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Total pollen (SPIn), Main Pollen Season start, end and duration, and seasonal peak values for each considered year.
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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...
Contexts in source publication
Context 1
... 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 (Table 1). ...Context 2
... 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 (Table 1). ...Context 3
... 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/m 3 ( Table 1). Temperature variations and precipitation events seem to produce a marked effect on the pollen presence in the atmosphere. ...Similar publications
Biological life-support systems could greatly increase the sustainability of crewed missions to the Moon or Mars. Understanding how bacteria react to hypobaria is critical to their optimization: if enclosed within crewed compartments, microbial modules may be exposed to the lower-than-Earth atmospheric pressure considered for future space vehicles...
Citations
... Under these circumstances, a reduction in leaf development promotes enhanced water use efficiency, enabling the vine to prioritize resource allocation toward berry maturation [42]. Heavy precipitation during the pollen season tends to have negative effects on yield due to inade-quate pollen dispersal [43], which is somewhat visible in Figure 5, although not entirely clear, due to the obfuscated results of RR_05 and RR_06 found herein. In sum, the intricate interplay between precipitation and grapevine development highlights the importance of meticulous water management practices in viticulture. ...
The Côa region in inner-northern Portugal heavily relies on viticulture, which is a cornerstone of its economy and cultural identity. Understanding the intricate relationship between climatic variables and wine production (WP) is crucial for adapting management practices to changing climatic conditions. This study employs machine learning (ML), specifically random forest (RF) regression, to predict grapevine yields in the Côa region using high-resolution climate data for 2004-2020. SHAP (SHapley Additive exPlanations) values are used to potentially explain the non-linear relationships between climatic factors and WP. The results reveal a complex interplay between predictors and WP, with precipitation emerging as a key determinant. Higher precipitation levels in April positively impact WP by replenishing soil moisture ahead of flowering, while elevated precipitation and humidity levels in August have a negative effect, possibly due to late-season heavy rainfall damaging grapes or creating more favorable conditions for fungal pathogens. Moreover, warmer temperatures during the growing season and adequate solar radiation in winter months favor higher WP. However, excessive radiation during advanced growth stages can lead to negative effects, such as sunburn. This study underscores the importance of tailoring viticultural strategies to local climatic conditions and employing advanced analytical techniques such as SHAP values to interpret ML model predictions effectively. Furthermore, the research highlights the potential of ML models in climate change risk reduction associated with viticulture, specifically WP. By leveraging insights from ML and interpretability techniques, policymakers and stakeholders can develop adaptive strategies to safeguard viticultural livelihoods and stable WP in a changing climate, particularly in regions with a rich agrarian heritage, such as the Côa region.
... Canopy condition, measured through spectral bands of remote sensing images or vegetation indices like NDVI, has been found to correlate with vineyard yield. Moreover, weather data significantly influences vineyard yield, improving estimation when combined with remote sensing data (Lobell et al., 2006;Agosta et al., 2012;González-Fernández et al., 2020). ...
... Using the same extent as the yield monitor data (see Fig. 1), time-series satellite Sentinel 2A-B (Sentinel2, 2022) images were downloaded using the Google Earth Engine (GEE) platform (Gorelick et al., 2017). The temporal resolution of Sentinel 2A satellite data is 10 days, but after lunching Sentinel 2B, this temporal resolution improved to 5 days. ...
... The fusion of RGBN satellite images and management practice data helped model to generalize the model better to different cultivar types, along with block-level variation in management practices. Fusing various data sources for crop yield estimation, such as multi-sensor fusion, fusion of meteorological, climate and remote sensing imagery, or soil and evapotranspiration have been used and their impacts on yield estimation has been discussed by previous studies (Jiang and Thelen, 2004;Epule et al., 2018;González-Fernández et al., 2020;Maimaitijiang et al., 2020;Sagan et al., 2021;Deines et al., 2021). However, based on our best knowledge at the time of writing, this experiment is the first to fuse data on categorical management practices with remote sensing imagery to not only improve yield estimation performance, but also to generalize the model to learn about different cultivar types and their different management practices. ...
