Editorial Type:
Article
Article Type:
Research Article

A Crop Model and Fuzzy Rule Based Approach for Optimizing Maize Planting Dates in Burkina Faso, West Africa

Moussa Waongo Institute of Geography, University of Augsburg, Augsburg, and Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

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Patrick Laux Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

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Seydou B. Traoré AGRHYMET Regional Centre, Niamey, Niger

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Moussa Sanon Institute de l’Environnement et de Recherches Agricoles, Ouagadougou, Burkina Faso

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Harald Kunstmann Institute of Geography, University of Augsburg, Augsburg, and Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany

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Print Publication:
01 Mar 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0116.1
Page(s):
598–613
Restricted access

Abstract

In sub-Saharan Africa, with its high rainfall variability and limited irrigation options, the crop planting date is a crucial tactical decision for farmers and therefore a major concern in agricultural decision making. To support decision making in rainfed agriculture, a new approach has been developed to optimize crop planting date. The General Large-Area Model for Annual Crops (GLAM) has been used for the first time to simulate maize yields in West Africa. It is used in combination with fuzzy logic rules to give more flexibility in crop planting date computation when compared with binary logic methods. A genetic algorithm is applied to calibrate the crop model and to optimize the planting dates at the end. The process for optimizing planting dates results in an ensemble of optimized planting rules. This principle of ensemble members leads to a time window of optimized planting dates for a single year and thereby potentially increases the willingness of farmers to adopt this approach. The optimized planting date (OPD) approach is compared with two well-established methods in sub-Saharan Africa. The results suggest earlier planting dates across Burkina Faso, ranging from 10 to 20 days for the northern and central part and less than 10 days for the southern part. With respect to the potential yields, the OPD approach indicates that an average increase in maize potential yield of around 20% could be obtained in water-limited regions in Burkina Faso. The implementation of the presented approach in agricultural decision support is expected to have the potential to improve agricultural risk management in these regions dominated by rainfed agriculture and characterized by high rainfall variability.

Denotes Open Access content.

Corresponding author address: Moussa Waongo, Institute of Geography, University of Augsburg, Universitätsstraße 10, Augsburg 86159, Germany. E-mail: moussa.waongo@imk.fzk.de

Abstract

In sub-Saharan Africa, with its high rainfall variability and limited irrigation options, the crop planting date is a crucial tactical decision for farmers and therefore a major concern in agricultural decision making. To support decision making in rainfed agriculture, a new approach has been developed to optimize crop planting date. The General Large-Area Model for Annual Crops (GLAM) has been used for the first time to simulate maize yields in West Africa. It is used in combination with fuzzy logic rules to give more flexibility in crop planting date computation when compared with binary logic methods. A genetic algorithm is applied to calibrate the crop model and to optimize the planting dates at the end. The process for optimizing planting dates results in an ensemble of optimized planting rules. This principle of ensemble members leads to a time window of optimized planting dates for a single year and thereby potentially increases the willingness of farmers to adopt this approach. The optimized planting date (OPD) approach is compared with two well-established methods in sub-Saharan Africa. The results suggest earlier planting dates across Burkina Faso, ranging from 10 to 20 days for the northern and central part and less than 10 days for the southern part. With respect to the potential yields, the OPD approach indicates that an average increase in maize potential yield of around 20% could be obtained in water-limited regions in Burkina Faso. The implementation of the presented approach in agricultural decision support is expected to have the potential to improve agricultural risk management in these regions dominated by rainfed agriculture and characterized by high rainfall variability.

Denotes Open Access content.

Corresponding author address: Moussa Waongo, Institute of Geography, University of Augsburg, Universitätsstraße 10, Augsburg 86159, Germany. E-mail: moussa.waongo@imk.fzk.de
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Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Ati, O. F., C. J. Stigter, and E. O. Oladipo, 2002: A comparison of methods to determine the onset of the growing season in northern Nigeria. Int. J. Climatol., 22, 731–742.

    • Search Google Scholar
    • Export Citation

  • Badini, O., O. S. Claudio, and H. F. Eldon, 1987: Application of crop simulation modeling and GIS to agroclimatic assessment in Burkina Faso. Agric. Ecosyst. Environ., 64, 233–244.

    • Search Google Scholar
    • Export Citation

  • Belohlavek, R., and G. J. Klir, 2011: Concepts and Fuzzy Logic. The MIT Press, 287 pp.

  • Biazin, B., G. Sterk, M. Temesgen, A. Abdulkedir, and L. Stroosnijder, 2012: Rainwater harvesting and management in rainfed agricultural systems in sub-Saharan Africa—A review. Phys. Chem. Earth, 47–48, 139–151.

    • Search Google Scholar
    • Export Citation

  • Birch, C. J., 1996: Testing the performance of two maize simulation models with a range of cultivars of maize (Zea mays) in diverse environments. Environ. Softw., 11, 91–98.

