The impacts of climate change on ecosystem services are complex in the sense that effective prediction requires consideration of a wide range of factors. Useful analysis of climate-change impacts on crops and native plant systems will often require consideration of the wide array of other biota that interact with plants, including plant diseases, animal herbivores, and weeds. We present a framework for analysis of complexity in climate-change effects mediated by plant disease. This framework can support evaluation of the level of model complexity likely to be required for analysing climate-change impacts mediated by disease. Our analysis incorporates consideration of the following set of questions for a particular host, pathogen, host-pathogen combination, or geographic region. 1. Are multiple biological interactions important? 2. Are there environmental thresholds for population responses? 3. Are there indirect effects of global change factors on disease development? 4. Are spatial components of epidemic processes affected by climate? 5. Are there feedback loops for management? 6. Are networks for intervention technologies slower than epidemic networks? 7. Are there effects of plant disease on multiple ecosystem services? 8. Are there feedback loops from plant disease to climate change? Evaluation of these questions will help in gauging system complexity, as illustrated for fusarium head blight and potato late blight. In practice, it may be necessary to expand models to include more components, identify those components that are the most important, and synthesize such models to include the optimal level of complexity for planning and research prioritization.

Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY). Summary and Statement of Need nasapower is an R (R Core Team, 2018) package providing functionality to interface with the NASA POWER API (Stackhouse et al., 2018) for reproducible data retrieval using R. Three functions, get_power(), create_met() and create_icasa() are provided. The get_power() function provides complete access to all functionality that the POWER API provides, which includes three user communities, AG (agroclimatology), SSE (Surface meteorology and Solar Energy) and SB (Sustainable Buildings); three temporal averages, Daily, Interannual and Climatology; three geographic options, single point, regional and global for the appropriate parameters offered. nasapower uses lubridate (Grolemund & Wickham, 2011) internally to format and parse dates which are passed along to the the query constructed using crul (Chamberlain, 2018) to interface with the POWER API. The query returns a json response, which is parsed by jsonlite (Ooms, 2014) to obtain the url of the .csv file that has been requested. The .csv file is downloaded to local disk using curl (Ooms, 2018) and read into R using readr (Wickham, Hester, & Francois, 2017). Data are returned in a tidy data frame (Wickham, 2014) as a tibble (Müller & Wickham, 2018) with a custom header, which provides POWER metadata. Two other functions provide functionality to generate weather input files for agricultural crop modelling. The create_met() function is a wrapper for the get_power() function coupled with the prepareMet() and writeMet() functions from APSIM (Fainges, 2017) to simplify the process of querying the data and creating text files in the .met format for use in Agricultural Production Systems sIMulator (APSIM). While the create_icasa() function wraps the get_power() into a function that generates and locally saves a text file in the International Consortium for Agricultural Systems Applications (ICASA) format for use in the Decision Support System for Agrotechnology Transfer (DSSAT) framework (G. Hoogenboom et al., 2017; J. W. Jones et al., 2003). Extended documentation is provided with examples of converting it to spatial objects using raster (Hijmans, 2017).

Rice brown spot (BS) is a chronic disease that affects millions of hectares of rice every growing season, grown by some of the most resource-poor farmers. Despite its widespread occurrence and impact, much still needs to be understood about BS. Reported yield losses in relative terms vary widely from 4 to 52 %. However, accurate, systematic estimates are lacking. BS is conventionally perceived as a secondary problem that reflects rice crops that experience physiological stresses, e.g. drought and poor soil fertility, rather than a true infectious disease. Much remains to be understood about the mechanisms leading to epidemics and crop losses. Quantitative and qualitative knowledge gaps exist in our understanding of the epidemiological processes, sources of resistance and biocontrol methods. In this review we identify several of these gaps, which if filled, could lead to a strong impact on the management of brown spot. We also use the architecture of a simulation model to position and prioritize these knowledge gaps, assess the epidemiological consequences of disease management options on

This article is the 13th contribution in the Fungal Diversity Notes series, wherein 125 taxa from four phyla, ten classes, 31 orders, 69 families, 92 genera and three genera incertae sedis are treated, demonstrating worldwide and geographic distribution. Fungal taxa described and illustrated in the present study include three new genera, 69 new species, one new combination, one reference specimen and 51 new records on new hosts and new geographical distributions. Three new genera, Cylindrotorula ( Torulaceae ), Scolecoleotia ( Leotiales genus incertae sedis ) and Xenovaginatispora ( Lindomycetaceae ) are introduced based on distinct phylogenetic lineages and unique morphologies. Newly described species are Aspergillus lannaensis , Cercophora dulciaquae , Cladophialophora aquatica , Coprinellus punjabensis , Cortinarius alutarius , C. mammillatus , C. quercoflocculosus , Coryneum fagi , Cruentomycena uttarakhandina , Cryptocoryneum rosae , Cyathus uniperidiolus , Cylindrotorula indica , Diaporthe chamaeropicola , Didymella azollae , Diplodia alanphillipsii , Dothiora coronicola , Efibula rodriguezarmasiae , Erysiphe salicicola , Fusarium queenslandicum , Geastrum gorgonicum , G. hansagiense , Helicosporium sexualis , Helminthosporium chiangraiensis , Hongkongmyces kokensis , Hydrophilomyces hydraenae , Hygrocybe boertmannii , Hyphoderma australosetigerum , Hyphodontia yunnanensis , Khaleijomyces umikazeana , Laboulbenia divisa , Laboulbenia triarthronis , Laccaria populina , Lactarius pallidozonarius , Lepidosphaeria strobelii , Longipedicellata megafusiformis , Lophiotrema lincangensis , Marasmius benghalensis , M. jinfoshanensis , M. subtropicus , Mariannaea camelliae , Melanographium smilaxii , Microbotryum polycnemoides , Mimeomyces digitatus , Minutisphaera thailandensis , Mortierella solitaria , ...

Many modern rice varieties (MVs) have been released but only a few have been widely adopted by farmers. To understand farmers’ preferences, we characterized MVs released in the Philippines from 1966 to 2013 and identified important characteristics of the varieties that were widely adopted in Central Luzon using farm surveys conducted in 1966–2012. We found that farmers adopt MVs that are high yielding, mature faster, and have long and slender grains, high milling recovery, and intermediate amylose content. The amylose content of adopted varieties has been declining, suggesting value in developing softer rice. To have a high potential for adoption, new MVs should have characteristics within the ranges of values observed for the adopted MVs. In addition, new MVs should have higher head rice recovery, less chalky grains, and better resistance to pests and diseases. Most MVs released in 2005–2013 compared poorly in these three traits. To reduce the risk of severe outbreaks, broad spectrum resistance should be incorporated into new MVs. This analysis of five decades of farm surveys provides insights into the varietal characteristics preferred by farmers which could contribute to the establishment of a product profile for developing improved MVs that are more targeted and, hence, would have high potential for adoption by farmers in Central Luzon and similar areas. We recommend a similar analysis be done in other major rice growing regions to aid the development of MVs that are more responsive to farmers’ needs and preferences.