Relevant bibliographies by topics / Blooming date prediction

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  3. Conference papers

Academic literature on the topic 'Blooming date prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Blooming date prediction"

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Kitamura, Yuto, Hisayo Yamane, Akira Yukimori, Hiroyoshi Shimo, Koji Numaguchi, and Ryutaro Tao. "Blooming Date Predictions Based on Japanese Apricot ‘Nanko’ Flower Bud Responses to Temperatures during Dormancy." HortScience 52, no. 3 (2017): 366–70. http://dx.doi.org/10.21273/hortsci11253-16.

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Flower bud development and the timing of blooming are mainly affected by genotype-dependent chilling requirements (CRs) during endodormancy and subsequent heat requirements (HRs) during ecodormancy. However, little information is available regarding the responses of flower buds to temperatures during endodormancy and ecodormancy in japanese apricot. We exposed japanese apricot ‘Nanko’ trees to various temperatures to estimate the CRs and HRs using development index (DVI) models specific for the endodormant (DVIendo) and ecodormant (DVIeco) stages. These models were based on the experimentally determined development rate (DVR). The DVRendo value was calculated as the reciprocal of the chilling time required to break endodormancy. The relationship between the DVRendo value and temperature was estimated using a three-dimensional curve. Our results indicated that 5–6 °C was the most effective temperature for breaking endodormancy in ‘Nanko’ flower buds. Additionally, exposure to −3 °C negatively affected endodormancy release, whereas 15 °C had no effect. We also determined that the DVReco values for temperatures between 5 and 20 °C were the reciprocal values of the time required for blooming after endodormancy release. The values outside this range were estimated using linear functions. The DVI was defined as the sum of the DVR values ranging from 0 to 1. Models for predicting the blooming date were constructed using the functions of sequentially combined DVIendo and DVIeco models. The accuracy of each model was assessed by comparing the predicted and actual blooming dates. The prediction of the model in which DVIeco = 1 corresponded to a 40% blooming level and DVIeco = 0 was set to DVIendo = 0.5 had the lowest root mean square error (RMSE) value (i.e., 3.11) for trees in commercial orchards exposed to different climates. Our results suggest that the developed model may have practical applications.
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Alonso, J. M., J. M. Ansón, M. T. Espiau, and R. Socias i. Company. "Determination of Endodormancy Break in Almond Flower Buds by a Correlation Model Using the Average Temperature of Different Day Intervals and its Application to the Estimation of Chill and Heat Requirements and Blooming Date." Journal of the American Society for Horticultural Science 130, no. 3 (2005): 308–18. http://dx.doi.org/10.21273/jashs.130.3.308.

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Almond (Prunus amygdalus Batsch.) blooming date is determined by the temperatures during the dormancy period, from the onset of endodormancy to just before blooming. In this work we have developed a model, based on several years data, to estimate the mean transition date from endodormancy to ecodormancy in 44 almond cultivars covering the whole range of almond bloom, through the significance of correlation coefficients between the temperatures occurring during dormancy and the date of full bloom. The estimation of this date for each cultivar has allowed the calculation of its chill and heat requirements. It was found that most cultivars have chilling requirements between 400 and 600 chill units, whereas the span of heat requirements was wider, from 5500 to 9300 growing degree hours Celsius. Some cultivars show high chilling requirements and low heat requirements whereas others show opposite requirements. These differences confirm the wide almond adaptability to different climatic conditions and offer the possibility of being utilized in breeding programs. The good fit shown by the application of this model in the prediction of bloom time may sustain its application in chilling and heat requirement estimation in other fruit species if blooming dates and climatic data for several years are available.
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Cardina, John, Catherine P. Herms, Daniel A. Herms, and Frank Forcella. "Evaluating Phenological Indicators for Predicting Giant Foxtail (Setaria faberi) Emergence." Weed Science 55, no. 5 (2007): 455–64. http://dx.doi.org/10.1614/ws-07-005.1.

