Spatial Comparison and Quality Check of Farmer-recorded Daily Rainfall Data; A Case of Nyakach and Soin-sigowett, Kenya
Science Journal of Applied Mathematics and Statistics
Volume 8, Issue 1, February 2020, Pages: 11-18
Received: Dec. 11, 2019; Accepted: Jan. 2, 2020; Published: Jan. 9, 2020
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Authors
Mawora Thomas Mwakudisa, Department of Statistics and Actuarial Science, School of Mathematics, Statistics and Actuarial Science, Maseno University, Maseno, Kenya
Edgar Ouko Otumba, Department of Statistics and Actuarial Science, School of Mathematics, Statistics and Actuarial Science, Maseno University, Maseno, Kenya
Joyce Akinyi Otieno, Department of Statistics and Actuarial Science, School of Mathematics, Statistics and Actuarial Science, Maseno University, Maseno, Kenya
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Abstract
Small scale farming is currently still heavily dependent on rainfall in developing nations. With the challenge of climate change, many innovations are proposed to help the farmers mitigate and adapt. The use of historical data provides a starting point in development of decision support tools for them. However, most climate data are not local, but far from the farmers. Thus, the challenge of representability of the data is questioned. In order to use the decision support tools with farmers at Nyakach and Soin-Sigowett, Kenya, historical data was used from a synoptic station 20 km away. The locals felt it was not representative enough, hence the need to look for more local data. In 2014, a CCAFS project empowered 100 farmers from the region with low cost rain gauges to collect and record their own data for use in decision support tools. In this paper, we look at the quality of the data comparing it to the KMS data. Line graphs were used to compare the total seasonal rain for more than 30 years with the farmers perception. In addition, pairwise t-tests have been used to compare difference in farmers recorded rain to the value at the synoptic station. Data from volunteer stations have also been used to confirm the validity of the spatial difference in the data. The results showed that quality of the farmers data is adequate for use. Further, data from farmers deviated from the main synoptic station half of the time. The results clearly show that there is need to allow locals collect their own data to help capture the spatial differences in climate. The farmers recorded data was good quality hence can be used in decision support tools to help them adapt to possible climate change.
Keywords
Rainfall, Quality, Farmers Data, T-test, Climate Change, Famers Perception
To cite this article
Mawora Thomas Mwakudisa, Edgar Ouko Otumba, Joyce Akinyi Otieno, Spatial Comparison and Quality Check of Farmer-recorded Daily Rainfall Data; A Case of Nyakach and Soin-sigowett, Kenya, Science Journal of Applied Mathematics and Statistics. Vol. 8, No. 1, 2020, pp. 11-18. doi: 10.11648/j.sjams.20200801.12
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
APSIM. (2019, 12 3). APSIM. Retrieved from APSIM: https://www.apsim.info/
[2]
Ashouri, H., Hsu, K.-L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., Prat, O. P. (2015). PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96 (1), 69-83.
[3]
Atta-Krah, A., & Francis, P. A. (1987). The role of on-farm trails in the evaluation of composite technologies: The case of alley farming in Southern Nigeria. Agricultural Systems, 23 (2), 133-152.
[4]
Behnke, R., Vavrus, S., Allstadt, A., Albright, T., Thogmartin, W. E., & Radeloff, V. C. (2016). Evaluation of downscaled, gridded climate data for the conterminous United States. Ecological Applications, 26 (5), 1338-1351.
[5]
CCAFS. (2019, 12 3). CCAFS. Retrieved from CCAFS: https://ccafs.cgiar.org/regions/east-africa
[6]
Dietrich, H., Wolf, T., Kawohl, T., Wehberg, J., Kandler, G., Mette, T.,... Bohner, J. (2019). Temporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI). Annals of Forest Science, 76 (1), 6.
[7]
Gadgil, M., Berkes, F., & Folke, C. (1993). Indigenous Knowledge for Biodiversity Conservation. Ambio, 22 (no. 2/3), 151-56. Retrieved from www.jstor.org/stable/4314060
[8]
Jones, D. A., & Wang, W. F. (2009). High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal, 58 (4), 233.
[9]
KMS. (2019, 12 3). KMS. Retrieved from KMS: http://www.meteo.go.ke/
[10]
Kyei-Mensah, C., Kyerematen, R., & Adu-Acheampong, S. (2019). Impact of Rainfall Variability on Crop Production within the Worobong Ecological Area of Fanteakwa District, Ghana. Advances in Agriculture, 1-5.
[11]
Lightfoot, C., & Barker, R. (1988). On-farm trials: a survey of methods. Agricultural Administration and Extension, 30 (1), 15-23.
[12]
Luschei, E. C., Wychen, L. R., Maxwell, B. D., Bussan, A. J., Buschena, D., & Goodman, D. (2001). Implementing and conducting on-farm weed research with the use of GPS. Weed Science, 49 (4), 536-542.
[13]
Rockström, J., Kaumbutho, P., Mwalley, J., Nzabi, A. W., Temesgen, M., Mawenya, L., Damgaard-Larsen, S. (2009). Conservation farming strategies in East and Southern Africa: yields and rain water productivity from on-farm action research. Soil and Tillage Research, 103, 23-32.
[14]
Shilenje, Z. W., & Ogwang, B. A. (2015). The role of Kenya meteorological service in weather early warning in Kenya. International Journal of Atmospheric Sciences, 2015, 1-5.
[15]
Stabinsky, D., & Brush, S. B. (1996). Valuing local knowledge: indigenous people and intellectual property rights.
[16]
Stern, R., & Coe, R. (1984). A model fitting analysis of daily rainfall data. Journal of the Royal Statistical Society: Series A (General), 1-18.
[17]
Stern, R., & Cooper, P. (2011). Assessing climate risk and climate change using rainfall data--a case study from Zambia. Experimental Agriculture, 241-266.
[18]
Wang, T., Hamann, A., Spittlehouse, D. L., & Murdock, T. Q. (2012). ClimateWNA—high-resolution spatial climate data for western North America. Journal of Applied Meteorology and Climatology, 51 (1), 16-29.
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