The selection of productive varieties of modern Criollo cocoa, showing fine aromatic qualities in their beans, is of major interest for some producing countries, such as Venezuela. Cultivated populations of Modern Criollo or Trinitario varieties may be suitable for admixture mapping analysis, as large blocks of alleles derived from two identified divergent ancestors, recently admixed, are still preserved, after a few generations of recombination, similar to experimental mapping progenies. Two hundred and fifty-seven individuals from a cultivated population of Modern Criollo were selected and analysed with 92 microsatellite markers distributed along the genome. This population exhibited a wide range of variability for yield factors and morphological features. Population structure analysis identified two main subgroups corresponding to the admixture from the two ancestors Criollo and Forastero. Several significant associations between markers and phenotypic data (yield factors and morphological traits) were identified by a least squares general linear model (GLM) taking into account the population structure and the percentage of admixture of each individual. Results were compared with classical QTL analyses previously reported for other cacao populations. Most markers associated to quantitative traits were very close to QTLs detected formerly for the same traits. Associations were also identified between markers and several qualitative traits including the red pigmentation observed in different organs, mainly associated to common markers in linkage group 4.

A sound understanding of crop history can provide the basis for deriving novel genetic information through admixture mapping. We confirmed this, by using characterization data from an international collection of cocoa, collected 25 years ago, and from a contemporary plantation. We focus on the trees derived from three centuries of admixture between Meso-American Criollo and South American Forastero genomes. In both cacao sets of individuals, linkage disequilibrium extended over long genetic distances along chromosome regions, as expected in populations derived from recent admixture. Based on loose genome scans, genomic regions involved in useful traits were identified. Fifteen genomic regions involved in seed and fruit weight variation were highlighted. They correspond to ten previously identified QTLs and five novel ones. Admixture mapping can help to add value to genetic resources and thus, help to encourage investment in their conservation.

Effective well management and a productive wellwork program are valuable and integral business objectives. Wellwork involves various well interventions and optimisation activities for enhancing and extending hydrocarbon production. These remedial processes involve substantial CAPEX and OPEX, as well as other resource allocations. Failure to prioritize objectives and improper selection of candidate wells can have significant implications on both derived value and potential risk. A primary challenge is to ensure that wellwork is delivering production growth while maintaining cost efficiency. Well-by-well reviews with actionable decision support information will provide the best method for identifying potential production improvements. The selection and prioritisation of candidate jobs is a critical investment decision. This paper addresses the business problem of reducing the uncertainty of well work program outcomes’ so that more informed choices can be made from all the options such that the benefits and value of an overall well work program is enhanced and optimized. It illustrates the use of data-driven models for estimating key performance indicators for wellwork jobs and predicting the likely outcome for a new planned job using pre-determined success criteria. Nine different machine learning and advanced analytics learning schemes were applied to the training dataset of wellwork history. The competing models performance was evaluated on a separate validation data set for a balance between best fit and prediction accuracy. The application of developed models provided intelligence augmentation for the decision-making process. This methodology embeds learning from past wellwork activities to streamline and guide complex workflows. The business value for embedding quantitative predictions into strategic and operational decision-making processes is realized in reducing less-favorable investments and maximizing the value of wellwork.