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Soil fertility evaluation and management by smallholderfarmer communities in northern TanzaniaJeremias G. Mowo a,*, Bert H. Janssen b,1, Oene Oenema b, Laura A. German c,Jerome P. Mrema d, Riziki S. Shemdoe ea Directorate of Research and Development, P.O. Box 5088 Tanga, Tanzaniab Wageningen University, Department of Soil Quality, P.O. Box 8005, 6700 EC Wageningen, The Netherlandsc African Highland Initiative, P.O. Box 26416, Kampala, Ugandad Sokoine University of Agriculture, P.O. Box Morogoro, Tanzaniae University of Dar es Salaam, P.O. Box 31576 Dar es Salaam, TanzaniaAvailable online 3 May 2006AbstractThe objective of this paper is to compare soil fertility evaluation based on experience and knowledge of smallholder farmer communitieswith the evaluation by scientists based on soil analysis and model calculations. The role of the smallholder farmer community in soil fertilityevaluation and management was examined from two research for development projects in northern Tanzania. These are the AfricanHighlands Initiative (AHI) and the Soil Water Management Research Group (SWMRG). Participatory approaches were applied by bothprojects. Farmers experience and knowledge of local indicators of soil quality were used in identifying soil fertility constraints and ingenerating resource flow maps. The farmers evaluation of soil fertility was compared with soil analytical data and with calculations of maizeyields by the model QUEFTS. Farmers indigenous knowledge in soil fertility evaluation mostly agreed with laboratory analysis and modelcalculations by QUEFTS. Model calculations identified potassium as the most limiting nutrient in the highlands in northeastern Tanzania foryields less than 3 t ha1 and phosphorus for yields higher than 4 t ha1. In Maswa (Lake Victoria Basin) nitrogen was most limiting. Giventhat farmers evaluation of soil fertility is relative to what they see around them, there is a need to verify their observations, but also theinterpretation of laboratory data by models like QUEFTS requires continuous and critical validation. Both projects have shown that there isscope to reverse the trends of declining soil fertility in smallholder farms in northern Tanzania. Essential was that the interaction withscientists has built confidence in the farmers because their knowledge in addressing soil fertility constraints was recognized.# 2006 Elsevier B.V. All rights reserved.Keywords: Farmer decision environment; Farmer empowerment; Local indicators; Organic nutrient resources; Participatory approaches; QUEFTS; Soilfertility evaluation; Tropical smallholder farmerswww.elsevier.com/locate/ageeAgriculture, Ecosystems and Environment 116 (2006) 47591. IntroductionSustained agricultural production in most Sub-Saharancountries is under threat due to declining soil fertility andloss of topsoil through erosion (Hellin, 2003; Sanchez,2002). The smallholder farmers in these countries are quiteaware of the declining trends in soil fertility, the reasons forthis and its impact on yields and household food security* Corresponding author.1 Present address: Wageningen University, Plant Production SystemsGroup, P.O. Box 430, 6700 AK Wageningen, The Netherlands.0167-8809/$ see front matter # 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.agee.2006.03.021(Lyamchai and Mowo, 2000; Defoer and Budelman, 2000;Ouedraogo, 2004; Sadou et al., 2004). Many farmers also doknow to some extent how to practice judicious managementof their soils, using nutrients available in their vicinity andadopting agricultural practices geared towards soil fertilityimprovement such as improved fallow, agroforestry andbiomass transfer (Wickama and Mowo, 2001; Johansson,2001; Rwehumbiza et al., 2003). On the other hand, themany endogenous and exogenous complicating factors (Vander Ploeg, 1993; Ondersteijn et al., 2003), most of which arebeyond the smallholder capability to handle, account for thecontinual trends in soil fertility decline. Admittedly, soilhttp://dx.doi.org/10.1016/j.agee.2006.03.021J.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 475948fertility management is highly complex given the myriad ofinteracting factors that dictate the extent to which farminghouseholds invest in the fertility of their soils. Theseinteracting factors must be understood, as judicious soilfertility management is of vital importance for sustainingfood production in smallholder communities.The complexity of soil fertility management in small-holder farms calls for active participation of farmers andother stakeholders. Consequently, two projects in Tanzania;the African Highlands Initiative (AHI) and the Soil WaterManagement Research Group (SWMRG) have adoptedmulti-stakeholder, multi-institutional and participatoryapproaches to soil fertility management with emphasis oninterdisciplinary teamwork, to share and exploit expertisefrom different professionals and institutions (Mowo et al.,2004; SWMRG, 2002). The premise of the projects is thatthe wealth of indigenous knowledge coupled with thefarmers comparative advantage of being familiar with theirown environment should be exploited for better manage-ment of soil fertility for improved system productivity.In this paper, we discuss the role of smallholder farmercommunities in soil fertility evaluation and managementbased on results and experiences from these two researchfor development projects in Tanzania. Research questionsaddressed in the projects include: (i) What approaches are inplace for enabling farmers to effectively manage soilfertility? (ii) How is farmers indigenous knowledge appliedto evaluate soil fertility and take decisions on nutrientmanagement? (iii) Is it possible to translate farmersknowledge and information into formal