Irrigation energy consumption in a tropical lowland rice field

Available and limited water resources are seeing high demand from many sectors such as agriculture, industry and domestic households. In water allocation, the quantification of water in terms of energy used for water supply is more appropriate in terms of economic aspects. This study was undertaken to assess the embodied in irrigation for a lowland tropical rice production system. The irrigation energy requirements for the off and main cropping seasons were estimated based on crop water requirements at different cropping stages. Experimental results indicate that there are significant differences among the irrigation energy requirements of the various cropping stages, with the highest values of 4625.34 MJ ha-1 and 3843.93 MJ ha-1 observed for the mid-season stage in the off-season and main season, respectively. The off-season irrigation energy requirements for the rice variety MR 219 were found to be 32.6%, 26.8%, 20.3% and 271.7% higher than the mainseason for the initial stage, crop development stage, mid-season stage and late-s eason stage, respectively. There is a significant difference in irrigation energy requirement among the seasons and crop growth stages. Crop Water Use Efficiency (CWUE) in the off season (0.26 kg/m3 ) is higher than the CWUE in the main season(0.23kg/m3) which is attributed to better water management and yields during the off season due to water shortage. Irrigation energy productivity (IEP) of rice in the off season and the main season at Tanjong Karang is 0.26 and 0.29, respectively. In terms of irrigation energy spent, the main season shows better performance where a part of irrigation requirement is met by rainfall. Irrigation energy, CWUE and IEP can be used in decision making on the profitability of growing rice in different seasons

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Virus genomes reveal factors that spread and sustained the Ebola epidemic

The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics