
@article{salam_blackleg_2003,
	title = {Blackleg {Sporacle}: {A} {Model} for {Predicting} {Onset} of {Pseudothecia} {Maturity} and {Seasonal} {Ascospore} {Showers} in {Relation} to {Blackleg} of {Canola}},
	volume = {93},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18944090},
	doi = {10.1094/PHYTO.2003.93.9.1073},
	abstract = {ABSTRACT A simple model has been developed to predict the onset of pseudothecia maturity and seasonal ascospore showers in relation to blackleg disease in canola, caused by the fungus Leptosphaeria maculans. The model considers a combination of two weather factors, daily mean temperature and daily total rainfall, to drive progress of maturity of pseudothecia on the infested canola stubble left from past crops. Each day is categorized as suitable or not suitable for progress of the maturation process. The onset of pseudothecia maturity occurs when approximately 43 suitable days have occurred. Following the onset of maturity, ascospore showers are triggered when daily rainfall exceeds a threshold. The model satisfactorily predicted the timing of the onset of pseudothecia maturity when tested with 3 years of field observations at four locations in Western Australia, which characteristically has a Mediterranean climate. The model also agreed reasonably well with the daily pattern of ascospore release observed in two locations. Sensitivity analysis was performed to show the relative importance of the parameters that describe the onset of pseudothecia maturity.},
	number = {9},
	journal = {Phytopathology},
	author = {Salam, M. U. and Khangura, R. K. and Diggle, A. J. and Barbetti, M. J.},
	month = sep,
	year = {2003},
	pages = {1073--1081},
	file = {2003_Salam et al._Blackleg Sporacle A Model for Predicting Onset of Pseudothecia Maturity and Seasonal Ascospore Showers in Relation to:C\:\\Users\\uqpmello\\Zotero\\storage\\7SIKP4KQ\\2003_Salam et al._Blackleg Sporacle A Model for Predicting Onset of Pseudothecia Maturity and Seasonal Ascospore Showers in Relation to.pdf:application/pdf},
}

@article{salam_g1_2011,
	title = {G1 {Blackspot} {Manager} model predicts the maturity and release of ascospores in relation to ascochyta blight on field pea},
	volume = {40},
	doi = {10.1007/s13313-011-0035-0},
	abstract = {A simple model, G1 Blackspot Manager, has been developed to predict the seasonal pattern of release of ascospores in relation to ascochyta blight in field pea. The model considers a combination of two weather factors, daily mean temperature and daily total rainfall, to drive progress of maturity of pseudothecia on infested field pea stubble from past crops. Each day is categorised as suitable or not suitable for continuation of the maturation process. The onset of pseudothecial maturity has been found to take place when approximately ten suitable days have occurred. Following the onset of maturity, ascospore release is triggered when daily rainfall exceeds a threshold. The model was satisfactorily calibrated using three datasets from Western Australia. The calibrated model performed well when independently tested with 21 datasets, 17 from Western Australia and 4 from South Australia. It is concluded that G1 Blackspot Manager model has the potential to be used to formulate sowing guides for field pea in southern Australia that minimise the risk of ascochyta blight.},
	number = {6},
	journal = {Australasian Plant Pathology},
	author = {Salam, Moin U and Galloway, Jean and Macleod, William J and Davidson, Jennifer A and Seymour, Mark and Pritchard, Ian and Salam, Kawsar P and Diggle, Art J and Maling, Tim},
	year = {2011},
	keywords = {Quantitative epidemiology, Introduction, Mycosphaerella pinodes, Ascochyta blight, Ascospore shower, Blackspot, Field pea, Didymella pinodes, Didymella pisi, Disease forecasting, Phoma koolunga, Phoma medicaginis var pinodella},
	pages = {621--631},
	file = {2011_Salam et al._G1 Blackspot Manager model predicts the maturity and release of ascospores in relation to ascochyta blight on field pe:C\:\\Users\\uqpmello\\Zotero\\storage\\U67NKTGD\\2011_Salam et al._G1 Blackspot Manager model predicts the maturity and release of ascospores in relation to ascochyta blight on field pe.pdf:application/pdf},
