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Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.

There are currently 180 million hectares under pasture in Brazil, and despite the country being one of the largest meat producers, there remain around 64 million hectares that show signs of degradation and contribute to the substantial loss of soil organic carbon (SOC). The aim of this study, therefore, was to derive the factors for SOC stock changes in managed pastures and evaluate the potential for SOC sequestration when converting degraded pastures to well-managed or recovered pastures in Brazil. The study involved 169 paired comparisons, including different types of pasture spread over 14 states in Brazil, and analysed the data in linear mixed-effect models deriving the SOC stock change factors for various soil depths (30 to 100 cm) over 30 years since the change in management. The results showed that for 30 years at a depth of 0–30 cm, compared to native vegetation, nominal pasture (non-degraded grassland, but with no significant management improvements) and improved pasture increased SOC stocks by 15% and 8%, whilst degraded pastures reduced the stocks by 10%. However, the recovery of degraded pastures enhances the SOC by 23%. In terms of the rates of SOC change, pasture degradation leads to losses of 0.25 Mg C ha−1 year−1, whilst nominal or recovered pastures can sequester SOC at rates from 0.25 to 0.54 Mg ha−1 year−1. Overall, it was estimated that the recovery of degraded pastures can sequester up to 3445 Tg of CO2. Nominal management or simple improvement practices can maintain or enhance SOC stocks, helping to mitigate the GHG emissions of livestock in Brazil.

Parallel monitoring of 222 Rn and its short-lived progeny (218 Po and 214 Pb) were carried out from November 2007 to April 2008 close to the top of the Schauinsland mountain, partly covered with forest, in South-West Germany. Samples were aspired from the same location at 2.5 m above ground level. We measured 222 Rn with a dual flow loop, two-filter detector and its short-lived progeny with a one-filter detector. A reference sector for events, facing a steep valley and dominated by pasture, was used to minimize differences between 222 Rn and progeny-derived 222 Rn activity concentrations. In the two major wind sectors covered by forest to a distance between 60 m and 80 m towards the station progeny-derived 222 Rn activity concentration was on average equal to 87% (without precipitation) and 74% (with precipitation) of 222 Rn activity concentration. The observations show that most of the time both detector types follow the same pattern. Still, there is no single disequilibrium factor that could be used to exactly transform short-lived progeny to 222 Rn activity concentration under all meteorological conditions.

Understanding root growth and phenology is essential for improving the productivity, resilience, and sustainability of pasture-based systems. However, roots remain one of the most difficult components of plant systems to measure and monitor, particularly in managed, high-turnover pastures, such as those in New Zealand (NZ) dairy systems. As a result, root processes are often underrepresented in both experimental studies and pasture system models. This perspective paper identifies critical, but underdeveloped areas in root research, with particular focus on root phenology. Current studies are limited by insufficient temporal resolution, a lack of species- and cultivar-specific trait data in mixed swards, and weak integration of root dynamics into breeding programmes and farm system models. These constraints limit our ability to link root processes to pasture persistence, nutrient cycling, and climate resilience. To address this gap, we propose that root phenology should be treated as a dynamic functional trait that links plant responses to environmental and management drivers with ecosystem-level outcomes. This framing provides a conceptual foundation for integrating root dynamics into pasture research and modelling, particularly in systems subject to frequent defoliation and environmental variability. We further highlight opportunities arising from rapid advances in sensing technologies, automation, and data analytics, which enable continuous, high-resolution root monitoring systems at multiple scales. However, realising this potential requires integration of complementary measurement approaches and alignment with system-level research questions. In this context, NZ provides a unique platform for developing scalable, pasture-based root monitoring framework that integrates science, management and policy. We argue for a coordinated effort that bridges fundamental root biology with applied pasture management, supported by long-term datasets, methodological integration, and engagement with end users. Embedding root traits and phenological dynamics into the next generation of pasture models and decision-support tools will be critical for improving system performance and environmental outcomes. This perspective aims to stimulate a shift towards more integrated, temporally explicit approaches for studying root systems in pasture environments, with relevance to grazing system beyond NZ and across temperate regions.

Nomadic Pastoralism among the Mongol Herders: Multispecies and Spatial Ethnography in Mongolia and Transbaikalia is based on anthropological research carried out by the author between 2008 and 2016 and addresses the spatial features of nomadic pastoralism among the Mongol herders of Mongolia and Southern Siberia from a cross-comparative perspective. In addition to classical methods of survey, Charlotte Marchina innovatively used GPS recordings to analyze the ways in which pastoralists envision and concretely occupy the landscape, which they share with their animals and invisible entities. The data, represented in abundant and original cartography, provides a better understanding of the mutual adaptations of both herders and animals in the common use of unfenced pastures, not only between different herders but between different species. The author also highlights the herders' adaptive strategies at a time of rapid sociopolitical and environmental changes in this area of the world.