knowledge? (iv)Why are some farmers better soil fertility managers thanothers? (v) Which questions require concerted action offarmers communities rather than individual activities? (vi)Which factors influence farmer ability to invest in soilfertility management? (vii) How does soil fertility manage-ment affect sustainability of the physical environment?The paper starts with our conceptual framework on therole of the farmer in soil fertility and nutrient management.Next, the approaches, activities and results of AHI andSWMRG are considered in relation to the conceptualframework. A major part of the paper deals with thecomparison of farmers evaluation of soil fertility with soilanalytical data and related yields as calculated with themodel Quantitative Evaluation of the Fertility of TropicalSoils (QUEFTS). A sequence of steps is proposed tostimulate investment in soil fertility management. Finallysuggestions are made for addressing research gaps.2. Conceptual framework on decision making in soilfertility management by farmers2.1. Farming in a dynamic environmentSoil fertility and nutrient management are functions ofsocio-economic processes associated with a household andits management. To understand the farmers role in soilfertility and nutrient management we need to know who thefarmer is and what factors are influencing his decisionmaking. It is Phase 1 in a Participatory Learning and ActionResearch (PLAR) process (Defoer, 2002). Smallholder farmsusually consist of small parcels of land. A farmer, also thesmallholder, has triple functions as entrepreneur, manager andcraftsman (laborer) (Fig. 1). As entrepreneur the farmerdefines the mission and long-term objectives of the farm anddevises strategies to realize set objectives. As manager thefarmer allocate resources to achieve the objectives. Inprinciple, he or she makes an analysis of all farm operations,decides what to do, and plans, executes and controls the thingsthat have to be done. As craftsman, the farmer needs thetechnical skills and capabilities to carry out required activitiesfor optimal performance of the farm. Several of the activitiescovered by the two projects in this study, such as PRA,training, and farmer tours, contribute to empowering thefarmer as an entrepreneur, a manager and a craftsman.2.2. Decision making process at farm levelDifferences in performance between farms and in the wayfarmers respond to for example changes in markets,governmental policies, soil fertility and weather can beexplained by the fact that not all farmers are equally skilful asentrepreneur, manager and craftsman. Further, the extent ofand capability to sustain what Bebbington (1999) refers to asthe five capital assets available to rural communities, that ishuman, physical, natural, social and financial capitals, differamong farmers. In Fig. 2, a range of dominant driving forcesfor the decision making process at farm level is depicted.In general, farmers have various objectives, which theytry to realize as best as possible. Some objectives arecommon, like sufficient income (financial capital) andcontinuity of the farm (natural capital), while others aremore specific, related to the drives, conviction and style ofthe farmer. Decisions about the objectives are influenced bythe drives and skill of the farmer (human capital), theinternal (farm) environment and the external (social,political, technological and economic) environment. Theexternal environment is becoming increasingly important(Keeney, 1997; Bontems and Thomas, 2000). This isbecause of an increasing number of actors and an increasingpressure by different groups such as environmentalists andconsumers to change production processes into environ-mentally friendly, socially acceptable and economicallyviable ways. The decision making process itself is governedby a complex framework of comparisons (Gomez-Limonet al., 2000). Some farmers are led by tradition while othersare influenced by what is usual at the time. The presence andcost of alternatives also influence decisions by farmers.Identifying different styles of farming may help in under-standing the decision making process and in the selection ofproper tools for soil fertility and nutrient management.Selection of skilful retailers and transparent marketsJ.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 4759 49Fig. 1. Operation of a smallholders farm. Modified after David (2001) and Ondersteijn et al. (2003).(physical capital), and knowledge of marketing techniquesare expected to improve income, which in turn mightstimulate investment in soil fertility management (White-side, 1998). Informal networks (social capital in theterminology of Bebbington, 1998, 1999) such as influentialmembers of the community, clan elders and collective actionFig. 2. Conceptualization of the decision making process at farm level, showing thVan der Ploeg (1993) and Ondersteijn et al. (2003).groups and age mates also play a vital role in influencingfarmers ability to access sources of information andwillingness to invest in soil fertility management technol-ogies (Mowo et al., 2004; German et al., in press) anddetermine to a great extent whether collective actions will besuccessful or not.e external and internal driving forces and the farmers own drives. Based onJ.