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 farmingsystems 4. Anisotropy and heterogeneity Agroecosystems: complex socio- ecological systemsAnisotropyHeterogeneity Ecological nichesLandscape organisation Connectivity ContingencySoil C gradients inMr. Olukas farmResource allocation(Ouganda) Local knowledge andperceptions of heterogeneity Differential responses tointerventionsEbanyat, 2010 Need to target technologies 5. veau dinfestation. Anisotropy and heterogeneity our visualiser les diffrences spatialises dans la dynamique dinfestation, les sont compares selon les sous-zones cologiques dans la Figure 20.ISTOM Variation spatio-temporelle Ecole dIngnieur en Agro-Dveloppement International Index dinfestation moyen32, Boulevard du PortF.-95094 - Cergy-Pontoise Cedex 4,5 tl : 01.30.75.62.60 tlcopie : 01.30.75.62.61istom@istom.net4 3,5 MMOIRE DE FIN DTUDES3ZE 1 2,5 Les dterminants de la variabilit spatiale et temporelleZE 22de la pression des pucerons et de leurs ennemis naturels 1,5ZE 3dans une rgion agricole du Kenya1 ZE 4 0,50 S1 S2 S3 S4 S5 Index dinfestation moyen des champs en fonction des semaines de relevs, pour les quatre zones s. Kajulu, Kenya, 2011 ur la base de ces donnes, des dynamiques dinfestation diffrentes se dessinent selons-zones cologiques. La sous-zone cologique 3 prsente en effet un index tion suprieur celui des autres sous-zones, en dbut de priode : jusqu lane. Or cette sous-zone cologique est caractrise par un intense rseau de haies, et aque de champs trs fine. Si la concentration en plantes htes des pucerons Aphis (Photographie de la zone dtude : Kajulu, Kenya (Source : Andr, 2011)) ra et Aphis fabae joue le rle de refuge pour les pucerons, ceci pourrait expliquer uneon plus importante dans les champs, ds le dbut du cycle de culture du haricot.SOUTENU EN SEPTEMBRE 2011Concernant linfestation en sous-zone cologique 4, elle commence un niveau plusAndr Laure VaitiarePromotion 97 ais sa pente est plus forte. Or cette zone-ci se caractrise par labsence de haies, et un Stage ralis Kajulu, Kisumu, Kenya.plus ouvert que les autres zones. La sous-zone cologique 3 pourrait donc jouer leAinsi qu Montpellier, FranceDu 15/02/11 au 31/07/11 servoir pucerons pour les autres sous-zones alentours.Au sein du CIRAD, URSCA.Matres de stage : Pierre SILVIE et Pascal CLOUVELes index dinfestation dessous-zones cologiques 1 et 2 sont reprsents dans ceTuteur de mmoire : Claire LAVIGNE, INRA Avignone partir dun seul jeu de donnes : un seul champ tait 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 ms homogneaTittonell et al., 2005a,b - AGEE 7. On-farm systems analysis MKTCS HOE 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 1T yp e 3 MKTLV S TKFOO DMKT CS H CNSHOM E O F F -F A R MWealthier householdsOE Mid-class to poor households CS HW OOD LV S TK T yp e 2 ResourceHO M ECSH allocation CNS W OOD strategies MK TLV S T KT yp e 4 MKTLV S T K C NS C NS FO O DHO ME FO O DHO M E O F F -F A R M W OODW OOD T yp e 5 C a sh MKTFO O DHOM Labour CNSEO F F -F A R M N u trie n tsW OODCSHTittonell et al., AGEE 2005a,b; AgSys 2010 9. Functional farm types and system states Performance (well-being) T2T1Stepping outP Stepping upT3P T4Hanging inT5 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 00Aboveground