Quantifying how climatic factors affect variation in plant disease severity: A general method using a new way to analyze meteorological data

A general method is presented for analyzing how climatic conditions affect plant disease severity. An example of its application is given for the analysis of stripe rust (caused by Puccinia striiformis) data on winter wheat cultivar Gaines and climatic data collected at Pullman, WA. for 1968–1986. A computer program WINDOW was written to identify the climatic factors most highly correlated with disease. This program is designed to utilize meteorological data for an entire growing season of a crop as well as to include climatic conditions preceding planting. This program uses an iterative process to examine variable-length segments of meteorological data in a more exhaustive analysis than previously possible. Climatic factors considered include: mean maximum, minimum, and average temperature; total and frequency of precipitation; consecutive days with and without precipitation; accumulation of negative and positive degree days; and number of days with extreme temperature events. Variables that were highly correlated with disease were the basis for regression models that were developed to predict disease severity index for each of the three cultivars. Two- and three-variable models explained, respectively, 75 and 76% of the variation in disease from year to year. Predictions (which could be made early enough in the growing season to allow application of chemical control) were evaluated on the basis of whether years with severe disease were accurately predicted. Models were validated using Allen's PRESS statistic and by application to new data. The method is potentially applicable to studies of how climatic conditions affect the populations or productivity of other types of organisms.This research was supported by a National Science Foundation Grant (ATM 85-03115), Climate Dynamics Program, Division of Atmospheric Sciences.     

Climate variability in the Pacific Northwest and its effect on stripe rust disease of winter wheat

Wheat stripe rust (Puccinia striiformis West.) epidemics are confined predominantly to the Pacific Northwest in the U. S. A. because of climate. This disease was frequently reported until the late 1930's and then virtually absent until the late 1950's. Since the severe epidemic in 1961, stripe rust has been frequently severe on winter wheat and has caused losses in susceptible cultivars in many years. Because of the unusual history of stripe rust in this region, the possibility that climate variability affected the pattern of rust occurrence was investigated. Meteorological data for seven locations in Oregon, Washington, and Idaho were analyzed. In 1961–1974 for the Columbia Basin locations, January and February temperatures averaged 1.20° C higher than during the period 1935–1960; however, April temperatures averaged 1.28° C lower in 1961–1974 than during the earlier period. Monthly precipitation averages have not varied more than 12.7 mm in any month. Between 1961–1974, December snowfall almost doubled over that in 1935–1960; snowfall in February decreased over 50% from the earlier period. Data was computed on a seasonal basis since 1901 and considered in respect to stripe rust epidemics. Since 1961, above-normal winter and below-normal spring temperatures have increased the frequency and severity of stripe rust epidemics in the Pacific Northwest. The direction of temperature and precipitation trends varied with the time period considered. How the climate variability which has occurred may have affected winter wheat growth and yields is postulated. Studies such as this should be useful to researchers modelling crop-yields, agronomists evaluating results from field experiments and to researchers studying fluctuations in pest populations.This research was supported by a National Science Foundation Grant (ATM 76-21725); Climate Dynamics Program, Division of Atmospheric Sciences.     

四川省小麦条锈病冬繁农业气候风险区划

本文针对盆地区代表点的小麦条锈病冬繁与气候条件的关系进行相关性分析,由此确定四川小麦条锈病冬繁的适宜、次适宜、不适宜气候条件指标。从风险基本理论出发,建立了包括气候条件出现频率和寄主存在数量两个环境因子的四川省小麦条锈病冬繁农业气候风险模型,并划分了高、中、低风险等级。基于GIS技术,对四川省小麦条锈病冬繁农业气候风险进行了区划。区划结果表明,四川省的川西高原地区和川西南山地是小麦条锈病冬繁阶段低风险或无风险区,而盆地区是中、高风险的集中区。其中,盆地中部地区是主要高风险区。      

