3 research outputs found
Assessing the impact of the adoption of agroforestry technology on food production and poverty reduction among farming households in Oyo State, Nigeria
Article Details: Received: 2020-06-22 | Accepted: 2020-08-08 | Available online: 2021-03-31 https://doi.org/10.15414/afz.2021.24.01.25-34This study determines the impact of agroforestry practices on food production, income generation and poverty reduction among farming households in Oyo State, Nigeria. A multi-stage sampling technique was used to select the respondents. Both descriptive statistics such as frequencies and percentages as well as inferential statistics such as Propensity Score Matching (PSM) and Foster Greer Thorbecke (FGT) analysis were used in the study. It was discovered that the propensity score distribution and common support for propensity score estimation shows the results from the covariate balancing tests both before and after matching in which the treatment (adopters) and comparison (non-adopters) groups are said to be balanced. The result of the impact of the adoption of agroforestry practices on farmers’ income from the PSM analysis shows that the adoption produces a positive and significant impact on the farmers’ income, while the result of the impact of the adoption on farmers’ output was found to be negative, though not significant. This could be attributed to improper adoption or practices of the technologies by the farmers. It was also discovered that about 27% of the adopters fell below the poverty line (102.21) and can therefore be described as poor. FGT poverty index was then used to show the extent of poverty among the farming households and it was found that the adopters of agroforestry technology were faring better than the non-adopters of agroforestry technology.Keywords: agroforestry technology, food production, poverty reduction, Propensity Score Matching (PSM), Foster Greer Thorbecke (FGT) ReferencesADAMS, W.M. et al. (2004). Biodiversity conservation and the eradication of poverty. Science, (306), 1146.ADEOLA, A.O. (2015). Principles and Practice of Agroforestry. YEMPET PUBLISHERS, Akure.ADEPOJU, A.O. et al. (2010). Households’ Vulnerability to Poverty in Ibadan Metropolis, Oyo State, Nigeria. Journal of Rural Economics and Development, 20, pp. 1–14.AJAYI, O.C. et al. (2012). Role of externality in the adoption of smallholder agroforestry: Case studies from Southern Africa and Southeast Asia. In S. Sunderasan (Ed.). Externality: Economics, Management and Outcomes, NY: NOVA Science Publishers, pp. 167–188.AKINWALERE, B.O. (2016). Agroforestry Practices among Farmers in SouthWest Nigeria: An Analysis of Benefits. Asian Journal of Agricultural Extension, Economics & Sociology, 10(2), 1–9.ALI, A. et al. (2010). The Adoption of Genetically Modified Cotton and Poverty Reduction in Pakistan. J. Agric. Econ., 61(1), 175–192.AMONUM, J.I. et al. (2019). Adoption Level of Agroforestry Practices in Katsina State, Nigeria. Asian Research Journal of Agriculture, 11(2), 1–10.BEEGLE, K. et al. (2016). Poverty in a rising Africa. World BankPublications. https://doi.org/10.1596/978-1-4648-0723-7CHARLES, A. et al. (2019). Addressing the climate change and poverty nexus: a coordinated approach in the context of the 2030 agenda and the Paris agreement. Rome. FAO.DANAAN, V.V. (2018). Analysing Poverty in Nigeria through Theoretical Lenses. Journal of Sustainable Development, 11(1), 20–31.GARRITY, D. et al. (2011). More trees on farms. Farm. Matt., 27(2), 8–9.GRIGGS, D. et al. (2013). Policy: sustainable development goals for people and planet. Nature, (495), 305–307.GUO, S. et al. (2010). Advanced quantitative techniques in the social sciences: Propensity score analysis: Statistical methods and applications. Sage Publications, Inc.FFOSTER J. E. et al. (1984). A class of decomposable poverty indices. Econometrica, (5), 761–766.IDUMAH, F.O. et al. (2019). Determinants of Yam Production and Resource use Efficiency under Agroforestry System in Edo State, Nigeria. Tanzania Journal of Agricultural Sciences, 18(1), 35–42.IDUMAH, F.O. et al. (2018). Balancing Food Production and Forest Conservation in Nigeria: The Agroforestry Option. IOSR Journal of Agriculture and Veterinary Science (IOSR–JAVS), 11(11), pp. 63–68.IDUMAH, F.O. et