Relationship between flowering phenology of perennial herbs and meteorological data in deciduous forests of Sweden

1996 ◽  
Vol 74 (4) ◽  
pp. 528-537 ◽  
Author(s):  
Martin Diekmann
Keyword(s):  
Deciduous Forest ◽  
Reference Model ◽  
Meteorological Data ◽  
Flowering Phenology ◽  
Data Sets ◽  
Base Temperature ◽  
Degree Days ◽  
Data Set ◽  
Air Temperatures ◽  
Full Flowering

The relationship between flowering phenology and meteorological measurements, in particular air temperature, was studied for 29 herbaceous species in four areas of deciduous forest near Uppsala, Sweden. Altogether 16 models were tested for their accuracy of predicting flowering. These were cumulative sum models based on the heat unit concept of an accumulation of (modified) temperatures above a threshold base temperature from a chosen starting date. Average temperature was tested as an alternative method. All models were first applied to a data set from the years 1990 to 1992 and then to an independent test data set from 1993. The accumulation of daily mean air temperatures (degree-days) above 5 °C from January 1 was chosen as the basic reference model. Despite its simplicity, it was a very accurate model in predicting flowering for these species. Only two models were superior to the reference model in both data sets: the summation of degree hours instead of degree-days from optimized starting dates for each species, and the addition of photoperiod (daylength) to daily mean temperature. In general, the models performed better for the late-flowering species than for the early-flowering species. The accuracy of the models was partly dependent on the actual course of temperature accumulation in a year. Keywords: degree-days, full flowering, photoperiod, solar radiation, temperature sum.

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

2021 ◽  
Vol 18 ◽  
pp. 93-97
Author(s):  
Gunta Kalvāne ◽  
Zane Gribuste ◽  
Andis Kalvāns
Keyword(s):  
Malus Domestica ◽  
Meteorological Data ◽  
Fruit Trees ◽  
Flowering Phenology ◽  
Observation Data ◽  
Apple Trees ◽  
Phenological Model ◽  
Data Set ◽  
Phenological Data ◽  
Full Flowering

Abstract. 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.

Quantification of vegetative development of faba bean, oats, and Italian ryegrass

2012 ◽  
Vol 63 (12) ◽  
pp. 1097 ◽  
Author(s):  
M. Mohammed Yusoff ◽  
D. J. Moot ◽  
B. A. McKenzie ◽  
G. D. Hill
Keyword(s):  
Soil Temperature ◽  
Faba Bean ◽  
Simulation Models ◽  
Italian Ryegrass ◽  
Base Temperature ◽  
Degree Days ◽  
Vegetative Development ◽  
Forage Crops ◽  
Air Temperatures ◽  
Leaf Appearance

This study quantified the relationship between vegetative development and temperature of ‘Old New Zealand’ faba bean, ‘Milton’ oats, and ‘Feast II’ Italian ryegrass using thermal time (Tt, degree-days) calculations. Each species was sown on five dates in autumn and winter 2008 and three dates in autumn 2009. The linear model for rate of development calculated the Tt requirement of faba bean for 75% emergence as 217 degree-days (base temperature (Tb) = 1.2°C), compared with 132 (Tb = 1.6°C) for oats and 132 (Tb = 1.8°C) for Italian ryegrass. Leaf appearance had a Tb of 2.4°C for faba bean, 3.0°C for oats, and 0.7°C for Italian ryegrass. The mean phyllochron (degree-days leaf–1) was 66 ± 1 for faba bean, 123 ± 3.90 for oats, and 120 ± 4.21 for Italian ryegrass. Soil temperature at 20 mm depth was the most accurate predictor of Tb and the Tt requirements to reach 75% emergence. Conversely, air temperature on-site was required to predict the phyllochron for faba bean because of its elevated growing point. Either air or soil temperature at the experimental site or at a nearby meteorological station could be used to define the phyllochron for oats and Italian ryegrass. These results highlight the importance of both soil and air temperatures to accurately define vegetative development before the processes are included in simulation models for these winter annual forage crops.

scholarly journals Observations and modelling of algal growth on a snowpack in northwest Greenland

2017 ◽  
Author(s):  
Yukihiko Onuma ◽  
Nozomu Takeuchi ◽  
Sota Tanaka ◽  
Naoko Nagatsuka ◽  
Masashi Niwano ◽  
...  
Keyword(s):  
Algal Blooms ◽  
Meteorological Data ◽  
Algal Growth ◽  
Data Sets ◽  
Snow Surface ◽  
Snow Algae ◽  
Alpine Regions ◽  
Air Temperatures ◽  
Algal Abundance ◽  
Study Sites

