prediction of corn yield potential using the hybrid-maize

Prediction of corn yield potential using the Hybrid-Maize model

Patricio Grassini

Department of Agronomy and

Horticulture

University of Nebraska-Lincoln

Yie

ldGap 1

Gap 2

Yield potential Water-limitedyield potential

Actual yield

Other limiting factors:

NutrientsWeedsPestsOthers

CO2

Solar radiationTemperature

Growth periodPlant density

Attainable yieldwith availablewater supply:

SoilRainfall

Irrigation

Daily intercepted solar radiation

f(x)= solar radiation, LAI

Length crop cycle

Cumulative intercepted

solar radiation

Gross assimilation

Dry matter

production Maintenance

Respiration

Growth

respiration

YIELD

POTENTIAL

Kernel #

Kernel

growth rate

Grain-filling

duration

[around silking]

Kernel weight

[grain-filling]

Temperature

Crop models: tools to predict yield potential

Water

supply

Mission impossible: to have a perfect model Desired attribute Explanation

Daily step simulation Simulation of daily crop growth and development

based on weather, soil, and crop physiological

attributes

Flexibility to simulate management practices Key management practices include: sowing date,

plant density, cultivar maturity, and irrigation

Simulation of fundamental physiological processes Simulation of key physiological processes such as

crop development, net carbon assimilation, biomass

partitioning, crop water relations, and grain growth

Crop specificity Should reflect crop-specific physiological attributes

for respiration and photosynthesis, critical stages

and growth periods that define vegetative and grain

filling periods, and canopy architecture

Minimum requirement of crop ‘genetic’ coefficients Minimum requirement of crop-site ‘genetic’

coefficients, such as maximum leaf area index, date

of flowering, etc.

Validation against data from field crops that

approach YP and YW

Comparison of model outcomes (grain yield,

aboveground dry matter, crop evapotranspiration)

against actual measured data from field crops that

received management practices conducive to

achieve YP (irrigated) or YW (rainfed crops)

User friendly Models embedded in user-friendly interfaces, where

required data inputs and outputs can be easily

visualized, and flexibility to modify default values for

internal parameters

Hybrid-Maize model

Simulates growth and development of maize for yield

potential and water-limited situations.

T-driven growth and development functions from CERES-Maize

Mechanistic descriptions of light interception, photosynthesis and organ-specific respiration from generic models (SUCROS/INTERCOM/WOFOST)

A linear relationship between growing degree-days (GDD) from emergence to silking and GDD from emergence to physiological maturity is used for prediction of day of silking

Yang, H.S., A. Dobermann, J.L. Lindquist, D.T. Walters, T.J. Arkebauer, and K.G. Cassman. 2004. Hybrid-Maize - a maize simulation model that combines two crop modeling approaches. Field Crops Res. 87:131-154.

Champaign, IL

Manchester, IA

North Platte, NE

Clay Center, NE

Mead, NE

Fully-irrigated crops ( )

Fully-irrigated crops

n = 30, RMSE = 1.0 Mg ha-1

Rainfed crops:

+15%

-15%

1:1

Rainfed crops

n = 13, RMSE = 1.2 Mg ha-1

0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18 20

Simulated grain yield (Mg ha-1)

Ob

serv

ed

gra

in y

ield

(M

g h

a-1

)

Champaign, IL

Manchester, IA

North Platte, NE

Clay Center, NE

Mead, NE

Fully-irrigated crops ( )

Fully-irrigated crops

n = 30, RMSE = 1.0 Mg ha-1

Rainfed crops:

+15%

-15%

1:1

Rainfed crops

n = 13, RMSE = 1.2 Mg ha-1

0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18 20

Simulated grain yield (Mg ha-1)

Ob

serv

ed

gra

in y

ield

(M

g h

a-1

)

Hybrid-Maize description and validation

Validation of the Hybrid-Maize model for

irrigated and rainfed crops:

