The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a public health emergency that turns the year 2020–2021 into annus horribilis for millions of people across international boundaries. The interspecies transmission of this zoonotic virus and mutated variants are aided by exposure dynamics of infected aerosols, fomites and intermediate reservoirs. The spike in the first, second and third waves of coronavirus confirms that herd immunity is not yet reached and everyone including livestock is still vulnerable to the infection. Of serious concern are the communitarian nature of agrarians in the livestock sector, aerogenous spread of the virus and attendant cytocidal effect in permissive cells following activation of pathogen recognition receptors, replication cycles, virulent mutations, seasonal spike in infection rates, flurry of reinfections and excess mortalities that can affect animal welfare and food security. As the capacity to either resist or be susceptible to infection is influenced by numerous factors, identifying coronavirus-associated variants and correlating exposure dynamics with viral aerosols, spirometry indices, comorbidities, susceptible blood types, cellular miRNA binding sites and multisystem inflammatory syndrome remains a challenge where the lethal zoonotic infections are prevalent in the livestock industry, being the hub of dairy, fur, meat and egg production. This review provides insights into the complexity of the disease burden and recommends precision smart-farming models for upscaling biosecurity measures and adoption of digitalised technologies (robotic drones) powered by multiparametric sensors and radio modem systems for real-time tracking of infectious strains in the agro-environment and managing the transition into the new-normal realities in the livestock industry.
The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely land management of sustainability indicators such as ground cover and grassland biomass, given the uncertainty of future climate and drought conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while maximising pasture biomass productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 hectare) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months) with each compared with control treatments with lighter stocking rates for longer periods (2,000 DSE). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica, and cocksfoot (Dactylis glomerata) were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We then invoked a machine learning model using Sentinel-2 imagery to quantify TSDM, standing green biomass and standing dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM and green biomass. However, regenerative treatments significantly impacted litter fall and trampled material, with the high intensity grazing treatments causing more dry matter trampling, increasing litter, enhancing decomposition rates and surface organic matter. Pasture digestibility was greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were significantly greater for the treatment with 15-month spell periods. Estimates of TSDM using machine learning with Sentinel-2 imagery underestimated TSDM in treatment plots but explained spatiotemporal variability associated within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3-6 months) are most conducive to increasing pasture production under high rainfall conditions, and we speculate that high intensity grazing is likely to positively impact on soil organic carbon through increased litterfall and trampling. Our study paves the way forward for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling management of pastures from afar.
The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely management of sustainability indicators, given the uncertainty of future climate conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while improving pasture productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 ha) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months), with each compared with controls characterised by lighter stocking rates for longer periods (2000 DSE/ha). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica), and cocksfoot (Dactylis glomerata), and were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We invoked a machine learning model forced with Sentinel-2 imagery to quantify TSDM, standing green and dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM, green biomass and recovery. However, regenerative treatments significantly impacted litterfall and trampled material, with high-intensity grazing treatments trampling more biomass, increasing litter, enhancing surface organic matter and decomposition rates thereof. Pasture digestibility and sward uniformity were greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were greater for the 15-month spelling treatment. TSDM prognostics from machine learning were lower than measured TSDM, although predictions from the machine learning approach closely matched observed spatiotemporal variability within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3–6 months) was more conducive to increasing pasture production under high rainfall conditions, and we speculate that – in this environment - high-intensity grazing with 3-month spelling is likely to improve soil organic carbon through increased litterfall and trampling. Our study paves the way for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling the management of pastures within small fields from afar.
We investigate 85 129 MODIS satellite active fire events from 2007 to 2015 in the Niger Delta of Nigeria. The region is the oil base for Nigerian economy and the hub of oil exploration where oil facilities (i.e. flowlines, flow stations, trunklines, oil wells and oil fields) are domiciled, and from where crude oil and refined products are transported to different Nigerian locations through a network of pipeline systems. Pipeline and other oil facilities are consistently susceptible to oil leaks due to operational or maintenance error, and by acts of deliberate sabotage of the pipeline equipment which often result in explosions and fire outbreaks. We used ground oil spill reports obtained from the National Oil Spill Detection and Response Agency (NOSDRA) database (see www.oilspillmonitor.ng) to validate MODIS satellite data. NOSDRA database shows a total of 10 072 spill events from 2007-2015. The spill events were filtered to include largest spills by volume and events occurring only in the Niger Delta (i.e. 386 spills). By projecting both MODIS fire and spill as 'input vector' layers with 'Points' geometry, and the Nigerian pipeline networks as 'from vector' layers with 'LineString' geometry in a geographical information system, we extracted the nearest MODIS events (i.e. 2192) closed to the pipelines by 1000m distance in spatial vector analysis. The extraction process that defined the nearest distance to the pipelines is based on the global practices of the Right of Way (ROW) in pipeline management that earmarked 30m strip of land to the pipeline. The KML files of the extracted fires in a Google map validated their source origin to be from oil facilities. Land cover mapping confirmed fire anomalies. The aim of the study is to propose a near-real-time monitoring of spill events along pipeline routes using 250 m spatial resolution of MODIS active fire detection sensor when such spills are accompanied by fire events in the study location.
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