天水地区条锈病的发生与气象条件关系研究

利用1994～2006年天水市条锈病监测资料、天水市7县（区）气象站的常规观测资料和冬小麦生长发育期资料,用数理统计方法分析条锈病的发生发展特征及气象因子与冬小麦生长发育期的关系。结果表明：全球气候变暖使条锈病的越冬、越夏条件优越,自1994年以来发生面积呈线性增长,发病程度天水市东南部重于西北部。条锈病的爆发流行既受当时气象条件制约,又受周边地区条锈病发生发展趋势的影响。根据分析制作的条锈病预测预报模型,为条锈病的积极防治及科学决策生产提供了依据。     

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change

Investigating the relationships between climate extremes and crop yield can help us understand how unfavourable climatic conditions affect crop production. In this study, two statistical models, multiple linear regression and random forest, were used to identify rainfall extremes indices affecting wheat yield in three different regions of the New South Wales wheat belt. The results show that the random forest model explained 41–67% of the year-to-year yield variation, whereas the multiple linear regression model explained 34–58%. In the two models, 3-month timescale standardized precipitation index of Jun.–Aug. (SPI_JJA), Sep.–Nov. (SPI_SON), and consecutive dry days (CDDs) were identified as the three most important indices which can explain yield variability for most of the wheat belt. Our results indicated that the inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation, and pre-growing season rainfall played a secondary role. Frequent shortages of rainfall posed a greater threat to crop growth than excessive rainfall in eastern Australia. We concluded that the comparison between multiple linear regression and machine learning algorithm proposed in the present study would be useful to provide robust prediction of yields and new insights of the effects of various rainfall extremes, when suitable climate and yield datasets are available.     

成都市2009年小麦条锈病特重发生的气象特征

本文从小麦条锈病浸染、繁殖、流行的气象特征角度,分析了2009年成都市在小麦条锈病生物环境存在的背景下,小麦条锈病特重发生的原因:秋季温适湿重、冬季温暖露重、春季暖和多雨。初步总结出条锈病爆发流行的定量指标:在条锈病流行期内日平均气温≥10℃时,雨日(≥0.5mm)达到10天有利于爆发,该结论可为成都市小麦条锈病的预报和防治提供科学的参考。      

成都市2009年小麦条锈病特重发生的气象特征

本文从小麦条锈病浸染、繁殖、流行的气象特征角度,分析了2009年成都市在小麦条锈病生物环境存在的背景下,小麦条锈病特重发生的原因：秋季温适湿重、冬季温暖露重、春季暖和多雨。初步总结出条锈病爆发流行的定量指标：在条锈病流行期内日平均气温≥10℃时,雨日（≥0．5mm）达到10天有利于爆发,该结论可为成都市小麦条锈病的预报和防治提供科学的参考。     

Quantifying spatial patterns of bioclimatic zones and controls in Turkey

Summary The study was aimed at inferring spatial patterns of climatic zones as well as identifying significant discriminating bioclimatic controls for distribution of major ecosystems in Turkey, based on multivariate analyses. A total of 12 climate variables and 11 bioclimatic indices for the period of 1968–2004 at 272 meteorological stations, and four location data (latitudes, longitudes, altitudes, and distance to sea) were analyzed using discriminant analysis (DA), hierarchical and non-hierarchical cluster analyses (CA), principal components analysis (PCA), and multiple linear regression (MLR) models. The first three and four linear discriminant functions (LDFs) explained 88 and 95% of the variation in the dataset, respectively. The efficacy of the discriminant model was high (85.5%) based on the cross-validation method. The hierarchical and non-hierarchical CA pointed to seven clusters (climate types) that can be observed on the basis of broad climatic similarity of 97%. PCA elucidated 78% of variation in the dataset. MLR models that accounted for variations in the 12 climatic response variables as a function of the four location variables and aspect had R ² values ranging from 28.8% for precipitation to 89.8% for mean air temperature and soil temperature for a depth of 5 cm. The multivariate analyses indicated that the meteorological stations are heterogeneous clusters consisting of the seven climatic zones. However, differences in the bioclimatic variables at the boundaries complicate the natural clustering scheme of a multidimensional cloud of data points and were detected in a climatologically plausible manner by the Ward and K-means CA, and PCA. Our multivariate approach revealed that the commonly used climatic zones are insufficient representations of the inferred climatic zones: (1) the coastal Black Sea; (2) the inland Black Sea; (3) the southeastern Anatolia; (4) the eastern Anatolia; (5) the central Anatolia; (6) the Mediterranean; and (7) the Aegean. Authors’ addresses: F. Evrendilek, Department of Environmental Engineering, Faculty of Engineering and Architecture, Abant Izzet Baysal University, G?lk?y Cambus, 14280 Bolu, Turkey; S. Berberoglu, Department of Landscape Architecture, Cukurova University, Balcali-Adana, Turkey.     

