The forest plays a large role both nationally and internationally towards a sustainable planet. Therefore, understanding non-industrial private forest owners’ preferences is important to achieve the environmental targets, since they are a large ownership group in Sweden. The present study focusses on non-industrial private forest owners’ harvesting decisions in Norrbotten. Furthermore, the study extends to analyze how the forest owners’ preferences towards promotion of ecosystem services are affected if compensation is offered. To understand the harvesting decisions of non-industrial private forest owners’, an empirical approach was used where a questionnaire was constructed. The data were analyzed through logistic regression. The results suggest that non-industrial private forest owners’ harvesting decision is positively affected by previous experience in the forest, days present in the forest, membership in a forest cooperative and a price increase of timber. Forest owners with economic objectives harvest more than owners with no or other objectives. No significant relationship between nature- and economic objectives in the harvesting decisions were identified. The results related to willingness to promote ecosystem services for compensation indicated ambiguous results.

<p dir="ltr">Precision agriculture has been around for many, many years but as technology has rapidly grown with the population, farmers are looking for more ways to improve their operation with the help of these tools. These tools help farmers manage, understand, and decide when, how and what should be done regarding the tough decisions in the field based on their machinery statues. The tools that utilize GPS location and provide farm managers with useful information and feedback on large scales of value in the Harvesting and planting operation. With previous works done focusing on identify state machine activity utilizing GPS location data with the use of loggers and algorithms this study carries on from one field to the next identifying the truth data set for each and the accuracy of the algorithm. The goal is to add a more realistic view to the states identifying turning and transporting throughout the harvesting operation in truth data and from algorithm results. Also diving into truck activity with lower standard GPS tracking to see how accurately they can be predicted to complete the harvesting cycle from vehicle to vehicle. Overall, the combine and grain cart held at 88% accuracy for labeling all state activity during the harvesting operation for multiple fields, while for the model algorithm with the grain trucks having an overall accuracy of 94%.</p>