Using simulation modelling as a policy option in coping with agrometeorological risks and uncertainties

Transcription

1 Using simulation modelling as a policy option in coping with agrometeorological risks and uncertainties Simone Orlandini, Luca Martinelli, Anna Dalla Marta Department of Agronomy and Land Management University of Florence simone.orlandini@unifi.it

2 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

3 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

4 State of the art There is an increasing need and possibility of model development and application to rationalise crop and land management. Several points are basic for this situation: Widening of biological knowledge Development of computer science and telecommunications High level of energy and chemical inputs utilisation Increasing need of information concerning agricultural systems to improve planning and management Increasing possibility to use weather forecast data

5 State of the art Widening of biological knowledge

6 Widening of biological knowledge

7 State of the art Widening of biological knowledge Development of computer science and telecommunications

8 Development of computer science and telecommuncations

9 Development of computer science and telecommuncations

10 Development of computer science and telecommuncations

11 Development of computer science and telecommuncations

12 Development of computer science and telecommuncations

13 Development of computer science and telecommuncations

14 Development of computer science and telecommuncations

15 State of the art Widening of biological knowledge Development of computer science and telecommunications High level of energy and chemical inputs utilisation

16 High level of energy and chemical inputs utilisation

17 World Pesticide Consumption, Region North America Latin America Western Europe Eastern Europe Africa/Mideast Asia/Oceania World Total 1998 Value (US$ millions) ,991 1,258 5,847 2, ,572 20, ,377 2,307 7,173 2,571 1,258 6,814 27, ,980 3,000 9,000 3,190 1,610 8,370 34,150 Source: (a) Yudelman et al. 1998:10. (b) IFPRI calculation based on Yudelman et al. 1998:10 and FAOSTAT 1999

18 State of the art Widening of biological knowledge Development of computer science and telecommuncations High level of energy and chemical inputs utilisation Increasing need of information concerning agricultural systems to improve planning and management

19 Increasing need of information concerning agricultural systems to improve planning and management

20 State of the art Widening of biological knowledge Development of computer science and telecommuncations High level of energy and chemical inputs utilisation Increasing need of information concerning agricultural systems to improve planning and management Increasing possibility to use weather forecast data

21 Numerical weather models Seasonal forecast

22 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

23 Year of formulation (% of proposed models)

24 Role of continents (% of proposed models) Europe Asia Africa S. Aṃ N. Aṃ Oceania

25 Application areas Crop protection: pathogens, insects, frosts Water balance and irrigation Crop growth and development Production and yield Soil erosion Early warning system

26 Coltura Malat. Mod. ABETE 3 3 AGRUMI 1 1 AVENA 2 2 AVOCADO 1 1 BANANA 2 4 BARBABIET. 2 2 BEGONIA 1 1 CACAO 1 1 CAFFÈ 1 1 CANNA ZUC. 1 1 CAROTA 2 2 CASTAGNO 1 1 CAUCCIÙ 2 3 CAVOLO 2 3 CEREALI 4 6 CILIEGIO 2 2 CIPOLLA 2 2 COCOMERO 1 1 COTICO ERB. 1 1 COTONE 3 4 CRESCIONE 1 1 DUGLASIA 1 1 FAGIOLO 4 4 FRAGOLA 4 5 GINEPRO 1 1 GIRASOLE 2 2 WHEAT LUPPOLO 1 3 Main epidemiological models Coltura Malat. Mod. MAIS 4 4 MANDORLO 1 1 MANGO 1 1 MEDICA 2 3 APPLE 4 18 MELONE 1 1 PEANUT 5 13 NOCCIOLO 1 1 OLMO 1 1 BARLEY 5 13 POTATO 4 21 PESCO 1 1 PINO 4 4 PIOPPO 3 3 PISELLO 1 1 POMODORO 4 6 QUERCIA 1 1 RAPA 2 4 RICE 4 17 SEDANO 1 1 SEGALE 1 1 SOIA 5 9 SORGO 7 7 SPINACI 1 1 SUSINO 1 1 TABACCO 2 2 TRIFOGLIO 1 1 GRAPEVINE 4 17

27 Irrigation management

28 Crop growth and development Yield Nitrogen content in soil Leaf area

29 Wine quality NAO

30 Farm characterization: quality areas

31 Soil erosion

32 Early Warning System

33 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

34 Implementation of the model Tables for manual calculations Simplicity of application, difficulty to obtain information for an efficacious use Electronic plant stations Collocation in field, complete automation, often imprecise results, frequent damages Computer Rapidity of intervention (tactic), possibility to simulate past and future conditions, possible simulation with future scenarios (strategic), automatic collection and production of data, use for different aims, precision of results Integrated systems They combine models, monitoring networks and GIS for the produc-tion of information, spatially distributed on the territory

35 Abacus for frost protection

36 Mills table LEAF WETNESS HOURS Temperature Light Medium Severe

37 Electronic plant station

38 Computer

39 Online software

40 Online software

41 Integrated system Integrated systems can be realised combining models and GIS to provide the users with information represented using text files, tables, graphs and thematic maps of the most important parameters. Number of infections Rainfall

42 Integrated system input: meteorological data: Meteorological stations Remote sensing Weather forecasts Data flow for agromet. models topograph. data (elevation, orientation, slope) Geograph. Info System (GIS) (e.g. AMBER in DWD) topoclimate models phenological data, crop architecture etc. microclimate models Simulation / forecast by models output: with standard data sets with calculated met. data

43 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

44 Aims of application Field monitoring and forecasts Future climatic scenario for climate change and variability analysis Climatic classification

