Skip to content
BY-NC-ND 4.0 license Open Access Published by De Gruyter Open Access July 11, 2018

How big is the potato (Solanum tuberosum L.) yield gap in Sub-Saharan Africa and why? A participatory approach

  • Dieudonné Harahagazwe , Bruno Condori , Carolina Barreda , Astère Bararyenya , Arinaitwe Abel Byarugaba , Danbaba Anthony Kude , Charles Lung’aho , Carolino Martinho , Daniel Mbiri , Bouwe Nasona , Bruce Ochieng , John Onditi , Jean Marc Randrianaivoarivony , Christopher M. Tankou , Alemu Worku , Elmar Schulte-Geldermann , Victor Mares , Felipe de Mendiburu and Roberto Quiroz Quiroz EMAIL logo
From the journal Open Agriculture

Abstract

According to potato experts from ten Sub-Saharan Africa (SSA) countries working together in a community of practice (CoP) over a 3-years period, potato farmers across SSA can increase their current annual production of 10.8 million metric tons by 140% if they had access to high quality seed along with improved management practices. This paper describes this innovative new methodology tested on potato for the first time, combining modelling and a comprehensive online survey through a CoP. The intent was to overcome the paucity of experimental information required for crop modelling. Researchers, whose data contributed to estimating model parameters, participated in the study using Solanum, a crop model developed by the International Potato Center (CIP). The first finding was that model parameters estimated through participatory modelling using experts’ knowledge were good approximations of those obtained experimentally. The estimated yield gap was 58 Mg ha-1, of which 35 corresponded to a research gap (potential yield minus research yield) and 24 to farmers’ gap (research yield minus farmer’s yield). Over a 6-month period, SurveyMonkey, a Web-based platform was used to assess yield gap drivers. The survey revealed that poor quality seed and bacterial wilt were the main yield gap drivers as perceived by survey respondents.

References

Anderson T.W., Estimating linear statistical relationships, Ann. Stat., 1984, 12, 1-4510.1214/aos/1176346390Search in Google Scholar

Caldiz D.O., Analysis of seed and ware potato production systems and yield constraints in Argentina, PhD thesis, Wageningen University, Wageningen, The Netherlands, 2000Search in Google Scholar

Chai T., Draxler R.R., Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)? - Arguments against Avoiding RMSE in the Literature, Geosci. Model Dev., 2014, 7, 1247-125010.5194/gmd-7-1247-2014Search in Google Scholar

Condori B., Hijmans R.J., Ledent J.F., Quiroz R., 2014. Managing Potato Biodiversity to Cope with Frost Risk in the High Andes: A Modeling Perspective, PLOS ONE, 2014, 9, 1, e8151010.1371/journal.pone.0081510Search in Google Scholar PubMed PubMed Central

Condori B., Hijmans R.J., Quiroz R., Ledent J.F., Quantifying the expression of potato genetic diversity in the high Andes through growth analysis and modeling, Field Crops Res., 2010, 119, 135-14410.1016/j.fcr.2010.07.003Search in Google Scholar

Cronbach L.J., Coefficient alpha and the internal structure of tests, Psychometrika, 1951, 16, 297-33410.1007/BF02310555Search in Google Scholar

Devaux A., Kromann P., Ortiz O., Potatoes for Sustainable Global Food Security, Potato Res., 2014, 57, 185-19910.1007/s11540-014-9265-1Search in Google Scholar

FAO, FAOSTAT online databases www.fao.org/faostat/, 2017Search in Google Scholar

FAO, International Year of the Potato 2008: New light on a hidden treasure, FAO, Rome, Italy, 2009Search in Google Scholar

Fleisher D.H., Condori B., Quiroz R., Alva A., Asseng S., Barreda C., et al., A potato model intercomparison across varying climates and productivity levels, Glob. Change Biol., 2017, 23, 1258-128110.1111/gcb.13411Search in Google Scholar PubMed

Fuglie K.O., Priorities for potato research in developing countries: Results of a survey, Am. J. Potato Res., 2007, 84, 35310.1007/BF02987182Search in Google Scholar

Grassini P., van Bussel L.G.J., Van Wart J., Wolf J., Claessens L., Yang H., et al., How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis, Field Crops Res., 2015, 177, 49-6310.1016/j.fcr.2015.03.004Search in Google Scholar

Harahagazwe D., Ledent J.F., Rusuku G., Growth analysis and modelling of CIP potato genotypes for their characterization in two contrasting environments of Burundi, Afr. J. Agric. Res, 2012, 7, 46, 6173-618510.5897/AJAR10.781Search in Google Scholar

Haverkort A.J., Struik P.C., Yield levels of potato crops: Recent achievements and future prospects, Field Crops Res., 2015, 182, 76-8510.1016/j.fcr.2015.06.002Search in Google Scholar

Hochman Z., Gobbett D., Holzworth D., McClelland T., van Rees H., Marinoni O., et al., Reprint of “Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia.” Crop Yield Gap Anal. - Ration, Methods Appl, 2013, 143, 65-7510.1016/j.fcr.2013.02.001Search in Google Scholar

Kooman P.L., Haverkort A.J., 1995. Modelling development and growth of the potato crop influenced by temperature and daylength: LINTUL-POTATO, In: Haverkort A.J., MacKerron D.K.L. (Eds.), Potato Ecology And Modelling of Crops under Conditions Limiting Growth: Proceedings of the Second International Potato Modeling Conference (17-19 May 1994, Wageningen, The Netherlands), Dordrecht, 1994, 41-5910.1007/978-94-011-0051-9_3Search in Google Scholar

