 1901–2006

AbstractData of ice cover for deep Alpine lakes contain relevant climatological information since ice cover and winter temperature are closely related. For the first time, ice cover data from 11 lakes on the Swiss plateau have been collected and analysed for the period 1901–2006. The ice cover data used stem from systematic registration by individuals or groups (fishermen, an ice club and lake security service) and from several national, regional and local newspapers. It is found that in the past 40 years, and especially during the last two decades, ice cover on Swiss lakes was significantly reduced. This is in good agreement with the observed increase in the winter temperature in this period. The trend of reduced ice cover is more pronounced for lakes that freeze rarely than for the lakes that freeze more frequently. This agrees well with the stronger relative decrease in the probability to exceed the sum of negative degree days (NDD) needed for freezing the lakes that rarely freeze. The ice cover data are related with the temperature measurements such as the sum of NDD of nearby official meteorological stations by means of binomial logistic regression. The derived relationships estimate the probability of a complete ice cover on a lake as function of the sum of NDD. The sums of NDD needed are well related to the average depth of the lake (rNDD–Depth = 0.85). Diagnosing lake ice cover on the basis of the sum of NDD is much better than a prediction on the basis of a climatological freezing frequency. The variance of lake ice cover that cannot be explained by the sum of NDD is important for judging the uncertainty associated with climate reconstruction on the basis of data on lake ice cover. Copyright © 2007 Royal Meteorological Society

Sugarcane maturity estimation through edaphic-climatic parameters

Sugarcane (Saccharum officinarum L.) grows under different weather conditions directly affecting crop maturation. Raw material quality predicting models are important tools in sugarcane crop management; the goal of these models is to provide productivity estimates during harvesting, increasing the efficiency of strategical and administrative decisions. The objective of this work was developing a model to predict Total Recoverable Sugars (TRS) during harvesting, using data related to production factors such as soil water storage and negative degree-days. The database of a sugar mill for the crop seasons 1999/2000, 2000/2001 and 2001/2002 was analyzed, and statistical models were tested to estimate raw material. The maturity model for a one-year old sugarcane proved to be significant, with a coefficient of determination (R²) of 0.7049*. No differences were detected between measured and estimated data in the simulation (P &lt; 0.05).

Association Between Ice Conditions in the Baltic Sea along the Estonian Coast and the North Atlantic Oscillation

Variations in time series of sum of negative degree-days, number of days with ice, date of ice break-up and maximum ice thickness on the Baltic Sea along the Estonian coast have been examined within context of the atmospheric circulation above the North Atlantic. It was found out that 20 – 50 % of ice conditions along the Estonian coast might be explained by the state of the NAO winter index. However, correlation coefficients between time series of ice conditions and the NAO winter index varies with time; moving correlation coefficient can reach -0.98.

A Model for Predicting Boll Weevil (Coleoptera: Curculionidae) Overwintering Survivorship

Boll weevil, Anthonomus grandis grandis Boheman, overwintering survivorship was quantified monthly throughout the overwintering period (October to May) in Texas High Plains and Rolling Plains for 12 yr. A negative exponential model was developed to dynamically predict survivorship throughout the overwintering months. Survivorship was modeled as a function of the number of days that weevils were in the habitat, negative degree-days ( 6.1°C), rainfall, and mortality during the first month of overwintering. First month mortality was modeled as a function of overwintering survival potential of weevils determined by dissection examination of their body lipid content and gonad atrophy. A nonlinear iterative multiple regression analysis showed that the model explained 94% of the variability in parameterization-verification data; a goodness-of-fit test showed that 97% of the estimated survival values did not significantly depart from their corresponding observed values. With independent validation data, 94% of the variability was explained by the survival model; a goodness-of-fit test for validation data showed that 96% of the predicted survival values did not significantly depart from their corresponding observed values. This model offers a greater understanding of boll weevil overwintering biology as it demonstrates a link between biological and climatic parameters. The model can be used to forecast weevil survivorship throughout the overwintering period in the Texas Plains.

