Prediction of Grapevine Yield Based on Reproductive Variables and the Influence of Meteorological Conditions

agronomy Article Prediction of Grapevine Yield Based on Reproductive Variables and the Influence of Meteorological Conditions Estefanía González-Fernández 1,* , Alba Piña-Rey 1, María Fernández-González 1,2 , María J. Aira 3and F. Javier Rodríguez-Rajo 1 1CITACA, Agri-Food Research and Transfer Cluster, Campus da Auga, University of Vigo, 32004 Ourense, Spain ; [email protected] (A.P.-R.); [email protected] (M.F.-G.); [email protected] (F.J.R.-R.) 2Earth Sciences Institute (ICT), Pole of the Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal 3Department of Biology University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; [email protected] *Correspondence: [email protected] Received: 20 April 2020; Accepted: 13 May 2020; Published: 16 May 2020   Abstract: Climate has a direct influence on crop development and final yield. The consequences of global climate change have appeared during the last decades, with increasing weather variability in many world regions. One of the derived problems is the maintenance of food supply in this unstable context and the needed changes in agricultural systems, looking for sustainable and adaptation strategies. The study was carried out from 2008 to 2017. Aerobiological data were obtained with a Lanzoni VPPS-2000 volumetric sampler, following the Spanish Aerobiological Network protocol. The pollen and flower production was studied on ten vines of the Godello grapevine cultivar. A HOBO Micro Station and a MeteoGalicia station were used to obtain meteorological information. We observed the detrimental effect of rain on airborne pollen presence, and we statistically corroborated the negative effect of high temperatures on fruit set and ripening. We developed an accurate multiple regression model to forecast the grape yield, applying a Spearman’s correlation test to identify the most influential variables. The use of aerobiological and meteorological studies for crop yield prediction has been widely used in different crops that suppose important engines for economy development. This enables growers to adapt their crop management and adjust the spent resources. Keywords: pollination dynamics; reproductive biology; grapevine; yield forecast; Godello 1. Introduction Aerobiological studies applied on crop yield prediction are becoming a valuable tool for agricultural practices in many crops. The use of pollen information with a forecast purpose has been widely explored for olive crops in the Mediterranean region [ 1 – 3 ], as well as for other important crops in this bioclimatic region, such as almond and grapevine [ 4 , 5 ]. Usually, climatological information is considered for aerobiological analyses because of their influence on aerobiological processes, such as pollen release or dispersion, and crop development. Meteorological factors exert a direct effect on the onset and duration of the phenological stages, acting as one of the main inputs that plants need to complete their vegetative-productive cycle [6]. Climatic conditions have a marked influence on grape and wine production, as factors such as temperature, light and humidity affect vegetal growth and development [ 7 , 8 ]. Combined with the edaphic elements (as soil water reserve or effective soil depth), these environmental components are considered the most important factors to define the winegrowing and production suitability of a region [ 9 ]. Furthermore, adverse weather conditions such as hailstorms may reduce grape yield by damaging flowers and fruits, affecting the total leaf area and the phenolic profile of berries at Agronomy 2020,10, 714; doi:10.3390/agronomy10050714 www.mdpi.com/journal/agronomy Agronomy 2020,10, 714 2 of 19 harvest [ 10 ]. Hail damage on grapevine yield increased across the European winegrowing regions due to climate change impacts [ 11 ]. Rainfalls wash pollen grains from the atmosphere or the stigma surface, which leads to a loss of fertilisation efficiency and a decrease in fecundated flowers [12]. Climate change has important consequences on biodiversity and ecosystem functioning. There are being registered many climate shifts in different world regions, such as the contraction of polar climate zones and the expansion of arid ones. This is the result of changes in intensity and frequency of extreme weather phenomena, such as extreme temperature events [ 13 ]. The intensity, frequency and duration of heat waves are projected to increase during the 21st century, as well as the frequency and intensity of droughts, which are projected to increase, particularly in the Mediterranean region and Southern Africa [ 14 ]. Studies conducted by [ 15 ] had confirmed that recent observed changes along European viticultural regions followed the climate change predictions, especially for the Iberian Peninsula, where a high increase of drought risk was detected. At a long-term scale, weather factors such as late spring frost in a warmer climate are