Journal of Resources and Ecology ›› 2018, Vol. 9 ›› Issue (3): 273-280.doi: 10.5814/j.issn.1674-764x.2018.03.007

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Application of Multi-Temporal MODIS NDVI Data to Assess Practiced Maize Calendars in Rwanda

MUGABOWINDEKWE Maurice1, *, MUYIZERE Aline2, LI Fadong3, 4, QIAO Yunfeng3, 4, RWANYIZIRI Gaspard1, 5   

  1. 1. Centre for Geographic Information Systems and Remote Sensing, College of Science and Technology, University of Rwanda, 3900 Kigali, Rwanda;
    2. Department of Environmental Management, Bright Green Nature Ltd, 2916 Kigali, Rwanda;
    3. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China;
    5. Department of Geography and Urban Planning, College of Science and Technology, University of Rwanda, 3900 Kigali, Rwanda
  • Received:2017-11-23 Revised:2018-04-05 Online:2018-05-30 Published:2018-06-06
  • Contact: Maurice Mugabowindekwe, E-mail: mugabomaurice@gmail.com
  • Supported by:
    The Natural Science Foundation of China (41561144011, 41761144053); International Partnership Program of Chinese Academy of Sciences (121311KYSB20170004).

Abstract: Crop calendar is an important tool providing relevant information on crops cycles in a specific area for effective agricultural management. Crop calendars vary in different areas given dissimilarities in agro-ecosystems’ characteristics. This research used multi-temporal MODIS NDVI stratification to assess differences in practiced maize crop calendars in various areas of Rwanda. Four (4) sample NDVI strata dominated by agriculture were purposively chosen, and 433 local farmers were randomly selected from the strata for interviews. The collected information helped to know about their maize planting as well as harvesting dates in order to generate maize calendars per NDVI strata. The generated crop calendars were later classified using k-means unsupervised classification, and produced 4 groupings of practiced maize calendars irrespective of NDVI strata. ANOVA results revealed significant differences between both the generated maize crop calendars by NDVI strata and the practiced crop calendars irrespective of NDVI strata, at p = 0.05. Moreover, chi-square tests and t-tests revealed not only a significant relationship between maize calendars and number of crop growing seasons, but also a significant relationship between maize calendars and NDVI strata, at p = 0.05. Finally, findings of this research contrasted the present conviction that there exist a single general maize calendar all over the country. Instead, the results were in accordance with the fact that Rwanda agro-ecosystems differ from East to West in terms of, mainly, altitude and rainfall patterns variations.

Key words: crop calendar, maize growing season, NDVI data stratification, Rwanda