biomass (t ha-1) organic C (t230250270 290 Building soilfrom (Kenya) (2007)310 0Data C Solomon et al.Market0 200 400600 800 0 0 3060 90 1 Julian day Cumulative rainfall (mm)Saturation Long-term soil C changes C Effect Dof long-term manuringPeriod of cultivation (years) 200 Root mean square error: 13.3 t ha-140EControlF ySoil25NPKc Decision1.23y = 1.01x + rule Soil organic C (t ha-1)ienficResponseY5 t manureSoil organic C (t ha-1) Ef 1602N Simulated3020 r 10 0.71= t manureNPK 120 MeasuredYield response > NPK15 cost of fertiliser2080 ExcessIntercept101040 5 Nutrient inputSensible input et al. Data from Solomonrates (2007) Data from Micheni et al. (2004)All treatments pooled 0 0 0306090 0 16 11 162126 051015 20 25 5Variable of cultivation(on-farm)Period responses (years)Period of cultivation (seasons)Aboveground biomass (t ha-1)Crop yield EFHome fields25 Poorly-responsive fertile fieldsAboveground biomass (t ha-1) y = 1.01x + 1.23 Measured on NPK plots r 2 = 0.71 Simulated water-limited yield20Responsive fieldsMiddle fieldsYield without nutrient inputs15ient Outfields10 gradtil i ty 5Poorly-responsive infertile fieldsilfer2 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 152025 5101520 25 Aboveground biomass (t ha )Nutrient input -1Tittonell and Giller (2012)kg-1) Crop Res. Soil organic C (g Field 12. Where do organic resources come from?Livestock-mediated nutrient transfersVillage landVariation in(600 ha) manure quality across farms in western KenyaWealthier farmers croplandManure originContent (%)Dry matterFZ4CFZ2 FZ2 N PK(25 ha) (46 ha) (43 FZ2ha)-1Experimental Farm82 39 3 t ha5 t ha-12.1 0.224.0Wet and dryMaseno FTC80season35 1.40.181.8grazingFarm A 56 30 1.20.322.0Farm B Communal grazing land 59 29Livestock1.00.301.6Cattle densitiesFarm C 77 25 1.00.100.6400 haFarm D 43 35 1.50.123.3Grazing of cropFarm E 41 230.5 residues 0.100.6Manure from the farm at Maseno Farmer Training Centre, Maseno, western Kenya; n/a: Not available Poorer farmers cropland Fodder FZ4 Manure 86 ha Diverse livestockZingore 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 33Indicator FertiliserGrain (Wealthier)Calculation ReferenceFertiliser + seeds Grain(A)(B) Biomass productionIndicatorsMaize of network size, activity and integration Maize- MaizeMaizeVegetablesSweet GroundFeed beans potatoes SorghumMaizeMaize VegetablesnnutsImports 2 IN z io cropsFood 2 (t capita-1)Food crops12i 1 14Effective # of nodesCompost Food Random networks n nCompostFoodTotal InflowTIN z io Natural ecosystems xiFinn (1980)10i 1 i 1121 n AgroecosystemsFood Manure 1 Household Food Manure Waste storageWaste Roles (#) PastureHousehold storageCompartmental Throughflow Ti f ijz io Excreta x i 108 Excreta j 1 ExcretaAnimal products Animal products n8 6 FallowTotal System Throughflow 0TST Ti 0 Excreta 0ni 1 20 406 6080 Excreta0.00 0.05 Goats 0.10 Chicken0.15 0.20Feed 4 PastureChicken Cattle Natural ecosystemsTotal System Throughput T .. T ij Patten and Higashi (1984)FeedLivestock, j 1i N import (kg N capita ) 4 (Medium-poor)-1 LivestockFinns cycling index Agroecosystems Fodder crops FeedProducts N flows=302 FeedProductsTST c flows=43 N2Finns Cycling Index FCIFinn (1980) Food self-sufficiency ratioTST0 404Dependency Fertiliser + seedsGrain DIN / TST(C) Tigray (D) 0246810 12 14 0 510 15 20 25 30 35 40 45Indicators of organisation and diversityMaizeMaizeMaizeVegetables 3 Ground Feed 3 Fertiliser + seeds MurewaConnectivity (flows noden-1Tnutsn 2) ij T ij T ..Effective # of flowsUlanowicz (2001), Latham andAverage Mutual Information AMIFoodkcropslog 2FeedScully (2002) Maize-Maize MaizeKakamegaVegetables Groundnuts- i 1 j 0 T .. T i .T . j sunflower beans 2 2Compost Food Food cropsn T T. j (Medium-wealthy)Statistical uncertainty (Diversity)HR.j log 2 ExcretaT ..T .. Manure Waste 1j 0 Food 1 Food Notation: zio are NHousehold inflows to each system compartment(H i) from the external environment, xi represents the change in storage of a compartmentWasteFood storage and fij represents internal flows between compartments (e.g., fromExcretaHi) ExcretaH j toChickenHouseholdProducts ExcretaAnimal products 0 Livestock 0 050 100 150N flows=21 00.5 (Poor) 1.512 Excreta PastureChickenCattleGoatsFeed Total system throughput Average mutual Livestock Fodder cropsFeedProducts (kg N capita-1)N flows=43 information (bits-1)Ecological Network Analysis 14. Integrated soillossesManure storage:fertility management100Improving livestock feeding and Mineral nitrogen SUSU-1)Pit open airFarmers try-outs and adaption plotsHeap open airmanure production Nitrogen (kg (g -1) 80 Heap under roof 60 40 2000 30 60 90120150 1800.6 Phosphorus (kg SU-1)0.5 Long rains Short rains0.4 (cropping seasons)0.3On-farm trials managed by researchersRainfall Improving compost management0.20.100 30 60 90120150 180 Jan FebMarApr May Jun JulAugSep OctNovDec1.2 Potassium (kg SU-1) Manure (compost)0.9 CRCR managementA+MAddition + Maturing Addition + Maturing0.60.3 Application Applicationto cropsMarketto crops Market 00 30 60 90120 150180Days of storage 15. Maize prNapier grasAllocation of manure to different crops202100 02040 6080 100120Productivity Soil organic Cand Napier of Maize (tha-1) Sweet potato 1B 1Maize field 3field 1(0.18 ha)(0.24 ha) Effects on soil fertility Relative Napier grass yield 100.8 A700.8 Relative maize yieldNapier grass production (t farm-1)Napier grass Napier grass productionMaize Maize production (t farm-1)0.660 0.6Napier grass 8field 2(0.15)Manure 50 allocation60.4 0.4 40 Maize field 2 strategies(0.25 ha) (10 year 4 0.2 Maize production 30 0.2simulations) 20 20 0 Napier grass 12 3 456 789 10Maize field 1 Even spread Concentrationfield 1 (0.15 ha)(0.06 ha)0020 40 Manure allocation strategy 60 80100120 Soil organic C (t ha-1) Manure 1B 1heappier grass yield0.8 0.8 maize yieldHomestead 2 cowsNapier grass production0.6 0.6 16. CumulPhotosNUANCES-FARMSIM: farm-scale, dynamic bio-economic model Activity calendars: seasonal800 labour and resource allocation0 0.71st ploughing Climatic and parameters SIMulator Livestock management effects230 250 270 2903102nd PloughingBodyweight (kg)600 FArm-scale Resource Management0.6Soil parametersJulian dayMaize plantingA 40 BBeans planting12 Long-term soil C changesEff 0.5 C1st weeding D Photosynthetically active radiation 460IncidentControl, no manure 0.4 AludekaSimulatedEmuhaya 460 400Shinyalu Control AludekaEmuhayaShinyalu weeding2nd Cumulative transpiration (mm) CROPFIELD SOIL Measured20020040 440 16Beans harvest 9 440 5 t manure -1 0.3 Root mean square