Relationship Between Ambient and Leaf Litter Temperatures in Overwintering Habitats of Boll Weevil (Coleoptera: Curculionidae)

Temperature data are unavailable for most boll weevil overwintering habitats. As a result, ambient data often are used to estimate overwintering survival and emergence. In this study, relationship of ambient and overwintering habitat temperatures was determined for 6 habitats in the Rolling Plains of Texas. The amplitude of the daily temperature cycle in the leaf litter of each habitat was dampened compared with that recorded for ambient. Average minimum daily leaf litter temperature was 3.19°C higher in the shelterbelt habitat, followed by mesquite–grass pasture (3.54°C), tall shinnery motte (3.64°C), low shinnery brush (3.81°C), lovegrass (4.01°C), and pecan (6.01°C). Likewise, the average maximum daily leaf litter temperature was 3.77°C lower in shelterbelt, followed by pecan (3.86°C), lovegrass (3.87°C), mesquite-grass pasture (4.07°C), low shinnery brush (5.51°C), and tall shinnery motte (6.12°C). Stepwise multiple regression of hourly leaf litter temperatures explained 93% of the variability, with hourly ambient temperature and week of year being significant variables. Similarly, multiple regressions of daily leaf litter minimum temperatures and daily leaf litter maximum temperatures explained 96 and 90% of the variability, respectively, with ambient temperatures and week of year being significant variables for both regressions. The effect of temperature quality (ambient versus leaf litter) and sampling frequency (hourly versus daily) on positive (>6.1°C) and negative (<0.0°C) degree-days experienced by overwintering boll weevils was quantified for each of the 6 habitats for 4 yr of independent data, representing 2 mild and 2 severe winters. Degree-day calculations based on fitting minimum–maximum data to an asymmetrical double sine curve overestimated positive degree-day accumulation by 2.5%. Ambient temperature positive degree-days were similar to that for mesquite–grass pasture habitat, but the ambient temperatures overestimated positive degree-days experienced by overwintering weevils in the pecan by 6.5%, followed by low shinnery brush (6.7%), lovegrass (8.7%), tall shinnery motte (9.2%), and shelterbelt (15.7%). All habitats had significantly fewer cumulative negative degree-days compared with that calculated for ambient. Pecan leaf litter accumulated 99% fewer negative degree-days, followed by shelterbelt (95%), tall shinnery motte (88%), low shinnery brush (82%), lovegrass (71%), and mesquite–grass pasture (50%).

Climatic Data-Based Analysis of Boll Weevil (Coleoptera: Curculionidae) Overwintering Survival and Spring Emergence

Analysis of data from a 15-yr study, composed of 97 boll weevil cohorts from overwintering habitats in the Rolling Plains of Texas, showed that overwintering survivorship and spring/summer emergence were influenced by the day of the year ( DOY ) when the weevils entered overwintering habitat ( DOY in), the amount of rainfall, and habitat temperatures experienced by the weevils during diapause. The earlier the date that weevils entered overwintering habitat or the greater the amount of rain occurring before emergence of the 1st weevil, the greater the positive degree-days (>6.1°C) required for weevils to emerge from overwintering habitat. There was no significant correlation between the timings of weevil emergence and the winter severity, measured in terms of negative degree-days(<0.0°C). Multiple least squares regression analyses describing the positive degree-days required for the weevils to start emergence ( DD start) and the time required for all weevils to emerge (duration of emergence) ( DD emerg) explained 88 and 77% of the variability in the data, respectively. With independent data, 75 and 36% of the variability were explained for DD start and DD emerg, respectively. Overwintering survival ranged from 0 to 50% in the 15-yr study. Survivorship was positively correlated with DOY in, and negatively correlated with NDD start and DD start. Multiple regression analysis with DOY in, negative degree-days, rainfall, positive degree-days, and their 1st order interactions explained 99% of the variability in overwintering survival. With independent data, 74% of the variability was explained by the survival function. A chi-square test for goodness-of-fit showed that 74% of the survival estimates did not differ significantly from their observed values. In contrast, when compared with independent data, 37% of the survival estimates did not differ significantly from their observed values. The spring/summer emergence pattern for the overwintering weevils was described by a sigmoid function that explained 95 and 92% of the variability for the verification data set and the independent data set, respectively. Boll weevil overwintering emergence patterns can be predicted with considerable accuracy, but greater knowledge of factors affecting boll weevil overwintering survival and the time to completion of emergence is required for these to be predicted accurately.

