Weldemichael A. Tesfuhuney Dept. Soil, Crop & Climate Sciences, University of the Free State Global Workshop Montpellier, France JUN 2016

Transcription

1 Future Climate Change and its Effect on Maize Yield in Selected Semi-arid Areas of Southern Africa Weldemichael A. Tesfuhuney Dept. Soil, Crop & Climate Sciences, University of the Free State Global Workshop Montpellier, France 28-3 JUN 216 1

2 Structure of talk Maize Production in SADC Approach / Rationale Methodology Results o Calibration / Evaluation o Yield Simulation (South Africa, Namibia, Botswana, Lesotho) Summary / Way Forward SAAMIP Pres._SADC-Weldemichael-216 2

3 Maize Production in Southern Africa Define livelihoods of millions of people in southern Africa, Partly serving as the most important source of calorie (7%) Arid and Semi-arid areas, maize crop stands out as the primary crop both in terms of acreage and absolute yield levels. E.g., In South Africa 2. million ha under dryland rainfed &.23 million ha cultivated under irrigated Southern Africa s maize harvest is expected to shrink (2-26% in 215/16) o o Constrained by natural forces / changing in climate & in addition to the formidable institutional bottlenecks. DROUGHT, i.e., huge challenge of maize production in the region SADC, 213 / FAO.. Report 8 million people in the developing countries do not have sufficient food. 13 million poor subsistence farmers in sub-saharan Africa that affect by the impact of climate change Big concern food insecurity will increase in SADC due to Climate Change SAAMIP Pres._SADC-Weldemichael-216 3

4 Problem Identification Household food security in the region is extremely vulnerable to the negative effect of climate changes and variability, with focus to low maize productivity Therefore, the climate change impact assessments using crop models expected to assist in exploring of crop yield variability as a result of climate uncertainties + management options Objectives To assess and evaluate the impacts of a projected climate change scenarios on major stable crop / maize using crop simulation model (APSIM) and to initiate adaptation strategies To develop capacity building to explore and evaluate the impacts of climate change through the application of crop models. SAAMIP Pres._SADC-Weldemichael-216 4

5 Approach and Rationale Integrate or link existing knowledge of Climate Change impacts & crop models that are in continuous under improvement o Atmosphere: microclimate / atmospheric interactions o Above ground: hydrology, soil/plant in cropped land o Below ground: soil water, water storage modeling, nutrient dynamics o Socio economics factors: communication, dissemination??? as an important farming system, continue to improve by researchers through continue innovative research, BUT limited.... Q. Merit of considering alternative / potential adaptation strategies for improved maize production in SADC SAAMIP Pres._SADC-Weldemichael-216 5

6 METHODOLOGY Study Areas / Regions Geo-WRSI Study areas Crop Modelling Simulations for baseline and Future scenarios Source: Climate Hazard Group (USGS) Model Calibration and Evaluation Participatory Processes: Stakeholders, scientists, policymakers, farmers, end users Research Simple ADP Package Shift to Improved cultivar Fertilization inc. 2-3% Irrigation Simulation with / out Adaptation SAAMIP Pres._SADC-Weldemichael-216 6

7 Regional approach to climate change SADC experienced rainfall shortages, mostly arid, semi-arid, erratic, variable Mostly lies on drought belt, receive lower ~ 2 mm higher 465 mm Maize is summer crop with mean temp. 23 o C yield is negatively affected above 3 o C. e.g., Scholes & Biggs 24; Using HADCM3 Climate Model scenario Spatial Distribution of Changes in 25s relative to mean Temp RF Climate Data: SAAMIP Pres._SADC-Weldemichael-216 Climate : Mid Century Future Scenario: mid-century, RCP 8.5, with 5 GCMs

8 Soil data: Different sources of literature, extension officers, experiments Land types with uniform microclimate, morphology and characteristics, e.g., in three locations of South Africa Attribute Bothavile (BO) Bethlehem (BH) Bainsvlei (BV) General description of the regime Medium Wet Dry Quaternary catchment indicators Bd18 Ca6 Ca22 Land form within land type Heidelberg Av36 Soetmelk Av 36 Bainsvlei Bv3 Land form covers (ha) Genera soil properties Thickness of Top soil horizon (cm) Thickness of subsoil horizon Dominant soil textural class Sandy clay loam Sandy clay loam Fine sandy loa Clay content (%) Soil profile description Horizon A B21 B22 A B1-3 C A B1-5 Depth (cm) BD (g cc -1 ) DUL (mm mm -1 ) LL15 (mm mm -1 ) OC (%) PH water Crop Management Practice: used common small scale practices, planting date, planting densities, row width, fertilization... etc SAAMIP Pres._SADC-Weldemichael-216 8

9 Model / APSIM Above and below Resources Capturing Soil shading and light interception / temperature Plant water uptake / changes in soil water SAAMIP Pres._SADC-Weldemichael-216 9