error:efficiency:Rainfall capture 13.3 t haMaize harvestPotential, water- andSoil C dynamics30 CLIMATE Body weight (kg) 420 42020010 t manureAludek a 0.23Aboveground biomass (t ha-1) organic C (t ha-1) organic C (t ha-1)nutrient-limited yields 0.26 400 160Water, N, P and K Actual variability Emuhaya 0.2715040012(MJ m-2 day-1)Weed competition availabilityShinyalu 0.31 30 3800.1 Scenarios Simulated0 3803 00 201 2 3 4 5 6 7 8 9 10 11 12Measured 360 360120MARKETd l. w . w r.h n.d b. t w b. w . t w r. w . w . y t w c. w . w . . y yd l.t w l. w .. w . d n.w . w .d n. tw . t w r.d n. tw ..d p. yd g. tw .d r.h r.bd g.4t Junp4t eb ep r3r Jan r 4t Jul1s Jan 1s un Aboveground biomass (t ha-1)rcg1s ug4t Sep 3r Ju100 1s Ju3r M a3r M a1s M a4t Ma1s M a2n Ju 2n Ma 2n Ma4t Ma3r A p1s A p3r Fe4t Ja 2n Fe1s Fe 2n Ap 3r S e1s De4t Ap 84t De3r Ju 2n Se 3r A u 4t Au 2n Ja 2n Ju 2n Au F S J Awww w w w w w w w w w w w tw w Age (y) w w0 340w20h 340 d dddhhhhdddhhddhHOUSEHOLD 3r 1 Factors Table 3: Reference prices and calculated costs used for 21 simulation scenarios; data collected duringthe 1 320 61116Objectives & 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 4Crop suppliers and technicians of Investment, allocation= 9 16). Exchange rate 75 KSh0.8 1 US$.research institutes (n 300 radiation=50Calving rate12 Soil Item [unit] 280 manure per and expenditurePrice CV 280 Use*Cost** from Micheni et al. (2004) 105tha COMMONLAND (KSh) (%) (units 0.6 ) Clay soilsSandy soils-1 40Data -1 ha0(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 12January to June** 1620Homefield 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- 61121 D LIVSIM230 250270290310 00 200 0 0 0 Prototyping: 600 ideal800 0 400the farmJuly to December6 14HEAPSIM860 1 210.4 -000 -12 Milk production (L day-1) 20 3060 901 Aboveground biomass yield (kg ha-1) 14 y = -0.039x + 0.0836x + 0.9 Feed supply andJulian day 0.2OFF-FARM 2 Cumulative rainfall (mm)Manure collection, 0 0 135 9 0 R = 0.8605 90003Long-term soil storage & quality 4.012 30 Maize seed (hybrids 513, 614) [kg] demand 12C changes 354050 Effect of long-term manuring EmploymentC Period of cultivation (years)D 600060000 Fertilisermeat, traction 50 kg] *** Milk, prices [Bag of10 200 -110 40RemittancesE 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 -- 25Soil FARMSIM 003000 Calcium1 8ammonium nitrate (46:0:0)6 1116 21 1870 2616.4 8 -- y = 1.01x + 1.23 5 t manure Soil organic C (t ha-1) -1160 Triple super phosphate (0:46:0)2000- -25- Bodyweight change (% mo 0) 0 30r 2 10 0.71= t manure12 66Simulated 30 1 0020 2 0 30 010 20 30Kaitho et al 2001 Manure [wheelbarrow ca. perkg FW] 10 t manure 30 ha 20 4 120MeasuredJenet et al 2004 Good quality manure (e.g. 3% N)50 1 526.7 4 -00025-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- 15OutfieldLanyasunya et al 2001M anure 2 2020 12000 2 Kabuga and 0 1 2 0 0 Agyemang 1984 SSP 680 Hired labour [person-day] M anure s im ulate d090001510 10 9000 First ploughing (hoe)160 14.0 0 20.03200S S P s im ulate d Second ploughing, manure3application and planting 9 10 3 0 1 24 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 04 101140000 5 Weeding380 50.6 11.14222r 2 = 0.67 Harvesting (including chopping of crop residues)97 313.5 26.65 5 25900003000 