}

@article{silva_cercospora_2018,
	title = {Cercospora leaf spot in {Toona} ciliata: {Epidemiology} and infection process of {Cercospora} cf. alchemillicola},
	volume = {48},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057997973&doi=10.1111%2fefp.12451&partnerID=40&md5=d489f8a82fadd75ed8eeed14812a90d3},
	doi = {10.1111/efp.12451},
	number = {6},
	journal = {Forest Pathology},
	author = {Silva, H.R. and Pozza, E.A. and de Souza, P.E. and Ferreira, M.A. and de Freitas, A.S. and Moreira, S.I.},
	year = {2018},
}

@article{el_jarroudi_weather-based_2021,
	title = {Weather-{Based} {Predictive} {Modeling} of {Cercospora} beticola {Infection} {Events} in {Sugar} {Beet} in {Belgium}},
	volume = {7},
	issn = {2309-608X},
	url = {https://www.mdpi.com/2309-608X/7/9/777},
	doi = {10.3390/jof7090777},
	abstract = {Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease management and farm profitability. In this study, we propose a weather-based modelling approach for predicting infection by C. beticola in sugar beet fields in Belgium. Based on reported weather conditions favoring CLS epidemics and the climate patterns across Belgian sugar beet-growing regions during the critical infection period (June to August), optimum weather conditions conducive to CLS were first identified. Subsequently, 14 models differing according to the combined thresholds of air temperature (T), relative humidity (RH), and rainfall (R) being met simultaneously over uninterrupted hours were evaluated using data collected during the 2018 to 2020 cropping seasons at 13 different sites. Individual model performance was based on the probability of detection (POD), the critical success index (CSI), and the false alarm ratio (FAR). Three models (i.e., M1, M2 and M3) were outstanding in the testing phase of all models. They exhibited similar performance in predicting CLS infection events at the study sites in the independent validation phase; in most cases, the POD, CSI, and FAR values were ≥84\%, ≥78\%, and ≤15\%, respectively. Thus, a combination of uninterrupted rainy conditions during the four hours preceding a likely start of an infection event, RH {\textgreater} 90\% during the first four hours and RH {\textgreater} 60\% during the following 9 h, daytime T {\textgreater} 16 °C and nighttime T {\textgreater} 10 °C, were the most conducive to CLS development. Integrating such weather-based models within a decision support tool determining fungicide spray application can be a sound basis to protect sugar beet plants against C. beticola, while ensuring fungicides are applied only when needed throughout the season.},
	number = {9},
	urldate = {2023-03-22},
	journal = {Journal of Fungi},
	author = {El Jarroudi, Moussa and Chairi, Fadia and Kouadio, Louis and Antoons, Kathleen and Sallah, Abdoul-Hamid Mohamed and Fettweis, Xavier},
	month = sep,
	year = {2021},
	pmid = {34575815},
	pmcid = {PMC8470031},
	pages = {777},
	file = {PubMed Central Full Text PDF:C\:\\Users\\uqpmello\\Zotero\\storage\\AMEL4BPM\\El Jarroudi et al. - 2021 - Weather-Based Predictive Modeling of Cercospora be.pdf:application/pdf},
}

@article{wolf_integrated_2002,
	title = {An {Integrated} {Pest} {Management} {System} in {Germany} for the {Control} of {Fungal} {Leaf} {Diseases} in {Sugar} {Beet}: {The} {IPM} {Sugar} {Beet} {Model}},
	volume = {86},
	issn = {0191-2917, 1943-7692},
	shorttitle = {An {Integrated} {Pest} {Management} {System} in {Germany} for the {Control} of {Fungal} {Leaf} {Diseases} in {Sugar} {Beet}},
	url = {https://apsjournals.apsnet.org/doi/10.1094/PDIS.2002.86.4.336},
	doi = {10.1094/PDIS.2002.86.4.336},
	language = {en},
	number = {4},
	urldate = {2023-03-22},
	journal = {Plant Disease},
	author = {Wolf, P. F. J. and Verreet, J. A.},
	month = apr,
	year = {2002},
	pages = {336--344},
	file = {Wolf and Verreet - 2002 - An Integrated Pest Management System in Germany fo.pdf:C\:\\Users\\uqpmello\\Zotero\\storage\\IVKE3Z3Q\\Wolf and Verreet - 2002 - An Integrated Pest Management System in Germany fo.pdf:application/pdf},
}

@article{rossi_simulation_1994,
	title = {A simulation model for {Cercospora} leaf spot epidemics on sugarbeet},
