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Introduction to Plant Disease Epidemiology
16 Jan 1990-
TL;DR: This paper presents the results of a two-year study into the design and modelling of Spatial Pattern Simulation Models of Plant Disease Designing Experiments and Sampling Crop Loss Assessment and Modelling Forecasting Plant Diseases.
Abstract: Epidemics and Plant Disease Epidemiology Monitoring Epidemics: Host Monitoring Epidemics: Environment Monitoring Epidemics: Pathogen Monitoring Epidemics: Disease Modeling and Data Analysis Temporal Analysis of Epidemics I Temporal Analysis of Epidemics Spatial Aspects of Plant Disease Epidemics I: Dispersal Gradients of Long Range Transport Spatial Aspects of Plant Disease Epidemics II: Analysis of Spatial Pattern Simulation Models of Plant Disease Designing Experiments and Sampling Crop Loss Assessment and Modelling Forecasting Plant Diseases.
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TL;DR: This review focuses on the population dynamics and activity of soilborne pathogens and beneficial microorganisms, and mechanisms involved in the tripartite interactions between beneficialmicroorganisms, pathogens and the plant.
Abstract: The rhizosphere is a hot spot of microbial interactions as exudates released by plant roots are a main food source for microorganisms and a driving force of their population density and activities. The rhizosphere harbors many organisms that have a neutral effect on the plant, but also attracts organisms that exert deleterious or beneficial effects on the plant. Microorganisms that adversely affect plant growth and health are the pathogenic fungi, oomycetes, bacteria and nematodes. Most of the soilborne pathogens are adapted to grow and survive in the bulk soil, but the rhizosphere is the playground and infection court where the pathogen establishes a parasitic relationship with the plant. The rhizosphere is also a battlefield where the complex rhizosphere community, both microflora and microfauna, interact with pathogens and influence the outcome of pathogen infection. A wide range of microorganisms are beneficial to the plant and include nitrogen-fixing bacteria, endo- and ectomycorrhizal fungi, and plant growth-promoting bacteria and fungi. This review focuses on the population dynamics and activity of soilborne pathogens and beneficial microorganisms. Specific attention is given to mechanisms involved in the tripartite interactions between beneficial microorganisms, pathogens and the plant. We also discuss how agricultural practices affect pathogen and antagonist populations and how these practices can be adopted to promote plant growth and health.
1,370 citations
TL;DR: In this paper, the effects of climate variability and change on food production, risk of malnutrition, and incidence of weeds, insects, and diseases are discussed, and projected scenarios of future climate change impacts on crop production and risk of hunger in major agricultural regions are presented.
Abstract: 90 © Kluwer Academic Publishers Challenges to Food Production and Nutrition Current and future energy use from burning of fossil fuels and clearing of forests for cultivation can have profound effects on the global environment, agriculture, and the availability of low-cost, highquality food for humans. Individual farmers and consumers are expected to be affected by changes in global and regional climate. The agricultural sector in both developing and developed areas needs to understand what is at stake and to prepare for the potential for change wisely. Despite tremendous improvements in technology and crop yield potential, food production remains highly dependent on climate, because solar radiation, temperature, and precipitation are the main drivers of crop growth. Plant diseases and pest infestations, as well as the supply of and demand for irrigation water are infl uenced by climate. For example, in recent decades, the persistent drought in the Sahelian region of Africa has caused continuing deterioration of food production[1,2]; the 1988 Midwest drought led to a 30% reduction in U.S. corn production and cost taxpayers $3 billion in direct relief payments to farmers[3] and, weather anomalies associated with the 1997-98 El Niño affected agriculture adversely in Nordeste, Brazil and Indonesia[4]. Earlier in the century, the 1930s U.S. Southern Great Plains drought caused some 200,000 farm bankruptcies in the Dust Bowl; yields of wheat and corn were reduced by as much as 50%[5]. The aim of this article is to discuss the effects of climate variability and change on food production, risk of malnutrition, and incidence of weeds, insects, and diseases. It focuses on the effects of extreme weather events on agriculture, looking at examples from the recent past and to future projections. Major incidents of climate variability are contrasted, including the effects of the El NiñoSouthern Oscillation. Finally, projected scenarios of future climate change impacts on crop production and risk of hunger in major agricultural regions are presented. Altered weather patterns can increase crop vulnerability to infection, pest infestations, and choking weeds. Ranges of crop weeds, insects, and diseases are projected to expand to higher latitudes[6,7]. Shifts in climate in different world regions may have different and contrasting effects. Some parts of the world may benefi t from global climate change (at least in the short term), but large regions of the developing world may experience reduced food supplies and potential increase in malnutrition[2,3]. Changes in food supply could lead to permanent or semi-permanent displacement of populations in developing countries, consequent overcrowding and associated diseases, such as tuberculosis[8]. Climate change and extreme weather events
1,007 citations
TL;DR: The status of global food security, i.e., the balance between the growing food demand of the world population and global agricultural output, combined with discrepancies between supply and demand at the regional, national, and local scales, is alarming as mentioned in this paper.