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 4759503. Approach and activities of AHI and SWMRG;methods of soil fertility evaluation3.1. About the projectsAHI is a regional program concerned with integratednatural resource management (INRM) research in the humidhighlands ecosystem of Eastern Africa while SWMRG isdealing with nutrient management under rainwater harvest-ing systems in the dry lowlands of Tanzania. The AHIbenchmark site is Lushoto District in northeastern Tanzaniaand the SWMRG is working in Same (Western PareLowlands (WPLL) in north eastern Tanzania) and Maswa(Lake Victoria basin) districts. Campbell et al. (2001) defineINRM as a process of incorporating the multiple aspects ofnatural resource use (biophysical, socio-political, oreconomic) into a system of sustainable management tomeet production goals of producers and other direct users(e.g., food security, profitability, risk aversion) as well asgoals of the wider community (e.g., poverty alleviation,welfare of future generations, environmental conservation).INRM research approach aims at identifying land-usepractices that increase production while maintaining naturalcapital and continuing to provide ecosystem services at localand global scales (Izac and Sanchez, 2001).A Participatory Rural Appraisal (PRA) was conducted toanalyze the situation at farm level specifically identifyingconstraints limiting agricultural production. Farmers wereexposed to different methods of priority setting includingmatrix and pair-wise ranking to determine the most pressingconstraints that need immediate attention. Special emphasiswas given on identifying communal goals requiring jointactivities of the neighborhood. Community action plans(CAP) for orderly execution of set priorities were preparedby farmers in collaboration with researchers. For qualitycontrol participatory monitoring and evaluation was adoptedto monitor and evaluate the implementation of the CAP.Table 1Courses and study tours organized by AHIa and SWMRGbNo. Course/tour Participants Proje1 Soil water conservation (SWC) 45 AHI2 Composting 65 AHI3 Local indicators of soil fertility 76 SWM4 Marketing and enterprise selection 40 AHI5 Tour to markets in large cities 40 AHI/6 Tour to Kenya 10 AHI7 Tour to Lushoto, Kilimanjaro and Arusha 46 AHI8 Tour to Tabora and Same 15 SWMa AHI: African Highlands Initiative (AHI).b SWMRG: Soil Water Management Research Group.To improve access to information which is one of thefactors in the external environment (Fig. 2), training andstudy tours were organized for farmers (Table 1) and simplereading materials were put at their disposal, like leaflets andpamphlets written in Kiswahili, the common language in thecountry. The design of the reading materials was based onone topic for each leaflet or pamphlet. These approaches alsocontributed to enabling farmers to set out realistic objectivesand devising appropriate strategies, and thus challengedtheir entrepreneurship. This was established through focusgroup discussion where farmers were asked to indicate withreasons, the technologies they have adopted and anymodification they have made on them as a result of thedifferent exposures they had (German et al., in press).3.2. Examining farm internal environmentVarious assessments were performed to identify con-straints limiting agricultural production. Local indicators(Barrios et al., 2002) were used in the participatoryassessment of soil quality (an internal environment factor)while participatory soil fertility and resource flow mappingwere used to assess nutrient flows (De Jager et al., 2004;Defoer, 2002). Transect walks (Lightfoot et al., 1992) werecarried out to ground truth farmers observations whilelaboratory analysis of selected soil samples was done tofurther verify farmers observations.In the AHI Project, farmers were categorized into threewealth categories (rich, intermediate and poor) based onindicators of resource endowment established by the farmersthemselves and agreed upon by consensus. These includefarm size (an internal environment factor, Fig. 2), type ofhouse (mud, burnt or concrete bricks; grass thatched or ironroofed). The proportions of farmers in each category were 5,45 and 50% for rich, intermediate and poor respectively.Farmers were then requested to draw resource flow mapsshowing the flow of resources within the farm and betweenct Course/tour objectiveEmpower local communities in establishingSWC structuresImprove quality of, and provide alternative organicnutrient sourcesRG Create local capacity in soil fertility assessmentImprove farmers marketing skills and selectionof enterprises in reaction to market demandsFarm Africa Enable farmers establish contacts with potential clientsExchange experience in SWC and in crop andlivestock husbandryExchange experience in SWC and in crop andlivestock husbandryRG Exchange experience in nutrient management underrainwater harvesting systemJ.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 4759 51farm and suppliers, retailers and markets (NRI, 2000). Atime scale of 1 year was used. The yield and destination ofthe previous year crop, crop residue and livestock producewere estimated and added into the map. Farmers identifiedand estimated the use of fertilizers on different fields.In the SWMRG Project, farmers were categorized on thebasis of gender, wealth, location of their farms in thelandscape and type of enterprise. Local indicators of soilquality were used to assess the fertility of soils in threevillages each in Same and Maswa districts. The villagesrepresented upper, mid and lower slope positions. Farmersclassified soil fertility as good (fertile) or bad (infertile) withrespect to crop growth (suitability classes). Each farmercategory roughly sketched soil fertility maps of theirvillages. Outcomes of observations during transect walkswere compared with the results of soil analyses.3.3. Examining farm external environmentTo advance the knowledge about the demands andcharacteristics of markets, tours were organized providingfarmers from different categories (gender, wealth andlocation) the opportunity to interact with suppliers, retailersin different markets and owners of big hotels (Table 1).Targeting big hotels was mainly confined to Lushoto farmersproducing specialized fruits and vegetables