	volume = {33},
	issn = {0031-9465},
	url = {https://www.jstor.org/stable/42685930},
	abstract = {The CERCODEP (CERCOspora Development of EPidemics) model simulating development of Cercospora leaf spot epidemics on sugarbeet is shown. Disease severity is calculated daily as the summation of the amount of disease severity of the previous day and the daily increase of disease severity, both expressed as percentage of diseased leaf area. The daily increase of disease severity depends on the infection rate, on the infectious leaf area and on the affectable leaf area. Infection rate is calculated as a function of some weather (hourly values of temperature and relative humidity and rainfall) and cultural factors (cultivar susceptibility and sugarbeet growth stage). The validation of the model, using data not used for model development, was made by subjective (visual comparison) and objective tests (Kolmogorov-Smirnov test and regression analysis). A comparison was made between field observations and simulations produced by CERCODEP. Visual comparison and Kolmogorov-Smirnov test always showed agreement of model simulations with field observations, while regression analysis showed one case of a significant difference.},
	number = {2},
	urldate = {2023-03-22},
	journal = {Phytopathologia Mediterranea},
	author = {Rossi, V. and Racca, P. and Battilani, P.},
	year = {1994},
	note = {Publisher: Firenze University Press},
	pages = {105--112},
	file = {JSTOR Full Text PDF:C\:\\Users\\uqpmello\\Zotero\\storage\\D5FLTGL3\\Rossi et al. - 1994 - A simulation model for Cercospora leaf spot epidem.pdf:application/pdf},
}

@article{bu_sugar_2016,
	title = {Sugar {Beet} {Yield} and {Quality} {Prediction} at {Multiple} {Harvest} {Dates} {Using} {Active}‐{Optical} {Sensors}},
	volume = {108},
	issn = {0002-1962, 1435-0645},
	url = {https://onlinelibrary.wiley.com/doi/10.2134/agronj2015.0268},
	doi = {10.2134/agronj2015.0268},
	abstract = {Yield prediction in sugar beet (Beta vulgaris L.) is important as a basis for in-season N application. Active optical sensors have been researched in sugar beet for yield estimation. A common field method for using active-optical sensors is to establish an N non-limiting area, and compare the yield predicted from sensor readings with readings from the rest of the field. Yield difference is the basis for calculation of N rate. Sugar beet gains root mass and sugar content with time. The objectives of these experiments were to utilize two active-optical sensors at two timings with canopy height measurements and relate readings to root yield and recoverable sugar yield at consecutive harvest dates. A 2-yr study in the Red River Valley of North Dakota and Minnesota was conducted on four sites to compare two active-optical sensors, GreenSeeker and Holland Crop Circle, red normalized differential vegetative index (NDVI), red edge NDVI, with and without canopy height for use in sugar beet yield prediction. The red NDVI and red edge NDVI, used at V 6–8 and V 12–14 were similar in their relationship to sugar beet yield over several harvest dates. The r2 of sensor measurement and yield relationships at V 6–8 improved when canopy height was considered but not at V 12–14. Active-optical sensors when canopy height is considered could be used to predict sugar beet root yield and recoverable sugar yield over a range of harvest dates, which would be useful in developing algorithms for in-season N fertilization.},
	language = {en},
	number = {1},
	urldate = {2023-05-29},
	journal = {Agronomy Journal},
	author = {Bu, Honggang and Sharma, Lakesh K. and Denton, Anne and Franzen, David W.},
	month = jan,
	year = {2016},
	pages = {273--284},
	file = {Bu et al. - 2016 - Sugar Beet Yield and Quality Prediction at Multipl.pdf:C\:\\Users\\uqpmello\\Zotero\\storage\\5AM4234D\\Bu et al. - 2016 - Sugar Beet Yield and Quality Prediction at Multipl.pdf:application/pdf},
}

@article{rimaz_predicting_2020,
	title = {Predicting the seedling emergence time of sugar beet ({Beta} vulgaris) using beta models},
	volume = {26},
	issn = {0971-5894},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772125/},
	doi = {10.1007/s12298-020-00884-1},
