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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
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
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Systems approaches to support ecological intensification

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  • 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. 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 interventionsEbanyat, 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 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. 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 CSHTittonell 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 1.6Cattle 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. 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 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. 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
  • 16. Cumul PhotosNUANCES-FARMSIM: farm-scale, dynamic bio-economic model Activity calendars: seasonal800 labour and resource allocation 0 0.7 1st ploughing Climatic and parameters SIMulator Livestock management effects 230 250 270 290 310 2nd Ploughing Bodyweight (kg) 600 FArm-scale Resource Management 0.6 Soil parameters Julian day Maize planting A 40 B Beans planting 12 Long-term soil C changes Eff 0.5 C 1st weeding D Photosynthetically active radiation 460 Incident Control, no manure 0.4 Aludeka Simulated Emuhaya 460 400 Shinyalu Control Aludeka Emuhaya Shinyalu weeding 2nd Cumulative transpiration (mm) CROP FIELD SOIL Measured 200 200 40 440 16 Beans harvest 9 440 5 t manure -1 0.3 Root mean square error:efficiency: Rainfall capture 13.3 t ha Maize harvest Potential, water- and Soil C dynamics 30 CLIMATE Body weight (kg) 420 420 200 10 t manureAludek a 0.23 Aboveground biomass (t ha-1) organic C (t ha-1) organic C (t ha-1) nutrient-limited yields 0.2 6 400 160 Water, N, P and K Actual variability Emuhaya 0.27 150 400 12 (MJ m-2 day-1) Weed competition availability Shinyalu 0.31 30 380 0.1 Scenarios Simulated 0 380 3 0 0 201 2 3 4 5 6 7 8 9 10 11 12 Measured 360 360 120 MARKET d l. w . w r. h n. d b. t w b. w . t w r. w . w . y t w c. w . w . . y y d l. t w l. w . . w . d n. w . w . d n. tw . t w r. d n. tw . . d p. y d g. tw . d r. h r. b d g. 4t Jun p 4t eb ep r 3r Jan r 4t Jul 1s Jan 1s un Aboveground biomass (t ha-1) r c g 1s ug 4t Sep 3r Ju 100 1s Ju 3r M a 3r M a 1s M a 4t Ma 1s M a 2n Ju 2n Ma 2n Ma 4t Ma 3r A p 1s A p 3r Fe 4t Ja 2n Fe 1s Fe 2n Ap 3r S e 1s De 4t Ap 8 4t De 3r Ju 2n Se 3r A u 4t Au 2n Ja 2n Ju 2n Au F S J A w w w w w w w w w w w w w w tw w Age (y) w w 0 340 w 20 h 340 d d d d h h h h d d d h h d d h HOUSEHOLD 3r 1 Factors Table 3: Reference prices and calculated costs used for 21 simulation scenarios; data collected during the 1 320 6 11 16 Objectives & decisions 26 Intercepted 320 10 80 Products Market prices and their variability responses across heterogeneous farms January-February 2005 through interviews with key informants: farmers, extension agents, input 300 4 Crop suppliers and technicians of Investment, allocation= 9 – 16). Exchange rate 75 KSh0.8 1 US$. research institutes (n 300 radiation = 50 Calving rate 12 Soil Item [unit] 280 manure per and expenditure Price CV 280 Use* Cost** from Micheni et al. (2004) 10 5t ha COMMONLAND (KSh) (%) (units 0.6 ) Clay soils Sandy soils -1 40 Data -1 ha 0(KSh ha ) 9 260 Maize grain [Bag of090 kg] Labour availability 15000 260 Rangeland 0 15000 0 1 2 3 4 5 6 7 8 9 10 11 12 January to June** 1620 Homefield 3 1 4 5 6 7 811 9 et 16 12 Homefield 26 7.3 - 0 0.4 2 Woodlots Data from Solomon 10 al. (2007) 1 - 6 11 21 D LIVSIM 230 250 270 290 310 0 0 200 0 0 0 Prototyping: 600 ‘ideal’800 0 400 the farm July to December 6 14 HEAPSIM 860 1 210.4 - 000 - 12 Milk production (L day-1) 2 0 30 60 90 1 Aboveground biomass yield (kg ha-1) 14 y = -0.039x + 0.0836x + 0.9 Feed supply and Julian day 0.2 OFF-FARM 2 Cumulative rainfall (mm) Manure collection, 0 0 135 9 0 R = 0.8605 9000 3 Long-term soil storage & quality 4.012 30 Maize seed (hybrids 513, 614) [kg] demand 12 C changes 35 4050 Effect of long-term manuring EmploymentC Period of cultivation (years) D 6000 6000 0 Fertilisermeat, traction 50 kg] *** Milk, prices [Bag of 10 200 -1 10 40 Remittances E F and 0manure 1.0 30 0.0 Control -1.0 -2.0 -3.0 Di-ammonium phosphateRoot mean square error: 13.3 t ha (18:46:0) 2100 3 06.7 - - 25 Soil FARMSIM 00 3000 Calcium1 8ammonium nitrate (46:0:0) 6 11 16 21 1870 2616.4 8 - - y = 1.01x + 1.23 5 t manure Soil organic C (t ha-1) -1 160 Triple super phosphate (0:46:0) 2000 - - 25- Bodyweight change (% mo 0) 0 30 r 2 10 0.71 = t manure 12 6 6 Simulated 30 1 0 0 20 2 0 30 0 10 20 30 Kaitho et al 2001 Manure [wheelbarrow ca. perkg FW] 10 t manure 30 ha 20 4 120 Measured Jenet et al 2004 Good quality manure (e.g. 3% N) 50 1 526.7 4 - 000 25 - Vargas 5 0 0 2000 1 et al 0 Milk yield (l d-1) 9 Poor quality manure (e.g. 0.7% N) 32 49.6 - Outfield 15- 15 Outfield Lanyasunya et al 2001 M anure 2 20 20 12000 2 Kabuga and 0 1 2 0 0 Agyemang 1984 SSP 6 80 Hired labour [person-day] M anure s im ulate d 0 9000 15 10 10 9000 First ploughing (hoe) 160 14.0 0 20.0 3200 S S P s im ulate d Second ploughing, manure3application and planting 9 10 3 0 1 2 4 5 6 7 8 11 12 87 626.6 24.4 1 102 3 2120 5 6 7 8 9 10 6 0 0 012 0 4 10 11 40 000 5 Weeding 380 50.6 11.1 4222 r 2 = 0.67 Harvesting (including chopping of crop residues) 97 313.5 26.6 5 5 2590 000 3000 0 General farm husbandry (e.g. animal feeding, al. (2007) Data from Solomon et milking) 55 15.7 - 0- Data from Micheni et al. (2004) All treatments pooled 1 06 11 Soil movement (digging, trenching) 16 21 150 26 0- - 0 - 00 5 10 15 200 25 30 35 W 0 30 60 0 02 1 4 6 36 8 10 12 21 090 10 20 0 11 0 16 10 26 20 30 Ox ploughing [acre] Number of growing seasons1350 15.7 2.2 3000 0 Lactation 5 10 15 -1 20 25 5 length (mo) P application rate (kg ha ) Period of cultivation (years) Period of cultivation (seasons) Aboveground biomass (t ha-1) Tittonell et al., 2007a,b;2008;2009; van Wijk et al., 2009
