• Amisigo, B., A. McCluskey, and R. Swanson, 2015: Modeling impact of climate change on water resources and agriculture demand in the Volta Basin and other basin systems in Ghana. Sustainability, 7, 69576975, https://doi.org/10.3390/su7066957.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aronson, S. M., 2007: Local Science vs. Global Science: Approaches to Indigenous Knowledge in International Development. Berghahn Books, 288 pp.

    • Search Google Scholar
    • Export Citation
  • Balehegn, M., S. Balehey, C. Fu, and W. Liang, 2019: Indigenous weather and climate forecasting knowledge among Afar pastoralists of north eastern Ethiopia: Role in adaptation to weather and climate variability. Pastoralism, 9, 8, https://doi.org/10.1186/s13570-019-0143-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnett, I., and Coauthors, 2017: External evaluation of mobile phone technology-based nutrition and agriculture advisory services in Africa and South Asia. Institute of Development Studies Doc., 104 pp., https://opendocs.ids.ac.uk/opendocs/bitstream/handle/20.500.12413/13465/mNutrition%20Inception%20Report_for%20publication%20Nov17.pdf?sequence=1&isAllowed=y.

  • Berkes, F., 1999: Sacred Ecology: Traditional Ecological Knowledge and Management Systems. Taylor and Francis, 209 pp.

  • Briggs, J., 2005: The use of indigenous knowledge in development: Problems and challenges. Prog. Dev. Stud., 5, 99114, https://doi.org/10.1191/1464993405ps105oa.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buytaert, W., and Coauthors, 2014: Citizen science in hydrology and water resources: Opportunities for knowledge generation, ecosystem service management, and sustainable development. Front. Earth Sci., 2, 26, https://doi.org/10.3389/feart.2014.00026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chisadza, B., M. J. Tumbare, W. R. Nyabeze, and I. Nhapi, 2015: Linkages between local knowledge drought forecasting indicators and scientific drought forecasting parameters in the Limpopo River Basin in southern Africa. Int. J. Disaster Risk Reduct., 12, 226233, https://doi.org/10.1016/j.ijdrr.2015.01.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Codjoe, S. N. A., G. Owusu, and V. Burkett, 2014: Perception, experience, and indigenous knowledge of climate change and variability: The case of Accra, a sub-Saharan African city. Reg. Environ. Change, 14, 369383, https://doi.org/10.1007/s10113-013-0500-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crane, T. A., C. Roncoli, J. Paz, N. Breuer, K. Broad, K. T. Ingram, and G. Hoogenboom, 2010: Forecast skill and farmers’ skills: Seasonal climate forecasts and agricultural risk management in the southeastern United States. Wea. Climate Soc., 2, 4459, https://doi.org/10.1175/2009WCAS1006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daan, H., and A. H. Murphy, 1982: Subjective probability, forecasting in the Netherlands: Some operational and experimental results. Meteor. Rundsch., 35, 99112.

    • Search Google Scholar
    • Export Citation
  • Deloria, V., 1996: If you think about it, you will see that it is true. ReVision, 18, 3744.

  • Derbile, E. K., A. Abdul-Moomin, and I. Yakubu, 2016: Local knowledge and community-based assessment of environmental change in Ghana. Ghana J. Geogr., 8, 5983.

    • Search Google Scholar
    • Export Citation
  • Fitzpatrick, R. G., C. L. Bain, P. Knippertz, J. H. Marsham, and D. J. Parker, 2015: The West African monsoon onset: A concise comparison of definitions. J. Climate, 28, 86738694, https://doi.org/10.1175/JCLI-D-15-0265.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gbangou, T., M. B. Sylla, O. D. Jimoh, and A. A. Okhimamhe, 2018: Assessment of projected agro-climatic indices over Awun River basin, Nigeria for the late twenty-first century. Climatic Change, 151, 445462, https://doi.org/10.1007/s10584-018-2295-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gbangou, T., F. Ludwig, E. Van Slobbe, L. Hoang, and G. Kranjac-Berisavljevic, 2019: Seasonal variability and predictability of agro-meteorological indices: Tailoring onset of rainy season estimation to meet farmers’ needs in Ghana. Climate Serv., 14, 1930, https://doi.org/10.1016/j.cliser.2019.04.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gearheard, S., M. Pocernich, R. Stewart, J. Sanguya, and H. P. Huntington, 2010: Linking Inuit knowledge and meteorological station observations to understand changing wind patterns at Clyde River, Nunavut. Climatic Change, 100, 267294, https://doi.org/10.1007/s10584-009-9587-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghana Statistical Service, 2012: 2010 population and housing census: Summary report of final results. GSS Accra Rep., 117 pp., https://www.statsghana.gov.gh/gssmain/storage/img/marqueeupdater/Census2010_Summary_report_of_final_results.pdf.