Abstract: The status of global food security, i.e., the balance between the growing food demand of the world population and global agricultural output, combined with discrepancies between supply and demand at the regional, national, and local scales (Smil 2000; UN Department of Economic and Social Affairs 2011; Ingram 2011), is alarming. This imbalance is not new (Dyson 1999) but has dramatically worsened during the recent decades, culminating recently in the 2008 food crisis. It is important to note that in mid-2011, food prices were back to their heights of the middle of the 2008 crisis (FAO 2011). Plant protection in general and the protection of crops against plant diseases in particular, have an obvious role to play in meeting the growing demand for food quality and quantity (Strange and Scott 2005). Roughly, direct yield losses caused by pathogens, animals, and weeds, are altogether responsible for losses ranging between 20 and 40 % of global agricultural productivity (Teng and Krupa 1980; Teng 1987; Oerke et al. 1994; Oerke 2006). Crop losses due to pests and pathogens are direct, as well as indirect; they have a number of facets, some with short-, and others with long-term consequences (Zadoks 1967). The phrase “losses between 20 and 40 %” therefore inadequately reflects the true costs of crop losses to consumers, public health, societies, environments, economic fabrics and farmers. The components of food security include food availability (production, import, reserves), physical and economic access to food, and food utilisation (e.g., nutritive value, safety), as has been recently reviewed by Ingram (2011). Although crop losses caused by plant disease directly affect the first of these components, they also affect others (e.g., the food utilisation component) directly or indirectly through the fabrics of trade, policies and societies (Zadoks 2008). Most of the agricultural research conducted in the 20th century focused on increasing crop productivity as the world population and its food needs grew (Evans 1998; Smil 2000; Nellemann et al. 2009). Plant protection then primarily focused on protecting crops from yield losses due to biological and non-biological causes. The problem remains as challenging today as in the 20th century, with additional complexity generated by the reduced room for manoeuvre available environmentally, economically, and socially (FAO 2011; Brown 2011). This results from shrinking natural resources that are available to agriculture: these include water, agricultural land, arable soil, biodiversity, the availability of non-renewable energy, human labour, fertilizers (Smil 2000), and the deployment of some key inputs, such as high quality seeds and planting material (Evans 1998). In addition to yield losses caused by diseases, these new elements of complexity also include post harvest quality losses and the possible accumulation of toxins during and after the S. Savary (*) : J.-N. Aubertot INRA, UMR1248 AGIR, 24 Chemin de Borde Rouge, Auzeville, CS52627, 31326 Castanet-Tolosan Cedex, France e-mail: Serge.Savary@toulouse.inra.fr
720 citations
Cites background from "Introduction to Plant Disease Epide..."
...These relationships are neither linear (Large 1966; James 1974; Madden 1983; Teng 1987; Campbell and Madden 1990; Madden et al. 2000; Savary et al. 2006a; Madden et al. 2007) nor are they automatic: epidemics do not always lead to measurable injuries, neither do injuries necessarily lead to measurable crop losses, nor do crop losses necessarily lead to measurable economic losses (Zadoks 1985; Rabbinge et al....
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...These relationships are neither linear (Large 1966; James 1974; Madden 1983; Teng 1987; Campbell and Madden 1990; Madden et al. 2000; Savary et al. 2006a; Madden et al. 2007) nor are they automatic: epidemics do not always lead to measurable injuries, neither do injuries necessarily lead to…...
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01 Jan 1998
TL;DR: In this paper, the use of a Crop Simulation Model for Planning Wheat Irrigation in Zimbabwe J.T. Bowen, W.J. Boote, and W.W. Wilkens.
