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A Mechanistic Model of Botrytis cinerea on Grapevines That Includes Weather, Vine Growth Stage, and the Main Infection Pathways.

González-Domínguez E, Caffi T, Ciliberti N, Rossi V - PLoS ONE (2015)

Bottom Line: Results from DFA were also used to calculate the daily probabilities that an ongoing epidemic would be mild, intermediate, or severe.SEV1 was the most influential variable in discriminating between mild and intermediate epidemics, whereas SEV2 and SEV3 were relevant for discriminating between intermediate and severe epidemics.The model represents an improvement of previous B. cinerea models in viticulture and could be useful for making decisions about Botrytis bunch rot control.

View Article: PubMed Central - PubMed

Affiliation: Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy.

ABSTRACT
A mechanistic model for Botrytis cinerea on grapevine was developed. The model, which accounts for conidia production on various inoculum sources and for multiple infection pathways, considers two infection periods. During the first period ("inflorescences clearly visible" to "berries groat-sized"), the model calculates: i) infection severity on inflorescences and young clusters caused by conidia (SEV1). During the second period ("majority of berries touching" to "berries ripe for harvest"), the model calculates: ii) infection severity of ripening berries by conidia (SEV2); and iii) severity of berry-to-berry infection caused by mycelium (SEV3). The model was validated in 21 epidemics (vineyard × year combinations) between 2009 and 2014 in Italy and France. A discriminant function analysis (DFA) was used to: i) evaluate the ability of the model to predict mild, intermediate, and severe epidemics; and ii) assess how SEV1, SEV2, and SEV3 contribute to epidemics. The model correctly classified the severity of 17 of 21 epidemics. Results from DFA were also used to calculate the daily probabilities that an ongoing epidemic would be mild, intermediate, or severe. SEV1 was the most influential variable in discriminating between mild and intermediate epidemics, whereas SEV2 and SEV3 were relevant for discriminating between intermediate and severe epidemics. The model represents an improvement of previous B. cinerea models in viticulture and could be useful for making decisions about Botrytis bunch rot control.

No MeSH data available.


Related in: MedlinePlus

Weather data and model output at S. Michelle in 2012 (SM-12).A: daily data of temperature (red line, T in °C), relative humidity (green line, RH in %), rain (blue bars, in mm), and wetness duration (light blue area, WD in hours); B: predicted rate of spore production (SPOR) by Botrytis cinerea; C: predicted relative infection severity (RIS) on inflorescences and young clusters by conidia (RIS1; green bars) during the first infection period (green area), and on ripening berries by conidia (RIS2; blue bars) and mycelium (berry-to-berry infection) (RIS3; red bars) during the second infection period (orange area). Lines indicate the accumulated value of RIS1 (SEV1; green line) and of RIS2+RIS3 (SEV2+SEV3; purple line). Numbers in C indicate vine growth stages of full flowering (stage 65 of the scale of Lorenz et al. [31]), fruit set (stage 71), and berries developing (stage 83). The first infection window extends from “inflorescences clearly visible” (stage 53) to “berries groat-sized, bunches begin to hang” (stage 73). The second infection window extends from “majority of berries touching” (stage 79) to “berries are ripe for harvest” (stage 89).
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pone.0140444.g003: Weather data and model output at S. Michelle in 2012 (SM-12).A: daily data of temperature (red line, T in °C), relative humidity (green line, RH in %), rain (blue bars, in mm), and wetness duration (light blue area, WD in hours); B: predicted rate of spore production (SPOR) by Botrytis cinerea; C: predicted relative infection severity (RIS) on inflorescences and young clusters by conidia (RIS1; green bars) during the first infection period (green area), and on ripening berries by conidia (RIS2; blue bars) and mycelium (berry-to-berry infection) (RIS3; red bars) during the second infection period (orange area). Lines indicate the accumulated value of RIS1 (SEV1; green line) and of RIS2+RIS3 (SEV2+SEV3; purple line). Numbers in C indicate vine growth stages of full flowering (stage 65 of the scale of Lorenz et al. [31]), fruit set (stage 71), and berries developing (stage 83). The first infection window extends from “inflorescences clearly visible” (stage 53) to “berries groat-sized, bunches begin to hang” (stage 73). The second infection window extends from “majority of berries touching” (stage 79) to “berries are ripe for harvest” (stage 89).