	abstract = {Soil temperature, texture, water content and sowing depth are effective factors on the estimation of emergence time. This research aimed to test the Beta model for its adequacy in predicting the time of emergence for sugar beet. The Beta growth model as a phenological model have been used for evaluating the time of seedling emergences under both controlled environments in laboratory and field conditions. An experiment was conducted both in the laboratory with five soil textures, three sowing depths, five soil water contents and ten constant soil temperatures, under field conditions on five sowing dates (20 February, 28 March, 19 April, 10 May, and 31 May) and three sowing depths. The results demonstrated that the Beta model can predict the time of emergence. Based on the root mean square error (RMSE), the time of emergence estimated by the Beta model was in high agreement with the time of emergence measured in the laboratory. Estimation accuracy was reduced slightly by the Beta model under field conditions. The accuracy of the Beta model was influenced by the sowing date under field conditions. So, on the first and second sowing dates (with low air temperature), the estimation of time of emergence by the model was lower and on the fourth and the fifth sowing date (with warmer air temperature), was more than the duration measured. Estimation accuracy was increased by the Beta model under field conditions using soil temperature. In conclusion, the Beta model can predict the time to emergence of sugar beet seedlings in different levels of soil texture and soil water content under field conditions, and with that, the proper planting date for sugar beet seeds to overcome weeds in different soil water content can be predicted.},
	number = {12},
	urldate = {2023-05-29},
	journal = {Physiology and Molecular Biology of Plants},
	author = {Rimaz, Hamid Reza and Zand-Parsa, Shahrokh and Taghvaei, Mansour and Kamgar-Haghighi, Ali Akbar},
	month = dec,
	year = {2020},
	pmid = {33424150},
	pmcid = {PMC7772125},
	pages = {2329--2338},
	file = {PubMed Central Full Text PDF:C\:\\Users\\uqpmello\\Zotero\\storage\\NU3CXESN\\Rimaz et al. - 2020 - Predicting the seedling emergence time of sugar be.pdf:application/pdf},
}

@article{gehl_-season_2011,
	title = {In-{Season} {Prediction} of {Sugarbeet} {Yield}, {Quality}, and {Nitrogen} {Status} {Using} an {Active} {Sensor}},
	volume = {103},
	issn = {1435-0645},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.2134/agronj2011.0040},
	doi = {10.2134/agronj2011.0040},
	abstract = {Nitrogen fertilizer management of sugarbeet (Beta vulgaris L.) continues to increase in importance with rising fertilizer costs and industry payments weighted toward crop quality. Our objective was to evaluate the use of an optical sensor for assessment of in-season sugarbeet N status, yield, and quality prediction and total N in foliage on the day of harvest. Six N fertilizer treatments, from 0 to 225 kg N ha−1, were applied at three sites in Michigan in 2006 and four sites in 2007. Normalized difference vegetative indices (NDVIs) were measured at four growing degree day (GDD) intervals in 2006, five intervals in 2007, and at harvest in both years with a red-band active sensor. Leaf biomass and root yield were determined at harvest, and root samples were collected for determination of sucrose content and clear juice purity. The NDVI readings were useful for differentiating control treatments from fertilized plots but were not able to identify a yield response threshold until late in the season. Midseason 1200 to 1400 GDD, 1900 to 2300 GDD, and harvest NDVI values were strongly related to recoverable white sucrose per area (R2 = 0.89, 0.87, and 0.80, respectively). Harvest NDVI was strongly related to sugarbeet vegetation total N (R2 = 0.87). Active sensing during the growing season shows promise as a means to estimate root yield and recoverable sugar in sugarbeet fields. Sensing on the day of harvest may improve rotational N management by providing an indication of N return to the cropping system.},
	language = {en},
	number = {4},
	urldate = {2023-06-07},
	journal = {Agronomy Journal},