expected to reshape the grapevine cultivars distribution in Europe [ 16 ]. Nevertheless, other studies suggested the increase of agrobiodiversity as an effective buffer of climate change effects on winegrape crop, using more climatically suitable cultivars for the climate warming. The authors of [ 17 ] found drastic reductions in winegrowing losses under warming scenarios with cultivar diversity. Another important point to consider for a crop production study are the physiological and morphological characteristics influenced by environmental conditions but deeply related to genetic load and expression. Flower formation and development in grapevines are highly influenced by environmental, genetic and cultural factors, which contribute to its variability [18]. The aim of the present study was to develop a prediction model to forecast the grape production for the Godello cultivar, one of the four autochthonous dominant white varieties in the Ribeiro Designation Origin area, which represent an important percentage of the Northwest Spain total production [ 19 ]. Aerobiological and meteorological variables, combined with pollen, flowers and grape production data, were considered to achieve an accurate prediction model for the final grape production as well as to assess the climatic influence on vegetal growth and the main detrimental factors affecting the considered cultivar. The development of this kind of model makes possible an adjusted crop yield prediction some months in advance, enhancing possible fraud detection due to the introduction of foreign grape by the establishment of a preliminary grape weight value prior to the official measure of the allowed grape varieties produced by the wineries covered by a Designation of Origin, the optimisation of the cultural and post-harvest tasks or the crop insurance hiring. Additionally, this information could be used to know the adaptation of the cultivars to the changing environmental conditions. 2. Material and Methods 2.1. Temporal and Geographic Delimitation The study was conducted from 2008 to 2017 in a vineyard located in Cenlle (Ourense) in Northwest Spain, which belongs to the Ribeiro Designation of Origin that actually covers a total area of 2250 ha (Figure 1). This winemaking region, watered by the Minho river, is defined as temperate and warm, sub-humid and with very cold nights according to the Multicriteria Climatic Classification System (MCC) [ 19 ]. Natural barriers that protect this territory from sub-Atlantic storms and its southern situation in Galicia favour the Oceanic-Mediterranean transitional climate of this region, with warm temperatures and considerable precipitation. Steep valleys and hillsides characterise the geomorphological structure of this area, with soils that have a granitic origin with a significant content of stones and gravel [20]. Agronomy 2020,10, 714 3 of 19 Agronomy 2020, 10, 714 3 of 19 Figure 1. Location of the Ribeiro Designation of Origin area in South Galicia, one of the main five Galician wine Designations of Origin, and its situation in Europe. 2.1. Plant Material and Grape Production Data Ten vines of the Godello grapevine cultivar were considered for pollen and flower production studies. The same ten plants were analysed over the study years, from 2008 to 2017, which were initially randomly selected among the Godello plot. The studied vineyard is enclosed in a multivarietal plot comprising four autochthonous Galician white cultivars, Treixadura, Godello, Loureira and Albariño, with a distance of 10 m between plots. The Godello plot considered in the present study covers an area of 2641.71 m2, composed by 20 rows of vines each one with 50 vines at 1-m spacing trained on a vertical shoot trellis system and row spacing of 2 m. The grape production data of the entire Ribeiro D.O. are annually provided by the Ribeiro Regulatory Council and published on its official website www.ribeiro.wine/es. The company “Viña Costeira S.R.L.”, registered as part of this Designation of Origin and owner of the studied vineyard in the present study, records the grape production of each plot in kilograms per hectare, providing us these yield data after harvest. 2.3. Aerobiological Study A Lanzoni VPPS-2000 volumetric pollen trap [21] was used for the detection and identification of airborne pollen and the spores of the main phytopathogenic fungi (Botrytis cinerea, Plasmopara viticola and Erysiphe necator) in the atmosphere of the vineyard. It was located in the central part of the plot, at 2 m above ground to minimise difficulties in pollen trapping by plant growth. The sampling period took place during the active cycle of Vitis vinifera, from 1 April to 30 September. A continuous flow of 10 litres of air/minute was maintained in the sampler and a Melinex tape coated with a 20 g/litre silicone solution was used as trapping surface. The tape support spins at 2 mm/hour, giving an autonomy of seven days. After this period, the Melinex tape was changed and the exposed tape was cut into seven pieces that