  • Ghana Statistical Service, 2014: 2010 population and housing census. GSS Accra Rep., 81 pp., https://www2.statsghana.gov.gh/docfiles/2010_District_Report/Northern/Kumbungu.pdf.

  • Gilchrist, G., M. Mallory, and F. Merkel, 2005: Can local ecological knowledge contribute to wildlife management? Case studies of migratory birds. Ecol. Soc., 10, 12, https://www.jstor.org/stable/26267752.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Green, D., J. Billy, and A. Tapim, 2010: Indigenous Australians’ knowledge of weather and climate. Climatic Change, 100, 337354, https://doi.org/10.1007/s10584-010-9803-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanssen, A. J., and W. J. Kuipers, 1965: On the relationship between the frequency of rain and various meteorological parameters. K. Ned. Meteor. Inst., 81, 225.

    • Search Google Scholar
    • Export Citation
  • Huntington, H. P., 2000: Using traditional ecological knowledge in science: Methods and applications. Ecol. Appl., 10, 12701274, https://doi.org/10.1890/1051-0761(2000)010[1270:UTEKIS]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ingram, K., M. Roncoli, and P. Kirshen, 2002: Opportunities and constraints for farmers of West Africa to use seasonal precipitation forecasts with Burkina Faso as a case study. Agric. Syst., 74, 331349, https://doi.org/10.1016/S0308-521X(02)00044-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalanda-Joshua, M., C. Ngongondo, L. Chipeta, and F. Mpembeka, 2011: Integrating indigenous knowledge with conventional science: Enhancing localised climate and weather forecasts in Nessa, Mulanje, Malawi. Phys. Chem. Earth, 36, 9961003, https://doi.org/10.1016/j.pce.2011.08.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrence, M., P. Goodwin, M. O’Connor, and D. Önkal, 2006: Judgmental forecasting: A review of progress over the last 25 years. Int. J. Forecasting, 22, 493518, https://doi.org/10.1016/j.ijforecast.2006.03.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lazar, A. N., and Coauthors, 2015: A method to assess migration and adaptation in deltas: A preliminary fast-track assessment. DECCMA Working Paper, 29 pp., http://www.kulima.com/wp-content/uploads/2011/03/Lazar-et-al-15-WP5_Fast_Track_Report_2015.pdf.

  • Lebel, L., 2013: Local knowledge and adaptation to climate change in natural resource-based societies of the Asia-Pacific. Mitigation Adapt. Strategies Global Change, 18, 10571076, https://doi.org/10.1007/s11027-012-9407-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luseno, W. K., J. G. Mcpeak, C. B. Barrett, P. D. Little, and G. Gebru, 2003: Assessing the value of climate forecast information for pastoralists: Evidence from southern Ethiopia and northern Kenya. World Dev., 31, 14771494, https://doi.org/10.1016/S0305-750X(03)00113-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakashima, D., and M. Roué, 2002: Indigenous knowledge, peoples and sustainable practice. Social and Economic Dimensions of Global Environmental Change, P. Timmerman and T. Munn, Eds., Vol. 5, Encyclopedia of Global Environmental Change, John Wiley and Sons, 314324.

