Jeroen Groot, 26 March 2012Systems approaches and tradeoffsanalysis: smallholder agriculture                              ...
Systems approaches to ecological intensification A Farming Systems Decalogue:   (i) Deal with farm diversity;   (ii) Deal ...
Properties of smallholder farming            systems
Anisotropy and heterogeneity     Agroecosystems: complex socio-           ecological systems                              ...
veau d’infestation.   Anisotropy and heterogeneity our visualiser les différences spatialisées dans la dynamique d’infesta...
Heterogeneity and farmer diversity • Esta foto muestra dos granjas contiguas, separadas por una cerca, e ilustra la difere...
On-farm systems analysis             MKT        CS H                        OE                       LV S TK              ...
A functional typology for East African highland systems                             T yp e 1                              ...
Functional farm types and system states   Performance (well-being)                                                        ...
Nutrient management in crop-     livestock systems…
Phot                                                                                             Expected response (on-sta...
Where do organic resources come from?  Livestock-mediated nutrient transfers    Village land  Variation in    (600 ha)    ...
Complexity/organisation of crop-livestock systems Table 2: Some of the indicators used in the network analysis of N flows ...
Integrated soillosses  Manure storage:                  fertility management                            100               ...
Maize pr                                                                                                                  ...
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Systems approaches to support ecological intensification
Prochain SlideShare
Chargement dans…5
×

Systems approaches to support ecological intensification

961 vues

Publié le

0 commentaire
3 j’aime
Statistiques
Remarques
  • Soyez le premier à commenter

Aucun téléchargement
Vues
Nombre de vues
961
Sur SlideShare
0
Issues des intégrations
0
Intégrations
25
Actions
Partages
0
Téléchargements
24
Commentaires
0
J’aime
3
Intégrations 0
Aucune incorporation

Aucune remarque pour cette diapositive

Systems approaches to support ecological intensification

  1. 1. Jeroen Groot, 26 March 2012Systems approaches and tradeoffsanalysis: smallholder agriculture Linking concepts to practicePablo TittonellFarming Systems Ecology – Wageningen University, The Netherlands World Agroforestry Centre 13 February 2013
  2. 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. 3. Properties of smallholder farming systems
  4. 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 interventionsEbanyat, 2010 • Need to target technologies
  5. 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 dinfestation 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 selons-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 lane. 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 uneon plus importante dans les champs, dès le début du cycle de culture du haricot. SOUTENU EN SEPTEMBRE 2011Concernant 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. 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. 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. 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 CSHTittonell et al., AGEE 2005a,b; AgSys 2010
  9. 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. 10. Nutrient management in crop- livestock systems…
  11. 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. 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 1.6Cattle 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. 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. 14. Integrated soillosses Manure storage: fertility management 100 Improving livestock feeding and Mineral nitrogen SUSU-1) Pit open airFarmers’ 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. 15. Maize pr Napier grasAllocation 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

×