were mounted on separate glass slides. Vitis vinifera pollen grains were identified and counted using an optical microscope and following the proposed model by the Spanish Aerobiological Network (REA) [22]. The V. vinifera pollen grains were identified as subspherical to triangular, tricolporated with three furrows and three pores, and 17-28 μm in diameter [23,24]. The Main Pollen Season (MPS) was assessed by means of the method proposed in [25], following the [26] protocol. The MPS accounted for the 95% of the total annual pollen recorded, starting when the accumulated sum of pollen reached the 2.5% of the total annual pollen, and ending when 97.5% of the total pollen was reached. The Seasonal Pollen Integral (SPIn) was calculated by summing the daily pollen grains/m3 concentrations over the MPS. Results of pollen counts were expressed as pollen when they were referred to total values of the considered period, or pollen grains/m3 of air when referring to daily mean values [27]. 2.4. Pollen and Flower Production Study Figure 1. Location of the Ribeiro Designation of Origin area in South Galicia, one of the main five Galician wine Designations of Origin, and its situation in Europe. 2.2. Plant Material and Grape Production Data Ten vines of the Godello grapevine cultivar were considered for pollen and flower production studies. The same ten plants were analysed over the study years, from 2008 to 2017, which were initially randomly selected among the Godello plot. The studied vineyard is enclosed in a multi-varietal plot comprising four autochthonous Galician white cultivars, Treixadura, Godello, Loureira and Albariño, with a distance of 10 m between plots. The Godello plot considered in the present study covers an area of 2641.71 m 2 , composed by 20 rows of vines each one with 50 vines at 1-m spacing trained on a vertical shoot trellis system and row spacing of 2 m. The grape production data of the entire Ribeiro D.O. are annually provided by the Ribeiro Regulatory Council and published on its official website www.ribeiro.wine/es. The company “Viña Costeira S.R.L.”, registered as part of this Designation of Origin and owner of the studied vineyard in the present study, records the grape production of each plot in kilograms per hectare, providing us these yield data after harvest. 2.3. Aerobiological Study A Lanzoni VPPS-2000 volumetric pollen trap [ 21 ] was used for the detection and identification of airborne pollen and the spores of the main phytopathogenic fungi (Botrytis cinerea,Plasmopara viticola and Erysiphe necator) in the atmosphere of the vineyard. It was located in the central part of the plot, at 2 m above ground to minimise difficulties in pollen trapping by plant growth. The sampling period took place during the active cycle of Vitis vinifera, from 1 April to 30 September. A continuous flow of 10 L of air/minute was maintained in the sampler and a Melinex tape coated with a 20 g/L silicone solution was used as trapping surface. The tape support spins at 2 mm/h, giving an autonomy of seven days. After this period, the Melinex tape was changed and the exposed tape was cut into seven pieces that were mounted on separate glass slides. Vitis vinifera pollen grains were identified and counted using an optical microscope and following the proposed model by the Spanish Aerobiological Network (REA) [ 22 ]. The V. vinifera pollen grains were identified as sub-spherical to triangular, tricolporated with three furrows and three pores, and 17-28 µ m in diameter [ 23 , 24 ]. The Main Pollen Season (MPS) was assessed by means of the method proposed in [ 25 ], following the [ 26 ] protocol. The MPS accounted for the 95% of the total annual pollen recorded, starting when the accumulated sum of pollen reached the 2.5% of the total annual pollen, and ending when 97.5% of the total pollen was reached. The Seasonal Pollen Integral (SPIn) was calculated by summing the daily pollen grains/m 3 concentrations over the MPS. Results of pollen counts were expressed as pollen when they were referred to total values of the considered period, or pollen grains/m3of air when referring to daily mean values [27]. Agronomy 2020,10, 714 4 of 19 2.4. Pollen and Flower Production Study Pollen and flower production was studied on 10 selected vines of the Godello cultivar maintained over the studied years. Each annual value corresponded to the average value of the 10 considered plants for the different variables. The number of inflorescences, tertiary branches (third order ramification in the inflorescence) and flowers per plant were estimated in the phenological phase BBCH-57 [ 28 ], corresponding to inflorescences fully developed, with flowers separating. To estimate the number of flowers per vine, we observed that 25 flowers composed a tertiary branch as a mean value, obtained from the flower count of one inflorescence on each one of the ten selected vines. To assess the number of anthers per flower, we observed in all collected flower samples that the Vitis vinifera androecium of the