Jäckel, G.

Kunstmann, H.

Laux, P.

Munang, T.R.

English

Munang, T. R.

KITopen

KIT – Universität des Landes Baden-Württemberg und nationales Forschungszentrum in der Helmholtz-Gemeinschaft IINSTITUTE OF METEOROLOGY AND CLIMATE RESEARCH, GARMISCH-PARTENKIRCHEN, GERMANY www.kit.edu   Linking seasonal and long-term climate  information to agricultural productivity  A case study for Central Africa Institute of Meteorology and Climate Research  IMK-IFU  2       P. Laux1, H. Kunstmann1,2, G. Jäckel1, T. R. Munang3    1 Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research (IMK-IFU), Germany  2 University of Augsburg, Regional Climate and Hydrology, Germany  3 Climate Change Adaptation Unit, Division of Environmental Policy Implementation (DEPI), United    Nations Environment Programme (UNEP), Nairobi, Kenya RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  3  Rainfall = major limiting factor for agriculture in sub-Saharan Africa   Economies of SSA highly exposed to rainfall variability  Agriculture accounts for 35% of the GDP, employs 70% of population  > 95% of cropland managed under rainfed conditions  High rainfall variability on intra-annual, inter-annual and decadal scales   Crucial problem for rainfed agriculture: Decision about the optimal planting date for current season  Planting as early as possible to avoid wasting of valuable growth time  Planting too early may lead to crop failures and high economic losses   CC will aggravate rainfall variability and water scarcity in 21. Century   Motivation: RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  4 “Challenge” for agricultural management under rainfed conditions in sub-Saharan Africa RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  5 „Challenges“ … more specifically  Development of agriculturally relevant ORS approach   optimal planting rules accounting for intra-seasonal variability of rainfall   Estimate the impact of planting date on “attainable” crop yield under current and future climate conditions  Potential Solutions   Development  “optimal planting date following crop modeling system”   Application for past and future and comparison to traditional planting dates  RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  6 “Optimal planting date following crop modeling system”    CropSyst Management • Planting date • Ploughing • Fertilization • etc. Weather • Temperature • Precipitation • Radiation • (CO2)  • etc. Crop Yield  (1979-2003) Algorithm Onset Rainy Season Planting date for each year Soil properties RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  7 CropSyst model (Stöckle et al., 2003)  Multi-year, multi-crop process-based simulation model  to study the effect of climate, soil, and management on productivity and environment of cropping systems   Calibration: Parameterization of crop-specific values (e.g. phenology) by IRA (Cameroon) and literature review   Validation  Difference modeled and observed yields acceptable (< 10%)  Represents inter-annual and spatial variability of observed crop yield RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  8 Algorithm: Onset of the Rainy Season (ORS)  Literature review: … many rainfall-based (threshold) approaches  e.g. Stern et al., 1981: ORS as first DOY with rainfall > 20mm within 2 consecutive days)  Sivakumar, Hess, and many more   Problem:  existing approaches too strict (binary logic)   Solution:  Fuzzy logic-based ORS approach of Laux et al. (2008, 2009) for Volta Basin of West Africa  3 membership functions (criterions) RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  9 #1: Rainfall amount criterion   µ1: 2 parameters a1, a2  Sufficient water at planting stage µ1 Rainfall amount within 5-day-period [mm] a1 a2 RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  10 µ2 b1 b2 Number of wet days within 5-day-period [days]  µ2: 2 parameters b1, b2  exclude single heavy showers as ORS  #2: Multiple rainy days criterion  RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  11  µ3: 2 parameters c1, c2  exclude total