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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, Mucor harpali, Nigrograna jinghongensis, Odontia huanrenensis, O. parvispina, Paraconiothyrium ajrekarii, Parafuscosporella niloticus, Phaeocytostroma yomensis, Phaeoisaria synnematicus, Phanerochaete hainanensis, Pleopunctum thailandicum, Pleurotheciella dimorphospora, Pseudochaetosphaeronema chiangraiense, Pseudodactylaria albicolonia, Rhexoacrodictys nigrospora, Russula paravioleipes, Scolecoleotia eriocamporesi, Seriascoma honghense, Synandromyces makranczyi, Thyridaria aureobrunnea, Torula lancangjiangensis, Tubeufia longihelicospora, Wicklowia fusiformispora, Xenovaginatispora phichaiensis and Xylaria apiospora. One new combination, Pseudobactrodesmium stilboideus is proposed. A reference specimen of Comoclathris permunda is designated. New host or distribution records are provided for Acrocalymma fici, Aliquandostipite khaoyaiensis, Camarosporidiella laburni, Canalisporium caribense, Chaetoscutula juniperi, Chlorophyllum demangei, C. globosum, C. hortense, Cladophialophora abundans, Dendryphion hydei, Diaporthe foeniculina, D. pseudophoenicicola, D. pyracanthae, Dictyosporium pandanicola, Dyfrolomyces distoseptatus, Ernakulamia tanakae, Eutypa flavovirens, E. lata, Favolus septatus, Fusarium atrovinosum, F. clavum, Helicosporium luteosporum, Hermatomyces nabanheensis, Hermatomyces sphaericoides, Longipedicellata aquatica, Lophiostoma caudata, L. clematidis-vitalbae, Lophiotrema hydei, L. neoarundinaria, Marasmiellus palmivorus, Megacapitula villosa, Micropsalliota globocystis, M. gracilis, Montagnula thailandica, Neohelicosporium irregulare, N. parisporum, Paradictyoarthrinium diffractum, Phaeoisaria aquatica, Poaceascoma taiwanense, Saproamanita manicata, Spegazzinia camelliae, Submersispora variabilis, Thyronectria caudata, T. mackenziei, Tubeufia chiangmaiensis, T. roseohelicospora, Vaginatispora nypae, Wicklowia submersa, Xanthagaricus necopinatus and Xylaria haemorrhoidalis. The data presented herein are based on morphological examination of fresh specimens, coupled with analysis of phylogenetic sequence data to better integrate taxa into appropriate taxonomic ranks and infer their evolutionary relationships.

Despite the substantial economic impact of Curtobacterium flaccumfaciens pv. flaccumfaciens on legume production worldwide, the genetic basis of its pathogenicity and potential host association is poorly understood. The production of high-quality reference genome assemblies of C. flaccumfaciens pv. flaccumfaciens strains associated with different hosts sheds light on the genetic basis of its pathogenic variability and host association. Moreover, the study of recent outbreaks of bacterial wilt and microevolution of the pathogen in Australia requires access to high-quality reference genomes that are sufficiently closely related to the population being studied within Australia. We provide the first genome assemblies of C. flaccumfaciens pv. flaccumfaciens strains associated with mungbean and soybean, which revealed high variability in their plasmid composition. The analysis of C. flaccumfaciens pv. flaccumfaciens genomes revealed an extensive suite of carbohydrate-active enzymes potentially associated with pathogenicity, including four carbohydrate esterases, 50 glycoside hydrolases, 23 glycosyl transferases, and a polysaccharide lyase. We also identified 11 serine peptidases, three of which were located within a linear plasmid, pCff119. These high-quality assemblies and annotations will provide a foundation for population genomics studies of C. flaccumfaciens pv. flaccumfaciens in Australia and for answering fundamental questions regarding pathogenicity factors and adaptation of C. flaccumfaciens pv. flaccumfaciens to various hosts worldwide and, at a broader scale, contribute to unraveling genomic features of gram-positive, xylem-inhabiting bacterial pathogens. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Worldwide, most isolates of Macrophomina (Botryosphaeriaceae) have been attributed to the generalist phytopathogen M. phaseolina. Since 2014, three cryptic species of Macrophomina have been recognised by molecular methods. This study elucidates the taxonomy of Macrophomina species associated with broadacre and horticultural crops in Australia. A five-locus phylogenetic analysis of 80 isolates of Macrophomina from 28 plant species in Australia, combined with genealogical concordance phylogenetic species recognition and coalescent-based species delimitation approaches, found M. phaseolina, M. pseudophaseolina and supported the introduction of M. tecta sp. nov. Macrophomina phaseolina was the most frequently isolated (88%). Macrophomina pseudophaseolina is reported for the first time in Australia. Macrophomina tecta was isolated from stems of Sorghum bicolor and Vigna radiata with charcoal rot symptoms in New South Wales and Queensland. The potential for two or more Macrophomina species to co-infect the same host has implications for disease epidemiology and pathogen evolution. Future investigations into the distribution, biology, host range and population diversity of the new Macrophomina records are needed.

