Improving early-season estimates of olive production using airborne pollen multi-sampling sites

The aim of this study was to improve the accuracy of aeropalynological models to forecast yields in areas with heterogeneous characteristics by applying principal component analysis to integrate the airborne pollen sampled from more than one trap. The sampling was performed during the past seven years (1998–2004) in the main northeast olive regions of Portugal. Annual crop production was forecasted on the basis of airborne pollen concentration measured at flowering, comparing the performance of three different independent variables: total airborne pollen concentration sampled in each trap and a derived variable that was determined by principal component analysis of the total airborne pollen concentration sampled. The best predictive results were obtained using a logarithmic relationship with airborne pollen concentration principal component scores describing about 97% of olive fruit production variability over the last seven years. The use of this technique improved the ability of pollen to explain the production interannual variations by about 13%. The comparison between actual reported and the adjusted production showed an average spread deviation of 5%.     

Olive crop-yield forecasting based on airborne pollen in a region where the olive groves acreage and crop system changed drastically

Olive trees are one of the most economically important perennial crops in Portugal. During the last decade, the Alentejo olive-growing region has suffered a significantly change in the crop production system, with the regional pollen index (RPI) and olive fruit production registering a significant growth. The aim of this study was to ascertain the utility of this highly variable production and pollen data in crop forecasting modeling. Airborne pollen was sampled using a Cour-type trap from 1999 to 2015. A linear regression model fitted with the regional pollen index as the independent variable showed an accuracy of 87% in estimating olives fruit production in Alentejo. However, the average deviation between observed and modeled production was 32% with half of the tested years presenting deviations between 36 and 66%. The low accuracy of this model is a consequence of the great overall variation and significant upward trend observed in both the production and the RPI dataset that conceal the true association between these variables. In order to overcome this problem, a detrend procedure was applied to both time series to remove the trend observed. The regression model fitted with the fruit production and the RPI detrended data showed a lowest forecasting accuracy of 63% but the average deviation between observed and modeled production decrease to 14% with a maximum deviation value of 33%. This procedure allows focusing the analysis on the production fluctuations related to the biological response of the trees rather than with the changes in the production system.     

Comparison of Classical Models for Evaluating the Heat Requirements of Olive （Olea europeae L.） in Portugal

The aim of the present study was to compare the accuracy and reproducibility of six statistical models for the calculation of olive （Olea europeae L.） heat requirements to trigger the onset of flowering in three Portuguese regions： Reguengos de Monsaraz, Valenga do Douro, and Braga. Other alms were to ascertain the date on which the heat-accumulation period started and the threshold temperatures above which the development of reproductive structures starts in olives. The starting and peak dates for the regional O. europeae flowering season were estimated by monitoring airborne pollen from 1998 to 2004 using ＂Cour＂- type samplers. The threshold temperature values calculated for the three regions were very similar （9.0 ℃ for Valenca do Douro, 9.2 ℃ for Reguengos de Monsaraz, and 9.7 ℃ for Braga）. The accumulated daily mean temperature model had less interannual and inter-regional variation, showing best predictive results for 2004, with absolute differences between the observed and predicted dates of 4 d in Reguengos de Monsaraz and 2 d In Valenca do Douro and Braga for the onset of flowering date and of 2 d In Reguengos de Monsaraz, 7 d in Valenca do Douro, and 4 d in Braga for peak flowering dates. This model was the most accurate, reproducible, and operational to calculate heat requirements for olives to flower, with an average mean temperature accumulation of 1 446 ℃ In Reguengos, 1 642 ℃ in Valenga do Douro, and 1 703℃ In Braga to reach the onset of flowering. The best initial date for this accumulation was 1 January.     