Effect of topography on farm-scale spatial variation in extreme temperatures in the Southern Mallee of Victoria, Australia

Extreme temperatures around flowering of wheat have the potential to reduce grain yield and at farm scale their impact can be spatially variable depending on topography. Twenty-five data loggers were installed at 0.8-m height across a 164-ha farm in the southern Mallee of Victoria, Australia to spatially record the daily course of temperatures around the average date of flowering of wheat in the region. The experiment was conducted during 2-years period. In 1 year, the farm had no crop cover and in another year the farm had a wheat crop. Multiple linear regression analysis techniques were used to fit models relating daily extreme temperatures to the farm topographic features of elevation, aspect and slope, and the average maximum and minimum temperatures of each day at the farm in order to identify areas of high risk of extreme temperatures around the time of the flowering of wheat. The fitted regression models explained 90% and 97% of the variability in maximum and minimum temperatures, respectively, when the farm had no crop cover and 80% and 94% of the variability in maximum and minimum temperatures, respectively, when the farm had a wheat crop cover. When the farm had no crop, only minimum temperature was partially explained by the topography however, both maximum and minimum temperatures were partially explained by the topography when the farm had a wheat crop. From this study it was concluded that, (1) high temperature variations were found across the farm (2) temperature variations were only partially explained from the developed model presumably due to the flatter topography of the farm and (3) the relationships obtained from this study could be used in a crop model which can explain variation in grain yield based on the topography of a field.     

半干旱区春小麦受条锈菌侵染后光合作用和蒸腾作用的变化规律

采用美国CI-301PS型便携式光合作用测定仪,对半干旱区大田春小麦的健康叶片和受条锈菌侵染后病叶的光合作用和蒸腾作用进行活体监测。结果表明：在干旱环境下。受条锈菌侵染后小麦叶片的光合作用和蒸腾作用发生了明显变化,其光合速率比健叶明显降低,而病叶细胞间隙CO2浓度、气孔导度、蒸腾速率等有所升高,且日变化随病叶严重度的不同而明显不同。受干旱和病原物侵染的双重胁迫,小麦叶片的光合效率显著降低,水分利用率也随之下降。不仅与叶绿素含量的明显下降有关,而且与干旱造成的水分亏缺对小麦体内生理生化代谢造成损伤,碳同化过程受到抑制等有着密切的关系。     

The response of the ‘green revolution’ to climatic variability

The purpose of this research is to assess the climatic sensitivity of high yielding variety (‘HYV’) ‘green revolution’ wheat. Improved multiple regression models were constructed for yields in India and Sonora, Mexico — the two most intensively planted regions in the world. After isolating the most important climatic predictors (which, not surprisingly, are total rainfall over the irrigation basins), the models were reduced to the pre-HYV period, and then re-run with successively more years of HYV input. This test indicated that increased adoption of the HYV package is associated with a significant increase in yield sensitivity to the most important climatic determinants of yield. To serve as a control, the U.S. Winter Wheat region was also modelled with a similar method. Overall, there is no significant increase in yield sensitivity to climate during the same period that HYV's were adopted in Mexico and India. Assuming that there is no change in overall climatic variability, this study indicates that production will nonetheless become more variable, particularly as HYV culture is expanded. Ironically, countries with rapidly expanding populations, that rely increasingly on HYV's, will experience the most significant fluctuations.     