Abstract: Preface. Acronyms. 1. Overview of IBSNAT G. Uehara, G.Y. Tsuji. 2. Data for Model Operation, Calibration, and Evaluation L.A. Hunt, K.J. Boote. 3. Soil Water Balance and Plant Water Stress J.T. Ritchie. 4. Nitrogen Balance and Crop Response to Nitrogen in Upland and Lowland Cropping Systems D.C. Godwin, U. Singh. 5. Cereal Growth, Development and Yield J.T. Ritchie, et al. 6. The CROPGRO Model for Grain Legumes K.J. Boote, et al. 7. Modeling Growth and Development of Root and Tuber Crops U. Singh, et al. 8. Decision Support System for Agrotechnology Transfer: DSSAT v3 J.W. Jones, et al. 9. Modeling and Crop Improvement J.W. White. 10. Simulation as a Tool for Improving Nitrogen Management W.T. Bowen, W.E. Baethgen. 11. The Use of a Crop Simulation Model for Planning Wheat Irrigation in Zimbabwe J.F. MacRobert, M.J. Savage. 12. Simulation of Pest Effects on Crops Using Coupled Pest-Crop Models: The Potential for Decision Support P.S. Teng, et al. 13. The Use of Crop Models for International Climate Change Impact Assessment C. Rosenzweig, A. Iglesias. 14. Evaluation of Land Resources Using Crop Models and a GIS F.H. Beinroth, et al. 15. The Simulation of Cropping Sequences Using DSSAT W.T. Bowen, et al. 16. Risk Assessment and Food Security P.K. Thornton, P.W. Wilkens. 17. Incorporating Farm Household Decision-Making within Whole Farm Models G. Edwards-Jones, et al. 18. Network Management and Information Dissemination for Agrotechnology Transfer G.Y. Tsuji. 19. Crop Simulation Models as an Educational Tool R.A. Ortiz. 20. Synthesis G. Uehara.
719 citations
TL;DR: This review considers plant disease severity assessment at the scale of individual plant parts or plants, and describes the current understanding of the sources and causes of assessment error, a better understanding of which is required before improvements can be targeted.
Abstract: Reliable, precise and accurate estimates of disease severity are important for predicting yield loss, monitoring and forecasting epidemics, for assessing crop germplasm for disease resistance, and for understanding fundamental biological processes including co-evolution. Disease assessments that are inaccurate and/or imprecise might lead to faulty conclusions being drawn from the data, which in turn can lead to incorrect actions being taken in disease management decisions. Plant disease can be quantified in several different ways. This review considers plant disease severity assessment at the scale of individual plant parts or plants, and describes our current understanding of the sources and causes of assessment error, a better understanding of which is required before improvements can be targeted. The review also considers how these can be identified using various statistical tools. Indeed, great strides have been made in the last thirty years in identifying the sources of assessment error inherent to visual rating, and this review highlights ways that assessment errors can be reduced—particularly by training raters or using assessment aids. Lesion number in relation to area infected is known to influence accuracy and precision of visual estimates—the greater the number of lesions for a given area infected results in more overestimation. Furthermore, there is a widespread tendency to overestimate disease severity at low severities (<10%). Both interrater and intrarater reliability can be variable, particularly if training or rating aids are not used. During the last eighty years acceptable accuracy and precision of visual disease assessments have often been achieved using disease scales, particularly because of the time they allegedly save, and the ease with which they can be learned, but recent work suggests there can be some disadvantages to their use. This review considers new technologies that offer opportunity to assess disease with greater objectivity (reliability, precision, and accuracy). One of these, visible light photography and digital image analysis has been increasingly used over the last thirty years, as software has become more sophisticated and user-friendly. Indeed, some studies have produced very accurate estimates of disease using image analysis. In contrast, hyperspectral imagery is relatively recent and has not been widely applied in plant pathology. Nonetheless, it offers interesting and potentially discerning opportunities to assess disease. As plant disease assessment becomes better understood, it is against the backdrop of concepts of reliability, precision and accuracy (and agreement) in plant pathology and measurement science. This review briefly describes these concepts in relation to plant disease assessment. Various advantages and disadvantages of the different approaches to disease assessment are described. For each assessment method some future research priorities are identified that would be of value in better understanding the theory of disease assessment, as it applies to improving and fully realizing the potential of image analysis and hyperspectral imagery.
636 citations
Cites background from "Introduction to Plant Disease Epide..."
...Some strive to be generic, and others are specific to individual host-pathogen systems (Chester, 1950; Campbell and Madden, 1990; Nutter and Esker, 2006; Madden et al., 2007)....
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...Whether there is an underlying psychophysical cause for the early similarity in scale structure, or just coincidence is not known (Campbell and Madden, 1990), but the H-B category scale, and its derivatives, are still in use....
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...While achieving these ends it is also necessary to utilize limited resources (equipment, labor and time) as effectively and efficiently as possible (Campbell and Madden, 1990; Nutter and Gaunt, 1996)....
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