Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario

Viswanath, K.; Sinha, Parimal; Kumar, Santosh; Sharma, Taru; Saxena, Shalini; Panjwani, Shweta; Pathak, Himanshu; Shukla, Shalu Mishra

doi:10.1007/s10584-017-1942-z

Cited by 18 publications

(8 citation statements)

References 32 publications

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“…To relate the effect of temperature, a temperature function ƒ(T) was used [ 46 , 79 ], which utilizes the pathogen’s cardinal temperatures to estimate the shape and response: ƒ(T) = [(T upper − T air-h )/(T upper − T opt )] × [(T air-h − T lower )/(T opt − T lower )]^(T opt − T lower )/(T upper − T opt ) where ƒ(T) = temperature response to rust infection (scaled between 0 and 1); T lower and T upper are the lower and upper thresholds, respectively for infection beyond which infection is assumed to be zero; and T opt = optimum temperature for infection. For the combined effect of temperature (T air-h ) and high RH hours (h) on infection index ( INF ), Richards’s function that combines RH-hours (h) with temperature-dependent parameters m and ƒ(T) was used [ 36 , 48 , 49 , 80 ]: INF ( h ) = ƒ(T) × (1 − EXP(− b × ( h − m ))) where h = RH hours ≥95%; m = minimum RH-h required for barberry infection; ƒ(T) = maximum infection index (as asymptote); and b = measure of the rate of change in INF with h . A value b = 0.117 as estimated for the leaf rust pathogen Puccinia triticina [ 48 ] was applied for both Pst and Pgt ; and m values 32 and 24 h were used for Pst and Pgt , respectively [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

“…where ƒ(T) = temperature response to rust infection (scaled between 0 and 1); T lower and T upper are the lower and upper thresholds, respectively for infection beyond which infection is assumed to be zero; and T opt = optimum temperature for infection. For the combined effect of temperature (T air-h ) and high RH hours (h) on infection index (INF), Richards's function that combines RH-hours (h) with temperature-dependent parameters m and ƒ(T) was used [36,48,49,80]:…”

Section: Estimation Of Environmental Suitability For Infectionmentioning

confidence: 99%

“…Hourly leaf wetness or a high relative humidity (RH > 95%) period is an important input for the estimation of infection risk dynamics of rusts [ 36 ]. Mechanistic models have been used to study plant distributions [ 45 ] and the effects of temperature and leaf wetness on infection as a measure of favorable weather [ 46 , 47 , 48 , 49 ]. Micro-meteorologically, leaf wetness or high RH periods can be estimated from daily minimum and maximum temperatures and rainfall [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

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Potential Infection Risks of the Wheat Stripe Rust and Stem Rust Pathogens on Barberry in Asia and Southeastern Europe

Sinha

Chen

2021

Plants

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Barberry (Berberis spp.) is an alternate host for both the stripe rust pathogen, Puccinia striiformis f. sp. tritici (Pst), and the stem rust pathogen, P. graminis f. sp. tritici (Pgt), infecting wheat. Infection risk was assessed to determine whether barberry could be infected by either of the pathogens in Asia and Southeastern Europe, known for recurring epidemics on wheat and the presence of barberry habitats. For assessing infection risk, mechanistic infection models were used to calculate infection indices for both pathogens on barberry following a modeling framework. In East Asia, Bhutan, China, and Nepal were found to have low risks of barberry infection by Pst but high risks by Pgt. In Central Asia, Azerbaijan, Iran, Kazakhstan, southern Russia, and Uzbekistan were identified to have low to high risks of barberry infection for both Pst and Pgt. In Northwest Asia, risk levels of both pathogens in Turkey and the Republic of Georgia were determined to be high to very high. In Southwest Asia, no or low risk was found. In Southeastern Europe, similar high or very high risks for both pathogens were noted for all countries. The potential risks of barberry infection by Pst and/or Pgt should provide guidelines for monitoring barberry infections and could be valuable for developing rust management programs in these regions. The framework used in this study may be useful to predict rust infection risk in other regions.

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Section: Methodsmentioning

confidence: 99%

Section: Estimation Of Environmental Suitability For Infectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Potential Infection Risks of the Wheat Stripe Rust and Stem Rust Pathogens on Barberry in Asia and Southeastern Europe

Sinha

Chen

2021

Plants

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“…Magnaporthe oryzae and Zymoseptoria tritici are two of the most destructive pathogens of rice and wheat 1 , respectively, but infection temperature estimates are unavailable. We therefore included cardinal temperature for lesion development of M. oryzae 41 , and average growth in culture cardinal temperatures for 18 strains of Z. tritici 42 . It was assumed that average cardinal temperature for each pathogen was identical across all hosts, for each respective pathogen.…”

