climate variability, climate change and agriculture
Post on 31-Dec-2015
Embed Size (px)
DESCRIPTIONClimate variability, climate change and agriculture. Variability/Change?. Many African societies are well-adapted to the climate variability to which they are exposed - PowerPoint PPT Presentation
Seasonal Forecasting Maximising effectiveness for end-users
Climate variability, climate change and agriculture
1Variability/Change?Many African societies are well-adapted to the climate variability to which they are exposed This variability is a good proxy for risks associated with future climate change, provided that the rate of change is sufficiently slow (Mortimore, 1998, Brooks and Adger, 2003)23Inter-annual variability - Zimbabwe 1980-2004Source Thomas & Matsikwa (2004)
3Dealing with variabilityIdentification of response strategiesAvailability of Climate informationHistorical data and response strategies Community/indigenous knowledgeForecasts of differing scale, skill, time framesTrials (and error)4DECISION FRAMEWORK ON CLIMATE VARIABILITY ANDCLIMATE CHANGETYPE OF DECISIONCLIMATEWEATHERLong Term (10-50yrs)Medium Term (6-9mths)Short term (0-7days)Decadal Changes Seasonal ForecastsReal Time WeekStrategicTacticalOperationalCrop planning Reservoir planningLand managementCapital investmentsCrop managementWater allocationInput costs/planningIrrigation scheduling Operations planningTime management5Monthly forecast 1-3 lead time
www. gfcsa.netPrecipitation ForecastJuly 2006% of Normal6TerminologyNormal (237 +/- 10% = 210-260 mm) Above Normal (above 260mm)Below Normal (below 210mm)
% of normal predicted: 80% = 168mm60% = 126mm40% = 84mm40-60% of normal = 84-126mm100-120% of normal= 237-284mm = 237 mm
203545376355807Seasonal forecasts?3 month regional scale projections produced for 1,2,3 month lead times
OND 2006Precipitation forecast (%of Normal)
8User responses - Reliability Accuracy will it be correct?Probability/likely/confidence?Skill how often will a forecast be correct?Scale usefulness?Consistency will the forecast for a particular month change?Will I be able to interpret it?
9Not enough information!What will the ENSO event mean?Delayed onset?Dry months?Hot temperatures?Which crops are more susceptible?Worst and best case scenariosTypical responses in analogue years..
Climate ChangeLonger termMore extremesChanges in variabilityDifferent coping skills?Long term changes in pests, seasonal lengths, water availability12Agricultural responseHedging opportunities and strategiesDiversification of crops/cultivarsPlanting/fertilising strategyMarket/climate conditions/food pricesWeather forecast creativity.?
13Farmer Issues on Adaptation to Climate ChangeCan farmers who are adapting prioritise beyond their own experience?can they think out of the box?or does some top-down advice have to be offered?Climate change is a special case within interacting drivers of change on the livelihoods of farmers
Mainstreaming adaptation to climate change means a focus on vulnerability reductionfor this we need to seek multiple responses
14 PHYSICAL LIMITSUnsuitable soilsLack of waterPhysically feasible but politically, socially or environmentally difficultCapacity of organisationsCapacity of individualsAfter Arnell (2005)FINANCIAL LIMITSCAPACITY LIMITSFEASIBILITY LIMITSADAPTATIONi.e. Adjustment to altered circumstancesLIMITS TO ADAPTATION IN AGRICULTURE15Things to chew over..Can we make forecasts even more useful?Are tailored agricultural forecasts feasible?Do we have the required institutional capacity and skills?Will climate change projection information fit seamlessly into the dissemination networks that exist?
RainfallYield x 100mmyearsMALMESBURY Rainfall total/ave wheat yield 2000-2004
AVERAGE001LEGENDTotal of the daily rainfall (in mm) by month*** indicates data is missing or is not yet available in the current month--- indicates that data is unavailable or was not requested= indicates that the average is unreliable due to missing daily valuesMonthly Daily Rain (mm) Data for station [0006734 5] - CALEDON Measured at 08:00JANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECTOTALYield x 100Mean13.92226.16454.865.464.254.530.940.116.726.4479LT Mean200028.41.053.65.432.338.983.638.9184.108.40.2064.8355.9274.0200020010.05.45.054.245.214.5144.877.035.436.612.57.9438.5240.02001200220.127.116.119.764.154.273.250.734.324.116.925.0464.4311.02002200319.28.2103.721.165.910.427.3146.933.642.17.021.6507.0321.00200320049.621.817.350.010.843.368.121.130.0121.59.036.8439.32004Monthly Daily Rain (mm) Data for station [0021178A3] - CAPE TOWN WO Measured at 08:00YearJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECMean12.18.811.546.27194.883.282.344.618.104.22.16827.4LT Mean200016.1012.913.862.392.646.346.222.214.171.124.9376.1200020018.34.72.539.480.662.3207.897.34726.312.76.4595.32001200260.914.99.32871.976.498.265.726.132.52215.9521.8200220032.48.447.611.937.12533.4100.363.919.25.821.1376.1200320045.80.29.263.13.891.164.7169.7126.96.36.199.2544.2200420050.8Monthly Daily Rain (mm) Data for station [0041388 0] - MALMESBURY Measured at 08:00YearJANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECRainfallYield x 100Mean7.57.272245.569.367.957.9188.8.131.525.3364.4LT Mean364200050.210.63.223.439.85633.6184.108.40.206.4241.4236200020014.45.21.220.681.214.815778.246.21694.4438.22792001200235.8220.127.116.11=92.447.475.868.4114.813.48.2374.2236200220030.87.619.221.818.4920.613247.413.8025.8316.4106200320048015.425.62.462.671.24726.8581.20.6318.891200420052.2
TOTAL 479Yield x 100mmyearsCALEDON total 2000-2004
TOTAL 479 355.9 438.5 464.4 507.0 439.3 527.4mmyearsCAPE TOWN total 2000-2004
mmyearsMALMESBURY total 2000-2004