Mentions: Two examples of model output are shown in Figs 2 and 3 (epidemics at CO-09 and SM-12 in Table 2, respectively) as follows: weather data are shown in Figs 2A and 3A, sporulation on inoculum sources in Figs 2B and 3B, and the daily and accumulated values of relative infection severity in Figs 2C and 3C. The considered period (i.e., between grape growth stages 53 and 89) was 153 days long at CO-10 and 129 days long at SM-12. At both locations, > 350 mm of rain fell in this period. This rain was more regularly distributed at CO-10 than at SM-12, and the number of hours with wetness was higher at CO-10 than at SM-12 (1060 vs. 530 h, respectively), especially during the first infection window (619 h for CO-10 and 254 h for SM-12) (Figs 2A and 3A). As a consequence, sporulation was abundant and regular at CO-10 during the first infection window, and a constant increase of RIS1 was observed, with an accumulated value SEV1 = 2.64 at the end of this period (Fig 2B and 2C). In the same period at SM-12, the model predicted lower levels of sporulation and lower values of RIS1, with only two peaks at the end of April and the beginning of June, resulting in an accumulated value SEV1 = 1.10 at the end of the first infection period (Fig 3B and 3C). During the second infection period, the wetness duration decreased in both vineyards, and lower values of sporulation were predicted (Figs 2 and 3). At CO-10, high values of RIS2 and RIS3 were predicted only in early August and in the last 2 weeks before harvest. At SM-12, the model predicted favourable conditions for B. cinerea 1 week before harvest. Consequently, the values of SEV2+SEV3 were higher for CO-10 (2.61) than for SM-12 (0.5).


A Mechanistic Model of Botrytis cinerea on Grapevines That Includes Weather, Vine Growth Stage, and the Main Infection Pathways.

González-Domínguez E, Caffi T, Ciliberti N, Rossi V - PLoS ONE (2015)

Weather data and model output at S. Michelle in 2012 (SM-12).A: daily data of temperature (red line, T in °C), relative humidity (green line, RH in %), rain (blue bars, in mm), and wetness duration (light blue area, WD in hours); B: predicted rate of spore production (SPOR) by Botrytis cinerea; C: predicted relative infection severity (RIS) on inflorescences and young clusters by conidia (RIS1; green bars) during the first infection period (green area), and on ripening berries by conidia (RIS2; blue bars) and mycelium (berry-to-berry infection) (RIS3; red bars) during the second infection period (orange area). Lines indicate the accumulated value of RIS1 (SEV1; green line) and of RIS2+RIS3 (SEV2+SEV3; purple line). Numbers in C indicate vine growth stages of full flowering (stage 65 of the scale of Lorenz et al. [31]), fruit set (stage 71), and berries developing (stage 83). The first infection window extends from “inflorescences clearly visible” (stage 53) to “berries groat-sized, bunches begin to hang” (stage 73). The second infection window extends from “majority of berries touching” (stage 79) to “berries are ripe for harvest” (stage 89).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4601735&req=5