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We evaluated the use of ornamental plants as phenological indicators for predicting giant foxtail emergence and compared their performance with predictions based upon Julian day, cumulative growing degree–days (GDD), and the WeedCast program. From 1997 to 2001, we monitored giant foxtail emergence in a field experiment with and without fall and spring tillage to estimate the dates of 25, 50, and 80% emergence; we also recorded dates of first and full bloom of 23 ornamental plant species. Dates of weed emergence and ornamental blooming for 1997 to 2000 were compiled in a phenological calendar consisting of 54 phenological events for each year, and events were ordered by average (1997 to 2000) cumulative GDD (January 1 start date, 10 C base temperature). Bloom events occurring just before the giant foxtail emergence events were chosen as the phenological indicators for 2001. The Julian day method used the average (1997 to 2000) dates of foxtail emergence to predict 2001 emergence. The GDD model (October 1 start date, 0 C base temperature) was chosen by determining the combination of start date and base temperature that provided the lowest coefficient of variation for the 1997 to 2000 data. The WeedCast prediction was generated using local soil and environmental data from 2001. The rank order of the 54 phenological events in 2001 showed little deviation from the 4-yr (1997 to 2000) average rank order (R2= 0.96). The phenological calendar indicated that, on average, 25% of giant foxtail seedlings had emerged when red chokeberry was in first bloom, and 80% of seedlings had emerged around the time multiflora rose was in full bloom. We compared the phenological calendar predictions for 25, 50, and 80% emergence with those based on Julian day, cumulative GDD, and WeedCast. The average deviation in predictions ranged from 4.4 d for the phenological calendar to 11.4 d for GDD. In addition to being generally more accurate, the phenological calendar approach also offers the advantage of providing information on the order of phenological events, thus helping to anticipate the progress of emergence and to plan and implement management strategies.
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Cardina, John, Catherine P. Herms, and Daniel A. Herms. "Phenological Indicators for Emergence of Large and Smooth Crabgrass (Digitaria sanguinalisandD. ischaemum)." Weed Technology 25, no. 1 (2011): 141–50. http://dx.doi.org/10.1614/wt-d-10-00034.1.

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We studied the emergence phenology of large and smooth crabgrass in lawn and bare soil environments and identified ornamental plants as phenological indicators that predict the progress of emergence. From 2002 to 2004, we monitored emergence of large and smooth crabgrass in field plots to estimate the dates of first emergence, and 25, 50 and 80% emergence. Each year, we monitored 74 taxa of ornamental plants to determine dates of first and full bloom. We compiled dates of weed emergence and ornamental blooming to create a biological calendar of phenological events for each year, ordered by average cumulative degree days (DD) (January 1 start date, 10 C base temperature). Ornamental plant flowering events that occurred in a regular sequence before crabgrass emergence events were identified as the phenological indicators. We also evaluated DD and rule-based models for predicting crabgrass emergence and optimum time of PRE herbicide application. In general, smooth crabgrass reached each emergence stage earlier than large crabgrass. Differences in emergence between environments were not consistent over years for the two species. There was no consistent pattern in parameters for DD models predicting emergence events for either crabgrass species or environment. For published DD models, the deviation between observed and predicted emergence events ranged from 0 to > 60 d. Published rule-based predictions, though accurate in some cases, were sometimes difficult to implement. The order of ornamental plant blooming and crabgrass emergence events was generally consistent over years (R2= 0.977). The biological calendar provided useful crabgrass emergence predictions using real-time field-based indicators of sequential biological events that can help managers plan and optimize management strategies.
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Yun, Seok Kyu, Kyeong Ho Chung, Ik Koo Yoon, et al. "Developmental Rate Equations for Predicting Blooming Date of 'Yumyeong' (Prunus persica) Peach Trees." Korean Journal of Agricultural and Forest Meteorology 14, no. 4 (2012): 189–95. http://dx.doi.org/10.5532/kjafm.2012.14.4.189.

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6

Kim, Jin-Hee, Eun-Jung Lee, and Jin I. Yun. "Prediction of Blooming Dates of Spring Flowers by Using Digital Temperature Forecasts and Phenology Models." Korean Journal of Agricultural and Forest Meteorology 15, no. 1 (2013): 40–49. http://dx.doi.org/10.5532/kjafm.2013.15.1.040.

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7

Chun, Jong Ahn, Kwangmin Kang, Daeha Kim, Hyun-Hee Han, and In-Chang Son. "Prediction of full blooming dates of five peach cultivars ( Prunus persica ) using temperature-based models." Scientia Horticulturae 220 (June 2017): 250–58. http://dx.doi.org/10.1016/j.scienta.2017.04.007.

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Muthurajan, Vinothkumar, and Balaji Narayanasamy. "An Elliptic Curve Based Schnorr Cloud Security Model in Distributed Environment." Scientific World Journal 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/4913015.

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Cloud computing requires the security upgrade in data transmission approaches. In general, key-based encryption/decryption (symmetric and asymmetric) mechanisms ensure the secure data transfer between the devices. The symmetric key mechanisms (pseudorandom function) provide minimum protection level compared to asymmetric key (RSA, AES, and ECC) schemes. The presence of expired content and the irrelevant resources cause unauthorized data access adversely. This paper investigates how the integrity and secure data transfer are improved based on the Elliptic Curve based Schnorr scheme. This paper proposes a virtual machine based cloud model with Hybrid Cloud Security Algorithm (HCSA) to remove the expired content. The HCSA-based auditing improves the malicious activity prediction during the data transfer. The duplication in the cloud server degrades the performance of EC-Schnorr based encryption schemes. This paper utilizes the blooming filter concept to avoid the cloud server duplication. The combination of EC-Schnorr and blooming filter efficiently improves the security performance. The comparative analysis between proposed HCSA and the existing Distributed Hash Table (DHT) regarding execution time, computational overhead, and auditing time with auditing requests and servers confirms the effectiveness of HCSA in the cloud security model creation.
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Jenni, Sylvie, Katrine A. Stewart, Gaétan Bourgeois, and Daniel C. Cloutier. "Predicting Yield and Time to Maturity of Muskmelons from Weather and Crop Observations." Journal of the American Society for Horticultural Science 123, no. 2 (1998): 195–201. http://dx.doi.org/10.21273/jashs.123.2.195.