Ascochyta blight is one of the most devastating diseases of chickpea worldwide. In Australia, Ascochyta blight management strategy in a standing crop is solely based on applying protective fungicides before a forecast rainfall event. Despite this, studies on the likely interaction between variable natural rain amount, rain duration, environmental factors and Ascochyta blight development are rare. We used generalised linear mixed models to investigate the relationship between rain intensity, wind speed and Ascochyta blight development. Briefly, 7 g of infested chickpea residue were placed at the soil surface in a 1 m 2 plot, and three pots (3 trap plants per pot) of a susceptible chickpea cultivar were randomly placed on each side of the 1 m 2 plot (total 12 pots), preceding a forecast rainfall event. Trap plants were transferred to a controlled temperature room (20°C) for 48 h (100% humidity) after rain events. After a 48 h incubation period, trap plants were transferred to a glasshouse (20°C) to allow lesion development. The number of lesions on all plant parts were counted after two weeks. Lesions developed in rain amounts as low as 1.4 mm and rain durations as short as 0.7 h. The number of lesions significantly increased with increasing rain amount. There was positive effect of increasing rain duration and a negative effect of increasing wind speed. This study suggests that small rain amounts, shorter duration rains or a limited amount of primary inoculum are not barriers to conidial dispersal or infection.

Charcoal rot is an important soilborne disease caused by a range of Macrophomina species, which affects a broad range of commercially important crops worldwide. Even though Macrophomina species are fungal pathogens of substantial economic importance, their mechanism of pathogenicity and host spectrum are poorly understood. There is an urgent need to better understand the biology, epidemiology, and evolution of Macrophomina species, which, in turn, will aid in improving charcoal rot management strategies. Here, we present the first high-quality genome assembly and annotation of Macrophomina tecta strain BRIP 70781 associated with charcoal rot symptoms on sorghum. Hybrid assembly integrating long reads generated by Oxford Nanopore Technology and short Illumina paired-end reads resulted in 43 contigs with a total assembly size of ∼54 Mb, and an N50 of 3.4 Mb. In total, 12,926 protein-coding genes and 7,036 repeats were predicted. Genome comparisons detected accumulation of DNA transposons in Macrophomina species associated with sorghum. The first reference genome of M. tecta generated in this study will contribute to more comparative and population genomics studies of Macrophomina species.

Peanut smut ( Thecaphora frezii ) is one of the most important peanut diseases in Argentinian peanut production. This soil-borne pathogen transforms kernels into spore masses. Spore liberation from broken infected pods during the harvest process is supposed to be the main mechanism of inoculum spread, with the subsequent spread among fields. However, we are unaware of any published study on the role of wind in the spread of smut spores. Therefore, we conducted an observational study where spore traps were distributed at harvest around six fields placed at 100, 200, 300, and 400 m away from each field's centroid in four cardinal directions. Three time slices were sampled: from the beginning of harvest to 90-, 180-, and 270-minutes during harvest. Wind speed and direction were recorded at each trap. A generalised additive model was fitted to describe the spore spread. Modelling the dispersion shows that the spread is influenced by wind direction, and the smut intensity present at the harvested field. Additionally, spore size and proportion of different smut spore types were assessed (from a single unit spore to a 5-multinuclear propagule). No statistical differences were observed in the proportion of the spore types trapped. However, the spores' spread decreased over distances from the harvested area. This work led us to understand one fundamental component of the peanut smut cycle and epidemiology, which is, to design management strategies, such as avoiding harvest on windy days.