Influence of meteorological parameters on Olea flowering date and airborne pollen concentration in four regions of Portugal

For calculating the total annual Olea pollen concentration, the onset of the main pollen season and the peak pollen concentration dates, using data from 1998 to 2004, predictive models were developed using multiple regression analysis. Four Portuguese regions were studied: Reguengos de Monsaraz, Valença do Douro, Braga and Elvas. The effect of some meteorological parameters such as temperature and precipitation on Olea spatial and temporal airborne pollen distribution was studied. The best correlations were found when only the pre‐peak period was used, with thermal parameters (maximum temperature) showing the highest correlation with airborne pollen distribution. Independent variables, selected by regression analysis for the predictive models, with the greatest influence on the Olea main pollen season features were accumulated number of days with rain and rainfall in the previous autumn, and temperatures (average and minimum) from January through March. The models predict 59 to 99% of the total airborne pollen concentration recorded and the initial and peak concentration dates of the main Olea pollen season.     

Olive phenology as a sensitive indicator of future climatic warming in the Mediterranean

Experimental and modelling work suggests a strong dependence of olive flowering date on spring temperatures. Since airborne pollen concentrations reflect the flowering phenology of olive populations within a radius of 50 km, they may be a sensitive regional indicator of climatic warming. We assessed this potential sensitivity with phenology models fitted to flowering dates inferred from maximum airborne pollen data. Of four models tested, a thermal time model gave the best fit for Montpellier, France, and was the most effective at the regional scale, providing reasonable predictions for 10 sites in the western Mediterranean. This model was forced with replicated future temperature simulations for the western Mediterranean from a coupled ocean‐atmosphere general circulation model (GCM). The GCM temperatures rose by 4·5 °C between 1990 and 2099 with a 1% per year increase in greenhouse gases, and modelled flowering date advanced at a rate of 6·2 d per °C. The results indicated that this long‐term regional trend in phenology might be statistically significant as early as 2030, but with marked spatial variation in magnitude, with the calculated flowering date between the 1990s and 2030s advancing by 3–23 d. Future monitoring of airborne olive pollen may therefore provide an early biological indicator of climatic warming in the Mediterranean.     

Bioclimatic indices as a tool in pollen forecasting

The use of bioclimatic indices could be a major step forward in the methodology of pollen forecasting. The basis for this proposal is that simple meteorological parameters do not reflect the global status of the atmosphere, but merely some static measurements. However, pollen dispersal is, above all, a dynamic phenomenon, and this fact should be reflected in the variables we used to explain it. Here, we test the two methodologies for routine pollen forecasting by comparing correlation coefficients using the same daily Poaceae airborne pollen data base from León (6 years, from 1994 to 1999) as the dependent variable and either simple daily meteorological variables or compound daily bioclimatic indices as independent variables. Both simple and compound indices reproduced the same profile of evolution of plant eco-physiological requirements, as the length of the study period during the pollen season increased. However, for time frames larger than the main pollen period, bioclimatic indices gave superior coefficients, which seems to indicate that these could be more valuable for pre-season pollen forecasting. The continentality index produced the highest mean coefficient, higher than those generated by any meteorological variable. Furthermore, at least for a Mediterranean climate, site location and evapotranspiration in relation to precipitation seem to be the most promising factors for increasing success when forecasting Poaceae airborne pollen concentration.     

Influence of meteorological parameters on olea pollen concentrations in Córdoba (South-western Spain)

The influence of meteorological parameters on the dispersion of airborne pollen has been studied by several authors. Olive pollen is the major cause of allergy in southern Spain, where a large part of the arable surface area is given over to olive cultivation. Daily pollen forecasts provide important information both for pollen-allergy sufferers and for agronomists trying to achieve a better biological understanding of variations in airborne olive pollen levels. The main purpose of this paper is to study, by means of short-term statistical analysis, the effect of meteorological parameters on airborne olive pollen concentrations in the city of Cordoba (south-western Spain). Twenty-one-year (1982–2002) aerobiological and meteorological databases were used. Correlation and multiple regression analyses were used to study the relationships between olive pollen levels and several meteorological parameters. Statistical analysis was applied both to the whole pollen season and to the pre-peak period. Daily meteorological parameters, such as accumulated mean temperature, accumulated sunlight hours, and accumulated rainfall were used as independent variables in both statistical analyses. Accumulated meteorological variables were of the greatest value in most regression analysis equations, heat-related variables being the most important.     

