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Notes lectures World, Food and Ecosystems
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Hoorcollege 1Share production in global GHG emission —> 26%
Share of agriculture in global habitable land —> 50%
Share of agriculture in global water use —> 70%
Share of agriculture in global ocean and freshwater pollution by eutrophication —>
70%
Share of livestock biomass compared to all mammals —> 94%(van alle mammals is
94% voor voedselproductie)
Climate responsible food production
Global land use for food production
29% land —> 71% habitable land —> 50% agriculture —> 77% livestock
So high amount of animal food production
Shift is recent
Both forests as well as grasslands have been victim
Difference between 1900 and now
We are in a turbulent epoch: world is changing fast, and mostly because of us
Not in the least because of our food production
This has had has and will have consequences on other biotic life
How to make well-considered decisions in world-food-and ecosystem management
that balances these needs vs consequences?
Such decision are complex, require suitable description of the form/process
Challenge of description
Reasons as causes, causes as reasons
Our language is not tailored to complexity
Same question can be answered in different ways.
These different ways can occupy different scales, going from very concrete to less
tangible
Some elects aid in explaining causality some distract
Different scales can lead to confusion in debates and even disagreement
Different abstractions also imply different proposed solutions
Finding an adequate level of complexity to analytically approach the problem
Abstraction of homosimson
The idea of abstraction: Ones you have a convention that allows you to be on the
same page its easy to be on the same page. Simpson
Key processes and key concepts/descriptors
Process influence form: which process you explicitly account for and how you
represent them depends on the application
Additional challenges in understanding:
- spatio-temporal context and resolution matter
- Not all why’s and what’s are observable or understandable
- Whatis a why, and why something is a what depends on scale (Space and time)
- Abstraction depends on application
Simplifying the reality: simplification
Problem description is not trivial
Decisions on which processes and forms to include/exclude matters
Reducing complexity to manageable analytic questions
,Decide/simplify:
Geographic scale - spatial unit
Temporal scale
Boundary conditions/assumptions
Dimensions (which processes and structures will you account for, which not)
Dimensions for descriptions (how do you approximate/describe the dimension)
Assumptions important because you can’t explain and take into account all the
processes
Classification
- reduce complexities by introducing classes to order/structure
Several ways to do classification
Top down: subdivide population according to common traits/characteristics or
common
Bottom up: first build up classes and then look for communalities among individuals
(Agglomeration)
One or multiple dimensions for this classification (check for redudancy, PCA…)
Often a transition from continuous (complex) to discrete scale (subdivided scale) to
facilitate understanding, at the expense of nuance
Extremes or averages —> the GAP instinct
Average you loose the nuance, you forget the distribution or overlap
Static classes —> world is not static anymore, many countries are developed and not
anymore developing.
Making wrong estimates how the world is today. The idea about the dynamic character
is hampered by the idea of categorisation/classes
Classifications: Refuses the dimensions —> for example High GDP doesn’t mean high
average years of schooling
Climate: understanding the climate is extremely difficult.
Köppen climate class —> temperature and precipitation we separate the world —>
Hierarchical levels. Simple and able to understand.
BUT climate zones are shifting, entire classes have shrunk.
Soil: reduce classes too, many factors and processes. World Reference Base is
hierarchical classification, this is very complex. Combination of qualitative and
quantitative information. Expert-based classification.
Biomes: subdivision of the world in characteristic vegetation zones. These are strongly
imprinted by climate zones. Climate interaction with vegetation.
Global-scale zones with characteristic life forms (plant-animal): targeted to
describe/classifiy the living world
Temperature and moisture regimes
Clustering = similarity of dominant plants + characteristic animal communities
Tropical rain forest
Temperate forest
Desert
Tundra
Taiga
Grassland
Savanna
Soil characteristics
But Soil is also determined by a lot of things that codetermine vegetation —>
interaction between vegetation, climate and soil. So lets include soils in biome
classification, because similar controls. The struggle: human impact also influence,
, include more processes and dimension, which make it a difficult concept to work with.
Moreover regardless how many dimensions you use, you still have other limitation,
underlining distribution that is forgotten we saw earlier. Forget underlying distribution,
and nuance with a class.
Biome are not only used as a descriptor but also as a predictor. (What and why)
Remove one characteristic vegetation —> then impact on many factors. For example
tropical rain forest —> temperature increases (evapotranspiration).
SO: biomes have gone for lets describe vegetation pattern, to lets include climate,
soils, hydraulic processes and entrepegenic influences (so all the dimensions) to lets
not consider it as a descriptor but lets consider it as a predictor for what would happen
if we remove something.
Classificator comes down to a trade-off: how do we minimize the homogeneity within a
unit whilst maximizing the heterogeneity between units of different classes?
Categorization needs to be valid and useful: we need simplification whilst
acknowledging nuance
In this course we will take the spatiality of our food and ecosystems. The land use
(land cover) is in essence an example of a structure that influences processes but in
itself is also a process that influences subsequent structures
Multidimensional complex —> in the next classes you will hear experts talk about their
domain: notice the classifications/aggregations they apply to their field and the way in
which context and multidimensionality is integrated
Understanding the world and reducing the nuance we will loose biome classification.
We always have to consider and ask yourself which assumptions am I making by
classifying and which mental pitfalls will I be making when I consider these
classification
Laptopcollege 1
the basic building blocks of writing code (in python, R, Javascript…) is (i) the identifica-
tion of the necessary variables, (ii) the identification of the appropriate functions, (iii)
understanding which input parameters these functions require.
Type of data (vector)
• Point data: e.g. species occurrence data
• Line data: e.g. transect, river, fault line...
• Polygon data: e.g. country boundaries, parcel boundary,
Type of data (raster)
Derivatives of passive remote sensing:
• Blue band: atmospheric corrections
• Green band: helps visualize vegetation
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