There is a growing call for inventories that evaluate geographic patterns in diversity of plant genetic resources maintained on farm and in species' natural populations in order to enhance their use and conservation. Such evaluations are relevant for useful tropical and subtropical tree species, as many of these species are still undomesticated, or in incipient stages of domestication and local populations can offer yet-unknown traits of high value to further domestication. For many outcrossing species, such as most trees, inbreeding depression can be an issue, and genetic diversity is important to sustain local production. Diversity is also crucial for species to adapt to environmental changes. This paper explores the possibilities of incorporating molecular marker data into Geographic Information Systems (GIS) to allow visualization and better understanding of spatial patterns of genetic diversity as a key input to optimize conservation and use of plant genetic resources, based on a case study of cherimoya (Annona cherimola Mill.), a Neotropical fruit tree species. We present spatial analyses to (1) improve the understanding of spatial distribution of genetic diversity of cherimoya natural stands and cultivated trees in Ecuador, Bolivia and Peru based on microsatellite molecular markers (SSRs); and (2) formulate optimal conservation strategies by revealing priority areas for in situ conservation, and identifying existing diversity gaps in ex situ collections. We found high levels of allelic richness, locally common alleles and expected heterozygosity in cherimoya's putative centre of origin, southern Ecuador and northern Peru, whereas levels of diversity in southern Peru and especially in Bolivia were significantly lower. The application of GIS on a large microsatellite dataset allows a more detailed prioritization of areas for in situ conservation and targeted collection across the Andean distribution range of cherimoya than previous studies could do, i.e. at province and department level in Ecuador and Peru, respectively.

Growing energy demand heightened by climate change challenges has seen the oil and gas industry tightly embrace smarter and more sustainable technologies. The motivation is to quickly grasp net-zero targets, while safely optimising oil-gas production. By its nature, the industry has the ingenuity to eliminate unnecessary carbon emissions. However, traditional development plans relied on the use of wells with minimal or no emphasis on the well completion in terms of optimum deliverability. This would produce a mixture of oil and excessive unwanted fluids such as water and/or gas which requires costly energy-intensive processes. Although the process has been optimized to some extent and often re-injects these unwanted fluids back to the reservoir, there has been not enough attention to the environmental impacts as these repetitive treatment processes of the fluids results in discharging excessive and unnecessary Greenhouse Gas (GHG) into the atmosphere. The issue is now widely recognized to be one of the industry challenges in its drive toward net-zero energy delivery. A case study of a heavy crude oil field with a strong water drive, located in a natural reserve in the Marañon basin of the Peruvian Amazon is presented. Here, the implementation of autonomous inflow control devices (AICDs) technology, through a knowledge management process, has made it possible to significantly reduce the volumes of water produced, which are reinjected again, thus generating significant savings in fluid lifting, treatment and energy consumption associated with the operations in this field. The study introduces a workflow that uses a publicly available GHG footprint estimator to evaluate the carbon intensity of different oil and gas field development plans. The estimator predicts the amount of GHG emitted from any individual operation, process and treatment involved in a field development from exploration to delivery at the gate of a refinery. Having this calculation enables the operators to recognize the major GHG emitter operations and optimise the process toward net zero using new technologies, methods and/or workflows. The workflow has then been applied to the field located in the Peruvian Amazon to illustrate the significant impact of flow control technologies on the reduction of GHG emissions and achieving net-zero targets. For example, the amounts of carbon intensity, GHG emission and energy consumption from the field have been estimated to been reduced by up to 56%, 64% and 78% respectively with AICD completions compared to a case of non-AICD completion such as stand-alone screen (SAS) was installed in the wells instead. This study provides the engineers with a workflow to quantify the impacts of the use of new technologies especially flow control devices. It also illustrates the significant role of flow control technologies in achieving net-zero production.