crop failure Number of consecutive days after ORS,  in which no dry spell > 6 days occurs [days]   c1 c2 µ3 #3: False start criterion RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  12 Combining #1, #2, and #3 for planting decision   Total membership grade: µTOT = µ1 · µ2 · µ3 IF µTOT  > threshold k [0,...,1], THEN Onset Rainy Season (planting)  ORS approach with 7 parameters: k, a1, a2, b1, b2, c1, c2  Parameters depend on region (weather, soil) and plant physiological aspects   Optimization for each crop and station of interest RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  13 Parameter optimisation algorithm 20.02.2012 RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  14 Case study: Cameroon Garoua Ngaoundéré Batouri Yaoundé Bamenda 1090 mm/a 1514 mm/a 2380 mm/a 1500 mm/a 1600 mm/a March 15 May 15 Traditional planting dates: RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  15 Garoua Yaoundé Batouri Ngaoundere Bamenda Results optimisation: Yaoundé (maize) → Mean attainable crop yield (1979-2003) increases per iteration → Distribution narrows (CV decreases)  RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  16 Parameter space after 10 iterations 20.02.2012 RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  17 ORS algorithm vs. traditional planting calendar Proposed new method for planting dates would have allowed for: → Increase in mean attainable crop yield: Yaoundé 15%, Garoua 50% → Crop yield increases in anomalous dry years Garoua Yaoundé Batouri Ngaoundere Bamenda RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  18 Impact of climate change on crop productivity 1. Daily climate scenarios for 2020s and 2080s using ClimGen based on HadCM3 and GISS, and A2 and B2 scenario (8 scenarios for each station)  Tmin, Tmax (Solar radiation)  Precipitation   2. Atmospheric CO2 conditions for baseline period 1961-1990, 2020s, and 2080s  3. Crop yield simulations using future climate scenarios under baseline/future atmospheric CO2 conditions  4.  Crop yield simulations with/without adaptations of the planting date RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  19 Local climate scenarios RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  20 Impact of planting date adaptations at Garoua Compared to traditional planting dates:  → Increase of groundnut (maize) yields → But: widened distribution for future crop yields: increase in variability! Distribution of mean crop yield at Garoua (2020s) RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  21 Summary  Optimal planting date following crop modelling system   Optimal planting rules (crop + location)  Significant increase of mean attainable crop yield,       particularly at drier northernmost stations (Garoua, Batouri)  Not working for “wet conditions” (Bamenda)    Impact CC on future crop yield estimations  Groundnut yields are expected to increase in the 2020s and 2080s, Maize yields are expected to increase (decrease) in the 2020s (2080s)  Using ORS approach reduces negative impacts of CC on maize yield (2080s) at northernmost stations  RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  22 References Laux, P., Jäckel, G., Munang, R. - T., Kunstmann, H. (2010): Impacts of climate change on agricultural productivity under rainfed conditions in Cameroon - A method to improve attainable crop yields by planting date adaptations, Agricultural and Forest Meteorology 150, pp. 1258-1271, DOI: 10.1016/j.agrformet.2010.05.008.  Laux, P., Wagner, S., Wagner, A., Bárdossy, A., Jacobeit, J., Kunstmann, H. (2009) Modelling Daily Precipitation Features in the Volta Basin of West Africa, International Journal of Climatology, Volume 29, Issue 7, pp. 937-954, DOI: 10.1002/joc.1852.   Laux P., Kunstmann, H., Bárdossy, A. (2008) Predicting the Regional Onset of the Rainy Season in West Africa, International Journal of Climatology, Volume 20, Issue 3, pp. 329-342, DOI: 10.1002/joc.1542.   