Climate Change and Agriculture - OECD · climate change and agriculture studies started from the...

Climate change and agriculture: OECD/INEA/FAO June 23 2010, Rome

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Climate Change and AgricultureMARS-AGRI4CAST on-going activities

Simon Kayon behalf of the MARS-AGRI4CAST Team


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AGRI4CAST infrastructure

■ The development of the AGRI4CAST infrastructure for climate change and agriculture studies started from the MARS weather database

■ New modelling capabilities and tools have been developed mostly “in house”, either adopting approaches made available in the literature, or implementing new ones

■ Software tools are developed using the component-oriented programming paradigm, which leads to discrete units of software, which are also re-usable by third parties


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BioMA – Biophysical Model Applications

Presenter

Presentation Notes

As in the previous slide: BioMA – Biophysical Models Application – is a platform for running biophysical models on generic spatial units The key requirements in its design aim at maximizing: Extensibility with new modelling solutions Ease of customization in new environments autonomously by third parties Ease of deployment


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Lengthening of growing seasonAs a whole, in Europe a lengthening of growingseason (defined as frost-free period) wasobserved (0.8-1 day per year during the last 30years). However, in a few and localized areas,due to particular microclimatic conditions,reductions were recorded instead.

Shortening of crop growth cycle (agrophenology)Increase of crops development speed did lead to a shortening ofcrops cycle over the last decades. Winter crops were influencedmore than summer crops.

Increased plant heat stressWorse conditions were recorded in Spain (mainly southern areas), Italy andBlack Sea area (mainly Turkey). However, it must also be highlighted thatlocally along the Atlantic coast line and in Greece a reduction of frequency ofheat stress was recorded

Reduction of winter rainfallIn Italy, Portugal, Greece, southernFrance and Ireland a significantreduction of cumulated values ofrain during winter was recorded.

Increased rainfallIn Scandinavia, eastern EU, Balkansand Austria a significant increase ofcumulated rain both during winterand summer was recorded.

Reduction of summer rainfallItaly and southern France show a significantreduction of cumulated rain In spite of the smallcontribution of summer rain to the whole yearcumulated value the reduced summer rainincreased the water deficit noticeably.

Increased risk of late frostsThe frequency of late frosts has increasedwestwards of the dotted line bringing agreater vulnerability to this regions.

AGRI4CAST – IPSC - JRC

Observed agro-climatological changes (MARS database 1975 – 2007)

Increased irrigation demandIncrease of water deficit. Italy, centralSpain and southern Francepresented the largest increases.

Reduction of irrigationdemandIn Balkans, Austria, CzechRepublic, The Netherlands,Denmark, southern Sweden andnorthern Poland a reduction ofwater deficit was recorded,mainly due to the increase ofrain during the growing season.

Presenter

Presentation Notes

Stress on the fact that impacts presented on this slide are observed


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IPCC storylines

A1 storylineWorld: market-orientedEconomy: fastest per capita growthPopulation: 2020 peak, then declineGovernance: strong regional interactions;

income convergence Technology: three scenario groups:• A1FI: fossil intensive• A1T: non-fossil energy sources• A1B: balanced across all sources

A2 storylineWorld: differentiated Economy: regionally orientated; lowest per capita growthPopulation: continuously increasing Governance: self-reliance with preservation of local identitiesTechnology: slowest and most fragmented development

B1 storylineWorld: convergentEconomy: service and information based, lower growth than A1Population: same as A1Governance: global solution to economic, social and environmental sustainabilityTechnology: clean and resource-efficient

B2 storylineWorld: local solutions Economy: intermediate growthPopulation: continuously increasing at lower rate than A2Governance: local and regional solutions to environmental protection and social equityTechnology: more rapid than A2; less rapid, more diverse than A1/B2

Presenter

Presentation Notes

Large differences in regional population, income and technological development implied under alternative SRES storylines can produce sharp contrasts in exposure to climate change and in adaptive capacity and vulnerability. Therefore, it is best not to rely on a single characterisation of future conditions. Scenarios. A scenario is a coherent, internally consistent and plausible description of a possible future state of the world. Scenarios are not predictions or forecasts (which indicate outcomes considered most likely) but are alternative images without ascribed likelihoods of how the future might unfold. They may be qualitative, quantitative, or both. Exploratory scenarios describe the future according to known processes of change, or as extrapolations of past trends. Normative scenarios describe a pre-specified future, either optimistic, pessimistic or neutral, and a set of actions that might be required to achieve (or avoid) it. Such scenarios are often developed using an inverse modelling approach, by defining constraints and then diagnosing plausible combinations of the underlying conditions that satisfy those constraints (see Nakicenovic et al., 2007). Storylines. Storylines are qualitative, internally consistent narratives of how the future may evolve. They describe the principal trends in socio-political-economic drivers of change and the relationships between these drivers. Storylines may be self standing, but more often support quantitative projections of future change that, together with the storyline, constitute a scenario.


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IPCC WG-I (2007)

Projected increases of temperature

2020 2050

Current AGRI4CAST

analysis

baseline

Presenter

Presentation Notes

The choice of time frames for the analysis are due to: baseline: current weather, rather than the traditional 1960-1990 2020: conditions urgent to address, in which we can assume that agricultural production will use technical means similar to current ones 2050: to explore a future when weather condition could be substantially changed


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Issues in estimating CC impact

■ GCM scale■ GCM variability of estimates■ Extreme events:

● GCM simulation ● Crop models simulation

■ Pests and diseases simulation■ Adaptation strategies building■ Metrics■ Data! Depending upon the goal of the analysis, data needs

can be a limiting factor.(http://mars.jrc.ec.europa.eu/mars/Bulletins-Publications/Data-Demand-CC-Analysis)


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Biophysical models

■ Several biophysical models are available to estimate crop development and growth; WOFOST, CropSyst, and WARM are used in the analysis

■ The models are integrated with implementations of submodels for abiotic damage, and more in general to introduce the possibility of crop failure in extreme conditions

■ Models for pest and diseases are being implemented; they can be linked to crop models, or used stand alone for studies of potential infection under new climatic conditions


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Grids of GCM derived weather data

■ The LARS-WG (Rothamstead Research, UK) and the CLIMA-WG (JRC AGRI4CAST) were used to respectively to downscale GCM simulations and to estimate/generate weather variables at different temporal resolution

■ Trends from runs of several GCMs are used to perturbate parameters (averages) representing current weather for each grid cell

■ Dataset based on the IPCC scenarios (A1B and B2) are generated by applying different values to parameters representing variability – not directly available from GCM runs

■ The weather data series are generated for each cell of the grid, to be used as inputs to simulation models


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Current weather DB for CC analysis

■ Baseline - 25 years of daily data (capability to add at run-time hourly values when needed)

■ Two GCMs used: Hadley3 and ECHAM-5■ Three time frames:

● “baseline” (based on recorded series1982-2008)● 2020● 2050

■ Two emission scenarios: A1B and B1

Currently the database (25 km grid) consists of:

Presenter

Presentation Notes

We have purposely selected 1982-2008 as baseline, instead of 1961-90 which is commonly used, to use as reference the current climate.


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Hadley A1B “2020” vs. baseline: Tmax

Presenter

Presentation Notes

Examples: most of the data presented are difference map, that is, the value of the scenario – the value of the baseline


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Hadley A1B “2020” vs. baseline: Rain


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Sample analysis

■ Maize: yield, water demand

■ Impacts on phenology of grape vines

■ Impacts on crop disease: potential infection

■ A case of integrated analysis: rice

AGRI4CAST analysis is run abstracting to a 25 x 25 spatial scale, EU level.Simulations are run on weather data representing a sample of years of baseline, 2020, and 2050 scenarios. Different models are run via the BioMA platform according to the simulation target.

The sample simulations unedrtaken so far are:

Presenter

Presentation Notes

The 25 x 25 grid level analysis does not allow running analysis which are context specific like the ones related to cropping systems


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Phytophtora infestans (e.g. potato)

Emission scenario = A1B2020 - baseline 2050 - baseline

Presenter

Presentation Notes

These and the two following slides to show examples of heterogeneity of disease development for different pathogens. In this case: improvements and worsening


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Sclerotinia sclerotiorum(e.g. sunflower)

2020 - baseline 2050 - baselineEmission scenario = A1B

Presenter

Presentation Notes

In this case: almost no projected change


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Puccinia recondita (e.g. wheat)

A1B 2050 - 2020 B1 2050 - 2020

Presenter

Presentation Notes

In this case: a basically generalized worsening. Pathogen complexes of each crop need to be evlauted case by case


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Rice yield – A1B

Presenter

Presentation Notes

Paddy rice yield is expected to increase in most areas with the exception of warmest areas. The increase of temperatures suggest also the need of less water for thermal buffering.


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On going developments

■ Enriching the database of agro-management;■ Extending sets of parameters for diseases potential

infection;■ Further development of a component to impact on yields

via diseases;■ Including olive trees simulations;■ Developing modules for insects simulation;■ Building agro-management rules for semi-automatic

adaptation strategies development;■ Adding soil water simulation;■ Running sample simulations of adaptation on different

systems.


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Thank you for your attention

JRC IPSC MARS-AGRI4CASThttp://mars.jrc.ec.europa.eu/mars/About-us/AGRI4CAST

Software and documentation downloadhttp://mars.jrc.ec.europa.eu/mars/About-us/AGRI4CAST/Software-Tools

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