基于经验法则的水稻细菌性条斑病气候年型分析

利用广东省化州市1989-2016年晚稻细菌性条斑病资料和同期气象资料,采用合成分析和秩相关分析,筛选出影响细菌性条斑病发生的关键气象因子;基于经验法则,利用水稻细菌性条斑病发生阶段的温度、降水距平,判别细菌性条斑病发病程度,并确定了细菌性条斑病发生流行的气候年型与预测指标。经历史回代,判别细菌性条斑病发病程度符合率为82.1%,并进行了2017-2019年的外延指标判别,符合率达100%,综合判别符合率在83%以上。     

Seasonal precipitation reconstruction and teleconnections with ENSO based on tree ring analysis of Pinus cooperi

Tendencies of climatic variability indicate that northern Mexico will soon suffer from severe drought. Modeling the influence of climate and ecological processes would help researchers better understand the future implication of climatic variations. Here, we reconstructed historical seasonal precipitation using dendrochronological indices of Pinus cooperi and El Niño southern oscillation (ENSO). Correlation analysis was conducted to establish the precipitation response period; then a reconstruction model using independent variables was constructed using regression procedures. Available data were calibrated and verified to strengthen and validate the modeled reconstruction. Precipitation from the previous winter was best correlated with tree growth. Regression procedures showed that the residual chronology associated in a linear model with El Niño 3.4 explained 47 % of seasonal precipitation variability. This study contributes to a better understanding of historical variations in precipitation and the influence of ENSO in common tree species of northern Mexico to help land managers improve local forest management in a climate change scenario.     

基于青藏高原春季感热异常信号的中国东部夏季降水统计预测模型

使用1980-2014年由青藏高原中东部的地面气象观测台站观测资料计算得到的地表感热通量以及中国东部高分辨率的降水格点资料,在年代际变化和年际变率两个时间尺度上,使用最大协方差分析方法研究了青藏高原春季感热与中国东部夏季6、7和8月降水的关系,基于最大协方差关联因子的时间尺度分解回归分析方法建立了一个降水统计预测模型。青藏高原春季感热的各个关联预报因子与中国东部夏季各月降水的相关分析表明,在年代际成分中,6、7和8月在中国东部绝大部分地区均存在显著相关,方差贡献分别为75.6%、99.9%和79.7%;在年际成分中,相关区域在6月是华南地区、华北沿海地区和江淮流域,7月是华南地区西南部、长江流域、东北地区东南部和黄河中下游地区,8月是东北地区和华南地区西部,方差贡献分别为42.7%、43.4%和32.0%。预测模型的解释方差分析和后报试验检验表明,7月对整个中国东部地区预测效果最好,6月主要在长江以南地区,而8月主要在东北地区和华南地区西部预测效果较好。该预测模型能很好描述青藏高原春季感热与中国东部夏季各月降水的关联性,并对局地降水实现较好的定量预测,具有在短期气候预测业务应用的价值。      

河北省干热风对小麦千粒重影响分析

利用河北省冬麦区1971~2005年5月10日至6月10日逐日降水、气温、湿度、风速及1981~2005年逐年小麦千粒重等资料,采用小波分析、回归分析等统计方法,对冬麦区35年来干热风时空分布、周期等特征进行分析;同时还就河北省干热风对小麦千粒重的影响进行了初步分析。分析表明:轻度和重度干热风年平均发生日数分布具有一致性,而轻度和重度干热风的年代际变化等也具有相同特征,干热风总日数与小麦千粒重具有负相关关系。     

Insolation estimates for the LITFASS area derived from high resolution satellite data

Summary ?For the LITFASS-98 experiment, from June 1 until June 30, 1998, the spatially resolved insolation at surface could be computed from NOAA-14 AVHRR data applying the modular analysis scheme SESAT (Strahlungs- und Energiebilanzen aus Satellitendaten). The satellite inferred insolation for this period shows for clear-sky regions a good agreement with surface based observations with a rms error of 76 Wm⁻². For cloudy conditions the insolation is overestimated with respect to ground based observations, with a rms error between 83 and 118 Wm⁻², depending on the cloud optical thickness. This overestimation can be explained by the surface heterogeneity, leading to underestimated cloud optical thickness, and also by a fixed relative humidity below clouds (55%, dry atmosphere) and a fixed horizontal visibility (50 km, clear atmosphere). A detailed study of comparable scales in space and time, considering the different observation geometries and sampling intervals, shows that a 30 min ground based observation can be compared with a 8 × 8 km² mean by the satellite data. Received July 12, 2001; revised April 29, 2002; accepted June 7, 2002     

