In eastern Africa, Maize lethal necrosis (MLN) is caused by the co-infection of maize plants with Maize chlorotic mottle virus (MCMV) (Tombusviridae: Machlomovirus) and Sugarcane mosaic virus (SCMV) (Potyviridae: Potyvirus). With the disease being new to Africa, minimal effective management strategies exist against it. This study examined the potential of 10 fungal isolates to colonize maize plants and induce resistance against MCMV and SCMV. Maize seeds were soaked in fungal inoculum, sown and evaluated for endophytic colonization. Fungus-treated plants were challenge-inoculated with SCMV and/or MCMV to assess the effects of fungal isolates on the viruses in terms of incidence, severity and virus titers over time. Isolates of Trichoderma harzianum, Trichoderma atroviride and Hypocrea lixii colonized different plant sections. All plants singly or dually-inoculated with SCMV and MCMV tested positive for the viruses by reverse transcription-polymerase chain reaction (RT-PCR). Maize plants inoculated by T. harzianum and Metarhizium. anisopliae resulted in up to 1.4 and 2.7-fold reduced SCMV severity and titer levels, respectively, over the controls but had no significant effect on MCMV. The results show that both T. harzianum and M. anisopliae are potential candidates for inducing resistance against SCMV and can be used for the integrated management of MLN.

Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer’s accuracy and UA: user’s accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10–20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.