Godello cultivar is comprised of five stamens, each one with a bilocular anther, which coincided with previous descriptions of the Vitis vinifera floral biology and morphology [18]. The number of pollen grains per anther was calculated following the volumetric method described in [ 29 ]. Anthers were collected from grapevine flowers near to anthesis, in the BBCH-57 stage. Each anther was placed in 0.5 mL of ethanol 70% (v/v) with two drops of 1% basic fuchsin for pollen grains staining. The sample was crushed in the tube and vortexed to its homogenisation. A volume of 10 µ L of this dilution was transferred to a slide for microscope analysis and total pollen grains were counted with an optical microscope NIKON ECLIPSE E100 at 400 × magnification. A total of 27 anthers were analysed corresponding to three inflorescences of each vine, selecting three flowers per bunch and three anthers per flower. Three replicates were analysed for each sample. The number of pollen grains per anther was obtained by extrapolation to the total dilution volume. We calculated the pollen grains per vine considering the number of pollen grains per anther, the constants of 5 anthers per flower and 25 flowers per tertiary branch, the number of tertiary branches per inflorescence and the number of inflorescence. With all this information, it is possible to know the number of pollen grains per flower and flowers per vine, which defines the number of pollen grains per vine. 2.5. Meteorological Data Meteorological information was obtained from a HOBO Micro Station data logger, located at the central part of the cultivar Godello plot and in the same bracket than the aerobiological trap, at 0.75 m above ground level. The monitored daily parameters with this station were maximum temperature, average temperature and minimum temperature. Information about the precipitation was obtained from the Galician Institute for Meteorology and Oceanography METEOGALICIA station in Leiro (at 5 km from the studied vineyard). Based on these daily data, an average of 10 days, 15 days and one month periods were calculated for each temperature parameters from 1 April to 30 September for each studied year, from 2008 to 2017. The rainfall values were obtained from the sum of rainfall daily values during 10 days, 15 days and one month periods from 1 st April to 30 th September, from 2008 to 2017. Each one of the considered variables for statistical analysis were formed by the corresponding ten-days, fifteen-days and monthly periods over the studied years. 2.6. Statistical Analysis A Spearman’s correlation test was applied to determine the influence of meteorological conditions, pollen and flowers production parameters on grape production using the 2008–2016 data set. We applied this non-parametric statistical analysis due to the non-normal distribution of data. Significance was calculated for p ≤ 0.01 and p ≤ 0.05. In order to detect possible redundant correlation among the variables and prevent its use for model development, we calculated correlation matrix in six blocks for meteorological variables, considering the ten-days, fifteen-days and monthly periods, and a separated block for pollen and flower production variables. We used the R software version 3.5.3 [ 30 ] for this purpose, with the ‘corrplot’ 0.84 package for graphs generation [31]. Agronomy 2020,10, 714 5 of 19 Based on the correlation results, we developed a multiple regression model to forecast the final grape production considering the 2008–2016 data set. The 2017 data were used for model validation. Pollen and morphological production variables were considered as discrete variables: inflorescences/vine, tertiary branches/vine, flowers/vine, pollen/anther, pollen/vine and the total sum of pollen grains/m 3 of air over the season (SPIn). The accuracy of the obtained model was assessed by means of a lineal regression analysis between the observed grape production and the expected grape production values obtained by the model, and a Leave-One-Out Cross Validation (LOOCV) to determine the forecast reliability by the iterative prediction of each observation by means of all surrounding data as training data set [ 32 ]. The 2008–2016 data set was used as a training set in order to calculate the training error rate as the Root Mean Square Error (RMSE) of the LOOCV iterations. The standard deviation was calculated in order to estimate the error magnitude. The 2017 year was used to describe the test error rate since it was not included for model development, with the application of a t-test for dependent samples for the evaluation of significant statistical differences between real and forecast grape production in 2017. We used the IBM SPSS Statistics 25 software for these statistical analyses. 