Variability and Trends in Australian Wheat Yield

Transcription

1 Variability and Trends in Australian Wheat Yield Graeme Hammer Andries Potgieter Agricultural Production Systems Research Unit

2 Overview I. Predicting wheat yield at shire scale throughout Australia II. Annual wheat yield variation and its association with ENSO III. Residual trends in wheat yield after removing effects of variation in annual rainfall IV. Association of residual trends with climate trends

3 (Source: ABARE)

4 I. Predicting Shire Wheat Yield Study to compare modelling approaches to predicting Australian wheat yield at shire scale Model types Empirical regression on rainfall Agro-climatic simple crop water balance and stress index Dynamic crop growth and development simulation Climate (BoM) and shire wheat production (ABS) data for 18-year period for 285 shires urce Hammer GL, Stephens D and Butler D (1996) Development of a national drought alert strategic information system. Vol Report to the Land and Water Resources R&D Corporation. phens DJ (1995) Crop yield forecasting over large areas in Australia. Ph.D Thesis, Murdoch University, Western Australia.

5 I. Predicting Shire Wheat Yield Model Comparison Merredin, WA 0.66

6 I. Predicting Shire Wheat Yield Model Comparison Waggamba, Qld 0.51

7 I. Predicting Shire Wheat Yield R 2 and MAE (t/ha) values from a fit of observed weighted yield (ABS) vs predicted weighted yield for Model Comparison State and National Scales each method for each state and nationally. Region Regression SI APSIM Wheat R 2 MAE R 2 MAE R 2 MAE QLD NSW na na VIC na na S.A na na W.A na na Australia na na

8 I Wheat Model: Predicted vs Actual Yield ( ) SA VIC WA 2 Predicted PredY Yield (t/ha) 2 Aus NSW QLD Actual ABS Yield (t/ha)

9 II. Wheat Yield Variation Shire scale yield simulation using long-term rainfall data for 20 th century with average temperature, radiation and vpd Assume current production technology Simulate spatial and temporal variability in wheat yield associated with rainfall effects Examine effects of ENSO phases urce Potgieter, A.B., Hammer, G.L. and Butler, D. (2002). Spatial and temporal patterns in Australian wheat yield and their ationship with ENSO. Aust J Agric Res 53: tgieter, A.B., Hammer, G.L., Meinke, H., Stone, R.C., and Goddard, L. (2005). Spatial variability in impact on Australian wheat

10 II. Wheat Yield Variation - Spatial NT Legend No Data QLD WA SA NSW Simulated median 20 th century shire wheat yield (t/ha) VIC TAS

11 II. Wheat Yield Variation - Spatial Legend: 0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% NT QLD 70-80% 80-90% % WA SA ± ,040 Kilometers NSW VIC 2004 Shire wheat yield decile map

12 II. Wheat Yield Variation - Spatial NT Legend: 0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% % No data N WA SA NSW Kilometers QLD VIC 2002 Shire wheat yield decile map TAS

13 II. Wheat Yield Variation - Temporal Simulated Australian Wheat Production 1996 planted area Production (Mt) Year

14 II. Wheat Yield Variation ENSO Effect Wheat Yield Relative to All Years shift in mean (no of sd) WA N 135 La Nina El Nino El Nino - El The Nino - The Qld -footprint Eastern The Everywhere footprint footprint Legend: < -2 Legend : < NT 1951 QLD QLD QLD QLD 1969 > NT NT No data 1-2 NT > 2 No data WA WA SA Kilometers WA SA NSW VIC TAS 145 SA NSW VIC TAS Emerald # Roma # SA Goondiwindi # Dalby # NSW 155 VIC TAS Em erald # Legend : < > 2 No data 1987 Roma # Dalby # 25 Goondiwindi NSW # VIC TAS Em erald # Roma # > No data Legend : < -2 Goondiwindi # Dalby #

15 III. Wheat Yield Trends Use simulated and actual shire wheat production (ABS) data for 18-year period for 285 shires Examine association between residual (i.e. after accounting for rainfall effect) and year Linear regression to derive yield trend for each shire

16 III. Wheat Yield Trends

17 III. Wheat Yield Trends - % per annum

18 IV. Associations with Wheat Yield Trends Wheat yield trends can be caused by - Technology change varieties, agronomy Climate change rainfall, temperature, CO 2 Explore associations of yield trends with climate trends Use shire scale data for yield and climate trends to avoid spatial averaging effects

19 IV. Associations with Rainfall Trends Yield% Y = X *** R 2 = 4.7% Shire wheat yield trend (%) vs in-crop rainfall trend (mm/year) Rain

20 IV. Associations with Min Temp Trends Yield% Y = X ns R 2 = 1.3% Shire wheat yield trend (%) vs in-crop min temp trend ( o C/year MinT

21 IV. Associations with Temp Range Trends Yield (%) Y = X *** R 2 = 10.0% TRange (%) ire wheat yield trend (%) vs trend in temp range ( o C/year)

22 Summary Wheat yield can be reliably predicted at shire scale throughout Australia using simple models High spatial and temporal variability in wheat yield generated by rainfall variability Phases of ENSO leave spatially coherent footprints on annual wheat yield variation Residual underlying long-term trends in wheat yield are small (0-5% per annum) Climate trends, other than in temperature range (i.e. atmospheric demand), have minimal effects on yield trends

23 Variability and Trends in Australian Wheat Yield Graeme Hammer Andries Potgieter Agricultural Production Systems Research Unit