How did the characteristics of the growing season change during the past 100 years at a steep river basin in Japan?

The effects of climate change on plant phenological events such as flowering, leaf flush, and leaf fall may be greater in steep river basins than at the horizontal scale of countries and continents. This possibility is due to the effect of temperature on plant phenology and the difference between vertical and horizontal gradients in temperature sensitivities. We calculated the dates of the start (SGS) and end of the growing season (EGS) in a steep river basin located in a mountainous region of central Japan over a century timescale by using a degree-day phenological model based on long-term, continuous, in situ observations. We assessed the generality and representativeness of the modelled SGS and EGS dates by using phenological events, live camera images taken at multiple points in the basin, and satellite observations made at a fine spatial resolution. The sensitivity of the modelled SGS and EGS dates to elevation changed from 3.29 days (100 m)−1 (−5.48 days °C−1) and −2.89 days (100 m)−1 (4.81 days °C−1), respectively, in 1900 to 2.85 days (100 m)−1 (−4.75 days °C−1) and −2.84 day (100 m)−1 (4.73 day °C−1) in 2019. The long-term trend of the sensitivity of the modelled SGS date to elevation was −0.0037 day year−1 per 100 m, but the analogous trend in the case of the modelled EGS date was not significant. Despite the need for further studies to improve the generality and representativeness of the model, the development of degree-day phenology models in multiple, steep river basins will deepen our ecological understanding of the sensitivity of plant phenology to climate change.

Full flowering phenology of apple tree (<i>Malus domestica</i>) in Pūre orchard, Latvia from 1959 to 2019

The Pūre orchard is one of the oldest apple orchards in the Baltic, where thousands of varieties of fruit trees from throughout the world are grown and tested. Over time, a huge knowledge base has been accumulated, but most of the observational data are stored in archives in paper format. We have digitized a small part of the full flowering phenological data of apple trees (Malus domestica) over the period of 1959 to 2019 for 17 varieties of apple trees, a significant step for horticulture and agricultural economics in Latvia. Climate change has led to significant changes in the phenology of apple trees as all varieties, autumn, summer and winter, have begun to flower earlier: from 2002 to 2019, on average full flowering was recorded to have taken place around 21 May, whereas for the period 1959–1967 it occurred around 27–28 May. To develop better-quality phenological predictions and to take account of the fragmentary nature of phenological data, in our study we assessed the performance of three meteorological data sets – gridded observation data from E-OBS, ERA5-Land reanalysis data and direct observations from a distant meteorological station – in simple phenological degree-day models. In the first approximation, the gridded E-OBS data set performs best in our phenological model.

A Phenological Model for Olive (Olea europaea L. var europaea) Growing in Italy

The calibration of a reliable phenological model for olive grown in areas characterized by great environmental heterogeneity, like Italy, where many varieties exist, is challenging and often suffers from a lack of observations, especially on budbreak. In this study, we used a database encompassing many phenological events from different olive varieties, years, and sites scattered all over Italy to identify the phases in which site-enlarged developmental rates can be well regressed against air temperature (Developmental Rate function, DR) by testing both linear and nonlinear functions. A K-fold cross-validation (KfCV) was carried out to evaluate the ability of DR functions to predict phenological development. The cross-validation showed that the phases ranging from budbreak (BBCH 01 and 07) to flowering (BBCH 61 and 65) and from the beginning of flowering (BBCH 51) to flowering can be simulated with high accuracy (r2 = 0.93-0.96; RMSE = 3.9–6.6 days) with no appreciable difference among linear and nonlinear functions. Thus, the resulting DRs represent a simple yet reliable tool for regional phenological simulations for these phases in Italy, paving the way for a reverse modeling approach aimed at reconstructing the budbreak dates. By contrast, and despite a large number of phases explored, no appreciable results were obtained on other phases, suggesting possible interplays of different drivers that need to be further investigated.