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 pooled10611 Soil movement (digging, trenching) 16 21 150 26 0- - 0- 00 5 10 1520025 30 35 W 0 30 600 02 146 368 1012 21090 10 200 110 161026 20 30 Ox ploughing [acre] Number of growingseasons1350 15.7 2.2 3000 0 Lactation 510 15 -1 2025 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 livestockNutrient Cycling through105Manure application rateAverage biomass yieldFT1(kg ha-1 season-1)(kg ha-1 season-1) 8 Farm type 2 (2.8 ha) 4FT2 63 FT3 Farm type 1 (0.5 ha)Napier Napiergrass FT4 grass4 2plot Maize & MaizeLabour foodbeans self resource conservationofobjectives (cf. indicatorsNapier productivity and 2financial resultsTable 5: Average values and standard deviationharmonising Foodgrassproduction and sufficiency farm-scaleFig. 9).and model parameters when& 1 Napier beans grassTeaTea Indicator/parameter 0Scenario0Sweet Sweet 40 NapierNapier400.0 0.5potato potatoFarm Type 1 0.0grassgrass2000 KSh0.5 1.0 5000 KSh 1.5 10000 KShFarm Type 2 1.5 1.0market Cattle stocking rate (t LW ha )-1Cattle stocking rate (t LW ha-1) Caloric energy (MJ farm-1 season-1) Objective indicators 30 30Stover sold/ exchangedNPK Maize production (t farm-1 season-1)NPK3.5 4.3 (0.0) SweetSweet5.7 (0.1) 7.1 1.5(0.1)Manure available to cropsMaize stover and thinnings D airy potatopotato N losses (kg N farm-1 season-1)NPKm eal FT184 (1) 87 (2)109 (3) 203.0 20 Soil erosion (t farm-1 season-1)FT218 (1) 18 (0) 1.2 17 (0) (kg season-1) (t DM season-1) Napier grass sold/ exchanged 2.5 FT3 NPK0.9 Summary of model parameters 2.0 10 10 Stover sold/ N P K airyexchanged D Total N fertiliser used (kg farm-1) FT45 (3) 18 (8)NPKm eal128 (16)FT1 Labour used (man-days farm-1) 1.50.6FT2Farm type 3 and planting Ploughing (1.2 ha) 01.0 49 (1)53 (1)063 (4) Weeding0.3 FT3 1 4 7 0.51013 (1) 16 21 19 34 (1) 1 Farm type 4 (0.9 ha)4 43 (2) 7 1013 1619 Ridge cropping and mulchingMaize21 (1)26 (2)38 (4) FT4 Total Maize 0.0 & 91 (1) 113 (2) 0.0 145 (3) 40 & 40 Maize beans 0.00.51.0 (94) Investment in N fertiliser (KSh season-1) 3Napier beans Farm Type187 1.5 2.0 2.5 (321) 3.5 673 3.0 &034787 (624) productionOn-farm 6 912Farm Type 415grass Total investment in labour (KSh season )-1 -1Excreted DM (kg season ) (333) beans 16872 (668)4151 (122) 10250-1 Total feed on offer (t DM season )Napier 30grass 30 Household requirement Napier Complementary indicators50Napiergrass 25 SweetgrassFT1FT1 Rainfall use efficiency (kg grain mm-1)N input to manure heap SweetSweetN output after storage plot12.6 (0.3)16.6Sweet(0.2)20.6 (0.2) potato potato2020 20 N productivity (kg grain kg N applied-1)40FT2 1913 (6411) 531 potatopotato (957)75 (7)FT2(kg season-1) (kg season-1) Gross N use efficiency (kg grain kg N available-1)ColleFT3 18 (70)23 (86) 24 (3)FT3 ing Value of production (KSh season-1)1 hc30 tio NPK59340herd 78660 1597980 10 -1 1,2er din n/ N P K airyion/ 10 FT4 FT455040 Collect DNPK Gross benefit (KSh season ) g20m eal 6773076230 Return to labour (KSh man-day-1)1,2 618 60510 548 Roadside Benefit/cost ratio1,2 0 grass12.86.203.5 10 5 Daily gross benefit (KSh family-1 day-1) 1,2 7 1 410 13 1511619186 14 209 710 13 1619 Gross benefit per capita (KSh person-1 day-1) 1,2,3 0 22 27031 1Calculations done considering the average values for the objective