CIMMYT gathers partners to discuss biotic stress and crop model integration

MLN CIMMYT

Maize crop infected with the maize lethal necrosis disease in Kenya. Photo: Florence Sipalla/CIMMYT, Flickr

When crops are damaged by other living organisms such as bacteria, viruses, fungi, insects and other pests, weeds or even cultivated plants competing for space and nutrients, we talk of the biotic stress. Biotic stresses are a major constraint to agricultural productivity in low and middle income countries. They affect poor producers and consumers the most and undermine food security in general.

Examples of some biggest current concerns related to biotic stress are the wheat diseases fusarium head blight (FHB), wheat blast (caused by fungi), and the maize lethal necrosis (MLN) caused by viruses (also read here). 

Scientists in the International Maize and Wheat Improvement Center (CIMMYT) know all about biotic stresses to crops. They also know that combatting these stresses is a task beyond the scope of any one organization or discipline. This was evident during the workshop in Addis Ababa, Ethiopia, on June 20-22 that brought together breeders, physiologists, entomologists, pathologists, modelers, and socio-economists from CIMMYT and partner organizations including Auburn University, University of Passo Fundo, and the International Food Policy Research Institute (IFPRI). The workshop titled "How can we take biotic stress into consideration with crop growth modeling in maize and wheat?" was organized by CIMMYT as part of the Global Futures & Strategic Foresight (GFSF) project, a CGIAR initiative led by IFPRI under the CGIAR Research Program on Policies, Institutions, and Markets (PIM).

Crop growth (or simulation) models are computer programs processing data on weather, soil, and crop management to predict crop yield, maturity date, efficiency of fertilizers and other elements of crop production. Accuracy of the predictions is based on the existing knowledge of the physics, physiology and ecology of crop responses to the environment[1]. So, the more we know about this responsiveness to the environment, including biotic stress, the more accurate these predictions can be. Existing crop growth models do not adequately simulate biotic stress to calculate possible yield reduction. Colleagues who came to Addis Ababa were eager to expand this knowledge and increase the accuracy of the predictions through integrating biotic stress and crop models.

Biotic stress workshop at CIMMYT

Workshop participants. Photo: CIMMYT

Dr. Gideon Kruseman, an ex-ante and foresight specialist at CIMMYT, and Dr. Bekele Abeyo, a wheat breeder and CIMMYT’s Ethiopia country representative, opened the workshop by reviewing the use of crop models in maize and wheat production systems. Dr. Kruseman explained the importance of integrating the models for biotic stress with crop models for a holistic assessment of the potential impact of new technologies in several environments. Dr. Abeyo emphasized the need for the partners to work together across disciplines.

Workshop discussions were dedicated, among other topics, to CIMMYT’s experiences in applications of crop models (for example, see: Chung et al., 2014; Gbegbelegbe, Chung, Shiferaw, Msangi, & Tesfay, 2014; Tesfaye et al., 2015, 2016), opportunities and challenges of incorporating biotic stress directly into crop growth models, linking crop growth models with biotic stress models through soft coupling[2], phenotyping for biotic stresses[3], and the probabilistic approaches to linking biotic stress into crop growth models. Apart from that, colleagues focused on the scale of biotic stress as a challenge, data gaps, and future action points, emphasizing the importance of collaboration with other initiatives such as AgMIP.

Biotic stress workshop at CIMMYT chart

Example of linkages among biophysical and economic models

As a way forward, participants agreed that soft coupling biotic stress models with crop models is a feasible approach in the short- and mid-term perspective whereas full integration can remain a long-term strategy. The soft coupling efforts presented by colleagues from Auburn University, USA, and University of Passo Fundo, Brazil, should serve as a springboard to link the major maize and wheat biotic stresses with current crop models such as those comprised in the Decision Support System for Agrotechnology Transfer (DSSAT). Moreover, an approach that considers probability of disease incidence, probability of disease severity, and probability of damage can also offer scope for linking crop growth models and biotic stress either separately or in combination with soft coupled models. The probabilistic approach can be especially useful when linking crop growth models with economic models, for example, to see how the chance of a disease outbreak shapes the choices made by farmers.

As a result of the workshop, partners agreed to start a small pilot project on integrating biotic stress with crop models to prove of concept. Concept notes shall be submitted to the competitive grant by the CGIAR Research Program on Maize. At the next stage partners shall come together to develop a bigger project and approach donors.

“I really enjoyed the workshop because it brought together a very diverse range of scientists that I would never normally get to interact with. Modeling abiotic stresses allowed us to quantify the potential impacts of improved varieties at the regional and national level. I’m excited to be able to do this for biotic stresses”. -- Jill Cairns Maize physiologist at CIMMYT

[1] What Are Crop Simulation Models? United States Department of Agriculture. Agricultural Research Service. http://www.ars.usda.gov/main/docs.htm?docid=2890 accessed on 7/27/16

[2] Soft coupling refers to linking two separate models through an interface that allows information to be exchanged amongst them.

[3] Phenotyping for biotic stress refers to trials conducted specifically to obtain information on how varieties react to pests and diseases, by subjecting the trials to substantial levels of the specified stressors.

References

Chung, U., Gbegbelegbe, S., Shiferaw, B., Robertson, R., Yun, J. I., Tesfaye, K., … Sonder, K. (2014). Modeling the effect of a heat wave on maize production in the USA and its implications on food security in the developing world. Weather and Climate Extremes, 5-6, 67–77.

Gbegbelegbe, S., Chung, U., Shiferaw, B., Msangi, S., & Tesfay, K. (2014). Quantifying theimpactofweatherextremesonglobalfoodsecurity: A spatialbio-economic approach. WeatherandClimateExtremes, 4, 97–108. Retrieved from http://crossmark.crossref.org/dialog/?doi=10.1016/j.wace.2014.05.005&domain=pdf

Tesfaye, K., Gbegbelegbe, S., Cairns, J. E., Shiferaw, B., Prasanna, B. M., Sonder, K., … Robertson, R. (2015). Maize systems under climate change in sub-Saharan Africa. International Journal of Climate Change Strategies and Management, 7(3), 247 – 271. http://doi.org/10.1108/IJCCSM-01-2014-0005

Tesfaye, K., Kai Sonder, Jill Cairns, Cosmos Magorokosho, Amsal Tarekegne, Girma T. Kassie, Fite Getaneh, Tahirou Abdoulaye, Tsedeke Abate, and Olaf Erenstein (2016). Targeting Drought-Tolerant Maize Varieties in Southern Africa: A Geospatial Crop Modeling Approach Using Big Data. International Food and Agribusiness Management Review, 9 (A): 75-92.

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