N7 Jamieson

Global Change and Wheat Production:Assessing the Future: 

Global Change and Wheat Production: Assessing the Future PD Jamieson (NZ) JR Porter (Denmark) MA Semenov (UK)

How is world wheat production likely to change in the next century?: 

How is world wheat production likely to change in the next century?

The drivers of change: 

The drivers of change Atmospheric [CO2] Climate Geopolitics International trade Technology

The drivers of change: 

The drivers of change Atmospheric [CO2] Climate Geopolitics International trade Technology

The drivers of change: 

The drivers of change Atmospheric [CO2] Going up (definitely) Climate Warmer (probably) Changes in rainfall patterns Technology Information Agrichemicals Machinery Cultivars

Sensitivity to global change: 

Sensitivity to global change Changes in suitable land area Improvements associated with technology Direct responses to changes in [CO2], temperature and water supply

Sensitivity to global change: 

Sensitivity to global change Changes in suitable land area Improvements associated with technology Direct responses to changes in [CO2], temperature and water supply

On what basis can we make assessments: 

On what basis can we make assessments Experimental work to determine and responses to temperature, water and CO2 Simulation modelling that incorporates known responses The GCTE Networks Wheat SOM Rice Tropical cereals

On what basis can we make assessments: 

On what basis can we make assessments Experimental work to determine and responses to temperature, water and CO2 Simulation modelling that incorporates known responses The GCTE Networks Wheat SOM Rice Tropical cereals

GCTE International Wheat Network: 

GCTE International Wheat Network Formed Saskatoon Canada, 1992 Meetings at Lunterin, Netherlands 1993 Reading, UK 1995 Casa Grande, Arizona, USA 1997 Potsdam, Germany 1998 KBS, Michigan, USA 2000 To come Clermont Ferrand, France 2004

A loose alliance of researchers…: 

A loose alliance of researchers… Experimenters USDA Arizona, FACE experiments Rothamsted Research – CE experiments Others from NZ, Australia, Canada…. Modellers from UK, USA, Canada, Germany, New Zealand, Denmark, Australia Publications from the collaborations Many in the international literature Plus many conference presentations

At the simplest: 

At the simplest production = growth rate x growth duration x harvest index [CO2 ] increase  growth rate increase Temperature increase  growth duration decrease Temperature increase  harvest index change (?) Combined [CO2 ] & T increase  Increased WUE  more suitable land (?)

Therefore: 

Therefore The system is complex We need to use a physiological model to analyse impacts and assess the balance of competing effects

Some of the available models: 

Some of the available models AFRCWHEAT2 CERES-Wheat CropSim Demeter FASSET SWHEAT and the Dutch models Sirius

Some of the available models: 

Some of the available models AFRCWHEAT2 CERES-Wheat CropSim Demeter FASSET SWHEAT and the Dutch models Sirius

Some of the available models: 

Some of the available models AFRCWHEAT2 CERES-Wheat CropSim Demeter FASSET SWHEAT and the Dutch models Sirius

Some of the available models: 

Some of the available models AFRCWHEAT2 CERES-Wheat CropSim Demeter FASSET SWHEAT and the Dutch models Sirius

Some of the available models: 

Some of the available models Sirius

Sirius: 

Sirius Phenology based on leaf appearance and numbers Canopy growth a function of thermal time, modified by stress Biomass accumulation from light interception and light use efficiency Grain production based on simple partitioning rules Widely tested

Model Validation: 

Model Validation

What are the predicted impacts of global atmospheric change?: 

What are the predicted impacts of global atmospheric change?

Canterbury NZ - maturity: 

Canterbury NZ - maturity

Canterbury NZ - Yields: 

Canterbury NZ - Yields

On a straight race between yield reducing duration changes (temperature) and yield enhancing growth rate changes (CO2): 

On a straight race between yield reducing duration changes (temperature) and yield enhancing growth rate changes (CO2) CO2 wins

Lest you think this applies only to high production systems: 

Lest you think this applies only to high production systems Australia – “most likely” climate change and double CO2 Yield increases of 9-37% assuming current practice Yield increases of 13-46% with adapted management Howden, Reyenga and Meinke (1999) using APSIM I-Wheat Average yields < 2 t/ha The variations are with location Risks may increase – extreme events Temperature, droughts….