al. (2014). Contribution of agroforestry to food production and income generation in Sapoba forest area, Edo State, Nigeria. Journal of Horticulture and Forestry, 6(8), pp. 64–71.INSTITUTE FOR FOOD SECURITY, ENVIRONMENTAL RESOURCES AND AGRICULTURAL RESEARCH(IFSERAR). Federal University of Agriculture, Abeokuta (FUNAAB), Cartographic, Laboratory, 2016.JAMA, B. et al. (2006). Role of Agroforestry in Improving Food security and Natural Resource Management in the Drylands: A Regional Overview. Journal of the Drylands, 1(2), 206–211.KANDJI, S.T. et al. (2006). Opportunities for linking climate change adaptation and mitigation through agroforestry systems. In Garrity DP, Okono A, Grayson M, Parrott S (Eds) World Agroforestry into the Future (Edn) World Agroforestry Centre (ICRAF). Nairobi, Kenya.KAREEM, I.A. et al. (2017) Evaluation of selected agroforestry practices and farmers’ perception of climate change in Ogun State, Nigeria. Forest Research Engineering: International Journal, 1(1), 9–16.KENNEDY, N. et al. (2016). Adoption of soil and water conservation practices in central Haiti. J. Soil Water Conserv., 71(2), 83–90.MADUKA, S.M. (2007). Role of Agroforestry Products in Household Income and Poverty Reduction in Semi-Arid Areas of Misungwi District, Mwanza, Tanzania. A dissertation submitted in partial fulfilment of the requirements for the degree of Master of Sciencein Forestry of Sokoine University of Agriculture, Morogoro, Tanzania.MAREN, O. et al. (2011). Climate Change Adaptation using Agroforestry Practices: A Case Study from Costa Rica. Stefano Casalegno (Ed.) Global Warming Impacts – Case Studies on the Economy, Human Health, and on Urban and Natural Environments. https://doi.org/10.5772/241727MUTUA, J. et al. (2014). Conservation Agriculture with Trees: Principles and Practice. A simplified guide for Extension Staff and Farmers. World Agroforestry Centre, (ICRAF), Nairobi, Kenya.OLAJUYIGBE, S. (2016). Potential role of traditional agroforestry in climate change mitigation in rural communities of Oyo State Nigeria. In Conference Paper presented at the 38th Annual Conference of the Forestry Association of Nigeria, held at Port Harcourt, Rivers State, Nigeria.OSOWOLE, O.I. (2011). An Analysis of Rural Poverty In Oyo State: A Principal Component Approach. JORIND, 9(2), 100–104. www.ajol.info/journals/jorindOWOMBO, P.T. et al. (2017). Determinants of agroforestry technology adoption among arable crop farmers in Ondo state, Nigeria: an empirical investigation. AgroforestSyst., (91), 919– 926. https://doi.org/10.1007/s10457-016-9967-2OZOWA, B. (2005). Making the Most of Agricultural Investment. PDF adobe acrobat document at www.ifad.org/agri_investment.pdf Retrieved in November, 2011.RAHMAN, S.A. et al. (2010). SustainableForest Management for Poverty Reduction Through Agroforestry Options:Lesson from the Remote Uplands of Eastern Bangladesh. Libyan Agriculture Research Center Journal International, 1(3), 134–141.RUBIN, D. R. (2001). Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation. Health Services & Outcomes Research Methodology, 2, 169–88.SARVADE, S. et al. R. (2014). Role of Agroforestry in Food Security. Popular Kheti, 2(2), 25–29.SILESHI, G.W. (2012). Can Integration of Legume Trees Increase Yield Stability in Rain-fed Maize Cropping Systems in South Africa? Agronomy Journal, (104), 1392–1398.THOEMMES, F.J. et al. (2011). A Systematic Review of Propensity Score Methods in the Social Sciences. Multivariate Behavioral Research, 46(1), 90–118.THORNTON, P. K. et al. (2006). Mapping climate vulnerability and poverty in Africa. Report to the Department for International Development, ILRI, Nairobi, Kenya, pp 171.TIWARI, P. et al. (2017). Agroforestry for Sustainable Rural Livelihood: A Review. International Journal of Pure Applied Biosciences, 5(1), 299–309. http://dx.doi.org/10.18782/2320-7051.2439NATIONAL HUMAN DEVELOPMENT REPORT OF THE UNITED NATIONS DEVELOPMENT PROGRAMME. (2019). Inequalities in Human Development in the 21st Century Briefing note for countries on the 2019 Human Development Report. Retrieved on http://hdr.undp.org/sites/all/themes/hdr_theme/country-notes/NGA.pdfUNITED NATIONS DEVELOPMENT PROGRAMME, HUMAN DEVELOPMENT REPORTS, GDP per capita (2011 PPP$).WORLD BANK. (2018). Poverty and Equity Brief in Sub-Saharan Africa, Nigeria. April, 2018. https://databank.worldbank.org/data/download/poverty/33EF03BB-9722-4AE2-ABC7-AA2972D68AFE/Archives-2018/GlobalPOVEQ_NGA. pdfWorld BankWORLD BANK. (2001). Attacking poverty. World development report 2000/2001 (draft copy). West African, USA, 352 p
