Modeling for crop production - indico.ictp.itindico.ictp.it/event/a12165/session/27/contribution/21/material/0/... · production Energy and mass ... Plant growth ↓Ethylene ... Profit

2444-14

College on Soil Physics - 30th Anniversary (1983-2013)

DOURADO NETO Durval

25 February - 1 March, 2013

Universidade de Sao Paulo Escola Superior de Agricultura 'Luiz de Queiroz'

Departamento de Produçao Vegetal, Avenida padua Dias, n. 11, Caixa Postal 9, Piracicaba 13.418-900

BRAZIL BRAZIL

Modeling for crop production

Crop Science Department, Esalq, University of São Paulo

College on Soil Physics 2013 – 30th Anniversary

Durval Dourado Neto

26 February 2013


Energy and mass balances at the Land-Water-Atmosphere continuum

Modeling: the philosophic procedure…

Observation

Abstraction The treatment applied to the FACT as function of experience and theoretical knowledge

Model as Resulted of VALUE applied to the FACT

FACT

VALUE

RULE

Productivity

Level of Productivity

Prod

uctivity

typ

e

Rs, T, H, Gen, Popmax

Determinant variables

Limiting variables W, Pop

Depleting variables... W*, Nut*, D*, P*, PD*, $$$

Potential Productivity (PP)

Obtainable Productivity

Maximum economical Productivity

MINIMUM intervention…

WITH intervention…

165

pl/ha 1

Corn Plant Population and productivity g/plant kg/ha

critical point

(550 grain/ear). (1 ear/plant).(0,3g/grain) =165g/plant

0,165 0,330

1.650

?

7.560

2 10.000 Pc 70.000

Popmax

108

Without competition

With competition

C

pl/ha

g/plant kg/ha

Pc 70.000

Popmax

50.000

Pop

Pc

C

C’ B

A

B’

A’

Corn Plant Population and productivity

pl/ha

g/plant kg/ha

70.000 (Popm

ax )

50.000

C

C’ B

A

B’

A’ A’’

B’’

C’’

60.000

Corn Plant Population and productivity

Productivity

Level of Productivity Pr

oduc

tivity

typ

e


Determinant variables

Limiting variables

W, Pop

Depleting variables... W*, Nut*, D*, P*, PD*, $$$

Potential Productivity (PP)

Obtainable Productivity

Maximum economical Productivity

De Wit

P, D e PD

Qo

Soil

Plant

Atmosphere

Productivity (xls)

Summary 1.  What is water deficiency? 2.  Genotype - Cultivar

(a)  Productivity (potential, attainable and actual productivity) (b)  Photosynthesis (c)  Leaf area index, row spacing and plant population (d)  Carbon partition, Conversion efficiency, hormones and

crop cycle (e)  Light and carbon use efficiency

3.  Environment (a)  Soil water holding capacity and flux density (b)  Water and nutrients use efficiency

What is water deficiency?

ETpETa =

SWHCSWHETpETa =

Thornthwaite & Mather (1955)


ETpETa =( )SWHCp

SWHETpETa.1−

=

Rijtema & Abouckhaled (1975)


( ) ⎥⎦

⎤⎢⎣

⎡

⎭⎬⎫

⎩⎨⎧

−−=

SWHCpSWHETpETa.1

.cos12

πETpETa =

Dourado Neto & Jong van Lier (1993)

DOURADO NETO, Durval ; JONG VAN LIER, Q. Estimativa do armazenamento de água no solo para realização de balanço hídrico. Revista Brasileira de Ciência do Solo, Campinas,SP, v. 17, n. 1, p. 9-15, 1993.


What is water deficiency? ETpETa =

Thornthwaite & Mather (1955)

( )ZeSWHC pwpfc θθ −=10( )ZeSWH pwpa θθ −=10


Rijtema & Abouckhaled (1975)

Plant physiologist

Matric flux potential M procedure

TpTa

=αWater stress reduction factor

JONG VAN LIER, Q. ; Dourado Neto, D.; METSELAAR, K. Modeling of transpiration reduction in van Genuchten Mualem type soils. Water Resources Research, v. 45, p. 1-9, 2009.

HW


HW (12,5%)

SWHC (12,5%)

Solids (50%)

Air (25%)

SWHC per unit of Ze...