The effect of meteorological parameters on diurnal patterns of airborne olive pollen concentration

Aerobiological studies carried out in the atmosphere of Granada using a Hirst-type volumetric spore trap during the period 1993-1996 show that there is not a single diurnal pattern for olive pollen (Olea europaea L.) over the course of the main pollen season. Examination of the behaviour of airborne olive pollen concentration allows the establishment of either regular (54.4% of the studied days) or irregular (45.6% of the time) patterns of diurnal variation. On a given day, the pattern found will depend on a combination of different factors: the origin of the captured pollen (either local or regional), source distribution in relation to the pollen sampler, topography, and different meteorological variables (mean air temperature, sunshine hours, total rainfall, relative humidity, wind speed and direction, and periods of calm). Regional sources were significant contributors to city centre pollen concentrations when moderate (< 10 km/h) winds from the 4th quadrant and warm temperatures (19-26 C) allow swift transport from the W-NW of the province.     

北京主要气传致敏花粉年浓度峰值日期预测

花粉是我国北方引发过敏性鼻炎最主要过敏原,花粉症发病期与花粉浓度高峰期吻合。基于北京地区2012至2020年花粉季多站、逐日分类花粉浓度观测数据分析,得出北京地区花粉浓度在3月上旬至5月中旬(可进一步划分为3月中旬至4月上旬和4月下旬至5月上旬两个高峰期)和8月中旬至9月中旬分别存在两个高峰期,第一个高峰期内优势致敏花粉种类为柏科、杨柳科和松科,第二个高峰期内优势致敏花粉种类为桑科、菊科蒿属和藜科。根据优势致敏花粉年浓度峰值日期观测数据,使用与花粉采样站点位置相匹配的逐日气象观测数据累积值,基于作物模型概念和模糊逻辑原理建立了北京地区主要气传致敏花粉年浓度峰值日期预测模型。经检验,柏科、杨柳科、松科、桑科、菊科蒿属和藜科花粉模型预测准确率分别为87.8%、80.0%、64.4%、86.7%、78.8%和81.8%。基于北京地区主要气传致敏花粉年浓度峰值日期预测模型可为本地花粉症防治提供理论参考。     

Dynamics and behaviour of airborne Quercus pollen in central Iberian Peninsula

Quercus pollen is one of the most abundant pollen types in the atmosphere of central Iberian Peninsula (Spain), as a consequence of the extensive representation of well-preserved forests and shrub communities dominated by species of the genus Quercus in this area. This paper analysed key features of the Quercus pollination season in the central Iberian Peninsula and the influence of weather-related variables on airborne Quercus pollen concentration through statistical techniques of correlation analysis and the use of a decision tree model for predicting pollen concentrations. Quercus species are very common in Spain and Portugal, dominating a number of ecosystems including Mediterranean forests. This gives rise to very high airborne Quercus pollen concentrations, particularly in spring. Sampling was carried out over a 6-year period using a Hirst volumetric sampler, and the sampling procedure established by the Spanish Aerobiology Network. Results show that between 92 and 98.5 % of total annual airborne Quercus pollen was recorded in the April–June period. Annual pollen index were high in all study years, averaging 12,344 grains, but it should be highlighted that pollen production was highly variable between years. Correlations between mean daily Quercus pollen concentration and weather-related variables showed that in the pre-peak period, a significant positive correlation was observed with the mean daily temperature and the hours of sunshine and a negative correlation was observed with the humidity and the rainfall. In the post-peak period, a significant negative correlation was found with the mean daily temperature and the hours of sunshine. The predictions obtained in the decision tree model showed a moderate significant correlation (r = 0.42) with the daily Quercus pollen concentration predicted and the one observed. Temperature is the most influential variable in the release of Quercus pollen.     

