Determinants of Adoption of Improved Cassava Varieties among Farming Households in Oyo, Benue, and Akwa Ibom States of Nigeria. HarvestPlus Working Paper 20

Biofortified pro-vitamin A cassava varieties are being developed and deployed in Nigeria and other countries. Understanding the adoption pathways of already released non-biofortified improved cassava varieties can inform decision makers on how best to disseminate the newly developed varieties. This paper empirically investigated factors influencing adoption of the improved cassava varieties in Akwa Ibom, Benue, and Oyo states in Nigeria. A multi-stage sampling technique was used to select a sample of 1,609 farming households. Data were analyzed using descriptive statistics and the Probit regression model. The results* showed that Oyo State had the highest reported rate of improved cassava use (69 percent of farmers surveyed), followed by Benue (52 percent), with the lowest in Akwa Ibom (38 percent). The variables that significantly influenced adoption of improved cassava varieties include education (p<0.01), livestock ownership (p<0.05), access to extension services (p<0.01), farmers’ organizations (p<0.05), participation in demonstration trials, and location-specific variables (p<0.01). The positive influence of the location-specific variable in favor of Oyo compared with Benue could be linked to proximity to, and the activities of, international and national research institutes. Within states, regression analysis reveals significant differences across agricultural extension zones. This suggests the need to develop localized strategies that account for applicable socioeconomic and institutional conditions. To increase adoption, an intensive program for farmers’ participation in on-farm demonstration trials should be considered. This can be achieved by facilitating group formation to encourage increased knowledge sharing among members, thereby promoting uptake of newly developed pro-vitamin A cassava varieties. 1 International Institute of Tropical Agriculture (IITA), Ibadan 2 Obafemi Awolowo University, Ile-Ife 3 HarvestPlus, International Food Policy Research Institute (IFPRI), Washington DC * These adoption rates were based on the percentage of respondents who have planted some improved cassava on part or all of their farms. However, these estimates could be biased due to the problem of identification of improved varieties based on farmers’ and experts’ opinions, because of different names given to the same varieties in different locations. Future work would benefit from using DNA fingerprinting to improve estimates. Acknowledgments: We gratefully acknowledge funding from HarvestPlus for this study. Many thanks to Ekin Birol (HarvestPlus) and the following IITA staff for valuable comments, suggestions, and fruitful discussions: Victor Manyong, Arega Alene, Peter Kulakow, Peter Uluebey, Ibrahim Kingsley, Akomolafe Funke, Ayedun Bamikole, Sadeeq Ibrahim, Kingsley Elughariwe, and James (RIP) and Smith Ikpan. We are also grateful to Ugonna Nwosu and Adedayo Adepitan of HarvestPlus Nigeria for all their support with the logistics of this project and field varietal identification, and to SEED Solutions Infotech for excellent software development and support. Anonymous reviewers who have helped improve the quality of this work are also acknowledged here. Any errors and mistakes remain those of the authors.

[1]  V. Manyong,et al.  Land tenurial systems and the adoption of Mucuna planted fallow in the derived savannas of West Africa , 2000 .

[2]  L. Drake,et al.  Soil and water conservation decision behavior of subsistence farmers in the Eastern Highlands of Ethiopia: a case study of the Hunde-Lafto area , 2003 .

[3]  A. S. Bamire,et al.  Adoption pattern of fertiliser technology among farmers in the ecological zones of south-western Nigeria: a Tobit analysis , 2002 .

[4]  B. Kotu,et al.  Adoption of improved wheat technologies in Adaba and Dodola Woredas of the Bale highlands, Ethiopia , 2000 .

[5]  T. Abdoulaye,et al.  Stages and determinants of fertilizer use in semiarid African agriculture: the Niger experience , 2005 .

[6]  Workneh Negatu,et al.  The impact of perception and other factors on the adoption of agricultural technology in the Moret and Jiru Woreda (district) of Ethiopia , 1999 .

[7]  Amos A. Akinola An application of peobit analysis to the adoption of tractor hiring service scheme in Nigeria , 1987 .

[8]  Manfred Zeller,et al.  Market access by smallholder farmers in Malawi: implications for technology adoption, agricultural productivity and crop income , 1998 .

[9]  R. Allen,et al.  Economic theory , 2018, Integrative Governance.

[10]  E. Birol,et al.  Information and Consumer Willingness to Pay for Biofortified Yellow Cassava: Evidence from Experimental Auctions in Nigeria. HarvestPlus Working Paper 13 , 2014 .

[11]  J. Tobin Estimation of Relationships for Limited Dependent Variables , 1958 .

[12]  B. Okigbo Nutritional implications of projects giving high priority to the production of staples of low nutritive quality: the case for cassava (Manihot esculenta, Crantz) in the humid tropics of West Africa. , 1980 .

[13]  W. Oluoch-Kosura,et al.  Soil fertility management in maize-based production systems in Kenya: current options and future strategies , 2001 .

[14]  B. Shiferaw,et al.  Resource degradation and adoption of land conservation technologies in the Ethiopian Highlands: A case study in Andit Tid, North Shewa , 1998 .

[15]  O. Capps,et al.  Analysis of Food Stamp Participation Using Qualitative Choice Models , 1985 .

[16]  T. Amemiya QUALITATIVE RESPONSE MODELS: A SURVEY , 1981 .

[17]  T. Amemiya Tobit models: A survey , 1984 .

[18]  F. Ellis Rural Livelihoods and Diversity in Developing Countries , 2000 .

[19]  David J. Strauss,et al.  Do One Percent of the Forest Fires Cause Ninety-Nine Percent of the Damage? , 1989, Forest Science.

[20]  T. Schroeder,et al.  FACTORS AFFECTING ADOPTION OF IMPROVED MAIZE SEED AND FERTILISER IN NORTHERN TANZANIA , 1997 .

[21]  D. Gujarati Basic econometrics (+ cd) , 2014 .

[22]  R. Hassan,et al.  DETERMINANTS OF ADOPTION AND INTENSITY OF USE OF IMPROVED MAIZE VARIETIES IN THE CENTRAL HIGHLANDS OF ETHIOPIA: A TOBIT ANALYSIS , 2000 .

[23]  A. Adesina,et al.  Technology characteristics, farmers' perceptions and adoption decisions: A Tobit model application in Sierra Leone , 1993 .

[24]  D. Rubinfeld,et al.  Econometric models and economic forecasts , 2002 .

[25]  D. Gujarati,et al.  Essentials of Econometrics , 1992 .

[26]  Felix I. Nweke,et al.  The Cassava Transformation: Africa's Best-Kept Secret , 2001 .