Grassini et al. (2009): Agric Forest Meteorology

http://www.hybridmaize.unl.edu/

Hybrid-Maize does not account

for nutrient deficiencies, insect

pests, diseases or weeds

Yang et al., 2004

http://www.hybridmaize.unl.edu/

Uses of Hybrid-Maize model

• Quantify site-specific yield potential and its variability

• Diagnosis of actual yields by comparison against yield potential

• Estimate yield goals for fertilizer recommendations

• Evaluate changes in yield potential using different choices of planting date, hybrid maturity, and plant density

• Analyze corn growth in specific years

• Diagnose and explore options for irrigation water management

• Conduct in-season simulations to evaluate actual growth and forecast final grain yield

Input data

Daily weather (solar radiation, max. and min. T,

rainfall)

Crop management (date of planting, GDD for hybrid,

plant density, sowing depth)

For simulating water-limited yield: max. rooting depth,

texture class and bulk density in topsoil and subsoil),

and soil water content at planting date

Optional: change model parameters

Hybrid-specific crop coefficients

General model coefficients describing crop growth

and development

Soil physical properties for different soil texture

classes

Hybrid-Maize model

Hands-on session on Hybrid Maize

Irrigated maize in central USA

• Central US Great Plains includes

one of the largest irrigated areas

cultivated with maize in the world

(3.2 million ha)

• Irrigated maize accounts for 60%

of total annual maize production

in the region (~60 million Mg)

• High and stable irrigated maize

production supports economic

viability of associated industries

such as cattle feeding operations

and ethanol plants

Distribution of US irrigated maize cropland:

Low : 0

High : 4456

Data mapped by P. Grassini

based on Portmann et al. (2010):

Global Biogeochemical Cycles

20-d

to

tal ra

infa

ll o

r c

rop

ET

(m

m)

0

50

100

150

200

0 20 40 60 80 100 120 140 Days after sowing

0

50

100

150

200

ETC

Rainfall

ETC

Rainfall

maturity

Silking maturity

Akron (WEST)

Mead (EAST)

Silking

Water availability central US Great Plains

0

200

400

600

800

1000

1200

-104 -102 -100 -98 -96 -94 -92 Longitude (º)

Maize water

demand (mm)

Total sowing-to-maturity

rainfall (mm)

County-average yields in Nebraska

NASS-USDA (2004-2008)Data compiled and mapped

by Patricio Grassini

0 70 140 21035 Kilometers

Maize production < 1500

ha in white counties

N Irrigated maize Rainfed maize

3.7 – 5.6

9.4 – 11.3

7.5 – 9.4

5.6 – 7.5

1.8 – 3.7

Rainfed yield (Mg/ha)

9.4 – 10.4

12.4- 13.4

11.4 – 12.4

10.4 – 11.4

8.4 – 9.4

Irrigated yield (Mg/ha)

#1. Perform separate single-year simulations for

irrigated and rainfed maize in 2004 at Mead, NE based

on the following inputs (and fill out requested data)

Inputs Irrigated Yp Rainfed Yw

Location (choose weather file) Mead, NE Mead, NE

Year 2004 (single year) 2004 (single year)

Start from: planting planting

Planting date April 30 April 30

Seed depth (cm) 4 4

Seed brand Pioneer Pioneer

Maturity (leave other options

unchecked)

GDD 1510 GDD 1510

Plant population (x1000/ha) 80 64

Water Optimal Rainfed

Maximum rooting depth (cm) - 150

Texture - Silty clay loam [top- and sub-soil]

Initial moisture status - Wet (100% FC) [top- and sub-soil]

Bulk density (g/cm3) - 1.3

Output for irrigated maize in Mead, NE in 2004: Results Tab

#1. Perform separate single-year simulations for

irrigated and rainfed maize in 2004 at Mead, NE based

on the following inputs (and fill out requested data):



Year 2004 (single year) 2004 (single year)



Seed depth (cm) 4 4



unchecked)

