Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long‐term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water‐limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well‐maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA‐POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location‐specific observed daily weather databases combined with an appropriate upscaling method.     

基于GIS的澳大利亚蝗虫治理决策支持系统的运行

澳大利亚疫蝗委员会（APLC）负责澳大利亚内陆四个州2000000km^2上严重威胁农业生产的蝗虫种群的监测与防治。应用一个基于GIS的“决策支持系统（DSS）”采集,处理,分析并显示多种空间数据,以预测蝗虫种群的发育并辅助实施防治。所做预测有助于对蝗群集聚区做出早期定位并采取有效的预防措施。该DSS的数据采集包括田间调查资料的无线直传和网上逐日天气资料的下载,蝗群的分布和年龄结构信息通过定期GPS定位调查获得,并连同农场主和地方农技推广人员的虫情报告通过高频调制直接传至APLC总部的GIS服务器上;当前的调查资料用于估测蝗群的分布并作为空间发育模型的输入数据以确定实施防治的最适发育阶段的始期;降水分布和风温场资料从网上自动下载并与栖境条件和蝗群分布信息集成。同时,澳大利亚联邦气象局的在线天气资料及产品也从网上定时存取用于决策支持。     

Rainfall, ambient temperature, and Clethrionomys californicus capture frequency

Sixty Western Red-backed Voles Clethrionomys californicus inhabiting an old-growth mixed conifer forest in north-western California (USA) were studied over 2 years to assess relationships among vole capture frequency, rainfall, and ambient temperature. Red-backed Voles were encountered more often and recapture rates per vole doubled with the tripling of yearly rainfall. In addition, more voles were captured during wet months than dry months. Analysed over weekly intervals, captures were positively correlated with the amount of weekly rainfall, a greater proportion of the known vole population was encountered during wet weeks than dry weeks, and increased weekly rainfall resulted in an increased proportion of the vole population captured. Significant relationships were not found between daily rainfall and vole captures, but both minimum and maximum daily ambient temperatures were lower when voles were trapped. Vole captures increased with decreases in daily and weekly minimum and maximum ambient temperatures. Weekly rainfall in combination with minimum weekly temperatures accounted for the greatest explanation of variability in vole capture frequencies. These results document that rainfall and ambient temperature have an impact on Western Red-backed Vole capture frequency; thus these weather variables should be addressed as covariates in analyses focused on capture data for this species.     

Fixed-point observation of Oncomelania nosophora in Kofu Basin -- establishment of monitoring system of schistosomiasis japonica in Japan

There are still many Oncomelania snails that inhabit the Kofu Basin, Yamanashi Prefecture, Japan, which had been declared free of schistosomiasis japonica. Due to the need to monitor the situation, a fixed-point observation system using GIS from GPS is being examined. In addition, in broad present or former endemic areas, survey areas are being managed by remote sensing with satellite images or aerial photographs. A simple and effective monitoring method by mobile GIS using PDAs was developed, risk or hazard maps were prepared and a system that would enable a response in the event of reemergence is being examined.     

Virtual Fire: A web-based GIS platform for forest fire control

Α web-based Geographic Information Systems (GIS) platform – named Virtual Fire – for forest fire control has been developed to easily, validly and promptly share and utilize information and tools among firefighting forces. This state-of-the-art system enables fire management professionals to take advantage of GIS capabilities without needing to locally install complex software components. Fire management professionals can locate fire service vehicles and other resources online and in real-time. Fire patrol aircrafts and vehicles may use tracking devices to send their coordinates directly to the platform. Cameras can augment these data by transmitting images of high-risk areas into the graphical interface of the system. Furthermore, the system provides the geographical representation of fire ignition probability and identifies high-risk areas at different local regions daily, based on a high performance computing (HPC) pilot application that runs on Windows HPC Server. Real-time data from remote automatic weather stations and weather maps based on a weather forecasting system provide vital weather data needed for fire prevention and early warning. By using these methods and a variety of fire management information and tools, the end-users are given the ability to design an operational plan to encompass the forest fire, choosing the best ways to put the fire out within the proper recourses and time.     