  • Nyadzi, E., and Coauthors, 2018: Diagnosing the potential of hydro-climatic information services to support rice farming in northern Ghana. NJAS Wageningen J. Life Sci., 8687, 5163, https://doi.org/10.1016/j.njas.2018.07.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nyadzi, E., E. S. Werners, R. Biesbroek, P. H. Long, W. Franssen, and F. Ludwig, 2019: Verification of seasonal climate forecast toward hydroclimatic information needs of rice farmers in northern Ghana. Wea. Climate Soc., 11, 127142, https://doi.org/10.1175/WCAS-D-17-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olsson, P., and C. Folke, 2001: Local ecological knowledge and institutional dynamics for ecosystem management: A study of Lake Racken watershed, Sweden. Ecosystems, 4, 85104, https://doi.org/10.1007/s100210000061.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orlove, B., C. Roncoli, M. Kabugo, and A. Majugu, 2010: Indigenous climate knowledge in southern Uganda: The multiple components of a dynamic regional system. Climatic Change, 100, 243265, https://doi.org/10.1007/s10584-009-9586-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Owusu, K., and P. Waylen, 2009: Trends in spatio-temporal variability in annual rainfall in Ghana (1951-2000). Weather, 64, 115120, https://doi.org/10.1002/wea.255.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palinkas, L. A., S. M. Horwitz, C. A. Green, J. P. Wisdom, N. Duan, and K. Hoagwood, 2015: Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Adm. Policy Ment. Health, 42, 533544, https://doi.org/10.1007/s10488-013-0528-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pierotti, R., and D. Wildcat, 2000: Traditional ecological knowledge: The third alternative. Ecol. Appl., 10, 13331340, https://doi.org/10.1890/1051-0761(2000)010[1333:TEKTTA]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quinn Patton, M., 2002: Qualitative Research and Evaluation Methods. SAGE, 598 pp.

  • Radeny, M., A. Desalegn, D. Mubiru, F. Kyazze, H. Mahoo, J. Recha, P. Kimeli, and D. Solomon, 2019: Indigenous knowledge for seasonal weather and climate forecasting across East Africa. Climatic Change, 156, 509526, https://doi.org/10.1007/s10584-019-02476-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riedlinger, D., and F. Berkes, 2001: Contributions of traditional knowledge to understanding climate change in the Canadian Arctic. Polar Rec., 37, 315328, https://doi.org/10.1017/S0032247400017058.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roncoli, C., and Coauthors, 2009: From accessing to assessing forecasts: An end-to-end study of participatory climate forecast dissemination in Burkina Faso (West Africa). Climatic Change, 92, 433460, https://doi.org/10.1007/s10584-008-9445-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roudier, P., B. Muller, P. D’Aquino, C. Roncoli, M. A. Soumaré, L. Batté, and B. Sultan, 2014: The role of climate forecasts in smallholder agriculture: Lessons from participatory research in two communities in Senegal. Climate Risk Manage., 2, 4255, https://doi.org/10.1016/j.crm.2014.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santha, S. D., B. Fraunholz, and C. Unnithan, 2010: A societal knowledge management system: Harnessing indigenous wisdom to build sustainable predictors for adaptation to climate change. Int. J. Climate Change, 2, 4964, https://doi.org/10.18848/1835-7156/CGP/v02i01/37293.

    • Search Google Scholar
    • Export Citation
  • Speranza, C. I., B. Kiteme, P. Ambenje, U. Wiesmann, and S. Makali, 2010: Indigenous knowledge related to climate variability and change: Insights from droughts in semi-arid areas of former Makueni District, Kenya. Climatic Change, 100, 295315, https://doi.org/10.1007/s10584-009-9713-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vellinga, M., A. Arribas, and R. Graham, 2013: Seasonal forecasts for regional onset of the West African monsoon. Climate Dyn., 40, 30473070, https://doi.org/10.1007/s00382-012-1520-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wohling, M., 2009: The problem of scale in indigenous knowledge: A perspective from northern Australia. Ecol. Soc., 14, 1, https://doi.org/10.5751/ES-02574-140101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yaro, J. A., 2013: The perception of and adaptation to climate variability/change in Ghana by small-scale and commercial farmers. Reg. Environ. Change, 13, 12591272, https://doi.org/10.1007/s10113-013-0443-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ziervogel, G., 2001: Global science, local problems: Seasonal forecast use in a Basotho village. Open Meeting of the Global Environmental Change Research Community, Rio de Janeiro, Brazil, Brazilian Academy of Sciences.

  • Ziervogel, G., and T. E. Downing, 2004: Stakeholder networks: Improving seasonal climate forecasts. Climatic Change, 65, 73101, https://doi.org/10.1023/B:CLIM.0000037492.18679.9e.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 83 83 1
Full Text Views 190 190 106
PDF Downloads 193 193 117

Harnessing Local Forecasting Knowledge on Weather and Climate in Ghana: Documentation, Skills, and Integration with Scientific Forecasting Knowledge

View More View Less
  • 1 Water System and Global Change Group, Wageningen University and Research, Wageningen, Netherlands
  • 2 University for Development Studies, Tamale, Ghana
© Get Permissions
Restricted access

