This document discusses using a systems analysis approach to understand tradeoffs and synergies between agricultural production, nutrition security, and the environment in different landscapes. It proposes examining how food choices and food systems impact ecosystem services and how to manage landscapes for better nutrition while considering other objectives. Case studies are proposed in Vietnam, Kenya, and Zambia using methods like participatory mapping, dietary surveys, and farm and landscape modeling to assess landscapes at multiple scales and functions. The goal is to explore adjustments to crop areas, livestock management, and other farm choices to improve performance across nutritional, economic, environmental and other indicators and understand the tradeoffs faced.

1. Systems analysis in nutrition sensitive landscapes
Jeroen Groot, Stéphanie Alvarez, Carl Timler, Wim Paas, Nester Mashingaidze, Trinidad del
Rio, Minke Stadler, Katrien Descheemaeker , Lummina Horlings, Inge Brouwer (WUR), Gina
Kennedy, Celine Termote, Jessica Raneri, Fabrice Declerck, Natalia Estrada Carmona
(Bioversity International), Roseline Remans, Monica Marie Pasqualino (Columbia University),
Alan Debrauw (IFPRI), Ray-Yu Yang (World Vegetable Center), Shakuntala Thilsted, Kate
Longley, Andrew Ward, Steve Cole (World Fish), Mwansa Songe (ILRI), Busie Maziya-Dixon
(IITA)

2. Nutrition sensitive landscapes
 What are tradeoffs and synergies between agricultural
production, nutrition security and the environment in a given
landscape?
 How do women’s and men’s food choices and food system
processes impact ecosystem services in a given landscape?
 How can we manage ecosystems for better nutrition while also
managing for other competing objectives of the landscape?
 Central role for a systems approach

3. Vietnam
Case study areas in Vietnam, Kenya, Zambia
Kenya
Zambia

4. Learning cycles
Action:
Implementing a
‘bright idea’
Observation:
Find out
consequences
Analysis:
What are
implications?
Plan:
Which
improvements?
Describe:
What?
Explain:
Why?
Explore
Diversify
What if?
Design
Select
Which?

5. Goal-oriented approach
Goal definition
Formulation of a case-specific
perception of multifunctionality
Indicator set
Metrics to evaluate the
performance of the system
System definition
Definition of the system,
components and process
Integrative model
Expresses system performance
in terms of indicator set
Multifunctionality
assessment
Rossing et al., 2007. Agriculture, Ecosystems & Environment.

6. Methods
 Transect walks
 Focus Group Discussions
 Participatory mapping
 Resource flow maps
 Dietary composition, 24 h recall
 AgroBioDiversity survey
 Farm survey (Impact Lite)
 Farm and landscape modeling

7. Perceptions, values and practices (by Minke Stadler)

8. Participatory mapping (by Trinidad del Rio)
2.3.1 Mwanja Litema
2.3.2 Matamakisi Lilako
2.3.2 Matamakisi Lilako
2.3.2 Impwa Lilako
2.3.2 Khabichi Lilako
2.3.3 Mbonyi Litapa
2.3.3 Namundalangwe Litapa
2.4.4 Mbonyi Lizulu
2.4.4 Namundalangwe Lizulu
Simu n˚ Sicalo
Mufuta
wa mubu
simu
Sept Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug
Kucala
Kucala Kukutula
Kucala Kukutula Kucala Kukutula
Kucala Kukutula Plant Kukutula
Kucala Kukutula Kucala Kukutula
Harvest Kucala
Harvest Kucala
Kucala Kukutula
Kucala Kukutula
Linanga
Maliha
Nako ya tala
Ku kwla litapi
Munda
Mbumbi Litabula

9. Multi-scale and multifunctional assessment
 Field – farm – household – landscape
Farm indicators:
Economic results (operating profit)
Nutrient flows, balances (C, NPK)
Water balance
Manure production and breakdown
Organic matter balance
Labor balance
Feed balance (E, P)
Bio-energy production
Greenhouse gas emissions
Field indicators:
Crop yield
Nutrient uptake, crop composition
Water content / dynamics
Soil nutrient dynamics
Organic matter content / dynamics
Erosion rate
Landscape indicators:
Ecological coherence
Nutritional functional diversity
Land-use diversity
Household indicators:
Dietary diversity
Nutrition adequacy
Household budget

10. Nutrition indicators
 Dietary diversity scores, based on 9-16 food groups
● HDDS, WDDS, MDD-W (Kennedy et al., 2010, 2014)
 Food pattern
● Balancing demand and supply of food groups
 Nutrient adequacy
● Balancing requirement and supply of energy, nutrients
 Nutritional Functional Diversity
● Fraction of foods diversity available in an area or farm,
relative to the ‘potential’ diversity in that landscape

11. Relation land-use and farm mgt. to indicators
 How can we adjust:
● Crop areas, product composition, use
● Animal number, mgt., productivity, product composition, use
● Manure management
● Food acquisition
● Labor use
● And other farm and household choices
 To improve performance of multiple functions?...
 …and which tradeoffs and synergies do we face?

12. Exploration / optimization
EXPLORATION
MODEL
Maize area
Milk cow number
Fertilizer amount
Inputs
(Describe):
Groundnut area
Calves number
Operating profit
Rotation area
Nitrogen soil losses
Outputs
(Explain):
Labour balance
Organic matter balance
Decision
variables:
Objectives
and constraints:
Minimum = 0
Maximum = 10
✔ Objective
Direction= minimize, or
maximize
✔ Constraint
Minimum = 1000
Maximum = 2000

13. Populations of farms or landscapes
Soil Org.
Matter
Gross margin
Housing
Intensive grassland
Extensive grassland
Maize
Wheat
Woodland
Groot & Rossing, 2011. Methods in Ecology and Evolution.
Original farm
configuration

14. Farm DESIGN
DESCRIBE – current
farm configuration
EXPLAIN – indicators
of farm performance
DESIGN – adjusted
farm configurations
EXPLORE – tradeoffs
and synergies
Groot et al (2012) Agric Syst.

15. Landscape IMAGES Groot et al (2007) Agric Ecosyst Environ.; Groot et al (2010) Eur J Agron.
DESCRIBE – current
land-use configuration
EXPLAIN – indicators of
landscape performance
DESIGN – adjusted
landscape configurations
EXPLORE – tradeoffs
and synergies

16. Populations of solutions
 Why diversify options?
● Explore tradeoffs and synergies
● Something to choose from
● Avoid lock-in onto undesirable paths
 Discussion support
Groot & Rossing, 2011. Methods in Ecology and Evolution.

17. Conclusions
 Multi-scale and multifunctional
 Explore tradeoffs and synergies
 Multi-methods and tools
 Multiple options to inform discussions

18. Nutrition Sensitive Landscapes
Thank you
jeroen.groot@wur.nl

19. Pareto-based multi-objective optimization
 Existing farm configuration as starting point
 Population of alternative farming systems, generated by changing
decision variables with an evolutionary algorithm
 Evaluate performance and select most promising on the basis of the
concept of Pareto optimality
 Iterative improvement to find the Pareto frontier (trade-offs) and to
visualize the solution space

20. Pareto optimality
Soil OM
Operating profit
2
2
2
3
4
1
1
1
1
1

21. Pareto-based optimization
Generate
by adjusting
decision variables
Evaluate
for multiple
objectives
Select
using non-weighing
Pareto-based methods
Soil OM
Operating profit
Original farm
configuration