Powdery mildew (PM), caused by two fungal species, Podosphaera xanthii and Erysiphe vignae, is a yield-limiting foliar disease commonly found in mungbean (Vigna radiata) cropping areas of the eastern region of Australia. Effective control of the disease relies largely on fungicide applications, mainly of the triazole group. Uncertainty in the current fungicide spray schedule recommendations, which advise commencing with a spray at the first signs of PM, prompted this study to evaluate PM severity and crop yield data obtained from fungicide trials, which also tested spray schedules starting before (early) or after (late) first signs, applied singly or combined with a follow-up spray. A meta-analytic approach was used to obtain mean differences of the PM severity and crop yield between plots sprayed with specific triazole-based spray schedules and nontreated plots. From 26 trials, 14 trials and 15 trials met the criteria for inclusion in PM severity and yield analyses, respectively. The schedule with the first spray starting at first sign, with a follow-up spray 14 days later, resulted in significantly lower disease severity compared with all other schedules. However, the yield protected was only numerically higher and not statistically different compared with single-spray at first sign, single-spray late, or two-spray starting late. PM severity and yield in the early sprayed plots did not differ from the nontreated plots. These findings support the current recommendations and provide additional evidence that yields are still protected when delaying the first spray up to a week after disease onset. They also suggest that additional sprays may not always be necessary, thus reducing direct fungicide costs, indirect costs related to fungicide insensitivity, and potential adverse effects to the environment.

Macrophomina phaseolina is a soil-borne phytopathogenic fungus that causes charcoal rot in several plant species, including sorghum. We constructed a draft genome of M. phaseolina isolate BRIP 70780a from sorghum, using long-read native DNA from MinION sequencing, which was error-corrected using short-read Illumina MiSeq reads. The draft genome, consisting of 22 contigs with an N50 of 4,257,441 bp, 99.3% complete benchmarking universal single-copy orthologs, and 14,471 genes, is a valuable resource to aid future studies in population genomics and molecular diagnostic marker development for rapid detection of the pathogen.

Phytophthora sojae is a significant pathogen of soybean worldwide. Pathotype surveys for Phytophthora sojae are conducted to monitor resistance gene efficacy and determine if new resistance genes are needed. Valuable measurements for pathotype analysis include the distribution of susceptible reactions, pathotype complexity, pathotype frequency, and diversity indices for pathotype distributions. Previously the Habgood-Gilmour Spreadsheet (HaGiS), written in Microsoft Excel, was used for data analysis. However, the growing popularity of the R programming language in plant pathology and desire for reproducible research made HaGiS a prime candidate for conversion into an R package. Here we report on the development and use of an R package, hagis, that can be used to produce all outputs from the HaGiS Excel sheet for P. sojae or other gene-for-gene pathosystem studies.

First release of spatiotemporaldynamics repository

Powdery mildew (PM), caused by two fungal species, Podosphaera xanthii and Erysiphe vignae , is a yield limiting foliar disease commonly found in mungbean ( Vigna radiata) cropping areas of eastern region of Australia. Effective control of PM afflicting mungbeans relies largely on fungicide applications, mainly of the triazole group. Uncertainty in the current fungicide spray schedule recommendations, which advise commencing with a spray at the first sign of PM, prompted this study to evaluate PM severity and crop yield data obtained from fungicide trials which tested spray schedules starting before ( Early ) or after ( Late ) first signs, applied singly or combined with a follow-up spray. A meta-analytic approach was employed to obtain estimates of the yield and PM severity difference between plots sprayed with specific triazole-based sprays schedules and untreated plots. From 26 trials, 15 and 14 met the criteria for inclusion in the yield and PM severity analysis respectively. The schedule with the first spray starting at first sign with a follow-up spray 14 days later, resulted in significantly lower disease severity compared to all other schedules, however, the yield protected was only numerically higher and not statistically different compared to: single-spray at first sign, single-spray Late or two-spray starting Late . While PM severity was lower in the Early sprayed plots compared to untreated plots, yield did not differ. These findings support the current recommendations and provide evidence that the first spray could be delayed up to a week and save an additional spray that may be unnecessary, thus reducing direct fungicide costs as well as indirect costs due to fungicide resistance.

