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Ascochyta rabiei asexual spores (conidia) were assumed to spread over short distances (∼10 m) in a combination of rain and strong wind. The potential distance of conidial spread was investigated in three rainfall and three sprinkler irrigation events. Chickpea trap plants were distributed at the distances of 0, 10, 25, 50 and 75 m from infected chickpea plots before scheduled irrigation and forecast rainfall events. Trap plants were transferred to a controlled temperature room (20 °C) for 48 h (100% humidity) after being exposed in the field for 2–6 days for rainfall events, and for one day for irrigation events. After a 48 h incubation period, trap plants were transferred to a glasshouse (20 °C) to allow lesion development. Lesions on all plant parts were counted after two weeks, which gave an estimate of the number of conidia released and the distance travelled. Trap plants at all distances were infected in all sprinkler irrigation and rainfall events. The highest number of lesions on trap plants were recorded closest to the infected plots – the numbers decreased as the distance from the infected plots increased. There was a positive relationship between the amount of rainfall and the number of lesions recorded. A generalised additive model was developed that efficiently described spatial patterns of conidial spread. With further development, the model can be used to predict the spread of A. rabiei. This is the first systematic study to show that conidia distribute A. rabiei over longer distances than previously reported.

Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC BY 4.0). The chirps package provides functionalities for reproducible analysis in R (R Core Team, 2020) using the CHIRPS (Funk et al., 2015) data. CHIRPS is daily precipitation data set developed by the Climate Hazards Group (Funk et al., 2015) for high resolution precipitation gridded data. Spanning 50◦ S to 50◦ N (and all longitudes) and ranging from 1981 to nearpresent (normally with a 45 day lag), CHIRPS incorporates 0.05 arc-degree resolution satellite imagery, and in-situ station data to create gridded precipitation time series for trend analysis and seasonal drought monitoring (Funk et al., 2015). Additionally, the package provides the API client for the IMERG (Huffman et al., 2014) and ESI (SERVIR Global, 2019a) data. The Integrated Multi-satelliE Retrievals for GPM (IMERG) data provides near-real time global observations of rainfall at 0.5 arc-degree resolution, which can be used to estimate total rainfall accumulation from storm systems and quantify the intensity of rainfall and flood impacts from tropical cyclones and other storm systems. IMERG is a daily precipitation dataset available from 2015 to near-present. The evaporative stress index (ESI) data describes temporal anomalies in evapotranspiration produced weekly at 0.25 arc-degree resolution for the entire globe (Anderson et al., 2011). The ESI data is based on satellite observations of land surface temperature, which are used to estimate water loss due to evapotranspiration (the sum of evaporation and plant transpiration from the Earth’s land and ocean surface to the atmosphere). The ESI data is available from 2001 to near-present. When using these data sets in publications please cite Funk et al. (2015) for CHIRPS, Huffman et al. (2014) for IMERG and SERVIR Global (2019a) for ESI.

This article addresses the modelling of crop health and its impact on crop losses, with a special emphasis on plant diseases. Plant disease epidemiological models have many different shapes. We propose a summary of modelling structures for plant disease epidemics, which stem from the concepts of infection rate, of site, of basic infection rate corrected for removals (Rc), and of basic reproductive number (R0). Crop losses, the quantitative and qualitative impacts of diseases and pests on crop performances, can be expressed along many different dimensions. We focus on yield loss, defined as the difference between the attainable yield and the actual yield, in a production situation. The modelling of yield loss stems from the concept of damage mechanism, which can be applied to the wide range of organisms (including pathogens, weeds, arthropods, or nematodes) that may negatively affect crop growth and performances. Damage mechanisms are incorporated in crop growth models to simulate yield losses. In both fields, epidemiology and crop loss, we discuss the process of model development, including model simplification. We emphasize model simplification as a main avenue towards model genericity. This is especially relevant to enable addressing the diversity of crop pathogens and pests. We also discuss the usefulness of considering differing evaluation criteria depending on the stage of model development, and thus, depending on modelling objectives. We illustrate progress made on two global key crops where model simplification has been critical; rice and wheat. Modelling pests and diseases, and of the yield losses they cause on these two crops, lead us to propose the concept of crop health syndrome as a set of injury functions, each representing the dynamics of an injury (such as, for example, the time-course of an epidemic). Crop health in a given context can be represented by the set of such injury functions, which in turn can be used as drivers for crop loss models.

