Soil Carbon in Grazing Lands: What's been done and we need to do and Highlights from Nitrogen Efficiency Study in Cropping Systems
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Soil Carbon in Grazing Lands: What's been done and we need to do and Highlights from Nitrogen Efficiency Study in Cropping Systems

Soil Carbon in Grazing Lands: What's been done and we need to do and Highlights from Nitrogen Efficiency Study in Cropping Systems

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Soil Carbon in Grazing Lands: What's been done and we need to do and Highlights from Nitrogen Efficiency Study in Cropping Systems Presentation Transcript

  • 1. Agricultural and climatechangeEmissions and solutions in contextPresented by Richard Conant
  • 2. CO2 concentrations are increasing: Human activities are driving increases in atmospheric CO2 10000 Fossil fuel emissions 8000 Tropical LUC Temperate LUC 6000 MmtC yr-1 4000 2000 0 1860 1880 1900 1920 1940 1960 1980 2000
  • 3. CO2 concentrations are increasing: Population and CO2 emissions 10000 Fossil fuel emissions Tropical LUC 8000 Temperate LUC World population 6000 MmtC yr-1 4000 2000 0 1860 1880 1900 1920 1940 1960 1980 2000
  • 4. Deriving the Kaya Identity: Understanding the driving forces for CO2 emissions people × CO2CO2 emissions ≡ person
  • 5. Deriving the Kaya Identity: Understanding the driving forces for CO2 emissions Problems Solutions • Increased populations • Decreased populations • Procreation • Abstention • Motherhood • contraception/abortion • Large families • Immigration • Small families • Medicine • Stop immigration • Public health • Disease • Sanitation • War • Peace • Murder/violence • Law and order • Famine • Scientific agriculture • Accidents • Accident prevention (drive 55) • Pollution (smoking) • Clean air • Ignorance of the population problem
  • 6. Deriving the Kaya Identity: Understanding the driving forces for CO2 emissions people × GDP × CO2CO2 emissions ≡ person GDP
  • 7. Deriving the Kaya Identity: Understanding the driving forces for CO2 emissions people × GDP × Energy × CO2CO2 emissions ≡ person GDP Energy
  • 8. The REVISED Kaya identity: Agriculture is differentGHGemissions people× GDP × Energy × CO2 + GHG FoodCO2 emissions ≡ ≡ person GDP Energy food Food five Food of food prod. food food production system food  GHG intensity CO2, N2O & CH4 per unit food
  • 9. The REVISED Kaya identity: Agriculture is differentGHG emissions ≡ people× Food × Energy × CO2 + GHG person Food Energy food food consumption is increasing (a good thing) slight declines in developed countries increasing in developing countries slow improvement until recently…  GHG intensity  GHG intensity CO2, N2O & CH4 per unit food
  • 10. Evaluating intensity and efficiency: N2O/crop and N recovery efficiency (REN) constant N inputs × Ef GHG N2O ≡ yield food If crop [N] is constant  N2O ≈ N inputs food N in yield ≡ REN
  • 11. Field-scale N recovery efficiencyIncreased fertilizer use >> increased yield  decreased efficiency Cassman et al. 2003 Ann. Rev. Environ. Reourc.
  • 12. Field-scale N recovery efficiencyIncreased fertilizer use Decreased efficiency Cassman et al. 2003 Ann. Rev. Environ. Reourc.
  • 13. Field-scale N recovery efficiency Decreased efficiencyIncreased fertilizer use Increased production Tilman et al. 2002 Nature
  • 14. Field-scale N recovery efficiency ^ changes in Frink et al. 1999 PNAS
  • 15. N recovery efficiency: what do we know? 1. Knowledge derived at the field-scale suggests that as fertilizer application rates increase in the field, N use efficiency decreases 2. Global-scale analysis of fertilizer data suggests that as application rates increased over time, N use efficiency decreased – dramatically 3. But the growth rate for fertilizer application rates has declined over time.
