Improving the Quantification of Pests, Disease, and Weeds Impact on Crops
Contributed by Serge Savary and Carolyn Mutter
Rice leaf with symptoms of brown spot (India), one of the main global rice diseases. Photo: S. Savary
An international conference on global crop losses recently convened to assess how plant diseases, pests, and weeds negatively affect crop health, crop performances, ecosystems and society. The conference was motivated by the fact that the negative impacts are well recognized, but their quantification is still fragmented or incomplete.
The Global Crop Loss Conference, held in mid-October in Paris, France, was organized by INRA, the French National Agricultural Research Institute, with inputs from 80 colleagues including contributions from INRA, CIRAD (Centre for International Development), MACSUR (Modeling Climate Change with Agriculture for Food Security), AgMIP (Agricultural Model Intercomparison and Improvement Project), and others.
Over the course of three days, 59 experts from 14 countries shared knowledge and recent findings through keynote presentations and workgroup sessions on key issues related to the characterization of pest, disease, and weed influence in agricultural system models, including impacts associated with changing climate.
“The convening of a strong team of experts allows us to understand and better approach complex problems of crop yield loss,” said Ken Boote (University of Florida, AgMIP Crop Modeling Co-Lead). “For example, we may need to predict characteristic pest effects on crops via dynamic pest models that couple to crop models rather than input scouted pest damage data into crop models.”
Together, the conference participants agreed that
|•||Crop loss assessment and modelling addresses legitimate and important issues|
|•||There are methods to quantify crop losses, especially yield losses.|
|•||Process-based modelling is a valid and valuable approach to yield loss analysis. |
|•||Crop loss information is part of the global data revolution. |
|•||Looking forward, we need to be mapping global pest and disease yield losses and crop loss data ontologies. |
“I believe this conference has significantly increased our shared understanding of current knowledge and forward looking priorities in the characterization of pest, disease, and weed systems as well as the data that is needed to simulate the impacts in models”, said Serge Savary, (INRA, AgMIP PeDiMIP Co-Lead and Conference Chair).
Keynote presentations and workgroup findings can be found online at the INRA website: http://www.smach.inra.fr/en/All-the-news/crop-losses-conference-en.
Bulleted points are elaborated in the Global Crop Loss Conference Executive Summary that follows.
Global Crop Loss Conference Executive Summary
Paris France, October 16-18, 2017
Crop loss assessment and modelling addresses legitimate and important issues
|•||Plant diseases and pests affect global crop production in many different ways, including a reduction of crop yield, but also a reduction in the shelf-life, organoleptic, or appearance of products, as well as impacts on the nutritional value of food as a result of toxin accumulation.|
|•||The importance of crop losses needs to be related to the variable contexts (economic, social, environmental) of agriculture, where the private and the public sectors have different goals.|
|•||There are several facets to crop losses caused by crop diseases, pests, and weeds |
|•||Aside from the quantitative, direct, and primary losses – i.e., yield losses, it is necessary to consider the massive losses which are: (1) qualitative, direct, and primary, such as mycotoxins accumulation, and (2) quantitative, direct, and secondary, such as the weakening of perennial crops exposed to plant disease epidemics.|
|•||Different types of crop losses must therefore be distinguished. The FAO has proposed a double classification: first, direct and indirect, and second, primary and secondary. Losses may be a direct result of the activity of pests and diseases, or be indirect via multiple impacts on the economic make-up of agriculture, or on the production system. Losses may be primary – occurring in a given growing season, or secondary – occurring over several successive seasons. |
|•||Further, several attributes of global crop losses to diseases, pests, and weeds need consideration: (1) their spatial and temporal variation (i.e., chronic vs. acute crop losses); (2) the level of potential losses in absence of control; and (3) the availability and efficiency of management tools. |
|•||There is overall consensus to consider that the increasing emergence of plant diseases is not associated with the appearance of “new” pathogens, but to the recently amplified pathogen dissemination resulting mainly from trade and human transport. Accelerated evolution of plant pathogens is also associated with selection pressures generated by specific resistance genes. Experimental work enables understanding some of the processes involved in the evolution of plant health under climate change. |
There are methods to quantify crop losses, especially yield losses