江苏省冬小麦气候适宜度动态模型建立及应用

用1961—2010年江苏57个气象站常规气象资料和小麦产量资料,结合前人的研究思路,应用生态适宜度、模糊数学理论,引入权重分析等方法,建立了江苏省冬小麦气候适宜度的动态模型,模型检验结果良好。对1961—2010年冬小麦历年全生育期和各生育期的气候适宜度进行初步分析,结果表明:江苏省各地冬小麦气候适宜度均维持在较高水平,其中冬小麦生育期内温度适宜度最高,降水适宜度维持在较低水平,日照是其生产过程中的关键性制约气象因子。     

Analysis of Energy Balance Components as a Function of Orography and Land Use and Comparison of Results with the Distribution of Variables Influencing Local Climate

Summary During 1992 and 1995 in the Upper Rhine valley between Karlsruhe in the north and Basel in the south 36 energy balance stations were installed to analyze the spatial and temporal behavior of the components of the energy balance. A second aim of the project ‘Regio-Klima-Projekt’ (REKLIP) was to study the dependence of climatic variables on the energy balance. Three main influences on spatial variation in energy balance components were detected: orography, precipitation and land use. Concerning the dependence of the climatic variables on the energy balance it can be stated that the mean diurnal amplitude of temperature shows a good correlation with the mean diurnal sensible heat flux, while the diurnal amplitude of the specific humidity correlates with the mean diurnal latent heat flux. Both these results are in good agreement with theoretical considerations. Consequently, areas with enhanced sensible heat flux values show higher monthly mean temperature maxima and also a greater numbers of summer days, while areas with higher latent heat flux values indicate enhanced monthly mean humidity maxima. Received February 26, 1998 Revised June 5, 1998     

山东省主要粮食作物气候生产潜力时空变化特征

根据山东省1961-2008年的气象资料,利用逐级订正法计算了山东省冬小麦和夏玉米等主要粮食作物的气候生产潜力,并进一步采用经验正交函数分解方法,探讨了其时空变化特征.结果表明:山东省冬小麦及夏玉米的气候生产潜力存在有明显的年际波动和空间差异,其中冬小麦优、劣年景气候生产潜力相差3～9倍,夏玉米相对较小,为2～3倍;全省冬小麦、夏玉米气候生产潜力的高值区位于水热条件匹配较好的鲁南地区,低值区在半岛东部沿海地区;冬小麦、夏玉米气候生产潜力与实际单产的年际变化基本一致,山东省粮食产量,特别是夏玉米产量的年际波动受作物生长期间气候条件影响较大;全省冬小麦、夏玉米气候生产潜力在空间上具有较好的一致性,区域互补性较差.     

Changes in Climate and Crop Production During the 20th Century in Argentina

This work was focused on the assessment of changes occurring in crop production and climate during the 20th century in Argentina. The study was carried out for nine sites located in the Pampas region that are representative of contrasting environments. We have considered the four main crops cultivated in this area (wheat, maize, sunflower and soybean). Historical climatic data and crop production related variables (yield, planted area, harvested area) were analyzed and, by means of crop simulation models, we quantified the impact of climate on crop yields. Changes occurring in climate during the three last decades of the 20th century were characterized by important increases in precipitation especially between October and March, decreases in maximum temperature and solar radiation in particular during spring and summer and increases in minimum temperature during almost all of the year. These changes contributed to increases in yields, especially in summer crops and in the semiarid zone, mostly due to increases in precipitation, although changes in temperature and radiation also affected crop yields but to a lesser extent. Comparing the period 1950–1970 with 1971–1999, yields increases attributable to changes in climate were 38% in soybean, 18% in maize, 13% in wheat, and 12% in sunflower while mean observed yield increases were 110% for maize, 56% for wheat and 102% for sunflower.