1. Jeroen Groot, 26 March 2012
Systems approaches and tradeoffs
analysis: smallholder agriculture
Linking concepts to practice
Pablo Tittonell
Farming Systems Ecology – Wageningen University, The Netherlands
World Agroforestry Centre
13 February 2013

2. Systems approaches to ecological intensification
A Farming Systems Decalogue:
(i) Deal with farm diversity;
(ii) Deal with spatio-temporal variability;
(iii) Deal with crop-livestock interactions;
(iv) Capture decision-making on factor allocation at farm scale;
(v) Scale from cropping systems to multifunctional landscapes;
(vi) Deal with collective decisions in communities/territories;
(vii) Prospect farming futures and scenarios;
(viii) Analyse (quantify and map out) tradeoffs;
(ix) Involve actors and embrace lay knowledge systems;
(x) Inform design and targeting of innovations.

3. Properties of smallholder farming
systems

4. Anisotropy and heterogeneity
Agroecosystems: complex socio-
ecological systems
Anisotropy
Heterogeneity Ecological niches
Landscape organisation
• Connectivity
• Contingency
Soil C gradients in
Mr. Oluka’s farm Resource allocation
(Ouganda)
• Local knowledge and
perceptions of heterogeneity
• Differential responses to
interventions
Ebanyat, 2010 • Need to target technologies

5. veau d’infestation.
Anisotropy and heterogeneity
our visualiser les différences spatialisées dans la dynamique d’infestation, les
sont comparées selon les sous-zones écologiques dans la Figure 20.
ISTOM
Variation spatio-temporelle Ecole d’Ingénieur en Agro-Développement International
Index d'infestation moyen 32, Boulevard du Port F.-95094 - Cergy-Pontoise Cedex
4,5 tél : 01.30.75.62.60 télécopie : 01.30.75.62.61 istom@istom.net
4
3,5 MÉMOIRE DE FIN D’ÉTUDES
3
ZE 1
2,5 Les déterminants de la variabilité spatiale et temporelle
ZE 2
2 de la pression des pucerons et de leurs ennemis naturels
1,5
ZE 3 dans une région agricole du Kenya
1 ZE 4
0,5
0
S1 S2 S3 S4 S5
Index d’infestation moyen des champs en fonction des semaines de relevés, pour les quatre zones
s. Kajulu, Kenya, 2011
ur la base de ces données, des dynamiques d’infestation différentes se dessinent selon
s-zones écologiques. La sous-zone écologique 3 présente en effet un index
tion supérieur à celui des autres sous-zones, en début de période : jusqu’à la
ne. Or cette sous-zone écologique est caractérisée par un intense réseau de haies, et
aïque de champs très fine. Si la concentration en plantes hôtes des pucerons Aphis
(Photographie de la zone d’étude : Kajulu, Kenya (Source : André, 2011))
ra et Aphis fabae joue le rôle de refuge pour les pucerons, ceci pourrait expliquer une
on plus importante dans les champs, dès le début du cycle de culture du haricot. SOUTENU EN SEPTEMBRE 2011
Concernant l’infestation en sous-zone écologique 4, elle commence à un niveau plus André Laure Vaitiare
Promotion 97
ais sa pente est plus forte. Or cette zone-ci se caractérise par l’absence de haies, et un Stage réalisé à Kajulu, Kisumu, Kenya.
plus ouvert que les autres zones. La sous-zone écologique 3 pourrait donc jouer le Ainsi qu’à Montpellier, France
Du 15/02/11 au 31/07/11
éservoir à pucerons pour les autres sous-zones alentours. Au sein du CIRAD, URSCA.
Maîtres de stage : Pierre SILVIE et Pascal CLOUVEL
es index d’infestation dessous-zones écologiques 1 et 2 sont représentés dans ce Tuteur de mémoire : Claire LAVIGNE, INRA Avignon
e à partir d’un seul jeu de données : un seul champ était suivi pour chacune de ces

6. Heterogeneity and farmer diversity
• Esta foto muestra dos granjas contiguas, separadas por una cerca, e ilustra la diferencia entre campesinos.
Soil fertility gradients = ‘Soilscape’ + History of use + Current management
• Mientras que en el campo de la izquierda se ve un gradiente de productividad muy marcado, en el campo
del vecino la productividad es más homogénea
Tittonell et al., 2005a,b - AGEE

