Serge Savary

Crop losses due to diseases and their implications for global food production losses and food security

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

How to implement biodiversity-based agriculture to enhance ecosystem services: a review

Intensive agriculture has led to several drawbacks such as biodiversity loss, climate change, erosion, and pollution of air and water. A potential solution is to implement management practices that increase the level of provision of ecosystem services such as soil fertility and biological regulation. There is a lot of literature on the principles of agroecology. However, there is a gap of knowledge between agroecological principles and practical applications. Therefore, we review here agroecological and management sciences to identify two facts that explain the lack of practical applications: (1) the occurrence of high uncertainties about relations between agricultural practices, ecological processes, and ecosystem services, and (2) the site-specific character of agroecological practices required to deliver expected ecosystem services. We also show that an adaptive-management approach, focusing on planning and monitoring, can serve as a framework for developing and implementing learning tools tailored for biodiversity-based agriculture. Among the current learning tools developed by researchers, we identify two main types of emergent support tools likely to help design diversified farming systems and landscapes: (1) knowledge bases containing scientific supports and experiential knowledge and (2) model-based games. These tools have to be coupled with well-tailored field or management indicators that allow monitoring effects of practices on biodiversity and ecosystem services. Finally, we propose a research agenda that requires bringing together contributions from agricultural, ecological, management, and knowledge management sciences, and asserts that researchers have to take the position of “integration and implementation sciences.”

Looking Ahead in Rice Disease Research and Management

Rice production is subject to increasing environmental and social constraints. Agricultural labor and water, which are key resources for rice production, illustrate this point. Nearly all rice-producing countries face reduced availability of agricultural water and shortage of farm labor. Plant pathologists should be concerned with such large-scale evolutions because these global drivers have an impact on not only the rice production system but also on the individual field and single-rice-plant levels. These concerns are closely associated with the long-term sustainability and environmental consequences of the intensification of agricultural systems brought about by problems of feeding a rapidly growing human population. Furthermore, genetic diversity in rice production has been reduced, thus inducing frequent disease epidemics and pest outbreaks. Looking ahead, we need to realize the need to maintain the diversity and yet retain the high productivity of the system. Natural resources, including genetic resources, are not infinitely abundant. We have to be efficient in utilizing genetic resources to develop durable resistance to rice diseases. Developing resistance is an important first step in tackling the disease problem, but it is not the only step available to achieve durability. Deployment of resistance must be considered in conjunction with development of host plant resistance. To attain durability, we need a better understanding of the coevolution process between the pathogen and the host resistance gene. Our target is an integrated gene management approach for better disease control and more effective utilization of genetic resources. Plant pathology, as an applied science, derives its strengths from various disciplines. To do the job right, we need a better understanding of the pathosystems, the epidemiology, and the coevolution process between the pathogen and the host resistance gene. The challenge, as pointed out by pioneers in our profession, is to prove the usefulness and the relevance of our research. Thus, we need to strike a balance between mission-oriented and fundamental research and make sure that our profession is (still) useful in the information technology and genomic era. We believe that a gene-based and a resource-based disease management approach should allow us to incorporate these new scientific developments. However, we do need to incorporate the new science for fundamental research to solve practical problems of rice production.

Resistance to rice sheath blight ( Rhizoctonia solani Kühn) [(teleomorph: Thanatephorus cucumeris (A.B. Frank) Donk.] disease: current status and perspectives

Sheath blight (ShB) disease, caused by Rhizoctonia solani, is an economically important rice disease worldwide, especially in intensive production systems. Several studies have been conducted to identify sources for ShB resistance in different species of rice, including local accessions and landraces. To date, none of the genotypes screened are immune to ShB, although variation in levels of resistance have been reported. Several quantitative trait loci (QTL) for ShB resistance have been identified using mapping populations derived from indica or japonica rice. A total of 33 QTL associated with ShB resistance located on all 12 rice chromosomes have been reported, with ten of these co-localizing with QTL for morphological attributes, especially plant height, or for heading date. Sixteen QTL, from the same or differing genetic backgrounds, have been mapped at least twice. Of these, nine QTL were independent of morphological traits and heading date. We hypothesize that two main, distinct, mechanisms contribute to ShB resistance: physiological resistance and disease escape. Strategies to improve our understanding of the genetics of resistance to ShB are discussed.

