Skip to main content Accessibility help
×
Home
Hostname: page-component-99c86f546-45s75 Total loading time: 0.714 Render date: 2021-12-01T20:09:54.166Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

Yield gap analysis of rainfed wheat demonstrates local to global relevance

Published online by Cambridge University Press:  18 August 2016

D. L. GOBBETT*
Affiliation:
CSIRO, Waite Campus, PMB 2, Glen Osmond, SA 5064, Australia
Z. HOCHMAN
Affiliation:
CSIRO, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, QLD 4067, Australia
H. HORAN
Affiliation:
CSIRO, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, QLD 4067, Australia
J. NAVARRO GARCIA
Affiliation:
CSIRO, Ecosciences Precinct, PO Box 2583, Brisbane, QLD 4001, Australia
P. GRASSINI
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, PO Box 830915, Lincoln, NE 68583-0915, USA
K. G. CASSMAN
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, PO Box 830915, Lincoln, NE 68583-0915, USA
*
*To whom all correspondence should be addressed. Email: david.gobbett@csiro.au

Summary

Australia has a role to play in future global food security as it contributes 0·12 of global wheat exports. How much more can it contribute with current technology and varieties? The present paper seeks to quantify the gap between water-limited yield potential (Yw) and farmer yields (Ya) for wheat in Australia by implementing a new protocol developed by the Global Yield Gap and Water Productivity Atlas (GYGA) project. Results of past Australian yield gap studies are difficult to compare with studies in other countries because they were conducted using a variety of methods and at a range of scales. The GYGA project protocols were designed to facilitate comparisons among countries through the application of a consistent yet flexible methodology. This is the first implementation of GYGA protocols in a country with the high spatial and temporal climatic variability that exists in Australia.

The present paper describes the application of the GYGA protocol to the whole Australian grain zone to derive estimates of rainfed wheat yield gap. The Australian grain zone was partitioned into six key agro-climatic zones (CZs) defined by the GYGA Extrapolation Domain (GYGA-ED) zonation scheme. A total of 22 Reference Weather Stations (RWS) were selected, distributed among the CZs to represent the entire Australian grain zone. The Agricultural Production Systems sIMulator (APSIM) Wheat crop model was used to simulate Yw of wheat crops for major soil types at each RWS from 1996 to 2010. Wheat varieties, agronomy and distribution of wheat cropping were held constant over the 15-year period. Locally representative dominant soils were selected for each RWS and generic sowing rules were specified based on local expertise. Actual yield (Ya) data were sourced from national agricultural data sets. To upscale Ya and Yw values from RWS to CZs and then to national scale, values were weighted according to the area of winter cereal cropping within RWS buffer zones. The national yield gap (Yg = Yw–Ya) and relative yield (Y% = 100 × Ya/Yw) were then calculated from the weighted values.

The present study found that the national Yg was 2·0 tonnes (t)/ha and Y% was 47%. The analysis was extended to consider factors contributing to the yield gap. It was revealed that the RWS 15-year average Ya and Yw were strongly correlated (R 2 = 0·76) and that RWS with higher Yw had higher Yg. Despite variable seasonal conditions, Y% was relatively stable over the 15 years. For the 22 RWS, average Yg correlated positively and strongly with average annual rainfall amount, but surprisingly it correlated poorly with RWS rainfall variability. Similarly, Y% correlated negatively but less strongly (R 2 = 0·33) with RWS average annual rainfall, and correlated poorly with RWS rainfall variability, which raises questions about how Australian farmers manage climate risk. Interestingly a negative relationship was found between Yg and variability of Yw for the 22 RWS (R 2 = 0·66), and a positive relationship between Y% and Yw variability (R 2 = 0·23), which suggests that farmers in lower yielding, more variable sites are achieving yields closer to Yw. The Yg estimates appear to be quite robust in the context of estimates from other Australian studies, adding confidence to the validity of the GYGA protocol. Closing the national yield gap so that Ya is 0·80 of Yw, which is the level of Yg closure achieved consistently by the most progressive Australian farmers, would increase the average annual wheat production (20·9 million t in 1996/07 to 2010/11) by an estimated 15·3 million t, which is a 72% increase. This indicates substantial potential for Australia to increase wheat production on existing farmland areas using currently available crop varieties and farming practices and thus make a substantial contribution to achieving future global food security.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

