Kunstmann, H.

Laux, P.

Waongo, M.

English

KITopen

EGU2014-13259Crop planting date optimization: An approachfor climate change adaptation in West AfricaM. Waongo1,2, P. Laux1, H. Kunstmann1,2Contact: moussa.waongo@kit.edu1 Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), 82467 Garmisch-Partenkirchen, Germany2 University of Augsburg, Institute for Geography, Regional Climate and Hydrology, 86135 Augsburg, Germany1. MotivationSub-Saharan Africa (SSA) is known as the most vulnerable to climatevariability and climate change since crop low yield and limited agri-cultural inputs (fertilizers, machines) are compounded with a highdependence on rainfed agriculture and high poverty level.XWith regards to low crop yield worsen by low adaptive capacity of farm-ers, crop management strategy such as optimized planting date (OPD)might belong to climate change adaptation options in SSA.XCrop planting date is a crucial tactical decision for subsistence agricul-ture in SSA since a wrong decision can lead to crop failure, which canthreaten farmers livelihood.XScientifically sound information on crop planting date aiming to alleviatecrop water stress can contribute to improve crop production in SSA.The main purpose of this study was to develop a crop planting dateoptimization approach for agricultural management decision support.2. Research Area & DataFig. 1: Annual rainfall distribution andrain gauges locations in Burkina FasoBurkina Faso (BF) is a West African coun-try located in the mid-west SSA region. Theclimate is characterized by two distinct sea-sons: a rainy season and a dry season.Mean annual precipitation (period 1981-2010) decreases from more than 1100 mmin South to 300 mm in North BF (Fig. 1).Crop growing season lies between three to sixmonths and decreases from South to Northin BF.•Climate data on daily basis, encompassing precipitation, temperatureand incoming shortwave radiation from BF Met Services, ERA-Interimreanalysis and regional climate models (RCMs) future projections.•Soil hydrological properties and annual maize yields for BF.3. MethodsOptimized planting dates (OPDs) are defined as planting dates whichachieve highest crop yield and lowest inter-annual variabilityof yield.A fuzzy rule is used to compute potential planting dates (Fig. 2).Optimization of the derived potential planting dates for maizecropping is performed using the large scale crop model GLAMin combination with a genetic algorithm (Fig. 3a).OPDs performance have been evaluated for present (Fig. 5) andfuture climate (Fig. 3b and Fig .6).The planting date approach ofDiallo (2001)1 is used as reference. This referenced approach is currentlyused in SSA.1Diallo, 2001: The date after 1st May, when rainfall accumulated over 3 consecutive days is at least 20 mm and when no dryspell more than 10 days within the next 30 days is recorded.(a) (b)Fig. 2: Fuzzy logic memberships for planting date definition (a) and flowchart of planting datecomputation (b).Waongo et al.(2014)(a) (b)Fig. 3: Flowchart of (a) planting date optimization using aGenetic Algorithm (Waongo et al., 2014) and (b) maize yieldsimulation using 8 RCM projections and two planting datesoptions.Ref:Waongo, M., Laux, P., Traore, S. B., Sanon, M., Kunstmann, H., 2014. A crop model and fuzzy rule basedapproach for optimizing maize planting dates in burkina faso, west Africa.Journal of Applied Meteorologyand Climatology 53, 598-6134. ResultsThe mean OPD ranged from May 1st (South-West) to July 11th (North)with a standard deviation less than 3 weeks across the country (Fig. 4).For present climate in comparison with Diallo (2001), the OPDapproach yielded earliest planting dates (Fig. 5a). With respect to thepotential yields, the OPD approach indicated that an average increasein maize potential yield of around 20% could be obtained inwater limited regions in Burkina Faso (Fig. 5b).For future climate projections, OPDs achieved an increase inmaize mean yield. In general, the minimum increase rate is 10%higher than yield obtained by Diallo (2001) , irrespectively of RCMs andemission scenarios (Fig. 6).(a) (b)Fig. 4: Mean OPDs (a) and standard deviation (b) for maize croping in Burkina Faso.(a) (b)Fig. 5: Comparison of (a) planting dates : OPD-Diallo (2001) and (b) maize relativeyield deviation [(OPD-Diallo (2001))/Diallo (2001)].Fig. 6: Relative deviation (%) of potential mean yield for maize obtained by OPD in comparison with Diallo (2001) for8 RCMs under two emission scenarios (RCP4.5 , RCP8.5). RCMs data have been retrieved from CORDEX-Africa.4. ConclusionThis approach achieves higher potential yields for maize croping acrossBF compared with the methods currently in operation. However, in-fieldvalidation is required before being operationally used in SSA.

Crop planting data optimization: An approach for climate change adaptation in West Africa

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Crop planting data optimization: An approach for climate change adaptation in West Africa

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