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.

Implementing the systems approach in pest management

Abstract Three broad phases may be distinguished in the application of the systems approach to pest management. Prior to about 1970, much emphasis was placed on the development of concepts and modeling techniques; in the 1970s and early 1980s, a series of pest simulation models for key pests was developed; and, in the late 1980s, some crop and socio-economic factors were incorporated to develop decision-aids for farmers and extensionists. Significant advances have been made in methodology at three levels—Level 1 on pest constraint characterization and pest management domain definition, Level 2 on quantitative and qualitative descriptions of the pest-crop-ecosystem interfaces, and Level 3 on the development of specific tools for applying systems techniques in pest management. The three levels are interlinked, as shown in several crop-pest ecosystems such as potato pests in the Mid-Western USA, groundnut diseases in West Africa, and rice pests in Tropical Asia. Analysis of pest profiles over time, resulting in the characterization of relationships among components of a system and improved system definition, has resulted in more focussed analysis of sub-systems. Pest simulation models commonly simulate the dynamics of single diseases or insects as they are affected by the host and physical environment. Pest or pest-crop models find little application for pest management unless they are used within the context of the socio-economic factors influencing the considered system and are adapted to the application domain. This has been accomplished in several ways. Simplified pest models or simplified decision rules from crop-pest models with economic values assigned to their outputs have been used for managing several pathosystems. Predictive models have been used to define zones of equivalent pest risk to guide extrapolation of pest management technology. from key sites to a broader area, and to deploy host plant resistance. For agricultural development and, more specifically, for an accelerated adoption of the systems approach in pest management, a toolkit may have to be developed to reduce the lag time between generation of global principles and development of site-specific management tactics and strategies.

An Epidemiological Simulation Model with Three Scales of Spatial Hierarchy

ABSTRACT An epidemiological model integrating three organizational scales of host plant populations (e.g., sites, leaves, and plants) is presented. At the lowest (site) scale, the model simulates the dynamics of vacant, latent, infectious, and removed sites. Three types of vacant sites are distinguished, depending on presence of infections at higher scales (leaf or plant). The rate of infection of each type of vacant site is computed according to ratios of autodeposition, allo-leaf-deposition, and allo-plantdeposition. At the leaf and plant scales, the rate of victimization is a function of the rate of infection of vacant sites. Sensitivity analyses showed that deposition patterns (the relative proportions of auto-, allo-leaf-, and allo-plant-depositions) and host structure (leaf size and number of leaves per plant) affected the speed of epidemics at the different scales. Model outputs conformed with results from other approaches in the case of random distribution of the disease. The model hypotheses concerning infection from autodeposited propagules, and their implications for disease epidemics, are discussed. The model can be used to derive relationships between allo-deposition ratios and disease incidences at the three scales. These relationships become simple when disease intensity is low. These relationships may be useful, e.g., to assess the potential efficiency of cultivar mixture to control epidemics. Integration of different organization scales and allo-deposition parameters enables the model to capture important features of epidemics developing in space without using explicitly spatialized variables. Such an approach could be useful to analyze other ecological processes that involve a variety of scales.

Structure and validation of RICEPEST, a production situation-driven, crop growth model simulating rice yield response to multiple pest injuries for tropical Asia