Ze

Z, cm

0 θcc θpmp

SWHC=10.(θfc- θwp).Ze

12.500 L.ha-1.cm-1

solids

ar

θ, cm3.cm-3

ETa, mm.dia-1

Ze

SWHC θcrit

1.25 mm.cm-1

0,50mm.cm-1

2.00 mm.cm-1

q < ET

Water stress...

q > ET

NO water stress...

ETa = q ETp

1 cm

NBF

N

0 θfc

θwp θ, cm3.cm-3 θcrit

q < ET

Water stress... q > ET

NO water stress...

ETa = q ETp

CO2+H2O CH2O+O2 44g 18g 30g 32g

ETa

DH

The effect of water deficiency on productivity

2,0 mm/cm

Ze

L/ha

0,5 mm/cm

1,25 mm/cm

1 cm

12.500

Comments about higher SWHC/Ze:

-  Higher plant population

-  Short dry season: less problem

- Superficial soil fertility: not impeditive to root growth

The effect of water deficiency on productivity

Less CO2... Less CH2O... Less Nutrients... Less DM production... ...Less productivity

Water…

Demand

Offer

Stomatal cavity

Wat

er(li

quid

pha

se)

Vapo

ur

( ) ( )RHTR ln2735.55 +=ψ

( )[ ] ( ) atmKKmol

LatmLmolpm

air 9465.0ln27327.

.082.05.552 −=+⎟⎠

⎞⎜⎝

⎛=ψAIR: Day...

AIR: Night (Dew point)... ( )[ ] ( ) atmK

KmolLatm

Lmolam

air 01ln27318.

.082.05.552 =+⎟⎠

⎞⎜⎝

⎛=ψ

( )[ ] ( ) atmKKmol

LatmLmol

leaf 1598907.0ln27327.

.082.05.55−=+⎟

⎠

⎞⎜⎝

⎛=ψ

( )[ ] ( ) atmKKmol

LatmLmol

fcsoil 14.09999.0ln27327.

.082.05.55−=+⎟

⎠

⎞⎜⎝

⎛=ψ

LEAF...

SOIL...

⎟⎟⎠

⎞⎜⎜⎝

⎛ −=

LgradTp leafair ψψ

ψ

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛ −===

LgradgradKqTa soilsoil

root ψψψψθ

Transpiration and C assimilation Transpiration Assimilation

Transpiration and C assimilation rate (TCAR

Transpiration (T) Assimilation (A)

TCAR

PAR

Evapotranspiration

t, day

ETa, mm.day-1

0 120

5mm.day-1

5mm.day-1.120days=600 mm or 6.000.000 L.ha-1

3.000 kg.ha-1

3mm.day-1

3mm.day-1.120days=360 mm or 3.600.000 L.ha-1

1.500 kg.ha-1

WUE=2.000 L.kg-1

WUE=2.400 L.kg-1

Transpiration

t, day

Ta, mm.day-1

0 120

4.75mm.day-1

4.75mm.day-1.120days=570 mm or 5.700.000 L.ha-1

3.000 kg.ha-1

2.85mm.day-1


1.500 kg.ha-1

WUE=1.900 L.kg-1

WUE=2.280 L.kg-1

E=0.05xETa

Water in the TDM and exported by grain

t, day

Ta, mm.day-1

0 120

4.75mm.day-1


3.000 kg.ha-1

2.85mm.day-1


1.500 kg.ha-1

WUE=295 mL.kg-1

WUE=362 mL.kg-1

HI=0.25/0.35 kg/kg u=0.13 g/g

E=0.05xETa

Productivity Level

Prod

uctivity CO2, Rs, T, H and

Genotype

Defining factors

Limiting

factors Water and Nutrients

Reducing factors Weeds, Pests, Diseases, pollutants, R$ and management (technology)

Potential productivity

Attainable productivity

Actual productivity

Productivity

Pests

Solar radiation

Soil

Plant

Atmosphere

Diseases

Weeds

Productivity

Productivity depends on:

•  Environment – Sowing date

•  Solar radiation, temperature and photoperiod •  Rainfall

– Soil type •  Soil water holding capacity and Flux density

•  Genotype – Soybean cultivar •  Crop management

– Based on phenology and economic decision

Photosynthesis

Gross photosynthesis

Enzyme activity

RuBisCO: Ribulose bisphosphate carboxylase-oxygenase

Respiration

Net photosynthesis

Temperature, °C

kg.ha-1 CH2O Respiration

Gross photosynthesis

Net photosynthesis

Tmín Tmax Toptimum

Genotype definition…

Net photosynthesis

Leaf Area Index (LAI, m2.m-2)

Leaf Area Index (LAI, m2.m-2)

Biomass and CO2 assimilation

LAI

Gross photosynthesis Respiration

Net photosynthesis

Water

Plant population

Carbon Partition

Soybean phenology and critical periods

DDays após o florescimento 0 10 20 30 40 50 60 70

R1 R2 R3 R4 R5 R6 R7 R8

Grain filling

Pod development

Flowering

Vegetative growth

Reproductive phase

Undeterminated and Determinated growth

Dry Matter Partition

Cycle: Superprecoceous Emergence V1 V2 V3 V4 V5 V6 V7 R1 R2 R3 R4 R5 R6 R7 R8 R9

1 2 3 4 5 6 11 16 21 26 31 36 42 43 44 45 46 48 49 52 53 54 55 64 65 66 67 81 Days Cycle: Precoceous Emergence V1 V2 V3 V4 V5 V6 V7 R1 R2 R3 R4 R5 R6 R7 R8 R9

1 2 3 4 5 6 12 18 24 30 36 42 47 48 49 50 51 53 54 57 58 70 71 82 83 93 94 110 Days Cycle: Medium Emergence V1 V2 V3 V4 V5 V6 V7 V8 R1 R2 R3 R4 R5 R6 R7 R8 R9

1 2 3 4 5 6 13 20 27 34 41 48 55 62 63 64 65 66 67 69 74 75 85 86 97 98 108 109 124 125 Days Cycle: Late Emergence V1 V2 V3 V4 V5 V6 V7 V8 V9 R1 R2 R3 R4 R5 R6 R7 R8 R9

1 2 3 4 5 6 13 20 27 34 41 48 55 62 66 67 68 69 71 72 77 78 88 89 100 101 112 113 123 124 139 140 Days

Phenology – critical periods

Water for stablishment No water for

harvesting Water during critical period

CO2 Photosynthesis

CH2O

CO2 Respiration (Maintence)

Respiration (Growth) CO2

Dry Matter

Dry Matter composition: Fats Lignins Proteins Carbohydrates Organic acids Minerals

H2O

45% C 45% O 6% H 96.00%

3.50% N,P,K,Ca,Mg,S

100.00%

CO2+H2O CH2O+O2

N

0.03% B,Cl,Cu,Fe,Mn,Mo,Zn

0.47% others

Conversion efficiency

ppm Nutriente %

C = Carbon 450,000 ppm 450,000 C 45.00H = Hydrogen 60,000 ppm 60,000 H 6.00O = Oxygen 450,000 ppm 450,000 O 45.00

96.00

P = Phosphorus 2,000 ppm 2,000 P 0.20K = Potassium 10,000 ppm 10,000 K 1.00N = Nitrogen 15,000 ppm 15,000 N 1.50S = Sulfur 1,000 ppm 1,000 S 0.10Ca = Calcium 5,000 ppm 5,000 Ca 0.50Mg = Magnesium 2000 ppm 2,000 Mg 0.20

3.50

Fe = Iron 100 ppm 100 Fe 0.01000Mo = Molybdenum 0.1 ppm 0.1 Mo 0.00001B = Boron 20 ppm 20 B 0.00200Cu = Copper 6 ppm 6 Cu 0.00060Mn = Manganese 50 ppm 50 Mn 0.00500Zn = Zinc 20 ppm 20 Zn 0.00200Cl = Chlorine 100 ppm 100 Cl 0.01000

