Improving the use of aerobiological and phenoclimatological data to forecast the risk of late blight in a potato crop

Seijo-Rodríguez, Ana; Escuredo, Olga; Rodríguez-Flores, María Shantal; Seijo, María Carmen

doi:10.1007/s10453-018-9515-9

Cited by 9 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Frequently, these models use weather variables and plant phenology but do not consider the presence of pathogen in field, being this the third support for the disease. For this reason, the development of statistical models with aerobiological data to prevent phytopathological damages in crops has increased in recent years [44][45][46][47][48]. Most of the predictive models concluded daily Alternaria conidia in air can be predicted using the spore concentrations of days before, and meteorological variables like maximum and minimum temperature and minimum relative humidity [23,46,49].…”

Section: Discussionmentioning

confidence: 99%

Modification of the TOMCAST Model with Aerobiological Data for Management of Potato Early Blight

et al. 2020

View full text Add to dashboard Cite

The present study focuses on establishing thresholds of weather variables for predict early blight in potato crops. For this, the TOMCAST model was adjusted using weather variables and Alternaria conidia levels (mainly A. solani and A. alternata) during six growing seasons in A Limia (Northwest Spain). TOMCAST for the effective management of early blight considers leaf wetness and air temperature to calculate daily severity values (DSVs). Spearman correlations between temperature (minimum and average), mean temperature during leaf wetness period and Alternaria concentration showed the highest positive significant coefficients (0.386, 0.230 and 0.372, respectively; p < 0.01). Specifically, Alternaria levels higher than 50 spores/m3 were found the days with air mean temperature above 18 °C, more than 7 h of leaf wetness. Leaf wetness was decisive to estimate the concentration of Alternaria, resulting in a significant linear regression model (R2 = 0.41; p < 0.001). TOMCAST was adapted to the area, considering 10 °C the minimum threshold for the mean value of temperature during the wet period and 10–15 accumulated disease severity values (DSV). Using TOMCAST, it was possible to predict the first Alternaria peak in most of potato growing seasons. Combining aerobiological and meteorological data to control fungal diseases during crops are a useful tool for sustainable agriculture.

show abstract

Section: Discussionmentioning

confidence: 99%

Modification of the TOMCAST Model with Aerobiological Data for Management of Potato Early Blight

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In recent years, diverse modelling approaches such as control strategies for the prediction of fungal diseases have been proposed. Among them, linear regression models, the autoregressive integrated model of running mean time-series, and neural network models were applied in potato, grapevine, rice, and wheat [8,35,[37][38][39]. As a first approximation, it can be concluded that the combination of aerobiological data with weather data (specially wet periods) collected during nine crop cycles in A Limia was efficient in that it could predict several days of attack in advance during the development of the crop.…”

Section: Discussionmentioning

confidence: 99%

Interrupted Wet Period (IWP) to Forecast the Aerial Alternaria in Potato Crops of A Limia (Spain)

et al. 2019

View full text Add to dashboard Cite

Potato early blight caused by Alternaria solani generates significant economic losses in crops worldwide. Forecasting the risk of infection on crops is indispensable for the management of the fungal disease, ensuring maximum economic benefit but with minimal environmental impact. This work aimed to calculate the interrupted wet periods (IWP) according to the climate conditions of A Limia (Northwest of Spain) to optimize the prediction against early blight in potatoes. The study was performed during nine crop cycles. The relative hourly humidity and Alternaria concentration in the crop environment were taken into account. Alternaria levels were monitored by aerobiological techniques using a LANZONI VPPS-2000 volumetric trap. The relationships between weather conditions and airborne Alternaria concentration were statistically analyzed using Spearman correlations. To establish the effectiveness of wetness periods, the first important Alternaria peak was taken into account in each crop cycle (with a concentration greater than 70 spores/m 3 ). Considering the six interrupted wet periods of the system, it was possible to predict the first peak of Alternaria several days in advance (between 6 and 38 days), except in 2007 and 2018. Automated systems to predict the initiation of early blight in potato crop, such as interrupted wet periods, could be an effective basis for developing decision support systems. The incorporation of aerobiological data for the calculation of interrupted wet periods improved the results of this system.

show abstract

“…Application recommendations are only relevant for the farmer if potatoes on the plot have reached the growth stage of "formation of basal side shoots" (BBCH ≥ 20), which is the minimum growth stage for fungicide applications against late blight. Based on the plot-specific planting date, we derive the earliest start of the critical crop growth phase (BBCH ≥ 20) for each plot using a growing degree days based approach 29 . For each plot, we then identify the date of the first application recommendation against late blight (greater than BBCH ≥ 20 dates).…”

Section: Fig 1 Locations Of Sample Farms and Misp Stations In Switzerlandmentioning

confidence: 99%

Why farmers deviate from recommended pesticide timing: the role of uncertainty and information

Möhring

Wuepper

Musa

et al. 2020

Pest Management Science

View full text Add to dashboard Cite

BACKGROUNDPrecise timing of pesticide applications, as recommended by decision support systems, can ensure crop protection, while maintaining efficient use of pesticides. Yet, farmers often deviate from recommended timing strategies. Here, we assess and explain farmers' choices to follow or not follow recommendations for the timing of fungicide applications against potato late blight in Switzerland. RESULTBased on daily fungicide application records as well as regional application recommendations and disease pressure we found that 36% of applications take place earlier than recommended.Using regression analysis, we identified the exposure to economic risks of infection, susceptibility of the planted potato varieties to late blight infections, as well as yearly differences in disease occurrence as the most important determinants of farmers' application decision. CONCLUSIONOur results indicate that decisions to not follow application recommendations and apply early are linked to available information and uncertainty with respect to disease predictions. Based on our results, we make recommendations on how to account for farmers' uncertainty with regard to the timing of pesticide applications in the design of pesticide policies and agricultural decision support systems. These include the use of new technologies and data, mandatory reporting and tailor made taxes and insurance solutions. Although the focus of this article is on late blight in Switzerland, our analysis can easily be extended to other countries and important plant diseases like powdery mildew in grapevines or Fusarium head blight in winter wheat.

show abstract

Improving the use of aerobiological and phenoclimatological data to forecast the risk of late blight in a potato crop

Cited by 9 publications

References 18 publications

Modification of the TOMCAST Model with Aerobiological Data for Management of Potato Early Blight

Modification of the TOMCAST Model with Aerobiological Data for Management of Potato Early Blight

Interrupted Wet Period (IWP) to Forecast the Aerial Alternaria in Potato Crops of A Limia (Spain)

Why farmers deviate from recommended pesticide timing: the role of uncertainty and information

Contact Info

Product

Resources

About