Phenological Model to Predict Budbreak and Flowering Dates of Four Vitis vinifera L. Cultivars Cultivated in DO. Ribeiro (North-West Spain)

The aim of this study was to assess the thermal requirements of the most important grapevine varieties in northwestern Spain to better understand the impact of climate change on their phenology. Different phenological models (GDD, GDD Triangular and UniFORC) were tested and validated to predict budburst and flowering dates of grapevines at the variety level using phenological observations collected from Treixadura, Godello, Loureira and Albariño between 2008 and 2019. The same modeling framework was assessed to obtain the most suitable model for this region. The parametrization of the models was carried out with the Phenological Modeling Platform (PMP) platform by means of an iterative optimization process. Phenological data for all four varieties were used to determine the best-fitted parameters for each variety and model type that best predicted budburst and flowering dates. A model calibration phase was conducted using each variety dataset independently, where the intermediate-fitted parameters for each model formulation were freely-adjusted. Afterwards, the parameter set combination of the model providing the highest performance for each variety was externally validated with the dataset of the other three varieties, which allowed us to establish one overall unique model for budburst and flowering for all varieties. Finally, the performance of this model was compared with the attained one while considering all varieties in one dataset (12 years × 4 varieties giving a total number of observations of 48). For both phenological stages, the results showed no considerable differences between the GDD and Triangular GDD models. The best parameters selected were those provided by the Treixadura GDD model for budburst (day of the year (t0) = 49 and base temperature (Tb) = 5) and those corresponding to the Godello model (t0 = 52 and Tb = 6) for flowering. The modeling approach employed allowed obtaining a global prediction model that can adequately predict budburst and flowering dates for all varieties.

Near-Ground-Based Optical Plant Phenology Measurements at China Ecological Meteorology Sites

&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt; Climate change is a hot issue in the global scale. The some varieties of phenological phase of plants (trees, grasslands and crops et al.) can directly and objectively reflected climate change and Commonly, the response of plant phenology to climate change is sensitive, especially to climatic factors such as precipitation, temperature, soil characteristics in the growing environment, and sometimes can be considered as an indicator of climate change. Those meteorology and soil factors must be taken into account when we build phenological model so as to quantitatively study the relationship between climate change and plant phenology. Beside of those factors, the high frequency and multi-scale acquisition of phenological observation data is also the basis for phenological model researches. Since February of 2020, China Meteorological Administration (CMA) has established 25 vegetation ecological observation sites in Inner Mongolia autonomous region, Shaanxi, Hebei, Sichuan, Guangxi, Fujian and Anhui provinces. The automatic vegetation eco-meteorological observation instruments, whichi are composed of image sensor (digital camera), multispectral sensor, laser altimeter, point cloud laser radar and sound sensor, have been installed in the sites. They can provide so much products as image of plant community, normal difference vegetation index (NDVI), plant height, canopy height and animal sound at present. Of all these products, image data of plant community can be further retrieved to generate the greenness chromatic coordinate (Gcc) data, which can be widely applied into the phenological studies and the validations of satellite terrestrial vegetation products. After months of experimental operation, these equipments show the great ability to monitor the growth and development of terrestrial plants in China. This ability also lays a foundation for the establishment of the plant ecological observation network in China (China Vegetation Ecological Meteorological Observation Network).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;KEYWORDS&lt;/strong&gt;&amp;#65306;Plant phenology, near-surface-based measurement, observation network&lt;/p&gt;

Development of a new phenological model based on the carbon balance of tree in boreal conifers

&lt;p&gt;Traditional phenological models use the concepts of chilling and thermal forcing (temperature sum or degree-days) to predict buds break. Even if new model formulations get more sophisticated with time, the bases of phenological model still rely on the effect of the time of chilling and forcing temperature in interaction, or not, with photoperiod. Because of the increasing impact of climate or other related biotic or abiotic stressors, a model with more biological support is urgently needed in order to accurately predict bud break. We have developed and calibrated a new mechanistic model that is based on the physiological processes taking place before and during budbreak in several conifers species. This model describes the phenology and growth dynamics of a conifer branch as representative of the whole tree. As a general assumption, we assume that phenology will be driven by the carbon status, which is closely related to the annual cycle of dormancy &amp;#8211; activity state through the year and to the environmental variables. The carbon balance of a branch was thus modelled i) from autumn to winter&amp;#8211;when aboveground parts exhibit cold acclimation and dormancy&amp;#8211; and ii) from winter to spring and summer &amp;#8211;when deacclimation and growth resumption occurs. After being calibrated in a field experiment, the model was tested across a large area in Qu&amp;#233;bec (Canada), based on observed phenological data. For the 20 field sites in Quebec, the model proved to be accurate in predicting the date of budbreak with an average error of &amp;#177;3.8 days (R2=0.72). This model also allowed us to better understand the effects of winter and spring temperature on bud burst, offering new simulation perspectives under global warming and insect defoliation.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions

&lt;p&gt;Phenological models applied to grapevines are valuable tools to assist in the decision of cultural practices related to winegrowers and winemakers. The two-parameter sigmoid phenological model was used to estimate the three main phenological stages of the grapevine development, i.e., budburst, flowering, and veraison. This model was calibrated and validated with phenology data for 51 grapevine varieties distributed in four wine regions in Portugal (Lisboa, Douro, D&amp;#227;o, and Vinhos Verdes). Meteorological data for the selected sites were also used. Hence, 153 model calibrations (51 varieties &amp;#215; 3 phenological stages) and corresponding parameter estimations were carried out based on an unprecedented comprehensive and systematized dataset of phenology in Portugal. For each phenological stage, the centroid of the estimated parameters was subsequently used, and three generalized sigmoid models were constructed (budburst: d =&amp;#8722;0.6, e = 8.6; flowering: d = &amp;#8722;0.6, e = 13.7; veraison: d = &amp;#8722;0.5, e = 13.2). Centroid parameters show high performance for approximately 90% of the varieties and can thereby be used instead of variety-specific parameters. Overall, the RMSE (root-mean-squared-error) is &lt; 7 days, while the EF (efficiency coefficient) is &gt; 0.5. Additionally, according to other studies, the predictive capacity of the models for budburst remains lower than for flowering or veraison. Furthermore, the F-forcing parameter (thermal accumulation) was evaluated for the Lisboa wine region, where the sample size is larger, and for the varieties with model efficiency equal to or greater than 0.5. A ranking and categorization of the varieties in early, intermediate, and late varieties was subsequently undertaken on the basis of F values. In this way, these results of the present study will be incorporated on a web platform, where the sigmoid model must convey valuable information regarding the development/evolution of the vineyard with short-term predictions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;grapevine; phenology modeling; sigmoid model; wine regions; short-term predictions; Portugal&lt;/p&gt;

Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model

Understanding plant phenological change is of great concern in the context of global climate change. Phenological models can aid in understanding and predicting growing season changes and can be parameterized with gross primary production (GPP) estimated using the eddy covariance (EC) technique. This study used nine years of EC-derived GPP data from three mature subtropical longleaf pine forests in the southeastern United States with differing soil water holding capacity in combination with site-specific micrometeorological data to parameterize a photosynthesis-based phenological model. We evaluated how weather conditions and prescribed fire led to variation in the ecosystem phenological processes. The results suggest that soil water availability had an effect on phenology, and greater soil water availability was associated with a longer growing season (LOS). We also observed that prescribed fire, a common forest management activity in the region, had a limited impact on phenological processes. Dormant season fire had no significant effect on phenological processes by site, but we observed differences in the start of the growing season (SOS) between fire and non-fire years. Fire delayed SOS by 10 d ± 5 d (SE), and this effect was greater with higher soil water availability, extending SOS by 18 d on average. Fire was also associated with increased sensitivity of spring phenology to radiation and air temperature. We found that interannual climate change and periodic weather anomalies (flood, short-term drought, and long-term drought), controlled annual ecosystem phenological processes more than prescribed fire. When water availability increased following short-term summer drought, the growing season was extended. With future climate change, subtropical areas of the Southeastern US are expected to experience more frequent short-term droughts, which could shorten the region’s growing season and lead to a reduction in the longleaf pine ecosystem’s carbon sequestration capacity.