Forage selection by herbivores is a major driver of plant diversity in pasture vegetation. Yet, we know relatively little about how plant traits influence decisions of different herbivore species and breeds to select or avoid a certain plant species on semi‐natural pastures. We quantified the influence of the traits leaf N and P content, leaf dry matter content (LDMC), specific leaf area (SLA) and physical defence mechanisms on plant species selection for three cattle breeds: high‐yielding Angus × Holstein crossbreed, dual‐purpose Original Braunvieh and undemanding Highland Cattle. The cattle grazed a series of adjacent paddocks in different alpine pastures. Plant species selection was quantified by assessing the difference in biomass proportions of all plant species in 66 vegetation subplots per breed before and after grazing. Plant traits and indicator values were extracted from the TRY database. Data on 152 plant species were analysed using a local mixed‐effects model and a global multivariate hierarchical regression model. Plant traits had a clear impact on forage behaviour. Plants with high SLA, leaf N and P contents were significantly selected, whereas plants with high LDMC (e.g. woody plants) and defence mechanisms (e.g. thistles) were generally avoided. Species with high forage quality indicator values as defined by Briemle et al. (2002) were significantly preferred. More importantly, significant differences between forage behaviour of cattle breeds were detected. Selection by less‐productive Highland Cattle was much less influenced by plant traits than the selection by the two higher‐yielding breeds. Results indicate a clear impact of plant traits on forage selection and demonstrate breed‐specific influences. Highland Cattle (and possibly other robust breeds) graze less selectively and impose less selective exclusion on plants. Thereby, they likely influence plant species composition of pastures in a different way than high‐yielding breeds, thereby creating a distinct habitat. A free Plain Language Summary can be found within the Supporting Information of this article. Abstrakt Der Artenreichtum und die Pflanzenzusammensetzung von beweidetem Grünland werden maßgeblich durch die Futterselektion der Weidetiere bestimmt. Auf artenreichen, extensiven Weideflächen können Herbivoren aus einer großen Anzahl von Pflanzenarten von ganz unterschiedlicher Beschaffenheit auswählen. Allerdings ist wenig darüber bekannt, welche Eigenschaften einer Pflanze dazu führen, dass sie vom Vieh gefressen oder ge‐mieden wird und ob sich Weidetierarten und ‐rassen in diesen Vorlieben unterscheiden. Analysiert wurde der Einfluss mehrerer Pflanzeneigenschaften (Gehalt von N, P und Trockensubstanz in den Blättern, spezifische Blattfläche (SLA), Abwehrmechanismen und Verholzung) auf die Futterselektion von drei Rinderrassen: (1) produktionsorientierte Rinder mit hoher Milch‐ und Fleisch‐Leistung (Angus × Holstein‐Kreuzung), eine weniger intensive Zweinutzungsrasse (Original‐Braunvieh) und eine anspruchslose Robustrasse (Hochlandrinder). Die Rassen beweideten in drei alpinen Vegetationstypen jeweils benachbarte Koppel. In 66 Vegetationsteilflächen pro Rasse wurde der Verzehr jeder einzelnen Pflanzenart bestimmt, indem der Biomasseanteil jeder Art vor und nach der Beweidung geschätzt und die Differenz berechnet wurde. Daten zu Pflanzeneigenschaften und Indikatorwerte wurden von der TRY‐Datenbank bezogen. Die Daten der 152 Pflanzenarten wurden mit Hilfe eines lokalen Mixed‐Effects‐Modells und eines globalen multivariaten hierarchischen Regressionsmodells analysiert. Die Eigenschaften der Pflanzen hatten einen sichtbaren Einfluss auf das Fressverhalten der Rinder. Pflanzen mit hohem SLA, N‐ und P‐Gehalt wurden von den Rindern signifikant ausgewählt, wohingegen Pflanzen mit hohem Trockensubstanzgehalt (z.B. Holzpflanzen) und mit Abwehrmechanismen (z.B. Disteln) im Allgemeinen gemieden wurden. Arten mit hohem Futterwert (nach Briemle et al., 2002) wurden signifikant bevorzugt. Bemerkenswert sind die deutlichen Unterschiede, die zwischen den Rinderrassen festgestellt wurden. Das Fressverhalten der extensiven Hochlandrinder war weniger von den Eigenschaften der Pflanzen abhängig als die Selektion der beiden produktionsorientierteren Rassen. Die Studie zeigt einen deutlichen Einfluss der Eigenschaften von Pflanzen auf die Futterselektion von Rindern und belegt ein rassenspezifisches Fressverhalten. Hochlandrinder (und möglicherweise auch andere Robustrinder) weiden weniger selektiv und verschmähen Pflanzen, die Abwehrstrategien oder einen geringen Futterwert besitzen, weniger stark. Dadurch beeinflussen sie langfristig die Zusammensetzung der Weidevegetation und schaffen einen spezifischen Lebensraum, in dem typische Weidezeiger weniger dominant sind als auf den Weiden produktionsorientierter Rinderrassen. A free Plain Language Summary can be found within the Supporting Information of this article.