Mer F - Use of climate predictions for impact studies, Nairobi Aug 2012
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This document discusses using climate predictions from global climate models (GCMs) for agricultural impact studies. It presents several crop modeling approaches that can be used with downscaled climate data to quantify the impacts of climate change on crops and design effective adaptation strategies. These include the EcoCrop, MaxEnt, DSSAT and GLAM models. The models vary in their complexity and data requirements, but can provide information on changes in crop suitability, yields and impacts under future climate scenarios. The document advocates using ensemble modeling approaches to decrease uncertainties and inform stakeholders about adaptation needs.
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Mer F - Use of climate predictions for impact studies, Nairobi Aug 2012
- 1. Using climate predictions for impact studies Nairobi, August 2012 Flora Mer
- 2. The World is changing…. Population growth Industrial revolution Non-environmentally friendly technologies/practices LEAD TO GREENHOUSE GASES EMISSIONS INCREASING
- 5. Changes in climates affect crops we grow... There will be winners… Number of crops with more than 5% gain …But much more losers in developing countries Number of crops with more than 5% loss
- 6. We need models to quantify impacts and design effective adaptation options
- 7. GCMs Statistical Downscaling MarkSim Dynamical downscaling: Regional Climate Model DSSAT Statistical Downscaling GLAM EcoCrop Effective MaxEnt adaptation options Bias correction Any model
- 8. Crop Models Probability EcoCrop Environmental gradient MaxEnt Models based on crop niches DSSAT Models based on processes GLAM
- 9. The Model: EcoCrop – A simple algorithm to look at the broad niche of each species only based on climate data – Ten growing parameters to set up the model • Absolute rainfall interval • Absolute temperature interval • Optimum rainfall interval • Optimum temperature interval • Length of the growing season • Crop freezing temperature – Use of climate data • Statistical downscaling of GCMs (IPCC4) • Present-day climates from WorldClim = Interpolations of observed data, representative of 1950-2000 • 24 different climate models (GCMs) to sample uncertainties
- 10. The Model: EcoCrop • So, how does it work? It evaluates on monthly basis if there are adequate climatic conditions within a growing season for …and calculates the climatic suitability of the temperature and precipitation… resulting interaction between rainfall and temperature…
- 11. Common Bean Current Suitability Kiling temperature (°C) 0 Growing season (days) 90 Minimum absolute temperature (°C) 13.55 Minimum absolute rainfall (mm) 200.0 Minimum optimum temperature (°C) 17.45 Minimum optimum rainfall (mm) 362.5 Maximum optimum temperature (°C) 23.05 Maximum optimum rainfall (mm) 449.5 Maximum absolute temperature (°C) 25.63 Maximum absolute rainfall (mm) 710.0
- 12. Common Bean Future Suitability and Change 2030s SRES-A1B 2030s SRES-A1B
- 14. Average change in suitability for 50 food crops in 2050s
- 15. Suitability changes Crop Comparison in Africa
- 16. MaxEnt Maximum Entropy Modelling • Model predicting the potential distribution of a species • Statistical dowscaling method apply to climate data. • Many modellers use the set of the bioclimatic variables Maxent use the principle of the maximum entropy Maxent use only presence point of specific species and environmental variables • One of the most accurate model for the prediction of shifts in suitable growth ranges of species
- 17. MaxEnt Application on Kenyan coffee Main coffee-producing areas in Kenya are located in two areas: - the central region around Mount Kanya - in the Rift Valley in the west - The most suitable areas: in the higher areas of Bungoma, Embu, Kericho, Kiambu, Kirinyaga, Kisii, Machakos, Meru, Muranga, Nithi, Nyamira, Nyeri and Trans-Nzoia New markets Management Alternatives to tea
- 18. DSSAT Decision Support System for Agrotechnology Transfer • Based on crop processes • Integrate the interaction of weather, soil, management and genetic factors • Prediction of yields, plant phenologic stages, plant weight,harverst date, water soil quantity, N quantity… • Current & Future predictions • Need precise and daily data
- 19. DSSAT predicts yields Jones and Thornton, 2003 Maize Yield negatively impacted by CC in most areas in Africa Need effective adaptation options
