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Ouma - Technology adoption in banana-legume systems of Central Africa
Presentation delivered at the CIALCA international conference 'Challenges and Opportunities to the agricultural intensification of the humid highland systems of sub-Saharan Africa'. Kigali, Rwanda, October 24-27 2011.
1.Framework shows CIALCA’s outcome and impact process. 2. Inputs – projects resources were committed to identified and mutually agreed upon activities for technology identification, demonstration and testing in collaboration with the NARS. Baseline surveys were carried out carried out to identify the constraints. Some of the constraints identified were highlighted during yesterday’s presentations. Soil fertility, Soil nutrient deficiency, pests and diseases among others. 3. This has led to a number of outputs including identification of CIALCA products, partnerships and capacity strengthening of boundary partners. 4. The boundary partners disseminate the technologies to the population, they are the ones who effect change to the population since the power to influence devt rest with them. 5. Their influence leads to development outcomes and impact.
The ISFM technologies promoted by CIALCA are productivity enhancing and the IPM technologies are to mitigate pest and disease risk. CIALCA’s main objective at the end of the day is to improve livelihoods through improvement in system productivity resulting in better income and improved nutrition.
Adoption of ISFM technologies is market driven to a large extent as the cash income from output sales particularly with lucrative output prices provides incentives to invest in the technologies. However, the market forces alone do not provide the necessary “PUSH” for technology adoption by farmers. In many cases increase in agricultural output prices do not necessarily translate into a supply response by the farmers. CIALCA has developed a marketing framework and facilitation support that has been tested over the years, in some mandate areas. Based on this framework, the ISFM technologies are operationalised through group-based business plans. Business plans articulate the production plans as well as finances needed to achieve enterprise goals. It articulates the resources needed for the enterprises, viability of the enterprises and plan for reaching the business goals. Business plans require support in order to be operationalized: One of the critical resources needed is credit, both in cash and in kind (farm inputs). Support for business plans can be internal or external through appropriate platforms as shown in the figure below. With support from research and policy environment, improved economic livelihoods would result. Important role of road infrastructure in reducing transaction costs.
Action sites: A Set of 30 geographically selected sites in each mandate area in which the field activities related to technology identification, evaluation, and adaptation are implemented by the project. Satellite site: outscaling sites by development partners. Control villages: Criteria for selection included similar agroecology as action sites, within a distance of 10-15km from action sites and if development partners are present they are not promoting CIALCA te4chnologies.
Adoption occurs when farmer perceived benefits with adoption of technology is greater than without. Not all farmers may be exposed to the technologies, making it difficult to obtain consistent estimates of population adoption rates and their determinants using direct sample estimates and classical adoption models such as probit/logit. The non-exposure bias results from the fact that farmers who have not been exposed to a new technology cannot adopt it even if they might have done so if they had been exposed. As a result the observed sample adoption rate would always underestimate the true population adoption rate. On the other hand, the sample adoption rate among the exposed is likely to overestimate the true population adoption rate because of +ve population selection bias. (e.g. farmers self selection into exposure and targeting of progressive farmers by researchers/devt agents. The ATE parameter measures the effect of a “treatment” on a person randomlyt selected in the population.
1. These proportions are not representative for entire countries but are the result of the sampling procedure described above. Accounting for the deliberate oversampling of adopters, the weighted share of awareness and adoption is presented. 2. Adoption rates are higher in Kigali-Kibungo mandate area of Rwanda and Bas-Congo. Adoption intensity levels are also highest in Rwanda followed by Bas-Congo and lowest in Burundi mandate areas.
1. Importance of social networks and learning in explaining technology exposure and adoption. Many farmers may initially not adopt a new technology because of imperfect knowledge about its management; however adoption eventually occurs due to own experience and neighbour’s experience.