|•||Quantitative estimates of crop yield loss from pests and diseases are commonly considered to be highly uncertain or imprecise, as a result of strong reliance to expert assessments or of observations which do not make use of standardized and uniform protocols.|
|•||Yet, International standards and procedures exist to: (1) conduct field experiments that are specifically designed to quantify crop losses; (2) quantify disease and pest injuries; and (3) measure crop losses.|
|•||In practice, the quantification of yield losses involves four necessary elements: (1) a quantification of injury(ies) caused by diseases, pests, and/or weeds; (2) a quantification of the attainable yield (i.e., the yield level achieved in absence of injuries); (3) a damage function translating injury into yield loss; (4) the quantification of actual (harvested) yields.|
|•||Yield loss data standards – “gold” and “silver” standards – may therefore be defined. The gold and silver standards would include: (1) the levels of disease or pest injuries; (2) measurements of crop growth; (3) successive development stages (phenology) of the crop; (4) crop yield. The gold standard would additionally include: (5) information on crop history; (6) details of crop management; (7) geographical location; (8) weather variables.|
Process-based modelling is a valid and valuable approach to yield loss analysis
|•||A major advantage of (process-based) modelling over empirical methods lies in the ability of such models to simulate yield losses under new, different, and therefore future conditions. Yield loss modelling may thus become a key instrument for policy-development and strategic research.|
|•||A number of mechanistic simulation models have been developed for several of the most important world crops. These models can successfully be linked with disease or pest models to simulate yield variation and yield losses. Implementation of these models for large-scale assessment of yield losses require (1) the development of (generic) models accounting for the most frequent diseases and pests, and (2) baseline global data on crop health.|
|•||Main challenges in modelling yield losses through the dynamic interaction between crop growth and development, on the one hand, and pest and disease dynamics, on the other hand, include: (1) the number of (pest- or pathogen-) specific processes considered, possibly with time-steps smaller than 1 day; (2) the need to consider elements of the microclimate; (3) the inclusion of several diseases and/or pests on the same crop; (4) the lack of standardized data for disease and pest injuries; and (5) the lack of quantitative and qualitative information on production situations, including crop management and cropping system.|
Crop loss information is part of the global data revolution
|•||The data revolution heralded by the United Nation has led to rapid improvements in the availability and quality of data related to agriculture and in turn crop health, but gaps still remain.|
|•||Technological advances mean that we can collect, process and distribute more data on the state of the biosphere and of societies than ever before. |
|•||Growth in available data related to agriculture is related to the increasing number of international collaboration networks, such as GODAN (Global Open Data for Agriculture & Nutrition), the CGIAR Platform for Big Data in Agriculture, the Global Yield Gap and Water Productivity Atlas (GYGA), the Agricultural Model Intercomparison and Improvement Project (AgMIP), and many others, some which make use of Creative Commons licences to make many of their results globally accessible.|
|•||Some journals and data repositories have started to publish and recognise datasets in a similar way to peer reviewed articles. This echoes new policies among the donor community.|
Looking forward: mapping global pest and disease yield losses and crop loss data ontologies
A farmers field in Côte d’Ivoire Photo: S. Savary
|•||Global food production will have to increase substantially to meet increased demands due to population growth and changing diets. Knowing where and how much crop yields can still increase on existing land through a process of so-called sustainable intensification is therefore a relevant question.|
|•||The Global Yield Gap Atlas initiated in 2012 aims to map yield gaps of key food crops in all food producing countries of the world. Addition of quantitative information on yield reduction due to weeds, pests and diseases would largely complete the list of biophysical factors that explain yield gaps.|
|•||Based on their importance towards global food security, targets for crop loss assessment can be identified and proposed for wheat, rice, maize, potato, and soybean.|
|•||Generic approaches are for instance available to model yield losses in wheat and rice. These models are congruent and compatible with the on-going (crop) modelling efforts engaged in the international networks AgMiP and MacSur, as well as with the Global Yield Gap Atlas initiative.|
|•||A necessary step towards advancing the analysis, understanding, prediction and management of disease and pest losses is the development of a data ontology and of generic data structures where diseases and pests are properly addressed.|