Climate Change Impact on the productivity of crops in smallholder systems in West Africa: The case of Navrongo, Ghana and Nioro, Senegal

Transcription

1 Climate Change Impact on the productivity of crops in smallholder systems in West Africa: The case of Navrongo, Ghana and Nioro, Senegal D. S. MacCarthy 1, B. S. Freduah 1, S.G.K. Adiku 1, M. Ly 2, S.B. Traore 2, S. Narh 1, P.S. Traore 3 1 University of Ghana (Ghana), 2 AGRHYMET (Niger), 3 ICRISAT (Mali) Montpellier - June 28, 2016 University of Ghana

2 Introduction Climate change is increasingly becoming a major subject of study globally. Sub- Sahara Africa is said to be particularly vulnerable because: (i) Its very high dependence on rain-fed agriculture (60%) and on climate-sensitive natural resources for their livelihoods, (ii) poverty levels are relatively high in these countries with little or no insurance against environmental stressors, and (iii) the increasing rate of degradation of natural resources renders them less resilient to the impact of rising temperatures and more variable rainfall. (iv) There is therefore the need to characterize the potential effects of climate change in this region and explore possible adaptation measures to address any negative impact.

3 Introduction Thus, smallholder farmers are likely to be impacted. Studies of this kind are usually limited to the use of a typical field to represent the study region. This study utilizes a unique approach that simulated multiple fields with multiple crop management practices simultaneously to assess the sensitivity of crop production to climate change. Objectives Assess the sensitivity of crop production to climate change in Nioro & Navrongo

4 Any evidence of climate change? Methodology

5 Methodology Two crop simulation models: Agricultural Productions Systems simulator (APSIM v. 7.4) and Decision Support Systems for Agro-technological Transfer (DSSAT v. 4.5) The crop models are dynamic with daily time steps and utilize - weather, soil, crop management, crop genetic information- soil processes, crop growth and yield Calibrated and evaluated for maize (MacCarthy et al., 2014) and millet (Akponikpe et al., 2010), Sorghum (MacCarthy et al, 2009) and peanut (Naab et al. 2004) The models simulated multiple farms, with multiple crop management practices multiple soil and weather data simultaneously. Input data source for CC impact assessment: Household survey conducted: Navrongo, Ghana in fields crop management practices -sowing dates, quantities and time of fertilizer/manure application, etc 6 Soil profile data from Upper East (Navrongo Bolgatanga) Nioro, Senegal in fields Crop management practices

6 Methodology Baseline climate data ( ) and Projected climate for near future-( ). Five GCMs with 8.5; & Five GCMs with 4.5 Climate change impact was accessed as :

7 Results Climate characteristics for Navrongo, Ghana Climate Scenario Temp Δ C Rainfall Δ % Rainfall Events % Baseline C 891mm 63 days Cool/Wet (E) Hot/Wet (K, W) Middle (S) Cool/dry Hot/dry Climate characteristics for Nioro, Senegal Climate Scenario Temp Δ C Rainfall Δ % Rainfall Events % Baseline C 741mm 46 days Cool/Wet Hot/Wet Middle Cool/dry Hot/dry

8 Challenges: Household survey as input data Ø Lack of data on initial soil N and C Ø Reliance on farmer memory for observed yield data Simulated DSSAT y = x R² = Series1 Linear (Series1) Ø Lack on actual planting date Observed Ø Lack of soil data at the household level 4000 APSIM y = 0.563x R² = Ø No time indicated for fertilizer application Ø Different crop varieties used not captured in models Simulated Series1 Linear (Series1) Observed

9 Challenges: Household survey as input data DSSAT y = x R² = APSIM y = x R² = Simulated 2000 Series1 Series2 Simulated 2000 Series1 Series Linear (Series1) 1000 Linear (Series1) Observed Observed Ø Re-initialising soil N and C Using literature e.g. Porter et al 2010 Ø Use of planting window Ø Increasing no of soils to introduce more variability

10 Climate change impact on maize yield Baseline yield: 1205, 1096 kg/ha for DSSAT & APSIM Yield reduction simulated across GCMs, s and models 4.5: -11 to -19% for DSSAT, 0 to -5% APSIM DSSAT: More pessimistic, even more under to -25% Differences in model output: higher under higher stress environment. Climate change impact on maize is more severe in Nioro and variability among farmers are also higher Baseline yield: 873, 769 kg/ha: DSSAT & APSIM

11 Climate change impact on sorghum/millet yield Baseline yield: 555/480 kg/ha -- DSSAT/APSIM Sensitivity of sorghum to climate change is less that of maize. DSSAT: -7 to 8% & APSIM:-4 to 3%. As with maize, variability among farmers were higher with DSSAT. Millet yields are also less sensitive to climate change compared to maize in Nioro. DSSAT simulated yield increases (8 to 15%) among farmers, the reverse was the case for APSIM ( 4.5: -5 to -21) Baseline yield: 521/484 kg/ha -- DSSAT/APSIM

12 Climate change impact on peanut yield Base yield: 736/672 kg/ha DSSAT/APSIM Unlike with the cereals, Climate change impact on peanut is largely positive 4.5: DSSAT 17 to 24 % APSIM, 3 to 11% 8.5: DSSAT 22 to 34% APSIM 6 to 26% Yield increases higher in Nioro 4.5: DSSAT 30 to 140 % APSIM, 19 to 31% 8.5: DSSAT -17 to 125% APSIM 14 to 41% Base yield: 524/605 kg/ha DSSAT/ APSIM

13 Concluding Statements ü Temperature increases of between o C for Navrongo & o C for Nioro projected in the near future. ü Rainfall generally will increase marginally in Navrongo and reduce significantly in Nioro in the near future. ü Climate change impact on the yield of all three cereals is generally negative, with sorghum and millet being less sensitive. ü The two crop models largely agreed on the direction of change in yield but differed in the magnitude of change. ü Increased temperature resulting mainly in reduced crop phenology resulted in yield reductions. ü The production of cereals in Nioro, Senegal is more prone to the negative impact of climate change than is the case for Navrongo, Ghana.

14 Thanks