GDD 1510 GDD 1510



Maximum rooting depth (cm) - 150

Texture - Silty clay loam [top- and sub-soil]

Initial moisture status Wet (100% FC) [top- and sub-soil]

Bulk density (g/cm3) 1.3

2005

2006

2007

Challenges to simulate Yw:

I. Spatial variability in

rainfall

Cumulative growing-season

rainfall (mm):

Example: spatial

variation in rainfall

across central Nebraska

Rain gauges

120 km

Rain gauge

A

Rain gauge

B

Rain gauge

C

Example: We have a field surrounded

by three rain gauges (A, B, C). The

distance from the field to the rain gauge

is 5, 9, and 13 miles, respectively

The relative contribution (‘weight’) of

each gauge to the rain in the field #55 is

inversely related to its distance:

(Rain gauge X – field)-2 /

[(A-field)-2 + (B-field)-2 + (C-field)-2]

e.g.

gauge A – field = 0.040 / 0.058 = 0.69

gauge B – field = 0.012 / 0.058 = 0.21

gauge C – field = 0.006 / 0.058 = 0.10

Then, for a given rainy day, given rain A=10 mm; rain B=50 mm, and rain C=20 mm;

the interpolated rain in field #55 can calculated as:

10 mm * 0.69 + 50 mm * 0.21 + 20 mm * 0.10 = 19.4 mm

Inverse distance weighting: interpolation of daily rainfall

Challenges to simulate Yw: II. Soil water at planting

Plant available soil water, expressed as % of maximum available water,

in Nebraska around maize planting date (first week of May) in 2012

Source: High Plains Climate Center, http://www.hprcc.unl.edu/awdn/soilm/

0

0.25

0.5

0.75

1

2 6 10 14

Maize grain yield (Mg/ha)

Cu

mu

lati

ve

fre

qu

en

cy

Low soil water

High soil water LOW

WATER

HIGH WATER

Yield (Mg/ha)

7.8 8.9

CV (%) 30 15

Influence of initial soil water content on Yw

Simulated maize grain yields in North Platte, NE based on 20-y of weather data and actual

management and soil properties, for two scenarios of soil water content by sowing date: ‘low’

and ‘high’ water (33% and 100% of total plant available water)

Grassini et al. (2009): Agric Forest Meteorology

Output for rainfed maize in Mead, NE in 2004: Results tab

Stress index: 1 – (Actual

transpiration-to-potential

transpiration)

[more severe water stress

with increasing index)

#2. Perform separate long-term simulations for irrigated and

rainfed maize at Mead, NE based on the following inputs:


Location (choose weather

file)

Mead, NE Mead, NE

Year Long-term runs (1982-2011) Long-term runs (1982-2011)



Seed depth (cm) 4 4



unchecked)

GDD 1510 GDD 1510


Water Optimal (check “estimate water

requirement” option)

Rainfed

Maximum rooting depth (cm) 150 150

Texture Silty clay loam [top- and sub-soil] Silty clay loam [top- and sub-soil]

Initial moisture status Wet (100% FC) [top- and sub-soil] Wet (100% FC) [top- and sub-soil]

Bulk density (g/cm3) 1.3 1.3

Long-term simulation of irrigated maize Yp in Mead, NE

#2. Perform separate long-term simulations for irrigated and

rainfed maize at Mead, NE based on the following inputs:



Year Long-term runs (1982-2012) Long-term runs (1982-2012)



Seed depth (cm) 4 4



unchecked)

GDD 1510 GDD 1510



Maximum rooting depth (cm) 150 150

Texture Silty clay loam [top- and sub-soil] Silty clay loam [top- and sub-soil]

Initial moisture status Wet (100% FC) [top- and sub-soil] Wet (100% FC) [top- and sub-soil]