  • 17. Integrated analysis of different farm types livestock Nutrient Cycling through 10 5 Manure application rate Average biomass yield FT1 (kg ha-1 season-1) (kg ha-1 season-1) 8 Farm type 2 (2.8 ha) 4 FT2 6 3 FT3 Farm type 1 (0.5 ha) Napier Napier grass FT4 grass4 2 plot Maize & MaizeLabour foodbeans self resource conservationofobjectives (cf. indicators Napier productivity and 2financial resultsTable 5: Average values and standard deviationharmonising Food grass production and sufficiency farm-scale Fig. 9). and model parameters when & 1 Napier beans grass Tea Tea Indicator/parameter 0 Scenario 0 Sweet Sweet 40 Napier Napier 40 0.0 0.5 potato potato Farm Type 1 0.0 grass grass 2000 KSh 0.5 1.0 5000 KSh 1.5 10000 KSh Farm Type 2 1.5 1.0 market Cattle stocking rate (t LW ha ) -1 Cattle stocking rate (t LW ha-1) Caloric energy (MJ farm-1 season-1) Objective indicators 30 30 Stover sold/ exchanged NPK Maize production (t farm-1 season-1) NPK 3.5 4.3 (0.0) Sweet Sweet 5.7 (0.1) 7.1 1.5 (0.1) Manure available to crops Maize stover and thinnings D airy potato potato N losses (kg N farm-1 season-1) NPK m eal FT1 84 (1) 87 (2) 109 (3) 20 3.0 20 Soil erosion (t farm-1 season-1) FT2 18 (1) 18 (0) 1.2 17 (0) (kg season-1) (t DM season-1) Napier grass sold/ exchanged 2.5 FT3 NPK 0.9 Summary of model parameters 2.0 10 10 Stover sold/ N P K airy exchanged D Total N fertiliser used (kg farm-1) FT4 5 (3) 18 (8) NPK m eal 128 (16) FT1 Labour used (man-days farm-1) 1.5 0.6 FT2 Farm type 3 and planting Ploughing (1.2 ha) 0 1.0 49 (1) 53 (1) 0 63 (4) Weeding 0.3 FT3 1 4 7 0.5 10 13 (1) 16 21 19 34 (1) 1 Farm type 4 (0.9 ha) 4 43 (2) 7 10 13 16 19 Ridge cropping and mulching Maize 21 (1) 26 (2) 38 (4) FT4 Total Maize 0.0 & 91 (1) 113 (2) 0.0 145 (3) 40 & 40 Maize beans 0.0 0.5 1.0 (94) Investment in N fertiliser (KSh season-1) 3 Napier beans Farm Type 187 1.5 2.0 2.5 (321) 3.5 673 3.0 & 0 3 4787 (624) production On-farm 6 9 12 Farm Type 4 15 grass Total investment in labour (KSh season )-1 -1 Excreted DM (kg season ) (333) beans 16872 (668) 4151 (122) 10250 -1 Total feed on offer (t DM season ) Napier 30 grass 30 Household requirement Napier Complementary indicators 50Napier grass 25 Sweet grass FT1 FT1 Rainfall use efficiency (kg grain mm-1) N input to manure heap Sweet Sweet N output after storage plot 12.6 (0.3) 16.6Sweet (0.2) 20.6 (0.2) potato potato 20 20 20 N productivity (kg grain kg N applied-1) 40 FT2 1913 (6411) 531 potato potato (957) 75 (7) FT2 (kg season-1) (kg season-1) Gross N use efficiency (kg grain kg N available-1) Co lle FT3 18 (70) 23 (86) 24 (3) FT3 ing Value of production (KSh season-1)1 h c30 tio NPK 59340 herd 78660 15 97980 10 -1 1,2 er din n/ N P K airy ion/ 10 FT4 FT4 55040 Collect D NPK Gross benefit (KSh season ) g20 m eal 67730 76230 Return to labour (KSh man-day-1)1,2 618 605 10 548 Roadside Benefit/cost ratio1,2 0 grass 12.8 6.20 3.5 10 5 Daily gross benefit (KSh family-1 day-1) 1,2 7 1 4 10 13 151 16 19 186 1 4 209 7 10 13 16 19 Gross benefit per capita (KSh person-1 day-1) 1,2,3 0 22 27 031 1 Calculations done considering the average values for the objective indicators and 90 0 30 60 model parameters 150 120 0 Number of growing seasons 10 20 30 40 50 2 Calculations done considering only the direct costs of N fertiliser use and labour hired season-1) N intake by livestock (kg in; fixed costs and/or other variable N input to manure heap (kg season-1) costs such as buying seeds were not considered.