This chapter reviews approaches taken to assessing the impact of diseases on rice crops, primarily in tropical Asia, compared with insect pests and weeds. Published estimates of yield loss show great variation between and within reports. This reflects the diversity of metrics used and differences in method, location, scale, crop ecosystem etc., exposing the inconsistency of the estimates as representative of rice production. Results are then summarised of >1000 surveys of farmers’ fields conducted over 25 years to a standardised protocol. Estimates were made of the levels of injury to the growing crop attributed to each disease or pest species present, and of the crop yield. Records of physical, biological, and socio-economic characteristics showed the importance of the influence of the crop’s “production situation” on the impact of pathogen and pest species. These surveys were complemented by the results of yield-loss experiments, and by epidemic modelling (based on EPIRICE) and yield-loss modelling (based on RICEPEST). An overview of the results of this research programme is presented, together with a summary of recent trends in rice crop health in Asia. In contrast with views widely held in the 1980s, plant diseases now appear to have a much greater impact on rice production than insect pests.

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 study revisits the relationship between income inequality and economic growth using provincial-level data from the Philippines over a ten-year period (1991–2000). This study demonstrates that ...

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.

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.

This chapter analyzes the spatial and temporal patterns of rice production and productivity in India. Of the 20 agroecological zones, five account for more than 60% of the total rice area and these zones unfortunately house a large number of low-productivity districts. Most of these low-productivity districts fall under the rainfed rice ecosystem that seems to lack appropriate production technologies. Average total factor productivity (TFP) growth for rice was estimated at 3.28% from 1991–92 to 2012–13, and much of this growth was contributed by technical change with little improvement in technical efficiency change. Among the states, Punjab registered the highest TFP growth of 5.71%, whereas Assam showed a negative growth rate. With the exception of Andhra Pradesh, all states failed to show technical efficiency gains, meaning higher quantities of rice are produced using a higher quantity of input per hectare. Overall, analysis revealed that technical change was the main driver of TFP growth in rice.

Weather affects the severity of many plant diseases, and climate change is likely to alter the patterns of crop disease severity. Evaluating possible future patterns can help focus crop breeding and disease management research. We examined the global effect of climate change on potato late blight, the disease that caused the Irish potato famine and still is a common potato disease around the world. We used a metamodel and considered three global climate models for the A2 greenhouse gas emission scenario for three 20-year time-slices: 2000-2019, 2040-2059 and 2080-2099. In addition to global analyses, five regions were evaluated where potato is an important crop: the Andean Highlands, Indo-Gangetic Plain and Himalayan Highlands, Southeast Asian Highlands, Ethiopian Highlands, and Lake Kivu Highlands in Sub-Saharan Africa. We found that the average global risk of potato late blight increases initially, when compared with historic climate data, and then declines as planting dates shift to cooler seasons. Risk in the agro-ecosystems analyzed, varied from a large increase in risk in the Lake Kivu Highlands in Rwanda to decreases in the Southeast Asian Highlands of Indonesia.

Potato late blight caused by Phytophthora infestans is one of the most important plant diseases known, requiring high pesticide inputs to prevent disease occurrence. The disease development is highly dependent on weather conditions, and as such, several forecasting schemes have been developed worldwide which seek to reduce the inputs required to control the disease. The Irish Rules, developed in the 1950s and calibrated to accommodate the meteorological network, the characteristics of potato production and the P. infestans population at the time, is still operationally utilized by the national meteorological agency, Met Éireann. However, numerous changes in the composition and dynamics of the pathosystem and the risks of production/economic consequences associated with potato late blight outbreaks have occurred since the inception of the Irish Rules model. Additionally, model and decision thresholds appear to have been selected ad hoc and without a clear criteria. We developed a systematic methodology to evaluate the model using the empirical receiver operating curve (ROC) analysis and the response surface methodology for the interpretation of the results. The methodology, written in the R language, is provided as an open, accessible and reproducible platform to facilitate the ongoing seasonal re-evaluation of the Irish Rules and corresponding decision thresholds. Following this initial analysis, based on the available data, we recommend the reduction of the thresholds for relative humidity and an initial period duration from 90% and 12 h to 88% and 10 h, respectively. Contrary to recent reports, we found that the risk of blight epidemics remains low at temperatures below 12 °C. With the availability of more comprehensive outbreak data and with greater insight into the founder population to confirm our findings as robust, the temperature threshold in the model could potentially be increased from 10 °C to 12 °C, providing more opportunities for reductions of pesticide usage. We propose a dynamic operational decision threshold between four and 11 effective blight hours (EBH) set according to frequency of the disease outbreaks in the region of interest. Although the risk estimation according to the new model calibrations is higher, estimated chemical inputs, on average, are lower than the usual grower’s practice. Importantly, the research outlined here provides a robust and reproducible methodological approach to evaluate a semi-empirical plant disease forecasting model.