The development of linear regression models using environmental variables to explain the spatial distribution of Fasciola hepatica infection in dairy herds in England and Wales

Fasciolosis caused by Fasciola hepatica is a major cause of economic loss to the agricultural community worldwide as a result of morbidity and mortality in livestock. Spatial models developed with the aid of Geographic Information Systems (GIS) can be used to develop risk maps for fasciolosis for use in the formulation of disease control programmes. Here we investigate the spatial epidemiology of F. hepatica in dairy herds in England and Wales and develop linear regression models to explain observed patterns of exposure at a small spatial unit, the postcode area. Exposure data used for the analysis were taken from an earlier study of F. hepatica infection, performed in the winter of 2006/7. Climatic, environmental, soil, livestock and pasture variables were considered as potential predictors. The performance of models that used climate variables for 5 years average data, contemporary data and a combination of both for England and Wales, and for England only, was compared. All models explained over 70% of the variation in the prevalence of exposure. The best performing models were those built using 5 year average and contemporary weather data. However, the fit of these models was only slightly better than the fit of models using weather data from one time period only. Rainfall was a consistent predictor in all models. Other model covariates included temperature, the negative predictors of soil pH and slope and the positive predictors of poor quality land, as determined by the Agricultural Land Classification, and very fine sand content of soil. Choroplethic risk maps showed a good match between the observed F. hepatica exposure values and exposure values fitted by the models. The development of these detailed spatial models is the first step towards the development of a spatially specific, temporal forecasting system for liver fluke in the United Kingdom.     

Fixed-point observation of Oncomelania nosophora in Kofu Basin--establishment of monitoring system of schistosomiasis japonica in Japan

There are still many Oncomelania snails that inhabit the Kofu Basin, Yamanashi Prefecture, Japan, which had been declared free of schistosomiasis japonica. Due to the need to monitor the situation, a fixed-point observation system using GIS from GPS is being examined. In addition, in broad present or former endemic areas, survey areas are being managed by remote sensing with satellite images or aerial photographs. A simple and effective monitoring method by mobile GIS using PDAs was developed, risk or hazard maps were prepared and a system that would enable a response in the event of reemergence is being examined.     

澳大利亚迁飞性害虫的预测与治理

Decision Support System(DSS)是澳大利亚疫蝗治理委员会用于治理几种重要迁飞性害虫的决策支持系统。该系统以地理信息系统（GIS）为平台,将天气资料和害虫栖境条件与害虫的迁飞,发育及分布数据进行整合以发布预测并辅助防治决策。该系统呈模块化结构,其模块的数量和性质可根据目标害虫治理所需的具体资料方便地调整。     

FORECASTING AND MANAGEMENT OF MIGRATORY PESTS IN AUSTRALIA

Abstract  The Decision Support System (DSS) used by the Australian Plague Locust Commission for management of several important migratory insect pests in Australia is described. The DSS is based on a Geographic Information System that integrates data on weather and habitat condition with the migration, development and distribution of the pest to prepare forecasts and aid decisions for control. The GIS is module based with the number and nature of the modules easily modified depending on the detail of data required to manage the pest concerned.     

Spatial co‐localisation of extreme weather events: a clear and present danger

Extreme weather events have become a dominant feature of the narrative surrounding changes in global climate with large impacts on ecosystem stability, functioning and resilience; however, understanding of their risk of co‐occurrence at the regional scale is lacking. Based on the UK Met Office’s long‐term temperature and rainfall records, we present the first evidence demonstrating significant increases in the magnitude, direction of change and spatial co‐localisation of extreme weather events since 1961. Combining this new understanding with land‐use data sets allowed us to assess the likely consequences on future agricultural production and conservation priority areas. All land‐uses are impacted by the increasing risk of at least one extreme event and conservation areas were identified as the hotspots of risk for the co‐occurrence of multiple event types. Our findings provide a basis to regionally guide land‐use optimisation, land management practices and regulatory actions preserving ecosystem services against multiple climate threats.     

