Contribution of shade trees to wind dynamics and pathogen dispersal on the edge of coffee agroforestry systems: A functional traits approach

Gagliardi, Stephanie; Avelino, Jacques; Beilhe, Leïla Bagny; Isaac, Marney E.

doi:10.1016/j.cropro.2019.105071

Cited by 29 publications

(37 citation statements)

References 53 publications

(67 reference statements)

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“…Shading practices can also reduce wind speed, resulting in reductions in aerial spore dispersal (Jaramillo and Gomez 1989). More recently, Gagliardi et al (2020) demonstrated the importance of shade tree leaf and canopy traits on wind speed dynamics and spore dispersal at the edges of coffee agroforestry systems.…”

Section: Deforestation and Coffee Rust Management At The Farm Levelmentioning

confidence: 99%

“…Shade trees exhibit great variability and porós are regularly pruned as part of the management, but the most effective shade trees are dense porós with thick leaves. However, this reduction of wind speed comes with a trade-off as coffee rust spores settle more at the edges of a coffee plot (Gagliardi et al 2020). Combined plot-level strategies can be beneficial and the investment of replacement of individual plants with coffee rust-resistant varieties placed at the edge of a coffee plot.…”

Section: Deforestation and Coffee Rust Management At The Farm Levelmentioning

confidence: 99%

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The spore of the beans: Spatially explicit models predict coffee rust spread in fragmented landscapes

Beasley

Aristizábal

Bueno

et al. 2020

Preprint

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Landscape structure influences the spread of plant pathogens, primarily by affecting pathogen dispersal. Coffee leaf rust (Hemileia vastatrix), a fungal disease that causes heavy economic losses in the coffee industry, is likely to be affected by landscape structure via dispersal of its wind-borne spores. Previous studies have found positive associations between leaf rust incidence and the proportion of pasture cover, suggesting that deforestation may facilitate rust spore dispersal. We explored this idea by modeling the spread of rust transmission in simulated landscapes. Specifically, we modeled within-patch transmission using a probabilistic cellular automata model, and between-patch transmission using a random walk with spore movement inhibited by forest canopy cover. We used this model to understand how the spread of coffee rust is affected by: 1) clustering of coffee plants, 2) clustering of deforestation, and 3) proportion of landscape deforestation. We found that clustering of coffee plants is the primary driver of rust transmission, affecting the likelihood and severity of rust outbreak. Deforestation is important in landscapes with high clustering of coffee: rust outbreaks are more severe in landscapes with a higher proportion of deforested areas, and more variable in landscapes where deforested areas are more evenly dispersed throughout the landscape.

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Section: Deforestation and Coffee Rust Management At The Farm Levelmentioning

confidence: 99%

Section: Deforestation and Coffee Rust Management At The Farm Levelmentioning

confidence: 99%

The spore of the beans: Spatially explicit models predict coffee rust spread in fragmented landscapes

Beasley

Aristizábal

Bueno

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…Investigating the variability in these traits allows us to understand the mechanistic underpinnings of plant responses to and plant effects on other species (e.g., Fulthorpe et al, 2020). This approach has furthered our understanding of biodiverse agroforestry systems, specifically in relation to nutrient cycling (Blesh, 2018), litter decomposition (García‐Palacios et al, 2013), crop yield (Gagliardi et al, 2015), and wind‐borne pathogen dispersal patterns (Gagliardi et al, 2020). Shade tree architectural characteristics and leaf traits, such as canopy shape and leaf area, are regularly considered by farmers during agroforestry farm design, because of their perceived influence on coffee production and pest incidence (Cerdán et al, 2012; Isaac et al, 2018; Valencia et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The microclimate modifications possible in biodiverse agroforestry systems largely depend on shade tree selection (van Oijen et al, 2010), due to large differences in shade tree architectural characteristics and leaf functional traits, both interspecifically (Hallé et al, 1978) and intraspecifically (Gagliardi et al, 2020). Investigating the variability in these traits allows us to understand the mechanistic underpinnings of plant responses to and plant effects on other species (e.g., Fulthorpe et al, 2020).…”

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confidence: 99%

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confidence: 99%

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Shade tree traits and microclimate modifications: Implications for pathogen management in biodiverse coffee agroforests

et al. 2021

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Diversified agroforestry systems are characterized by the deliberate planting and retention of both native and non-native tree species.This enhanced diversity contributes many important ecosystem features (e.g., habitat; Perfecto et al., 1996) and processes (e.g., nutrient cycling; Isaac & Borden, 2019). However, one often overlooked but important modification in biodiverse agroforests is the modifica-

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Using local knowledge to reconstruct climate‐mediated changes in disease dynamics and yield—A case study on Arabica coffee in its native range

Ayalew,

Hylander,

Börjeson

et al. 2024

Plants People Planet

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Societal Impact StatementAdapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships.Summary While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies. We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee. The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution. Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist.

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Contribution of shade trees to wind dynamics and pathogen dispersal on the edge of coffee agroforestry systems: A functional traits approach

Cited by 29 publications

References 53 publications

The spore of the beans: Spatially explicit models predict coffee rust spread in fragmented landscapes

The spore of the beans: Spatially explicit models predict coffee rust spread in fragmented landscapes

Shade tree traits and microclimate modifications: Implications for pathogen management in biodiverse coffee agroforests

Using local knowledge to reconstruct climate‐mediated changes in disease dynamics and yield—A case study on Arabica coffee in its native range

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