Methods of Agroclimatology: Modeling Approaches for Pests and Diseases

Orlandini, Simone; Magarey, Roger D.; Park, Eun Woo; Sporleder, M.; Kroschel, J.

doi:10.2134/agronmonogr60.2016.0027

Cited by 22 publications

(34 citation statements)

References 136 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Aerobiological models associated with climatic variables and the use of local cultivars, adapted to the specific area conditions, would enhance the control of grey mould, powdery mildew, and downy mildew [35,36]. Furthermore, these models are useful optimization tools for wine growers to achieve more effective pest management and crop protection as they can predict, in advance, the inoculum concentration of these pathogens [37].…”

Section: Discussionmentioning

confidence: 99%

“…Models are powerful tools that can be applied to agricultural pest management and prevention by analyzing an organism's reply to environmental conditions and by improving epidemiological studies to achieve considerable crop protection optimization [37]. Several predictive models that consider the environmental conditions influence on the spore release process have been developed for many important viticulture regions, but they cannot be applied to areas with different climatic characteristics [66].…”

Section: Predictive Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Fungal Diseases in Two North-West Spain Vineyards: Relationship with Meteorological Conditions and Predictive Aerobiological Model

et al. 2020

View full text Add to dashboard Cite

Grey mould, powdery mildew, and downy mildew are the most frequent fungal diseases among vineyards worldwide. In the present study, we analysed the influence of the fungi causing these diseases (Botrytis, Erysiphe, and Plasmopara, respectively) on two viticulture areas from North-western (NW) Spain during three growth seasons (2016, 2017, and 2018). The obtained results showed the predominant concentration of the Botrytis airborne spores, mainly from the beginning of the Inflorescence emerge phenological stage (S-5) until the end of the Flowering phenological stage (S-6). Erysiphe and Plasmopara airborne spore peak concentrations were more localised around Flowering (S-6) and Development of fruits (S-7) phenological stages. We applied a Spearman’s correlation test and a Principal Component Analysis to determine the influence of the meteorological parameters on the concentration of airborne spores. Taking into account the variables with the highest correlation coefficient, we developed multiple regression models to forecast the phytopathogenic fungal spore concentrations. The Botrytis model regression equation explained between 59.4–70.9% of spore concentration variability. The Erysiphe equation explained between 57.6–61% and the Plasmopara explained between 39.9–55.8%. In general, we found better prediction results for mean daily concentrations than sporadic spore peaks.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Predictive Modelsmentioning

confidence: 99%

Fungal Diseases in Two North-West Spain Vineyards: Relationship with Meteorological Conditions and Predictive Aerobiological Model

et al. 2020

View full text Add to dashboard Cite

show abstract

“…A major difficulty of integrating crop and pest and disease models is their different spatial and temporal scales of operation [93]. Additionally, field research is limited on how pests and diseases and crop interactions may vary in a wide range of environments [94,95]. Donatelli et al [94] proposed a framework to improve the availability and quality of data for pest and disease model improvement and validation, which may be used as a tool to complement crop model simulations.…”

Section: Crop Stress Factorsmentioning

confidence: 99%

Cropping Systems and Climate Change in Humid Subtropical Environments

Hernández-Ochoa

Asseng

2018

Agronomy

View full text Add to dashboard Cite

Abstract:In the future, climate change will challenge food security by threatening crop production. Humid subtropical regions play an important role in global food security, with crop rotations often including wheat (winter crop) and soybean and maize (summer crops). Over the last 30 years, the humid subtropics in the Northern Hemisphere have experienced a stronger warming trend than in the Southern Hemisphere, and the trend is projected to continue throughout the mid-and end of century. Past rainfall trends range, from increases up to 4% per decade in Southeast China to −3% decadal decline in East Australia; a similar trend is projected in the future. Climate change impact studies suggest that by the middle and end of the century, wheat yields may not change, or they will increase up to 17%. Soybean yields will increase between 3% and 41%, while maize yields will increase by 30% or decline by −40%. These wide-ranging climate change impacts are partly due to the region-specific projections, but also due to different global climate models, climate change scenarios, single-model uncertainties, and cropping system assumptions, making it difficult to make conclusions from these impact studies and develop adaptation strategies. Additionally, most of the crop models used in these studies do not include major common stresses in this environment, such as heat, frost, excess water, pests, and diseases. Standard protocols and impact assessments across the humid subtropical regions are needed to understand climate change impacts and prepare for adaptation strategies.

show abstract

“…Estimates of climatic suitability identify areas to concentrate surveillance or management resources and efforts [6,7], whereas real-time (i.e. current) or forecasted predictions of phenology can improve the timing of surveillance and integrated pest management (IPM) efforts such as monitoring device installation, pesticide applications, and biological control release [8][9][10]. Additionally, estimates of climatic suitability, phenology, and voltinism (number of generations per year) can help growers predict the impact of pests and diseases on agricultural production and associated economic losses [11].…”

Section: Introductionmentioning

confidence: 99%

DDRP: Real-time phenology and climatic suitability modeling of invasive insects

et al. 2020

View full text Add to dashboard Cite

Rapidly detecting and responding to new invasive species and the spread of those that are already established is essential for reducing their potential threat to food production, the economy, and the environment. We describe a new spatial modeling platform that integrates mapping of phenology and climatic suitability in real-time to provide timely and comprehensive guidance for stakeholders needing to know both where and when invasive insect species could potentially invade the conterminous United States. The Degree-Days, Risk, and Phenological event mapping (DDRP) platform serves as an open-source and relatively easy-to-parameterize decision support tool to help detect new invasive threats, schedule monitoring and management actions, optimize biological control, and predict potential impacts on agricultural production. DDRP uses a process-based modeling approach in which degree-days and temperature stress are calculated daily and accumulate over time to model phenology and climatic suitability, respectively. Outputs include predictions of the number of completed generations, life stages present, dates of phenological events, and climatically suitable areas based on two levels of climate stress. Species parameter values can be derived from laboratory and field studies or estimated through an additional modeling step. DDRP is written entirely in R, making it flexible and extensible, and capitalizes on multiple R packages to generate gridded and graphical outputs. We illustrate the DDRP modeling platform and the process of model parameterization using two invasive insect species as example threats to United States agriculture: the light brown apple moth, Epiphyas postvittana, and the small tomato borer, Neoleucinodes elegantalis. We then discuss example applications of DDRP as a decision support tool, review its potential limitations and sources of model error, and outline some ideas for future improvements to the platform.

show abstract

Methods of Agroclimatology: Modeling Approaches for Pests and Diseases

Cited by 22 publications

References 136 publications

Fungal Diseases in Two North-West Spain Vineyards: Relationship with Meteorological Conditions and Predictive Aerobiological Model

Fungal Diseases in Two North-West Spain Vineyards: Relationship with Meteorological Conditions and Predictive Aerobiological Model

Cropping Systems and Climate Change in Humid Subtropical Environments

DDRP: Real-time phenology and climatic suitability modeling of invasive insects

Contact Info

Product

Resources

About