Abstract
ABSTRACT Shade effects on coffee diseases are ambiguous because they vary depending on the season and environment. Using Coffee Leaf Rust (CLR) as an example, we demonstrate relationships between the environment and shading systems and their effects on disease intensity. We characterized seasonal variations in microclimate and CLR incidence across different altitudes and shading systems, and integrated effects between the environment, shading systems, microclimate and CLR into a piecewise structural equation model. The diurnal temperature range was higher in unshaded systems, but differences decreased with altitude. Humidity related indicators in shaded systems decreased with altitude. At mid and high altitudes, high CLR incidence occurred in the shading system showing a low diurnal temperature range and a high dew point temperature. Our study demonstrates how microclimatic indicators vary as a function of the season, altitude and the coffee shading system, and how this in turn is related to CLR.
Highlights
Coffee agroecosystems are interaction networks consisting of anthropogenic, topographic, meteorological, edaphic and biological components, which vary in space and time (Wagenet 1998)
Monitoring dates: (1) March/April, (2) May/June, (3) July/August, (4) September, (5) October/November (6) January (7) February aTopographic variables of the study area were generated from a digital elevation model (90 m DEM) of the shuttle radar topography mission. bMicroclimatic indicators resulting from the selection procedure described in the subsequent data analysis section. cThe maximum incidence of the season was reported to be a good indicator of epidemic intensity (Kushalappa and Chaves 1980; Silva-Acuña and Zambolim 1999; Avelino et al 2006)
At mid-altitude, the mean Dew point (DP) over the season was lower in CT systems, while differences were marginal at high altitudes
Summary
Coffee agroecosystems are interaction networks consisting of anthropogenic, topographic, meteorological, edaphic and biological components, which vary in space and time (Wagenet 1998). Shade modifies the environment for many other components of the system, e.g. coffee physiology and productivity, soil, water, as well as biodiversity, which in turn may be related amongst themselves and with pests and diseases (Muschler 2004). These ecological mechanisms of shade are altered by greater spatial factors, such as macroclimatic variations along altitudinal or latitudinal gradients (Staver et al 2001; Avelino et al 2011; Cerda et al 2017). We (ii) integrated effects between the environment, shading systems, microclimate and CLR into a conceptual and statistical framework to understand directional relationships
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