3. Results 3.1. Aerobiological Analysis Vitis vinifera L. Godello main pollen seasons were monitored between 2008 and 2017 from the middle of May to the end of June (Table 1). This period, which identifies the main pollen presence in the atmosphere, varied between the considered seasons, with a mean duration of 19 days and ranging from 15 to 27 days (in 2011 and 2012, respectively). The mean start date for the MPS was on 29 May and the mean end date on 16 June. In both cases, there is a month gap between the earliest and latest dates for starting and ending MPS among the considered years. The latest dates for the start and the end dates were detected in 2013, on 14 June for the start and 30 June for the end. The earliest dates for the MPS start and end also coincided in the same year, in 2011, with the beginning on 14 May and the end on 28 May. The 2011 season was additionally the year with the shortest MPS duration with 15 days (Table 1). The earliest peak date of airborne pollen concentrations was recorded on 23 May 2011, while the latest was registered on 23 June 2013. This monthly deviation among the studied years for the peak date indicates a marked year-to-year difference on the pollination season. The maximum peak value of daily pollen concentration was registered in 2011 with 64 pollen grains/m 3 , which coincided with the earliest peak date among the studied years. On the other hand, the minimum daily pollen concentration was registered in 2008 with 7 pollen grains/m3(Table 1). Table 1. Total pollen (SPIn), Main Pollen Season start, end and duration, and seasonal peak values for each considered year. Year SPIn (Total Pollen) Main Pollen Season (MPS) Seasonal Peak Start Date End Date Duration (Days) Peak Date Peak Value (Pollen Grains/m3) 2008 84 8-June 27-June 20 12,13,14,20-June 7 2009 222 30-May 17-June 19 1-June 34 2010 225 29-May 21-June 24 7-June 34 2011 226 14-May 28-May 15 23-May 64 2012 142 29-May 24-June 27 6-June 17 2013 336 14-June 30-June 17 23-June 48 2014 224 29-May 13-June 16 6-June 56 2015 94 24-May 10-June 18 29-May 28 2016 293 6-June 24-June 19 21-June 42 2017 282 15-May 1-June 18 24-May 49 Agronomy 2020,10, 714 6 of 19 Temperature variations and precipitation events seem to produce a marked effect on the pollen presence in the atmosphere. By representation of the airborne pollen concentrations evolution with mean and maximum temperature and precipitation (Figure 2), we observed that rain events during the pollination period coincided with marked decreases in the airborne pollen. In 2008, this direct effect was observed on 15 and 16 June, with 2.2 and 6.6 mm of rain, respectively, and a decrease in pollen concentrations that were the highest into the MPS (of 7 pollen grains/m 3 ) to very low values of 2 pollen grains/m3. Agronomy 2020, 10, 714 7 of 19 Figure 2. Airborne concentration of Vitis vinifera pollen (grey area), mean daily temperature (grey line), maximum daily temperature (black line) and precipitation (bars) during the Main Pollen Season (MPS) (grey squares) for each studied year. Pollen sampling was interrupted in 2011, from 21 to 22 of May, due to power cuts. 3.2. Pollen and Flower Production Figure 2. Airborne concentration of Vitis vinifera pollen (grey area), mean daily temperature (grey line), maximum daily temperature (black line) and precipitation (bars) during the Main Pollen Season (MPS) (grey squares) for each studied year. Pollen sampling was interrupted in 2011, from 21 to 22 of May, due to power cuts. Agronomy 2020,10, 714 7 of 19 In 2009 this detrimental effect was observed in the middle of the MPS, with a period of rain in consecutive days from 4 to 10 June and much rain accumulated on 5 June (16.8 mm), 8 June (18 mm) and 9 June (25.6 mm). The continuous rain period made the airborne pollen concentrations descend, which previously were the maximum MPS values, containing the seasonal peak of 34 pollen grains/m 3 on first June. During this precipitation period, the pollen concentrations went down to a range of 2–5 pollen grains/m 3 for the 6–10 June period. The same situation was observed in 2010 and 2016, with heavy rain events that promoted a marked decrease in airborne pollen concentrations. In the 2012 MPS, the airborne pollen records were irregular and oscillating. Pollen concentrations were not very high in this year and rain was widely distributed over the entire MPS, with several events on 1–3 June (0.8, 4.2, 0.2 mm, respectively), 7–8 June (2.6 and 0.2 mm), 10–13 June (0.8, 0.6, 0.4 and 0.2 mm), 15–17 June (7.4, 2.2 and 10 mm) and 20–21 June (9.8 and 0.4 mm). This descend on the pollen concentrations promoted by rain was also observed in 2017, where the occurrence of rain on 25 May, 26 May and 28 May (8.8, 24.2 and 20 mm, respectively), just after the seasonal pollen peak, of 49 pollen grains/m 3 on 24 May, promoted a fast decrease in the airborne pollen concentration. Moreover, temperature dropped during the precipitation events, which has an added adverse effect on the atmospheric pollen presence (Figure 2). Conversely, temperature rises coincided with high airborne pollen concentrations and in many cases with the highest seasonal pollen peak. The beneficial effect of the mean and maximum temperature rise on the presence of atmospheric pollen was clearly observed in 2008, 