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# Recuperación de áreas degradadas e intensificación sostenible de sistemas silvoagropecuarios como una respuesta al cambio climático en América Latina

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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

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### Recuperación de áreas degradadas e intensificación sostenible de sistemas silvoagropecuarios como una respuesta al cambio climático en América Latina

1. 1. Production Efficiency Models for Decision Support in Livestock Production on PasturesProduction on Pastures Luís Gustavo Barioni Computacional Mathematics Laboratory Embrapa Informática Agropecuária
2. 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. 3. The Concept of Efficiency Inputs Outputs The efficiency concept is concerned with the relationship of inputs and outputs of a system System
4. 4. Evaluating Efficiency Inputs Outputs Easy to evaluate when inputs and outputs are of the same dimension System 100 W 70 W
5. 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. 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. 7. Evaluating Efficiency Maximum Efficiency is always a comparative measure! Optimal (or the most desirable possible outcome) is always a good reference!
8. 8. Production efficiency can be evaluated in relation to an reference (optimal) extremereference (optimal) extreme value!
9. 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. 10. Production Seasonality AFD =3300 kg DM/ha
11. 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. 12. Criteria Production Efficiency Inputs Outputs EnvironmentManagement System’s Boundaries
13. 13. Economic x Biological
14. 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. 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. 16. Decision Support Systems
17. 17. How to get there? Optimal (Desired) Current
18. 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. 19. Pastagens (172 M ha) Vegetação Natural Outros Cultivos (78 M ha)
20. 20. Planning at the country level World Bank, 2010
21. 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. 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. 23. Land productivity projections Baseline Low Carbon World Bank, 2010 year
24. 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. 25. Efficiency projections World Bank, 2010
26. 26. Obrigado! Gracias!Gracias! Thank you! barioni@cnptia.embrapa.br