not commonlyconsumed by the local population. These tours werefollowed up by a course on marketing to impart thenecessary skills on enterprise selection and marketingtechniques including formation of farmers marketinggroups.Different groups of farmers were interviewed using astructured questionnaire to identify policies related to soilfertility management, their adequacy and factors influencingtheir enforcement. Policies are an important externalenvironment factor influencing soil fertility management.Interviewees were classified into three groups. These were:(i) all farmers group made up of a sample from the villagelist and representing the categories gender, wealth andlocation in the landscape, (ii) leaders group made up oflocal government and religious leaders and (iii) influentialpeoples group made up of men and women who are highlyrespected by the community for example traditional healers,elders and retired civil servants. The different groupsanswered questions on how policies were formulated, whowere involved and to what extent farmers were aware ofthese policies. Interviewees were also asked to list the localinstitutions and farmer networks including informal net-works (Fig. 2) relevant to soil fertility management, theirpotential and weaknesses and the ways they can be exploitedin soil fertility management. Other questions focused ontraditional beliefs to understand how they influencetechnologies uptake.To address another important external environmentfactor, namely technologies, three on farm trials managedby farmers and based on farmers identified soil fertilitymanagement options were conducted in order to fine-tunetechnologies developed in research centers to the farmersconditions. These were: (i) enhancing the effectiveness ofMinjingu phosphate rock (MPR) using organic nutrientsources (ii) the use of multipurpose leguminous trees andforage species for soil conservation, soil fertility improve-ment and provision of fodder and (iii) the role of improvedfallow in soil fertility management. Trials were conductedfor two seasons from 1999 to 2000. In this paper only thefirst trial is considered, consisting of completely randomizedblocks with four treatments (farmers practice as control,MPR applied alone, MPR + farmyard manure (FYM) andMPR + Vernonia subligera green manure). The role offarmers was to provide land and labor and with the help ofthe extension partners to evaluate the different treatmentsand collecting data. Researchers took a leading role indesigning and monitoring the trials and in data analysis,report writing and feedback to farmers.3.4. Soil analysis and QUEFTS calculationsIn both projects composite soil samples (020 cm) weretaken from farmers fields representing the upper, middle andlow slope positions and analyzed at the National Soil Servicein Mlingano for soils from the AHI Project, and at SokoineUniversity in Morogoro for soils from the SWMRG Project.The methods are described by NSS (1990) and Okalebo et al.(1993), respectively. The model QUEFTS (Janssen et al.,1990) was run to evaluate the soil data. The model uses soilorganic carbon and nitrogen (SOC, SON), P-Olsen,exchangeable K, and pH (H2O) to calculate the uptake ofN, P and K by a maize (Zea mays) crop. Next, it expressessoil fertility in one parameter being estimated maize grainyield on unfertilized soil.In the present study P-Bray-I has been used as soil Pindicator, and not P-Olsen. Mowo (2000) found that P-Olsenmay vary between 0.5 and 5 times P-Bray-I, but mostly isbetween 0.75 and 2.5 times P-Bray-I. The results of Menonet al. (1990) are also within these boundaries. Thereforewe have made the QUEFTS calculations twice assumingP-Olsen/P-Bray-1 ratios of 0.75 or 2.5. Application ofQUEFTS to the four Maswa soils with pH above 7 was toorisky because the original QUEFTS has not been tested onsoils with pH above 7.0. The version of QUEFTS modifiedby Smaling and Janssen (1993) included soils with pH up to8, but that version asks for some parameters that had notbeen analyzed by SWMRG (2003).4. Results4.1. Farm internal environment4.1.1. Farm characteristics and resource flow mapsIn Kwalei (AHI Project), there was a consistentrelationship between wealth category, age and farm sizeJ.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 475952Table 2AHI Project: some characteristics of farmers and farms in Kwalei (sample size = 110)Wealth category of farmers Rich (n = 5) Intermediate (n = 50) Poor (n = 55)FarmerCharacteristics Hard working Active in development projects Limited participation indevelopment projectsTraining Copy and learn from others Received some training insoil fertility managementAge >50 3540 2535Farm size (ha)Homestead 12 1 0.4Away 2.8 2.4 1.2Food self sufficiency Yes Not NotNumber of enterprisesCrops 12 12 7Cattle 5 6 2Nutrients inputs Nil, rely on tightnutrient recycling10 kg NPK or urea, manureand fodder from neighborsSmall amounts of NPKon high value cropsNutrients outputs Coffee, tea and wood Potato, tomato, sugar cane Vegetables, coffee, woodSource: NRI (2000).Table 3SWMRG Project: most important local indicators of soil fertilitya,bLocal indicators Technical equivalentGood soil1. Black color Rather high organic matter content2. Cracks during dry season High clay content3. Good crop performance Adequate supply of growth factors4. Presence/vigorousgrowth of certain plantscLarge supply of plant nutrients5. Presence of plants ina dry environmentHigh water holding capacity (WHC)6. Low frequencyof wateringHigh infiltration rate and WHC7. Abundance ofearth wormsHigh biological activity, high organicmatter content and neutral pHPoor soil1. Yellow and red colors Low soil fertility/low organicmatter content2. Compacted soils Presence of cementing materials(Al, Fe2O3 heavy clays) and lowbiological activity3. Stunted growth Physical, chemical andbiological limitation4. Presence ofbracken fernsLow pH5. Salt visible on surface High pH, high osmotic pressure6. Presence of rocksand stonesShallow soilsa Source: Mrema et al. (2003).b According to participatory assessments in Same and Maswa districts.c e.g., Solunium indicum, Commelina spp., a.o.