- 20. DSSAT predicts yields In 2055: Maize Yield would be negatively impacted by CC in most areas in Ethiopia Need to develop adaptation strategies Jones and Thornton, 2003
- 21. GLAM - General Large Area Model Challinor et al. (2004) • Designed at climate model scale to capitalize on known large-scale relationships between climate and crop yield, thus avoiding over-parameterization. Uses grid-scaled agricultural statistics To simulate yields at to simulate yields climate model scale Large-area models are able to reproduce large-scale historical yield responses to climate and inter-annual variability Observed peanut yields (kg/ha) Rate of simulated to observed yields
- 22. Conclusions • Different models exist for evaluating the impact of CC on crops • The application of each model depends on what information we have and what we want to know: • Daily/Monthly data • Crop suitability • Yield • Agricultural management practices • Impact studies at agricultural level benefit from having climate data of higher resolution • Inform adaptation to stakeholders: policy makers, donors, other researchers, but also farmers. • Gaps: Need more research to understand better crop responses to climate change To decrease uncertainties
- 23. Future research plans • Input climate data quality and its effects on impact predictions • Analysis of trial data to better understand crop responses to environment (soil, nutrients, CO2, heat stress, drought stress, and their interactions) • Expansion of crop model parameterisation, including multi-Ag- Model ensembles • Impacts of future climate change and future variability on crop yields • Uncertainty quantification (crop and climate) • Design of crop genotypic adaptation strategies (“ideotypes”) and link with analogues to find useful germplasm • Scale up adaptation strategies to national/ international level (policy-making)
- 24. Thank you ! Any questions?
Notas del editor
- Existing different change in our world such as population growth, industrial revolution and non-environmentally friendly technologies and practices, which affect the global concentration of greenhouse gases in the atmosphere.
- Those change are happening and are affecting crop around the world. We need to know how and how much climate change is going to have an impact on crops to be able to build adaptation strategy and decrease the potential impact of CC on crops and agricultural systems.
- Grado de cobertura diff segun modelo. Y resultados tambien yield o suitability. Tambien difieren en escala espacio-temporal a la que se usan.
- Remover este slide. Esta info la puedes DECIR en el slide siguiente
- Speak about food security problem around the world. Here we can see that food security will be negatively affected by CC in West Afrcia, India with a decrease of crop suitability between 1 and 10 %.
- Looking at regional level, for Africa, only sorghum will be positively impacted by CC, the 5 other crops would be negaltively affected. We can think about adaptation strategy such as cropping more sorghum in the future which will not be negatively impacted to CC according to our model.
- JRV: puede remover, o convertir en grafico. Esto es demasiado texto. Puede decirlo, no necesidad de escribirlo Statistical dowscaling method apply to climate data to produce 1km resolution surfaces of the monthly mean of max, min temperature and monthly precipitation. Interpolation between centroids of the GCM grid (same to produce WorldClim) and add the predicted climate anomaly for the respective grid cell to the WorldClim data points.
- Remover. Esta informacion lo puedes dar en slide siguiente. No necesidad de tenerlo escrito
- Recuerda decir: GLAM has been successfully used in various applications in India (groundnut), China (wheat), Brazil (maize), the Sahel (sorghum and groundnut), Nigeria (sorghum, maize, groundnut), and globe (soybean, maize, wheat) GLAM is able to simulate inter-annual variability in crop yield…as well as picking out areas where climate extremes are likely to affect crops and assesing how a change in crop avreity can be used to adapt to these changes. It is designed for use with regional and global climate model output. It is similar to DSSAT with the benefits of empirical models in order to simulate yields.
- We need to work to fill the gaps about how crops are responding to CC ; for example how crops are responding to dry period? … We need to understand better all crop processes to different climate to decrease uncertainties in crop modelling.