    • Search Google Scholar
    • Export Citation

  • Birch, C. J., G. L. Hammer, and K. G. Rickert, 1998: Temperature and photoperiod sensitivity of development in five cultivars of maize (Zea mays L.) from emergence to tassel initiation. Field Crops Res., 55, 93–107.

    • Search Google Scholar
    • Export Citation

  • Brock, J., and J. E. Brink, 1996: Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso. Agric. For. Meteor., 83, 95–112.

    • Search Google Scholar
    • Export Citation

  • Carberry, P. S., 1991: Test of leaf area development in CERES-Maize—A correction. Field Crops Res., 27, 159–167.

  • Carberry, P. S., R. C. Muchow, and R. L. McCown, 1989: Testing the CERES-Maize simulation model in a semi-arid tropical environment. Field Crops Res., 20, 297–315.

    • Search Google Scholar
    • Export Citation

  • Carroll, D. L., 1996a: Chemical laser modeling with genetic algorithms. AIAA J., 34, 338–346.

  • Carroll, D. L., 1996b: Genetic algorithms and optimizing chemical oxygen–iodine lasers. Developments in Theoretical and Applied Mechanics: Proceedings of Sectam XVIII—A Collection of Technical Papers (Developments in Theoretical & Applied Mechanics), H. B. Wilson, Ed., University of Alabama, 411–424.

  • Challinor, A. J., T. R. Wheeler, J. M. Slingo, P. Q. Craufurd, and D. I. F. Grimes, 2004: Design and optimisation of a large-area process-based model for annual crops. Agric. For. Meteor., 124, 99–120.

    • Search Google Scholar
    • Export Citation

  • Challinor, A. J., T. R. Wheeler, J. M. Slingo, P. Q. Craufurd, and D. I. F. Grimes, 2005: Simulation of crop yields using the ERA-40: Limits to skill and nonstationarity in weather–yield relationships. J. Appl. Meteor., 44, 516–531.

    • Search Google Scholar
    • Export Citation

  • Challinor, A. J., T. R. Wheeler, C. Garforth, P. Q. Craufurd, and A. Kassam, 2007: Assessing the vulnerability of food crop systems in Africa to climate change. Climatic Change, 83, 381–399.

    • Search Google Scholar
    • Export Citation

  • Chamberlin, P., and M. Diop, 2003: Application of daily rainfall principal component analysis to the assessment of the rainy season characteristics in Senegal. Climate Res., 23, 159–169.

    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597.

    • Search Google Scholar
    • Export Citation

  • Diallo, M. A., 2001: Statistical analyse of agroclimatic parameters based on regional meteorological data based. Centre Régional AGRHYMET Tech. Rep., Niamey, Niger, 88 pp.

  • Dodd, D. E. S., and I. T. Jolliffe, 2001: Early detection of the start of the wet season in semiarid tropical climates of western Africa. Int. J. Climatol., 21, 1251–1262.

    • Search Google Scholar
    • Export Citation

  • FAO, 1991: Digitized Soil Map of the World. World Soil Resources Tech. Rep. 67, FAO, Rome, Italy, CD-ROM.

  • Folberth, C., T. Gaiser, K. C. Abbaspour, R. Schulin, and H. Yang, 2012: Regionalization of a large-scale crop growth model for sub-Saharan Africa: Model setup, evaluation, and estimation of maize yields. Agric. Ecosyst. Environ., 151, 21–33.

    • Search Google Scholar
    • Export Citation

  • Holland, J. H., 1975: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press, 183 pp.

  • Hoogenboom, G., 2000: Contribution of agrometeorology to the simulation of crop production and its application. Agric. For. Meteor., 103, 137–157.

    • Search Google Scholar
    • Export Citation

  • Janicot, S., A. Harzallah, B. Fontaine, and V. Moron, 1998: West African monsoon dynamics and eastern equatorial Atlantic and Pacific SST anomalies (1970–88). J. Climate, 11, 1874–1882.

    • Search Google Scholar
    • Export Citation

  • Janin, S., 2010: Burkina Faso: Pays des Hommes Intègres. Olizane, 319 pp.

  • Kaboré, P. D., and C. Reij, 2004: The emergence and spreading of an improved traditional soil and water conservation practice in Burkina Faso. Environment and Production Technology Division Discussion Paper 114, IFPRI, Washington DC, 28 pp.

  • Kniveton, D. R., R. Layberry, C. J. R. Williams, and M. Peck, 2009: Trends in start of the wet season over Africa. Int. J. Climatol., 29, 1216–1225.

    • Search Google Scholar
    • Export Citation

  • Laux, P., H. Kunstmann, and A. Bárdossy, 2008: Predicting the regional onset of the rainy season in West Africa. Int. J. Climatol., 28, 329–342.

    • Search Google Scholar
    • Export Citation

  • Laux, P., M. T. G. Jäckel, and H. Kunstmann, 2010: Impact of climate change on agricultural productivity under rainfed conditions in Cameroon—A method to improve attainable crop yields by planting date adaptations. Agric. For. Meteor., 150, 1258–1271.