45 Field monitoring for crop protection To treat Not to treat

46 Irrigation management Day of irrigation Quantity of water needed

47 Climate change effect on pests Number numero of anni years (%) (%) Anni Years IV Gen. IV Gen. Giorni Days IV Gen Number of days (dd) numero giorni (gg) PRE-1 PRE-2 FUT-1 FUT-2 Scenarios

48 Climate changes

49 Climate changes Effects on grapevine production Yield Not suitable Spreading areas Sugar content Acidity. 0.50t/ha -0.50t/ha-1.50t/ha 2.50 % 1.50% 0.50 % 2.00g/l 1.00 g/l 0.00 g/l

50 Climatic characterisation: Olive fly risk in Tuscany

51 Argentina: De Martonne index

52 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

53 Conditions of application Local: the model is applied directly by farmers, with evident benefits in the evaluation of real condition and microclimate evaluation. On the other hand, the management of the simulations and the updating of the systems represent big obstacles. Territory: it is probably preferable, because it allows a better management and updating of the system. This solution requires the application of suitable methods for the information dissemination among the users.

54 Local Map of temperature Map of relative humidity

55 Local Map of number of days for the outbreak of the current infection Map of number of current infections

56 Territory

57 Agrometeorological and extension services

58 Information dissemination: the bulletins Advises and information to the users can be disseminated by using: personal contact, newspaper and magazines, radio and television, videotel, televideo, telefax, mail, phone, INTERNET, SMS.

59 Televideo

60 SMS Does not require use of computer Two type of warnings: Push-type warnings regularly sent Pull-type warnings sent on user s request by SMS

61 SMS examples Grapevine downy mildew SMS: Primary infection (started on 4 May) is at 72% development SMS: Forecasted second infection is at 18% development SMS: Lasting leaf wetness events (occurred on June) determine probable presence of infections ranging from 44% to 81% development Olive fly SMS: Monitored olive groves are free of pest SMS: Low level of pest (1%) within monitored olive groves. Suggested threshold value for olive protection is 10%

62 Internet advantages o Fast dissemination and utilisation of information o Interaction and feedback with the users o Immediate visualisation of information o Easy comprehension of information and advises o Increase computer use by farmers o Cost reduction o Fast updating and improving of the system o Control of system performing o Application of multimedia tools (texts, graphics, maps, figures, audio, video, etc.)

63 Agrometeorological bulletin

64 Aphid bulletin

65 Bulletin University of Illinois Integrated Pest Management Bulletin

66 Powdery mildew risk

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72 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

73 Example of weather data for disease models Variable Effect Temperature Solar radiation High temperature Low temperature Leaf wetness Precipitation Relative humidity Wind Phenological development Biomass assimilation and growth Rate of infection Higher threshold of development and survival Spore and insects conservation Lower threshold of development and survival Inoculation Survival of organism Dispersion of spore and insect Survival of organism Presence of saturation conditions Survival of organism Dispersion of spore and insect Modification of temperature and humidity

74 Other required data Topographic data (elevation, orientation, slope, etc.) Phenological and crop data (LAI, crop height, bud and leaf development, flowering, senescence, etc.) Cultivation conditions (training system, watering, fertilisation, etc.) Soil characteristics (water capacity, infiltration)

75 Source of data Field monitoring using meteorological stations Spatial interpolation Remote sensing Forecasts

76 Weather stations Low spatial representativeness (punctual measures) Possibility of using different time steps (from minutes to mean daily values) Purchase and maintenance costs

77 Spatial interpolation Different Spatial interpolation techniques (multiregressive, kriging, integrated kriging, etc.) Difficulties when interpolating weather parameters with high spatial and temporal variability

78 Spatial interpolation Rainfall Spatial interpolation of weather station data Radar measures Radar vs spatial interpolation

79 Remote sensing Advantages: Already geo-referenced information High spatial (up to 1m) and temporal (up to 15min) resolution Information comes from large surfaces Quick access to data Disadvantages: The data coming from satellites need to be calibrated by using the in situ -determined information Difficult validation of some variables due to high spatial and temporal variability and lack of representative ground measurements

80 Weather forecasts Availability of different temporal resolution (from hours to seasons) Low cost Already geo-referenced information Low spatial resolution, not always sufficient for agrometeorology

81 Numerical weather models Seasonal forecast

82 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

83 Constraints Limited confidence of farmers High costs of weather stations Difficulties in disseminating information among the growers Technical problems of management, rarely compatible with model output, or model output not easily understandable by the user

84 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

85 Uncertainties Inaccurate definition of some agrometeorological input variables (ex: leaf wetness) Uncertainty with the numerical weather forecast data Microclimate of a crop can be calculated for mean conditions, but plant density etc. in real world may differ Are the weather-driven biological cycles fully understood (ET, water balance, N fixation, disease effect)?

86 Outline - State of the art - Worldwide simulation models realisation and application areas - Implementation of the model - Aims of application - Conditions of application - Required data - Constraints - Uncertainties - Advantages of application

87 Advantages of application Economical and ecological benefits as result of enhanced rationalization of farming. reduction of chemical inputs in the ecosystem soil fertility conservation smaller amount of chemical residuals in food work quality improvement reduction in the development of resistant forms safeguarding of natural predatory more acceptance of the farmers work in public

88 Benefits example for crop protection Reduced risk of production losses Increase of farmers income Benefits/costs ratio from 120/1 to 27/1 Reduction in the number of treatments 20-40% Wheat in Europe (about euros per treatment per hectare) a saving of 1/2 treatment on 20 mill. ha would mean mill. of euros. Grapevine in Europe (about 16 euros per treatment per ha) a saving of about millions of euros per year is possible (it corresponds to about tons of fungicides). Crop Country Annual saving Grapevine France 7.2 millions of euros Potatoes Germany 2.9 millions of euros Potatoes Great 1.5 millions Sugar beet Britain Great Britain of euros 1.5 millions of euros

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