ABARE-BRS (2010). Land Use of Australia, Version 4, 2005–06. Canberra, Australia: Australian Bureau of Agricultural and Resource Economics - Bureau of Rural Sciences (ABARE-BRS).Google Scholar
ABARES (2012). Agricultural Commodity Statistics 2012. Canberra, Australia: Australian Bureau of Agricultural and Resource Economics and Sciences.Google Scholar
ABARES (2015). Australian Agricultural and Grazing Industries Survey (AAGIS). Canberra, Australia: Australian Bureau of Agricultural and Resource Economics and Sciences.Google Scholar
ABS (2012). Agricultural Census: Value of Agricultural Commodities 2010–11. Canberra, Australia: Australian Bureau of Statistics.Google Scholar
ACLEP (2012). Australian Soil Classification (ESRI Grid). Australian Collaborative Land Evaluation Program (ACLEP) endorsed through the National Committee on Soil and Terrain (NCST). Canberra, Australia: ACLEP. Available from: http://www.clw.csiro.au/aclep/asc_re_on_line/soilhome.htm (verified 8 March 2016).Google Scholar
Anderson, W. K. (2010). Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Research 116, 1422.CrossRefGoogle Scholar
Barlow, K. M., Christy, B. P., O'Leary, G. J., Riffkin, P. A. & Nuttall, J. G. (2015). Simulating the impact of extreme heat and frost events on wheat crop production: a review. Field Crops Research 171, 109119.CrossRefGoogle Scholar
BoM (2009). The Australian Data Archive of Meteorology. Melbourne, Australia: Commonwealth of Australia, Bureau of Meteorology.Google Scholar
Brown, H. E., Huth, N. I., Holzworth, D. P., Teixeira, E. I., Zyskowski, R. F., Hargreaves, J. N. G. & Moot, D. J. (2014). Plant modelling framework: software for building and running crop models on the APSIM platform. Environmental Modelling & Software 62, 385398.CrossRefGoogle Scholar
Carberry, P. S., Liang, W. L., Twomlow, S., Holzworth, D. P., Dimes, J. P., McClelland, T., Huth, N. I., Chen, F., Hochman, Z. & Keating, B. A. (2013). Scope for improved eco-efficiency varies among diverse cropping systems. Proceedings of the National Academy of Sciences of the United States of America 110, 83818386.CrossRefGoogle Scholar
Cornish, P. S. & Murray, G. M. (1989). Low rainfall rarely limits wheat yields in southern New South Wales. Australian Journal of Experimental Agriculture 29, 7783.CrossRefGoogle Scholar
CSIRO (2015). Australian National Outlook 2015: Economic Activity, Resource Use, Environmental Performance and Living Standards, 1970–2050. Canberra, Australia: CSIRO.Google Scholar
Dalgliesh, N. P., Foale, M. A. & McCown, R. L. (2009). Re-inventing model-based decision support with Australian dryland farmers. 2. Pragmatic provision of soil information for paddock-specific simulation and farmer decision making. Crop & Pasture Science 60, 10311043.CrossRefGoogle Scholar
Edwards, J., Umbers, A. & Wentworth, S. (2012). GRDC Farm Practices Survey 2012. Kingston, ACT: Grains Research & Development Corporation.Google Scholar
ESRI (2010). ArcGIS 10·0. Redlands, CA, USA: Environmental Systems Research Institute.Google Scholar
Feldman, D., Thomas, Q., Farre Codina, I., Plunkett, B. & Kingwell, R. (2015). Is a low-input strategy a sound business defence in a drying climate? In 59th Australian Agricultural and Resource Economics Society Annual Conference. Rotorua, New Zealand: Copyright by authors. Available from: http://ageconsearch.umn.edu/handle/205077 (verified 8 March 2016).Google Scholar
Fischer, R. A., Byerlee, D. & Edmeades, G. O. (2014). Crop Yields and Global Food Security: Will Yield Increase Continue to Feed the World? ACIAR Monograph No. 158. Canberra, Australia: Australian Centre for International Agricultural Research.Google Scholar
Freebairn, D. M., Wockner, G. H., Lawrence, D., Cawley, S. & Hamilton, A. N. (1998). A framework for extending principles of conservation cropping. In Proceedings of the 9th Australian Agronomy Conference (Eds Michalk, D. L. & Pratley, J. E.), pp. 89. Wagga Wagga, NSW, Australia: Australian Society of Agronomy.Google Scholar
