Effect of Meteorological and Micrometeorological Parameters on Stripe Rust (Puccinia striformis f. sp. tritici) Severity of Wheat

Sandhu, Sarabjot Kaur; Dhaliwal, L. K.; Pannu, P.P.S.

doi:10.20546/ijcmas.2017.607.470

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The measured database yielded a hierarchical variables distribution of RH, Rad G and T DWpnt , while the indirect database unveiled T DWpnt , T night and T max . Temperature and RH are the weather variables necessary to explain the development of SR (Sandhu et al ., ) but these results show that more weather variables are necessary.…”

Section: Resultsmentioning

confidence: 82%

Weather‐data‐based model: an approach for forecasting leaf and stripe rust on winter wheat

Rodríguez-Moreno

Jiménez‐Lagunes²,

Estrada-Ávalos³

et al. 2020

Meteorological Applications

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Classification and regression trees (CARTs) for data analysis, an hourly weather dataset, and a 3 year field incidence and severity dataset of winter wheat rust were integrated to forecast pathogens’ presence/absence. The field dataset of incidence and severity was collected for three production cycles. Measured records of 88 Automatic Meteorological Stations and the indirect weather dataset generated in the Weather Research and Forecasting environment interpolated to each Automatic Meteorological Station location were analysed in the Python ecosystem. The focal point of the analysis was the severity of the disease. The analysis of direct weather data revealed the association of leaf rust severity with a night temperature of <14.25°C and global radiation of <521.67 W·m–2, while the estimated dataset showed that its severity is better explained by the dew point temperature of <13.7°C and a mean temperature of <19.06°C. The direct dataset also indicated that stripe rust severity was associated with relative humidity of <88.73%, global radiation of <597.39 W·m–2 and dew point temperature of <16.09°C, whereas the estimated data revealed that pathogen severity is better explained by a model composed of a dew point temperature of <14.6°C, night temperature of <20.4°C and a maximum temperature of <27.9°C. The severity and intensity analysis indicated the pathogen's preference for non‐dry ambient conditions and the preference of stripe rust pathogen for humid and warmer temperatures than leaf rust. The weather thresholds of both pathogens, and CART analysis, unveiled that winter wheat rust can be forecasted. This constitutes the foundation of a more efficient extension programme based on the internet of things.

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Section: Resultsmentioning

confidence: 82%

Weather‐data‐based model: an approach for forecasting leaf and stripe rust on winter wheat

Rodríguez-Moreno

Jiménez‐Lagunes²,

Estrada-Ávalos³

et al. 2020

Meteorological Applications

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“…Temperature and relative humidity are the most critical meteorological variables to explain the development of stripe rust [40]. It has been reported previously that the causal agent of stripe-rust disease, P. striiformis f. sp.…”

Section: Discussionmentioning

confidence: 99%

MISSR: A Mentoring Interactive System for Stripe Rust

et al. 2022

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Wheat is one of the most important crops in the world and was considered the major grain crop grown in Egypt. Nowadays, Egypt is the largest wheat importer in the world and consumes an extensive amount of it. To decrease the gap between production and consumption and increase the yield, we need to control wheat diseases, especially stripe rust, due to its major damage to wheat. Further, we need to advise farmers as early as we can to control and treat them. The paper proposed an interactive intelligent system to monitor, predict and give the correct advice at the right time to farmers. This system is called MISSR (Mentoring Interactive System for Stripe Rust). The system is considered an important means to effectively prevent risks in agricultural production. It also plays an important role in guiding farmers and decision-makers to plan and implement suitable practices to increase yield and mitigate stripe-rust disease. On the other hand, it can acquire relevant and timely information in the areas where this information or data is unavailable. To build this model for the wheat crop in Egypt, we used wheat experts’ knowledge and climate data API. MISSR is available as a mobile application to provide access for more farmers and increase its availability.

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Effect of Meteorological and Micrometeorological Parameters on Stripe Rust (Puccinia striformis f. sp. tritici) Severity of Wheat

Cited by 2 publications

References 9 publications

Weather‐data‐based model: an approach for forecasting leaf and stripe rust on winter wheat

Weather‐data‐based model: an approach for forecasting leaf and stripe rust on winter wheat

MISSR: A Mentoring Interactive System for Stripe Rust

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