0.03995,296

1,000,0004,704 0.47Outros

Cl = Chlorine 100 ppm

Macronutrientes e nutrientes orgânicosNutrientes orgânicos

C = Carbon 450,000 ppmH = Hydrogen 60,000 ppmO = Oxygen 450,000 ppm

Zn = Zinc 20 ppm

P = Phosphorus 2,000 ppmK = Potassium 10,000 ppm

SUBTOTAL

SUBTOTAL

SUBTOTAL

B = Boron 20 ppmCu = Copper 6 ppmMn = Manganese 50 ppm

SUBTOTAL

Microutrientes

N = Nitrogen 15,000 ppm

Macronutrientes

Fe = Iron 100 ppmMo = Molybdenum 0.1 ppm

TOTAL

S = Sulfur 1,000 ppmCa = Calcium 5,000 ppmMg = Magnesium 2000 ppm

Soybean composition:

•  Protein = 40 %

•  Oil = 20 %

1 kg of CH2O from Net Photosynthesis produces

0.55 kg of Dry Matter of seeds

Conversion efficiency

Plant growth

↑Cytokinin

↑Auxin

↑gibberellin

Plant growth

↓Ethylene ↓ABA (Abscisic Acid)

Promotor and inhibitor hormones and effect of Ca, B and enzymes

↓Senecence ↑Leaf DM and LAI

↑Leaf DM and LAI

↑RuBisCO ↑ division and cell expansion

↑IAA (Indole Acetic Acid) ↓Senescence ↑division and cell expansion

↓Chlorophyl degradation ↑division and cell expansion

↑activity of POD, SOD and CAT enzymes

Antioxidant effect (↓ROS): Keep membrane integrity and inhibit the premature senescence and damage on

DNA

ff

Ca and B: less flower aborption

Soybean: environment and plant physiology

Soybean - USA

The economical actual productivity defines TECHNOLOGY (Pme, sc.ha-1)

P (sc.ha-1)

Fixed cost

Variable cost

Total Cost

Gross revenue

R$.ha-1

Pme

Profit Maximum profit

Selection of cultivars

Diseases management

Sowing date

Row spacing and plant population

Weeds management

Soil fertility

P

Iwoa State University

Higher productivities: 6000 kg/ha in Whiting, Iowa

4800 kg/ha - Est of Iwoa

3900 to 4200 kg/ha in Des Moines, Iwoa

Pests management

Management for high productivity (P)

1. Limiting values (h or θθ) of macroscopic root water extraction models are a function of potential transpiration rate, soil hydraulic properties and root density.

2. hmean,lim and θmean,lim can be calculated from the matric flux potential at 0.53 rm, which is a function of transpiration rate and root density and which depends on a soil-specific K(h) function.

Water deficiency

Final consideration

Final consideration

1. Water use efficiency 2. Nutrients use efficiency 3. Genotype

(a) Chlorophyll content – water and N (b) Leaf area index (c) Carbon partition and Conversion efficiency:

Total dry matter (d) Hormones (ethylene) (e) Crop cycle duration (f) Productivity

4. Knowledge and technology

80 100 120 1400

5

10

15

20

100 120 140

5

10

15

20

5

10

15

20

B

CDSecOP CDSecN

D

BRSSecOP BRSSecN

E

CNQIrrOP CNQIrrN

D ias após semeadura

F

CNQSecOP CNQSecN

D ias após semeadura

A

CDIrrOP CDIrrN

C

BRSIrrOP BRSIrrN

Teor

de cl

orofi

la (m

g l-1 )

Chlorophyll content: water and N

Final consideration

Leaf area index

0

5

10

15

0

5

10

0 35 70 1050

5

10

0 35 70 105 140

B

C D

E F

A

CDIrrOP CDIrrN

CDSecOP CDSecN

BRSIrrOP BRSIrrN

Índi

ce d

e ár

ea fo

liar

BRSSecOP BRSSecN

CNQSecOP CNQSecN

Dias após semeadura

CNQIrrOP CNQIrrN


Final consideration

Total Dry Matter

0

5000

10000

15000

20000

25000

0

5000

10000

15000

20000

0 35 70 1050

5000

10000

15000

20000

0 35 70 105 140

B

C D

E F

A

CDIrrOP CDIrrN

CDSecOP CDSecN

BRSIrrOP BRSIrrN

Bio

ma

ssa

se

ca t

ota

l (kg

ha

-1)

BRSSecOP BRSSecN

CNQIrrOP CNQIrrN


CNQSecOP CNQSecN


25000B

Final consideration

Ethylene

0,5

1,0

1,5

2,0

2,5CBA

Co

nce

ntr

açã

o d

e e

tile

no

(p

pm

) CD OP CD N

BRS OP BRS N

IHG

FED

CNQ OP CNQ N

0,01

0,02

0,03

Bio

ma

ssa

se

ca (

kg)