Grassini, P., Eskridge, K. M. & Cassman, K. G. (2013). Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature Communications 4, article no. 2918.CrossRefGoogle ScholarPubMed
Grassini, P., van Bussel, L. G. J., Van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., de Groot, H., van Ittersum, M. K. & Cassman, K. G. (2015). How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Research 177, 4963.CrossRefGoogle Scholar
Hochman, Z., Holzworth, D. & Hunt, J. R. (2009). Potential to improve on-farm wheat yield and WUE in Australia. Crop & Pasture Science 60, 708716.CrossRefGoogle Scholar
Hochman, Z., Gobbett, D., Holzworth, D., McClelland, T., van Rees, H., Marinoni, O., Navarro Garcia, J. & Horan, H. (2012). Quantifying yield gaps in rainfed cropping systems: a case study of wheat in Australia. Field Crops Research 136, 8596.CrossRefGoogle Scholar
Hochman, Z., Prestwidge, D. & Carberry, P. S. (2014). Crop sequences in Australia's northern grain zone are less agronomically efficient than implied by the sum of their parts. Agricultural Systems 129, 124132.CrossRefGoogle Scholar
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., Chenu, K., van Oosterom, E. J., Snow, V., Murphy, C., Moore, A. D., Brown, H., Whish, J. P. M., Verrall, S., Fainges, J., Bell, L. W., Peake, A. S., Poulton, P. L., Hochman, Z., Thorburn, P. J., Gaydon, D. S., Dalgliesh, N. P., Rodriguez, D., Cox, H., Chapman, S., Doherty, A., Teixeira, E., Sharp, J., Cichota, R., Vogeler, I., Li, F. Y., Wang, E., Hammer, G. L., Robertson, M. J., Dimes, J. P., Whitbread, A. M., Hunt, J., van Rees, H., McClelland, T., Carberry, P. S., Hargreaves, J. N. G., MacLeod, N., McDonald, C., Harsdorf, J., Wedgwood, S. & Keating, B. A. (2014). APSIM – evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327350.CrossRefGoogle Scholar
Hunt, J. R. & Kirkegaard, J. A. (2011). Re-evaluating the contribution of summer fallow rain to wheat yield in southern Australia. Crop and Pasture Science 62, 915929.CrossRefGoogle Scholar
Jeffrey, S. J., Carter, J. O., Moodie, K. B. & Beswick, A. R. (2001). Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software 16, 309330.CrossRefGoogle Scholar
Keating, B. A., Carberry, P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I., Hargreaves, J. N. G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J. P., Silburn, M., Wang, E., Brown, S., Bristow, K. L., Asseng, S., Chapman, S., McCown, R. L., Freebairn, D. M. & Smith, C. J. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267288.CrossRefGoogle Scholar
Keating, B. A., Herrero, M., Carberry, P. S., Gardner, J. & Cole, M. B. (2014). Food wedges: framing the global food demand and supply challenge towards 2050. Global Food Security 3, 125132.CrossRefGoogle Scholar
Lobell, D. B., Cassman, K. G. & Field, C. B. (2009). Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources 34, 179204.CrossRefGoogle Scholar
Monjardino, M., McBeath, T., Ouzman, J., Llewellyn, R. & Jones, B. (2015). Farmer risk-aversion limits closure of yield and profit gaps: a study of nitrogen management in the southern Australian wheatbelt. Agricultural Systems 137, 108118.CrossRefGoogle Scholar
Nidumolu, U. B., Hayman, P. T., Howden, S. M. & Alexander, B. M. (2012). Re-evaluating the margin of the South Australian grain belt in a changing climate. Climate Research 51, 249260.CrossRefGoogle Scholar
Oliver, Y. M. & Robertson, M. J. (2013). Quantifying the spatial pattern of the yield gap within a farm in a low rainfall Mediterranean climate. Field Crops Research 150, 2941.CrossRefGoogle Scholar
Oliver, Y. M., Robertson, M. J. & Weeks, C. (2010). A new look at an old practice: benefits from soil water accumulation in long fallows under Mediterranean conditions. Agricultural Water Management 98, 291300.CrossRefGoogle Scholar