Abstract. Snow algal bloom is a common phenomenon on melting snowpacks in polar and alpine regions and can substantially increase melting rates of the snow due to the effect of albedo reduction on the snow surface. In order to reproduce algal growth on the snow surface using a numerical model, temporal changes in snow algal abundance were investigated on the Qaanaaq Glacier in northwest Greenland from June to August 2014. Snow algae first appeared at the study sites in late June, which was approximately 94 hours after air temperatures exceeded the melting point. Algal abundance increased exponentially after the appearance, but the increasing rate became slow after late July, and finally reached 3.5 × 107 cells m-2 in early August. We applied a logistic model to the algal growth curve and found that the algae could be reproduced with an initial cell concentration of 6.9 × 102 cells m-2, a growth rate of 0.42 d-1, and a carrying capacity of 3.5 × 107 cells m-2 on this glacier. This model has the potential to simulate algal blooms from meteorological data sets and to evaluate their impact on the melting of seasonal snowpacks and glaciers.

scholarly journals Base temperature as a function of genotype: a foundation for modeling phenology of temperate fruit species

2016 ◽  
Vol 37 (4) ◽  
pp. 1811
Author(s):  
Anzanello Rafael ◽  
Luiz Antonio Biasi
Keyword(s):  
Meteorological Data ◽  
Growing Season ◽  
Fruit Trees ◽  
Thermal Time ◽  
Base Temperature ◽  
Degree Days ◽  
Single Node ◽  
Fruit Species ◽  
Lower Base ◽  
Phenological Phases

To complete each phase of the growing season, plants must accumulate thermal time at lower base temperature (Tb). Little information exists on Tb variation between either fruit species or cultivars of the same species. We therefore aimed to determine the lower base temperature for contrasting genotypes in precocity of peach, plum, grape, pear, and kiwi. Twigs 25-35 cm long for the following cultivars: peach, Tropic Beauty (TB) and Eragil (ER); plum, Gulf Blaze (GB) and Letícia (LE); grape, Chardonnay (CH) and Cabernet Sauvignon (CS); pear, Smith (SM) and Packham’s (PA); and kiwi, Golden King (GK) and Hayward (HA) were collected in orchards in Veranópolis, RS Estate, on 06/13/2014, with 0 h at temperatures ? 7.2°C (chilling hours; HC) in the field. Intact twigs packed in black plastic film were subjected to 1,008 HC at 0°C in incubators to overcome dormancy and then transferred to temperatures of 2, 4, 6, 8, 10, and 12°C on single-node cuttings planted in phenolic foam to define effective heat temperature for the genotypes. Over 110 d, budburst of the buds was evaluated in 2-3-d intervals in the green-tip stage. The resulting inverse data of number of days to budburst (1/days to budburst) was inserted into regression curves to estimate Tb for each genotype. Historical phonological series comprised of 10 years for the analyzed cultivars and meteorological data of the cultivation sites were used to determine thermal time (degree-days) for the fruit trees during the growing season, considering different phenological phases. Temperate fruit species exhibited different Tb behaviors: Tb was lower for early cultivars (TB and GB = 2.2°C; CH = 2.1°C; SM = 4.4°C; GK = 4.3°C) and higher for late cultivars (ER = 6.3°C; LE = 6.2°C; CS = 4.3°C; HA and PA = 8.2°C) for all cultures. The Tb f fruit cultivars related directly with genotype chilling requirements: the higher the chilling requirement, the higher the Tb of the cultivar. Cultivars of the same fruit species yielded a sum of degree-days almost equal to finalize the growing season, regardless of the degree of precocity (TB = 1720; ER = 1801; GB = 1680; LE = 1718; CH = 2310; CS = 2369; SM = 2096; PA = 2003 GD; GK = 2775; HA = 2691). Regarding phenological phases, 82% of the assessed cases responded more to thermal time (degree-days) than to chronological time (d) to complete phenological steps. Differences in Tb between genotypes are a relevant factor for improving the accuracy and applicability of phenology models in agriculture.

scholarly journals A Heat Unit Model to Predict Growth and Development of Muskmelon (Cucumis melo var. reticulatus Naud.) to Anthesis

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 790D-790
Author(s):  
S. Jenni ◽  
D.C. Cloutier ◽  
G. Bourgeois ◽  
K.A. Stewart
Keyword(s):  
Cucumis Melo ◽  
Dry Weight ◽  
Base Temperature ◽  
Degree Days ◽  
Heat Unit ◽  
Air Temperatures ◽  
Unit Model ◽  
Dependent Variables ◽  
Soil Temperatures ◽  
Unit Formula

