Phenological changes in olive (<i>Ola europaea L.</i>) reproductive cycle in southern Spain due to climate change

Introduction Modifications of crop species phenology due to a changing environment are of interest because of their impact on fruit set and final harvest. Pre-flowering and flowering phenophases in olive groves at different sites of southern Spain were examined, in order to chart potential trends and determine major correlations with weather-related parameters, especially temperature and water availability. The high prevalence of olive pollen allergy in the Mediterranean population makes this study highly relevant. Material and Methods Ten sites in Cordoba province (Spain) during a 17-year period (1996–2012). BBCH phenology scale. Meteorological data from 1960 were analyzed; data from 1996 included on modeling analysis. Linear Mixed Models (LMMs) were developed, combining phenological and meteorological data. Results Since 1960, local spring temperatures have increased 1.5ºC, the number of spring rainfall days has fallen 11 days, total rainfall has declined 150 mm. Despite phenological differences between sites, attributable to altitude, phenological development during the season followed a similar pattern. Flowering dates advanced 2 days, while inflorescence emergence was delayed 24 days. Trend slopes revealed differences, an earlier period (1996–2002) with a sharp flowering advance of 15 days, and a later period (2003–2012) characterized by a gradual advance and a high bud emergence delay of 22 days. Conclusions LMMs was revealed as an appropriate technique for phenology behaviour analysis displaying both fixed and random interactions. Cultivars grown in the study province are adapted to climate with a synchronized response, although climate change is affecting theolive reproductive cycle in southern Spain; therefore, the timing of pollen release, with subsequent consequences on allergic population as phenological changes, could have impacts on flowering period and pollen production. Further investigation is required of the implications for crop production in Mediterranean ecosystems

Automatic and online pollen monitoring

BACKGROUND: Pollen are monitored in Europe by a network of about 400 pollen traps, all operated manually. To date, automated pollen monitoring has only been feasible in areas with limited variability in pollen species. There is a need for rapid reporting of airborne pollen as well as for alleviating the workload of manual operation. We report our experience with a fully automated, image recognition-based pollen monitoring system, BAA500. METHODS: The BAA500 sampled ambient air intermittently with a 3-stage virtual impactor at 60 m(3)/h in Munich, Germany. Pollen is deposited on a sticky surface that was regularly moved to a microscope equipped with a CCD camera. Images of the pollen were constructed and compared with a library of known samples. A Hirst-type pollen trap was operated simultaneously. RESULTS: Over 480,000 particles sampled with the BAA500 were both manually and automatically identified, of which about 46,000 were pollen. Of the automatically reported pollen, 93.3% were correctly recognized. However, compared with manual identification, 27.8% of the captured pollen were missing in the automatic report, with most reported as unknown pollen. Salix pollen grains were not identified satisfactorily. The daily pollen concentrations reported by a Hirst-type pollen trap and the BAA500 were highly correlated (r = 0.98). CONCLUSIONS: The BAA500 is a functional automated pollen counter. Its software can be upgraded, and so we expected its performance to improve upon training. Automated pollen counting has great potential for workload reduction and rapid online pollen reporting

Predicting the main pollen season of Broussonetia Papyrifera (paper mulberry) tree

Paper mulberry pollen, declared a pest in several countries including Pakistan, can trigger severe allergies and cause asthma attacks. We aimed to develop an algorithm that could accurately predict high pollen days to underpin an alert system that would allow patients to take timely precautionary measures. We developed and validated two prediction models that take historical Nov 15, 2023 2/18 pollen and weather data as their input to predict the start date and peak date of the pollen season in Islamabad, the capital city of Pakistan. The first model is based on linear regression and the second one is based on phenological modelling. We tested our models on an original and comprehensive dataset from Islamabad. The mean absolute errors (MAEs) for the start day are 2.3 and 3.7 days for the linear and phenological models, respectively, while for the peak day, the MAEs are 3.3 and 4.0 days, respectively. These encouraging results could be used in a website or app to notify patients and healthcare providers to start preparing for the paper mulberry pollen season. Timely action could reduce the burden of symptoms, mitigate the risk of acute attacks and potentially prevent deaths due to acute pollen-induced allergy