Abstract RICEPEST, a model simulating yield losses due to several rice pests (sheath blight, brown spot, sheath rot, bacterial leaf blight, stem borers, brown plant hopper, defoliating insects, and weeds) under a range of specific production situations of tropical Asia was developed. The model was assessed, using: (1) combined data sets generated by a series of test-experiments conducted in different sites of the Philippines, India, and China; and (2) one additional, independent, validation-experiment where a wide range of production situations and injury profiles were manipulated at a single site. Model evaluation was based on the analysis of two output variables: grain yield and relative yield loss. The paper reports results of qualitative and quantitative methods used to assess RICEPEST. Qualitative evaluation involved visual examination of graphs where deviations (simulated minus observed) are plotted against simulated values, and displaying an area of acceptance. This method showed that, in general, RICEPEST accounted well for the yield reducing effects of rice pests. Two areas for potential improvement of RICEPEST were however, identified: the simulation of damage caused by dead hearts in water-stressed environments, and the simulation of damage caused by weeds. Quantitative evaluations made use of equivalence- and χ 2 -tests. The equivalence tests rejected ( P ≤0.05) the hypothesis of difference between simulated and observed yield and relative yield loss larger than a preset tolerance in both test- and validation-experiments. Conversely, the χ 2 -tests did not reject the hypothesis of difference in categorised simulated and observed yields and relative yield losses ( P ≤0.05) in both test- and validation-experiments. RICEPEST proved to simulate adequately yield losses and can be used as a tool to set research priorities for rice crop protection in tropical Asia.

Spatiotemporal Relationships Between Disease Development and Airborne Inoculum in Unmanaged and Managed Botrytis Leaf Blight Epidemics

Comparatively little quantitative information is available on both the spatial and temporal relationships that develop between airborne inoculum and disease intensity during the course of aerially spread epidemics. Botrytis leaf blight and Botrytis squamosa airborne inoculum were analyzed over space and time during 2 years (2002 and 2004) in a nonprotected experimental field, using a 6 x 8 lattice of quadrats of 10 x 10 m each. A similar experiment was conducted in 2004 and 2006 in a commercial field managed for Botrytis leaf blight using a 5 x 5 lattice of quadrats of 25 x 25 m each. Each quadrat was monitored weekly for lesion density (LD) and aerial conidium concentration (ACC). The adjustment of the Taylors power law showed that heterogeneity in both LD and ACC generally increased with increasing mean. Unmanaged epidemics were characterized in either year, with aggregation indices derived from SADIE (Spatial Analysis by Distance Indices). For LD, the aggregation indices suggested a random pattern of disease early in the season, followed by an aggregated pattern in the second part of the epidemic. The index of aggregation for ACC in 2002 was significantly greater than 1 at only one date, while it was significantly greater than 1 at most sampling dates in 2004. In both years and for both variables, positive trends in partial autocorrelation were observed mainly for a spatial lag of 1. In 2002, the overall pattern of partial autocorrelations over sampling dates was similar for LD and ACC with no significant partial autocorrelation during the first part of the epidemic, followed by a period with significant positive autocorrelation, and again no autocorrelation on the last three sampling dates. In 2004, there was no significant positive autocorrelation for LD at most sampling dates while for ACC, there was a fluctuation between significant and non-significant positive correlation over sampling dates. There was a significant spatial correlation between ACC at given date (t(i)) and LD 1 week later (t(i + 1)) on most sampling dates in both 2002 and 2004 for the unmanaged and managed sites. It was concluded that LD and ACC were not aggregated in the early stage of epidemics, when both disease intensity and airborne conidia concentration were low. This was supported by the analysis of LD and ACC from a commercial field, where managed levels of disease were low, and where no aggregation of both variables was detected. It was further concluded that a reliable monitoring of airborne inoculum for management of Botrytis leaf blight is achievable in managed fields using few spore samplers per field.

Modelling sheath blight epidemics on rice tillers

Abstract The structure of a model for rice sheath blight which integrates processes at the tiller level and their contribution to epidemics is reported. The model considers two processes, primary and secondary infection, leading to disease increase. It incorporates disease aggregation in terms of accessibility of healthy tillers to infection by the pathogen from diseased tillers. Parameters from the model were derived either empirically or by numerical optimisation from published data, and from a field experiment. A separate field experiment was conducted for model evaluation. The model has the potential to adequately account for actual epidemics. Sensitivity analysis showed that the intrinsic rate of secondary infection had a large effect on simulated epidemics, while effect of the intrinsic rate of primary infection was comparatively small. The aggregation parameter had a strong effect on simulated epidemics in their later stages. The model adequately simulated the pattern of actual epidemiological data, except in the later stage of epidemics, when it failed to account for decrease in disease incidence at the tiller level. The model was considered to comply with the requirements of a preliminary simulation model, and approaches to improve its performances are discussed.