2009, 2016 and 2017. In these years, the pollen increases seem to adjust with temperature rises (Figure 2). The average of mean temperatures during the MPS of the considered years 2008–2017 was 18.5 ◦ C, and the average for maximum temperatures was 26.2 ◦C. 3.2. Pollen and Flower Production The production of inflorescences, flowers, pollen per vine and pollen per anther showed similar fluctuations among the considered years, from 2008 to 2017 (Figure 3). Agronomy 2020, 10, 714 8 of 19 The production of inflorescences, flowers, pollen per vine and pollen per anther showed similar fluctuations among the considered years, from 2008 to 2017 (Figure 3). Figure 3. Total annual production of flower, inflorescences, pollen grains per anther and pollen grains per vine for the studied seasons 2008–2017. Standard error of the mean for each variable in bars. The flowers per vine, which presented an average value of 2238 flowers/vine for the considered plants among the complete studied period, showed the maximum in 2013 with 3581 flowers/vine and a minimum in 2012 with 1308 flowers/vine. The maximum annual record of flowers/vine coincided with the highest inflorescences´ number that was also registered in 2013 with 28 inflorescences, while the minimum was registered in 2008 with 15 inflorescences. The minimum records of pollen per anther and the pollen per vine coincided in the same year, in 2012, with 1863 pollen grains/anther and 12,138,912 pollen grains/vine, respectively. The maximum record of pollen per anther was obtained in 2017 with 4937 pollen grains/anther, while the maximum record of pollen per vine was registered in 2009 with 61,631,026 pollen grains/vine (Figure 3). Nevertheless, important differences were found in 2011, 2014 and 2017, showing a dissimilar behaviour on the different pollen-flowers-inflorescences production variables. In 2011, despite the increase of 855 flowers/vine with respect to the previous year, the pollen/anther markedly decreased (from 3780 in 2010 to 2506 pollen/anther) and the pollen/vine had a slight descend (from 37,377,906 in 2010 to 35,083,187 pollen/vine). In 2014, these variables changed inversely, with a notable increase in pollen/anther (from 2546 to 4084 pollen/anther) and a slight increase in pollen/vine respect to the previous year (from 44,739,674 to 46,331,441 pollen/vine), but the number of flowers/vine descended to 1258 flowers with respect to the previous year. In 2017, the pollen/anther and pollen/vine markedly increased, from 2953 in 2016 to 4937 pollen/anther, and 38,773,450 in 2016 to 53,637,941 pollen/vine. Despite this, the number of flowers slightly descended to 504 flowers (Figure 3). 3.3. Statistical Analysis A Spearman´s correlation test was applied to assess the main weather, pollen and flower production variables that have a significant influence on final grape production. The statistically significant correlations were mostly related to meteorological variables (Figure 4). Regarding the correlations of meteorological variables with grape production, we found the highest significance level (p < 0.01) for the variables of the first ten-days of August maximum Temperature, the second ten-days of July maximum Temperature and the second ten-days of April Rain, and the monthly July maximum Temperature, April Rain and May Rain, all of them with a negative coefficient, which indicated a negative influence on the final grape production (Figure 4). In the case of pollen and flower production variables, we only found one correlation with grape production, the number of inflorescences per vine, but with a low p-value (0.1 > p > 0.05) (Figure 5). 0 1000 2000 3000 4000 5000 6000 7000 8000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 pollen grains, flowers, inflorescences Pollen/anther (Pollen/vine)/10000 Flowers/vine (Inflorescences/vine)*100 Figure 3. Total annual production of flower, inflorescences, pollen grains per anther and pollen grains per vine for the studied seasons 2008–2017. Standard error of the mean for each variable in bars. Agronomy 2020,10, 714 8 of 19 The flowers per vine, which presented an average value of 2238 flowers/vine for the considered plants among the complete studied period, showed the maximum in 2013 with 3581 flowers/vine and a minimum in 2012 with 1308 flowers/vine. The maximum annual record of flowers/vine coincided with the highest inflorescences’ number that was also registered in 2013 with 28 inflorescences, while the minimum was registered in 2008 with 15 inflorescences. The minimum records of pollen per anther and the pollen per vine coincided in the same year, in 2012, with 1863 pollen grains/anther and 12,138,912 pollen grains/vine, respectively. The maximum record of pollen per anther was obtained in 2017 with 4937 pollen grains/anther, while the maximum record of pollen per vine was registered in 2009 with 61,631,026 pollen grains/vine (Figure 3). Nevertheless, important differences were found in 2011, 2014 and 2017, showing a dissimilar behaviour on the different