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

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

This review considers the cascade of events that link injuries caused by plant pathogens on crop stands to possible (quantitative and qualitative) crop losses (damage), and to the resulting economic losses. To date, much research has focused on injury control to prevent this cascade of events from occurring. However, this cascade involves a complex succession of components and processes whereby knowledge on crop loss generates entry points for management. Proposed here is a framework linking different types of knowledge on crop loss to a range of decision categories, from tactical to strategic short- or long-term. Important advances in this field are now under way, including a probabilistic treatment of the injury-damage relationship, or analyses of the sources of uncertainty attached to some components of the decision process. Management of injury profiles, rather than individual injuries, and shifts in dimensionality of crop losses are anticipated to contribute to the design of sustainable agricultural systems, and address global issues concerning food security and food safety.

Quantification and modeling of crop losses: a review of purposes.

A series of experiments was conducted where a range of injuries due to rice pests (pathogens, insects, and weeds) was manipulated simultaneously with a range of production factors (fertilizer input, water supply, crop establishment method, variety) in different seasons and years. These factors were chosen to represent lowland rice production situations characterized in surveys conducted in tropical Asia and their corresponding range of attainable yield. Experiments complemented one another in exploring the response surface of rice yields to yield-limiting and yield-reducing factors. The resulting experimental data base consisted of 445 individual plots and involved 11 manipulated injuries in a range of attainable yields of 2 to 11 t ha-1. A first, nonparametric, multivariate analysis led to a hierarchy of potential injuries, from marginally (e.g., bacterial leaf blight) to extremely harmful (e.g., rice tungro disease). A second, parametric, multivariate approach resulted in a multiple regression ...

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Rice Pest Constraints in Tropical Asia: Quantification of Yield Losses Due to Rice Pests in a Range of Production Situations

Rice pest constraints in tropical Asia: quantification of yield losses due to rice pests in a range of production situations

Modelling the impacts of pests and diseases on agricultural systems

https://horizon.documentation.ird.fr/exl-doc/pleins_textes/pleins_textes_6/b_fdi_33-34/36521.pdf

Analysis of crop loss in the multiple pathosystem groundnut-rust-late leaf spot. II. Study of the interactions between diseases and crop intensification in factorial experiments

J.C. Zadoks

Papers

Analysis of crop loss in the multiple pathosystem groundnut-rust-late leaf spot. II. Study of the interactions between diseases and crop intensification in factorial experiments