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Agricultural Sustainability: Opportunities for contributions from computational sciences. Laurie Drinkwater Cornell University. Solving a complex equation, by Owen Schuh. Overview of this talk. Context Global situation in terms of food, hunger and environmental change

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agricultural sustainability opportunities for contributions from computational sciences

Agricultural Sustainability: Opportunities for contributions from computational sciences

Laurie Drinkwater

Cornell University

Solving a complex equation, by Owen Schuh

overview of this talk
Overview of this talk
  • Context
    • Global situation in terms of food, hunger and environmental change
  • Transitioning to sustainable agricultural systems
    • What are the challenges that must be addressed?
    • Ideology and values play a role in defining sustainable agriculture
    • Social-ecological systems: A useful conceptual framework
  • Potential questions for computational sciences
    • Three examples
global food situation
Global food situation
  • Nearly 900 million people -- about 13% of the world population -- do not have enough food (FAO)
  • About 40% of the world population relies on subsistence agricultural systems
  • Currently, we have sufficient food for our population– political and economic conditions prevent access
  • Can food production continue to keep pace with population growth?
slide4
Humans occupy 75% of the ice-free terrestrial surface and manage 90% of the NPP
  • Agricultural systems dominate a majority of the most productive biomes

Ellis and Ramankutty 2008

consequences of intensification
Consequences of intensification
  • Resource consumption by industrial agriculture is enormous and is undermining long term productivity of our agricultural lands.
    • Fossil fuels, water, soil, land area
  • Agricultural systems disrupt the integrity of natural ecosystems and contribute to widespread losses of biodiversity.
    • Movement of nutrients, toxic chemicals and sediments
    • Extensive land use
  • Agriculture is a major contributor to global climate change.
    • 52% and 84% of global anthropogenic CH4 and N2O, significant C02
  • Despite progress in food output on a per acre and per capita basis, the quality of life for farm families and rural communities continues to decline.
    • Reduced income over time, health issues
slide6

Human activities and environmental consequences are changing rapidly

Steffan et al. 2004. Global Change and the Earth System.

slide7

Transitioning to sustainable agricultural systems

http://waterweek.wordpress.com/2007/09/19/

slide8

Ecosystem Services The benefits people obtain from ecosystems

Millennium Ecosystem Assessment- www.millenniumassessment.org

what are our goals for agriculture
What are our goals for agriculture?
  • Multifunctional: Food and fiber plus ecosystems services
  • Provide healthy, nutritional food in sufficient quantities
  • Production and food systems must be resilient
  • Quality of life for farm families and vibrant rural communities
  • Reverse environmental degradation: local to global scales
super eco
Super eco

http://www.holocene.net/

agriculture as a social ecological system
Agriculture as a social-ecological system
  • Food production is still largely dependent on ecological processes despite intensification
  • Social processes govern the design and development of production systems and agricultural technology as well as land use patterns
  • So far, social processes have not proven to be adept at responding to ecosystem change or mitigating undesirable consequences
slide14

EU: DPSIR Framework

Pressure on the environment

State of the environment changes

Social and economic

Driving forces

Societal Response

Impacts on

human health, [Impacts on other species or undermining Ecosystem Services(?)]

what areas could benefit from computer science and mathematics
Ecosystem modeling—Flows of energy and materials

Resource accounting– Ecological footprint and life cycle analysis

Spatial and temporal analysis of existing (massive) data sets

What areas could benefit from computer science and mathematics?

Three examples…

ecosystem modeling
Ecosystem modeling

Ecosystem process models have five components

  • Forcing functions or external variables-naturally imposed variables that influence the state of the ecosystem
  • State variables-variables that describe the state of the ecosystem
  • Mathematical equations-represent biological, chemical and physical functions and describe the relationship between forcing functions and state variables
  • Parameters-coefficients in the mathematical representation of processes
  • Universal constants-naturally occurring constants

Jorgensen and Bendoricchio 2001

slide17

ecological

drivers

Climate

Soil

Vegetation

Human activity

annual

average

temp.

litter

water uptake

potential

evapotrans.

very labile

labile

resistant

N-demand

N-uptake

water stress

daily growth

root respiration

water demand

microbes

LAI-regulated

albedo

evap.

trans.

labile

resistant

vertical

water

flow

humads

labile

resistant

grain

stems

roots

soil moist

profile

soil Eh

profile

O2

diffusion

soil temp

profile

O2 use

Plant growth

passive humus

Soil climate

Decomposition

effect of temperature and moisture on decomposition

soil

environmental

factors

Temperature

Moisture

pH

Eh

Substrates: NH4+, NO3-, DOC

nitrate

denitrifier

nitrite

denitrifier

N2O

denitrifier

DOC

soil Eh

nitrifiers

DOC

NH4+

NH3

NO3-

DOC

NO3-

NH4+

NO2-

DOC

CH4 transport

CH4 oxidation

CH4 production

clay- NH4+

aerenchyma

CO2

N2O

NO

NH3

N2

N2O

CH4

NO

Denitrification

Nitrification

Fermentation

The Decomposition-Denitrification Model

slide18

Modeling flows of energy and materials

Water drainage from watersheds planted with corn and soybean on tile-drained Mollisols in Illinois

Graph courtesy of Christina Tonitto

comparison of six widely used process models daily n 2 n 2 o flux predictions across models
Comparison of six widely used process models: Daily N2+N2O flux predictions across models
  • Models had different predictions for water flux, soil moisture status
  • They also had very different seasonal predictions of N gas flux
  • With limited field data we can not distinguish which model perform best
  • Options for resolving this: massive data collection and increased complexity of models
  • Can we develop simple models to serve need of policymakers?
  • Can we combine spatial and process modeling?

David et al. 2009

the ecological footprint
The Ecological Footprint
  • Developed by Wackernagel and Rees, 1996: Our Ecological Footprint: Reducing Human Impact on the Earth
  • Attempts to answer a single question: How much of the planet’s capacity do we use compared to how much is available?
  • Compares the area we demand to how much area is available
  • Currently, it provides the only available comprehensive answer to this research question.
  • Based on real data: consumption and production
  • How does this approach differ from carrying capacity?
global ecological footprint by component 1961 2005
Global ecological footprint by component, 1961-2005

http://www.footprintnetwork.org Living Planet Report 2008

ecological footprint per person by country for 2005
Ecological Footprint per person, by country for 2005

http://www.footprintnetwork.org Living Planet Report 2008

ecological footprint summary
Ecological Footprint summary
  • Provides insights on sustainability of natural capital use at large scales: regions, nations and global
  • Example: Because the calculations are based on actual consumption rates that reflect current technology, we can conclude that technology has not kept pace with increased consumption
  • Limitations in terms of env. Impact of toxins, materials that do not decompose, and resource depletion outside the biosphere
  • Strong ecological basis: datasets and calculation methods have improved since 1996
  • Focus on natural capital, efficiency of providing human needs
  • Does not assess social indicators of sustainability
  • Application to farm-scale production has yet to be clearly demonstrated but has the potential to be very useful in combination with other approaches
life cycle assessment
Life cycle assessment
  • Developed in the 1960’s, “industrial ecology”
  • A “cradle-to-grave” approach for assessing industrial systems.
  • Begins with the gathering of raw materials from the earth to create the product and ends at the point when all materials are returned to the earth.
  • Quantifies environmental releases to air, water, and land in relation to each life cycle stage and/or major contributing process in the course of the product’s life-span
lca summary
LCA summary
  • Focus is on assessing natural capital: source-sink and environmental impacts
  • Integrates known environmental impacts
  • Interpretation of trade-off across env. Impacts depends on subjective decision-making of the evaluators
  • Sound ecological basis but estimating emissions in agricultural systems presents a formidable challenge (open versus point emissions)
  • Units for reporting resource use and emissions determine performance of contrasting systems
crop distribution in illinois 2004
Crop distribution in Illinois, 2004

Corn Soybean Pasture

There is a vast amount of biophysical and social data available. USDA and Census Bureau, NRCS

understand spatial patterns and temporal dynamics of agricultural landscapes
Understand spatial patterns and temporal dynamics of agricultural landscapes

Average N loss at a county level in the Mississippi River Basin

Mark David et al., in prep

conclusions
Conclusions
  • Tremendous potential for computational sciences to contribute
  • Vast amounts of information are not being exploited to their full potential
  • The pace of change presents a significant challenge
  • Quantitative analysis of systemic trends can help society adapt and respond appropriately to environmental change