indicators and 90030 60model parameters 1501200 Number of growing seasons 10 20 30 4050 2Calculations 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 variableN 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 enoughConservation agricultureEffect of in crop productivityChangeslong-term fertiliser use on soil fertility (Togo)25 2.55Carbon Maize grain yield (t ha-1) ATillage-NoCropNo-fertiliserA Nitrogen B Soil organic C (g kg-1)20No-fertilizerFertiliser-RR2.0Total soil N (g kg-1)4Fertiliser-1.5RRFertilizer-RR15 1.53 Fertilizer-1.5RR1021.0 510.5 000.003 69121518036 912 15 181972 197619801984 19881992 100 6.5Phosphorus5C pH D Seed-cotton yield (t ha-1)B 6.25 Available P (mg kg-1) 804 6 pH (1:2.5)5.75 603 5.5 405.2525 201 4.7504.50 03 69121518036 912 15 181972 197619801984 19881992 Time (years)YearTerres de barre,Kintch 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 objectivesNaudin et al. 2011 21. Landsacape level interactionsHow can agricultural intensification and wildlife beFigure 2 Schematic representation of the multi-agent modelAgent-based modellingbest accommodated in a village territory? Baudron, Delmotte, Herrera, Corbeels, TittonellIntensification through conservation agriculture to preserve habitats and biodiversity 22. Tradeoffs analysisObjective B B1A1 A2 A3 Objective A 23. Services cosystemiques: biodiversit et squestration de CAVihiga B SiayaAboveground C stock (Mg ha-1)40 40 homegarden annual crop permanent crop30 30 pasture A) Trees 20 B) Hedgerows20 4020 Delta C stock (Mg farm-1) Vihiga 10Vihiga 10 Siayal Siayantia 30p ote 0 150tion 0.0 0.5 1.01.5 2.02.50.0 0.5 1.0 1.5 2.0 2.5 stra queC-seC 10Vihiga D SiayaC-sequestration potential 20Aboveground C density (kg m-2)88WindrowIndividual treeWoodlot66 1054400 05 1015220 0510 21520 it wt Current aboveground C stock (Mg farm-1) 0 00.00.5 1.01.5 2.02.5 0.00.5 1.0 1.5 2.02.5 gHomegarden indexShannonb lh Food crophh wltmh PastureteCash cropSlopWoodlot Henry et al. (2009), Agriculture Ecosystems and Environment 129 24. Farming Systems Ecology Group 25. Extra slides 26. Planting basins and za 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 SahelUnderstanding traditional soil fertility managementPiliostigma reticulatum Guiera senegalensis 28. Stepwise aggradation Rabah Lahmar (2009) 29. Soil rehabilitationQuick responses Performance/ Efficiencies/ Stocks Slow responsesPerformance/ Efficiencies/ Stocksht100% Reh ha bil itati o nht50% De gra da t25% tio nPeriod of degradation (t) Period of rehabilitation (t) Period of degradation (t)Period of rehabilitation (t) 30. Tradeoffs analysis: methodsInverse modellingModlisation directe Paramtres Rsultat Modlisation inverseRsultatEnsemble de paramtres = dcisions de lagriculteur Tittonell et al. (2007), Agricultural Systems 95 31. Analysing tradeoffs at farm scale A spatially heterogeneous farmTrade-offs between objectives 200 A limited availability of cash2510000 K S h180 A limited availability of labour Farm farm scale (kg) 24 5000 K S h2000 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)160N losses at N loss [kg] S tro oil io n ion los [tons production, minimise N losses,22 etc 140 21 Simulated management decisions 20120AB arm sProfile.8019.8 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 