Adopters are resource constrained: Don’t have access to credit and off farm income. 1. Adopters and non-adopters largely differ based on institutional factors (extension and membership to farmer groups). 2. Frequency of extension contact in a year is higher for adopters than non-adopters, implying +ve correlation b/w extension contact and adoption, Contact with CIALCA, as expected. Value of crop income for season A: Sept 10-March11 (short wet season) Season B usually is from March-July11
Results from a probit estimation of factors that affect the propensity of exposure to CIALCA technologies. Membership to a farmer group has a +ve and significant effect on the propensity to get exposed to CIALCA technologies. This shows the important role of social networks and interactions in technology diffusion processes through information sharing and social learning. Radio ownership and extension contact which are avenues for technology information delivery returned insignificant but expected sign coefficients. Radio in most sites may not be used to transmit agricultural technology and advisory information. As expected, farmers who are aware of CIALCA and have participated in technology evaluation have a propensity to get exposed to CIALCA technologies. The location specific variables show that farmers located in Gitega, Rusizi and Umutara mandate areas had a lower propensity to get exposed to the CIALCA technologies compared to Kigali-Kibungo in Rwanda.
Results show the coefficients of the ATE probit model and the classic adoption model (not controlling for exposure). Larger coefficient values than the classical probit model. Human capital effects - Household heads with no formal education are least likely to adopt CIALCA technologies. Primary level and secondary level education are positive and significant. The ability to conceptualise and comprehend the effects of adopting technological improvements is enhanced by education. Wealth related variables (off farm income and credit access) returned significant and unexpected coefficient signs. Other variables such as farm size was not significant and has not been included for brevity reasons. This shows that farmers with access to credit and off farm income are least likely to adopt CIALCA technologies. Most adopters are liquidity constrained and are thus subsidized to access the technologies. Households with access to extension services have a higher probability to adopt CIALCA technologies. Although this variable does not influcence awareness of technology, possibly once farmers are aware of technology existence they link up with extension agents to acquire more information. Membership to farmer association is significant showing that farmers who are members to such groups have a higher probability to adopt improved technologies. Being in the mandate areas of Rusizi and Gitega reduces one’s probability to adopt CIALCA technologies compared to Kigali-Kibungo in Rwanda. This is possibly because Rwanda farmers are more willing to intensify production due to favourable policy environment. Variables such as distance to the markets were not significant, hence were not included in the results.
Rwanda:Vision 2020 Roadmap where it wants to be by 2020. Policy revisions to meet with underdevelopment challenges after the crisis. The Agricultural Policy Outline prepared by the Ministry of Agriculture, Animal Resources and Forestry (MINAGRI) calls for “a radical change of approach” to transform and modernize Rwandan agriculture through “the development of a modern agriculture” Strategies include intensification of agriculture through increased use of agricultural inputs, agricultural professionalization” that promotes high enterprise profitability, the promotion of soil fertility and protection, improved marketing initiative and greater role of farmer cooperatives an association. 2. Encouraging private sector involvement in fertiliser importation. They have put in place mechanisms to develop fertilizer distribution systems in Rwanda such as developing enabling policy and investment environment for fertiliser market development (reduce non tariff barriers and improving lending environment and increasing investments in rural roads and marketing infrastructure. 3. These policies are also being enforced and there is a very high local level participation in monitoring that they are enforced by those entrusted to do so in each local unit. Disadvantages have been cited as being too top-down and may be perceived differently by farmers who may react by devising their own coping mecahnisms. Burundi : MINAGRI produced a 5 year action plan through the NAP in line with the I-PRSP (interim poverty reduction strategy paper) to reduce degradation of natural resources, food insecurity, and poverty in Burundi. The policy is being implemented through different projects such as PRASAB. CPPs – Crop Protection Products. Consignments are usually low. Government’s involvement in procurement. There is a fertilizer revolving fund managed by BNDE, through which cost of importation is funded by the government. DRC : Some attempts were done by the central government in Kinshasa to develop Agricultural, Trade and Food related policies, but nothing seems to come through to implementation. The last time government facilitated extension service in terms of funds release for extension service in DRC is 1997.
A set of enabling conditions can favor the uptake of ISFM. One of the factors that is expected to catalyze uptake of productivity enhancing technologies in CIALCA is the linkage to defined markets. CIALCA market intervention seeks to achieve the objectives of i mproving the economic livelihoods of men and women in the rural areas, (ii) creating sustainable market linkages and relations for actors; and (iii) enhancing adoption and reaching scale.