  • 18. Tradeoffs analysis
  • 19. Fertiliser use + residue restitution may not be enough…Conservation agriculture Effect of in crop productivity Changeslong-term fertiliser use on soil fertility (Togo) 25 2.5 5 Carbon Maize grain yield (t ha-1) A Tillage-NoCrop No-fertiliser A Nitrogen B Soil organic C (g kg-1) 20 No-fertilizer Fertiliser-RR 2.0 Total soil N (g kg-1) 4 Fertiliser-1.5RR Fertilizer-RR 15 1.5 3 Fertilizer-1.5RR 10 2 1.0 5 1 0.5 0 0 0.0 0 3 6 9 12 15 18 0 3 6 9 12 15 18 1972 1976 1980 1984 1988 1992 100 6.5 Phosphorus 5 C pH D Seed-cotton yield (t ha-1) B 6.25 Available P (mg kg-1) 80 4 6 pH (1:2.5) 5.75 60 3 5.5 40 5.25 2 5 20 1 4.75 0 4.5 0 0 3 6 9 12 15 18 0 3 6 9 12 15 18 1972 1976 1980 1984 1988 1992 Time (years) Year Terres de barre, Kintché et al (2011) southern Togo (20% clay)
  • 20. Biomass allocation at in CA scale Biomass tradeoffs village (Yilou, Burkina Faso)Trade-offs between crop & livestock objectives Naudin et al. 2011
  • 21. Landsacape level interactionsHow can agricultural intensification and wildlife be Figure 2 – Schematic representation of the multi-agent model Agent-based modellingbest accommodated in a village territory? Baudron, Delmotte, Herrera, Corbeels, TittonellIntensification through conservation agriculture to preserve habitats and biodiversity
  • 22. Tradeoffs analysis Objective B B1 A1 A2 A3 Objective A
  • 23. Services écosystemiques: biodiversité et séquestration de C A Vihiga B Siaya Aboveground C stock (Mg ha-1) 40 40 homegarden annual crop permanent crop 30 30 pasture A) Trees 20 B) Hedgerows 20 40 20 Delta C stock (Mg farm-1) Vihiga 10 Vihiga 10 Siaya l Siaya ntia 30 p ote 0 15 0 tion 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 stra que C-se C 10 Vihiga D Siaya C-sequestration potential 20 Aboveground C density (kg m-2) 8 8 Windrow Individual tree Woodlot 6 6 10 5 4 4 0 0 0 5 10 15 2 20 0 5 10 2 15 20 it wt Current aboveground C stock (Mg farm-1) 0 0 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0 1.5 2.0 2.5 g Homegarden index Shannon b lh Food crop hh wlt mh Pasture t e Cash crop Slop Woodlot Henry et al. (2009), Agriculture Ecosystems and Environment 129
  • 24. Farming Systems Ecology Group
  • 25. Extra slides
  • 26. Planting basins and zaï systems: are these CA? CA CP
  • 27. Use of native resources and local knowledge in CA Facilitation of crop production through association with native woody species in the Sahel Understanding traditional soil fertility managementPiliostigma reticulatum Guiera senegalensis
  • 28. Stepwise ‘aggradation’ Rabah Lahmar (2009)
  • 29. Soil rehabilitation Quick responses Performance/ Efficiencies/ Stocks Slow responses Performance/ Efficiencies/ Stocks h t100% Re h’ ha bil itati o n h’’ t50% De gra da t25% tio n Period of degradation (t) Period of rehabilitation (t) Period of degradation (t) Period of rehabilitation (t)
  • 30. Tradeoffs analysis: methodsInverse modelling Modélisation directe Paramètres Résultat Modélisation inverse Résultat Ensemble de paramètres = décisions de l’agriculteur Tittonell et al. (2007), Agricultural Systems 95
  • 31. Analysing tradeoffs at farm scale • A spatially heterogeneous farm Trade-offs between objectives 200 • A limited availability of cash 25 10000 K S h 180 • A limited availability of labour Farm farm scale (kg) 24 5000 K S h 2000 K S h • Objectives: maximise food 23 R e la tiv e in v e s tm e n t in e ro s io n c oFn o il elro serosa t fa rms sca le] (t) 160 N losses at N loss [kg] S tro oil io n ion los [tons production, minimise N losses, 22 etc… 140 21 Simulated management decisions 20 120 A B arm sProfile.8 0 19.8 0 R e la tiv e in v e s tm e n t in w e e d in g 2000 KSh 2000 KSh Homestead 100 18 5000 KSh 5000 KSh Napier grass 0 .6 10000 KSh 17.6 0 10000 KSh Compound fields Home garden 80 0 1000 1 2000 2 3000 3 4000 4 5000 5 6000 6 7000 7 8000 8 Maize fields Woodlot 16 Farm grain yield [kg]Living fence 0 1000 2000 3000 4000 5000 Tea 0 1 2 production (tones)8000 Farm-scale3 maize4grain 5 6000 6 7000 7 8 0 .4 0 .4 F arm grain y ield [k g] Maize F a rm -sca le m a ize g ra in p ro d u ctio n (t)Layout 0 .2 0 .2 Maize 1 Sweet Maize 2 potato Maize 5 (+) (-) (+/-) 0 Maize 6 Woodlot 0 .0 Maize 4 (-) 0 0 .2 (+/-) 0 .4 Tea 0 .6 0 .8 0 .0 0 .2 0 .4 0 .6 0 .8 1 .0 Maize 3 Home R e la tiv e in v e s tm e n t in N fe rtilis e r (+) R e la tiv e in v e s tm e n t in la n d p re p a ra tio n garden Tittonell et al. (2007), Agricultural Systems 95