Section: Methodsmentioning

confidence: 99%

Plant pathogen infection risk tracks global crop yields under climate change

Chaloner

Gurr

Bebber

2020

Preprint

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7Global food security is strongly determined by crop production. Climate change will not only 8 affect crop yields directly, but also indirectly via the distributions and impacts of plant 9 pathogens that can cause devastating production losses. However, the likely changes in 10 pathogen pressure in relation to global crop production are poorly understood. Here we show 11 that disease risk for 79 fungal and oomycete crop pathogens will closely track projected yield 12 changes in 12 major crops over the 21 st Century. For most crops, yields are likely to increase 13 at high latitudes but disease risk will also grow. In addition, the USA, Europe and China will 14 experience major changes in pathogen assemblages. In contrast, while the tropics will see 15 little or no productivity gains, the disease burden is also likely to decline. The benefits of 16 yield gains will therefore be tempered by the increased burden of crop protection. 17 Main text 18Plant pests and pathogens exert a growing burden on crop production around the world 1,2 . 19The burden can be measured directly in yield losses or indirectly in the social, environmental 20 and economic costs of control 1 . Like all species, crop pests and pathogens have particular 21 tolerances to, or requirements for, particular environmental conditions 3 . These tolerances 22 define their ecological niche, which determines the geographical regions and periods of the 23year that allow pests and pathogens to proliferate and attack crops 3 . As climate changes, 24 suitable conditions for pest outbreaks shift in time and space, altering the threats that farmers 25 face and the management regimes required for their control 4 . Modelling the pattern and 26 process of future changes in pest and pathogen burdens is therefore a key component in Latitudinal range shifts of pests and pathogens are expected as the planet warms and 29 populations track their preferred temperature zones 4 . Spatial movements in geographical 30 distributions and temporal shifts in phenologies of wild populations are among the clearest 31 signs of anthropogenic global warming 6 . Though distribution data for crop pests and 32 pathogens are noisy and incomplete 5 , similar changes have been detected for hundreds a 33 species of pests and pathogens over recent decades 7 . Increasing burdens of insect pests at 34 high latitudes, and decreasing burdens at low latitudes, have been projected using ecological 35 niche models (ENM) 8 . ENMs attempt to reconstruct the environmental tolerances of species 36 from contemporary climates within the observed species range using statistical models 9 . 37Alternatively, species' responses to microclimate can be directly measured, and these 38 responses incorporated into physiologically-based models of species performance 10 . Such 39 mechanistic models are commonly used to project future crop yields 11 , and models have also 40 been developed for some plant diseases 12,13 . However, we know little about how plant 41 disease pressure is likely to change in future, nor h...

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“…[ 35 , 37 ]) and various plant-pathogen interactions (e.g. [ 38 – 41 ]). For comparative purposes we also fitted a linear model, with lesion size as the response variable and temperature as the independent variable.…”

Section: Methodsmentioning

confidence: 99%

Threat of establishment of non-indigenous potato blackleg and tuber soft rot pathogens in Great Britain under climate change

et al. 2018

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Potato blackleg and soft rot caused by Pectobacterium and Dickeya species are among the most significant bacterial diseases affecting potato production globally. In this study we estimate the impact of future temperatures on establishment of non-indigenous but confirmed Pectobacterium and Dickeya species in Great Britain (GB). The calculations are based on probabilistic climate change data and a model fitted to disease severity data from a controlled environment tuber assay with the dominant potato blackleg and soft rot-causing species in GB (P. atrosepticum), and three of the main causative agents in Europe (P. carotovorum subsp. brasiliense, P. parmentieri, Dickeya solani). Our aim was to investigate if the European strains could become stronger competitors in the GB potato ecosystem as the climate warms, on the basis of their aggressiveness in tubers at different temperatures. Principally, we found that the tissue macerating capacity of all four pathogens will increase in GB under all emissions scenarios. The predominant Pectobacterium and Dickeya species in Europe are able to cause disease in tubers under field conditions currently seen in GB but are not expected to become widely established in the future, at least on the basis of their aggressiveness in tubers relative to P. atrosepticum under GB conditions. Our key take-home messages are that the GB potato industry is well positioned to continue to thrive via current best management practices and continued reinforcement of existing legislation.

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Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario

Cited by 18 publications

References 32 publications

Potential Infection Risks of the Wheat Stripe Rust and Stem Rust Pathogens on Barberry in Asia and Southeastern Europe

Potential Infection Risks of the Wheat Stripe Rust and Stem Rust Pathogens on Barberry in Asia and Southeastern Europe

Plant pathogen infection risk tracks global crop yields under climate change

Threat of establishment of non-indigenous potato blackleg and tuber soft rot pathogens in Great Britain under climate change

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