Kunstmann, H.

Laux, P.

Pedroso, R.

Waongo, M.

English

KITopen

Analyzing the impacts of climate change on attainable groundnut yields under rainfed conditions in Central Vietnam  1 Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research (KIT, IMK-IFU), 82467 Garmisch-Partenkirchen, Germany 2 University of Augsburg, Chair for Regional Climate and Hydrology, Augsburg, Germany 3 Institute for Technology and Resources Management in the Tropics and Subtropics (ITT), Cologne University of Applied Sciences,  Cologne, Germany  P. Laux1, M. Waongo1,2, R. Pedroso3, H. Kunstmann1,2  Introduction •  In Central Vietnam studies of the impacts of climate on agri-cultural productivity are rare due to missing data such as climate, soil, crop management. •  Complex interactions between plants and climate need physically-based approaches, particularly if non-linear feedbacks are considered as induced by climate change •  The regional crop model GLAM (Challinor et al., 2003) is applied to model the complex crop-climate interactions. •  Groundnut, predominantly grown in the winter-spring season under rainfed conditions, is one of the major staples (often processed to cooking oil) Setup of the regional crop model GLAM    GLAM set up for groundnut driven by WRF-ERA Interim (Fig. 1) for the period 2004-2012, using: •  Shortwave solar radiation, maximum and minimum temp-erature, and precipitation; •  Soil texture from the Harmonized World Soil Database (HWSD) for each grid cell; •  Yield information derived for each grid cell based on observed provincial yields (Quang Nam Statistical Yearbook). •  Planting date: Crop planted if soil water content exceed half of soil water holding capacity. Calibration of GLAM Fig. 2: Yield Gap Parameter (YGP) [0, ...,1] for groundnut indicating the deviations between simulated and observed yields; YGP based on the parameter set P1 (left) and P2 (right). High (low) values show high (low) deviation. The YGP is calibrated locally (grid specific).   •  Two sets of crop specific parameters (including thermal durations, rate of change of harvest index, transpiration efficiency, max. rate change of LAI, etc.) are applied: •  Parameters used for India (P1) •  Parameters used for China (P2) à The YGP (Fig. 2) is an input data bias correction parameter and represents effects which are not explicitly modelled (pests, diseases, non-optimal management). Lon107 107.2 107.4 107.6 107.8 108 108.2 108.4 108.6 108.8Lat14.81515.215.415.615.81616.212345678910111213141516171819202122232425Grid cells from WRF dataVu Gia Thu Bon basinFig. 1: Provinces in the VGTB and raster grid for GLAM simulations     1 Bac Tra My   14 Phu Ninh  2 Dien Ban   15 Tam Ky 3 Dong Giang   16 Nui Thanh 4 Duy Xuyen   17 Hoi An 5 Hiep Duc   18 Que Son 6 Nam Giang   19 Lien Chieu 7 Nam Tra My   20 Hoa Vang  8 Nong Son   21 Cam Le 9 Phuoc Son   22 Thanh Khe  10 Son Tra   23 Hai Chau  11 Tay Giang   24 Ngu Hanh Son  12 Thang Binh   25 Dai Loc 13 Tien Phuoc  GLAM simulation results (2004 - 2012) Fig. 3: Simulated mean yield (2004-2012) for groundnut using parameter set P1 (left) and P2 (right). Fig. 4: Simulated planting dates following the GLAM standard routine (soil water content criterion).  à High yield variations for groundnut modelled in the VGTB basin (Fig. 3) à Provinces 9 & 11: Highest deviations based on the two different parameter sets P1 and P2 (Fig. 3) à Planting dates range from Nov, 1st in the South to Nov, 8th in the coastal regions (Fig. 4).   Depending on future funding opportunities: •  Field experimentations (e.g. together with Hue Uni-versity) to improve model parameterizations.  •  Improving attainable yields for the VGTB basin by implementing the Optimized Planting Date algorithmus for groundnut (Laux et al., 2010; Waongo et al., 2014).  •  Using future climate ensemble projections to quantify future attainable yields for better adapted agricultural management options.  Outlook à GLAM is generally able to model trends of observed groundnut yields under rain-fed conditions (Fig. 4). à Further improvement of the parameterization still re-quired. -  Challinor, A.J., T. Osborne, L. Shaffrey, H. Weller, A. Morse, T. Wheeler, P.L. Vidale. 2009. Methods and resources for climate impacts research. Bull. Am. Meteorol. Soc., 90, pp. 836–848. -  Laux, P., G. Jäckel, R.M. Tingem, 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. Meteorol., 150, pp. 1258–1271. -  Waongo, M., P. Laux, S.B. Traoré, M. Sanon, H. Kunstmann. 2014. A crop model and fuzzy rule based approach for optimizing maize planting dates in Burkina Faso, West Africa. J. Appl. Meteorol. Climatol., 53 (2014), pp. 598–613. Contact details:  Dr. Patrick Laux Fig. 5: Observed vs. simulated groundnut yields for Tai Gian (Province #11) using parameter set P1 (solid red line) and P2 (dashed-dotted red line).  year2004 2005 2006 2007 2008 2009 2010 2011groundnut yield [kg/ha]400600800100012001400160018002000simulated P1simulated P2observedProject changes of groundnut yields Fig. 6: Projected groundnut yield changes for 2001-2030 compared to the baseline period 1971-2000 using the ECHAM5 data and the A1B emission scenario based on  parameter set P1 (left) and P2 (right).  More favorable conditions for groundnuts expected in the highlighted regions 

Analyzing the impacts of climate change on attainable groundnut yields under rainfed conditions in Central Vietnam

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Analyzing the impacts of climate change on attainable groundnut yields under rainfed conditions in Central Vietnam

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