Volume 76, Issue 5
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645


Irrigation for rice cultivation increases the production of , the main vector of malaria in Mali. Mosquito abundance is highly variable across villages and seasons. We examined whether rice cultivation patterns mapped using remotely sensed imagery can account for some of this variance. We collected entomologic data and mapped land use around 18 villages in the two cropping seasons during two years. Land use classification accuracy ranged between 70% and 86%. The area of young rice explained 86% of the inter-village variability in abundance in August before the peak in malaria transmission. Estimating rice in a 900-meter buffer area around the villages resulted in the best correlation with mosquito abundance, larger buffer areas were optimum in the October and dry season models. The quantification of the relationship between abundance and rice cultivation could have management applications that merit further study.


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  1. Dale P, Ritchie S, Territo B, Morris C, Muhar A, Kay B, 1998. An overview of remote sensing and GIS for surveillance of mosquito vector habitats and risk assessment. J Vector Ecol 23 : 54–61. [Google Scholar]
  2. Hay S, Packer M, Rogers D, 1997. The impact of remote sensing on the study and control of invertebrate intermediate hosts and vectors for disease. Int J Remote Sens 18 : 2899–2930. [Google Scholar]
  3. Hay S, Snow R, Rogers D, 1998. From predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives. Parasitol Today 14 : 306–313. [Google Scholar]
  4. Hay S, Omumbo J, Craig M, Snow R, 2000. Earth observation, geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa. Hay SI, Randolph SE, Rogers DF, eds. Advances in Parasitology, Remote Sensing and Geographic Information Systems in Epidemiology. London: Academic Press, 173–215.
  5. Kitron U, 1998. Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. J Med Entomol 35 : 435–445. [Google Scholar]
  6. Liebhold A, Rossi R, Kemp W, 1993. Geostatistics and geographic information-systems in applied insect ecology. Annu Rev Entomol 38 : 303–327. [Google Scholar]
  7. Thomson M, Connor S, 2000. Environmental information systems for the control of arthropod vectors of disease. Med Vet Entomol 14 : 227–244. [Google Scholar]
  8. Rogers D, Randolph SE, Snow RW, Hay SI, 2002. Satellite imagery in the study and forecast of malaria. Nature 415 : 710–715. [Google Scholar]
  9. Omumbo JA, Hay SI, Snow RW, Tatem AJ, Rogers DJ, 2005. Modelling malaria risk in east Africa at high-spatial resolution. Trop Med Int Health 10 : 557–566. [Google Scholar]
  10. Diuk-Wasser MA, Toure MB, Dolo G, Bagayoko M, Sogoba N, Traore SF, Manoukis N, Taylor CE, 2005. Vector abundance and malaria transmission in rice-growing villages in Mali. Am J Trop Med Hyg 72 : 725–731. [Google Scholar]
  11. Diuk-Wasser MA, Dolo G, Bagayoko M, Sogoba N, Toure MB, Moghaddam M, Manoukis N, Rian S, Traore SF, Taylor CE, 2006. Patterns of irrigated rice growth and malaria vector breeding in Mali using multi-temporal ERS-2 synthetic aperture radar. Int J Remote Sens 27 : 535–548. [Google Scholar]
  12. Dolo G, Briet OJT, Dao A, Traore SF, Bouare M, Sogoba N, Niare O, Bagayogo M, Sangare D, Teuscher T, Toure YT, 2004. Malaria transmission in relation to rice cultivation in the irrigated Sahel of Mali. Acta Trop 89 : 147–159. [Google Scholar]
  13. Klinkenberg E, Takken W, Huibers F, Touré YT, 2003. The phenology of malaria mosquitoes in irrigated rice fields in Mali. Acta Trop 85 : 71–82. [Google Scholar]
  14. Sissoko MS, Dicko A, Briet OJ, Sissoko M, Sagara I, Keita HD, Sogoba M, Rogier C, Toure YT, Doumbo OK, 2004. Malaria incidence in relation to rice cultivation in the irrigated Sahel of Mali. Acta Trop 89 : 161–170. [Google Scholar]
  15. Service MW, 1993. Mosquito Ecology: Field Sampling Methods. New York: Elsevier Applied Science.
  16. U.S. Geological Service and the National Aeronautics and Space Administration, 2006. Earth Observing Systems Data Gateway: Land Processes Distributed Active Archive Center. Reston, VA: U.S. Geological Service and Washington, DC: National Aeronautics and Space Administration
  17. Diuk-Wasser MA, Bagayoko M, Sogoba N, Dolo G, Toure MB, Traore SF, Taylor CE, 2004. Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data. Int J Remote Sens 25 : 359–376. [Google Scholar]
  18. Lillesand T, Kiefer R, 1994. Remote Sensing and Image Interpretation. New York: John Wiley & Sons.
  19. Landsat Project Science Office, 2006. Landsat 7 Science Data Users Handbook. Greenbelt, MD: National Aeronautics and Space Administration.
  20. Chavez PS, 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24 : 459–479. [Google Scholar]
  21. Landis JR, Koch GG, 1977. Measurement of observer agreement for categorical data. Biometrics 33 : 159–174. [Google Scholar]
  22. Longley P, Goodchild M, Maguire D, Rhind D, 1999. Geographical Information Systems: Principles, Techniques, Management and Applications. New York: Wiley & Sons.
