Gender-differentiated stochastic meta-frontier analysis of production technology heterogeneity among smallholder cassava farmers in Ghana

References

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  • Bai X, Wang Y, Huo X, Salim R, Bloch H & Zhang H, 2019. Assessing fertilizer use efficiency and its determinants for apple production in China. Ecological Indicators 104: 268–78.
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  • Danso-Abbeam G & Baiyegunhi LJ, 2020. Technical efficiency and technology gap in Ghana’s cocoa industry: Accounting for farm heterogeneity. Applied Economics 52(1): 100–12.
  • Danso-Abbeam G, Baiyegunhi LJ & Ojo TO, 2020. Gender differentials in technical efficiency of Ghanaian cocoa farms. Heliyon 6(5): e04012.
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  • O’Donnell CJ, Rao DP & Battese GE, 2008. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics 34(2): 231–55.
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  • Otieno DJ, Hubbard L & Ruto E, 2011. Technical efficiency and technology gaps in beef cattle production systems in Kenya: A stochastic metafrontier analysis. A paper read at the 85th annual Conference of the Agricultural Economics Society (AES), 18–20 April, University of Warwick, Coventry, UK.
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  • Addai KN & Owusu V, 2014. Technical efficiency of maize farmers across various agro ecological zones of Ghana. Journal of Agriculture and Rural Development 3(1): 149–72.
  • Ahmed MH & Melesse KA, 2018. Impact of off-farm activities on technical efficiency: Evidence from maize producers of eastern Ethiopia. Agricultural and Food Economics 6(1): 1–15.
  • Alem H, Lien G, Hardaker JB & Guttormsen A, 2019. Regional differences in technical efficiency and technological gap of Norwegian dairy farms: A stochastic meta-frontier model. Applied Economics 51(4): 409–21.
  • Ali I, Huo XX, Khan I, Ali H, Khan B & Khan SU, 2019. Technical efficiency of hybrid maize growers: A stochastic frontier model approach. Journal of Integrative Agriculture 18(10): 2408–21.
  • Bai X, Wang Y, Huo X, Salim R, Bloch H & Zhang H, 2019. Assessing fertilizer use efficiency and its determinants for apple production in China. Ecological Indicators 104: 268–78.
  • Battese GE, Rao DP & O’Donnell CJA, 2004. Meta-frontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of Productivity Analysis 21(1): 91–103.
  • Danso-Abbeam G & Baiyegunhi LJ, 2020. Technical efficiency and technology gap in Ghana’s cocoa industry: Accounting for farm heterogeneity. Applied Economics 52(1): 100–12.
  • Danso-Abbeam G, Baiyegunhi LJ & Ojo TO, 2020. Gender differentials in technical efficiency of Ghanaian cocoa farms. Heliyon 6(5): e04012.
  • Dawson PJ, 1990. Farm efficiency in the England and Wales dairy sector. Oxford Agrarian Studies 18(1): 35–42.
  • Dimelu MU, Okoye AC, Okoye BC, Agwu AE, Aniedu OC & Akinpelu AO, 2009. Determinants of gender efficiency of small-holder cocoyam farmers in Nsukka agricultural zone of Enugu State Nigeria. Scientific Research and Essays 4(1): 28–32.
  • Dossah BO & Mohammed IU, 2016. Evaluation of gender differences in resource utilization and technical efficiency of irrigated vegetable farming in Plateau State, Nigeria. European Journal of Basic and Applied Sciences 3(2): 1–14.
  • Food and Agriculture Organization (FAO), 2019. Food and Agriculture Organization Statistics Division database. Available at http://www.fao.org/faostat/en/#data/QCL (Accessed 29 October 2020).
  • Food and Agriculture Organization (FAO), 2018. Food and Agriculture Organization Statistics Division Database. Available at http://www.fao.org/faostat/en/#data/QCL (Accessed 9 August 2019).
  • Gebre GG, Isoda H, Rahut DB, Amekawa Y & Nomura H, 2019. Gender differences in agricultural productivity: Evidence from maize farm households in southern Ethiopia. GeoJournal 86: 843–64. https://doi.org/10.1007/s10708-019-10098-y
  • Ghana Statistical Service (GSS), 2013. 2010 population & housing census: National analytical report. Accra, Ghana: Ghana Statistics Service.
  • Hayami Y, 1969. Sources of agricultural productivity gap among selected countries. American Journal of Agricultural Economics 51(3): 564–75.
  • Huang CJ, Huang TH & Liu NH, 2014. A new approach to estimating the metafrontier production function based on a stochastic frontier framework. Journal of Productivity Analysis 42(3): 241–54.
  • Koirala KH, Mishra AK & Sitienei I, 2015. Farm productivity and technical efficiency of rural Malawian households: Does gender make a difference? Paper read at the 2014 Annual Meeting of the Southern Agricultural Economics Association, 31 January–3 February, Atlanta, Georgia, USA.
  • Kuwornu JKM, Amoah E & Seini W, 2013. Technical efficiency analysis of maize farmers in the Eastern Region of Ghana. Journal of Social and Development Sciences 4(2): 84–99.
  • Makombe G, Namara R, Fitsum H, Awulachew SB, Mekonnen A & Deborah B. 2011. A comparative analysis of the technical efficiency of rain-fed and smallholder irrigation in Ethiopia. IWMI Working Paper no. 143. Colombo, Sri Lanka: International Water Management Institute.
  • Ministry of Food and Agriculture (MoFA), 2017. Facts and figures, agriculture in Ghana. Accra, Ghana: Ministry of Food and Agriculture.
  • Moreira VH & Bravo-Ureta BE, 2010. Technical efficiency and metatechnology ratios for dairy farms in three southern cone countries: A stochastic meta-frontier model. Journal of Productivity Analysis 33(1): 33–45.
  • Moreira López VH & Bravo-Ureta BE, 2009. A study of dairy farm technical efficiency using meta regression: An international perspective. Chilean Journal of Agricultural Research 69: 214–23.
  • Mupakati T & Tanyanyiwa VI, 2017. Cassava production as a climate change adaptation strategy in Chilonga Ward, Chiredzi District, Zimbabwe. Jàmbá: Journal of Disaster Risk Studies 9(1): 1–10.
  • Musa HA, Lemma Z & Endrias G, 2015. Measuring technical, economic and allocative efficiency of maize production in subsistence farming: Evidence from the Central Rift Valley of Ethiopia. Applied Studies in Agribusiness and Commerce 9(3): 63–73.
  • Ng’ombe JN, 2017. Technical efficiency of smallholder maize production in Zambia: A stochastic meta-frontier approach. Agrekon 56(4): 347–65.
  • O’Donnell CJ, Rao DP & Battese GE, 2008. Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics 34(2): 231–55.
  • Orea L & Kumbhakar SC, 2004. Efficiency measurement using a latent class stochastic frontier model. Empirical Economics 29(1): 169–83.
  • Otieno DJ, Hubbard L & Ruto E, 2011. Technical efficiency and technology gaps in beef cattle production systems in Kenya: A stochastic metafrontier analysis. A paper read at the 85th annual Conference of the Agricultural Economics Society (AES), 18–20 April, University of Warwick, Coventry, UK.
  • Oyakhilomen O, Murtala MO, Abraham F & Kwagyang SM. 2016. Technical efficiency of catfish farming in Alimosho local government area of Lagos State, Nigeria: A gender perspective. Agricultura Tropica et Subtropica 49(1–4): 45–9.
  • Poku AG, Birner R & Gupta S, 2018. Is Africa ready to develop a competitive bioeconomy? The case of the cassava value web in Ghana. Journal of Cleaner Production 200: 134–47.
  • Sauer J, Fronhberg K & Hockmann H, 2006. Stochastic efficiency measurement: The curse of theoretical consistency. Journal of Applied Economics 9(1): 139–65.
  • Senkoro CJ, Tetteh FM, Kibunja CN, Ndungu-Magiroi KW, Quansah GW, Marandu AE, Ley GJ, Mwangi TJ & Wortmann CS, 2018. Cassava yield and economic response to fertilizer in Tanzania, Kenya and Ghana. Agronomy Journal 110(4): 1600–6.
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