CD OP CD N

BRS OP BRS N

CNQ OP CNQ N

10/03/04 11/03/040

30

60

90

120

Etil

en

o/B

iom

ass

a s

eca

(p

pm

/kg

)

CD OP CD N

10/03/04 11/03/04

Datas de amostragem

BRS OP BRS N

10/03/04 11/03/04

CNQ OP CNQ N

Final consideration

Crop cycle

Final consideration

Productivity R2

CD Irr OP CD Irr N CD Sec OP CD Sec N0

2500

5000

C

B

A

BRS Irr OP BRS Irr N BRS Sec OP BRS Sec N0

2500

Pro

dutiv

idad

e de

grã

os (k

g ha

-1)

CNQ Irr OP CNQ Irr N CNQ Sec OP CNQ Sec N0

2500

Tratamentos

Final consideration

Light and carbon use efficiency

Final consideration

NC PPBPPBPPB +=

( ) cTcHnQoPPBC ⎟⎠

⎞⎜⎝

⎛+= ..36,02,107

j ΦJo n T

DCCICCCPPBPP WRIAF .....=

IAF DCIC u

( )uCw −

=100

100

E

R

C

CTseC

CTseC

oR

oR

20;5,0

20;6,0

≥=

<=

2210

2210

..

..

TcTcccTc

TnTnncTn

++=

++=

De Wit

The first model…

( ) cTnHnQoPPBN ⎟⎠

⎞⎜⎝

⎛ −+= 1..219,07,31

2.0175,0.185,00093,0 IAFIAFCIAF −+=

⎥⎦

⎤⎢⎣

⎡⎟⎠

⎞⎜⎝

⎛Φ+Φ⎟⎠

⎞⎜⎝

⎛⎟⎠

⎞⎜⎝

⎛=2

coscos2180

2 HsensensenHDdJQ oo δδ

π

( )δtgtgH Φ−= arccos.152

⎟⎠

⎞⎜⎝

⎛ −=

365)80(36045,23 jsenδ⎟

⎠

⎞⎜⎝

⎛+=⎟⎠

⎞⎜⎝

⎛365

.360cos033,012 j

Dd


fiRgiT iΦ cPr 70...aaGDpmf Tb20...bb

ligninalipidio

proteinaCHO

TT

TT

,

,E

C

R

IC utΔ

⎟⎠

⎞⎜⎝

⎛ −=

365)80(36045,23 iseniδ

GDpmf

TbTDr

i

jj

i

∑=

−

= 1

( )ii tgtgH δΦ−= arccos.152

( )i

ii H

cPARq Pr1. −=

ii IAFki eCRs .1 −−=

( )( ) ( )iiii

iiii TaTaqaqa

TaqaqaaAdc2

762

54

32

210

lnln1ln++++

+++=

tCRsCRmcIAFHAdcFL iiiiii Δ= 510

....4430

472,0.33,0.404,0.826,0.( ligninalipidioproteinaCHOii TTTTFLFST +++=Δ

iii FSTFSTFST Δ+= −1

( )uICFST

PP pmfi

−= =

1.

Stochastic Simulation

40...cc

ii RgfPAR .=

5,13

22

10 ..i

IAFii IAF

dedIAFddk i +++=

30...dd

( )[ ] ( )i

ii

Tii T

TcTcecTccCRmc iln.ln... 52

3210 ++++=

iii DrbDrbDrbIAFi

... 22

13

0 ++=


  Correct Techniques

  Economically feasible

  Social justice

  Environmental sustainability

Agriculture and Environment

Science and Technology

Final Consideration

Dr. Norman Borlaug (1914 - )

1970: Nobel Prieze (Peace)

“... 85% of future growth in

food production must come

from already used

agricultural areas.”

Durval Dourado Neto

“The essence of scientific knowledge is

its practical application”.

[email protected]

Documents

Modeling for crop production - indico.ictp.itindico.ictp.it/event/a12165/session/27/contribution/21/material/0/... · production Energy and mass ... Plant growth ↓Ethylene ... Profit