Agricultural Production Systems Research Unit

Variability and Trends in Australian Wheat Yield

Graeme HammerAndries Potgieter

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

(Source: ABARE)

I. Predicting Shire Wheat Yield

Study to compare modelling approaches to predicting Australian wheat yield at shire scale

Model types Empirical – regression on rainfallAgro-climatic – simple crop water balance and stress indexDynamic – crop growth and development simulation

Climate (BoM) and shire wheat production (ABS) data for 18-year period 1975-1992 for 285 shires

Source – Hammer GL, Stephens D and Butler D (1996) Development of a national drought alert strategic information system. Vol 6a. Report to the Land and Water Resources R&D Corporation.

Stephens DJ (1995) Crop yield forecasting over large areas in Australia. Ph.D Thesis, Murdoch University, Western Australia.

0.84

0.66

0.73

Model Comparison Merredin, WA

I. Predicting Shire Wheat Yield

0.75

0.51

0.73

Model Comparison Waggamba, Qld

I. Predicting Shire Wheat Yield

R2 and MAE (t/ha) values from a fit of observed weighted yield (ABS) vs predicted weighted yield for

each method for each state and nationally.

Region Regression SI APSIM Wheat

R2 MAE R2 MAE R2 MAE

QLD 0.86 0.15 0.82 0.17 0.73 0.22

NSW 0.89 0.12 0.87 0.12 na na

VIC 0.88 0.13 0.90 0.12 na na

S.A. 0.88 0.11 0.85 0.13 na na

W.A. 0.86 0.07 0.91 0.08 na na

Australia 0.92 0.07 0.90 0.07 na na

Model Comparison – State and National Scales

I. Predicting Shire Wheat Yield

0.0 0.8 1.6 2.4 0.0 0.8 1.6 2.4

0.0 0.8 1.6 2.4

ABS

1

2

1

2

Pre

dY

Aus NSW QLD

SA VIC WA

SI Wheat Model: Predicted vs Actual Yield (1975-93)

Actual Yield (t/ha)

Pred

icted

Yiel

d (t/

ha)

II. Wheat Yield Variation

Shire scale yield simulation using long-term rainfall data for 20th 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

Source – Potgieter, A.B., Hammer, G.L. and Butler, D. (2002). Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO. Aust J Agric Res 53:77-89.

Potgieter, A.B., Hammer, G.L., Meinke, H., Stone, R.C., and Goddard, L. (2005). Spatial variability in impact on Australian wheat yield reveals three putative types of El Nino. J Climate, in press.

II. Wheat Yield Variation - Spatial

WA

NT

QLD

SA

NSW

VIC

TAS

Legend0.5 - 1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0 3.0 - 3.5 3.5 - 4.0 No Data

Simulated median 20th century shire wheat yield (t/ha)

II. Wheat Yield Variation - Spatial

2004 – Shire wheat yield decile map

WA

NT

QLD

SANSW

VIC

Legend:0-10%

10-20%

20-30%

30-40%

40-50%

50-60%

60-70%

70-80%

80-90%

90-100%

±0 260 520 780 1,040130

Kilometers

II. Wheat Yield Variation - Spatial

2002 – Shire wheat yield decile map

WA

NT

SA

NSW

VIC

TAS

Legend:0-10%10-20%20-30%30-40%40-50%50-60%60-70%70-80%80-90%90-100%No data

QLD

N

0 200 400 Kilometers

II. Wheat Yield Variation - Temporal

Year

Pro

duct

ion

(Mt)

1901

1906

1911

1916

1921

1926

1931

1936

1941

1946

1951

1956

1961

1966

1971

1976

1981

1986

1991

1996

2001

10.0

12.5

15.0

17.5

20.0

22.5

Simulated Australian Wheat Production – 1996 planted area

II. Wheat Yield Variation – ENSO Effect

Wheat Yield Relative to All Years – shift in mean (no of sd)

QLD

N

WA

NT

SA

NSW

VIC

TAS35°

35°

25°25°

15°15°

105° 115°

115°

125°

125°

135°

135°

145°

145°

155°

155°

165°

165°

0 200 400 600 Kilometers

Legend:< -2-2 - -1-1 - -0.5-0.5 - -0.25-0.25 - 00 - 0.250.25 - 0.50.5 - 11 - 2> 2No data

La Nina

#

#

#

#WA

NT

SA

NSW

VIC

TAS

Roma

Dalby

Emerald

Goondiwindi

Legend :< -2-2 - -1-1 - -0.5-0.5 - -0.25-0.25 - 00 - 0.250.25 - 0.50.5 - 11 - 2> 2No data

QLD

190519111951195319691987199119921993

El Nino - The Qld footprint

#

#

#

#WA

NT

SA

NSW

VIC

TAS

Roma

Dalby

Emerald

Goondiwindi

Legend :< -2-2 - -1-1 - -0.5-0.5 - -0.25-0.25 - 00 - 0.250.25 - 0.50.5 - 11 - 2> 2No data

QLD

192519411946197219771997

El Nino - The Eastern footprint

#

#

#

#WA

NT

SA

NSW

VIC

TAS

Roma

Dalby

Emerald

Goondiwindi

Legend :< -2-2 - -1-1 - -0.5-0.5 - -0.25-0.25 - 00 - 0.250.25 - 0.50.5 - 11 - 2> 2No data

QLD

190219141919194019571965198219942002

El Nino - The Everywhere footprint

III. Wheat Yield Trends

Use simulated and actual shire wheat production (ABS) data for 18-year period 1975-1992 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

III. Wheat Yield Trends

III. Wheat Yield Trends - % per annum

IV. Associations with Wheat Yield Trends

Wheat yield trends can be caused by -Technology change – varieties, agronomyClimate change – rainfall, temperature, CO2

Explore associations of yield trends with climate trends

Use shire scale data for yield and climate trends to avoid spatial averaging effects

IV. Associations with Rainfall Trends

Rain

Yiel

d%

-5 0 5 10

-20

24

6

Shire wheat yield trend (%) vs in-crop rainfall trend (mm/year)Data for 285 shires for period 1975-1992

Y = 1.10 + 0.12X ***

R2 = 4.7%

MinT

Yiel

d%

-0.10 -0.05 0.0 0.05 0.10 0.15

-20

24

6

IV. Associations with Min Temp Trends

Shire wheat yield trend (%) vs in-crop min temp trend (oC/year)Data for 285 shires for period 1975-1992

Y = 1.28 + 2.84X ns

R2 = 1.3%

TRange (%)

Yiel

d (%

)

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8

-20

24

6

IV. Associations with Temp Range TrendsY = 1.45 - 1.35X ***

R2 = 10.0%

Shire wheat yield trend (%) vs trend in temp range (oC/year)Data for 285 shires for period 1975-1992

SummaryWheat 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

Agricultural Production Systems Research Unit

Variability and Trends in Australian Wheat Yield

Graeme HammerAndries Potgieter