	author = {Gehl, Ronald J. and Boring, Timothy J.},
	year = {2011},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.2134/agronj2011.0040},
	pages = {1012--1018},
	file = {Article summary, .pdf:C\:\\Users\\uqpmello\\Zotero\\storage\\66WAWEJ4\\Gehl and Boring - 2011 - In-Season Prediction of Sugarbeet Yield, Quality, .pdf:application/pdf;Gehl and Boring - 2011 - In-Season Prediction of Sugarbeet Yield, Quality, .pdf:C\:\\Users\\uqpmello\\Zotero\\storage\\8L9GDIUP\\Gehl and Boring - 2011 - In-Season Prediction of Sugarbeet Yield, Quality, .pdf:application/pdf;Snapshot:C\:\\Users\\uqpmello\\Zotero\\storage\\3SFAUE26\\agronj2011.html:text/html},
}

@article{rimaz_predicting_2020-1,
	title = {Predicting the seedling emergence time of sugar beet ({Beta} vulgaris) using beta models},
	volume = {26},
	issn = {0974-0430},
	url = {https://doi.org/10.1007/s12298-020-00884-1},
	doi = {10.1007/s12298-020-00884-1},
	abstract = {Soil temperature, texture, water content and sowing depth are effective factors on the estimation of emergence time. This research aimed to test the Beta model for its adequacy in predicting the time of emergence for sugar beet. The Beta growth model as a phenological model have been used for evaluating the time of seedling emergences under both controlled environments in laboratory and field conditions. An experiment was conducted both in the laboratory with five soil textures, three sowing depths, five soil water contents and ten constant soil temperatures, under field conditions on five sowing dates (20 February, 28 March, 19 April, 10 May, and 31 May) and three sowing depths. The results demonstrated that the Beta model can predict the time of emergence. Based on the root mean square error (RMSE), the time of emergence estimated by the Beta model was in high agreement with the time of emergence measured in the laboratory. Estimation accuracy was reduced slightly by the Beta model under field conditions. The accuracy of the Beta model was influenced by the sowing date under field conditions. So, on the first and second sowing dates (with low air temperature), the estimation of time of emergence by the model was lower and on the fourth and the fifth sowing date (with warmer air temperature), was more than the duration measured. Estimation accuracy was increased by the Beta model under field conditions using soil temperature. In conclusion, the Beta model can predict the time to emergence of sugar beet seedlings in different levels of soil texture and soil water content under field conditions, and with that, the proper planting date for sugar beet seeds to overcome weeds in different soil water content can be predicted.},
	language = {en},
	number = {12},
	urldate = {2023-06-07},
	journal = {Physiology and Molecular Biology of Plants},
	author = {Rimaz, Hamid Reza and Zand-Parsa, Shahrokh and Taghvaei, Mansour and Kamgar-Haghighi, Ali Akbar},
	month = dec,
	year = {2020},
	keywords = {Plantation date, Seedling emergence, Soil moisture, Soil texture, Sugar beet},
	pages = {2329--2338},
	file = {Full Text PDF:C\:\\Users\\uqpmello\\Zotero\\storage\\PG8GKSLS\\Rimaz et al. - 2020 - Predicting the seedling emergence time of sugar be.pdf:application/pdf},
}

@article{rossi_cercopri_1991,
	title = {{CERCOPRI}: a forecasting model for primary infections of cercospora leaf spot of sugarbeet},
	volume = {21},
	issn = {1365-2338},
	shorttitle = {{CERCOPRI}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2338.1991.tb01284.x},
	doi = {10.1111/j.1365-2338.1991.tb01284.x},
	abstract = {A computerized model for forecasting the appearance of primary infections of cercospora disease on sugarbeet was worked out using meteorological and epidemiological data collected in the western Po Valley from 1980 to 1989. This program, called CERCOPRI, gives a figure for the current risk of disease appearance, a forecast of the risk on a pre-established day or a forecast of the day on which risk will reach a pre-established threshold.},
	language = {en},
	number = {3},
	urldate = {2023-06-07},
	journal = {EPPO Bulletin},
	author = {Rossi, V. and Battilani, P.},
	year = {1991},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2338.1991.tb01284.x},
