Evaluation Of Normalized Difference Vegetation Index Of Common Vegetation Habitats For Monitoring Rodent Population And Outbreaks In Isimani, Tanzania

EXTENDED ABSTRACT

Rodent pest outbreaks are major concern for agriculture in Africa and Tanzania in particular, especially in drier areas such as arid and semi-arid agro ecosystems. This is due to the fact that, if the problem cannot be treated with seriousness it deserves, 80% of the potential harvest may be lost. Crop losses occur at all stages (i.e. field to market). However, higher losses occur at the field/harvest and storage where rodents play a major role. Severe rodent outbreaks have been reported in many areas in Tanzania e.g. the recent outbreak reported by farmers in Isimani division, Iringa, Tanzania. Based on literature, it is estimated that rodents cause 15% of the total crop pre and post-harvest losses. The problem is compounded by unpredictable rodent pest outbreaks, late control actions, and lack of adequate interventions guided by ecologically based rodent management strategies. Recently, efforts have been taken to develop an ecologically based rodent management strategy which requires knowledge about the pest species' ecology in order to reduce the damage experienced by farmers. However, this is constrained by the limited knowledge about rodent populations on individual farms to allow smarter approaches for control of rodent outbreaks. Structural characterization and mapping of vegetation habitats could contribute knowledge about rodent populations on individual farms. Such studies may include describing and measuring vegetation and habitat structural component using geo spatial and statistical approaches (i.e. life form and cover types, terrain, soil and management practices) across various landscapes in different seasons and their influence to small mammal abundance. Recently, it has been reported that remote sensing derived vegetation indices could be used to explain rodent pest abundance at fine scale. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have been reported to correlate well with vegetation productivity (i.e. biomass), forage quality and quantity iii (i.e. food) at moderate resolutions over a range of spatial-temporal scales. Such indices have been reported to be vital tools for studying vegetation habitat characteristics (i.e. vegetation cover) and their association with rodents in space and time. Therefore, the current study was envisaged to evaluate the potential of NDVI of common vegetation habitats derived from satellite remote sensing data for monitoring rodent population dynamics and outbreaks in order to contribute knowledge for refining ecologically based rodent management strategies. More specifically, the study was carried out to i) characterise and spatially map the vegetation habitats associated with small mammal abundance in smallholder farming agro-ecosystem; ii) determine the Normalised Difference Vegetation Index (NDVI) of common vegetation habitats and rainfall patterns in the study area; and iii) establish a relationship between NDVI of the common vegetation habitats and small mammals distribution and abundance in space and time. The study was conducted between September 2015 and June 2016 in Isimani Division, Iringa Tanzania. A combination of field survey and Geospatial approach including the use of Multi temporal Landsat 8 (Operational Land Imager (OLI)) images were applied to identify and map the vegetation habitats and estimation of NDVI. The NDVI of common vegetation habitats and rainfall patterns were also explored. Small mammals were trapped in the mapped vegetation habitat units, and counted for abundances. In total, nine main types of vegetation habitats were investigated. A total of 597 small mammals, potentially related to major rodent pests were trapped. Different levels of scales and resolutions were considered. Linear regression analysis was employed to establish the important habitat characteristics (predictor variables) for relating rodent abundance with rainfall and NDVI. Linear regression analysis was also used to clarify the relationships between ground measured rodents and predicted rodent abundance from rainfall and NDVI across seasons, and calculation of the Pearson correlation coefficient (r) at P ≤ 0.05.