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Climate Variability and Climate Change: Decision Making under Uncertainty Gerrit Hoogenboom Director, AgWeatherNet & Professor of Agrometeorology Washington.

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Presentation on theme: "Climate Variability and Climate Change: Decision Making under Uncertainty Gerrit Hoogenboom Director, AgWeatherNet & Professor of Agrometeorology Washington."— Presentation transcript:

1 Climate Variability and Climate Change: Decision Making under Uncertainty Gerrit Hoogenboom Director, AgWeatherNet & Professor of Agrometeorology Washington State University, USA AgMIP–Pakistan Kickoff Workshop & International Seminar on Climate Change University of Agriculture Faisalabad, Pakistan June 4-6, 2013 University of Agriculture Faisalabad, Pakistan June 4-6, 2013

2 4,000 km

3 Winter Outlook Weather and Agriculture Weather has an important impact on agriculture, both crop and animal production. For dryland agriculture more than 90% of the variability in yield can be explained by weather conditions.

4 Winter Outlook Climate and Weather Does a farmer have options to modify or change his production system? If so, what are these options? Can weather and climate information play a role? How do we provide this information?

5 National Weather Service Cooperative Weather Station Network – Volunteer data collection network – Limited set of data (temperature and rainfall) – Approximately 85 stations in Georgia – Long-term records – Data collected at Griffin Experiment Station since 1926

6 Georgia Automated Environmental Monitoring Network  First weather station was installed in 1991  Air temperature, rainfall, relative humidity, wind speed and direction, solar radiation, soil moisture

7 Georgia Automated Environmental Monitoring Network

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10 Winter Outlook Wind Machines & Frost

11 Weather Data and Applications

12 Climate in the southeastern USA Why should farmers care?

13 Climate Variability and Climate Change Climate Variability 2-3 months Inter-annual Decadal Climate Change Several decades 50+ years Centuries

14 Changing Ocean Temperatures Impacts the climateacross the globe

15 El Niño and La Niña El Niño: above-average sea-surface temperatures that develop across the east-central equatorial Pacific. La Niña: cold phase

16 Why are El Niño and La Niña important? Effects of El Niño

17 Why are El Niño and La Niña important? Effects of La Niña

18 El Niño, La Niña and Neutral Phases

19 Climate in the southeastern USA Why should farmers care?

20 Deviation from mean severity (%) Field observations (188 fields, 5 seasons) ENSO and Tomato Spotted Wilt Virus (TSWV) severity in peanut (a). Leaf symptoms of TSWV on peanut (b). Western Flower Thrips (vector)

21 Farmers and Climate: Why models? Traditional agronomic approach: – Experimental trial and error Systems Approach – Computer models – Experimental data Understand  Predict  Control & Manage – (H. Nix, 1983)  Options for adaptive management and risk reduction

22 Soil ConditionsWeather data Model Simulation Crop Management Genetics Growth Development Yield

23 Soil ConditionsWeather data Model Simulation Crop Management Genetics Growth Development Yield Net Income Pollution Resource Use

24 Linkage Between Data and Simulations Model credibility and evaluation Model credibility and evaluation Input data needs: Input data needs: Weather and soil data Weather and soil data Crop Management Crop Management Specific crop and cultivar information Specific crop and cultivar information Economic data Economic data

25 Observed and simulated soybean yield as a function of seasonal average rainfall (Georgia yield trials)

26 Observed and simulated soybean yield as a function of average max temperature (Georgia yield trials)

27 Three representative soil profiles for each county Soil surface data Soil horizons Crop management options: – Crop selection – Variety selection – Planting date – Irrigated versus rainfed – Fertilizer applications Prices and production costs Spatial Data Alabama, Florida and Georgia

28 Simulations: Cotton Yield Variety “DP555 BG/RR” 9 planting dates, rainfed vs irrigated 38 – 107 years of daily historical weather data

29 El Niño La Niña

30 Climate in the southeastern USA How do farmers make decisions?

31 Farmers and Climate Interviews 38 farmers 21 counties in GA Semi-structured interviews - Risk management strategies - Access of weather & climate information