Bulk density (g/cm3) 1.3 1.3

Long-term simulation of rainfed maize Yw in Mead, NE

1989

2007

2007

1989

25%-percentile year: 2007

Total rain: 403 mm

Yield: 10.6 Mg/ha

Median year: 1989

Total rain: 308 mm

Yield: 12.4 Mg/ha

Rainfall patterns

Simulation 1: Rainfed yield

Simulation 2: Analysis of farmer irrigation

Simulation 3: Addition of early season irrigation

on June 22, 2002

Simulation 4: Addition of late irrigation on

August 15, 2002

Simulation 5: Eliminate June 22 irrigation but keep

August 15 irrigation

#3. Post-season analysis of irrigation management

and water-use efficiency in 2002 at O’Neill, NE

Data inputs

Inputs Rainfed/Irrigated Yw

Location (choose weather file) Mead, NE

Year Long-term runs (1982-2012)

Start from: planting

Planting date April 30

Seed depth (cm) 4

Seed brand Generic

Maturity (leave other options unchecked) GDD 1400

Plant population (x1000/ha) 74

Water Rainfed/Irrigated

Maximum rooting depth (cm) 100

Texture Sandy loam [top- and sub-soil]

Initial moisture status Wet (100% FC) [top- and sub-soil]

Bulk density (g/cm3) 1.3

RAINFED SIMULATION

Simulation 1:

Rainfed

Simulation 1:

Rainfed

Farmers’ irrigation: irrigation starts on June 30 and is irrigated weekly @ 30mm / irrigation ending in late July because of significant rainfall in August

Month Day Inches

7 1 30

7 6 30

7 11 30

7 17 30

7 22 30

7 28 30

SIMULATION OF FARMER’ MANAGEMENT

Simulation 2:

Farmer irrigation

Simulation 2:

Farmer irrigation

To test the importance of early stress, we will add an additional 30 mm early irrigation on 6/22 to mitigate the early season stress

Month Day Applied irrigation (mm)

6 22 30

6 30 30

7 6 30

7 11 30

7 17 30

7 22 30

7 28 30

SIMULATION OF FARMER’ MANAGEMENT PLUS EARLY IRRIGATION

Simulation 3:

Farmer irrigation

Plus early 6/22

irrigation

Simulation 3:

Farmer irrigation

Plus early 6/22

irrigation

Farmer Irrigation

11.2 Mg/ha

Farmer Irrigation

plus early irrigation

11.2 Mg/ha

Rainfed

7.4 Mg/ha

Simulation 3:

Farmer irrigation

Plus early 6/22

irrigation

In addition to the early 6/22 irrigation we add as additional 30 mm on August 15 to avoid late season stress

Month Day Applied irrigation (mm)

6 22 30

6 30 30

7 6 30

7 11 30

7 17 30

7 22 30

7 28 30

8 15 30

Simulation 4:

Farmer irrigation

plus early 6/22

and late 8/15

irrigation

Since early (6/22) irrigation had no impact on yield we’ll look at the effect of adding only the late season (8/15) irrigation

Month Day Applied irrigation water (mm)

6 30 30

7 6 30

7 11 30

7 17 30

7 22 30

7 28 30

8 15 30

Simulation 5:

Farmer

irrigation

plus late 8/15

irrigation

Farmer

Irrigation

11.2 Mg/ha

Farmer

Irrigation

plus early

irrigation

11.2 Mg/ha

Rainfed

7.4 Mg/ha

Farmer

Irrigation

plus early

and late

irrigations

12.6 Mg/ha

Farmer

Irrigation

plus early

and late

irrigations

12.6 Mg/ha

Summary of 2002 irrigation analysis

Simulation

Water input (mm) Grain yield

(Mg/ha)

IWUE

Rainfall Irrigation Total

#1. Dryland 185 0 185 7.4 -

#2. Farmer’s management 185 180 365 11.2 21

#3. Farmer’s management +

early irrigation

185 210 395 11.2 18


early & late irrigation

185 240 425 12.6 22


late irrigation

185 210 395 12.6 25

IWUE = irrigation water use efficiency

(yield irrigated – yield rainfed) / applied irrigation

prediction of corn yield potential using the hybrid-maize

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