Adam H. Sparks

Beyond Yield: Plant Disease in the Context of Ecosystem Services

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.

International agricultural research tackling the effects of global and climate changes on plant diseases in the developing world

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.

Taking transgenic rice drought screening to the field

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.

Plant Pathogens as Indicators of Climate Change

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.

Climate change may have limited effect on global risk of potato late blight

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.

Spatial modelling of rice yield losses in Tanzania due to bacterial leaf blight and leaf blast in a changing climate

Rice is the most rapidly growing staple food in Africa and although rice production is steadily increasing, the consumption is still out-pacing the production. In Tanzania, two important diseases in rice production are leaf blast caused by Magnaporthe oryzae and bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae. The objective of this study was to quantify rice yield losses due to these two important diseases under a changing climate. We found that bacterial leaf blight is predicted to increase causing greater losses than leaf blast in the future, with losses due to leaf blast declining. The results of this study indicate that the effects of climate change on plant disease can not only be expected to be uneven across diseases but also across geographies, as in some geographic areas losses increase but decrease in others for the same disease.

Decision tools for bacterial blight resistance gene deployment in rice-based agricultural 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.

Anticipating and responding to biological complexity in the effects of climate change on agriculture

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.

nasapower: A NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client for R

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).

Spatial and Temporal Patterns of Rice Production and Productivity

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.