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

1. Agricultural and climate
change
Emissions and solutions in context
Presented 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 × CO2
CO2 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 ×
CO2
CO2 emissions ≡
person GDP

7. Deriving the Kaya Identity:
Understanding the driving forces for CO2 emissions
people × GDP × Energy ×
CO2
CO2 emissions ≡
person GDP Energy

8. The REVISED Kaya identity:
Agriculture is different
GHGemissions people× GDP × Energy × CO2 + GHG
Food
CO2 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 different
GHG 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 efficiency
Increased fertilizer use >> increased yield  decreased efficiency
Cassman et al. 2003
Ann. Rev. Environ. Reourc.

12. Field-scale N recovery efficiency
Increased fertilizer use Decreased efficiency
Cassman et al. 2003
Ann. Rev. Environ. Reourc.

13. Field-scale N recovery efficiency
Decreased efficiency
Increased 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 18
Country 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
11
Country 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 different
1. 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 implications
1. Practical reasons:
1. Less important (focus first on large sources)
2. Uncertainty in measurements
2. 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 outsourced
3. 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 programs
2. 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 progress
3. Reduce risk:
1. quantify co-benefits, production/adaptation benefits
2. Pilot projects to demonstrate feasibility