(Table 2). The rich farmers, who are also the older membersof the community, own significantly larger chunks of landthan the other categories. They also have proportionatelyless land away from their homestead (23%) compared to theintermediate (240%) and poor (300%) categories.Generally resource flows within and between farms arecomplex, partly because farmers have many plots and someplots have various crops. Use of farmyard manure is in mostcases confined to plots close to the homestead. Some youngfarmers can afford manure transportation to distant plots butaged farmers cannot. Rich farmers do not use inorganicfertilizers but rely instead on tight nutrient recycling. Theuse of some inorganic fertilizers by the intermediate andpoor farmers is explained by the fact that they are relativelyyounger and ambitious. Consequently, they can venture intohigh value crops, mainly vegetables (Table 2) which requiremuch attention in terms of management (laborinternalenvironment) and marketing (external environment). Ingeneral, there is very little use of inorganic fertilizers. This isattributed to the high prices of fertilizers and absence of twokey external environment factors (Fig. 2): suppliers andinformation on how to use fertilizers. Limited use offertilizers is characteristic for villages in Lushoto. In asurvey involving six villages, only 5% of farmers usedinorganic fertilizers and 9% used organic manure (Mowoet al., 2004).4.1.2. Comparison of local soil fertility indicators withsoil analytical data and QUEFTS-calculated nutrientuptake and yieldFarmers in Maswa and Same districts (SWMRG Project)using the most common local indicators of soil fertility madedistinction between good and poor soils. These localindicators and their technical equivalents refer to fertility ina wider sense than just the supply of nutrients (Table 3). Soilrelated local indicators include soil color, presence ofworms, cracks, salts, sand and gravel and drying upcharacteristics. Vegetation related indicators include dom-inance of certain types of plants, and crop performance.In Kwalei (AHI Project) farmers identified seven shrubsas being effective in improving soil fertility (Wickama andMowo, 2001). Soils around these shrubs were sampled andanalysed. Marks were assigned to the shrubs, with 10 for theJ.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 4759 53Table 4AHI Project: farmers preference (score) for and nutrient mass fractions in shrubs, and properties of soils around the shrubsShrub Score Nutrients shrubs (g kg1) SOC(g kg1)SON(g kg1)P-Bray1(mg kg1)Exch.K (mmol kg1)pH(H2O)N P KVernonia subligera 10 36 0.25 4.7 22 4.5 8 9.4 6.5Vernonia amyridiantha 9 34 0.23 4.5 21 4.5 10 10.8 5.0Albizia schimperiana 9 31 0.32 1.3 16 4.3 13 8.4 5.1Ficus vallis-choudae 8 30 0.23 4.4 57 6.6 9 3.6 6.4Kalanchoe crinata 2 21 0.23 3.8 27 5.2 9 4.6 5.1Bothriocline tementosa 2 21 0.27 1.5 20 4.3 7 1.6 4.9Justicia glabra 2 20 0.27 2.1 27 5.7 10 2.8 6.3Soils away fromthe shrubs16 2.0 6 3.0 5.1Source: Wickama and Mowo (2001).shrubs of highest effectiveness and 2 for the shrubs of lowesteffectiveness. In Table 4, the shrubs are arranged accordingto farmers preference. The plant analytical data confirm theobservations of the farmers. Highly ranked shrubs (scores 9and 10) contained more N than the shrubs with score 2(averages of 33.7 and 20.7 mg kg1 N, respectively).Vernonia spp. is not a legume and the high N contentmight be a reflection of its efficiency in extracting soil N.Soils around the plants with high scores (>8) were higher inexchangeable K than soils with low scoring shrubs (averagesof 8.1 and 3.0 mmol kg1 K, respectively) and than soilsaway from the plants.Maize yields were calculated with the model QUEFTS.In Table 5, ranges of yields and nutrient uptake arepresented. The lower values refer to a P-Olsen/P-Bray-1ratio of 0.75, the higher values to a ratio of 2.5. Two groupsmay be distinguished: high yields (>4 t ha1) and low yields(J.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 475954Fig. 3. AHI Project. Exchangeable K and calculated maize yield (QUEFTS)in relation to farmers preference of shrubs (top), and relation betweenexchangeable K and calculated maize yield (bottom). Ficus vallis-choudaeis plotted separately because it behaved differently than the other six shrubs(see text). The significance values (P) are J.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 4759 55Table 8SWMRG Project: some properties of the soils of different land suitability classes in MaswaaSuitabilityclassEC(mS/cm)Texture Org. C(g kg1)Org. N(g kg1)P-Bray-1(mg kg1)Exch. K(mmol kg1)Exch. Na(mmol kg1)CEC(mmol kg1)pH(H2O)Pb1 (nc = 3) 0.08 Loamy sand 5 0.4 16 3.0 28 87 7.3P2 (n = 2) 0.08 Sand clay loam 7 0.6 17 4.9 29 98 6.8P3 (n = 3) 0.04 Loamy sand 5 0.5 14 3.5 18 102 6.6Md1 (n = 3) 0.28 Sand clay loam 7 0.5 6 3.1 62 195 8.3M2 (n = 3) 0.08 Sand clay loam 5 0.4 6 1.7 44 134 7.6M3 (n = 2) 0.32 Sand clay loam 6 0.5 6 2.1 57 186 8.5a Source: SWMRG (2003).b P: soils suitable for cotton and maize cultivation.c n: number of composite soil samples.d M: soils suitable for paddy rice cultivation.exchangeable Na. Except for CEC which is increasing fromsub-class 1 to 3, and for pH which is decreasing from sub-class 1 to 3 in land suitability class P, there is no consistentpattern in the other parametres measured. Soils in landsuitability class M have higher pH and electrical con-ductivity (EC), and lower P-Bray-1 and exchangeable