Instrumental test to estimate a model of forecast yield: a non-parametric application to the pollen index in South Italy

A statistical test is described to verify the characteristics of the biological information contained in the dynamics of the flowering process. The test focuses on interactions between the pollen index and climatic variables to investigate if the biological indicator can synthesise the information of the pre-flowering phases. The multiple-regression model is built upon two pre-flowering climate macro-indicators extracted by Principal Component Analysis (PCA) and the optimised pollen index is obtained by non-parametric estimation. The empirical analysis is applied to 15 stations located in southern Italy in regions that have a longstanding tradition of olive production. Using the variance explained, we find that an optimised pollen index is fairly well predicted by the pre-flowering climatic data. We conclude that the optimised pollen index makes more parsimonious the modelling for predicting olive production.     

Distribution of Bactrocera oleae (Rossi, 1790) throughout the Iberian Peninsula based on a maximum entropy modelling approach

The Iberian Peninsula (Portugal and Spain) is a great production area of olives. The fruit production can be severely affected by the olive fruit fly, Bactrocera oleae (Rossi, 1790) (Diptera). Detailed geographical distribution maps of key pests, such as B. oleae, are essential for their integrated management. Although different sources reporting the occurrence of B. oleae are available for sub-regions of Portugal and Spain, the data available are dispersed and centralisation of this information considering the Iberian Peninsula as a faunistic geographical unit is currently lacking. In this work, we built two distribution maps of B. oleae throughout the Iberian Peninsula, one based on occurrence sites and another based on its bioclimatic habitat suitability. After modelling the bioclimatic suitability of B. oleae using a maximum entropy model, three potential distribution areas beyond the previously known occurrence range of the olive fruit fly were identified corresponding to the autonomous community of Galicia (Spain), the Spanish and Portuguese sides of the International Douro Natural Park, and the autonomous community of Castilla y León (Spain). Interestingly, each region houses nowadays autochthonous olive cultivars. The drivers that most contributed to the model were the precipitation of the coldest quarter and the precipitation of driest month which agrees with the B. oleae bioecology. Although our approach is not fully-comprehensive in terms of occurrence sites, we show how a maxent modelling approach can be useful to identify potential risk areas of B. oleae occurrence throughout a target geographical extent such as the Iberian Peninsula.     

Differences in atmospheric emissions of Poaceae pollen and Lol p 1 allergen

The allergens of different grass species share similar physicochemical and immunological features that account for the high incidence of allergenic cross-reactivity. We aimed to gain more information on the correlation between Poaceae airborne pollen and allergen concentration and hence make a reliable assessment of true pollen exposure in different bioclimatic areas. The release of Lol p 1 allergen from grass pollen differs between years and areas depending on variables like meteorological factors, biological sources, and cross-reactions with homologous allergens. This study monitored airborne pollen concentrations of grasses and Lol p 1 aeroallergen in León and Ourense, two cities with different climatic conditions located in northwestern Spain. Lol p 1 content in aerosol samples was quantified using specific ELISA antibody plates. Some our results show that Lol p 1 concentration increases when the atmospheric relative humidity is below 70%. This could explain the appearance of protein peaks at times when little or no grass pollen is present, especially after a short and heavy storm.     

A mathematical model for mugwort (Artemisia vulgaris L.) pollen forecasts

Pollen forecasts are a fundamental prerequisite to obtain prophylactic measures for allergic individuals. Mugwort belongs to the most relevant allergenic pollen types after grasses and birch. An approach to modeling of mugwort pollen concentrations has not been attempted previously in Germany. A process-oriented mathematical model for the relative local daily average mugwort airborne pollen concentration was developed on the basis of pollen and weather data measured during a 6-year period. The model depends on the daily minimum and maximum temperature, amount of precipitation and atmospheric pressure, which have to and can be supplied by measurement and prediction. The comparison of modeling results and pollen counting for an additional year confirms the fitness of the model. A computer program was written, which rests upon the model and supplies daily predictions of mugwort pollen flight during the period of the weather forecast. The latter should allow a pollen forecasting period of about 5 days, with an accuracy of about 32–63% explained variance, which in view of the low mugwort pollen counts (nine grains/m³ maximum in the validation year) represents a high relative measurement error. The mathematical model may serve to improve and rationalize of present pollen forecasts.     

Biometeorological and autoregressive indices for predicting olive pollen intensity

This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982–2010), a multivariate regression model based on five indices—the index-based model—was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.     

Pollen Ole e 1 content variations in olive cultivars of different Portugal regions

Olive is recognized as a crop with great impact in agricultural, socioeconomic, environmental and public health sectors. The last is becoming more important during recent years as consequence of the increase of the pollen allergy in south Europe prompted by the widespread Olea pollen allergic reactions. The aim of the study was to quantify, for the first time, the variations of the Ole e 1 allergen amount in Olea pollen grains from four cultivars in three regions of Portugal. How weather parameters can affect the allergen production was also assessed. The study was conducted in three olive producer areas of Portugal from 2010 to 2013, Santarém (Central), Elvas (Southeast) and Mirandela (Trás-os-Montes region, Northeast). Mature pollen of four different cultivars (Cobrançosa, Arbequina, Picual and Verdeal) was collected during the olive flowering season. Ole e 1 was quantified using specific 2-site antibody ELISA. Pollen of the olive groves at the boundary Olea bioclimatic distribution in the Mirandela registered the higher allergen content for all varieties in each study year. Arbequina was the variety that showed the lower Ole e 1 allergen concentration, whereas the higher content was registered for Cobrançosa. The main meteorological parameters that influenced the allergen Ole e 1 concentration in the pollen grains were the rainfall and temperatures related variables. The knowledge of the allergenicity in different olive cultivars is an important tool in the selection of the most adequate for planting as ornamental crop and to adjust the pollen extracts used for diagnosis or even immunotherapy.

     

Species traits are associated with the quality of bioclimatic models

Aim To analyse the effects of nine species trait variables on the accuracy of bioclimatic envelope models built for 98 butterfly species. Location Finland, northern Europe. Methods Data from a national butterfly atlas monitoring scheme (NAFI) collected from 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAMs) were constructed for 98 butterfly species to predict their occurrence as a function of climatic variables. Modelling accuracy was measured as the cross‐validation area under the curve (AUC) of the receiver–operating characteristic plot. Observed variation in modelling accuracy was related to species traits using multiple GAMs. The effects of phylogenetic relatedness among butterflies were accounted for by using generalized estimation equations. Results The values of the cross‐validation AUC for the 98 species varied between 0.56 and 1.00 with a mean of 0.79. Five species trait variables were included in the GAM that explained 71.4% of the observed variation in modelling accuracy. Four variables remained significant after accounting for phylogenetic relatedness. Species with high mobility and a long flight period were modelled less accurately than species with low mobility and a short flight period. Large species (>50 mm in wing span) were modelled more accurately than small ones. Species inhabiting mires had especially poor models, whereas the models for species inhabiting rocky outcrops, field verges and open fells were more accurate compared with other habitats. Main conclusions These results draw attention to the importance of species traits variables for species–climate impact models. Most importantly, species traits may have a strong impact on the performance of bioclimatic envelope models, and certain trait groups can be inherently difficult to model reliably. These uncertainties should be taken into account by downweighting or excluding species with such traits in studies applying bioclimatic modelling and making assessments of the impacts of climate change.     