Methods for interpolating missing data in aerobiological databases

The availability of extensive environmental time series is usually laborious and difficult, and sometimes unexpected failures are not detected until samples are processed. Consequently, environmental databases frequently have some gaps with missing data in it. Applying an interpolation method before starting the data analysis can be a good solution in order to complete this missing information. Nevertheless, there are several different approaches whose accuracy should be considered and compared. In this study, data from 6 aerobiological sampling stations were used as an example of environmental data series to assess the accuracy of different interpolation methods. For that, observed daily pollen/spore concentration data series were randomly removed, interpolated by using different methods and then, compared with the observed data to measure the errors produced. Different periods, gap sizes, interpolation methods and bioaerosols were considered in order to check their influence in the interpolation accuracy. The moving mean interpolation method obtained the highest success rate as average. By using this method, a success rate of the 70% was obtained when the risk classes used in the alert systems of the pollen information platforms were taken into account. In general, errors were mostly greater when there were high oscillations in the concentrations of biotic particles during consecutive days. That is the reason why the pre-peak and peak periods showed the highest interpolation errors. The errors were also higher when gaps longer than 5 days were considered. So, for completing long periods of missing data, it would be advisable to test other methodological approaches. A new Variation Index based on the behaviour of the pollen/spore season (measurement of the variability of the concentrations every 2 consecutive days) was elaborated, which allows to estimate the potential error before the interpolation is applied.This work was supported by the Spanish Ministry of Economy and Competitiveness [project CGL2014-54731-R]; by the Ministry of Science and Innovation [projects RTI2018-096392-B-C22]; by the Junta de Andalucía [contract 8.06/503.4764]; and by the Area of Environment and Sustainability of the Malaga City Council [contracts 8.06/5.03.4721 and 8.07/5.03.5159], and the Junta Comunidades de Castilla-La Mancha, which provides financial support for the Castilla-La Mancha Aerobiology Network (AEROCAM). Antonio Picornell was supported by a predoctoral grant financed by the Spanish Ministry of Education, Culture and Sport, in the Program for the Promotion of Talent and its Employability [grant number FPU15/01668]. The pollen trap installed in Sierra de las Nieves was funded by the Herbarium MGC of the SCAI (Central Services of Research Support) of the University of Malaga under the agreement signed between the Junta de Andalucía and the University of Malaga [contract 8.07/5.034764]. Acknowledgments: The authors specially want to thanks the SCAI (Central Service for Research Support) of the University of Malaga for supporting the acquisition of the pollen trap installed in Sierra de las Nieves; the Parauta City Council, the direction of Sierra de las Nieves Natural Park, Las Conejeras campsite for facilitating the installation of the pollen trap in Sierra de las Nieves; and the staff of Pérez de Guzmán High School for providing support to install and maintain the pollen trap in Ronda, and to Enresa for facilitating the installation and maintenance of the pollen trap in Hornachuelos Natural Park

Environmental drivers of the seasonal exposure to airborne Alternaria spores in Spain.

Alternaria conidia have high allergenic potential and they can trigger important respiratory diseases. Due to that and to their extensive detection period, airborne Alternaria spores are considered as a relevant airborne allergenic particle. Several studies have been developed in order to predict the human exposure to this aeroallergen and to prevent their negative effects on sensitive population. These studies revealed that some sampling locations usually have just one single Alternaria spore season while other locations generally have two seasons within the same year. However, the reasons of these two different seasonal patterns remain unclear. To understand them better, the present study was carried out in order to determine if there are any weather conditions that influence these different behaviours at different sampling locations. With this purpose, the airborne Alternaria spore concentrations of 18 sampling locations in a wide range of latitudinal, altitudinal and climate ranges of Spain were studied. The aerobiological samples were obtained by means of Hirst-Type volumetric pollen traps, and the seasonality of the airborne Alternaria spores were analysed. The optimal weather conditions for spore production were studied, and the main weather factor affecting Alternaria spore seasonality were analysed by means of random forests and regression trees. The results showed that the temperature was the most relevant variable for the Alternaria spore dispersion and it influenced both the spore integrals and their seasonality. The water availability was also a very significant variable. Warmer sampling locations generally have a longer period of Alternaria spore detection. However, the spore production declines during the summer when the temperatures are extremely warm , what splits the favourable period for Alternaria spore production and dispersion into two separate ones, detected as two Alternaria spore seasons within the same year.This work was partially financed by the Ministry of Science and Innovation of Spain and FEDER fundings inside the Operational Plurirregional Program of Spain 2014-2020 and the Operational Program of Smart Growing (Project Environmental and Biodiversity Climate Change Lab, EnBiC2-Lab). This work counted also with the economical support of the Spanish Ministry of Economy and Competitiveness [project CGL2014-54731-R]; the Ministry of Science and Innovation [project RTI2018-096392-B-C22]; the Junta de Andalucía [contract 8.06/503.4764]; the Area of Environment and Sustainability of the Malaga City Council [contracts 8.06/5.03.4721 and 8.07/5.03.5159]; the Junta Comunidades de Castilla-La Mancha, which provides financial support for the Castilla-La Mancha Aerobiology Network (AEROCAM); and the financial support of Health Department of Madrid region (Consejería de Sanidad de Comunidad de Madrid) to the palynological network PALINOCAM. The pollen trap installed in Sierra de las Nieves was funded by the Herbarium MGC of the SCAI (Central Services of Research Support) of the University of Malaga under the agreement signed between the Junta de Andalucía and the University of Malaga [contract 8.07/5.034764]. Antonio Picornell was supported by a predoctoral grant financed by the Spanish Ministry of Education, Culture and Sport, in the Program for the Promotion of Talent and its Employability [grant number FPU15/01668]. Navarra sampling locations were supported by the Navarra Institute of Public and Occupational Health (ISPLN) with funding from the LIFE+NADAPTA project. Valladolid and Salamanca sampling locations were supported by the Regional Health Authority, Junta of Castile and León, Spain [Project GRS 1862/A/18]. Seville sampling location was supported by the Project MEC I+D+I CGL2009-10683