Environmental and Edaphic Effects on Western Corn Root worm (Coleoptera: Chrysomelidae) Overwintering Egg Survival

Western corn root worm, Diabrotica virgifera virgifera LeConte, overwintering egg survival was studied at 3 soil depths (7.5, 15, and 30 cm), 3 soil textures (silty clay loam, loam, and sandy loam), and 2 surface residue treatments (with and without surface cover) at an eastern Nebraska site in 1989-1990 and 1990-1991. At a western Nebraska site, egg survival was evaluated at 3 soil depths (7.5, 15, and 30 cm), 2 surface residue regimes (with and without surface cover) within a fine sandy loam soil in 1989-1990. Overall, egg survival was low (30.0%) and intermediate (41.0%) at the eastern and western sites, respectively in 1989-1990 and high (64.7%) at the eastern Nebraska site in 1990-1991. Egg survival was significantly influenced by surface residue and by depth in all soil textures at the eastern site during both seasons. Percentage survival of D. v. virgifera eggs was <5 and ≈ 15% in the bare surface treatment at the 7.5 and 15 cm depths, respectively in 1989-1990. In 1990-1991, the lowest survival occurred in the bare surface treatment at 7.5 cm. Egg survival was not influenced by the surface cover or sample date at the western site. Simple regression equations showed significant relationships of percentage of egg survival with minimum soil temperature and with negative degree-days (1989-1990 only) at the eastern site. In 1990-1991, ≈80 negative degree days or a minimum temperature of ≈-7°C was needed to significantly decrease D. v. virgifera egg survival. Multiple regression improved the prediction and showed the importance of snowfall and snow cover at the western Nebraska site and in 1990-1991 at the eastern site.

Development of Regional Models that Use Meteorological Variables for Predicting Stripe Rust Disease on Winter Wheat

Meteorological variables can be used to predict stripe rust, a disease of wheat caused by Puccinia striiformis West., at Lind, Pullman, and Walla Walla, Washington and Pendleton, Oregon in the Pacific Northwest of the United States. Regional models developed using different methodologies are described and evaluated for accuracy. Disease intensity data, collected from 1968 to 1981, were converted to a 0–9 disease index (DI) and were used as the dependent variable in regression analysis. Meteorological data were expressed as standardized negative degree days (NDDZ) accumulated during December and January, the Julian date of spring (JDS) [defined as the date when 40 or more positive degree days (PDD) accumulated during the subsequent 14 days] and PDD for the 80-day period after the JDS. In one of the regional models, NDDZ was accumulated for adjusted time periods at sites other than Pullman. Mallow's Cp criterion was used to evaluate the regression equations with different numbers of independent variables. The most accurate model uses NDDZ and JDS as the independent variables. The models were cross-validated by randomly removing 2 years' data and reformulating the model based on the remaining data; the new model was then used to compare actual and predicted DI. Predicted DI was within one standard error of the actual DI 60% of the time. Incorrect predictions occurred during years when spring was unusually favorable or unfavorable for disease development. The methodology described is applicable to developing statistical models relating other pest occurrences to meteorological conditions.

Limatic variables that control development of stripe rust disease on winter wheat

The frequency and severity of the stripe rust disease (caused byPuccinia striiformis) on winter wheat in the Pacific Northwest of the U.S.A. has increased since 1958 in association with climatic variation. From 1968–1979, rust intensities on ‘Gaines’ wheat were most highly correlated with accumulated negative degree days (NDD) between 1 December and 31 January and positive degree days (PDD) between 1 April and 30 June. NDD and PDD were calculated from a 7 °C base. Linear regression equations using NDD alone accounted for 76% of the variation in stripe rust. When NDD and PDD were combined, 88% of the variation in stripe rust was explained. When a growth index (GI), and NDD plus PDD were used as independent variables in a multiple regression analysis, 91% of the variation in disease was explained. Frequency of precipitation in June was correlated with stripe rust intensity, but when it was added to the multiple regression analysis, it explained less than an additional 1% of the variation. The relationships between NDD, PDD, and disease index help to explain why stripe rust was not severe from 1941 to 1957. Methods used in this research should be applicable to similar studies of the effect of climatic variation on other pests.