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. (2012). Recent patterns of crop yield growth and stagnation. Nature Communications 3, article number 1293.CrossRefGoogle ScholarPubMed
Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8, e66428.CrossRefGoogle ScholarPubMed
Stephens, D., Anderson, W., Nunweek, M., Potgieter, A. & Walcott, J. (2011). GRDC Strategic Planning for Investment Based on Agro-Ecological Zones - Second Phase. Perth, Australia: Department of Agriculture and Food Western Australia.Google Scholar
Tilman, D., Balzer, C., Hill, J. & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America 108, 2026020264.CrossRefGoogle Scholar
van Bussel, L. G. J., Grassini, P., Van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., de Groot, H., Saito, K., Cassman, K. G. & van Ittersum, M. K. (2015). From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Research 177, 98108.CrossRefGoogle Scholar
van Herwaarden, A. F., Farquhar, G. D., Angus, J. F., Richards, R. A. & Howe, G. N. (1998). ‘Haying-off’, the negative grain yield response of dryland wheat to nitrogen fertiliser: I. Biomass, grain yield, and water use. Australian Journal of Agricultural Research 49, 10671081.CrossRefGoogle Scholar
van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P. & Hochman, Z. (2013). Yield gap analysis with local to global relevance – a review. Field Crops Research 143, 417.CrossRefGoogle Scholar
van Rees, H., McClelland, T., Hochman, Z., Carberry, P., Hunt, J., Huth, N. & Holzworth, D. (2014). Leading farmers in South East Australia have closed the exploitable wheat yield gap: prospects for further improvement. Field Crops Research 164, 111.CrossRefGoogle Scholar
Van Wart, J., Kersebaum, K. C., Peng, S., Milner, M. & Cassman, K. G. (2013 a). Estimating crop yield potential at regional to national scales. Field Crops Research 143, 3443.CrossRefGoogle Scholar
Van Wart, J., van Bussel, L. G. J., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H., Gerber, J., Mueller, N. D., Claessens, L., van Ittersum, M. K. & Cassman, K. G. (2013 b). Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research 143, 4455.CrossRefGoogle Scholar
Walcott, J. J., Zuo, H., Loch, A. D. & Smart, R. V. (2013). Patterns and trends in Australian agriculture: a consistent set of agricultural statistics at small areas for analysing regional changes. Journal of Land Use Science 9, 453473.CrossRefGoogle Scholar
Whitbread, A. M., Davoren, C. W., Gupta, V. V. S. R., Llewellyn, R. & Roget, D. (2015). Long-term cropping system studies support intensive and responsive cropping systems in the low-rainfall Australian Mallee. Crop and Pasture Science 66, 553565.CrossRefGoogle Scholar
You, L., Crespo, S., Guo, Z., Koo, J., Sebastian, K., Tenorio, M. T., Wood, S. & Wood-Sichra, U. (2009). Spatial Production Allocation Model (SPAM) 2000 Version 3 Release 6. Washington, D.C.: MapSPAM. Available from: http://mapspam.info/spam-2000/ (verified 8 March 2015).Google Scholar
Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research 14, 415421.CrossRefGoogle Scholar
Zajaczkowski, J., Wong, K. & Carter, J. (2013). Improved historical solar radiation gridded data for Australia. Environmental Modelling & Software 49, 6477.CrossRefGoogle Scholar
Supplementary material: File

Gobbett supplementary material

Appendix 1-3

Download Gobbett supplementary material(File)
File 47 KB
14
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Yield gap analysis of rainfed wheat demonstrates local to global relevance
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Yield gap analysis of rainfed wheat demonstrates local to global relevance
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Yield gap analysis of rainfed wheat demonstrates local to global relevance
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *