Abstract
This paper examines whether technology and energy have a role to play in agriculture value addition in nine sub-Saharan African countries for a sample period between 1981 and 2015. We employ the Autoregressive Distributed Lag modelling technique to examine the relationship among the variables. Models with technology, energy use, and a combination of technology and energy were considered, respectively. Only Nigeria recorded a positive and significant impact of technology on its agricultural value addition in both the short run and long run in the first model. In the second model, energy use positively and significantly influenced agricultural value addition in two countries (Botswana and South Africa) in the short run. However, the long-run coefficients of energy use were found to be positive and significant for Benin and South Africa. In the third model, short-run coefficients of technology were largely positive but statistically insignificant across the countries, while its long-run coefficient was largely positive but only statistically significant for Nigeria. Also, the short-run effect of energy use remained positively significant for Botswana and South Africa as observed in the earlier model. However, its long-run effect was only positive and statistically significant in three countries (Benin, South Africa, and Togo). Thus, the study recommends the need for huge investment in technology and energy use as well as the sensitisation of local farmers in adopting technology to further enhance the quality of their output.
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Notes
Between 1997 and 2017, the average agriculture value added (% of GDP) at regional level is 19%, 17.2%, 6.4%, 5.6%, 5.1%, 4.2%, and 1.2% for South Asia, Sub-Saharan Africa, Arab World, MENA, Latin America, and Caribbean and North America, respectively.
For example, the agricultural machinery, tract per 100 km2 of arable land, is 22 on average, for Sub-Saharan Africa compared to regions such as North America with 240, Latin America, and Caribbean 85 and world average of 157 between 1961 and 2006. This is an indication of poor mechanisation of agricultural activities in the SSA region.
For instance, the average energy use per capita between 1971 and 2014 at the regional level is 362, 687, 1156, 1345, 1108, 7686 for South Asia, Sub-Saharan Africa, Arab World, MENA, Latin America, and Caribbean and North America, respectively. Sub-Saharan Africa recorded the second lowest energy use per capita in the world. This continues to weaken the activities of the sector in the region.
See Table 9 in “Appendix 3”.
References
Abro ZA, Alemu BA, Hanjra MA (2014) Policies for agricultural productivity growth and poverty reduction in rural Ethiopia. World Dev 59:461–474
Ajao OA (2011) empirical analysis of agricultural productivity growth in sub-Saharan Africa: 1961–2003. Agricultural Economics and Extension Department, Ladoke Akintola University of Tech, Ogbomoso
Aksoy M, Beghin JC (eds) (2004) Global agricultural trade and developing countries. Washington D.C.: The World Bank. https://doi.org/10.1596/0-8213-5863-4
Akudugu MA, Guo M, Dadzie SK (2012) Adoption of modern agricultural production technologies by farm households in Ghana: what factors influence their decisions? J Biol Agric Healthc 2(3):1–13
Alene AD, Manyong VM (2016) The effects of education on agricultural productivity under traditional and improved technology in Northern Nigeria: an endogenous switching regression analysis. Empir Econ 32:141–159
Ball VE, Färe R, Grosskopf S, Margaritis D (2015) The role of energy productivity in U.S. agriculture. Energy Econ 49:460–471
Bannerjee A, Dolado JJ, Galbraith JW, Hendry DF (1993) Co-integration, error correction, and the econometric analysis of non-stationary data. Oxford University Press, Oxford
Becker B, Hall SG (2013) Do R&D strategies in high-tech sectors differ from those in low-tech sectors? An alternative approach to testing the pooling assumption. Econ Change Restruct 46:183. https://doi.org/10.1007/s10644-012-9122-7
Bhargava AK, Vagen T, Gassner A (2018) Breaking ground: unearthing the potential of high-resolution, remote-sensing soil data in understanding agricultural profits and technology use in Sub-Saharan Africa. World Dev 105:352–366
Block SA (1995) The recovery of agricultural productivity in sub-Saharan Africa. J Food Policy 20(5):385–405
Brady M, Sohngen B (2008) Agricultural productivity, technological change, and deforestation: a global analysis. Department of Agricultural, Environmental, and Development Economics, Ohio State University, Columbus
Brigham AM (2011) Agricultural exports and food insecurity in sub-Saharan Africa: a qualitative configurational analysis. Dev Policy Rev 29(6):729–748
CBN, BOI (2010) Unleashing agricultural development potentials in Nigeria through value chain financing. Working Paper, November 2010. United Nations Industrial Development Organization, Vienna, Austria
Change M (2016) Applying the energy productivity index that considers maximized energy reduction on SADC (Southern Africa Development Community) members. Energy 95:313–323
Christiaensen L, Demery L (2007) Down to earth: agriculture and poverty reduction in africa, directions in development. World Bank, Washington
Collier P, Dercon S (2014) African agriculture in 50 years: smallholders in a rapidly changing world? World Dev 63:92–101
Diao X, Hazell P, Resnick D, Thurlow J (2006) The role of agriculture in development: implications for sub-Saharan Africa. Development Strategy and Governance Division Discussion Paper No. 29. International Food Policy Research Institute (IFPRI): Washington, DC
Donovan J, Franzel S, Cunha M, Gyau A, Mithöfer D (2015) Guides for value chain development: a comparative review. J Agribus Dev Emerg Econ 5(1):2–23
Eberhardt M, Vollrath D (2016) The effect of agricultural technology on the speed of development. World Dev. https://doi.org/10.1016/j.worlddev.2016.03.017
Emran S, Shilpi F (2016) Agricultural productivity, hired labor, wages, and poverty: evidence from Bangladesh. World Dev. https://doi.org/10.1016/j.worlddev.2016.12.009
Fulginiti LE, Perrin RK, Yu Bingxin (2004) Institutions and agricultural productivity in Sub-Saharan Africa. Agric Econ 31:169–180
Giannakis E, Bruggeman A (2018) Exploring the labour productivity of agricultural systems across European regions: a multilevel approach. Land Use Policy 77:94–106
González V, Ibarrarán P, Maffioli A, Rozo S (2009) The impact of technology adoption on agricultural productivity: the case of the Dominican Republic, Inter-American Development Bank Office of evaluation and oversight. Working Paper: OVE/WP-05/09
Granger CWJ, Newbold P (1974) Spurious regression in econometrics. J Econom 2:111–120
Hair JF Jr, Anderson RE, Tatham RL, Black WC (1995) Multivariate data analysis, 3rd edn. Macmillan, New York
Lezin HW, Long-Bao W (2005) Agricultural productivity growth and agricultural technology progress in developing country agriculture, the case of China. J Zhejiang Univ Sci 6A(Suppl. 1):172–176
Lipton M (1988) The place of agricultural research in the development of sub-Saharan Africa. World Dev 16:1231–1257
Mahgoub F (2014) Current status of agriculture and future challenges in Sudan, current African issues 57. Nordiska Afrikainstitutet, Uppsala 2014
Mcmichael P (2013) Value-chain agriculture and debt relations: contradictory outcomes. Third World Q 34(4):671–690
Mukasa AN, Woldemichael AD, Salami AO, Simpasa AM (2017) Africa’s agricultural transformation: identifying priority areas and overcoming challenges. Africa Econ Brief 8(3):1–16
Muzari W, Kupika O, Danha C, Mapingure C (2013) The impacts of agricultural technology use on productivity and food security among smallholder farmers in Zimbabwe: the case of Makonde district. J Agric Ext Rural Dev 5(10):225–231. https://doi.org/10.1016/j.eneco.2014.05.006
Nkamleu GB, Gokowski J, Kazianga H (2003) Explaining the failure of agricultural production in sub-Saharan Africa. In: Contributed paper selected for presentation at the 25th international conference of agricultural economists, August 16–22, 2003, Durban, South Africa
Nyaribo-Roberts F, Ospina E (1992a) Methodologies and impacts of small ruminant technologies on small farm systems in Kenya. In: Presented at the symposium on the impact of technology on agricultural transformation in Africa, 14–16 October, Washington, DC
Nyaribo-Roberts F, Ospina E (1992b) Methodologies and impacts of small ruminant technologies on small farm systems in Kenya. In: Presented at the symposium on the impact of technology on agricultural transformation in Africa, 14–16 October
Oehmke J (1992) Technology, impact and agricultural trans- formation: lessons learned from impact studies. In: Presented at the symposium on the impact of technology on agricultural transformation in Africa, 14–16 October, Washington, DC
Oehmke JF, Crawford EW (1992) The impact of agricultural technology in sub-Saharan Africa. J Afr Econ 5(2):271–292
Ogunlesi A, Bokana K, Okoye C, Loy J (2018) Agricultural productivity and food supply stability in sub-Saharan Africa: LSDV and SYS-GMM approach MPRA paper no. 90204
Oyinlola MA, Adedeji A (2019) Human capital, financial sector development and Inclusive growth in sub-Saharan Africa. Econ Change Restruct 52(1):43–66
Pahlavani M, Wilson E, Worthington AC (2005) Trade-GDP nexus in Iran: an application of the autoregressive distributed lag (ARDL) model. Faculty of Commerce Papers, University of Wollongong, Australia. http://www.scipub.us/; http://ro.uow.edu.au/commpapers/144.
Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econom 16:289–326
Sheahan M, Barrett CB (2017) Ten striking facts about agricultural input use in Sub-Saharan Africa. Food Policy 67:12–25
Shrestha MB, Chowdhury K (2005) ARDL modelling approach to testing the financial liberalization hypothesis. Economics working paper series 2005 (WP 05-15). Australia: University of Wollongong, June. http://www.uow.edu.au/commerce/econ/wpapers.html. Accessed 20 Nov 2006
Solow RM (1956) A contribution to the theory of economic growth. Q J Econ 70(1):65
Sterns JA, Bernsten R (1994) Assessing the impact of cowpea and sorghum research and extension: lessons learned in Northern Cameroon. Department of Agricultural Economics, Michigan State University, East Lansing, MI, MSU International Development Working Paper No. 43. Washington, DC
Tang TC (2003) Japanese aggregate import demand function: reassessment from the ‘bounds’ testing approach. Jpn World Econ 15(4):419–436
Wang SL, McPhail L (2014) Impacts of energy shocks on US agricultural productivity growth and commodity prices—A structural VAR analysis. Energy Econ 46:435–444
Watts N, Scales IR (2020) Social impact investing, agriculture, and the financialisation of development: Insights from sub-Saharan Africa. World Dev 130:1–11
WDI (2016) World development indicators. World Bank Group. http://data.worldbank.org/data-catalog/world-development-indicators. Accessed 13 July 2018
Webber CM, Labaste P (2009) Building competitiveness in Africa’s agriculture: a guide to value chain concepts and applications (English). Agriculture and rural development. Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/715461468003020843/Building-competitiveness-in-Africas-agriculture-a-guide-to-value-chain-concepts-and-applications
Weintraub A, Romero C (2006) Operations Research Models and the Management of Agricultural and Forestry Resources: A Review and Comparison. Interfaces 36(5):446–457
World Bank (2010) Building competitiveness in Africa’s agriculture. World Bank. Washington, DC. http://www.worldbank.org/pdf. Accessed 13 July 2018
Acknowledgements
The authors are very thankful to the Editor-in-Chief, George Hondroyiannis, and the anonymous reviewers for their valuable comments and suggestions that have shaped the quality of our paper. Further thanks go to the University of Ibadan, Ibadan, Nigeria, for providing the enabling space for scholarship and the African Economic Research Consortium, Nairobi Kenya, for the training supports. The usual disclaimer applies.
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Oyinlola, M.A., Adedeji, A.A. & Olabisi, N. Technology, energy use, and agricultural value addition nexus: an exploratory analysis from SSA countries. Econ Change Restruct 54, 457–490 (2021). https://doi.org/10.1007/s10644-020-09286-5
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DOI: https://doi.org/10.1007/s10644-020-09286-5