• Energy Research

Climate change might increase plant diseases, reduce crop yields and threaten the livelihoods of millions of smallholder farmers globally. It is thus important to understand the relationships between climate, disease levels and yield to improve management strategies for sustainable agroforestry in a changing climate. One of the major threats to coffee production in Africa is the coffee berry disease (Colletotrichum kahawae). To investigate the effects of climatic and management variables on coffee berry disease (CBD) incidence and yield, we recorded minimum and maximum temperature and relative humidity, as well as CBD and yield, along a broad environmental and management gradient in southwestern Ethiopia during two consecutive years. CBD was affected by several climatic and management variables. For example, CBD incidence increased with minimum temperature during the fruit expansion stage, and decreased with minimum temperature during the endosperm filling stage. CBD incidence was negatively affected by the proportion of resistant cultivars, whereas the coffee structure index (pruning) had no effect on disease incidence. Coffee yield decreased with increasing minimum temperature during the flowering period in 2018 and maximum temperature during the fruit developmental period in 2019. Coffee yield was negatively affected by canopy cover and positively affected by the coffee structure index in both years. Our findings highlight that CBD and yield were affected by different climatic and management variables. Yet, managing for low disease levels and high yield is practically difficult due to season-dependent effects of several climatic variables. One way to break the correlation of climatic variables between seasons might be to take advantage of differences among shade trees in the presence or timing of leaf drop. To reduce CBD incidence, using resistant cultivars is an effective strategy, but this might threaten the wild coffee genetic reservoir.

# Impact of climate on a host-hyperparasite interaction on Arabica coffee in its native range GENERAL INFORMATION 1\. Title of Dataset: Impact of climate on a host-hyperparasite interaction on Arabica coffee in its native range Ayalew, Biruk; Hylander, Kristoffer; Adugna, Girma; Zewdie, Beyene; Tack J.M, Ayco 2\. Date of data collection: 2018–2020 3\. Geographic location of data collection: Gomma and Gera districts, southwestern Ethiopia 4\. Information about funding sources that supported the collection of the data: Swedish Research Council (2019-04493) and Bolin Centre for Climate Research SHARING/ACCESS INFORMATION 1\. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain 2\. Links to other publicly accessible locations of the data: None 3\. Links/relationships to ancillary data sets: None 4\. Was the data derived from another source? No 5\. Recommended citation for this dataset: Ayalew, B., Hylander, K., Adugna, G., Zewdie, B., & Tack, A. J. M. (2023). Data from: Impact of climate on a host-hyperparasite interaction on Arabica coffee in its native range. Dryad Digital Repository, DATA & FILE OVERVIEW 1\. Number of variables: 29 2\. Number of cases/rows: 58 3\. Variable List: * Site_ID: coffee farms (site) where data collected * Elevation: the geocoordinates of study sites * Canopy_cover (%): canopy cover * Min_temp_Wet_season_2018: Wet season minimum temperature (°C) during 2018 * Max_temp_Wet_season_2018: Wet season maximum temperature (°C) during 2018 * RH_Wet_season_2018: Wet season relative humidity (%) during 2018 * Min_temp_Dry_season_2019: Dry season minimum temperature (°C) during 2019 * Max_temp_Dry_season_2019: Dry season maximum temperature (°C) during 2019 * RH_Dry_season_2019: Dry season relative humidity (%) during 2019 * Min_temp_Wet_season_2019: Wet season minimum temperature (°C) during 2019 * Max_temp_Wet_season_2019: Wet season maximum temperature (°C)during 2019 * RH_Wet_season_2019: Wet season relative humidity (%)during 2019 * Min_temp_Dry_season_2020: Dry season minimum temperature (°C)during 2020 * Max_temp_Dry_season_2020: Dry season maximum temperature (°C)during 2020 * RH_Dry_season_2020: Dry season relative humidity (%) during 2020 * CLR_Sev_Dry_Season_2018: Coffee leaf severity (%) during dry season 2018 * CLR_Sev_Wet_Season_2018: Coffee leaf severity (%) during wet season 2018 * CLR_Sev_Dry_Season_2019: Coffee leaf severity (%) during dry season 2019 * CLR_Sev_Wet_Season_2019: Coffee leaf severity (%) during wet season 2019 * CLR_Sev_Dry_Season_2020: Coffee leaf severity (%) during dry season 2020 * Hyper_to_rust_ratio_Dry_2018_Log: Hyperparasite-to-rust ratio (%) during dry season 2018 (log transformed) * Hyper_to_rust_ratio_Wet_2018_Log: Hyperparasite-to-rust ratio (%) during wet season 2018 (log transformed) * Hyper_to_rust_ratio_Dry_2019_Log: Hyperparasite-to-rust ratio (%) during dry season 2019 (log transformed) * Hyper_to_rust_ratio_Wet_2019_Log: Hyperparasite-to-rust ratio (%) during wet season 2019 (log transformed) * Hyper_Sev_Dry_Season_2018_Log: Hyperparasite severity (%) during dry season 2018 (log transformed) * Hyper_Sev_Wet_Season_2018_Log: Hyperparasite severity (%) during wet season 2018 (log transformed) * Hyper_Sev_Dry_Season_2019_Log: Hyperparasite severity (%) during dry season 2019 (log transformed) * Hyper_Sev_Wet_Season_2019_Log: Hyperparasite severity (%) during wet season 2019 (log transformed) * Hyper_Sev_Dry_Season_2020_Log: Hyperparasite severity (%) during dry season 2020 (log transformed) 4\. Missing data codes: NA 5\. Specialized formats or other abbreviations used: None \# For more information on the data set: *Contact*: **** Natural enemies of plant pathogens might play an important role in controlling plant disease levels in natural and agricultural systems. Yet, plant pathogen-natural enemy interactions might be sensitive to climatic changes. Understanding the relationship between climate, plant pathogens, and their natural enemies is thus important for developing climate-resilient, sustainable agriculture. To this aim, we recorded shade cover, daily minimum and maximum temperature, relative humidity, coffee leaf rust, and its hyperparasite at 58 sites in southwestern Ethiopia during the dry and wet season for two years. Coffee leaf rust severity was positively related to the maximum temperature. Hyperparasite severity was higher when the minimum temperature was low (i.e. in places with cold night temperatures). While canopy cover did not have a direct effect on rust severity, it reduced rust severity indirectly by lowering the maximum temperature. Canopy cover had a direct positive effect on the hyperparasite severity during one surveying period. Synthesis and applications. Our findings highlight that coffee leaf rust and its hyperparasite are both affected by shade cover and temperature, but in different ways. On the one hand, these niche differences lead to the worrying prediction that levels of coffee leaf rust will increase, and its hyperparasite will decrease, with climate change. On the other hand, these niche differences between coffee leaf rust and its hyperparasite provide opportunities to develop strategies to manage the environment (such as shade cover and microclimate) in such a way that the rust is disfavored and the hyperparasite is favored. Usage Notes These datasets were collected in Gomma and Gera districts in southwestern Ethiopia at 58 sites during the dry and rainy seasons (2018–2020). Details for each dataset are provided in the README file. Datasets included: 1) Microclimate variables Average daily minimum temperature (°C): for each year was calculated by averaging the minimum temperature for both the dry (November to February) and rainy (April to July) seasons. Average daily maximum temperature (°C): for each year was calculated by averaging the maximum temperature for both the dry (November to February) and rainy (April to July) seasons. Monthly average of the daily mean relative humidity (%): for each year was calculated by averaging the relative humidity for both the dry (November to February) and rainy (April to July) seasons. 2) Coffee leaf rust and its hyperparasite Coffee leaf rust severity (%): the percentage of rust on a per-leaf basis of all leaves for both the dry and rainy seasons. Hyperparasite-to-rust ratio (%): the average percentage of rust covered by the hyperparasite for both the dry and rainy seasons. Hyperparasite severity (%): the percentage of hyperparasite on a per-leaf basis of all leaves, irrespective of whether they had rust or not, both for the dry and rainy seasons. 3) Canopy cover (%): was assessed based on five canopy pictures taken above coffee height and analysed using ImageJ software as the percentage of black pixels. The average of the five canopy cover percentages was used as a canopy cover (%) per site. 4) Elevation (m.a.s.l): was measured using Garmin GPS at the center of each plot.