Plant dry weight of muskmelon transplants to anthesis could be predicted from a multiple linear regression based on air and soil temperatures prevailing under 11 mulch and rowcover combinations. The two dependent variables of the regression model consisted of a heat unit formula for air temperatures with a base temperature of 14C and a maximum-reduced threshold at 40C, and a standard growing-degree-day formula for soil temperatures with a base temperature of 12C. Based on 2 years of data, 86.5% of the variation in the dry weight (on a log scale) could be predicted with this model. The base temperature for predicting time to anthesis of muskmelon transplants was established at 6.8C and the thermal time ranged between 335 and 391 degree-days during the 2 years of the experiment.

scholarly journals Application of time-lapse ERT imaging to watershed characterization

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. G7-G17 ◽  
Author(s):  
Carlyle R. Miller ◽  
Partha S. Routh ◽  
Troy R. Brosten ◽  
James P. McNamara
Keyword(s):  
Reference Model ◽  
Time Lapse ◽  
Data Sets ◽  
Data Set ◽  
Resistivity Tomography ◽  
Practical Applications ◽  
Data Inversion ◽  
Time Periods ◽  
The Difference ◽  
Base Data

Time-lapse electrical resistivity tomography (ERT) has many practical applications to the study of subsurface properties and processes. When inverting time-lapse ERT data, it is useful to proceed beyond straightforward inversion of data differences and take advantage of the time-lapse nature of the data. We assess various approaches for inverting and interpreting time-lapse ERT data and determine that two approaches work well. The first approach is model subtraction after separate inversion of the data from two time periods, and the second approach is to use the inverted model from a base data set as the reference model or prior information for subsequent time periods. We prefer this second approach. Data inversion methodology should be consideredwhen designing data acquisition; i.e., to utilize the second approach, it is important to collect one or more data sets for which the bulk of the subsurface is in a background or relatively unperturbed state. A third and commonly used approach to time-lapse inversion, inverting the difference between two data sets, localizes the regions of the model in which change has occurred; however, varying noise levels between the two data sets can be problematic. To further assess the various time-lapse inversion approaches, we acquired field data from a catchment within the Dry Creek Experimental Watershed near Boise, Idaho, U.S.A. We combined the complimentary information from individual static ERT inversions, time-lapse ERT images, and available hydrologic data in a robust interpretation scheme to aid in quantifying seasonal variations in subsurface moisture content.

Seasonal Occurrence, Phenological Indicators and Mortality Factors Affecting Five Scale Insect Species (Hemiptera:Diaspididae, Coccidae) in the Urban Landscape Setting

2004 ◽  
Vol 39 (4) ◽  
pp. 611-622 ◽  
Author(s):  
G. S. Hodges ◽  
S. K. Braman
Keyword(s):  
Urban Landscape ◽  
Flowering Phenology ◽  
Scale Insect ◽  
Scale Insects ◽  
Base Temperature ◽  
Degree Days ◽  
Mortality Factors ◽  
Factors Affecting ◽  
Standard Base ◽  
Euonymus Scale

Proper timing of pesticide applications is paramount when attempting to control scale insects (Hemiptera: Diaspididae, Coccidae) that are important pests of landscape plantings. Use of degree-days and phenological indicators can better time the applications and reduce the number of treatments. Seasonal appearance of five species of scale insects in the urban landscape along with flowering phenology of 40 plant species were systematically monitored during 1997, 1998, 1999 and 2000 in Athens, GA. Degree-day calculations for predicting first-generation crawler emergence were attained by two methods: use of standard-base or an experimentally determined base temperature. Predictions using a standard temperature resulted in high year-to-year variance. Use of a model-derived base temperature reduced the variance for degree-days needed for first crawler emergence. Mean base temperatures for European fruit lecanium, Indian wax scale, obscure scale, euonymus scale, and tea scale were, respectively, 12.78, 12.78, 5.0, 3.89, and 5.0°C. The range in degree-days required for first crawler emergence of each species using first the experimentally derived base, or the standard base temperature of 10.56 were 1184 to 1296 or 1064 to 1622 for European fruit lecanium; 846 to 1014 or 1150 to 1380 for Indian wax scale; 1246 to 1268 or 515 to 566 for obscure scale; 1366 to 1492 or 313 to 597 for euonymus scale; and 526 to 1502 or 202 to 776 for tea scale. Natural enemy complexes observed in association with each of the scale species are discussed.

scholarly journals Using Growing Degree Days, Agrometeorological Variables, Linear Regression, and Data Mining Methods to Help Improve Prediction of Sweetpotato Harvest Date in Louisiana

