Landscape Vegetation Productivity Influences Population Dynamics of Key Pests in Small Avocado Farms in Kenya

Abstract

<jats:p>Avocado (Persea americana Mill.) production contributes to the economic growth of East Africa. However, poor fruit quality caused by infestations of tephritid fruit flies (Tephritidae) and the false codling moth, Thaumatotibia leucotreta (Meyrick), hampers access to lucrative export markets. Remote sensing and spatial analysis are increasingly applied to crop pest studies to develop sustainable and cost-effective control strategies. In this study, we assessed pest abundance in Muranga, Kenya, across three vegetation productivity classes, viz., low, medium and high, which were estimated using the normalised difference vegetation index at a landscape scale. Population densities of the oriental fruit fly, Bactrocera dorsalis (Hendel) and T. leucotreta in avocado farms were estimated through specific baited traps and fruit rearing. The population density of T. leucotreta varied across the vegetation productivity classes throughout the study period, although not significantly. Meanwhile, B. dorsalis showed a clear trend of decrease over time and was significantly lower in high vegetation productivity class compared to low and medium classes. Ceratitis cosyra (Walker) was the most abundant pest reared from fruit with few associated parasitoids, Pachycrepoideus vindemmiae (Rondani) and Toxeumorpha nigricola (Ferriere).</jats:p>

Sophisticated Cropping Patterns Mapping in Kenya Using Vegetation Indices and Phenology from Sentinel-2 and Sentinel-1 Radar Backscatter Data

“…This is a robust and reliable classification accuracy assessment method compared to the traditional classification confusion matrix that considers the number of mapped instances of each class. Understanding the variability of cropping patterns and non-croplands can also contribute to the sustainable management of crop insect pests and pollinators that can be influenced by landscape structure [40].…”

“…We selected three sub-counties in Murang'a county (Kandara, Maragwa, and Gatanga), which are dominant for avocado production [39]. Mapping of the cropping patterns in this region can be utilized to improve the understanding of the contribution of each cropping pattern in the abundance of avocado pests and pollinators across different vegetation intensity for sustainable management [40]. Figure 2 illustrates the methodological approach used to map the cropping patterns in the study area using different remotely sensed variables extracted from S2 and S1 imageries.…”

“…A stratified random sampling approach was used for the field data collection, using three vegetation intensity classes (i.e. low, medium, and high) as strata at a landscape scale using the K-means clustering method [40]. The K-means clustering method groups similar data points together and uncovers underlying patterns [55].…”

Cropping Pattern Mapping in an Agro-Natural Heterogeneous Landscape Using Sentinel-2 and Sentinel-1 Satellite Datasets

“…Three sub-counties suitable for avocado faming in Murang'a County i.e., Kandara, Maragua, and Gatanga were selected. Mapping of the cropping patterns in this region can be utilized, for instance, to improve the understanding of the contribution of each cropping pattern to the abundance of avocado pests and pollinators across different vegetation intensities for sustainable management [50]. Location of the study area comprising the three sub-counties, i.e., Kandara, Maragua, and Gatanga, in Murang'a County, Kenya with overlaid field reference points and surface features.…”

“…A stratified random sampling technique was used for the collection of the field data (i.e., ground-truthing), within three vegetation intensity classes (i.e., low, medium, and high) as strata at a landscape scale that were generated using the K-means clustering method [70] and the NDVI composite image. The detailed methods and results of the three vegetation intensity classes can be found in Toukem et al [50]. Stratified random sampling was employed to provide a comprehensive representation of the cropping patterns within the different vegetation intensities in the study area.…”