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.

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.

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 BS monocyclic processes and explore the impact changing production situations on this important disease.

Climate change has a number of observed, anticipated, or possible consequences on crop health worldwide. Global change, on the other hand, incorporates a number of drivers of change, including global population increase, natural resource evolution, and supply–demand shifts in markets, from local to global. Global and climate changes interact in their effects on global ecosystems. Identifying and quantifying the impacts of global and climate changes on plant diseases is complex. A number of nonlinear relationships, such as the injury (epidemic)–damage (crop loss) relationship, are superimposed on the interplay among the three summits of the disease triangle (host, pathogen, environment). Work on a range of pathosystems involving rice, peanut, wheat, and coffee has shown the direct linkage and feedback between production situations and crop health. Global and climate changes influence the effects of system components on crop health. The combined effects of global and climate changes on diseases vary from one pathosystem to another within the tetrahedron framework (humans, pathogens, crops, environment) where human beings, from individual farmers to consumers to entire societies, interact with hosts, pathogens, and the environment. This article highlights international phytopathological research addressing the effects of global and climate changes on plant diseases in a range of crops and pathosystems