Modelling cherry full bloom using ‘space-for-time’ across climatically diverse growing environments

Darbyshire, Rebecca; Navas-López, Jose F.; Song, Xinxin; Wenden, Bénédicte; Close, DC

doi:10.1016/j.agrformet.2020.107901

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…In return, the updraft is suppressed when the inversion layer at the cloud top intensifies to a certain extent and the vertical motion at the cloud top turns into horizontal advection, which triggers the mixing and entrainment between dry and moist air at the boundary of the cloud body. Sensible heat and latent heat at the cloud bottom transfer to the cloud top by taking turbulence as a carrier, which makes up for the loss of radioactive and evaporative cooling [11,[27][28][29][30]. As a result, the sea of clouds is maintained; when the radiation from the sun gets stronger, the temperature increase at the cloud top is significantly greater than the cloud bottom because of the absorption of short-wave radiation.…”

Section: Atmospheric Stratification Feature Of the Sea Of Cloudsmentioning

confidence: 99%

Analysis of the Micro-Physical Characteristics of the Sea of Clouds Phenomena in Jiuxian Mountain Based on Multiple Source Observations

Cheng,

Lin,

Zhou

et al. 2024

Atmosphere

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The micro-physical characteristics of a typical sea of clouds process in Jiuxian Mountain are investigated by comprehensively analyzing parameters that delineate the micro-physical characteristics of clouds and atmospheric stratification based on data from a cloud radar, wind profiler, meteorological gradient observation in high mountains, and other observations. The results show that water vapor condenses into cloud particles via an entrained and mixing process accompanied by an updraft originating from orographic uplift. During the thickening stage of the sea of clouds, atmospheric motion within the clouds is featured as “downdraft on the top—updraft on the bottom”. The zero vertical velocity area is located closely to the maximum of liquid water content. The thermal inversion layer is formed during the maintenance stage; however, the enhancement of inversion on the cloud top could suppress updraft in areas with a high liquid water content. The values mainly concentrate on the cloud top, and repetitively lifting and falling processes caused by the atmospheric upward and downward motion are in favor of the coalescence growth of cloud particles, which result in the persistence of strong radar echo. At the dissipation stage, warming on the cloud top is greater than that on the cloud bottom due to the short-wave absorption of clouds as the solar radiation enhances. As a result, the inversion layer thickens and elevates, evaporation caused by heating outweighs the condensation caused by cooling, a strong radar echo band descends from the top to the middle part of clouds, a sea of clouds dissipates gradually as cloud particles evaporates, and the particle size and concentration number of cloud particles decrease simultaneously.

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Section: Atmospheric Stratification Feature Of the Sea Of Cloudsmentioning

confidence: 99%

Analysis of the Micro-Physical Characteristics of the Sea of Clouds Phenomena in Jiuxian Mountain Based on Multiple Source Observations

Cheng,

Lin,

Zhou

et al. 2024

Atmosphere

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“…The function predicts phenology using a user-defined starting date and forcing heat data (GDD or GDH), and a dataframe for starting dates and heat requirements. Although the thermal models are highly unrealistic in a biological sense, and not recommended for most species, so far is still the best approach to estimate phenology in grapevine, as more realistic models have not demonstrated superior efficacy (Parker et al, 2011;Prats-llinàs et al, 2018), in contrast to other species like apple (Darbyshire et al, 2017), cherry (Darbyshire et al, 2020) or almond (Diez-Palet et al, 2019).…”

Section: Fruclimadapt Package Featuresmentioning

confidence: 99%

“…Chill is accumulated up to the plant requirement, and then heat up to the forcing requirement follows, with no overlap between the two phases. This approach can be considered the reference method for many temperate fruit species, and is widely used by the industry and the scientific community (Darbyshire et al, 2020). In the function, chill can be supplied as chill hours, chill units, or chill portions, and forcing heat accumulation can be supplied either as GDD or GDH.…”

Section: B Phenology_sequential()mentioning

confidence: 99%

fruclimadapt: An R package for climate adaptation assessment of temperate fruit species

Jiménez

Urrestarazu

Santesteban

2021

Computers and Electronics in Agriculture

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“…An important consideration when developing phenology models is the quantity and quality of phenological observations needed to train and validate the model. Ideally, long time series data (or panel data) collected consistently over time should be used to model phenology, as this will reduce model error due to differences in crop management and will minimise observer errors (Darbyshire et al, 2020). Given the reality of available data in many studies some researchers have compiled data from different sites to construct a larger dataset (Darbyshire et al, 2020;Luedeling et al, 2009;Morales-Castilla et al, 2020;Parker et al, 2013;Parker et al, 2011;Parker et al, 2020;Pope et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modelling the phenological development of cv. Touriga Nacional and Encruzado in the Dão Wine Region, Portugal

Rodrigues

Pedroso²,

Henriques

et al. 2021

OENO One

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The grapevine vegetative cycle, which is morphologically described by its phenological stages, is strongly determined by weather conditions. Phenological models are widely applied in viticulture and are based on the assumption that air temperature is the preponderant environmental factor which determines vine development. In this study, phenological development models (PDMs) were calibrated and validated to simulate several intermediate stages between budbreak and veraison for cv. Touriga Nacional (TN) and cv. Encruzado (EN) winegrape varieties, which are widely grown in the Dao Wine Region, Portugal. These are thermal models, with which the daily sum of the rate of forcing (R) was calculated using a sigmoid function. For this purpose, a high-quality and comprehensive dataset was used which combines phenology data and weather station data in several vineyard sites spread over the region. The model showed an overall high performance (global RMSE of 5.4 days for EN and 5.0 days for TN), although it depended on the phenological stage and variety. The RMSE ranged from 3.2 to 6.2 for TN, and from 3.9 to 6.8 for EN. For both varieties and in all phenological stages, the RMSE was significantly lower than the standard deviation of the phenological observations. For TN, the model efficiency was greater than 0.71 for all phenological stages. In future studies, these models will be combined with specific models that simulate the evolution of winegrape berry quality indicators commonly used for harvest decision support. The relatively low complexity of the selected PDMs enables their use as a crop management and decision support tool. To our knowledge, no previous studies have been carried out on either of these two varieties and their intermediate phenological timings. The present study is an illustration of conceivable model development under diverse environmental conditions, thus allowing similar approaches to be adopted in other wine regions on a worldwide scale.

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Modelling cherry full bloom using ‘space-for-time’ across climatically diverse growing environments

Cited by 15 publications

References 36 publications

Analysis of the Micro-Physical Characteristics of the Sea of Clouds Phenomena in Jiuxian Mountain Based on Multiple Source Observations

Analysis of the Micro-Physical Characteristics of the Sea of Clouds Phenomena in Jiuxian Mountain Based on Multiple Source Observations

fruclimadapt: An R package for climate adaptation assessment of temperate fruit species

Modelling the phenological development of cv. Touriga Nacional and Encruzado in the Dão Wine Region, Portugal

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