Technology effects: 

Technology effects Farmers and researchers live in largely the same environment, and this moulds their expectations These get revised upwards by both groups as they learn more from each other In NZ, 25 years ago wheat yields of 5 t/ha were being sought by both researchers and growers 10 years ago it was 10 t/ha Now it is 15 t/ha This needs attention to detail in management

Projected production increases by 2030 (million tonnes pa): 

Projected production increases by 2030 (million tonnes pa) Developed world: 308 to 440 (43%) Developing world: 272 to 418 (54%) Mostly from increased yield/ha Marathée & Gomez-MacPherson (2001 – The World Wheat Book) By 2100? At least that much again is possible None of the above considers atmospheric changes

The Land Balance: 

The Land Balance Combined [CO2 ] & temperature increase increased WUE more suitable land (?) but low production Rural depopulation and urban sprawl Reduction in available land area Magnitude? Losses are likely to bigger than gains

The Trade Balance: 

The Trade Balance Increasing demand from the developing world will increase its wheat deficit, and the amount of wheat traded Net trade (millions of tonnes) by 2030 Developed world: Exports increase from 64 to 155 (142%) Developing world: Imports increase from 61 to 152 (150%) The biggest demand increases will be in China and North Africa/Near East Marathée & Gomez-MacPherson (2001 – The World Wheat Book)

Where to from here…..?: 

Where to from here…..? Whole systems – soils, rotations….. Food quality – balancing up protein and carbohydrate production Genetics Keeping pace with global change Conventional plant breeding Biotechnology Scaling from field to landscape

Whole systems:R-W sequences in the IGP: 

Fallow Legume GreenManure Jute ChickPea Pea OilSeed Whole systems: R-W sequences in the IGP

Actual v potential yieldsThe yield gap: 

Actual v potential yields The yield gap

Simulation of Phenology(Ortiz-Monasterio et al. 1994): 

Simulation of Phenology (Ortiz-Monasterio et al. 1994)

Simulation of grain number: 

Simulation of grain number

Simulation of yield: 

Simulation of yield

Causes of yield gap?: 

Causes of yield gap? The reasons that achieved yields are well below potential are not obvious from the experimental data What manifest as sowing date effects are likely to be soil and management influences Continuous cropping degrades soil We need better links between crop models and SOM models

Causes of yield gap?: 

Causes of yield gap? “Severe biological and/or technological limitations to productivity, and …. potential for substantial yield increase provided the environmental and management constraints can be identified and rectified, are evident” Timsina and Connor, (2001).

The protein gap: 

The protein gap Many of the genetic advances that have raised wheat yields have resulted in reduced protein levels We believe this can be addressed by appropriate attention to N metabolism in wheat cultivars Higher production, whether from CO2 fertilisation or just better management, requires more N fertiliser

Where does the N go?: 

Where does the N go? 15 kg/ha per unit green area 1% of non-green biomass 0.5% of non-grain 2% of Grain Jamieson and Semenov 2000. Field Crops Res.68, 21-29. Martre, Porter, Jamieson and Triboi 2003. Plant Physiology, (In press)

Issues of scale: 

Issues of scale Single plant scale is of most interest to the plant physiologist Plot scale is of most interest to the crop physiologist Field and farm scale is of most interest to the farmer Industries, commerce and governments must deal with regional scale

Regional scale: 

Regional scale Regional production depends on How much land is sown How much crop survives Storage and transportation Yield How best to represent/predict?

Spatial and temporal scales: 

Spatial and temporal scales

Downscaling or upscaling?: 

Downscaling or upscaling?

Scaling up –Simplifying a crop model: 

Scaling up – Simplifying a crop model Simplify a model by analysing model structure, model processes and its interactions

Scaling up –Simplifying a crop model: 

Scaling up – Simplifying a crop model Simplify a model by analysing model structure, model processes and its interactions Brooks, Semenov and Jamieson, 2001. Eur. J. Agron. 14, 43-60.

Spatial downscaling: HadRM climate regional model: 

Spatial downscaling: HadRM climate regional model HadCM3 global model

Temporal downscaling:LARS-WG stochastic weather generator: 

Temporal downscaling: LARS-WG stochastic weather generator Generates precipitation, min and max temperature and radiation Modelling is based on wet/dry series Flexible semi-empirical distributions are used Temperature and radiation are cross-correlated www.iacr.bbsrc.ac.uk\mas-models\larswg.html

Climate change impact assessment: high temporal and spatial resolutions: 

Climate change impact assessment: high temporal and spatial resolutions GCM Observations low resolution, 300 km high resolution, 1 km

Effect of changes in climatic variability on simulated grain yield (Nature, 1999): 

Effect of changes in climatic variability on simulated grain yield (Nature, 1999)

Conclusions: 

Conclusions Global atmospheric changes will have mostly positive effects on wheat production Other influences are likely to be just as big and just as positive It ain’t all doom! But…….. Risks may increase We need to continue to improve the methodology

Thanks to….: 

Thanks to…. GCTE Conference organisers International Wheat Network collaborators