Crop yield forecasting is essential for informed farm management decisions. However, most yield forecasting models have low spatial resolution, late-season predictions, and lack validation for unseen years or locations. These limitations likely stem from the scarcity of large-scale, high-resolution yield measurement data collected over multiple years, which is uncommon in commercial specialty crop operations. Additionally, these limitations raise concerns about the models’ utility and generalizability under new environmental and management conditions within or across farms. In this study, we develop a spatio-temporal deep learning model to forecast wine grape yield early in the season, utilizing a large dataset with high spatio-temporal resolution (i.e., yield data from million grapevines of eight cultivars observed over four years). The model is trained on weekly 10 m RGB-NIR time-series satellite data from Sentinel 2A-B, fused with categorical and continuous management inputs, including cultivar type, trellis type, row spacing, and canopy spacing. We assess the model’s generalizability by examining its performance on data from unseen years and/or locations and at multiple spatial resolutions. Our results show that combining management data with satellite imagery significantly improves model performance on entirely unseen vineyard blocks at 10 m resolution, achieving an of 0.76, a mean absolute error (MAE) of 4.21 tonnes/hectare, and a mean absolute percent error (MAPE) of 13%. We find that cultivars with considerable year-to-year yield variability tend to exhibit lower predictive performance and may benefit from longer time-series observations for model training to encompass a wide range of environmental and management conditions. We also observe improved estimations from the early season in April to the middle of the growing season in June. In conclusion, the yield forecasting and model validation framework established in this study lays the foundation for training spatio-temporally aware deep learning models on exceptionally large yield datasets with high spatio-temporal resolution. We anticipate that such models will become increasingly prevalent as yield monitors are more frequently deployed in specialty crop operations.
... Te gap was larger with high rainfall, and associated low radiation, between budburst and fowering ( Figure 7). For Godello in the Ribero Designation of Origin, the production of pollen spanned 2-3 weeks in May and June, and yield correlated negatively with rainfall in May [87]. Tree successive rainy days in late May (53 mm), just after the seasonal pollen peak, promoted a fast decrease in the airborne pollen concentration [87]. ...
... For Godello in the Ribero Designation of Origin, the production of pollen spanned 2-3 weeks in May and June, and yield correlated negatively with rainfall in May [87]. Tree successive rainy days in late May (53 mm), just after the seasonal pollen peak, promoted a fast decrease in the airborne pollen concentration [87]. Te association between yield gap and temperature shifted from positive early in the season to negative at later stages, particularly between fowering and veraison. ...
Background and Aims. Vineyard performance is impacted by water availability including the amount and seasonality of rainfall, evapotranspiration, and irrigation volume. We benchmarked water-limited yield potential (Yw), calculated yield gaps as the difference between Yw and actual yield, and explored the underlying environmental and management causes of these gaps. Methods and Results. The yield and its components in two sections of 24 Shiraz vineyards were monitored during three vintages in the Barossa zone (GI). The frequency distribution of yield was L-shaped, with half the vineyards below 5.2 t·ha−1, and an extended tail of the distribution that reached 24.9 t·ha−1. The seasonal ratio of actual crop evapotranspiration and reference evapotranspiration was below 0.48 in 85% of cases, with a maximum of 0.65, highlighting a substantial water deficit in these vineyards. A boundary function relating actual yield and seasonal rainfall was fitted to quantify Yw. Yield gaps increased with an increasing vine water deficit, as quantified by the carbon isotope composition of the fruit. The yield gap was smaller with higher rainfall before budburst, putatively favouring early-season vegetative growth and allocation to reproduction, and with higher rainfall between flowering and veraison, putatively favouring fruit set and berry growth. The gap was larger with higher rainfall and lower radiation between budburst and flowering. The yield gap increased linearly with vine age between 6 and 33 yr at a rate of 0.3 t·ha−1·yr−1. The correlation between yield gap and yield components ranked bunch weight ≈ berries per bunch > bunch number > berry weight; the minimum to close the yield gap was 185,000 bunches ha−1, 105 g bunch−1, 108 berries bunch−1, and 1.1 g berry−1. Conclusions. Water deficit and vine age were major causes of yield gaps. Irrigation during winter and spring provides an opportunity to improve productivity. The cost of dealing with older, less productive vines needs to be weighed against the rate of increase in yield gap with vine age. Significance of the Study. A boundary function to estimate water-limited yield potential returned viticulturally meaningful yield gaps and highlighted potential targets to improve vineyard productivity.