K thansoils in suitability class P.QUEFTS yields were calculated only for classes P2 andP3 which had pH below 7.0. The QUEFTS-predicted yieldsof maize (Table 9) were higher for class P2 than for class P3,in agreement with the farmers perception of soil fertilityand with org. C, org. N, P-Bray-1, Exch. K and pH (Table 8).From the N/P ratios it is inferred that the yields were Nlimited.4.2. Farm external environment4.2.1. Nutrient management strategiesIn the AHI Project, one of the on-farm trial managed byfarmers tested the response of common beans (Phaseolusvulgaris) to V. subligera or FYM applied with MPR assource of P. Unfortunately there was no FYM or V. subligeraalone treatments. Sixty percent (60%) of the farmersinvolved observed that the growth vigor of bean treated witheither FYM or the green manure V. subligera combined withMPR was higher than for the MPR alone treatment (Fig. 4).There was no significant difference in bean grain yieldbetween FYM or V. subligera mixed with MPR but the twotreatments differed significantly (P > 0.05) from the MPRalone treatment. The phosphorous content in the bean crop atflowering, following similar patterns as the bean grainyields, is high indicating that P was not the limiting factor.Probably the effect of FYM and V. subligera must beTable 9SWMRG Project: calculated (QUEFTS) maize yield and uptake of N, P and KSuitability class Yield (t ha1) UN (kg ha1) UP (kg haP2b 2.22.3 39 7.212.2cP3b 1.71.8 31 5.99.4a N/P, K/P: uptake ratios UN/UP and UK/UP, YL: yield limiting nutrient.b Land suitability classes P2 and P3 in Maswa (see text) P is the soils suitablc Ranges are based on assumed ratios of P-Olsen/P-Bray-1 between 0.75 andascribed to K, in agreement with the QUEFTS-calculationsin Section 4.1.2, and with the strong responses of beans to Kfound by Smithson et al. (1993) near the southern edge of theUsambara Mountains and by Anderson (1974) on HumicFerralsols at altitudes between 1460 and 1830 m.4.2.2. Access to information, and linking farmers tomarketsOne of the constraints identified by both projects was thelimited exposure farmers had to available technologies andmarket opportunities. To address this, eight courses andtours were conducted involving 337 farmers (Table 1). In theAHI Project 246 or 1.2% of target farmers participated, andin the SWMERG Project 91 or 0.5% of target farmers.Although no systematic impact study has been done, someobservations are worth noting as emanating from the studytours. For example, farmers in Kwalei (AHI Project) wereslow in accepting soil conservation technologies becausesome of them are laborious. After a tour to the highlands ofKenya, however, there was an appreciable increase in theestablishment of conservation structures (Meliyo et al.,2004). By the end of 2000 some 2500 m of bench terraces,400 m of hillside ditches and 100 m of cut off drains hadbeen constructed. Maize yield increased from 1.6 to 5.1 kg/plot due to soil conservation. Meanwhile, market toursproved important in market developments (Fig. 2) in thatthey enabled farmers to access and negotiate with traders onfavorable terms and hence to improve their incomes.4.2.3. Social networks and traditional beliefsIn the AHI Project the most important social network thatinfluenced soil fertility management was formed by localinstitutions related to sharing of labour (collective action).(UN, UP, UK)1) UK (kg ha1) N/Pa K/Pa YLa72 3.25.4 5.910.0 N61 3.35.2 6.510.4 Ne for cotton and maize cultivation.2.5. For explanation see text.J.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 475956Fig. 4. AHI Project. P content of bean (Phaseolus vulgaris) crop atflowering and bean grain yield as affected by MPR and combinations ofMPR with farmyard manure (FYM) or with Vernonia subligera (= Tughutu)in Kwalei. Coefficient of variation of yields is 24% (P = 0.05). Source:Wickama et al. (2000).Ngemo is the word used by the local people, and it meanspulling up of efforts to tackle issues jointly. Soil fertilitymanagement issues calling for collective action includedmanure transportation to distant plots and construction ofsoil conservation structures. Younger farmers were moreactive in this since they are still strong and ambitious.It is important to understand the traditional beliefs somefarmers indulge in (see quote below) and their relation to soilfertility management, as this can provide a good entry pointfor addressing soil fertility constraints. For example, somefarmers in Lushoto believe that giving away FYM will leadto poor performance of their crops, or will depress milkyields as is believed by some livestock keepers in Maswa(Rwehumbiza et al., 2003).Magic power?The teacher knew that the high paddy yield he was gettingwas due to improved management including fertilizer use.However, his neighbors were convinced that it was the workof a powerful magician capable of relocating nutrients tothe teachers field. One of the neighbors requested theteacher to introduce him to the magician for which heobliged on condition the neighbor follows whatever theteacher does. So when it was time for fertilizer applicationthe neighbor was asked to take with him US$ 15 the fee forthe magician. They went to a fertilizer store and bought a50-kg bag of Urea. The neighbors paddy performed verywell and he was convinced that it had nothing to do withmagical transfer of nutrients.5. Discussion5.1. Farmers constraints and knowledgeSeveral constraints related to the internal and externalenvironment (Figs. 1 and 2) make the smallholder farmersfind themselves in a vicious cycle. They do not import thenutrients into the system equivalent to what they areremoving with the harvested crops (Fig. 3). This imbalanceleads to the downward trend in soil fertility commonlyfound in Sub-Saharan countries (Stoorvogel