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A simple method to predict time from anthesis of perfect flowers to fruit maturity (full slip) and yield is presented here for muskmelon (Cucumis melo L.) grown in a northern climate. Developmental time for individual muskmelons from anthesis to full slip could be predicted from several heat unit formulas, depending on the temperature data set used. When temperature at 7.5 cm above soil level was used, the heat unit formula resulting in the lowest coefficient of variation (cv=6.9%) accumulated daily average temperatures with a base temperature of 11 °C and an upper threshold of 25 °C. With temperatures recorded at a meteorological station located 2 km from the experimental field, the method showing the lowest cv (8.9%) accumulated daily maximum temperatures with a base temperature of 15 °C. This latter method was improved by including a 60-degree-day lag for second cycle fruit. The proportion of fruit volume at full slip of 22 fruit from the first cycle could be described by a common Richards function (R2=0.99). Although 65% of the plants produced two fruit cycles, fruit from the first cycle represented 72% of total yield in terms of number and mass. The blooming period of productive flowers lasted 34 days, each cycle overlapping and covering an equal period of 19 days. Counting the number of developing fruit >4 cm after 225 degree days from the start of anthesis (when 90% of the plants have at least one blooming perfect flower) could rapidly estimate the number of fruit that will reach maturity.
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Liping, Wang, and Zheng Binghui. "Prediction of chlorophyll-a in the Daning River of Three Gorges Reservoir by principal component scores in multiple linear regression models." Water Science and Technology 67, no. 5 (2013): 1150–58. http://dx.doi.org/10.2166/wst.2013.679.

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After the impoundment of the Three Gorges Reservoir (TGR) since 2003, eutrophication has occurred and has become severe in Daning River. To predict chlorophyll-a (Chl-a) levels, the relationships between Chl-a and 11/13 routine monitoring data on water quality and hydrodynamics in Daning River were studied by principal component scores in the multiple linear regression model (principal component regression (PCR) model). In order to determine the hydrodynamic effect on simulated accuracy, two 0-day ahead prediction models were established: model A without hydrodynamic factors as variables, and model B with hydrodynamic factors (surface water velocity and water residence time) as variables. Based on the results of correlation analysis, score 1 and 2 with significant loads of phosphorus and nitrogen nutrients were omitted in developing model A (R2 = 0.355); while score 2 with significant loads of nitrogen was omitted in developing model B (R2 = 0.777). The results of validation using a new dataset showed that model B achieved a better fitted relationship between the predicted and observed values of Chl-a. It indicated hydrodynamics play an important role in limiting algal growth. The results suggested that a PCR model incorporating hydrodynamics processes has been suitable for the Chl-a concentration simulation and algal blooming prediction in Daning River of TGR.
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Dissertations / Theses on the topic "Blooming date prediction"

1

Kitamura, Yuto. "Studies on the temperature requirements for flower bud dormancy release in Prunus mume." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225642.

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Conference papers on the topic "Blooming date prediction"

1

Wagner, Bernhard, Bruno Frackowiak, Pierre Gajan, and Alain Strzelecki. "Fuel Vapor Concentration Measurements by Laser Induced Fluorescence and Infra-Red Extinction: An Investigation on a Monodisperse Droplet Stream." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59949.

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The vapor mole fraction field around a stream of monodisperse acetone droplets is investigated by Planar Laser Induced Fluorescence (PLIF) and Infra-Red Extinction (IRE). The PLIF works develop an interface positioning method based on the Lorenz-Mie Theory and on geometrical optics, which can be applied to the images despite the blooming effect caused by the liquid phase. Quantitative results obtained at two different injection temperatures concur with the numerical predictions. IRE results — taken at a high repetition rate on the same configuration — are presented. The dynamical behavior, possibilities and constraints of the employed techniques are discussed and an outlook to following investigations is given. This paper presents some PLIF and IRE basics, a description of the test rigs, the post processing of the obtained data and a comparison of the results to a simplified numerical calculation. Finally a discussion of the results and suggestions for improvements are proposed.
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Journal articles
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