Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Adapting production inputs site-specifically within a field and individually for each animal allows better use of resources to maintain the quality of the environment while improving the sustainability of the food supply. Precision agriculture provides a means to monitor the food production chain and manage both the quantity and quality of agricultural produce.

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible–near infrared (vis–NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. We hope that this work might reinvigorate our community’s discussion towards larger, more coordinated collaborations and encourage other contributions. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and push the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

This study examined the impact of robotics and geospatial technologies interventions on middle school youth’s learning of and attitudes toward science, technology, engineering, and mathematics (STEM). Two interventions were tested. The first was a 40-hour intensive robotics/GPS/GIS summer camp; the second was a 3-hour event modeled on the camp experiences and intended to provide an introduction to these technologies. Results showed that the longer intervention led to significantly greater learning than a control group not receiving the instruction, whereas the short-term intervention primarily impacted youth attitude and motivation. Although the short-term intervention did not have the learning advantages of a more intensive robotics camp, it can serve a key role in getting youth excited about technology and encouraging them to seek out additional opportunities to explore topics in greater detail, which can result in improved learning.