pollen-flowers-inflorescences production variables. In 2011, despite the increase of 855 flowers/vine with respect to the previous year, the pollen/anther markedly decreased (from 3780 in 2010 to 2506 pollen/anther) and the pollen/vine had a slight descend (from 37,377,906 in 2010 to 35,083,187 pollen/vine). In 2014, these variables changed inversely, with a notable increase in pollen/anther (from 2546 to 4084 pollen/anther) and a slight increase in pollen/vine respect to the previous year (from 44,739,674 to 46,331,441 pollen/vine), but the number of flowers/vine descended to 1258 flowers with respect to the previous year. In 2017, the pollen/anther and pollen/vine markedly increased, from 2953 in 2016 to 4937 pollen/anther, and 38,773,450 in 2016 to 53,637,941 pollen/vine. Despite this, the number of flowers slightly descended to 504 flowers (Figure 3). 3.3. Statistical Analysis A Spearman’s correlation test was applied to assess the main weather, pollen and flower production variables that have a significant influence on final grape production. The statistically significant correlations were mostly related to meteorological variables (Figure 4). Regarding the correlations of meteorological variables with grape production, we found the highest significance level (p<0.01) for the variables of the first ten-days of August maximum Temperature, the second ten-days of July maximum Temperature and the second ten-days of April Rain, and the monthly July maximum Temperature, April Rain and May Rain, all of them with a negative coefficient, which indicated a negative influence on the final grape production (Figure 4). In the case of pollen and flower production variables, we only found one correlation with grape production, the number of inflorescences per vine, but with a low p-value (0.1 >p>0.05) (Figure 5). Furthermore, we observed in the correlation matrix of the meteorological variables, divided in six blocks, as well as in the matrix for pollen and flower production variables, some strong correlations between variables tightly related, as the positive correlations found for mean and maximum temperatures in the same period, or the negative correlations of these mean and maximum temperatures with rain for the same studied period that could reflect the air temperature decrease the effect of rainfall from a cooler atmosphere layer, as raindrops exchange heat with the warmer air near to the ground due to the evaporation of part of the waterdrops. Among the considered pollen and flower production variables, we found strong correlations possibly related with some secondary variables derived from primary variables, as the flowers/vine deriving from the number of tertiary branches and inflorescences or the pollen/vine derived from the pollen per anther and the number of flowers. To prevent the effect of these relations in the regression model, we decided not to consider variables where redundant correlations were found, and instead just one of the pollen and flowers production variables, the inflorescences/vine, since it showed statistical influence on grapevine yield. Agronomy 2020,10, 714 9 of 19 Agronomy 2020, 10, 714 9 of 19 Furthermore, we observed in the correlation matrix of the meteorological variables, divided in six blocks, as well as in the matrix for pollen and flower production variables, some strong correlations between variables tightly related, as the positive correlations found for mean and maximum temperatures in the same period, or the negative correlations of these mean and maximum temperatures with rain for the same studied period that could reflect the air temperature decrease the effect of rainfall from a cooler atmosphere layer, as raindrops exchange heat with the warmer air near to the ground due to the evaporation of part of the waterdrops. Figure 4. Spearman´s rank correlation coefficients between grape production and meteorological variables with the considered period durations of ten-days, fifteen-days and month for the 2008–2016 data set. Coefficient values were expressed in colours (referred to the lower legend) and the significance level was represented as * p < 0.05, ** p < 0.01. Figure 4. Spearman’s rank correlation coefficients between grape production and meteorological variables with the considered period durations of ten-days, fifteen-days and month for the 2008–2016 data set. Coefficient values were expressed in colours (referred to the lower legend) and the significance level was represented as * p<0.05, ** p<0.01. Agronomy 2020,10, 714 16 of 19 References 1. Gal á n, C.; V á zquez, L.; Garc í a-Mozo, H.; Dom í nguez, E. Forecasting olive (Olea europaea) crop yield based on pollen emission. Field Crop. Res. 2004,86, 43–51. [CrossRef] 2. Oteros, J.; Orlandi, F.; Garc í a-Mozo, H.; Aguilera, F.; Dhiab, A.B.; Bonofiglio, T.; Abichou, M.; Ruiz-Valenzuela, L.; del Trigo, M.M.; D í az de la Guardia, C.; et al. Better prediction of Mediterranean olive production using pollen-based models. Agron. Sustain. 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