Presentación de Luis Barioni y Geraldo Bueno Martha, EMBRAPA, Brasil, durante la XI Reunión de la CODEGALAC, Capítulo Cono Sur, realizada del 16 y 18 de noviembre de 2010 en Buenos Aires, Argentina

1. Production Efficiency Models for
Decision Support in Livestock
Production on PasturesProduction on Pastures
Luís Gustavo Barioni
Computacional Mathematics Laboratory
Embrapa Informática Agropecuária

2. 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?

3. The Concept of
Efficiency
Inputs Outputs
The efficiency concept is concerned with the
relationship of inputs and outputs of a system
System

4. Evaluating Efficiency
Inputs Outputs
Easy to evaluate when inputs and outputs are
of the same dimension
System
100 W 70 W

5. Evaluating Efficiency
Inputs Outputs
Is compared with some reference value,
usually extremes (maximum or minimum)
System
100 W 70 W
Max = 100W
Eff =70 W/100W = 0.7 = 70%

6. 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

7. Evaluating Efficiency
Maximum
Efficiency is always a comparative measure!
Optimal (or the most desirable possible outcome) is
always a good reference!

8. Production efficiency can be
evaluated in relation to an
reference (optimal) extremereference (optimal) extreme
value!

9. Potential Stocking Rates
Where 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?

10. Production Seasonality
AFD =3300 kg DM/ha

11. 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

12. Criteria
Production Efficiency
Inputs Outputs
EnvironmentManagement
System’s
Boundaries

13. Economic x Biological

14. 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
AbsorçãoDecomposição
Pisoteio
Fezes e
Urina
Live Stem Dead Stem
Leaves above
growing point
Leaves 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 point
Leaves 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 point
Leaves 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

15. Optimally managed
systems
Mês
Nitrogênioaplicado
(kgN/ha)
Suplemento
Fornecido
(kgMS)
PesodeVenda
(kg)
Disponibilidade
diáriadeForragem
(kgMS/uo/dia)
Disponibilidade
emrelaçãoao
potencialde
ingestão
Massapós-pastejo
(kgMS/ha)
Massamédia
deforragem
(kgMS/ha)
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

16. Decision Support Systems

17. How to get there?
Optimal
(Desired)
Current

18. 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

19. Pastagens (172 M ha)
Vegetação
Natural
Outros Cultivos (78 M ha)

20. Planning at the country
level
World Bank, 2010

21. 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

22. Baseline Scenario
Production systems composition
0
50
100
150
200
Área(milhõesdeha)
Extensivo
Degradado
ILP - Pastagem
ILP - Cultivo
Low Carbon Scenario
Pasture recovery
Expansion of crop-livestock
Supplemented finishing
0
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
Ano
Low Carbon Scenario
World Bank, 2010

23. Land productivity
projections
Baseline
Low Carbon
World Bank, 2010
year

24. Emissions projections
255
260
265
270
275
Emissions106tCO2-e
Baseline Low Carbon
235
240
245
250
255
2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030
Emissions10
Year World Bank, 2010

25. Efficiency projections
World Bank, 2010

26. Obrigado!
Gracias!Gracias!
Thank you!
barioni@cnptia.embrapa.br