	pages = {527--531},
	file = {Full Text PDF:C\:\\Users\\uqpmello\\Zotero\\storage\\VV43ELQY\\Rossi and Battilani - 1991 - CERCOPRI a forecasting model for primary infectio.pdf:application/pdf;Snapshot:C\:\\Users\\uqpmello\\Zotero\\storage\\H2PWM75P\\j.1365-2338.1991.tb01284.html:text/html},
}

@article{racca_cercbet_2007,
	title = {{CERCBET} 3 – a forecaster for epidemic development of {Cercospora} beticola*},
	volume = {37},
	issn = {1365-2338},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2338.2007.01136.x},
	doi = {10.1111/j.1365-2338.2007.01136.x},
	abstract = {Cercospora beticola is the most prevalent and damaging fungal disease in German sugar beet growing. Control strategies are based on action thresholds. A model has been developed which forecasts epidemic development (expressed as disease incidence) and signals when action thresholds are overridden. The plot-specific model, CERCBET 3 uses as input meteorological parameters (temperature, relative humidity), easily accessible agronomic field characteristics and a single recording of C. beticola disease incidence. Extensive validation in 2001–03 showed that, in 80–95\% of the cases, CERCBET 3 correctly forecasted the dates when thresholds were overridden. Cultivar diversity in German sugar beet growing is increasing, thus a module has been included into CERCBET 3 which reflects susceptibility to C. beticola by introducing a sporulation factor. In some cases a second or even third fungicide treatment could be necessary to control Cercospora leaf spot and so a further module which models fungicide efficacy has been elaborated. CERCBET 3 is available for sugar beet growers in an interactive form on the Internet platform ISIP, which is provided by the governmental crop protection services of Germany.},
	language = {en},
	number = {2},
	urldate = {2023-06-13},
	journal = {EPPO Bulletin},
	author = {Racca, P. and Jörg, E.},
	year = {2007},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2338.2007.01136.x},
	pages = {344--349},
	file = {Full Text PDF:C\:\\Users\\uqpmello\\Zotero\\storage\\PKGHVMPP\\Racca and Jörg - 2007 - CERCBET 3 – a forecaster for epidemic development .pdf:application/pdf;Snapshot:C\:\\Users\\uqpmello\\Zotero\\storage\\W5RFHQ53\\j.1365-2338.2007.01136.html:text/html},
}

@article{wolf_factors_2005,
	title = {Factors {Affecting} the {Onset} of {Cercospora} {Leaf} {Spot} {Epidemics} in {Sugar} {Beet} and {Establishment} of {Disease}-{Monitoring} {Thresholds}},
	volume = {95},
	issn = {0031-949X},
	url = {https://doi.org/10.1094/PHYTO-95-0269},
	doi = {10.1094/PHYTO-95-0269},
	abstract = {Severe Cercospora leaf spots epidemics in sugar beet during the late 1980s and early 1990s in southern Germany prompted us to initiate investigations on the epidemiology of the causal agent, Cercospora beticola. The data set involved 69 field trials (1993 to 2003) focusing on factors affecting the epidemic onset of this disease. Observations were made at weekly intervals, recording the calendar week when canopy closure occurred (growth stage according to BBCH scale = 39) and symptom development by assessing the percentage of infected leaf area on a single-leaf basis (n = 40 plants). These monitoring trials revealed that epidemic onset varied between early July and mid-September. Hence, the target was to identify the reasons for this variation in order to deduct the most suitable approach for predicting epidemic onset. Differences in cultivar resistance explained part of epidemic onset variability, as did different timings of canopy closure, presumably due to associated microclimate changes. Moreover, meteorological variables were considered as potential reasons for variation in epidemic onset. The weather-dependent infection probability was assessed by daily infection values (DIV) in the range from 0 to 1 using hourly weather data. For calculating DIVs, the temperature effect was quantified by the proportions of the latent period (LP) relative to the optimum at 20 to 25°C, established by artificial inoculation of sugar beet plants in growth cabinets. Artificial infection experiments further established that air relative humidity (RH) {\textgreater}95\% or