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Manuela Coromaldi – University of Rome “Niccolò Cusano”

17 th ICABR Conference 18 th -21 th June, 2013, Ravello , Italy. Traditional and Improved Varieties in Uganda: food security, agriculture productivity and biodiversity conservation . Manuela Coromaldi – University of Rome “Niccolò Cusano”

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Manuela Coromaldi – University of Rome “Niccolò Cusano”

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  1. 17th ICABR Conference 18th -21th June, 2013, Ravello, Italy Traditional and Improved Varieties in Uganda: food security, agriculture productivity and biodiversity conservation. Manuela Coromaldi – University of Rome “Niccolò Cusano” Giacomo Pallante - University of Rome Tor Vergata Sara Savastano - University of Rome Tor Vergata

  2. Building blocks • Background and motivations • Our aim • Literature • Data description • Empirical model • Estimation results • Conclusions

  3. Background and motivations • Evidence from the Green Revolution show a strong impact of the adoption of improved varieties on agriculture productivity growth (Evenson and Gollin, 2007) , on food production and food security (Tillman, 1999). • High-yield-crop of Asian Green Revolution were bred to work better with greater applications of fertilizer than traditional varieties and to work better on irrigated land (Larson et al. 2010). • Levelofadoption in Sub-Saharan Africahasbeenlimited.

  4. Our aim • This paper aims at testing the factors affecting productivity and agricultural biodiversity in terms of local and improved varieties. • As the Sub-Saharan Africa countries heavily rely on traditional technologies, we study the potential of local species who are expected to perform better in marginal production environments and who can be more resistant to climatic stress. • We adapt the Steinfeld approach (2000) to empirically verify the effect of intensification of crop production on the opportunity costs for smallholders of conserving local species when there are factors market and agronomic constraints that inhibit the efficient and economic use, and availability of improved variety.

  5. Literature (1) • Evidences from Sub-Saharan Africa show low level of adoption of improved technology and inappropriate use of inputs: • 40% of fertilizer is used on maize (Morris et al., 2007; Heisey and Norton, 2007) • Sub-Saharan Africa : the average dose is about 17 kg/ha of nutrients • Developing countries: 100 kg/ha • Developed countries: 270 kg/ha on the same crop • Sub-Saharan Africa failing in adoption of agricultural technology is due not only to market constraints but also to low responsiveness of marginal land respect to external inputs (Sanchez et al., 1997).

  6. Literature (2) • The use of improved seeds has increased the farmers yield in Asia and Latin America, but has also let arise diffused concerning about the inter and intra species genetic diversity erosion (Harlan, 1992). • The variability is then dramatically reduced, when single lines or F1 hybrids varieties become dominant to the detriment of diversity-rich landraces (Lipper and Cooper, 2009). • When the farmers preserve a wide range of local landraces, they conserve a genetic portfolio that minimizes a set of private and public risks (Weitzman, 2000). • To the point where smallholders allow the conservation of local public goods as the resilience of the local ecosystem to face biotic and abiotic stresses (Jarvis et al., 2007) or global public goods as the maintenance of a pool of genetic material and the option value to use it (Bellon, 2009), those farmers are “custodians” of the agricultural biodiversity conservation (Silveri and Manzi, 2009).

  7. Literature (3)

  8. Agricultural intensification index (1) • The agricultural intensification concept is defined as the increase of agricultural production on a fixed portion of land, as opposite to a raising in production due to expansion of land (Netting, 1993). • It involves the substitution of other inputs, as well technology, for a constant land in order to let the yielding function rise (Brookefield, 1993).

  9. Agricultural intensification index (2) • Herzog et al. (2006) developed an intensification index with the aim to study the effect on biodiversity at the landscape level (Agricultural Intensity index, AI ). where yiis a variable of agricultural intensification, n is the number of intensification variables and yiminand yimaxare the minimum and maximum value of the agricultural intensification variables in the sample.

  10. Agricultural intensification index (3) The use of AI holds two interesting features: • The input’s application on per hectares basis is a widely comparable measure over regions. • It is a relative index. Since it accounts for maxima and minima administering of inputs into a community, the differences in soil qualities, climatic conditions and farm’s practices are endogeneized so that the context-specific traits are taken into consideration.

  11. Agricultural intensification index (4) • We estimate the AI index for each households by using the following three variable of agricultural intensification: • Chemical fertilizers (kg/ha): sum of Nitrate, Phosphate, Potash and Mixed. • Pesticides (Kg/ha): sum of Insecticides, Miticides/Acaricides, Fungicides, Rodenticides, Herbicides and growth regulators. • Years of fallow (number of years).

  12. Data description (1) • Data are taken from the Uganda National Panel Survey 2009/10 (UNPS). • UNPS is carried out annually over a twelve-month period on a nationally representative sample of households. • The survey includes 3,123 households that were distributed over 322 enumeration areas.

  13. Data description (2) * significant at 10%; ** significant at 5%; *** significant at 1%

  14. Conceptual framework (1) • We implement the conceptual framework developed by Steinfeld (2000) and adapted by Narloch et al. (2011); • the aim is to verify as the gross profit of farmers from the use of traditional or improved variety changes under the increase of the intensification degree of the farming system.

  15. Gross profit Improved gross profits Traditional gross profits Degree of intensification Conceptual framework (2)

  16. The empirical results

  17. Estimation strategy (1) • To control for selection bias in the assessment agriculture productivity in the adoption of improved variety, we make use of Heckman's two-step estimation (Heckman, 1978). • In the first stage, we compute a Probit regression in order to estimate the probability that a given farmers will adopt a new technology. • This regression is used to estimate the Inverse Mills Ratio (IMR) for each farmers, and this will be used as an instrument in the second regression where we will analyze the determinant farmers efficiency through an ordinary OLS

  18. Estimation strategy (2) • Following Maddala (1983) Amemiya (1985) and Johnston and DiNardo (1997), we will use different instruments to control for identification problems • The first stage Heckman procedure, namely the adoption equation is a standard Probit regression of the form: (1) where Y indicates adoption (Y=1 if farmers adopted the improved technology, 0 if they did not adopted). Z is a vector of explanatory variables such as household characteristics, asset’s endowment, credit access, and dummy for organic inputs used, fertilizers, geographical dummies).

  19. Estimation strategy (3) • In the second stage, we correct for self-selection by incorporating a transformation of these predicted farmers probabilities as an additional explanatory variable. • The agriculture productivity equation may be specified as (2) where AP* denotes an underlying level of technical efficiency, which is not observed if the farmers did not adopted. • The conditional expectation of efficiency if the farmer adopted is: (3)

  20. Estimation strategy (4) • Under the assumption that the error terms are jointly normal, we have: (4) • where ρ is the correlation between unobserved determinants of the probability of adopting the technology ε and the unobserved determinants of farmers technical efficiency u; • and λis the Inverse Mills Ratio.

  21. Conclusions • Low level of technology adoption in Uganda, but increasing probability if neighbor farmers’ adopt. • The perception of technology profitability increases with the number of households using improved seeds in the district. • Positive impact of Intensification Index on productivity but level of input use is still very low among adopters. Need to increase extension services for maximizing use of scarce seeds and agriculture productivity. • Evidence of the presence of an inverse farm size productivity relationship, but larger farmers have higher probability to adopt. • Adoption does not lead to substantial difference in agriculture productivity. • Extensions: Estimate Heckman Model with 2 selection equations: • Probability of adopting HYV • Probability that once adoption has taken place, fertilizer use will follow.

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