Abstract

Improved weather and climate forecast information services are important to sustain small-scale crop production in many developing countries. Previous studies recognized the value of integrating local forecasting knowledge (LFK) with scientific forecasting knowledge (SFK) to support farmers’ decision-making. Yet, little work has focused on proper documentation, quality verification, and integration techniques. The skills of local and scientific forecasts were compared, and new integration approaches were derived over the coastal zone of Ghana. LFK indicators were documented, and farmers were trained to collect indicators’ observations and record rainfall in real time using digital tools and rain gauges, respectively, in 2019. Dichotomous forecasts verification metrics were then used to verify the skills of both local and scientific forecasts against rainfall records. Farmers use a diverse set of LKF indicators for both weather and seasonal climate time-scale predictions. LFK indicators are mainly used to predict rainfall occurrence, amount of seasonal rainfall, dry spell occurrence, and onset and cessation of the rainy season. The average skill of a set of LFK indicators in predicting one-day rainfall is higher than individual LFK indicators. Also, the skills of a set of LFK indicators can potentially be higher than the forecasts given by the Ghana Meteorological Agency for the Ada District. The results of the documentation and skills indicate that approaches and methods developed for integrating LFK and SFK can contribute to increasing forecast resolution and skills and reducing recurring tensions between the two knowledge systems. Future research and application of these methods can help improve weather and climate information services in Ghana.

Significance Statement

Most African farmers still rely on local or traditional knowledge on weather and climate forecasts to manage climate variability and change, although there is much effort to reach farmers with the increasing availability of scientific forecasts and data. Exploring the potential of local forecasts and the possible integration with modern forecasts has been suggested as a path to reach out to farmers with more accessible and credible climate information services (CIS). We aimed to understand the contribution of this local knowledge by documenting and investigating its quality. We found that local forecast indicators used by farmers are diverse, and their level of quality can potentially improve the development of CIS, especially when they are combined or integrated with scientific forecasts.

ORCID: 0000-0002-8929-7108.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WCAS-D-20-0012.s1.

Denotes content that is immediately available upon publication as open access.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Gbangou Talardia, talardia.gbangou@wur.nl; talardia.gbangou@gmail.com

Abstract

Improved weather and climate forecast information services are important to sustain small-scale crop production in many developing countries. Previous studies recognized the value of integrating local forecasting knowledge (LFK) with scientific forecasting knowledge (SFK) to support farmers’ decision-making. Yet, little work has focused on proper documentation, quality verification, and integration techniques. The skills of local and scientific forecasts were compared, and new integration approaches were derived over the coastal zone of Ghana. LFK indicators were documented, and farmers were trained to collect indicators’ observations and record rainfall in real time using digital tools and rain gauges, respectively, in 2019. Dichotomous forecasts verification metrics were then used to verify the skills of both local and scientific forecasts against rainfall records. Farmers use a diverse set of LKF indicators for both weather and seasonal climate time-scale predictions. LFK indicators are mainly used to predict rainfall occurrence, amount of seasonal rainfall, dry spell occurrence, and onset and cessation of the rainy season. The average skill of a set of LFK indicators in predicting one-day rainfall is higher than individual LFK indicators. Also, the skills of a set of LFK indicators can potentially be higher than the forecasts given by the Ghana Meteorological Agency for the Ada District. The results of the documentation and skills indicate that approaches and methods developed for integrating LFK and SFK can contribute to increasing forecast resolution and skills and reducing recurring tensions between the two knowledge systems. Future research and application of these methods can help improve weather and climate information services in Ghana.

Significance Statement

Most African farmers still rely on local or traditional knowledge on weather and climate forecasts to manage climate variability and change, although there is much effort to reach farmers with the increasing availability of scientific forecasts and data. Exploring the potential of local forecasts and the possible integration with modern forecasts has been suggested as a path to reach out to farmers with more accessible and credible climate information services (CIS). We aimed to understand the contribution of this local knowledge by documenting and investigating its quality. We found that local forecast indicators used by farmers are diverse, and their level of quality can potentially improve the development of CIS, especially when they are combined or integrated with scientific forecasts.

ORCID: 0000-0002-8929-7108.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WCAS-D-20-0012.s1.

Denotes content that is immediately available upon publication as open access.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Gbangou Talardia, talardia.gbangou@wur.nl; talardia.gbangou@gmail.com

Supplementary Materials

    • Supplemental Materials (PDF 371 KB)
Save