Laux, P., Jäckel, G., Tingem, M., Kunstmann, H. (2009): Onset of the rainy season and crop yield in Sub Saharan Africa - Tools and perspectives for Cameroon. Thoms, M. Et al. (eds): Ecohydrology of Surface and Groundwater Dependent Systems: Concepts, Methods and Recent Developments, Hyderabad, India, September 6-12, 2009. IAHS Publication 328, 191-201.  RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  23 Relevance for RICE-EA  Optimized planting rules for rainfed rice under current and future climate conditions by coupling with dynamical and statistical crop models (e.g. ORYZA or GLAM)   Improvement of approach:  Latin Hypercube instead of Monte Carlo  Downscaling: RCM simulations & bias correction methods (e.g. Copulas)   Implementation of other constraints such as temperature thresholds for regions in higher altitudes (Kenyan highlands)   Might also be used in combination with seasonal predictions with more direct importance for farmers    RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  24 Thank you  for your attention RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  25   20.02.2012 Dr. Patrick Laux - PICREVAT workshop Embu (Kenya) Institute of Meteorology and Climate Research  IMK-IFU  26 RICE-EA workshop, Nairobi, Kenya 17.04. – 19.04.2012 Institute of Meteorology and Climate Research  IMK-IFU  27 Impact planting dates on future crop yield  GISS and HadCM3 A-OGCM, A2 und B2 scenario  ClimGen for statistical downscaling of GCM output 20.02.2012 Dr. Patrick Laux - PICREVAT workshop Embu (Kenya) Institute of Meteorology and Climate Research  IMK-IFU  28 Direct CO2 effects + ΔP & ΔT  + planting date adaptations Compared to baseline 1961-1990:  → Increase of groundnut yields for the 2020s and 2080s → Increase (decrease) of maize yields for the 2020s (2080s)   → Aggravation of growing conditions for Bamenda 20.02.2012 Dr. Patrick Laux - PICREVAT workshop Embu (Kenya) Institute of Meteorology and Climate Research  IMK-IFU  29 Calibration/Validation CropSyst  Parametrisation crop-specific values (maize, groundnut)  Publications (e.g. Tingem et al., 2008)  CropSyst user manual   Phenological parametrisation (e.g. GDD)  Institute of Agricultural Research (IRA) Cameroon   Validation: 5-year-period of observed yields:   Difference between modeled and observed yields acceptable  Interannual and spatial variability of crop yields  Bild Validation 20.02.2012 Dr. Patrick Laux - PICREVAT workshop Embu (Kenya) Institute of Meteorology and Climate Research  IMK-IFU  30 Initialization parameter domain (7 parameters) Randomisation (500 realisations) of  the 7 parameters Calculation of the ORS dates (1979-2003) using ORS approach Simulation Mean Crop Yield MCY (500 realisations) using CropSyst and ORS dates  Iterations Parameter domain of best 5% simulations Optimal parameter domain for definition ORS Δ (MCY) < threshold Δ (MCY) > threshold ORS parameter optimisation 20.02.2012 Dr. Patrick Laux - PICREVAT workshop Embu (Kenya) Institute of Meteorology and Climate Research  IMK-IFU  31   20.02.2012 Dr. Patrick Laux - PICREVAT workshop Embu (Kenya) Institute of Meteorology and Climate Research  IMK-IFU  32   20.02.2012 Dr. Patrick Laux - PICREVAT workshop Embu (Kenya) Institute of Meteorology and Climate Research  IMK-IFU  33 Cameroon: Factors affecting rainfall variability High spatial and temporal rainfall variability  Climate: semi-humid (South) to semi-arid (North)  Intertropical Convergence Zone (ITCZ)    South: bimodal (april/may & september/october)  North: unimodal (august/september)  Topography    Garoua Yaoundé Batouri Bamenda Ngaoundéré Elevation (m.a.s.l.) -26 - 250  251 - 500 501 - 750 751 - 1.000 1.001 - 1.250 1.251 - 1.500 1.501 - 1.750 1.751 - 2.000 2.001 - 3.000 3.001 - 4.024 20.02.2012 Dr. Patrick Laux - PICREVAT workshop Embu (Kenya) 

Linking seasonal and long-term climate information to agricultural productivity. A case study for Central Africa

https://publikationen.bibliothek.kit.edu/230087638/3816099

Linking seasonal and long-term climate information to agricultural productivity. A case study for Central Africa

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