et al., 1993).Further, due to limited nutrient use, yields are low andfarmers cannot produce surplus for the market and hencecannot purchase fertilizers. Organic nutrient sources, whichare about the only sources of nutrients available tosmallholder farmers (Lyamchai et al., 1998), cannot caterfor all the nutrients removed from the system, simplybecause the amounts are too small and also because of thenutrient losses associated with the poor management ofthese sources (Ramaru et al., 2000).The two projects discussed in this paper relied onparticipatory approaches with all actors in agriculturalproduction, and there are indications that it worked (Stroud,2003). Most important in this approach is to consider eachothers as equal partners in agricultural development(Ramaru et al., 2000; Lyamchai et al., 2004). The majorrole of farmers is therefore to collaborate with the otherstakeholders while the latter have to learn from farmers inorder to contribute effectively in addressing problemsconfronting them (Kanmegne and Degrande, 2002). Theapproach of the two projects is conducive to share thisknowledge because farmers are involved in all the stages ofthe research for development continuum. Farmers knowl-edge has been used in identifying soil fertility constraintsusing local indicators of soil quality and in providing thenecessary information in the generation of soil fertility andresource flow maps. This provides information on one of theinternal environment factors, the soil (Fig. 2), that will formthe basis for further interventions. Essential was that theinteraction with scientists has built confidence in the farmersbecause their knowledge in addressing soil fertilityconstraints was recognized.A sequence of steps is proposed to gradually enable andstimulate farmers to invest in soil fertility management. Themajor successive steps are: increase of use efficiency ofavailable nutrient resources, improvement of farmersaccess to information and links with markets, entrance intothe market economy, purchase of organic and inorganicfertilizers.5.2. Soil fertility evaluation and nutrient managementThe finding of this study that the results of the twoevaluation systems one based on indigenous farmersknowledge, the other on model calculations are inagreement is very encouraging. Although farmers indigen-ous knowledge on soil fertility is important, we haveencountered some striking examples that it does not alwaysagree with formal scientific knowledge. Nevertheless, it isremarkable that the soil related local indicators in Tanzania(soil color, presence of worms, cracks, salts, sand and graveland drying up characteristics) were practically the same asthe ones used by farmers in Benin (Sadou et al., 2004). It is,J.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 4759 57however, also remarkable that farmers did not mentionTithonia diversifolia as a preferred shrub, although it isknown in the area. T. diversifolia has been stronglysupported as a green manure by ICRAF (Palm et al.,1997) and it would be appropriate to demonstrate itseffectiveness to farmers in the AHI Project area. V. subligerahas been promoted since the German colonial era inTanzania for stabilization of conservation structures apartfrom its fertilizing value. It is also used as fodder for goatsduring dry seasons, as firewood and medicinally in thetreatments of wounds (Wickama and Mowo, 2001).Another discrepancy between farmer and science is therather high score of F. vallis-choudae by farmers while it hadby far the lowest yield according to QUEFTS (Tables 4 and5). Is the high SOC content of the surrounding soil aconsequence of low decomposability of Ficus leaves whichaccording to Palm et al. (2001) have a high lignin content?Further it may be questioned whether the shrubs identified asfertility promoters indeed have improved the soils or dogrow on soils that already were good.It is unfortunate that in the bean experiment (Section4.2.1) no treatments with FYM and V. subligera alone wereincluded, the more so because some farmers adoptedcombined application of organic sources and MPR. Theorganic sources (FYM and V. subligera) alone could havebeen as good as the combination of organic sources andMPR. The QUEFTS calculations suggest that K deficiencywas a major soil fertility problem in the AHI Project.Because of the emphasis on the use of MPR in the project, Kdeficiency may have been overlooked, although previousstudies in the area (Anderson, 1974; Smithson et al., 1993)had shown that good responses to K could be obtained.So,variousquestions still have tobe answered by scientists.Well-designed trials in the field are needed to find out whetherfarmers knowledge, laboratory analysis, interpretations ofchemical soil tests by QUEFTS calculations or otherwise, orall have to be adjusted. Hence, not only evaluation by farmersbut also yield predictions by QUEFTS need experimentalverification. It was noted that farmers perception of soilfertility is limited to what they see around them (SWMRG,2003). This underscores the need of verification of farmersobservations through scientific assessment. Scientists shouldbe able to show agreements and differences in evaluationTable 10Soil analytical data and calculated QUEFTS yields of five groups of soils rankeName Yield(t ha1)SOC(g kg1)SON(g kg1)C/NShrubs, high score 5.8 20 4.4 4.4Shrubs, low score 3.3 25 5.1 4.9Maswa P 2.0b 6 0.5 11.4Maswa M n.c.c 6 0.5 12.8Kwalei 0.5 41 2.1 19.4a Soil data and yields refer to the middle of the range of values found for theb Average of class P2 and P3.c Yields were not calculated because pH(H2O) was more than 7 (see text).criterions of farmers from different areas, and to identify theappropriate actions to be undertaken.In the present study, farmers soil fertility evaluationcould