Lemaga B., Siriri D., Ebanyat, P., Effect of soil amendments on bacterial wilt incidence and yield of potatoes in southwestern Uganda, Afr. Crop Sci. J., 2001, 910.4314/acsj.v9i1.27648Search in Google Scholar

Licker R., Johnston M., Foley J.A., Barford C., Kucharik C.J., Monfreda C., et al., Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world?, Glob. Ecol. Biogeogr., 2010, 19, 769-78210.1111/j.1466-8238.2010.00563.xSearch in Google Scholar

Lobell D.B., The use of satellite data for crop yield gap analysis. Crop Yield Gap Anal. - Ration, Methods Appl., 2013, 143, 56-6410.1016/j.fcr.2012.08.008Search in Google Scholar

Lobell D.B., Cassman K.G., Field C.B., Crop Yield Gaps: Their Importance, Magnitudes, and Causes, Annu. Rev. Environ. Resour., 2009, 34, 179-20410.1146/annurev.environ.041008.093740Search in Google Scholar

McCullagh P., Regression models for ordinal data, Ournal R. Stat. Soc. Ser. B Methodol., 1980, 42, 109-14210.1111/j.2517-6161.1980.tb01109.xSearch in Google Scholar

Monfreda C., Ramankutty, N., Foley, J.A., Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Glob. Biogeochem. Cycles, 2008, 22, 1-1910.1029/2007GB002947Search in Google Scholar

Neumann K., Verburg P.H., Stehfest E., Muller C., The yield gap of global grain production: A spatial analysis, Agric. Syst., 2010, 103, 316-32610.1016/j.agsy.2010.02.004Search in Google Scholar

Parsa S., Morse S., Bonifacio A., Chancellor T.C.B., Condori B., Crespo-Pérez V., et al., Obstacles to integrated pest management adoption in developing countries, Proc. Natl. Acad. Sci., 2014, 111, 388910.1073/pnas.1312693111Search in Google Scholar PubMed PubMed Central

Sadras V.O., Cassman K.G., Grassini P., Yield gap analysis of field crops: Methods and case studies, FAO, Rome, Italy, 2015Search in Google Scholar

Schulte-Geldermann E., Gildemacher P.R., Struik P.C., Improving Seed Health and Seed Performance by Positive Selection in Three Kenyan Potato Varieties, Am. J. Potato Res., 2012, 89, 429-43710.1007/s12230-012-9264-1Search in Google Scholar

Svubure O., Struik P.C., Haverkort A.J., Steyn J.M., Yield gap analysis and resource footprints of Irish potato production systems in Zimbabwe, Field Crops Res., 2015, 178, 77-9010.1016/j.fcr.2015.04.002Search in Google Scholar

Thiele G., Informal potato seed systems in the Andes: Why are they important and what should we do with them?, World Dev., 1999, 27, 83-9910.1016/S0305-750X(98)00128-4Search in Google Scholar

Thomas-Sharma S., Abdurahman A., Ali S., Andrade-Piedra J.L., Bao S., Charkowski A.O., et al., Seed degeneration in potato: the need for an integrated seed health strategy to mitigate the problem in developing countries, Plant Pathol., 2016, 65, 3-1610.1111/ppa.12439Search in Google Scholar

Tittonell P., Giller K.E., When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture. Crop Yield Gap Anal. - Ration, Methods Appl., 2013, 143, 76-9010.1016/j.fcr.2012.10.007Search in Google Scholar

van Ittersum M.K., Cassman K.G., Grassini P., Wolf J., Tittonell P., Hochman Z., Yield gap analysis with local to global relevance - A review. Crop Yield Gap Anal. - Ration, Methods Appl., 143, 4-1710.1016/j.fcr.2012.09.009Search in Google Scholar

van Ittersum M.K., Rabbinge R., Concepts in production ecology for analysis and quantification of agricultural input-output combinations, Field Crops Res., 1997, 52, 197-20810.1016/S0378-4290(97)00037-3Search in Google Scholar

van Wart J., van Bussel L.G.J., Wolf J., Licker R., Grassini P., Nelson A., et al., Use of agro-climatic zones to upscale simulated crop yield potential. Crop Yield Gap Anal. - Ration, Methods Appl., 2013, 143, 44-5510.1016/j.fcr.2012.11.023Search in Google Scholar

Willmott C.J., Matsuura K., Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance, Clim. Res., 2005, 30, 79-8210.3354/cr030079Search in Google Scholar

Winsor C.P., The Gompertz Curve as a Growth Curve, Proc. Natl. Acad. Sci. U. S. A., 1932, 18, 1-810.1073/pnas.18.1.1Search in Google Scholar PubMed PubMed Central

Yin X., Goudriaan J., Lantinga E.A., Vos J., Spiertz H.J., A flexible sigmoid function of determinate growth, Ann. Bot., 2003, 91, 361-37110.1093/aob/mcg029Search in Google Scholar PubMed PubMed Central

Received: 2018-02-20
Accepted: 2018-06-04
Published Online: 2018-07-11

© 2018 Roberto Quiroz Quiroz, et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Downloaded on 25.4.2024 from https://www.degruyter.com/document/doi/10.1515/opag-2018-0019/html
Scroll to top button