leaf wetness was required for infection and subsequent lesion development. Under field conditions, the probability of leaf wetness was 75\% at RH {\textgreater}90\%. Therefore, DIVs were set to 0 for RH ≤90\% in the absence of precipitation (moisture index I). Alternatively, the effect of moisture was modeled with a sigmoidal function describing the occurrence of leaf wetness in dependence of RH in the field (moisture index II). Using this approach, DIV values were cumulated (c-DIV) for each of the 69 trials beginning either at fixed starting dates (1 May, 16 May, or 1 June) or the dates of canopy closure. Accumulation of DIV ended at the time of epidemic onset. The two different moisture index definitions had no significant influence on c-DIV; whereas, for starting time of summation of DIV, the date of canopy closure was more suitable. Values of c-DIV ranged from 7 to 19 in highly susceptible cultivars and 12 to 25 in cultivars with lower susceptibility. Given this variation, c-DIV values were insufficient to explain differences in the date of epidemic onset and thus were not considered suitable for making accurate and precise management decisions. However, a negative prognosis assessing the most likely periods of disease absence was possible by determining the minimum c-DIV as a threshold. This threshold was 7 c-DIV for highly susceptible cultivars and 12 c-DIV for cultivars with low susceptibility. Crop monitoring is recommended as soon as these threshold values are exceeded so that the exact epidemic onset time can be observed.},
	number = {3},
	urldate = {2023-07-04},
	journal = {Phytopathology®},
	author = {Wolf, P. F. J. and Verreet, J. A.},
	month = mar,
	year = {2005},
	note = {Publisher: Scientific Societies},
	keywords = {Beta vulgaris, economic damage threshold},
	pages = {269--274},
	file = {Full Text PDF:C\:\\Users\\uqpmello\\Zotero\\storage\\PGF8Y8YJ\\Wolf and Verreet - 2005 - Factors Affecting the Onset of Cercospora Leaf Spo.pdf:application/pdf},
}

@article{meier_phanologische_1993,
	title = {Phänologische {Entwicklungsstadien} der {Beta}-{Rüben} ({Beta} vulgaris {L}. sspp.){Codierung} und {Beschreibung} nach der erweiterten {BBCH}-{Skala} mit {Abbildungen}},
	volume = {45},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	url = {https://www.openagrar.de/receive/openagrar_mods_00067183},
	abstract = {Mit den ausführlichen kulturspezifischen Beschreibungen ist diese spezielle Skala als Ergänzung zur „Allgemeinen BBCH-Skala" zu verstehen. Die phänologischen Entwicklungsstadien von Beta-Rüben werden beschrieben und in Entwicklungsphasen (Makrostadien) und stärker differenzierte Entwicklungsschritte (Mikrostadien) eingeteilt. Jedem Entwicklungsschritt wird ein zweistelliger Code zugeordnet, der nach dem Dezimalsystem aufgebaut ist. Damit wird Forderungen der Datenverarbeitung im pflanzenbaulichen Versuchswesen Rechnung getragen.},
	language = {en},
	number = {2},
	urldate = {2023-07-04},
	journal = {Heft 2},
	author = {Meier, Uwe and Bachmann, L. and Buhtz, E. and Hack, H. and Klose, R. and Märländer, B. and Weber, E.},
	year = {1993},
	pages = {37--41},
	file = {Meier et al. - 1993 - Phänologische Entwicklungsstadien der Beta-Rüben (.pdf:C\:\\Users\\uqpmello\\Zotero\\storage\\JIY3L8IA\\Meier et al. - 1993 - Phänologische Entwicklungsstadien der Beta-Rüben (.pdf:application/pdf},
}

@article{wolf_zum_2001,
	title = {Zum {Einfluss} des {Bestandesklimas} auf die {Prädisposition} der {Zuckerrübe} gegenüber {Infektionen} von {Cercospora} beticola ({Sacc}.) / {Influence} of sugar beet leaf mass development on predisposition of the crop to {Cercospora} beticola ({Sacc}.)},
	volume = {108},
	issn = {0340-8159},
	url = {https://www.jstor.org/stable/43215404},
