Climate change and environmental disturbances due to human activities are posing significant threats to biodiversity, which, in turn, affects food security and social stability. Many species are undergoing range shifts and fitness declines, while others have developed adaptive traits to avoid extinction. When genetically controlled, these traits undergo natural selection, leading to the development of locally adapted populations (LA), where specific alleles become prevalent due to their fitness advantage in specific environments. This has led to a growing research interest in landscape genomics (LSG), which combines landscape ecology and genomics to detect signatures of local adaptation. LSG is particularly valuable for identifying the loci responsible for adaptive genetic variation across landscapes. The approach, though relatively new, has shown its feasibility in crop and animal species, revealing adaptive traits in species like wheat, maize, barley, sorghum, eggplant, goats, and chicken. Modeling genomic and environmental data has allowed researchers to predict crop fitness in future climates, benefiting conservation efforts and crop improvement programs. For example, studies have identified populations at risk of extinction, guiding ex-situ conservation and assisted gene flow strategies. Using LSG to predict species' genetic responses to climate gradients is helping shape conservation strategies to maintain biodiversity. While LSG holds promise, there are significant challenges, particularly the issue of false positives when identifying loci linked to local adaptation. False positives may arise when genetic drift or demographic effects mimic local adaptation patterns. Fortunately, continuous improvement of methods in environmental association analyses is helping to control for false positives. Moreover, distinguishing between correlated environmental factors remains challenging in LSG studies, as some correlations may be due to covarying factors not included in the analysis. Despite these challenges, LSG's potential to mine adaptive genes for crop improvement and to inform conservation strategies is vast. This comprehensive manual provides valuable insights into the fundamental steps and critical considerations needed for undertaking successful LSG studies. Within its pages, we have carefully selected a few commonly applied methods. However, it is important to note that various techniques are available to researchers depending on their computational capacity and proficiency. We also assume that the user has basic R program skills to be able to apply the scripts provided in the manual. Furthermore, we present a collection of R scripts that have been curated to achieve specific outcomes and create simplified graphical displays of the results for easier interpretation. We acknowledge that study scenarios can vary widely, and the complexity of each study can differ significantly depending on the organism being studied and the diverse landscapes involved. Links to additional detailed resources have also been provided to broaden the users’ perspective.