7. On-farm systems analysis
MKT CS H
OE
LV S TK
HO M E
CNS
W OOD
C ash
Labour
N u trie n ts

8. A functional typology for East African highland systems
T yp e 1
T yp e 3
MKT LV S TK
FOO D
MKT CS H
CNS
HOM E
O F F -F A R M
Wealthier households
OE
Mid-class to poor households
CS H W OOD
LV S TK
T yp e 2
Resource HO M E
CSH
allocation CNS
W OOD
strategies
MK T
LV S T K
T yp e 4
MKT LV S T K
C NS
C NS FO O D
HO ME FO O D
HO M E
O F F -F A R M
W OOD
W OOD
T yp e 5
C a sh MKT FO O D
HOM
Labour CNS E
O F F -F A R M
N u trie n ts W OOD
CSH
Tittonell et al., AGEE 2005a,b; AgSys 2010

9. Functional farm types and system states
Performance (well-being)
T2
T1
‘Stepping out’
P’’
‘Stepping up’
T3
P’
T4
‘Hanging in’
T5
R’’ R’
Resources (natural, social, human)
Tittonell (2011) Farm typologies and resilience: The diversity of livelihood strategies seen as alternative system states

10. Nutrient management in crop-
livestock systems…

11. Phot
Expected response (on-station)
Cu
Crop yield
0 0
Aboveground biomass (t ha-1) organic C (t
230 250 270 290
Building soilfrom (Kenya) (2007)
310 0
Data C Solomon et al.
Market
0 200 400 600 800 0
0 30 60 90 1
Julian day Cumulative rainfall (mm)
Saturation
Long-term soil C changes
C Effect
D of long-term manuring
Period of cultivation (years)
200
Root mean square error: 13.3 t ha-1
40 EControl F
y
Soil
25
NPK
c
Decision1.23
y = 1.01x + rule
Soil organic C (t ha-1)
ien
fic
Response
ΔY 5 t manure
Soil organic C (t ha-1)
Ef
160 2
ΔN Simulated
30
20
r 10 0.71
= t manure
NPK
120 Measured Yield response > NPK
15 cost of fertiliser
20
80 Excess
Intercept
10
10
40
5
Nutrient input
‘Sensible’ input et al.
Data from Solomonrates (2007) Data from Micheni et al. (2004) All treatments pooled
0 0
0 30 60 90
0 1 6 11 16 21 26
0 5 10 15 20 25 5
Variable of cultivation(on-farm)
Period responses (years) Period of cultivation (seasons)
Aboveground biomass (t ha-1)
Crop yield
E F Home fields
25 Poorly-responsive fertile fields
Aboveground biomass (t ha-1)
y = 1.01x + 1.23 Measured on NPK plots
r 2 = 0.71 Simulated water-limited yield
20 Responsive fields
Middle fields
Yield without nutrient inputs
15
ient
Outfields
10
grad
til i ty
5 Poorly-responsive infertile fields
il fer 2
All treatments pooled So
Water capture efficiency = 0.093*SOC + 0.016 (r 0.99)
2
Water conversion efficiency = 0.79*SOC + 86.8 (r 0.98)
0
0 5 10 15 20 25 5 10 15 20 25
Aboveground biomass (t ha )
Nutrient input
-1 Tittonell and Giller (2012)kg-1) Crop Res.
Soil organic C (g Field

12. Where do organic resources come from?
Livestock-mediated nutrient transfers
Village land
Variation in
(600 ha) manure quality across farms in western Kenya
Wealthier farmers’ cropland
Manure origin Content (%)
Dry matter FZ4 CFZ2 FZ2 N P K
(25 ha) (46 ha) (43 FZ2
ha)
-1
Experimental Farm 82 39 3 t ha 5 t ha-1
2.1 0.22 4.0
Wet and dry
Maseno FTCφ 80
season 35 1.4 0.18 1.8
grazing
Farm A 56 30 1.2 0.32 2.0
Farm B Communal grazing land 59 29
Livestock 1.0 0.30 1.6
Cattle densities
Farm C 77 25 1.0 0.10 0.6
400 ha
Farm D 43 35 1.5 0.12 3.3
Grazing of crop
Farm E 41 23 0.5
residues 0.10 0.6
φManure from the farm at Maseno Farmer Training Centre, Maseno, western Kenya; n/a: Not available
Poorer farmers’ cropland
Fodder
FZ4
Manure 86 ha
Diverse livestock
Zingore et al., 2010 production systems