	abstract = {Für die Prognose des Cercospora-Befalis in Zuckerrüben sind u. a. Kenntnisse der Erregerbiologie sowie über den Einfluss der Bestandesentwicklung auf das 'Mikroklima von Bedeutung. Für die pilzliche Entwicklung stellen im Allgemeinen Temperaturen im Bereich von 25—30 °C das Optimum dar. Eine Keimung von Konidien ist, wenngleich überproportional verzögert, bis in die Nähe des Gefrierpunktes möglich, die Inkubationszeit jedoch geht bereits bei Temperaturen unter 10 °C gegen unendlich. Ein quantitativer Einfluss der relativen Luftfeuchte ist nur im optimalen Bereich von 95-100 \% gegeben, diese ist somit weitgehend als qualitativer Einflussfaktor zu charakterisieren. Unter optimalen Voraussetzungen benötigt der Pilz von der Inokulation bis zum Erscheinen von Symptomen eine Inkubationszeit von 6-7 Tagen. Aus den Labordaten abgeleitete Funktionen beschreiben den Charakter der meteorologischen Einflussparameter und können zur Kalkulation von witterungsbedingten Infektionswahrscheinlichkeiten dienen. Mit der sukzessiven Entwicklung von Blattmasse geht eine deutliche Veränderung des Mikroklimas einher, welche insbesondere ab dem Zeitpunkt des Reihenschlusses wirksam wird. Die Temperaturen im Bestand sind geringfügig gesenkt, die relative Luftfeuchte jedoch liegt erhöht vor, ebenso wie die Blattnässedauer verlängert wird. Die biologischen Ansprüche von Cercospora beticola sind daher nach dem Reihenschluss in höherem Maße erfüllt. Die Tagesperiodik der Luftfeuchte weist nach dem Reihenschluss insbesondere tagsüber eine durchschnittliche Steigerung der Luftfeuchte um bis zu 10 \% auf, die Häufigkeit von Blattnässe ist um bis zu 35 \% erhöht, verglichen mit der Situation vor Reihenschluss. Als Konsequenz der meteorologischen Untersuchungen im Freiland ist für Termin-Prognosen der Verwendung von stündlichen Werten der Vorzug einzuräumen. Als flexibler Ausgangspunkt von Prognosekalkulationen wird der Zeitpunkt des Reihenschlusses vorgeschlagen, bzw. fixiert nach Kalendarium der Anfang Juni, als erfahrungsgemäßer Zeitpunkt des Eintretens. Important points for the prognosis of Cercospora leaf spot infection in sugar beet include knowledge of the causal agent biology as well as the influence of the crop canopy development on the microclimate. Representing the optimum conditions for fungus development are generally temperatures in the range of 25—30 °C. Germination of conidia is possible, even though over-proportionately reduced in intensity, at just above freezing point. The incubation period, however, proceeds to infinite even below 10 °C. The influence of moisture is only quantitative in the optimum range of 95-100 \% relative air humidity and, therefore, it can be characterised as a more qualitative influence parameter. Under optimum conditions, the fungus requires an incubation period of from 6 to 7 days from inoculation until occurrence of first symptoms. Functions derived from laboratory data describe the character of meteorological influence parameters and can help in the calculation of weather-influenced infection forecasting. The successive development of leaf mass is accompanied by a distinct alteration of the microclimate, especially effective from row closure onwards. The temperatures within the crop canopy are then slightly reduced. Relative air moisture content is increased, however, and the duration of leaf wetness is lengthened. The biological requirements of Cercospora beticola are thus fulfilled to a large extent after row closure. The daily periodicity of the relative air humidity as recorded in closed crops, in particular during daytime, indicates an average increase of up to 10 \%, the frequency of leaf wetness is increased by up to 35 \% compared with before row closure. The influence of crop canopy development is reduced during nighttime. As a consequence of the meteorological investigations in-field, the use of hourly data proved advantageous for treatment date prognosis. As flexible starting point for forecast calculations, the time of row closure is advised, or the beginning of June when a fixed calendar date is preferred, as experience has shown that this is when canopy closure commences.},
	number = {6},
	urldate = {2023-07-05},
	journal = {Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz / Journal of Plant Diseases and Protection},
	author = {Wolf, P. F. J. and Heindl, M. and Verreet, J. A.},
	year = {2001},
	note = {Publisher: Verlag Eugen Ulmer KG},
	pages = {578--592},
	file = {JSTOR Full Text PDF:C\:\\Users\\uqpmello\\Zotero\\storage\\EIQYVVZW\\Wolf et al. - 2001 - Zum Einfluss des Bestandesklimas auf die Prädispos.pdf:application/pdf},
}