Homestead100 18 5000 KSh 5000 KShNapier grass0 .6 10000 KSh 17.6 010000 KShCompoundfieldsHome garden 80 010001200023000340004 50005 6000 6700078000 8Maize fieldsWoodlot16 Farm grain yield [kg]Living fence01000 20003000 40005000 Tea01 2 production (tones)8000 Farm-scale3 maize4grain 5 60006 7000780 .4 0 .4 F arm grain y ield [k g]MaizeF a rm -sca le m a ize g ra in p ro d u ctio n (t)Layout0 .2 0 .2Maize 1SweetMaize 2potatoMaize 5 (+)(-) (+/-) 0Maize 6Woodlot 0 .0Maize 4 (-) 0 0 .2(+/-) 0 .4Tea 0 .6 0 .80 .00 .20 .4 0 .6 0 .8 1 .0Maize 3 HomeR 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 gardenTittonell et al. (2007), Agricultural Systems 95 32. Conservation agriculture: tradeoffs at farm scale Rotation effects on pestand diseases Fodder availability Trade-offs between practical objectives C input and soil Cstocks Weed controlEvolving labour demands due to climate changeN fixation and nutrientcycling Soil biological activityand physical properties Erosion control Rainy season Dry season Rainy seasonRazakavololona, 2011 Naudin et al. 2011 33. Structural typologies: based on resource endowmentSmallholder households in NE ZimbabweFarm 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)Medium0.7 1.22411.0 1.2 40Rich> 1.2 4 - 2222.0 3.5 50% similarityWithin-group similarity (%) 60 80100 Farm samples 34. Conservation agricultureEvaluation des impacts et contraints a lchelle de lexploitation Field Survey Adoption rate (%land shifted to DMC) FarmSurvey Farmers management Superimposed, innovative managementSubsides (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) Labandon de la culture du cotonnier est-il momentan ou dfinitif ? AgroParisTech 36. Prospective analysis using models Are current crop models able to simulate water and nutrient dynamics under CA? Main structure Effect of/ fertilizermulching Genetic Soil coefficientsGenotype / CultivarGenotype / Ecotype Soil (water)A sensitivity analysis of Soil waterDSSAT calibrated for storageMonze 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 whichoptimisationan economic Data undemandingmodels thatbalance isrepresent bioadded, in whichprocesses asdecisions are fixed technicalinitialisation coefficients (noparameters dynamic feedbacks) 38. 100 40 Total hous IncomeIndicators of resources and performanceTotal household income (kSh yr ) -1 120300 Household type 1Income per capita (kSh yr ) 5020-1 Household type 2 250 100 Household type 3 Household type 4 20080 Household type 50 030012001 2 3 4 50.0 0.4 Total household income (kSh yr ) 15060 System state II-1 Income per capita (kSh yr ) -1 6250 1.01002 t ha-140Food production per capita (t dm) 100 Stepping out Food production (t dm farm-1) 1 us$ day-150 520200 0.8800 1 t ha-1 0 0 4 1 23450.0 0.4 0.81.21500.6 60 6-1 2 t ha 120 1.0 Food production per capita (t dm) 3Household type 1Food production (t dm farm-1)100 40come per capita (kSh yr ) 5-1 0.8Household type 2 100-10.41 t haHousehold type 3 4 2 500.620Household type 4 3800.2 Household type 5 1 0.4 0 0 2System state 1 0 I602 3450.0 00.0 0.2 101 2 Cropping land (ha)3 450.0 0.4Self-sufficiency6-1 1.0Cropping landt (ha) 40 2 ha(t dm) 0 0.0 Land rm-1)01 23 4 50.0 0.4 0.81.25Cropping land (ha) Land:labour ratio1 us$ day-1Tittonell, 2011 0.820