    • Search Google Scholar
    • Export Citation

  • Maddonni, G. A., and M. E. Otegui, 1996: Leaf area, light interception, and crop development in maize. Field Crops Res., 48, 81–87.

  • Moen, T. N., K. M. Kaiser, and S. J. Riha, 1994: Regional yield estimation using a crop simulation model: Concepts, methods and validation. Agric. Syst., 46, 79–92.

    • Search Google Scholar
    • Export Citation

  • Muchow, R. C., and P. S. Carberry, 1989: Environmental control of phenology and leaf growth in a tropically-adapted maize. Field Crops Res., 20, 221–236.

    • Search Google Scholar
    • Export Citation

  • Rasse, D. P., J. T. Ritchie, W. W. Wilhelm, J. Wei, and E. C. Martin, 2000: Simulating inbred-maize yields with CERES-IM. Agron. J., 92, 672–678.

    • Search Google Scholar
    • Export Citation

  • Ritchie, J. T., A. Gerakis, and A. A. Suleiman, 1999: Simple model to estimate field-measured soil water limits. Trans. ASAE, 42, 1609–1614.

    • Search Google Scholar
    • Export Citation

  • Robert, M. P., and R. C. Bruce, 1998: Agricultural System Modeling and Simulation. Marcel-Decker, 693 pp.

  • Rockstrom, J., J. Barron, and P. Fox, 2002: Rainwater management for increased productivity among smallholder farmers in drought prone environments. Phys. Chem. Earth, 27, 949–959.

    • Search Google Scholar
    • Export Citation

  • Roudier, P., B. Sultan, P. Quirion, and A. Berg, 2011: The impact of future climate change on West African agriculture: What does the recent literature say? Global Environ. Change, 21, 1073–1083.

    • Search Google Scholar
    • Export Citation

  • Sanon, M., and Y. Dembélé, 2001: Etude de quatre niveaux dirrigation de complément et du pluvial strict sur le maïs et le cotonnier dans la plaine du sourou. INERA Tech. Rep. 25, Ouagadougou, Burkina Faso, 30–45.

  • Sanon, M., Y. Dembélé, and L. Somé, 2002: Evapotranspiration du blé, de l’oignon, du maïs et du cotonnier au nord-ouest du Burkina Faso. Proc. Fifth Conf. Inter-Régionale sur l’Environnement, Ouagadougou, Burkina Faso, EIER-ETSHER, 38–48.

  • Sivakumar, M. V. K., 1988: Predicting rainy season potential from the onset of rains in southern Sahelian and Sudanian climate zones of West Africa. Agric. For. Meteor., 42, 295–305.

    • Search Google Scholar
    • Export Citation

  • Sivakumar, M. V. K., 1990: Exploiting rainy season potential from the onset of rains in the Sahelian zone of West Africa. Agric. For. Meteor., 51, 321–332.

    • Search Google Scholar
    • Export Citation

  • Sivakumar, M. V. K., and F. Gnoumou, 1987: Exploiting rainy season potential from the onset of rains in the Sahelian zone of West Africa. ICRISAT Research Bull. 23, Niamey, Niger, 192 pp.

  • Sivanandam, S. N., and S. N. Deepa, 2008: Introduction to Genetic Algorithms. Springer, 442 pp.

  • Stehfest, E., M. Heistermann, J. A. Priess, D. S. Ojima, and J. Alcamo, 2007: Simulation of global crop production with the ecosystem model DayCent. Ecol. Modell., 209, 203–219.

    • Search Google Scholar
    • Export Citation

  • Stern, R. D., M. D. Dennett, and D. J. Garbutt, 1981: The start of the rains in West Africa. J. Climatol., 1, 59–68.

  • Stern, R. D., M. D. Dennett, and I. C. Dale, 1982: Analyzing daily rainfall measurements to give agronomically useful results. 1. Direct methods. Exp. Agric., 18, 223–236.

    • Search Google Scholar
    • Export Citation

  • Suleiman, A. A., and J. T. Ritchie, 2001: Estimating saturated hydraulic conductivity from soil porosity. Trans. ASAE, 42, 235–239.

  • Sultan, B., and S. Janicot, 2000: Abrupt shift of the ITCZ over West Africa and intra-seasonal variability. Geophys. Res. Lett., 27, 3353–3356.

    • Search Google Scholar
    • Export Citation

  • Tao, F., M. Yokozawa, and Z. Zhang, 2009: Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis. Agric. For. Meteor., 149, 831–850.

    • Search Google Scholar
    • Export Citation

  • Wallach, D., D. Makowski, and J. W. Jones, 2006: Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications. Elsevier, 447 pp.

  • Ward, M. N., 1998: Diagnosis and short-lead time prediction of summer rainfall in tropical North Africa at interannual and multidecadal timescales. J. Climatol., 11, 3167–3191.

    • Search Google Scholar
    • Export Citation

  • Zadeh, L., 1965: Fuzzy sets. Inf. Control, 8, 338–353.

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