The figure shows fertilizer retail prices for August 2011 from AMITSA monthly price report. Amitsa is a regional agricultural input market information system that uses the esoko platform to push out input information and pull data in from the field. Fertilizer prices are generally high and beyond the reach of most smallholder farmers. The high prices include poorly developed fertilizer value chains, domination of fertilizer imports by governments which use tenders to import, high costs of import, high transport costs. Demand for fertiliser is affected by the profitability of the crop, value-to-cost ratio of the use of fertilizers, availability of small packs, availability of rural agro-dealer shops that sell farm inputs and distance to these farm input shops
In Sud-Kivu and Bas-Congo, prevalence of malnutrition among children is high. It is largely associated with poor quality diets characterized by low diversity. Through incorporation of legume-based products into local diets and demonstrating impact of improved dietary intake on nutrition, communities are encouraged to adopt ISFM technologies.
1. Farmer perceived attributes- Once exposed to the technology, farmer gather information about technology attributes and may subjectively evaluate technology differently from scientists.
Variety release procedures in some countries take a long time up to 6 years with financial and economic implications for both seed companies and farmers. Improving agricultural productivity through technical change is a major goal of most African governments’ development policies. Achievement of this has been a major challenge. What interventions have potentials of driving change ? A) Support to farmers through subsidies? B) Deregulation of seed sectors – increased role of private sector (access to foundation seed of publicly developed germplasm)?
One approach to the identification of ATE is based on conditional independence assumption, which states that the treatment status w is independent of the potential outcomes y0 and y1 conditional on the observed set of covariates z that determine exposure (z). ATE(x) which is the population mean adoption outcome is estimated conditional on a set of covariates x, given the exposure outcome. ATE0 is the expected adoption outcome in the nonexposed subpopulation.
Ouma - Technology adoption in banana-legume systems of Central Africa
Technology adoption in banana-legume systems of Central Africa
General framework Inputs/Activities Boundary partners: change agents (extension services, NGOs, farmer associations) Outcomes: Farmer awareness, adoption, productivity, profitability Impact: food security, incomes, nutrition Outputs Partnerships (NARS), capacity building Identification of best bet technologies (CIALCA products)
Categorisation of CIALCA products Category CIALCA product Productivity enhancing (ISFM) <ul><li>Improved germplasm. </li></ul><ul><li>Integrated crop components, </li></ul><ul><li>maize x legume, </li></ul><ul><li>cassava x legume, </li></ul><ul><li>banana x legume, </li></ul><ul><li>banana x coffee. </li></ul><ul><li>Management practices, </li></ul><ul><li>Banana zero-tillage mulch </li></ul><ul><li>Seed multiplication </li></ul><ul><li>Organic and inorganic fertilizer application </li></ul>Pest and disease risk mitigation (IPM) <ul><li>BXW control </li></ul><ul><li>BBTV control </li></ul>
Marketing framework for technology adoption Research Policy <ul><li>Private sector </li></ul><ul><li>Institutions of micro finances </li></ul><ul><li>Distributors, sellers of inputs </li></ul><ul><li>Bulk traders, buyers and processors </li></ul><ul><li>ICT/Information service providers </li></ul><ul><li>Infrastructure service providers (e.g. warehousing) </li></ul>Business plans Warrantage credit <ul><li>Input Kiosks </li></ul><ul><li>Fertilizers </li></ul><ul><li>Materials </li></ul><ul><li>Seeds </li></ul>MUSO Members’ guarantee Credit for input + Labour Credit for produce Financial capacity empowerment External credit External-Synergy Internal -Synergy