Concentric Ring Method for generating pollen maps. Quercus as case study.

Mapping pollen concentrations is of great interest to study the health impact and ecological implications or for forestry or agronomical purposes. A deep knowledge about factors affecting airborne pollen is essential for predicting and understanding its dynamics. The present work sought to predict annual Quercus pollen over the Castilla and León region (Central and Northern Spain). Also to understand the relationship between airborne pollen and landscape. Records of Quercus and Quercus pyrenaica pollen types were collected at 13 monitoring sites over a period of 8years. They were analyzed together with land use data applying the Concentric Ring Method (CRM), a technique that we developed to study the relationship between airborne particle concentrations and emission sources in the region. The maximum correlation between the Quercus pollen and forms of vegetation was determined by shrubland and "dehesa" areas. For the specific Qi pyrenaica model (Q. pyrenaica pollen and Q. pyrenaica forest distribution), the maximum influence of emission sources on airborne pollen was observed at 14km from the pollen trap location with some positive correlations up to a distance of 43km. Apart from meteorological behavior, the local features of the region can explain pollen dispersion patterns. The method that we develop here proved to be a powerful tool for multi-source pollen mapping based on land use

Automatic and Online Pollen Monitoring.

Pollen are monitored in Europe by a network of about 400 pollen traps, all operated manually. To date, automated pollen monitoring has only been feasible in areas with limited variability in pollen species. There is a need for rapid reporting of airborne pollen as well as for alleviating the workload of manual operation. We report our experience with a fully automated, image recognition-based pollen monitoring system, BAA500.The BAA500 sampled ambient air intermittently with a 3-stage virtual impactor at 60 m3/h in Munich, Germany. Pollen is deposited on a sticky surface that was regularly moved to a microscope equipped with a CCD camera. Images of the pollen were constructed and compared with a library of known samples. A Hirst-type pollen trap was operated simultaneously.Over 480,000 particles sampled with the BAA500 were both manually and automatically identified, of which about 46,000 were pollen. Of the automatically reported pollen, 93.3% were correctly recognized. However, compared with manual identification, 27.8% of the captured pollen were missing in the automatic report, with most reported as unknown pollen. Salix pollen grains were not identified satisfactorily. The daily pollen concentrations reported by a Hirst-type pollen trap and the BAA500 were highly correlated (r = 0.98).The BAA500 is a functional automated pollen counter. Its software can be upgraded, and so we expected its performance to improve upon training. Automated pollen counting has great potential for workload reduction and rapid online pollen reporting

Impact of Climate Change on Olive Crop Production in Italy

The effects of climate change on agricultural systems raise important uncertainties about the future productivity and suitability of crops, especially in areas suffering from intense environmental changes. Olive groves occupy Mediterranean areas characterized by seasonal temporary droughts, which cause this cultivation to be highly dependent on local microclimatic conditions. Olive crop production can be reliably estimated using pollen intensity metrics together with post-pollination environmental conditions. In this study, we applied this kind of statistics-based models to identify the most relevant meteorological variables during the post-pollination periods for olive fruit production. Olive pollen time-series for the period of 1999–2012 was analyzed in 16 Italian provinces. Minimum and maximum temperature during spring and summer (March–August) showed a negative relationship with olive production, while precipitation always showed a positive correlation. The increase in aridity conditions observed in areas of Italy during the summer represents an important risk of decreasing olive crop production. The effect of climate change on the olive production trend is not clear because of the interactions between human and environmental factors, although some areas might show an increase in productivity in the near future under different climate change scenarios. However, as more drastic changes in temperature or precipitation take place, the risk to olive production will be considerably greater