  23. Hobbs JH, Lowe RE, Schreck CE, 1974. Studies of flight range and survival of Anopheles albimanus Wiedemann in El Salvador. 1. Dispersal and survival during dry season. Mosq News 34 : 389–393. [Google Scholar]
  24. Touré YT, Dolo G, Petrarca V, Traoré SF, Bouare M, Dao A, Carnahan J, Taylor CE, 1998. Mark-release-recapture experiments with Anopheles gambiae sl in Banambani Village, Mali, to determine population size and structure. Med Vet Entomol 12 : 74–83. [Google Scholar]
  25. Costantini C, Li SG, DellaTorre A, Sagnon N, Coluzzi M, Taylor CE, 1996. Density, survival and dispersal of Anopheles gambiae complex mosquitoes in a West African Sudan savanna village. Med Vet Entomol 10 : 203–219. [Google Scholar]
  26. Sokal RR, Rohlf FJ, 1995. Biometry: The Principles and Practice of Statistics in Biological Research. New York: W. H. Freeman.
  27. Burnham KP, Anderson DR, 2004. Multimodel inference—understanding AIC and BIC in model selection. Sociol Methods Res 33 : 261–304. [Google Scholar]
  28. Van Niel TG, McVicar TR, 2004. Current and potential uses of optical remote sensing in rice-based irrigation systems: a review. Aust J Agric Res 55 : 155–185. [Google Scholar]
  29. Wood B, Beck L, Washino R, Palchick S, Sebesta P, 1991. Spectral and spatial characterization of rice field mosquito habitat. Int J Remote Sens 12 : 621–626. [Google Scholar]
  30. Wood B, Washino R, Beck L, Hibbard K, Pitcairn M, Roberts D, Rejmankova E, Paris J, Hacker C, Salute J, Sebesta P, Legters L, 1991. Distinguishing high and low anopheline-producing rice fields using remote-sensing and GIS technologies. Prev Vet Med 11 : 277–288. [Google Scholar]
  31. Wood BL, Beck LR, Washino RK, Hibbard KA, Salute JS, 1992. Estimating high mosquito-producing rice fields using spectral and spatial data. Int J Remote Sens 13 : 2813–2826. [Google Scholar]
  32. Rodriguez A, Rodriguez M, Hernandez J, Dister S, Beck L, Rejmankova E, Roberts D, 1996. Landscape surrounding human settlements and Anopheles albimanus (Diptera: Culicidae) abundance in southern Chiapas, Mexico. J Med Entomol 33 : 39–48. [Google Scholar]
  33. Beck L, Rodriguez M, Dister S, Rodriguez A, Rejmankova E, Ulloa A, Meza R, Roberts D, Paris J, Spanner M, Washino R, Hacker C, Legters L, 1994. Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. Am J Trop Med Hyg 51 : 271–280. [Google Scholar]
  34. Beck L, Rodriguez M, Dister S, Rodriguez A, Washino R, Roberts D, Spanner M, 1997. Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico. Am J Trop Med Hyg 56 : 99–106. [Google Scholar]
  35. Kitron U, Otieno L, Hungerford L, Odulaja A, Brigham W, Okello O, Joselyn M, Mohamedahmed M, Cook E, 1996. Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS. J Anim Ecol 65 : 371–380. [Google Scholar]
  36. Thomas C, Lindsay S, 2000. Local-scale variation in malaria infection amongst rural Gambian children estimated by satellite remote sensing. Trans R Soc Trop Med Hyg 94 : 159–163. [Google Scholar]
  37. Jacob BG, Arheart KL, Griffith DA, Mbogo CM, Githeko AK, Regens JL, Githure JI, Novak R, Beier JC, 2005. Evaluation of environmental data for identification of Anopheles (Diptera: Culicidae) aquatic larval habitats in Kisumu and Malindi, Kenya. J Med Entomol 42 : 751–755. [Google Scholar]
  38. Jacob BG, Regens JL, Mbogo CM, Githeko AK, Keating J, Swalm CM, Gunter JT, Githure JI, Beier JC, 2003. Occurrence and distribution of Anopheles (Diptera: Culicidae) larval habitats on land cover change sites in urban Kisumu and urban Malindi, Kenya. J Med Entomol 40 : 777–784. [Google Scholar]
  39. Gillies M, 1968. The Anophelinae of Africa South of the Sahara (Ethiopian Zoogeographical Region). Johannesburg: South African Institute for Medical Research.
  40. Thomson M, Connor S, Quinones M, Jawara M, Todd J, Greenwood B, 1995. Movement of Anopheles gambiae Sl malaria vectors between villages in the Gambia. Med Vet Entomol 9 : 413–419. [Google Scholar]
  41. Carter R, Mendis K, Roberts D, 2000. Spatial targeting of interventions against malaria. Bull World Health Organ 78 : 1401–1411. [Google Scholar]
  42. Briët O, Dossou-Yovo J, Akodo E, van de Giesen N, Teuscher T, 2003. The relationship between Anopheles gambiae density and rice cultivation in the savannah zone and forest zone of Côte d’Ivoire. Trop Med Int Health 8 : 439–448. [Google Scholar]
  43. Ijumba J, Lindsay S, 2001. Impact of irrigation on malaria in Africa: paddies paradox. Med Vet Entomol 15 : 1–11. [Google Scholar]
  44. Keiser J, Singer BH, Utzinger J, 2005. Reducing the burden of malaria in different eco-epidemiological settings with environmental management: a systematic review. Lancet Infect Dis 5 : 695–708. [Google Scholar]
  45. Keiser J, de Castro MC, Maltese MF, Bos R, Tanner M, Singer BH, Utzinger J, 2005. Effect of irrigation and large dams on the burden of malaria on a global and regional scale. Am J Trop Med Hyg 72 : 392–406. [Google Scholar]

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  • Received : 04 Dec 2006
  • Accepted : 23 Jan 2007

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