The response of plant disease to weather variables such as temperature and precipitation is well known, and has been the basis for disease forecasting models used in decision-making by farmers and policy makers. Thus, plant disease risk can readily shift under climate change. Predicting changes in risk under climate change is complicated by the many biological interactions that result in disease. For example, some plant diseases occur when the phenology of plant and pathogen are aligned, in the case of Fusarium head blight so that spores are ready to infect during wheat flowering. There are numerous examples of experiments involving simulated climate change that have shown changes in disease risk. Finding indicators of climate change effects on disease in real agricultural or natural systems is more challenging because of the correlative nature of the observations and the potential for many other factors to influence disease, such as changes in host abundance. Assessments of global disease databases suggest some pathogen range shifts consistent with predicted outcomes for climate change. There are also several cases of emerging tree diseases in which climate change has likely played a role.

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.

Cheatham, M. R., Rouse, M. N., Esker, P. D., Ignacio, S., Pradel, W., Raymundo, R., Sparks, A. H., Forbes, G. A., Gordon, T. R., and Garrett, K. A. 2009. Beyond yield: Plant disease in the context of ecosystem services. Phytopathology 99:1228-1236. The ecosystem services concept provides a means to define successful disease management more broadly, beyond short-term crop yield evaluations. Plant disease can affect ecosystem services directly, such as through removal of plants providing services, or indirectly through the effects of disease management activities, including pesticide applications, tillage, and other methods of plant removal. Increased plant biodiversity may reduce disease risk if susceptible host tissue becomes less common, or may increase risk if additional plant species are important in completing pathogen life cycles. Arthropod and microbial biodiversity may play similar roles. Distant ecosystems may provide a disservice as the setting for the evolution of pathogens that later invade a focal ecosystem, where plants have not evolved defenses. Conversely, distant ecosystems may provide a service as sources of genetic resources of great value to agriculture, including disease resistance genes. Good policies are needed to support conservation and optimal use of genetic resources, protect ecosystems from exotic pathogens, and limit the homogeneity of agricultural systems. Research is needed to provide policy makers, farmers, and consumers with the information required for evaluating trade-offs in the pursuit of the full range of ecosystem services desired from managed and native ecosystems.

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.

Numerous transgenes have been reported to increase rice drought resistance, mostly in small-scale experiments under vegetative-stage drought stress, but few studies have included grain yield or field evaluations. Different definitions of drought resistance are currently in use for field-based and laboratory evaluations of transgenics, the former emphasizing plant responses that may not be linked to yield under drought. Although those fundamental studies use efficient protocols to uncover and validate gene functions, screening conditions differ greatly from field drought environments where the onset of drought stress symptoms is slow (2-3 weeks). Simplified screening methods, including severely stressed survival studies, are therefore not likely to identify transgenic events with better yield performance under drought in the target environment. As biosafety regulations are becoming established to allow field trials in some rice-producing countries, there is a need to develop relevant screening procedures that scale from preliminary event selection to greenhouse and field trials. Multilocation testing in a range of drought environments may reveal that different transgenes are necessary for different types of drought-prone field conditions. We describe here a pipeline to improve the selection efficiency and reproducibility of results across drought treatments and test the potential of transgenic rice for the development of drought-resistant material for agricultural purposes.

Process‐based ecological models used to assess organisms' responses to environmental conditions often need input data at a high temporal resolution, e.g., hourly or daily weather data. Such input data may not be available at a high spatial resolution for large areas, limiting opportunities to use such models. Here we present a metamodeling framework to develop reduced form ecological models that use lower resolution input data than the original process models. We used generalized additive models to create metamodels for an existing model that uses hourly data to predict risk of potato late blight, caused by the plant pathogen Phytophthora infestans. The metamodels used daily or monthly weather data, and their predictions maintained the key features of the original model. This approach can be applied to other complex models, allowing them to be used more widely.

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.

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.