Yet another scary forecast of crop yields at SNWA, where fact checking has failed the author, and researchers, forecasting massive crop losses due to climate change if no action is taken. ‘Models say’: Climate projections show ways to improve crop yields…
Agronomists made the prediction after ‘virtually growing’ six key crops inside a computer, using climate projection data from the Bureau of Meteorology and APSIM (Agricultural Production Systems Simulator) software.
They chose to model wheat, barley, lupin, canola and field peas at Cunderdin and Katanning in WA, Hamilton in Victoria and Wagga Wagga in NSW, using three different soil types at each location.
Lead author and cropping system modeller Muhuddin Rajin Anwar, from the NSW Department of Primary Industries Wagga Wagga Agricultural Institute, says reduced rainfall is expected to reduce both crop biomass and grain yield in all four locations.
Cunderdin, the lowest rainfall area in the study, is likely to suffer a 26 per cent annual rainfall decline by 2090, resulting in 23.4 per cent less wheat production if current farming methods and varieties continue to be used.
Field pea production could be as much as 45 per cent lower. […]
“Climate projection data from BoM”?
A few points missed. Research into the yield of pea crops shows large increases in biomass here, with a 300ppm or 600ppm increases in CO2. Increases in Co2 also improves the various crop’s capacity to grow whilst receiving substantially less moisture. Add to that the improvements in plant breeding, and yields will improve further. Better forecasting from BoM? Fat chance.
For wheat, barley, lupins and canola, yields can more than double with large increases in atmospheric CO2.
To help modeller Muhuddin Anwar, growing their ‘virtual crops’, and columnist Geoff Vivian, look here, where real crop experiments take place, (co2science says):
Plant Dry Weight (Biomass) Responses to
Atmospheric CO2 Enrichment
For a description of how these data are arranged, click here. Otherwise, begin by clicking on the first letter of the common or scientific name of the individual plant for which you seek information in the alphabet below
or select the natural community or ecosystem for which you seek information from the following link:
I would also recommend reading up on the Dr John Abbot, Dr Jennifer Marohasy papers on forecasting Australian rainfall. Dr Marohasy:
[…] This work is focused on historic temperature reconstructions and monthly rainfall forecasting. The two are linked because, with Professor John Abbot at Central Queensland University, I am developing statistical models based on a machine learning technique to forecast monthly rainfall up to 18 months in advance. The skill of our forecasts is documented in past, and upcoming, technical papers in terms of root mean square errors (RMSE) and mean absolute error (MAE) relative to observed rainfall. This means that our forecasts could be easily compared in a rigorous and quantitative way against the forecasts from general circulation models including the Bureau’s POAMA. However, so far, the Bureau has resisted any comparisons being made, as I detailed in a letter to Simon Birmingham in August last year, which can be downloaded here
Update … Australia fares well under La Nina scenarios, with large increases in rainfall. I guess the modeller and his columnist did not see this, certainly not following the BoM ‘climate projection data’ very well:
Guest Post by Bob Tisdale The new 2015 paper by Cai et al Increased frequency of extreme La Niña events under greenhouse warming has been getting a lot of alarmist attention recently. Examples: see the CBS News story Climate change expected to bring more La Niñas, and the BBC News article Study: Global warming ‘doubles risk’…