pone.0140444.g003: Weather data and model output at S. Michelle in 2012 (SM-12).A: daily data of temperature (red line, T in °C), relative humidity (green line, RH in %), rain (blue bars, in mm), and wetness duration (light blue area, WD in hours); B: predicted rate of spore production (SPOR) by Botrytis cinerea; C: predicted relative infection severity (RIS) on inflorescences and young clusters by conidia (RIS1; green bars) during the first infection period (green area), and on ripening berries by conidia (RIS2; blue bars) and mycelium (berry-to-berry infection) (RIS3; red bars) during the second infection period (orange area). Lines indicate the accumulated value of RIS1 (SEV1; green line) and of RIS2+RIS3 (SEV2+SEV3; purple line). Numbers in C indicate vine growth stages of full flowering (stage 65 of the scale of Lorenz et al. [31]), fruit set (stage 71), and berries developing (stage 83). The first infection window extends from “inflorescences clearly visible” (stage 53) to “berries groat-sized, bunches begin to hang” (stage 73). The second infection window extends from “majority of berries touching” (stage 79) to “berries are ripe for harvest” (stage 89).
Mentions: Two examples of model output are shown in Figs 2 and 3 (epidemics at CO-09 and SM-12 in Table 2, respectively) as follows: weather data are shown in Figs 2A and 3A, sporulation on inoculum sources in Figs 2B and 3B, and the daily and accumulated values of relative infection severity in Figs 2C and 3C. The considered period (i.e., between grape growth stages 53 and 89) was 153 days long at CO-10 and 129 days long at SM-12. At both locations, > 350 mm of rain fell in this period. This rain was more regularly distributed at CO-10 than at SM-12, and the number of hours with wetness was higher at CO-10 than at SM-12 (1060 vs. 530 h, respectively), especially during the first infection window (619 h for CO-10 and 254 h for SM-12) (Figs 2A and 3A). As a consequence, sporulation was abundant and regular at CO-10 during the first infection window, and a constant increase of RIS1 was observed, with an accumulated value SEV1 = 2.64 at the end of this period (Fig 2B and 2C). In the same period at SM-12, the model predicted lower levels of sporulation and lower values of RIS1, with only two peaks at the end of April and the beginning of June, resulting in an accumulated value SEV1 = 1.10 at the end of the first infection period (Fig 3B and 3C). During the second infection period, the wetness duration decreased in both vineyards, and lower values of sporulation were predicted (Figs 2 and 3). At CO-10, high values of RIS2 and RIS3 were predicted only in early August and in the last 2 weeks before harvest. At SM-12, the model predicted favourable conditions for B. cinerea 1 week before harvest. Consequently, the values of SEV2+SEV3 were higher for CO-10 (2.61) than for SM-12 (0.5).

Bottom Line: Results from DFA were also used to calculate the daily probabilities that an ongoing epidemic would be mild, intermediate, or severe.SEV1 was the most influential variable in discriminating between mild and intermediate epidemics, whereas SEV2 and SEV3 were relevant for discriminating between intermediate and severe epidemics.The model represents an improvement of previous B. cinerea models in viticulture and could be useful for making decisions about Botrytis bunch rot control.

View Article: PubMed Central - PubMed

Affiliation: Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy.

ABSTRACT
A mechanistic model for Botrytis cinerea on grapevine was developed. The model, which accounts for conidia production on various inoculum sources and for multiple infection pathways, considers two infection periods. During the first period ("inflorescences clearly visible" to "berries groat-sized"), the model calculates: i) infection severity on inflorescences and young clusters caused by conidia (SEV1). During the second period ("majority of berries touching" to "berries ripe for harvest"), the model calculates: ii) infection severity of ripening berries by conidia (SEV2); and iii) severity of berry-to-berry infection caused by mycelium (SEV3). The model was validated in 21 epidemics (vineyard × year combinations) between 2009 and 2014 in Italy and France. A discriminant function analysis (DFA) was used to: i) evaluate the ability of the model to predict mild, intermediate, and severe epidemics; and ii) assess how SEV1, SEV2, and SEV3 contribute to epidemics. The model correctly classified the severity of 17 of 21 epidemics. Results from DFA were also used to calculate the daily probabilities that an ongoing epidemic would be mild, intermediate, or severe. SEV1 was the most influential variable in discriminating between mild and intermediate epidemics, whereas SEV2 and SEV3 were relevant for discriminating between intermediate and severe epidemics. The model represents an improvement of previous B. cinerea models in viticulture and could be useful for making decisions about Botrytis bunch rot control.

No MeSH data available.


Related in: MedlinePlus