Approaches for improved marketing <ul><li>Business plan development </li></ul><ul><ul><li>Training of facilitators - CIALCA, NARS, NGO partners. </li></ul></ul><ul><ul><li>Total of 8 business plans prepared and implemented by farmer associations in Rwanda and Sud-Kivu (beans, maize, soybean, cassava and sorghum). </li></ul></ul><ul><ul><li>Several still under development in Burundi and Nord-Kivu. </li></ul></ul><ul><li>Outcomes </li></ul><ul><ul><li>Bulking, storage and collective sales, </li></ul></ul><ul><ul><li>Linkages with MFIs </li></ul></ul><ul><ul><li>Increase in sales revenue (50% for 1 association in Sud-Kivu) through strategic storage facilitated by warrantage credit schemes. </li></ul></ul>
<ul><li>Assessing level of farmer awareness of CIALCA products, adoption rates and outcomes </li></ul>
Data <ul><li>Farm level cross sectional surveys in 7 out of 10 CIALCA mandate areas in July-Aug 2011, </li></ul><ul><ul><li>Purposive selection of mandate areas based on intensity of dissemination of CIALCA technologies and crop types. </li></ul></ul><ul><ul><li>Stratification of villages per mandate area, 3 strata: </li></ul></ul><ul><ul><ul><li>Action site </li></ul></ul></ul><ul><ul><ul><li>Satellite site </li></ul></ul></ul><ul><ul><ul><li>Control site </li></ul></ul></ul><ul><ul><li>Random selection of 5 villages per mandate area per stratum. </li></ul></ul><ul><ul><li>Random sample of households per stratum from village level lists proportional to size yielding a total N = 945 hh. </li></ul></ul>
Methods <ul><li>ATE estimation framework proposed by Diagne and Demont (2007) </li></ul><ul><ul><li>accounting for selection and non-exposure biases. </li></ul></ul><ul><li>Adoption context, “treatment” -> “exposure to a technology” </li></ul><ul><li>Adoption - use of 2 or more of the CIALCA technologies. </li></ul><ul><li>Exposure - awareness of the CIALCA technology </li></ul>
Proportion of households exposed to and adopting CIALCA technologies Mandate area % of exposed farmers % of sample adopters Adoption intensity (#adopted/ #disseminated) n Rusizi 73 34 0.47 124 Gitega 70 56 0.48 100 Kigali-Kibungo 81 72 0.61 140 Umutara 68 50 0.74 127 Bas-Congo 64 42 0.56 133
Mode of technology acquisition n =143 163 44 63 107
Household characteristics Variable Adopters N = 303 (56%) Non-adopters N = 234 (44%) Difference t-values Farming experience 21.9 22.7 -2.8 ** -1.9 Secondary education of head of hh (dummy) 0.2 0.1 0.1 *** 3.3 Credit access (dummy) 0.2 0.3 -0.1 *** -2.8 Radio ownership (dummy) 0.8 0.6 0.2 *** 1.9 Off farm income (dummy) 0.3 0.5 -0.1 ** -2.7 Extension contact frequency 4.4 2.6 1.9 *** 3.2 Contact with CIALCA 0.5 0.2 0.2 *** 5.8 Membership to farmer group (dummy) 0.5 0.2 0.3 *** 7.5
Determinants of probability of exposure to CIALCA technologies Dependent variable Dummy variable 1=ever heard of CIALCA technology Explanatory variables Coefficient Gender of head of hh 0.40 Value of asset owned (US$) 0.01* Membership to farmer group 1.05*** Radio 0.30 Credit access -0.61** Awareness of CIALCA 0.68** Participate in CIALCA tech evaluation 0.33** Extension contact frequency 0.03 Gitega -0.69** Rusizi -0.45 Bas-Congo -0.26 Pseudo R 2 =0.297; n=413; LR Chi 2 =79.29; P>Chi 2 =0.000
Determinants of CIALCA technologies adoption Variables ATE adoption coefficients No formal education-hh head (dummy) -0.94* Primary education-hh head (dummy) 0.19 Secondary education-hh head (dummy) 1.09** Off-farm income (dummy) -0.39** Credit access -0.44* No. of extension visits -year 0.03** Member of farmer group 0.17* Participate in CIALCA tech evaluation 0.21 Rusizi -0.89*** Gitega -0.23 Bas-Congo -0.18* Pseudo-R2;LRChi2 0.34;66.6
