Conservation Agriculture: strengthening crop production in marginal areas

Conservation Agriculture has the potential to enhance soil fertility and reduce erosion across 260,000 hectares (ha) of fragile and degraded cereal land in Tunisia, according to a joint study by the country’s National Institute of Agronomic Research (INRAT) and ICARDA.

A Zero Tillage seeder on fragile and degraded cereal land in North East Tunisia (Photo credit: INGC Tunisia).

A Zero Tillage seeder on fragile and degraded cereal land in North East Tunisia (Photo credit: INGC Tunisia).

Conservation Agriculture (CA) – the practice of not plowing and leaving crop residue in fields for enhanced soil fertility and moisture conservation – was first introduced to Tunisia in 1999, where it was pilot-tested on 11 farms in the country’s North-East. While the area cultivated under CA has since grown, the practice is still applied on only 12,000 ha of agricultural land – an area distributed among 200 farmers and operated by some 102 seeders.

Structural Approaches and Technology Adoption: A new paper in Global Food Security

By Shahnila Islam

Trends in population and income growth along with climate change pose significant risks to achieving sustainable food security. As challenges to the agricultural sector grow, we need improved tools to understand the risks, and to evaluate alternative solutions to mitigate some of these risks. In order for these tools to be useful, they need to capture the reality of a sector where farmers react to both biophysical changes in crop productivity, as well as to the economic impacts from the market they face.

In a new paper titled “Structural Approaches to Modeling the Impact of Climate Change and Adaptation Technologies and Crop Yields and Food Security”, Islam et al. (2016) look at the “structural approach” (Figure 1). This is a modeling method that combines both biophysical and economic modeling in order to answer some of the questions related to how climate change may affect agricultural production and what role improved crop varieties may have to reduce some of the negative impacts. The authors found that adoption of drought and heat tolerant maize, wheat, potatoes, sorghum, and groundnut in select countries have the potential to reduce the negative impacts from climate change, even though the biophysical yield gains are dampened through market responses.

Figure 1: Primary components of the structural approach used in research on climate impacts in agriculture and food systems.

Figure 1: Primary components of the structural approach used in research on climate impacts in agriculture and food systems.

The work in this in paper was possible due to our long-term collaboration with partner centers across the CGIAR and the collaboration of a multi-disciplinary team through the Global Futures & Strategic Foresight (GFSF) program. The methodology enabled us to combine information from crops trials to parameterize alternative drought and heat tolerant technologies into crop models (in this case the DSSAT crop model). The link between crop models and the IMPACT economic model (Robinson et al., 2015) allowed us to take into consideration market effects, and therefore obtain a better estimate of production following adoption of these improved varieties, as well as estimate effects on world prices and trade.

Global Futures and Strategic Foresight (GFSF) is a CGIAR initiative led by IFPRI and funded by the Bill and Melinda Gates Foundation, the CGIAR Research Program on Policies, Institutions and Markets (PIM), and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).


Islam, S., Cenacchi, N., Sulser, T.B., Gbegbelegbe, S., Hareau, G., Kleinwechter, U., Mason-D'Croz, D., Nedumaran, S., Robertson, R., Robinson, S. and Wiebe, K., 2016. Structural approaches to modeling the impact of climate change and adaptation technologies on crop yields and food security. Global Food Security, 10, pp.63-70.

Robinson, S., Mason-D’Croz, D., Islam, S., Cenacchi, N., Creamer, B., Gueneau, A., Hareau, G., Kleinwechter, U., Mottaleb, K., Nedumaran, S., Robertson, R., Rosegrant, M.W., Gbegbelegbe, S., Sulser, T.B., and Wiebe, K., 2015b. New Crop Varieties and Climate Change Adaptation: Ex-Ante Analysis of Promising and Alternative Technologies. IFPRI Discussion Paper Series, Washington, DC: International Food Policy Research Institute (IFPRI).


Building capacity and a forum for collaboration

by Kindie Tesfaye (CIMMYT), Evgeniya Anisimova (PIM)

group-work-300x169As part of its work under PIM (Flagship 1) and the Global Futures & Strategic Foresight (GFSF) project, the International Maize and Wheat Improvement Center (CIMMYT) organized a five-day training workshop titled “Crop and Bioeconomic Modeling under Uncertain Climate”. The training took place on 7-11 December 2015 in Addis Ababa, Ethiopia.

The workshop brought together representatives of Association for Strengthening Agricultural Research in Eastern and Central Africa (ASARECA) and West and Central Africa Council for Agricultural Research and Development (CORAF), as well as researchers from agricultural research institutes and universities from Ethiopia, Kenya, Uganda, Niger, Senegal, Nigeria, and Democratic Republic of the Congo.

Participants were trained to apply crop and bioeconomic models allowing to estimate biophysical and economic impacts of climate variability and change and to assess different adaptation options. The tools they worked with included the Decision Support System for Agrotechnology Transfer (DSSAT), the Agricultural Production Systems Simulator (APSIM), and Gtree (GAMS). The training involved plenary discussions, group work, and individual hands-on exercises.

This workshop was a follow up of a similar training conducted in November 2014 in Addis Ababa. A third training is planned for 2016. The series is designed to contribute to building of a core regional group of researchers who appreciate and use crop and bioeconomic models in addressing the impacts of climate change in Africa, and to create a forum for experience sharing and collaboration.

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