... However, the possible effect of biennial bearing on the amount of pollen grains per anther has been scarcely tested in olive and other fruit species Delgado et al., 2021). Also, the applicability of pollen per anther production in yield forecasting is controversial in other species with studies of this aspect, such as grapevine (Fernández-González et al., 2011;González-Fernández et al., 2020). ...
Background and aims:
Olive (Olea europaea subsp. europaea var. europaea) is the most extensively cultivated fruit crop worldwide. It is considered a wind-pollinated and strictly outcrossing crop. Thus, elevated pollen production is crucial to guarantee optimum fruit set and yield. Despite these facts, the variability of pollen production within the cultivated olive has been scarcely studied. This study is aimed to characterize this feature by analysing a representative set of worldwide olive cultivars.
Methods:
We evaluated the average pollen grains per anther in 57 principal cultivars over three consecutive years. We applied a standard generalized linear model (GLM) approach to study the influence of cultivar, year and previous year fruit load on the amount of pollen per anther. Additionally, the K-means method was used for cluster analysis to group cultivars based on their pollen production capacity.
Key results:
Pollen production per anther was highly variable among olive cultivars. The cultivar significantly accounted for 51.3% of the variance in pollen production and the year for 0.3%. The interaction between both factors explained 8.4% of the variance, indicating that not all cultivars were equally stable in producing pollen across the years. The previous year fruit load and its interaction with the year were significant, but barely accounted for 1.5% of the variance. Olive cultivars were classified into four clusters according to their capacity to produce pollen. Interestingly, the fourth cluster was composed of male-sterile cultivars, which presumably share this character by inheritance.
Conclusions:
Pollen production per anther varied extensively within the cultivated olive. This variation was mainly driven by the cultivar and its interaction with the year. The differential capacity of olive cultivars to produce pollen should be considered not only for designing new orchards but also gardens where this species is used as ornamental.
... SAFRAN tends to minimise extreme temperatures (Ollat et al., 2021), thus decreasing the number of Very Hot Days and Days of Frost. Because of the scale used, we could not consider certain local phenomena such as hail, that can drastically damage grapevine yield (González-Fernández et al., 2020). The analysis of a longer climate data series (i.e., over 20 or 30 years) would help to improve the relevance of climatic indicators, but they would require grapevine yield data from the same period and length of time. ...
Often unable to fulfill theoretical production potentials and to obtain the maximum yields set by wine quality labels, many vineyards and cellars need to solve the issue of so-called grapevine yield gaps in order to assure their durability. These yield gaps particularly occur in Mediterranean wine regions, where extreme events have intensified because of climate change. Yield gaps at the regional level have been widely studied in arable crops using big datasets, but much less so in perennial crops, such as grapevine. Understanding the environmental factors involved in yield gaps, such as those linked to climate and soil, is the first step in grapevine yield gap analysis. At a regional scale, there are numerous studies on ‘terroir’ linked to wine typicity and quality; however, none have classified spatial zones based on environmental factors identified as being involved in grapevine yield. In the present study, we aggregated into one big dataset information obtained from producers at the municipality level in the wine region Languedoc-Roussillon (South of France) between 2010 and 2018. We used a backward stepwise model selection process using linear mixed-effect models to discriminate and select the statistically significant indicators capable of estimating grapevine yield at the municipality level. We then determined spatial zones by using the selected indicators to create clusters of municipalities with similar soil and climate characteristics. Finally, we analysed the indicators of each zone related to the grapevine yield gap, as well as the variations among the grapevine varieties in the zones. Our selection process evidenced 6 factors that could explain annual grapevine yield annually (R2 = 0.112) and average yield for the whole period (R2 = 0.546): Soil Available Water Capacity (SAWC), soil pH, Huglin Index, the Climate Dryness Index, the number of Very Hot Days and Days of Frost. The clustering results show seven different zones with two marked yield gap levels, although all the zones had municipalities with no or high yield gaps. On each zone, grapevine yield was found to be driven by a combination of climate and soil factors, rather than by a single environmental factor. The white wine varieties showed larger yield gaps than the red and rosé wine varieties. Environmental factors at this scale largely explained yield variability across the municipalities, but they were not performant in terms of annual yield prediction. Further research is required on the interactions between environmental variables and plant material and farming practices, as well as on vineyard strategies, which also play an important role in grapevine yield gaps at vineyard and regional scale.
