Intensifying Maize Production Under Climate Change Scenarios in Central West Burkina Faso

  • Omonlola Nadine WorouEmail author
  • Jérôme Ebagnerin Tondoh
  • Josias Sanou
  • Thomas Gaiser
  • Pinghouinde Michel Nikiema
  • Jules Bayala
  • Paulin Bazié
  • Catherine Ky-Dembele
  • Antoine Kalinganiré
Reference work entry


Combination of poor soil fertility and climate change and variability is the biggest obstacle to agricultural productivity in Sub-Saharan Africa. While each of these factors requires different promising adaptive and climate-resilient options, it is important to be able to disaggregate their effects. This can be accomplished with ordinary agronomic trials for soil fertility and climate year-to-year variability, but not for long-term climate change effects. In turn, by using climate historical records and scenario outputs from climate models to run dynamic models for crop growth and yield, it is possible to test the performance of crop management options in the past but also anticipate their performance under future climate change or variability. Nowadays, the overwhelming importance given to the use of crop models is motivated by the need of predicting crop production under future climate change, and outputs from running crop models may serve for devising climate risk adaptation strategies. In this study we predicted yield of one maize variety named Massongo for the time periods 1980–2010 (historical) and 2021–2050 (2030s, near future) across agronomic practices including the fertilizer input rates recommended by the national extension services (28 kg N, 20 kg P, and 13 kg K ha−1). The performance of the crop model DSSAT 4.6 for maize was first evaluated using on-farm experimental data that encompassed two seasons in the Sudano-Sahelian zone in six contrasting sites of Central West Burkina Faso. The efficiency of the crop model was evidenced by reliable simulations of total aboveground biomass and yields after calibration and validation. The root-mean-square error (RMSE) of the entire dataset for grain yield was 643 kg ha−1 and 2010 kg ha−1 for total aboveground biomass. Three regional climate change projections for Central West Burkina Faso indicate a decrease in rainfall during the growing period of maize. All the three scenarios project that the decrease in rainfall is to the tune of 3–9% in the 2030s under RCP4.5 in contrast to climate scenarios produced by the regional climate model GCM ICHEC-EC-Earth which predicted an increase of rainfall of 25% under RCP8.5. Simulations using the CERES-DSSAT model reveal that maize yields without fertilizer show the same trend as with fertilizer in response to climate change projections across RCPs. Under RCP4.5 with output from the climate model ICHEC-EC-Earth, yield can slightly increase compared to the historical baseline on average by less than 5%. In contrast, under RCP8.5, yield is increased by 13–22% with the two other climate models in fertilized and non-fertilized plots, respectively. Nevertheless, the average maize yield will stay below 2000 kg ha−1 under non-fertilized plots in RCP4.5 and with recommended mineral fertilizer rates regardless of the RCP scenarios produced by ICHEC-EC-Earth. Giving the fact that soil fertility improvement alone cannot compensate for the adverse impact of future climate on agricultural production particularly in case of high rainfall predicted by ICHEC-EC-Earth, it is recommended to combine various agricultural techniques and practices to improve uptake of nitrogen and to reduce nitrogen leaching such as the splitting of fertilizer applications, low-release nitrogen fertilizers, agroforestry, and any other soil and water conservation practices.


DSSAT 4.6 Climate change projection Site-specific fertilization Validation 



This study relies partly on the outputs of the Work Package 1.2 (agroforestry and farm interventions) of the BIODEV Project (2012–2016) funded by the Finnish Government whose significant contribution to combat climate change in West Africa is greatly appreciated.

The assistance provided by the Swedish Meteorological and Hydrological Institute (SMHI) in providing and processing downscaled, bias-corrected regional climate information for climate impact studies is gratefully acknowledged.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Omonlola Nadine Worou
    • 1
    Email author
  • Jérôme Ebagnerin Tondoh
    • 2
  • Josias Sanou
    • 3
  • Thomas Gaiser
    • 4
  • Pinghouinde Michel Nikiema
    • 5
  • Jules Bayala
    • 6
  • Paulin Bazié
    • 3
  • Catherine Ky-Dembele
    • 6
  • Antoine Kalinganiré
    • 6
  1. 1.The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)BamakoMali
  2. 2.WASCAL and UFR des Sciences de la Nature, Nandjui Abrogoua UniversityAbidjanCôte d’Ivoire
  3. 3.Institut de l’Environnement et de Recherche Agricole (INERA)OuagadougouBurkina Faso
  4. 4.Institute of Crop Science and Resource ConservationUniversity of BonnBonnGermany
  5. 5.Agence Nationale de la Météorologie du Burkina FasoOuagadougouBurkina Faso
  6. 6.World Agroforestry Centre, West and Central Africa Regional Office – Sahel NodeBamakoMali

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