Climatic limits of pink bollworm in Arizona and California: effects of climate warming

The distribution and abundance of pink bollworm (Pectinophora gossypiella Saunders (PBW)) in cotton in Arizona and California was examined using a validated weather-driven, physiologically based demographic model of cotton and PBW integrated into a geographic information system (GIS). Survival of diapause larvae during winter as affected by low temperatures is a key factor determining the range of PBW. Winter survival was estimated using data from Gutierrez et al. (Can. Entomol. 109 (1977) 1457) and Venette et al. (Environ. Entomol. 29 (5) (2000) 1018). The model was run continuously over the period 1 January 1995 to 31 December 2003 using observed weather data from 121 locations. Three output variables were mapped as measures of PBW persistence: over-winter survival of diapause PBW larvae, cumulative daily PBW larval densities over the season, and the number of diapause larvae produced during the season. The distribution of pink bollworm is predicted to be restricted to the relatively frost-free cotton growing areas of Arizona and Southern California where it currently reaches pest status. The model predicts that extension of PBW's range into the Central Valley of California is unlikely. The analysis questions the efficacy of an ongoing area-wide effort to prevent the establishment of PBW in the Central Valley of California. Four global warming scenarios were examined to estimate the effects on the potential geographic range of PBW. Average observed daily temperatures were increased 1.0, 1.5, 2.0 or 2.5 °C, respectively, in the four scenarios. Scenarios with average increases of 1.5–2.5 °C predicted that the range of PBW would expand into the Central Valley of California and the severity of the pest would greatly increase in areas of current infestation.     

Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola

As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.     

Geographical Information Systems and on-line GIServices for health data sharing and management

Integrating Geographical Information Systems (GIS) technology and public health experience may represent a solution for a better comprehension of spatial and temporal trends of phenomena. Useful applications can be built that support practitioners in their daily tasks, from risk assessment to prevention programmes. Also, making available data on the Internet through GIServices represents an important goal. Institutions and public health practitioners may benefit from the technological integration of GIS, the Web, handheld and mobile global positioning systems (GPS) devices. Expert users may be supported in deriving thematic maps which represent a spatial synthesis documentation starting from an analytic study expressed in terms of numbers and features. In this paper we show an example of an on-line data sharing and processing application, emphasizing ways GIS can provide added value to health research and management.     

不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

Effects of landscape and management on ground‐dwelling insect assemblages of farmland in Jeju Island,Korea

Granite‐derived soils are widespread in the farmland of Korea in general. In contrast, Jeju Island has mainly volcanic ash soils. Soils and weather condition in Jeju Island created a unique agricultural system. We identified the features of ground‐dwelling insects in farmlands of Jeju Island. This study was conducted in four areas (Samdal‐ri and Susan‐ri in Seogwipo city, and Dongmyeong‐ri and Suwon‐ri in Jeju city) in Jeju Island, Korea. Field surveys were carried out twice in summer (June) and autumn (September) in 2013. Ground‐dwelling insects were sampled quantitatively by using pitfall traps. As a result, in total 3322 individuals, 137 species, 48 families and 8 orders were investigated in farmlands of Jeju Island. Especially, members of Coleoptera and Hymenoptera accounted for a large proportion of ground‐dwelling insect communities. The numbers of species and individuals for major taxonomic groups showed significant regional and seasonal differences. This study implied that the seasonal and regional differences of ground‐dwelling insect communities were affected by surrounding land use patterns, life history patterns of each taxonomic group and farmland management.     

Agrometeorology in Croatia

Meteorological and Hydrological Service was established 70 years ago and agrometeorological activities (soil temperature measurements and phenological observations) started in 1951. Scientific research is divided into an agricultural meteorology, forest meteorology and plant phenology. The basic purpose of research is the impact of regional climate change on the crop production, on the phenological stages of different plants as forest trees and shrubs, fruit-trees, olive-trees, grapevine and maize, and on the potential wildfire risk. In the operational tasks short-range and medium-range forecasts have been produced for TV and internet since 2004, radio since 1990 and specialized journals since 1988. For further development of Croatian agrometeorology, future plans are using monthly and seasonal forecasts for prediction of the development of yield of major agricultural crops, for irrigation purposes, as well as for potential risk of wildfires and applying satellite data and nowcasting for warning purposes in agriculture and in forest protection against fire.     

Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

Introduction

Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.

Methodology/Principal Findings

We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation.

Significance

These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.     

Weather factors in the short-term forecasting of daily ambulance calls

The daily ambulance demand for Hong Kong is rising, and it has been shown that weather factors (temperature and humidity) play a role in the demand for ambulance services. This study aimed at developing short-term forecasting models of daily ambulance calls using the 7-day weather forecast data as predictors. We employed the autoregressive integrated moving average (ARIMA) method to analyze over 1.3 million cases of emergency attendance in May 2006 through April 2009 and the 7-day weather forecast data for the same period. Our results showed that the ARIMA model could offer reasonably accurate forecasts of daily ambulance calls at 1–7 days ahead of time and with improved accuracy by including weather factors. Specifically, the inclusion of average temperature alone in our ARIMA model improved the predictability of the 1-day forecast when compared to that of a simple ARIMA model (8.8 % decrease in the root mean square error, RMSE?=?53 vs 58). The improvement in the 7-day forecast with average temperature as a predictor was more pronounced, with a 10 % drop in prediction error (RMSE?=?62 vs 69). These findings suggested that weather forecast data can improve the 1- to 7-day forecasts of daily ambulance demand. As weather forecast data are readily accessible from Hong Kong Observatory’s official website, there is virtually no cost to including them in the ARIMA models, which yield better prediction for forward planning and deployment of ambulance manpower.     

Effect of low temperatures on mortality and oviposition in conjunction with climate mapping to predict spread of the root weevil Diaprepes abbreviatus and introduced natural enemies

The tropical root weevil, Diaprepes abbreviatus (L.), has been a pest of citrus and ornamental plants since its introduction into Lake County, FL, in 1964. Since then, it has colonized the Florida peninsula to the south of its point of introduction but has not expanded its range to the north. A lower threshold for oviposition by D. abbreviatus was estimated as 14.9 degrees C. Eggs were highly susceptible to cold, with 95% mortality (LTime95) occurring in 4.2 d at 12 degrees C. Relative susceptibility of life stages to cold was eggs > pupae > larvae > adults. Archived weather data from Florida were examined to guide a mapping exercise using the lower developmental threshold for larvae (12 degrees C) and the lower threshold for oviposition (15 degrees C) as critical temperatures for mapping the distribution of D. abbreviatus and the potential for establishment of egg parasitoids. Probability maps using the last 10 yr of weather data examined the frequency of at least 10, 15, 20, 25, or 30 d per winter when soil temperature was 相似文献   

Climatic limits of pink bollworm in Arizona and California: effects of climate warming

The distribution and abundance of pink bollworm (Pectinophora gossypiella Saunders (PBW)) in cotton in Arizona and California was examined using a validated weather-driven, physiologically based demographic model of cotton and PBW integrated into a geographic information system (GIS). Survival of diapause larvae during winter as affected by low temperatures is a key factor determining the range of PBW. Winter survival was estimated using data from Gutierrez et al. (Can. Entomol. 109 (1977) 1457) and Venette et al. (Environ. Entomol. 29 (5) (2000) 1018). The model was run continuously over the period 1 January 1995 to 31 December 2003 using observed weather data from 121 locations. Three output variables were mapped as measures of PBW persistence: over-winter survival of diapause PBW larvae, cumulative daily PBW larval densities over the season, and the number of diapause larvae produced during the season. The distribution of pink bollworm is predicted to be restricted to the relatively frost-free cotton growing areas of Arizona and Southern California where it currently reaches pest status. The model predicts that extension of PBW's range into the Central Valley of California is unlikely. The analysis questions the efficacy of an ongoing area-wide effort to prevent the establishment of PBW in the Central Valley of California. Four global warming scenarios were examined to estimate the effects on the potential geographic range of PBW. Average observed daily temperatures were increased 1.0, 1.5, 2.0 or 2.5 °C, respectively, in the four scenarios. Scenarios with average increases of 1.5–2.5 °C predicted that the range of PBW would expand into the Central Valley of California and the severity of the pest would greatly increase in areas of current infestation.