... Manisha S. Sirsa et al. [20] predicted grapevine yield using climatic conditions, phenological dates, fertilizer information, soil analysis and maturation index data with the accuracy measure with low RMSE of 1459.4 (kg/ha) and low relative root mean squared error of 24.2% respectively. Estefanía Gonzalez Fernandez et al. [21] predicted grapevine yield using multiple regression model and applied Spearman rank correlation to identify influential variables based on reproductive variables and the influence of meteorological ...
Grapes vine originally a temperate fruit crop and it’s also grown successfully under tropical conditions. Grape is one of the economically important fruit crops grown in India. As Theni district is the leading producer of grape in Tamil Nadu, followed by Coimbatore and Dindigul, this study is centred on the Theni-Kambum block area. In this region, Muskat Humbug is a well-liked cultivar that yields more than other varieties. In this study, this cultivar was employed. In this study, an artificial neural network (ANN), multiple linear regression (MLR), and elastic net (ELNET) regression methods were used to construct a yield prediction model (ANN) using twelve years secondary data. Additionally, we evaluate our model over a two-year period using field-level data from GRS and neighbouring farms. Finding the best-fit model for predicting grape yield and PDI using meteorological parameters in the Theni district is the goal of this communication. The model is chosen according to many performance indicators including RMSE, MAPE, MAE, and R2. Among the three techniques developed in the study, the Artificial Neural Network is found to be best for prediction of grape yield based on weather and disease incidence for the available data in the studied region.
... From previous works, the authors found few examples of yield estimation for regional scales (Barriguinha et al., 2021), divided mainly into climate-based models (Fraga and Santos, 2017;Gouveia et al., 2011;Santos et al., 2020;Sirsat et al., 2019); pollen-based models (Besselat, 1987;Cunha et al., 2015;González-Fernández et al., 2020;Cristofolini and Gottardini, 2000); a combination of one or both adding phenological and phytopathological variables (Fernandez-Gonzalez et al., 2011;Fernández-González et al., 2011); Simulateur mulTIdisciplinaire pour les Cultures Standard, or multidisciplinary simulator for standard crops (STICS) models (Fraga et al., 2015); and models based on correlation with Vegetation Indices (VI) (Arab et al., 2021;Cunha et al., 2010). Only a few are referenced for real environment, producing estimation for decision-making (Barriguinha et al., 2021). ...
... The more commonly used for regional yield estimation are the ones based on the relationship between airborne pollen and yield, relying on the principle that more flowers per area unit in more productive years relates to higher airborne pollen concentrations (Besselat, 1987;Cunha et al., 2015;Fernandez-Gonzalez et al., 2011;Fernández-González et al., 2011González-Fernández et al., 2020;Cristofolini and Gottardini, 2000). The main disadvantages/difficulties of using pollen-based models (Barriguinha et al., 2021) are: choosing the best placement for sampling devices to represent effectively spatial variability; the number of observations for model calibration (historical data not commonly available); costly and complex laboratory processes; plant dynamics (high variations of the area with vineyards around the pollen traps); temperature and precipitation variations; vineyard management activities (fertilization impact); and identification of the beginning and final of the pollen season. ...
Estimating vineyard yield in advance is essential for planning and regulatory purposes at the regional level, with growing importance in a long-term scenario of perceived climate change. With few tools available, the current study aimed to develop a yield estimation model based on remote sensing and climate data with a machine-learning approach. Using a satellite-based time-series of Normalized Difference Vegetation Index (NDVI) calculated from Sentinel 2 images and climate data acquired by local automatic weather stations, a system for yield prediction based on a Long Short-Term Memory (LSTM) neural network was implemented. The study was conducted in the Douro Demarcated Region in Portugal over the period 2016-2021 using yield data from 169 administrative areas that cover 250,000 ha, in which 43,000 ha of the vineyard are in production. The optimal combination of input features, with an Mean Absolute Error (MAE) of 672.55 kg/ha and an Mean Squared Error (MSE) of 81.30 kg/ha, included the NDVI, Temperature, Relative Humidity, Precipitation, and Wind Intensity. The model was tested for each year, using it as the test set, while all other years were used as input to train the model. Two different moments in time, corresponding to FLO (flowering) and VER (veraison), were considered to estimate in advance wine grape yield. The best prediction was made for 2020 at VER, with the model overestimating the yield per hectare by 8 %, with the average absolute error for the entire period being 17 %. The results show that with this approach, it is possible to estimate wine grape yield accurately in advance at different scales.