32 Farmers and Climate Risk Reducing Options Potential UseFreq. Crop selection23 Planting timing16 Input management14 Land management13 Variety selection11 Marketing8 Harvesting dates4 Insurance3 Herd management2 Hog lagoon mgmt.1

33 Farmers and Climate Risk Reducing Options Potential UseFreq. Crop selection23 Planting timing16 Input management14 Land management13 Variety selection11 Marketing8 Harvesting dates4 Insurance3 Herd management2 Hog lagoon mgmt.1 Forecast Use: Irwin County Spring 2006 forecast for summer drought  widespread shift from long- to short-cycle peanut variety

34 Farmer Joe’s Questions El Niño La Niña

35 Management Decisions Crop selection Variety selection Planting dates Acreage allocation Irrigation Pest management Amount and type of crop insurance

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37 Historical weather data ( ) ENSO Phases Planting dates Soil types Select AL, FL, GA counties Yield Total amount of irrigation No. of irrigation events CSM-CROPGRO Peanut Model April 16, 23 May 1, 8, 15, 22, 29 June 5, 12 Crop Simulations

38 Georgia Crop Simulations: Research Analysis

39 Crop Simulations: AgroClimate Extension, Producers and Consultants

40 AgroClimate Tools

41 Interaction & Participation Forecasts, Climatology Web-based DSS Climate-based Management Options Stand alone Decision Aid Tools Needs for Specific Commodities Crop Models & Climate-based Tools Extension Agents & Specialists Farmers/ Growers Climate in the Southeastern USA: How do farmers make decisions?

42 Climate Change and Climate Variability The impact of climate change and climate variability on agricultural production and the potential for mitigation and adaptation Future issues can only be studied with simulation models “What-If” type of scenarios

43 Agriculture and Climate Change Impact and Adaptation Camilla, Mitchell County, Georgia Maximum and Minimum Temperature Precipitation

44 Maize Yield (kg/ha) Mitchell County, Georgia, USA 4 varieties, 3 soils, rainfed and irrigated Long-term historical weather data

45 Corn Yield (kg/ha) Agriculture and Climate Change Mitchell County, Georgia, USA 4 varieties, 3 soils, rainfed and irrigated Historical weather GCM-Modified CSIROMK2, Scenario IS92a,

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47 Agricultural Irrigation Water Demand forecast for 2011 to 2050 University of Georgia (UGA) and the State of Georgia Environmental Protection Division Purpose: – Prepare forecasts of irrigation water demand that meet the needs for the agricultural sector of the Georgia economy during the first half of this century.

48 Model Evaluation DSSAT Version 4.5 Five cropping seasons: Crop Management : UGA Extension Production Guidelines Field specific water use data: Agricultural Water Pumping (AWP) project

49 Irrigation Depth for Corn Mitchell County

50 Irrigation Water Amount by Crop

51 Estimation of Irrigation Amounts Use crop models to simulate irrigation depths for multiple years Weather data : 1950 to 2007 For major producing counties – 3 most important soils on which irrigation occurs – Average planting date for county or region – Estimate daily water application; sum by month Corn and Cotton 88 Peanut and Soybean 66

52 March April May June July Maize Monthly Irrigation Amounts

53 Soybean CottonMaize Peanut

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56 Winter Outlook Communication & Information

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58 Winter Outlook Cell Phone Alerts

59 Winter Outlook Weather Insurance

60 ““We like to tackle and solve some of the world’s most challenging problems,” Friedberg says, on what’s next for The Climate Corporation beyond insuring farmers, “A farmer is about as analog as it gets. To be able tell a farmer this is what’s going to happen at the end of a season, that’s mind-blowing.” TechCrunch, June 14, 2012 The Climate Corporation

61 Winter Outlook Rainfall Insurance

62 Why do we need a model?

63 Weather/Climate and Modeling: Crop and Pest Prediction Current WeatherWeather PredictionClimate Prediction Crop/Livestock/Pest/Disease/Irrigation Model Bud BreakFloweringHarvest Maturity Information delivery to producers Social scientists/agronomists/atmospheric scientists & engineers

64 DSSAT 2014 International Training Program on Crop Modeling May 19-24, 2014 The University of Georgia, Griffin, Georgia, USA

65 Weather conditions and weather-based decision support tools Southeast climate information and tools: For crop model information:


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