10 Simulated Yield (kg ha -1 ) Model Calibration / Evaluation a) y =.4498x R² =.47 RMSE= 681 kg ha Observed District Yield (kg ha -1 ) RESULTS / Regional Simulation Calibrated with range of common practices and field experiments, genetic coefficients (medium maturing hybrid maize), commonly growing in the semi-arid Evaluation made with district average yields and observed farm yields Model Evaluation Simulated Yield (kg ha-1) b) y =.3741x R² =.51 RMSE= 624 kg ha Observed District Yield (kg ha-1) Simulated Yield (kg ha -1 ) c) y =.2425x R² =.65 RMSE= 748 kg ha Observed District Yield (kg ha -1 ) Simulated Yield (kg ha -1 ) Katima Linyati Kongola Kabbe Sibbinda Linear (All) y =.3665x R² =.46 RMSE= 639 kg ha Observed farms Yield (kg ha -1 ) SAAMIP Pres._SADC-Weldemichael-216 Simulated Yield (kg ha -1 ) y =.4189x R² =.59 RMSE= 355 kg ha Observed farms Yield (kg ha -1 ) 1

11 SOUTH AFRICA 2 a) Bloemfontein, BF (No-Adaptation) 2 b) Bloemfontein, BF (With-Adaptation) Maize Pr. Area Yield (kg/ha) % GFDL_ESM Avg. GCMs GFDL_ESM Avg. GCMs 2 15 c) Bothavile, BO (No-Adaptation) d) Bothavilie, BO (With-Adaptation) % Yield (kg/ha) Study area District Main city GFDL_ES M Avg. GCMs GFDL_ESM Avg. GCMs South Africa SAAMIP Pres._SADC-Weldemichael

12 6 5 e) Bethlehem, BH (No-Adaptation) 6 5 f) Bethlehem, BH (With- Adaptation) Yield (kg/ha) % 1 1 Yield (kg/ha) GFDL_ESM Avg. GCMs GFDL_ESM Avg. GCMs a) IRRIGATION (No- Adaptation) GFDL_ESM Avg. GCMs Climate Scenario b) IRRIGATION (With-Adaptation) GFDL_ESM Avg. GCMs Climate scenario Bothaville Irrigation by 61.2 % compared to dryland Irrigation + Adaptation increased by 29.9 % Dryland Bethlehem yield with adaptation is better & ranges ( ) SAAMIP Pres._SADC-Weldemichael

13 NAMIBIA / Caprivi Region Yield (t/ha) All, Caprivi, Namibia (24-27) ( ) GFDL_ESM AvgGCMs RF < 5-65 mm 1. PROBABILITY OF EXCEEDANCE APSIM APSIM APSIM GFDL_ESM2 APSIM 2_ES APSIM APSIM _MR APSIM All GCMs MAIZE YIELD (KG/HA) SAAMIP Pres._SADC-Weldemichael

14 Yield is higher western & lower extreme eastern Caprivi Yield (kg/ha) Konglola, Caprivi, Namibia (24-27) ( kg ha -1 ) Sibinda, Caprivi, Namibia (24-27) ( kg ha -1 ) 1 1 More yield variability in western & central compared to eastern Yield (kg/ha) GFDL_ESM AvgGCMs Kabe, Caprivi, Namibia (24-27) (63 27 kg ha -1 ) 3 2 GFDL_ESM AvgGCMs Katima, Caprivi, Namibia (24-27) ( kg ha -1 ) 1 1 GFDL_ESM AvgGCMs GFDL_ESM AvgGCMs Katima & Kabe Yield (t/ha) Linyanti, Caprivi, Namibia (24-27) GFDL_ESM AvgGCMs SAAMIP Pres._SADC-Weldemichael

15 LESOTHO regions Yield (t/ha) Basline Liphir Ha-Malets Liphir- ADP Ha-Malets- ADP Avg- ADP Probability of exceedance 1. Maize yield (kg/ha) Basline Liphir Ha-Malets Liphir-ADP Ha-Malets-ADP Avg-ADP SAAMIP Pres._SADC-Weldemichael-216 Use only one GCM With climate changing yield reduced by 37% in Liphiring & 13% in Ha- Maletsene With adaptation yield increased by 66% & 44% on average Yield variation in Liphiring is much smaller 15

16 BOTSWANA Northern / Maun region 12 1 a) Botswana (No-Adaptation) Southern region Yield (kg/ha) Average annual RF: Northern region mm Southern Region - 25 mm GFDL_ESM Avg. GCMs Probability of Non Exceedance MAIZE YIELD Northern Southern yield Kg Ha -1 SAAMIP Pres._SADC-Weldemichael b) Botswana (With Adaptation) GFDL_ESM Avg. GCMs Climate scenario 16

17 Summary results of yield simulations Countries in SADC Sites No. of Farmers Summary / Way forward Performance Mean Future Scenarios (t ha -1 ) with Adaptation R 2 RMSE (t ha -1 ) CCSM GFDL HADG MICR MPI Mean South Africa Botswana Namibia Lesotho From the model simulations results, more research can be motivated to find an appropriate on farmmanagement adaptation strategies and a forward step to build the capacity of database and networking to reduce the vulnerability to wards the effect of the impact of climate changes in the region. Off course need to calibrate / verified all APSIM parameters in particular with improved phenological and soil data adjustment (Part of AgMIP-II) BUT what kind of adaptive strategy is appropriate??? Researching in diversified crops and cultivars & cropping systems o In south Africa & Lesotho regions high performing maize cultivar with high fertilization and Irrigation could be a strategy for the effect of CC o For Botswana & Namibia need to look alternative crops like sorghum & legumes / even livestock / mixed SAAMIP Pres._SADC-Weldemichael

18 THANK YOU! SAAMIP Pres._SADC-Weldemichael