... The "oldschool approach" still taken by many researchers is largely an expert choice. It might have been guided by selective correlation analyses for preselected candidate variables (González-Fernández et al. 2020;Ji et al. 2019), stepwise regression (Kern et al. 2018;Salehnia et al. 2020), or consideration of crop growth stages for suitable time windows (Butts-Wilmsmeyer et al. 2019;Zhang et al. 2017). In some cases, the selection effort is critically flattened, even if continental climate impact assessments are at stake: Lobell 2014, 2015) used temperature and precipitation averages of the growing season as sole meteorological basis for that purpose. ...
ABSOLUT v1.2 is an adaptive algorithm that uses correlations between time-aggregated weather variables and crop yields for yield prediction. In contrast to conventional regression-based yield prediction methods, a very broad range of possible input features and their combinations are exhaustively tested for maximum explanatory power. Weather variables such as temperature, precipitation, and sunshine duration are aggregated over different seasonal time periods preceding the harvest to 45 potential input features per original variable. In a first step, this large set of features is reduced to those aggregates very probably holding explanatory power for observed yields. The second, computationally demanding step evaluates predictions for all districts with all of their possible combinations. Step three selects those combinations of weather features that showed the highest predictive power across districts. Finally, the district-specific best performing regressions among these are used for actual prediction, and the results are spatially aggregated. To evaluate the new approach, ABSOLUT v1.2 is applied to predict the yields of silage maize, winter wheat, and other major crops in Germany based on two decades of data from about 300 districts. It turned out to be absolutely crucial to not only make out-of-sample predictions (solely based on data excluding the target year to predict) but to also consequently separate training and testing years in the process of feature selection. Otherwise, the prediction accuracy would be over-estimated by far. The question arises whether performances claimed for other statistical modelling examples are often upward-biased through input variable selection disregarding the out-of-sample principle.
... Manisha S. Sirsat [19] obtained the predictive model for each phenology that predicts yield during growing stages of grapevine and to identify highly relevant predictive variable by machine learning technique. Recently, a prediction model has been developed for the Godello cultivar, one of the preferential autochthonous white cultivars in the Northwest Spain Ribeiro Designation of Origin vineyards, by means of aerobiological, meteorological and flower production analysis by Estefanía González-Fernández [20] . More recently, Kadbhane et al. [21] have developed the grape yield (ACGY) model under climate change scenario using multiple linear regression analysis. ...
... Prolonged periods with high temperatures affect ripening rates, causing berry shrivelling and sunburns [9]. Accordingly, temperatures lower than the suboptimal levels lead to decreased photosynthesis rates [10], pollen concentration in the atmosphere, and consequently lower yield [11]. It is worth noting that indicators employing temperature information have been developed to anticipate grapes phenological stages and thus facilitating task planning (e.g. ...
Grape is considered as one of the world's most important crop. Climatic and soil parameters can significantly affect the yield and quality of grapes. The aim of this study was to analyse and assess this impact of soil and climatic conditions on table grape yield and quality. The experiments took place on a commercial table grape vineyard in Greece for three successive years (2015, 2016 and 2017). The climatic conditions were found to affect significantly the table grape yield and all the tested quality parameters apart from the berry density and titratable acidity. In addition, the climatic conditions affected the table grape yield and quality but in a different degree per phenological stage. The effect of soil conditions on table grape yield and quality parameters differed significantly per annum due to the climatic conditions variation in terms of temperature and precipitation. The conducted analysis highlighted that the information regarding the combined effect of weather and soil conditions on the spatiotemporal variability of the production of table grapes in terms of their quantity and quality is an essential component of the modern precision viticulture. Based on the contemporary principles of the precision viticulture, appropriate cultivation practices can be planned and implemented, reducing thus the impact of climate change on the vine production efficiency and quality.
