Location-specific weather and climate information together with related advisory services are one of the crucial drivers of development in the 21^st century particularly in Sub-Saharan Africa. However, there still exist significant gaps in provision of weather and climate information at scales that effectively address the needs of local people for instance; local farmers and pastoralists. This therefore force local people to rely on indigenous knowledge (IK), to observe and forecast weather conditions. This situation does not exclude Uganda, and thus the current study assessed the efficacy of integrating indigenous knowledge systems into modern weather observational instruments in order to boost and act as a backup mechanism for modern weather observational instruments to increase accuracy and wide coverage of weather observations within Uganda. Results indicated that, respondents across the two pilot sites use a combination of plants, animals, insects, and human behaviours, meteorological and astrological indicators to observe and predict local prevailing weather conditions. Majority of the respondents particularly in Masaka district, believed that the use of indigenous knowledge to observe weather and climatic events is very reliable compared to their counterparts in Entebbe Municipality. Therefore, the integration of indigenous knowledge system in scientific weather observations is very vital.

Lightning has received a lot of attention in scientific literature during the recent decade, not only because it is an impressive atmospheric phenomenon but also its associations with severe storms that cause unprecedented damages to agriculture, electric power networks, property, and life. This study assessed the Spatio-temporal characteristics of lightning occurrence with elevation in Uganda using lightning flash and elevation datasets for a period of fifteen years (1998-2013). Datasets used in this study included daily lightning flashes as captured by Lightning Imaging Sensor (LIS) aboard on Tropical Rainfall Measurement Mission (TRMM) satellite and elevation data in form of Digital Elevation Model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM). Spatio-temporal results indicated that ~80% of areas with an elevation that ranges from 800-1200 m above mean sea level (masl) in Uganda had severe lightning occurrences and ~20% of areas with an elevation greater than 1200 m (masl) had severe lightning occurrences. The country received an enhanced number of lighting events with the highest number in 1999. Subsequently, a reduced trend was observed from 2002 to 2007 followed by an increment in the number of lightning events in (2010, 2011, 2012, and 2013). The intensity of the events decreased gradually though two peaks were observed, (1998-2001) and (2010-2013). Furthermore, results indicate escalations in the frequency and duration of lightning events from 60 times in 1998 to approximately 200 times in 2013 and from 1000 microseconds in 1998 to more than 2000 microseconds in 2013. Generally, the country experienced an enhanced increase in lighting occurrences over the study period which therefore calls for urgent actions to combat the root cause and also provide effective measures to reduce the impacts of lightning strikes.

Drought occurrences in Rakai district take a strange model and it has been rampantly increasing causing reduced income levels for farmers, reduced farm yields, increased food insecurity and migration, wetland degradation, illness and loss of livestock. The purpose of this study was to investigate past and future characteristics of drought due to climate change in Rakai district. Datasets used include dynamically downscaled daily precipitation and temperature data from Coordinated Regional Climate Downscaling Experiment (CORDEX) at 0.44°×0.44° resolution over the Africa domain. R software (Climpact2 package), was used to generate SPI values, Mann Kendall trend test and Inverse Distance Weighting methods were used to examine temporal and spatial drought characteristics respectively. Results depicted more extreme and severe drought conditions for SPI12 under historical compared to SPI3,Kakuto, Kibanda and Lwanda sub counties were the most drought hot spot areas, positive trends of drought patterns for both time scales were observed, though only significant under SPI12. Projected results revealed extreme and severe drought conditions will be observed under RCP8.5 SPI12, and the least will be under RCP8.5 SPI3 and SPI12. Results further reveal that Kakuto, Kibanda, Kiziba, Kacheera, Kyalulangira, Ddwaniro and Lwanda sub counties will be the most drought hot spot sub counties across all time scales. Generally projected results reveals that the district will experience more drought conditions under RCP8.5 compared to RCP4.5 for time scale SPI12 and therefore urgent actions are needed.

Genomic selection (GS) can accelerate variety improvement when training set (TS) size, and its relationship with the breeding set (BS) are optimized for prediction accuracies (PA) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and BS was the remainder whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTS) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW resistance traits, and, for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and, these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant since a positive correlation (R=0.92***) between TS size and PAs was observed for RBTS and, for the PBTS, it was negative (R=0.44**). This study pioneers the use of GS for maize resistance to insect pests in sub-Saharan Africa.