Livelihood Strategies of Farming Households in Forest Fringe Communities of Niger State, Nigeria
This study assesses the different types of livelihood strategies as well as factors that influence the choice of these strategies among rural households in Niger State, Nigeria. One hundred copies of a structured questionnaire were used to obtain information from respondents in the study area. Two Local Government Areas (LGAs) were purposively selected for the study. Both descriptive statistics such as frequencies and percentages and inferential statistics such as Multinomial Logistic Regression were used in the study. The average household size, farm size and farming experience in the study were 10, 2.2 acre and 20 years respectively and most of the respondents were educated. The results of the Multinomial Logistic Regression show that age, household size, farm size, non-farm income, access to extension services, educational qualifications, farming experience and forest availability in their locality were factors that influenced the respondents’ choice of livelihood strategy relative to the reference category in the study
Adaptation of the Wound Healing Questionnaire universal-reporter outcome measure for use in global surgery trials (TALON-1 study): mixed-methods study and Rasch analysis
BackgroundThe Bluebelle Wound Healing Questionnaire (WHQ) is a universal-reporter outcome measure developed in the UK for remote detection of surgical-site infection after abdominal surgery. This study aimed to explore cross-cultural equivalence, acceptability, and content validity of the WHQ for use across low- and middle-income countries, and to make recommendations for its adaptation.MethodsThis was a mixed-methods study within a trial (SWAT) embedded in an international randomized trial, conducted according to best practice guidelines, and co-produced with community and patient partners (TALON-1). Structured interviews and focus groups were used to gather data regarding cross-cultural, cross-contextual equivalence of the individual items and scale, and conduct a translatability assessment. Translation was completed into five languages in accordance with Mapi recommendations. Next, data from a prospective cohort (SWAT) were interpreted using Rasch analysis to explore scaling and measurement properties of the WHQ. Finally, qualitative and quantitative data were triangulated using a modified, exploratory, instrumental design model.ResultsIn the qualitative phase, 10 structured interviews and six focus groups took place with a total of 47 investigators across six countries. Themes related to comprehension, response mapping, retrieval, and judgement were identified with rich cross-cultural insights. In the quantitative phase, an exploratory Rasch model was fitted to data from 537 patients (369 excluding extremes). Owing to the number of extreme (floor) values, the overall level of power was low. The single WHQ scale satisfied tests of unidimensionality, indicating validity of the ordinal total WHQ score. There was significant overall model misfit of five items (5, 9, 14, 15, 16) and local dependency in 11 item pairs. The person separation index was estimated as 0.48 suggesting weak discrimination between classes, whereas Cronbach's α was high at 0.86. Triangulation of qualitative data with the Rasch analysis supported recommendations for cross-cultural adaptation of the WHQ items 1 (redness), 3 (clear fluid), 7 (deep wound opening), 10 (pain), 11 (fever), 15 (antibiotics), 16 (debridement), 18 (drainage), and 19 (reoperation). Changes to three item response categories (1, not at all; 2, a little; 3, a lot) were adopted for symptom items 1 to 10, and two categories (0, no; 1, yes) for item 11 (fever).ConclusionThis study made recommendations for cross-cultural adaptation of the WHQ for use in global surgical research and practice, using co-produced mixed-methods data from three continents. Translations are now available for implementation into remote wound assessment pathways