not simply be related to one particular soil property. Itis here where the model QUEFTS shows to full advantage. Itintegrates the analytical data on SOC, SON, P, K and pH intoone criterion: maize yield. In Table 10, the evaluation resultshave been grouped in five classes of decreasing soil fertility.The data of each class are averages of the values of threesoils, presented in Tables 49. In the most right column themajor causes of differences in soil fertility are indicated. Asshown in Fig. 3, it was obviously K that caused thedifference between high and low valued shrubs. The shrubsoils and Maswa soils diverge in soil organic matter,quantitatively (SOC and SON), as well as qualitatively (C/N). Values of P and K are lower, and those of pH (Table 10)and electrical conductivity (EC in Table 8) are higher inMaswa M, suited for rice, than in Maswa P, suitable forcotton and maize. Striking is that the soils with the highestorganic matter content (Kwalei) were seen as very poor bythe farmers as well as by QUEFTS. Generally, soil organicmatter is considered one of the most important and positivesoil fertility characteristics. Black soil color is related toorganic matter, and it is mentioned as positive in mostfarmers soil fertility evaluations (Table 3; Sadou et al.,2004). The poor quality of Kwalei soils finds expression inlow P and K, and in high C/N. Farmers have obtained verylow yields on these degraded soils (Lyamchai and Mowo,1999). In Table 10, the soil parameters most clearly relatedto farmers evaluation and QUEFTS yields are C/N andexchangeable K. These parameters also proved important inthe study by Tittonel (2003) who observed that difference insoil fertility were explained by C, N, P, K and CEC.5.3. External and internal decision environmentsHeterogeneity among farmers leads to different decisionsby households. Most of these differences constitute theinternal environment influencing a farmers decision making(Figs. 1 and 2). Ley et al. (2002) observed that wealthyfarmers are more likely to afford expensive fertilizers, ormight own livestock and hence have more organic manure(Ouedraogo, 2004). Meanwhile, older couples might find itd in order of farmers soil fertility evaluationaP-Bray-1(mg kg1)Exch. K(mmol kg1)pH(H2O) Major differencewith lower class10.3 9.5 5.5 K8.7 3.0 5.4 SOC, SON, C/N15.7 3.8 6.9 P, K, pH6.0 2.3 8.1 C/N, P, K3.3 0.9 5.8particular group.J.G. Mowo et al. / Agriculture, Ecosystems and Environment 116 (2006) 475958difficult to transport manure to distant plots compared toyoung farmers. It is the role of researchers and extensionworkers to recognize and work with these differences forbetter targeting of technologies. The two projects have takenthis into consideration whereby interactions with farmersalways consider their different categories.Finally, links with markets and training on enterpriseselection will enable poor farmers to make some cashincome. Although the effects of such linkages are yet to bestudied, we believe that if farmers can have some cashincome to meet domestic needs they could be convinced toinvest more into soil fertility to derive even more cash. Giventhat most smallholder farmers are poor, a cascade of steps togradually enable them to get into the market economy isproposed. In this approach farmers should be provided withthe necessary knowledge to manage and use organicresources more efficiently. Through awareness creation onthe need to replenish nutrients taken out of the farmingsystem they could be convinced to start purchasing someinorganic fertilizers to supplement their organic sources.The project tours and courses have exposed the farmers tobetter land husbandry practices by their fellow farmerselsewhere. The impact of such exposures has beenencouraging. For example, Tenge (2005) working inLushoto observed that adoption of technologies by farmerswas related, besides to their level of education, to the extentof contact they have had with development projects.6. ConclusionsParticipatory approaches as used by the AHI andSWMRG Projects have shown that there is an opportunityto reverse the declining trends in soil fertility in smallholderfarms in northern Tanzania. This is because the approachesallow for participation of all stakeholders and notablyfarmers whose indigenous knowledge and experience areuseful in soil fertility evaluation and management.Results of model calculations suggested that in LushotoK was the most limiting nutrient for maize yields lower than3 t ha1, and P for yields higher than 4 t ha1. In Maswa(Lake Victoria Basin) nitrogen was most limiting. 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Environ. 30,203218.Soil fertility evaluation and management by smallholder farmer communities in northern TanzaniaIntroductionConceptual framework on decision making in soil fertility management by farmersFarming in a dynamic environmentDecision making process at farm levelApproach and activities of AHI and SWMRG; methods of soil fertility evaluationAbout the projectsExamining farm internal environmentExamining farm external environmentSoil analysis and QUEFTS calculationsResultsFarm internal environmentFarm characteristics and resource flow mapsComparison of local soil fertility indicators with soil analytical data and QUEFTS-calculated nutrient uptake and yieldComparison of local land suitability classification with soil analytical data and QUEFTS-calculated nutrient uptake and yieldFarm external environmentNutrient management strategiesAccess to information, and linking farmers to marketsSocial networks and traditional beliefsDiscussionFarmers constraints and knowledgeSoil fertility evaluation and nutrient managementExternal and internal decision environmentsConclusionsReferences

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