  • 16. Evaluating efficiency: N2O/crop and N recovery efficiency (REN) REN ≡ N in N inputs yield Challenges: 1.Crop composition has changed over time, [N] varies by crop 2.N inputs arise not just from fertilizer, but from legumes, manure; N input mix has changed over time 3.Therefore, we need a database on N inputs and yield by crop, over time to evaluate temporal and spatial trends in REN (and N2O/food)
  • 17. Evaluating efficiency: Allocating fertilizer N to each crop IPNIS, IFIA 1961 1962 1963 … 1998 … 2006 2007 2008 Maize 45 (kgN/ha) wheat 27 rice 54 … oats 18Country total 2019 2120 2199 4761 4993 5193 5318(tN/yr) We used a Bayesian model to integrate information on fertilizer appilcation rates (IPNIS, IFA – average and variation) and constraints from total N fertilizer rates by country to fill in this matrix
  • 18. Evaluating efficiency: Allocating fertilizer N to each crop IPNIS, IFIA 1961 1962 1963 … 1998 … 2006 2007 2008 Maize 45 (kgN/ha) 22 wheat 27 13 rice 54 12 … soybeans 22 lupin 13 oats 18 11Country total 2019 2120 2199 4761 4993 5193 5318(tN/yr) 877 903 910 1201 1311 1355 1398 572 596 610 712 744 784 819 We generated a new database of manure N inputs by allocating total N manure (from FAOstat livestock data) We generated a new database of N fixation rates by crop, by country to generate new estimates of N-fixation inputs
  • 19. The REVISED Kaya identity: Efficiency of N use and N2O production 6 1.0 5 (N harvested / N inputs) 0.8 (Mg DM ha-1 yr-1) 4 0.6 Yield REN 3 0.4 2 1 0.2 0 0.0 0.9 180 160 0.8 (g N2O-N Mg DM ) 140 -1 N2O per unit yield 0.7(kg N ha yr ) -1 120 N inputs 0.6 100 -1 80 0.5 60 0.4 40 0.3 20 0 0.2 1960 1970 1980 1990 2000 2010 1960 1970 1980 1990 2000 2010 OECD BRICS non-OECD World average
  • 20. The REVISED Kaya identity: Efficiency of N use and N2O production 1.0 1.0 former USSR Argentina 0.8 2005 Canada Brazil 0.8 India 0.6 0.6 Indonesia Bangladesh France 0.4 0.4 Proportion of total production USA 0.2 0.2 China 1.0 1.0 former USSR 1963 Canada 0.8 Argentina 0.8 Brazil China 0.6 0.6 India 0.4 0.4 OECD USA BRICS 0.2 0.2 France non-OECD Bangladesh Japan 0.0 0.0 400 300 200 100 0 0.5 1.0 1.5 2.0 N inputs REN (kg N ha-1 yr-1) (N harvested in crops / N inputs)
  • 21. Agriculture and climate change Emissions and solutions in context – agriculture is different1. Emissions are non-point sources of multiple greenhouse gases.2. Opportunities for C sequestration and CO2 drawdown.3. Reducing food consumption is unlikely and increasing food consumption is often a good thing.4. Decarbonization of energy sources has a role in reducing emissions, but it is limited.5. Increasing efficiency of our food production systems is central to reducing agricultural GHG emissions.
  • 22. Agricultural is different Source: IPCC 2007
  • 23. Why isn’t agriculture on the agenda? And implications1. Practical reasons: 1. Less important (focus first on large sources) 2. Uncertainty in measurements2. Political reasons: 1. No desire to limit food production 2. Most emitters with a large ag GHG footprint are developing countries (low emitters) 3. Detracts focus from reducing largest sources 4. A cynical reason: ag can’t be outsourced3. Implications of ag being on the outside: 1. Greater risk 2. Accounting issues 3. Limited investment
  • 24. Why isn’t agriculture on the agenda? How can we get it there?1. Address practical limitations 1. Measurement/uncertainty 1. Expand sampling networks 2. Conduct more/better syntheses 2. Feasibility 1. Carry out demonstration projects 3. Address offset limitations head on 1. Develop protocols for existing trading programs2. Address political concerns: 1. Argue for an all-in approach (working on the energy sector is not a reason to forego work on the ag sector) 2. ID win-win scenarios (production, adaptation, etc.) 3. A kaya-ag framework focused on overall systematic improvement 4. Understand limits to progress3. Reduce risk: 1. quantify co-benefits, production/adaptation benefits 2. Pilot projects to demonstrate feasibility