Production Efficiency Models for Decision Support in Livestock

Production on PasturesProduction on Pastures

Luís Gustavo Barioni

Computacional Mathematics Laboratory

Embrapa Informática Agropecuária

Where are the most

efficient systems?

Where are there greatest

Are we efficient?

Which municipalities

could improve

production?Where are there greatest

opportunities for

production efficiency

improvement?

Can we produce what will

be demanded?Is our production

environmentally

efficient?

The Concept of Efficiency

Inputs Outputs

The efficiency concept is concerned with the

relationship of inputs and outputs of a system

System

Evaluating Efficiency

Inputs Outputs

Easy to evaluate when inputs and outputs are

of the same dimension

System100 W 70 W

Evaluating Efficiency

Inputs Outputs

Is compared with some reference value,

usually extremes (maximum or minimum)

System100 W 70 W

Max = 100W

Eff =70 W/100W = 0.7 = 70%

Evaluating Efficiency

Inputs Outputs

A production system is said to be efficient if it produces

maximum output for a given set of inputs

System

Given monetary value to the inputs, a production system is

efficient when cost per unit of output is minimum

Evaluating EfficiencyMaximum

Efficiency is always a comparative measure!

Optimal (or the most desirable possible outcome) is

always a good reference!

Production efficiency can be evaluated in relation to an

reference (optimal) extreme reference (optimal) extreme value!

Potential Stocking RatesWhere are the most

efficient systems?

Are we efficient?

Can we produce what

will be demanded?

IBGE, 2006

Stocking rate (hd/ha)

Stocking rate (hd/ha)

Where are there greatest

opportunities for

production efficiency

improvement?

Production Seasonality

AFD =3300 kg DM/ha

Production Seasonality

Max Sustainable Stocking Rate

1.4 UA/ha

AFD =2500 kg DM/ha

Max Sustainable Herbage Consumption

28 kg.ha-1.dia-1

Criteria

Production Efficiency

Inputs Outputs

EnvironmentManagement

System’s

Boundaries

Economic x Biological

Process-based models

Animais

Venda

Produtos

Compra e

Venda de

Animais

Photosynthesis

Solar Radiation

Pasture growth multiplier LAI

Day Length

and Temperature

Photosynthesis

Solar Radiation

Pasture growth multiplier LAI

Day Length

and Temperature

PhotosynthesisPhotosynthesis

Solar Radiation

Pasture growth multiplier LAI

Day Length

and Temperature

Subsistema BiofísicoSubsistema Biofísico

Pastagem

Solo

Pastejo

Absorção Decomposição

Pisoteio

Fezes e

UrinaLive Stem Dead Stem

Leaves above

growing pointLeaves below

growing point

(mature)

Dead Leaves

Dies (Grazing/tiller death caused by competition or

environmental stress/ senescence )

Dies (tiller death caused by competition or

environmental stress)

Decomp. Stems

Assimilates

Respiration

(maintenance,

growth)

Stem Growth Rate

Decomp. Leaves

Leaves Growth Rate

Stem elongation

Realocation of carbohydrates from dying leaves.

Senescence

Live Stem Dead Stem

Leaves above

growing pointLeaves below

growing point

(mature)

Dead Leaves

Dies (Grazing/tiller death caused by competition or

environmental stress/ senescence )

Dies (tiller death caused by competition or

environmental stress)

Decomp. Stems

Assimilates

Respiration

(maintenance,

growth)

Stem Growth Rate

Decomp. Leaves

Leaves Growth Rate

Stem elongation

Realocation of carbohydrates from dying leaves.

Senescence

Live Stem Dead Stem

Leaves above

growing pointLeaves below

growing point

(mature)

Dead Leaves

Dies (Grazing/tiller death caused by competition or

environmental stress/ senescence )

Dies (tiller death caused by competition or

environmental stress)

Decomp. Stems

Assimilates

Respiration

(maintenance,

growth)

Stem Growth Rate

Decomp. Leaves

Leaves Growth Rate

Stem elongation

Realocation of carbohydrates from dying leaves.

Senescence

Optimally managed systems

Mês

Nit

rogênio

ap

lica

do

(kg N

/ha)

Sup

lem

ento

Fo

rneci

do

(kg M

S)

Pes

o d

e V

end

a

(kg

)

Dis

po

nib

ilid

ade

diá

ria

de

Fo

rrag

em

(kg M

S/

uo

/d

ia)

Dis

po

nib

ilid

ade

em r

elaç

ão a

o

po

tenci

al d

e

ing

estã

o

Mas

sa p

ós-

pas

tejo

(kg M

S /

ha)

Mas

sa m

édia

de

forr

agem

(kg M

S/h

a)

Mar 0.0 0 - 2.67 2.15 1042 1461

Abr 12.5 0 - 2.17 2.00 994 1433

Mai 0.0 0 - 1.56 1.10 1130 1579

Barioni, L.G.; Dake, C.K.G.; Parker, W.J .

Environment International, 25(6-7), 1999

Jun 0.0 0 - 3.11 2.75 1155 1470

Jul 0.0 0 - 2.27 2.30 887 1204

Ago 50.0 0 - 1.60 1.25 914 1291

Set 0.0 0 - 2.78 2.00 1303 1753

Out 0.0 0 35 3.53 2.00 1712 2127

Nov 0.0 0 35 5.00 3.50 1881 2259

Dez 0.0 0 37 3.46 3.00 1664 2167

Jan 0.0 0 33 2.28 1.55 1488 1986

Fev 0.0 0 - 3.41 2.75 1334 1708

Decision Support Systems

How to get there?

Optimal

(Desired)

Current

Low Carbon Brazil Study

Herd Dynamics/

Production systems

allocation models

Bovine Meat Demand Projections Land Availability Projections

allocation models

Farm model

Productivity of Land

Emissions projections

Economic Analysis

World Bank, 2010

Pastagens (172 M ha)

Vegetação

Natural

Outros Cultivos (78 M ha)

Planning at the country level

World Bank, 2010

Emissões dos sistemas prototípicos

Sistema produtivo Emissões por animal no rebanho (kg/ano) Emissões/produto

(kg CO2-e/ kg carcaça) CH4 N2O CO2-e

Pastagens degradadas 56,38 0,20 1,25 29,65

Pastagens extensivas 51,71 0,22 1,15 21,89 -26%Pastagens extensivas 51,71 0,22 1,15 21,89

ILP1 51,73 0,21 1,15 18,76

Confinamento2 51,53 0,21 1,15 17,64

-26%

-37%

-40%

World Bank, 2010

Baseline Scenario

Production systems composition

0

50

100

150

200

Áre

a (m

ilh

õe

s d

e h

a)

Extensivo

Degradado

ILP - Pastagem

ILP - Cultivo

Low Carbon Scenario

Pasture recovery

Expansion of crop-livestock

Supplemented finishing

0

20

10

20

12

20

14

20

16

20

18

20

20

20

22

20

24

20

26

20

28

20

30

Ano

Low Carbon Scenario

World Bank, 2010

Land productivityprojections

Baseline

Low Carbon

World Bank, 2010

year

Emissions projections

255

260

265

270

275

Emis

sio

ns

10

6t

CO

2-e

Baseline Low Carbon

235

240

245

250

255

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Emis

sio

ns

10

Year World Bank, 2010

Efficiency projections

World Bank, 2010

Obrigado!Gracias!Gracias!

Thank you!barioni@cnptia.embrapa.br