2009 ◽  
Vol 19 (1) ◽  
pp. 133-144 ◽  
Author(s):  
Arthur Villordon ◽  
Christopher Clark ◽  
Don Ferrin ◽  
Don LaBonte
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Daily Temperature ◽  
Multivariate Adaptive Regression Splines ◽  
Harvest Date ◽  
Support Vector ◽  
Growing Degree Days ◽  
Base Temperature ◽  
Degree Days ◽  
Data Set

Predictive models of optimum sweetpotato (Ipomoea batatas) harvest in relation to growing degree days (GDD) will benefit producers and researchers by ensuring maximum yields and high quality. A GDD system has not been previously characterized for sweetpotato grown in Louisiana. We used a data set of 116 planting dates and used a combination of minimum cv, linear regression (LR), and several algorithms in a data mining (DM) mode to identify candidate methods of estimating relationships between GDD and harvest dates. These DM algorithms included neural networks, support vector machine, multivariate adaptive regression splines, regression trees, and generalized linear models. We then used candidate GDD methods along with agrometeorological variables to model US#1 yield using LR and DM methodology. A multivariable LR model with the best adjusted r2 was based on GDD calculated using this method: maximum daily temperature (Tmax) – base temperature (B), where if Tmax > ceiling temperature [C (90 °F)], then Tmax = C, and where GDD = 0 if minimum daily temperature <60 °F. The following climate-related variables contributed to the improvement of adjusted r2 of the LR model: mean relative humidity 20 days after transplanting (DAT), maximum air temperature 20 DAT, and maximum soil temperature 10 DAT (log 10 transformed). In the DM mode, this GDD method and the LR model also demonstrated high predictive accuracy as quantified using mean square error. Using this model, we propose to schedule test harvests at GDD = 2600. The harvest date can further be optimized by predicting US#1 yield using GDD in combination with climate-based predictor variables measured within 20 DAT.

scholarly journals A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

2021 ◽  
Vol 25 (5) ◽  
pp. 2685-2703
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Sepp Hochreiter ◽  
Grey S. Nearing
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Data Sets ◽  
Rainfall Runoff ◽  
Data Set ◽  
Simulation Accuracy ◽  
Meteorological Forcing ◽  
Rainfall Runoff Model ◽  
Land Data Assimilation System

Abstract. A deep learning rainfall–runoff model can take multiple meteorological forcing products as input and learn to combine them in spatially and temporally dynamic ways. This is demonstrated with Long Short-Term Memory networks (LSTMs) trained over basins in the continental US, using the Catchment Attributes and Meteorological data set for Large Sample Studies (CAMELS). Using meteorological input from different data products (North American Land Data Assimilation System, NLDAS, Maurer, and Daymet) in a single LSTM significantly improved simulation accuracy relative to using only individual meteorological products. A sensitivity analysis showed that the LSTM combines precipitation products in different ways, depending on location, and also in different ways for the simulation of different parts of the hydrograph.

scholarly journals Multitemporal Landsat Imagery with Optimum Band Ratio Techniques for Deciduous Forest Classification

2021 ◽  
pp. 21-27
Author(s):  
W Paengwangthong
Keyword(s):  
Deciduous Forest ◽  
Forest Type ◽  
Landsat Imagery ◽  
Kappa Coefficient ◽  
Landsat 8 ◽  
Data Sets ◽  
Band Ratio ◽  
Data Set ◽  
Multitemporal Data ◽  
Type Classification

The objective of the study is to evaluate optimum band ratio combinations data set derived from monthly Landsat 8 imageries for forest type classification around the Sirikit dam reservoir using supervised classification with Maximum Likelihood Classifier (MLC). In this study, imageries data acquired from January 2014 to November 2017 were used to create the monthly band ratio data set of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Moisture Indices (NDMI), and Normalized Burn Ratios (NBR) and used to create the monthly multispectral (MS) data set represented as a case of without applying band ratio techniques. In classifying deciduous forest type, four data sets were used to classify two classes of deciduous forests, namely mixed deciduous forest and dry deciduous dipterocarp forest. After the accuracy assessment, the result showed that the overall accuracy and kappa coefficient of all data sets were between 78.33% – 86.21% and between 44.32% – 62.83%, respectively. Herein, the monthly NDVI multitemporal data set provided the highest overall accuracy and kappa coefficient which were better than the monthly MS multitemporal data set about 4% and 8%, respectively. In conclusion, applying monthly multitemporal data of Landsat 8 with band ratio technique, especially NDVI, can increase the accuracy of deciduous forest type classification.

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