  • 32. Conservation agriculture: tradeoffs at farm scale • Rotation effects on pest and diseases • Fodder availability Trade-offs between practical objectives • C input and soil C stocks • Weed controlEvolving labour demands due to climate changeN fixation and nutrient • cycling • Soil biological activity and physical properties • Erosion control Rainy season Dry season Rainy season Razakavololona, 2011 Naudin et al. 2011
  • 33. Structural typologies: based on resource endowment Smallholder households in NE Zimbabwe Farm type Farm size # Livestock # Scotch Maize yield (ha) carts (t ha-1) Poor 20 < 0.7 0 None 0.2 – 1.0 Clustering (e.g. multi-dimensional scaling) Medium 0.7 – 1.2 2–4 1 1.0 – 1.2 40 Rich > 1.2 4 - 22 2 2.0 – 3.5 50% similarity Within-group similarity (%) 60 80 100 Farm samples
  • 34. Conservation agricultureEvaluation des impacts et contraints a l’échelle de l’exploitation Field Survey Adoption rate (%land shifted to DMC) Farm Survey Farmer’s management Superimposed, innovative management Subsides (us$ ha-1)Affholder et al., 2010
  • 35. Land tenure and history of occupation Story lines: Diversity of farming trajectories and styles Local land use systems • Organic matter and nutrient flows at village scale • Land tenure, diversity of livelihoods and ethnicity • How to determine reference yield levels (potential?) over such heterogeneous agricultural landscapes? Debru, J. (2009) L’abandon de la culture du cotonnier est-il momentané ou définitif ? AgroParisTech
  • 36. Prospective analysis using models Are current crop models able to simulate water and nutrient dynamics under CA? Main structure Effect of / fertilizer mulching Genetic Soil coefficientsGenotype / Cultivar Genotype / Ecotype Soil (water) A sensitivity analysis of Soil water DSSAT calibrated for storage Monze trials, Zambia capacityChirat, Thierfelder, Nyagumbo, Corbeels and others… (CA2Africa EU FP7)
  • 37. Bio-economic models • How ‘bio’ and how ‘eco’ do we want them? • Strategies • Farm scaleBiophysical Economic • Simplicitymodels to which optimisationan economic • Data ‘undemanding’ models thatbalance is represent bioadded, in which processes as‘decisions’ are fixed technicalinitialisation coefficients (noparameters dynamic feedbacks)
  • 38. 100 40 Total hous IncomeIndicators of ‘resources’ and ‘performance’ Total household income (kSh yr ) -1 120 300 Household type 1 Income per capita (kSh yr ) 50 20 -1 Household type 2 250 100 Household type 3 Household type 4 200 80 Household type 5 0 0 300 120 0 1 2 3 4 5 0.0 0.4 Total household income (kSh yr ) 150 60 System state II -1 Income per capita (kSh yr ) -1 6 250 1.0 100 2 t ha-1 40 Food production per capita (t dm) 100 Stepping out Food production (t dm farm-1) 1 us$ day-1 50 5 20 200 0.8 80 0 1 t ha-1 0 0 4 1 2 3 4 5 0.0 0.4 0.8 1.2 150 0.6 60 6 -1 2 t ha 120 1.0 Food production per capita (t dm) 3 Household type 1 Food production (t dm farm-1) 100 40 come per capita (kSh yr ) 5 -1 0.8 Household type 2 100-1 0.4 1 t ha Household type 3 4 2 50 0.6 20 Household type 4 3 80 0.2 Household type 5 1 0.4 0 0 2 System state 1 0 I 602 3 4 5 0.0 0 0.0 0.2 1 0 1 2 Cropping land (ha) 3 4 5 0.0 0.4 Self-sufficiency 6 -1 1.0 Cropping landt (ha) 40 2 ha (t dm) 0 0.0 Land rm-1) 0 1 2 3 4 5 0.0 0.4 0.8 1.2 5 Cropping land (ha) Land:labour ratio 1 us$ day-1Tittonell, 2011 0.8 20

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