Agronomic and Economic Evaluation of Wheat-Chickpea Double Cropping on the Vertisol of Takusa, North Western Ethiopia

Takusa district in north Gondar zone has a high potential for double cropping per one growing season. Farmers in the area, however, do not practice double cropping so far, a reflection of lack of research outputs that addressed its feasibility. The objective of this study was, therefore, formulated around the need to evaluate the technical and economic feasibility of double cropping in the area using wheat-chickpea. Experiments were established at Takusa district during the 2015 main cropping season. The trials were laid down in factorial arrangement of randomized complete block design (RCBD) with three replications. Three bread wheat varieties (Senkegna, Tay and Dinknesh) and two chickpea varieties (Habru and Natoli) were used. The combined data showed that, wheat variety Dinknesh took the shortest days to mature (81 days) compared to Senkegna and Tay (97 days each) varieties. The highest thousand seed weight and grain yield was observed on variety Denknesh and has significance difference at P<0.05 with the other two varieties. Sole planting of Natoli (2926 kg/ha) and Habru (2103kg/ha) chickpea varieties gave relatively higher yield when compared with their respective double cropping combination. The Marginal rate of return (MRR) result showed that double cropping Natoli chickpea variety with Denekinesh wheat variety had 104% MRR. The land equivalent ration demonstrated double cropping rewards to a maximum of 1.99, implying the yield and benefit maximization per unit area per season. The highest grain yield in the double cropping system was obtained with Dinknesh wheat variety (2709kg/ha) double cropped with Natoli chickpea variety (2562 Kg/ha) and this combination could be recommended for similar agroecologies

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

An Adaptive Informative Path Planning Algorithm for Real-time Air Quality Monitoring Using UAVs

Environmental monitoring is a heavily data driven task where data sample efficiency is paramount due to the shear volumes of gathered data. In particular, air monitoring strongly depends on sensor location. Since the recent past, Unmanned Aerial Vehicles (UAVs) present themselves as a prospective solution for flexible and better air quality data gathering. In this paper, we present a novel adaptive Informative Path Planning (IPP) approach that enables UAVs navigate through a sample utility map based on adaptive Statistical Gas Distribution Models (GDM) for efficient surveying. The presented adaptive IPP approach maximises the amount of gathered information per mission within the system constraints in known and unknown environments with near optimal performance. The effectiveness of the algorithm is tested through extensive simulation. The results showed high quality sample collection, low computational costs and better model prediction metrics against other surveying strategies. Although framed in an air environmental monitoring context, the developed solution can be used for any generic IPP problem by adapting the sample utility map to the particular application

Robot vision: obstacle-avoidance techniques for unmanned aerial vehicles

In this article, a vision-based technique for obstacle avoidance and target identification is combined with haptic feedback to develop a new teleoperated navigation system for underactuated aerial vehicles in unknown environments. A three-dimensional (3-D) map of the surrounding environment is built by matching the keypoints among several images, which are acquired by an onboard camera and stored in a buffer together with the corresponding estimated odometry. Hence, based on the 3-D map, a visual identification algorithm is employed to localize both obstacles and the desired target to build a virtual field accordingly. A bilateral control system has been developed such that an operator can safely navigate in an unknown environment and perceive it by means of a vision-based haptic force-feedback device. Experimental tests in an indoor environment verify the effectiveness of the proposed teleoperated control