Climate Change and Agriculture

Transcription

1 Climate Change and Agriculture MARS-AGRI4CAST on-going activities Simon Kay on behalf of the MARS-AGRI4CAST Team 1

2 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 componentoriented programming paradigm, which leads to discrete units of software, which are also re-usable by third parties 2

3 BioMA Biophysical Model Applications 3

4 Lengthening of growing season As a whole, in Europe a lengthening of growing season (defined as frost-free period) was observed (0.8-1 day per year during the last 30 years). However, in a few and localized areas, due to particular microclimatic conditions, reductions were recorded instead. Increased rainfall In Scandinavia, eastern EU, Balkans and Austria a significant increase of cumulated rain both during winter and summer was recorded. Observed agro-climatological changes (MARS database ) Increased plant heat stress Worse conditions were recorded in Spain (mainly southern areas), Italy and Black Sea area (mainly Turkey). However, it must also be highlighted that locally along the Atlantic coast line and in Greece a reduction of frequency of heat stress was recorded Reduction of irrigation demand In Balkans, Austria, Czech Republic, The Netherlands, Denmark, southern Sweden and northern Poland a reduction of water deficit was recorded, mainly due to the increase of rain during the growing season. Reduction of winter rainfall In Italy, Portugal, Greece, southern France and Ireland a significant reduction of cumulated values of rain during winter was recorded. Increased irrigation demand Increase of water deficit. Italy, central Spain and southern France presented the largest increases. AGRI4CAST IPSC - JRC Increased risk of late frosts The frequency of late frosts has increased westwards of the dotted line bringing a greater vulnerability to this regions. Reduction of summer rainfall Italy and southern France show a significant reduction of cumulated rain In spite of the small contribution of summer rain to the whole year cumulated value the reduced summer rain increased the water deficit noticeably. Shortening of crop growth cycle (agrophenology) Increase of crops development speed did lead to a shortening of crops cycle over the last decades. Winter crops were influenced 4 more than summer crops.

5 IPCC storylines A1 storyline World: market-oriented Economy: fastest per capita growth Population: 2020 peak, then decline Governance: strong regional interactions; income convergence Technology: three scenario groups: A1FI: fossil intensive A1T: non-fossil energy sources A1B: balanced across all sources A2 storyline World: differentiated Economy: regionally orientated; lowest per capita growth Population: continuously increasing Governance: self-reliance with preservation of local identities Technology: slowest and most fragmented development B1 storyline World: convergent Economy: service and information based, lower growth than A1 Population: same as A1 Governance: global solution to economic, social and environmental sustainability Technology: clean and resource-efficient B2 storyline World: local solutions Economy: intermediate growth Population: continuously increasing at lower rate than A2 Governance: local and regional solutions to environmental protection and social equity Technology: more rapid than A2; less rapid, more diverse than A1/B2 5

6 Projected increases of temperature Current AGRI4CAST analysis baseline IPCC WG-I (2007) 6

7 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. ( 7

8 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 8

9 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 9

10 Current weather DB for CC analysis Currently the database (25 km grid) consists of: Baseline - 25 years of daily data (capability to add at runtime hourly values when needed) Two GCMs used: Hadley3 and ECHAM-5 Three time frames: baseline (based on recorded series ) Two emission scenarios: A1B and B1 10

11 Hadley A1B 2020 vs. baseline: Tmax 11

12 Hadley A1B 2020 vs. baseline: Rain 12

13 Sample analysis 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: Maize: yield, water demand Impacts on phenology of grape vines Impacts on crop disease: potential infection A case of integrated analysis: rice 13

14 Phytophtora infestans (e.g. potato) Emission scenario = A1B baseline baseline 14

15 Sclerotinia sclerotiorum(e.g. sunflower) Emission scenario = A1B baseline baseline 15

16 Puccinia recondita (e.g. wheat) A1B B

17 Rice yield A1B 17

18 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. 18

19 Thank you for your attention JRC IPSC MARS-AGRI4CAST Software and documentation download 19