Predicted adoption rates of CIALCA technologies Awareness exposure Estimate S. E. ATE-corrected popn estimates Predicted adoption rate-full popn – ATE 0.44*** 0.21 Predicted adoption rate-exposed sub-popn-ATT 0.46*** 0.02 Predicted adoption rate unexposed sub-popn-ATE0 0.29*** 0.04
Enabling agricultural policy environment Country Policy document Elements Rwanda (Vision 2020) <ul><li>Poverty Reduction Strategy Paper (PRSP), 2001. </li></ul><ul><li>Strategic Plan for Agricultural Transformation in Rwanda, 2004. </li></ul><ul><li>- Intensification of agriculture, </li></ul><ul><li>- Zero grazing policy and guardianage system, </li></ul><ul><li>- Promotion of soil fertility and protection, </li></ul><ul><li>Improved marketing initiatives. </li></ul>Burundi <ul><li>National Agricultural Policy (2006-2010). </li></ul>- Improving availability of fertilizer and CPPs - Capacity building of producer associations. DRC a) Lack of solid agricultural policy (post-colonial period) to guide agricultural production. <ul><li>- Governance centralized and concentrated in Kinshasa, </li></ul><ul><li>Poor enforcement at the local level. </li></ul>
Agricultural advisory services Country Attributes Rwanda <ul><li>Effective government extension system. </li></ul>Burundi <ul><li>Weak government extension system (human, technical and financial capacity lacking). </li></ul><ul><li>NGOs involved in extension </li></ul>DRC <ul><li>Agricultural extension – NGOs – uncoordinated, operating under emergency framework. </li></ul>
Fertilizer retail prices in selected countries MONO-PHOSPHATE International price 08/2011
Human nutrition and health <ul><li>Trainings on processing of soybeans and other legumes for improved nutrition and diet diversification. </li></ul><ul><ul><li>Soy milk </li></ul></ul><ul><ul><li>Soy bean curd (Tofu) </li></ul></ul><ul><ul><li>Soy bean flour </li></ul></ul><ul><li>Soybean product acceptability studies. </li></ul>Nutritional composition of soybean products Malnutrition prevalence among 2-5 year olds Calories (kcal) Protein (g) Soybean, dry roasted, ½ cup 386 32.0 Tofu, firm, raw, 120g 116 11.8 Soymilk,½ cup 162 3.2
Summary-adoption drivers <ul><li>Awareness of CIALCA products mainly influenced by information access variables: </li></ul><ul><ul><li>social networks </li></ul></ul><ul><ul><li>participation in technology evaluation </li></ul></ul><ul><li>Adoption is influenced by a number of factors; </li></ul><ul><ul><li>binding capital constraints </li></ul></ul><ul><ul><li>institutional and location factors </li></ul></ul><ul><ul><li>farmer perceived attributes of the technology. </li></ul></ul>
Outlook <ul><li>Resource constraints </li></ul><ul><ul><li>Financial - implications on affordability of inputs (fertilizer and seeds) </li></ul></ul><ul><ul><li>Institutional – supportive policy environment </li></ul></ul><ul><li>Long term measures to accelerate productivity growth and achieve impact at scale. </li></ul><ul><ul><li>Support to farmers through subsidies? Credit policy? </li></ul></ul><ul><li>How to unlock poverty traps for small scale farmers </li></ul><ul><ul><li>Mix of underlying challenges and a mix of interventions for different categories of farmers – not “one size fits all” </li></ul></ul>
Adoption and Exposure <ul><li>Determinants of adoption conditional on exposure – corresponds to the conditional ATE( x ). </li></ul><ul><li>Parametric estimation procedure of ATE (x): </li></ul><ul><li>w = binary exposure variable, w=f(z) </li></ul><ul><li>y = adoption outcome variable, y=f(x) </li></ul><ul><li>g= linear or non-linear function of the vector covariates and β unknown estimated parameter vector. </li></ul><ul><li>Conditional independence assumption </li></ul><ul><li>ATE, ATT an ATE0 are estimated. </li></ul>
Proportion of households (%) adopting CIALCA technologies, per mandate area Mandate area Banana germplasm Banana systems Banana IPM Legume germplasm Legume systems Market Rusizi 10 16 13 Gitega 15 30 44 53 7 Kig-Kib 17 56 8 43 72 23 Umutara 6 38 59 11 Bas-Congo 63 54 29