GeoFault: A well-founded fault ontology for interoperability in geological modeling

Geological modeling currently uses various computer-based applications. Data harmonization at the semantic level by means of ontologies is essential for making these applications interoperable. Since geo-modeling is currently part of multidisciplinary projects, semantic harmonization is required to model not only geological knowledge but also to integrate other domain knowledge at a general level. For this reason, the domain ontologies used for describing geological knowledge must be based on a sound ontology background to ensure the described geological knowledge is integratable. This paper presents a domain ontology: GeoFault, resting on the Basic Formal Ontology BFO (Arp et al., 2015) and the GeoCore ontology (Garcia et al., 2020). It models the knowledge related to geological faults. Faults are essential to various industries but are complex to model. They can be described as thin deformed rock volumes or as spatial arrangements resulting from the different displacements of geological blocks. At a broader scale, faults are currently described as mere surfaces, which are the components of complex fault arrays. The reference to the BFO and GeoCore package allows assigning these various fault elements to define ontology classes and their logical linkage within a consistent ontology framework. The GeoFault ontology covers the core knowledge of faults 'strico sensu,' excluding ductile shear deformations. This considered vocabulary is essentially descriptive and related to regional to outcrop scales, excluding microscopic, orogenic, and tectonic plate structures. The ontology is molded in OWL 2, validated by competency questions with two use cases, and tested using an in-house ontology-driven data entry application. The work of GeoFault provides a solid framework for disambiguating fault knowledge and a foundation of fault data integration for the applications and the users

Genetic Characterization of the Pee Dee Cotton Breeding Program

The history of cotton breeding in the southeastern United States is multifaceted and complex. Public and private breeding programs have driven cotton’s genetic development over the past two centuries. The Pee Dee breeding program in Florence, South Carolina, has had a substantial role in the development of well-adapted cotton cultivars with improved fiber strength, fiber length, and performance in farmers’ fields. Despite the historic importance of the cotton germplasm lines and varieties from the Pee Dee program, little has been done to characterize the population structure and genetic architecture of key traits in this closed breeding program. Here, I first provide an in-depth exploration of the rich history of cotton breeding and genetics over the past century to provide some context for the remainder of this thesis. Then, I discuss the interface of breeding goals, population genetics, and historical implications of a representative sample across 85+ years of cotton breeding in the Pee Dee program. Once the family structure had been evaluated, I applied modern statistical methodology to find gene haplotypes that are associated with improved fiber quality or field performance and attempted to trace the origin of some beneficial alleles. Lastly, I talk about the implications of our work and how it may influence future breeding efforts to utilize the germplasm from this diverse cotton collection

Carving out new business models in a small company through contextual ambidexterity: the case of a sustainable company

Business model innovation (BMI) and organizational ambidexterity have been pointed out as mechanisms for companies achieving sustainability. However, especially considering small and medium enterprises (SMEs), there is a lack of studies demonstrating how to combine these mechanisms. Tackling such a gap, this study seeks to understand how SMEs can ambidextrously manage BMI. Our aim is to provide a practical artifact, accessible to SMEs, to operationalize BMI through organizational ambidexterity. To this end, we conducted our study under the design science research to, first, build an artifact for operationalizing contextual ambidexterity for business model innovation. Then, we used an in-depth case study with a vegan fashion small e-commerce to evaluate the practical outcomes of the artifact. Our findings show that the company improves its business model while, at the same time, designs a new business model and monetizes it. Thus, our approach was able to take the first steps in the direction of operationalizing contextual ambidexterity for business model innovation in small and medium enterprises, democratizing the concept. We contribute to theory by connecting different literature strands and to practice by creating an artifact to assist managemen

World Cotton Germplasm Resources

Preservation of plant germplasm resources is vitally important for mankind to supply food and product security in the globalization and technological advances of the 21st century. Mankind preserved a wealth of available genetic resources of many plant species worldwide. One of the such worldwide plant germplasm resources is available for cotton, a unique natural fiber producing cash crop for mankind. Worldwide cotton germplasm collections exist in Australia, Brazil, China, India, France, Pakistan, Turkey, Russia, United States of America, and Uzbekistan. The objective of World Cotton Germplasm Resources book is to present readers with updated information on existing cotton germplasm resources, highlighting detailed inventory, description, storage conditions, characterization and utilization as well as challenges and perspectives. This book should be a comprehensive encyclopedic reading source for plant research community and students to gather important information on worldwide cotton germplasm resources

Validation Framework for RDF-based Constraint Languages

In this thesis, a validation framework is introduced that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, consists of a small lightweight vocabulary, and ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages

Genomic prediction applied to high-biomass sorghum for bioenergy production.

The increasing cost of energy and finite oil and gas reserves have created a need to develop alternative fuels from renewable sources. Due to its abiotic stress tolerance and annual cultivation, high-biomass sorghum (Sorghum bicolor L. Moench) shows potential as a bioenergy crop. Genomic selection is a useful tool for accelerating genetic gains and could restructure plant breeding programs by enabling early selection and reducing breeding cycle duration. This work aimed at predicting breeding values via genomic selection models for 200 sorghum genotypes comprising landrace accessions and breeding lines from biomass and saccharine groups. These genotypes were divided into two sub-panels, according to breeding purpose. We evaluated the following phenotypic biomass traits: days to flowering, plant height, fresh and dry matter yield, and fiber, cellulose, hemicellulose, and lignin proportions. Genotyping by sequencing yielded more than 258,000 single-nucleotide polymorphism markers, which revealed population structure between subpanels. We then fitted and compared genomic selection models BayesA, BayesB, BayesC?, BayesLasso, Bayes Ridge Regression and random regression best linear unbiased predictor. The resulting predictive abilities varied little between the different models, but substantially between traits. Different scenarios of prediction showed the potential of using genomic selection results between sub-panels and years, although the genotype by environment interaction negatively affected accuracies. Functional enrichment analyses performed with the marker-predicted effects suggested several interesting associations, with potential for revealing biological processes relevant to the studied quantitative traits. This work shows that genomic selection can be successfully applied in biomass sorghum breeding programs

Outputs: Potassium Losses from Agricultural Systems

Potassium (K) outputs comprise removals in harvested crops and losses via a number of pathways. No specific environmental issues arise from K losses to the wider environment, and so they have received little attention. Nevertheless, K is very soluble and so can be leached to depth or to surface waters. Also, because K is bound to clays and organic materials, and adsorbed K is mostly associated with fine soil particles, it can be eroded with particulate material in runoff water and by strong winds. It can also be lost when crop residues are burned in the open. Losses represent a potential economic cost to farmers and reduce soil nutritional status for plant growth. The pathways of loss and their relative importance can be related to: (a) the general characteristics of the agricultural ecosystem (tropical or temperate regions, cropping or grazing, tillage management, interactions with other nutrients such as nitrogen); (b) the specific characteristics of the agricultural ecosystem such as soil mineralogy, texture, initial soil K status, sources of K applied (organic, inorganic), and rates and timing of fertilizer applications. This chapter provides an overview of the main factors affecting K removals in crops and losses through runoff, leaching, erosion, and open burning

Digital agriculture: research, development and innovation in production chains.

Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil.Translated by Beverly Victoria Young and Karl Stephan Mokross