13. Complexity/organisation of crop-livestock systems
Table 2: Some of the indicators used in the network analysis of N flows in agroecosystems of the highlands of East and Southern
Africa by Rufino et al. (2009) + seeds 3 3
Indicator
Fertiliser Grain
(Wealthier)
Calculation Reference
Fertiliser + seeds Grain (A) (B)
Biomass production
IndicatorsMaize
of network size, activity and integration Maize-
Maize Maize Vegetables
Sweet Ground Feed
beans potatoes Sorghum Maize Maize Vegetables
n nuts
Imports 2
IN z io crops
Food 2
(t capita-1)
Food crops
12 i 1
14
Effective # of nodes
Compost Food Random networks
n n Compost Food
Total Inflow TIN z io
Natural ecosystems 
xi Finn (1980)
10 i 1 i 1 12
1 n
AgroecosystemsFood Manure 1
Household Food
Manure Waste storage Waste
Roles (#)
Pasture Household
storage
Compartmental Throughflow Ti f ij z io 
Excreta x i 10
8 Excreta j 1
Excreta
Animal products Animal products
n
8
6
Fallow
Total System Throughflow 0
TST Ti 0
Excreta 0n
i 1
20 40 6 60 80
Excreta 0.00 0.05 Goats 0.10
Chicken 0.15 0.20
Feed
4 Pasture Chicken Cattle Natural ecosystems
Total System Throughput T .. T ij Patten and Higashi (1984)
Feed Livestock, j 1
i N import (kg N capita ) 4
(Medium-poor)
-1 Livestock
Finn’s cycling index
Agroecosystems
Fodder crops Feed Products N flows=30
2 Feed Products
TST c flows=43
N
2
Finn’s Cycling Index FCI Finn (1980)
Food self-sufficiency ratio
TST
0
4 0 4
Dependency Fertiliser + seeds Grain D IN / TST (C) Tigray
(D)
0 2 4 6 8 10 12 14 0 5 10 15 20 25 30 35 40 45
Indicators of organisation and diversity
Maize Maize Maize Vegetables
3 Ground Feed 3
Fertiliser + seeds Murewa
Connectivity (flows noden-1T
nuts
n 2 ) ij T ij T .. Effective # of flows
Ulanowicz (2001), Latham and
Average Mutual Information AMIFoodkcrops log 2 Feed
Scully (2002)
Maize-
Maize Maize
Kakamega
Vegetables
Ground
nuts-
i 1 j 0 T .. T i .T . j sunflower beans
2 2
Compost Food Food crops
n T T. j
(Medium-wealthy)
Statistical uncertainty (Diversity) HR
.j
log 2 Excreta
T .. T ..
Manure Waste
1 j 0 Food 1 Food
Notation: zio are N Household
inflows to each system compartment
(H i) from the external environment, xi represents the change in storage of a compartment
Waste Food
storage
and fij represents internal flows between compartments (e.g., fromExcretaHi) Excreta
H j to Chicken Household
Products Excreta
Animal products
0 Livestock
0
0 50 100 150
N flows=21
0 0.5
(Poor) 1.5
1 2
Excreta
Pasture Chicken Cattle Goats
Feed
Total system throughput Average mutual
Livestock
Fodder crops
Feed Products (kg N capita-1)
N flows=43 information (bits-1)
Ecological Network Analysis

14. Integrated soillosses
Manure storage:
fertility management
100 Improving livestock feeding and
Mineral nitrogen SUSU-1)
Pit open air
Farmers’ try-outs and adaption plots
Heap open air
manure ‘production’
Nitrogen (kg (g -1)
80 Heap under roof
60
40
20
0
0 30 60 90 120 150 180
0.6
Phosphorus (kg SU-1)
0.5
Long rains Short rains
0.4
(cropping seasons)
0.3
On-farm trials managed by researchers Rainfall Improving compost management
0.2
0.1
0
0 30 60 90 120 150 180
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1.2
Potassium (kg SU-1)
Manure (compost)
0.9 CR CR
management
A+M Addition + Maturing Addition + Maturing
0.6
0.3
Application Application
to crops Market to crops Market
0
0 30 60 90 120 150 180
Days of storage

15. Maize pr
Napier gras
Allocation of manure to different crops
20
2
10
0 0
20 40 60 80 100 120
Productivity Soil organic Cand Napier
of Maize (t ha-1)
Sweet potato 1
B 1
Maize field 3
field 1
(0.18 ha) (0.24 ha) Effects on soil fertility
Relative Napier grass yield
10
0.8 A 70
0.8
Relative maize yield
Napier grass production (t farm-1)
Napier grass Napier grass production
Maize
Maize production (t farm-1)
0.6 60 0.6
Napier grass 8
field 2
(0.15)
Manure 50
allocation 6
0.4 0.4
40
Maize field 2
strategies
(0.25 ha) (10 year 4 0.2 Maize production
30 0.2
simulations) 20
2 0 0
Napier grass 1 2 3 4 5 6 7 8 9 10
Maize field 1 Even spread Concentration
field 1 (0.15 ha)
(0.06 ha) 0 0
20 40
Manure allocation strategy
60 80 100 120
Soil organic C (t ha-1)
Manure 1
B 1
heap
pier grass yield
0.8 0.8
maize yield
Homestead
2 cows Napier grass production
0.6 0.6