The literature on the importance of plant pathogens sometimes emphasizes their possible role in historical foodshortages and even in famines. Aside from such major crises, plant pathogens should also be seen as important reducers of crop performances, with impacts on system sustainability, from the ecological, agronomical, social, and economic standpoints – all contributing ultimately to affecting food security. These views need reconciliation in order to produce a clearer picture of the multidimensional effects of plant disease epidemics. Such a picture is needed for disease management today, but would also be useful for future policies. This article attempts to develop a framework that would enable assessment of the impacts of plant diseases, referred collectively to as crop health, on food security via its components. We have combined three different existing definitions of food security in order to develop a framework consisting of the following six components: (1) Availability. Primary production; (2) Availability. Import - Stockpiles; (3) Access. Physical and supply chain; (4) Access. Economic; (5) Stability of food availability; (6) Utility-Safety-Quality- Nutritive value. In this framework, components of food security are combined with three attributes of production situations: the nature of the considered crop (i.e. food- or non- food), the structure of farms (i.e. subsistence or commercial), and the structure of markets (i.e. weakly organized and local, to strongly organized and globalized). The resulting matrix: [Food security components] × [Attributes of production situations] provides a framework where the impacts of chronic, acute, and emerging plant disease epidemics on food security can be examined. We propose that, given the number of components and interactions at play, a systems modelling approach is required to address the functioning of food systems exposed to plant disease risks. This approach would have application in both the management of the current attrition of crop performances by plant diseases, and also of possible disease-induced shocks. Such an approach would also enable quantifying shifts in disease vulnerability of production situations, and therefore, of food systems, as a result of climate change, globalization, and evolving crop health.

Attempting to achieve long-lasting and stable resistance using uniformly deployed rice varieties is not a sustainable approach. The real situation appears to be much more complex and dynamic, one in which pathogens quickly adapt to resistant varieties. To prevent disease epidemics, deployment should be customized and this decision will require interdisciplinary actions. This perspective article aims to highlight the current progress on disease resistance deployment to control bacterial blight in rice. Although the model system rice-Xanthomonas oryzae pv. oryzae has distinctive features that underpin the need for a case-by-case analysis, strategies to integrate those elements into a unique decision tool could be easily extended to other crops. Global Rice Science Partnership (GRiSP) BIll & Melinda Gates Foundation (BMGF) Philippine Rice Information System

International audience; 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.

Publisher Summary Plant disease risk is strongly influenced by environmental conditions. While some animal hosts may provide their pathogens with a consistent range of body temperatures, plant pathogens are generally much more exposed to the elements. Plant diseases will tend to respond to climate change, though a number of interactions taking place among host, pathogen, and potential vectors. In some cases, the actions of land managers may also complicate interpretation of climate change effects. This chapter presents a brief introduction to plant diseases and a synthesis of research in plant pathology related to climate change. It discusses the types of evidence for climate change impacts that might be observed in plant disease systems and evaluates what evidence of climate change fingerprints currently exists. The battle against plant disease is not a new one, and plant disease management is essential to feed a growing human population. Plant pathogen groups include fungi, prokaryotes, oomycetes, viruses and viroids, nematodes, parasitic plants, and protozoa. The very different life histories of this diverse group of organisms and their different interactions with host plants produce a wide range of responses to environmental and climatic drivers. Pathogen species may quickly develop resistance to pesticides or adapt to overcome plant disease resistance, and may also adapt to environmental changes, where the rate of adaptation depends on the type of pathogen. Pathogen populations may explode when weather conditions are favorable for disease development. The potentially rapid onset of disease makes it difficult to anticipate the best timing of management measures, especially in areas with high levels of interannual variability in climatic conditions.

The effects of climate change on biological systems are complex. This is particularly apparent for multispecies systems such as plant diseases and plant-herbivore interactions where climate can affect each species individually as well as influencing the interactions between species. Climate change-driven shifts in agricultural patterns and practices add another layer of complexity (Savary et al., Field Crops Res., 2005, 91:263-271). Plant diseases and insect pests have important impacts on agricultural systems; for example, agricultural losses to plant disease are estimated at over 10% (Savary et al., Ann. Rev. Phytopathol., 2006, 44:89-112). Thus, as a first step it will be important to develop an adequate conceptual framework for anticipating the biological complexity of the responses of these systems to climate change. Secondly, an adequate conceptual framework for the effects of different adaptation and mitigation scenarios, with their own complexities, will be needed to evaluate appropriate responses. Our objective is to develop frameworks to help meet this need, and here we outline a modeling structure for these components, with an emphasis on plant disease. The impact of climate, through weather patterns, on plant disease has been studied in detail for several important plant diseases (Garrett et al., Ann. Rev. Phytopathol., 2006, 44:489-509). It is possible to predict with reasonable confidence whether disease will become more or less important within a field as a function of weather variables.