Identification of potential sources of airborne Olea pollen in the Southwest Iberian Peninsula

This study aims to determine the potential origin of Olea pollen recorded in Badajoz in the Southwest of the Iberian Peninsula during 2009–2011. This was achieved using a combination of daily average and diurnal (hourly) airborne Olea pollen counts recorded at Badajoz (south-western Spain) and Évora (south-eastern Portugal), an inventory of olive groves in the studied area and air mass trajectory calculations computed using the HYSPLIT model. Examining olive pollen episodes at Badajoz that had distinctly different diurnal cycles in olive pollen in relation to the mean, allowed us to identify three different scenarios where olive pollen can be transported to the city from either distant or nearby sources during conditions with slow air mass movements. Back trajectory analysis showed that olive pollen can be transported to Badajoz from the West on prevailing winds, either directly or on slow moving air masses, and from high densities of olive groves situated to the Southeast (e.g. Andalucía). Regional scale transport of olive pollen can result in increased nighttime concentrations of this important aeroallergen. This could be particularly important in Mediterranean countries where people can be outdoors during this time due to climate and lifestyle. Such studies that examine sources and the atmospheric transport of pollen are valuable for allergy sufferers and health care professionals because the information can be incorporated into forecasts, the outputs of which are used for avoiding exposure to aeroallergens and planning medication. The results of studies of this nature can also be used for examining gene flow in this important agricultural crop.     

Modelling pollen sedimentation in Danish lakes at c.ad 1800: an attempt to validate the POLLSCAPE model

Aims To validate the POLLSCAPE simulation model of pollen dispersal and deposition, and evaluate the effect of factors such as pollen productivity, wind speed and regional plant abundance, using a data set of ad 1800 pollen assemblages and historical land cover data. Location Denmark. Methods ad 1800 land cover from historical maps is digitized for 2000 m radii around 30 Danish lakes (3.5–33 ha). The simulation model POLLSCAPE is used to predict sedimentary pollen assemblages in the lakes from the plant abundance data inferred from these maps, with different model parameter settings for wind speed, pollen productivity, regional pollen loading, etc. The model predictions are compared with observed ad 1800 pollen assemblages from the lake sediment records. Furthermore, pollen productivity is estimated from the ad 1800 pollen and vegetation data using the Extended R‐value model. Results Generally the model reproduces the patterns in the observed pollen assemblages, and for most pollen types there are significant correlations between observed and predicted pollen proportions. The pollen proportions predicted by the POLLSCAPE model are sensitive to the pollen productivity estimates used, the regional background pollen loading and average wind speed. There is a difference in background pollen loading between eastern and western Denmark, especially of Calluna pollen. The fit between predicted and observed pollen assemblages is best at wind speeds around 2.5 m s^?1, and decreases rapidly at lower wind speeds. The pollen productivity estimates from the ad 1800 data set are comparable with estimates from moss polsters in modern analogues of traditional cultural landscapes in Sweden and Norway. Main conclusions The POLLSCAPE model reproduces the patterns in the observed pollen assemblages from the lakes well, considering the uncertainty in the historical plant abundance data. This study indicates that the simulation model can be a useful tool for investigating relationships between vegetation and pollen composition, but also that the simulated pollen proportions are sensitive to the pollen productivity estimates, the regional background and to wind speed.     

13. Lake Saloio (Nazaré, western Portugal)

The possible impact of altitude and the related microclimatic conditions on the total production of fruiting branches, inflorescences, flowers and pollen grains of olive trees Olea europaea was analysed. A total of 90 Picual cultivar trees, the most extensive olive cultivar in the Iberian Peninsula, were studied for a three-year period (2007–2009). The study shows that production of flowers and pollen grains in a cultivar of the olive tree varies according to the microclimate. Our study also indicates that the olive trees frequently can have up to half a million flowers per tree. Moreover, the total flower production differs between years and study areas. In the Picual cultivar, the average production of pollen grains per anther is usually more than 60?000 grains. The total production of pollen per tree is around 72?000 million on average. The most favourable microclimatic conditions for reproduction in olive trees are found in years and olive growing areas with low temperature and high precipitation records during the months prior to flowering of the olive trees. We hypothesise that olive trees tend to increase their pollen production rate as altitude increases, which can be interpreted as a reproductive strategy to ensure fertilisation.