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In semi-arid regions, such as the Mediterranean, seasonal changes in climate make the migration of ruminants essential for the efficient use of ecosystemic primary productivity. Transhumance is one of the oldest sheep husbandry systems which contributes to the conservation of natural biodiversity and to minimize the effects of climate change on livestock production. However, its use has decreased substantially, mainly because of livestock intensification. In Spain, particularly, interest in maintaining the traditional transhumant network has increased. Geotechnologies such as Global Positioning Systems (GPS) and Geographical Information Systems (GIS) might provide important information about the drove roads of the transhumant network. Therefore, in this study, we used several geotechnologies that described accurately and in detail the path followed by several sheep flocks and cattle herds in eastern Spain. Specifically, we monitored a Merino de los Montes Universales (MMU) sheep flock on the well-known Spanish Conquense route in Nov 2021. The flock traveled 349.8 km and climbed a total elevation of 1223 m in 23 days, moving 9–10 h per day. Maximum and minimum daily distances traveled were 19.06 km and 8.85 km, respectively, and the steepest slopes were encountered in the first few days of the journey, up to 60 %. The preferred land use/land covers (LU/LC) were scrublands, rainfed herbaceous crops, and rangelands. Indirectly, the study demonstrated the outstanding physiological aptitude of the MMU breed for withstanding the long journey, even when pregnant. • Seasonal changes in climate make the transhumance of ruminants essential. • Transhumance plays a crucial role in maintaining environmental sustainability. • Geotechnologies provide vital information about drove roads characteristics. • GPS and GIS revealed certain topographic and biotic features of the Conquense route. • Merino de los Montes Universales sheep are physiologically adapted to transhumance. [ABSTRACT FROM AUTHOR]

Abstract: A time series of more than two years of continuous measurements of EC, OC and TC obtained with a Rupprecht and Patashnick (R&P) thermal analyzer at a suburban site in Madrid has been evaluated in this work. Correction factors obtained from intercomparison exercises with thermo-optical methods were applied to the original measurements. Corrected OC and EC mean values are 3.7 and 1.3 μg m−3 respectively. The highest individual values have been recorded in winter. No seasonal variation has been observed in OC, whereas a slight decrease of EC is observed in summer, leading to higher OC/EC ratios. The mean value of the OC/EC ratio is 4.9. Daily patterns show maximum EC and OC associated to traffic rush hours, with a delay of the OC peak related to the formation of SOC. Daily oscillation is more pronounced in winter. Hourly estimations of SOC production have been obtained by the primary OC/EC ratio approach, after discounting the OC, EC background contamination. A mean primary ratio value of 0.59 has been used for SOC short-term estimations throughout the entire time period. As expected, the highest SOC production is observed in summer, related to the increasing in photochemical activity, which also influences daily patterns and the time of the daily SOC maximum. In summer the daily SOC production ranges from 80 to 92%, whereas in the winter months it varies from 70 to 84%. These SOC estimations do not discount primary biogenic or natural contributions to OC and are therefore maximum values. [Copyright &y& Elsevier]

The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X). [ABSTRACT FROM AUTHOR]