http://hdl.handle.net/10174/151 Thu, 30 Jul 2026 06:16:42 GMT 2026-07-30T06:16:42Z http://hdl.handle.net/10174/42294 Title: Within-field soil variability in agro-silvo-pastoral systems: a baseline for Precision Agriculture adoption Authors: Horta Marques, J.; Carreira, E.; Lloberas, D.; Martins, M.; Ribeiro, H.; Serrano, João Abstract: The Montado represents a dominant agro-silvo-pastoral system across the Mediterranean basin. It integrates pastures, trees (cork oak and/or holm oak), and livestock (ruminants or swine), While Precision Agriculture is more commonly applied to purely agricultural systems, it offers significant potential to optimize Montado management, thereby increasing economic, environmental, social, and cultural sustainability. This study aims to quantify spatial soil variability within Montado plots to identify which essential nutrients for plant production exhibit the greatest distribution range. This study was conducted on two permanent applied research plots, grazed by cattle under a rotational grazing system (total area = 20 ha; location: Mitra/University of Évora, Portugal, Figure 1). Sampling’s carried out at 24 points: 12 under the tree canopy influence and 12 outside the canopy influence. The methodology’s described in Serrano et al (2023) and Horta Marques (2026). Results indicate high within-field variability (Coefficient of Variation around 40% or higher) in the levels of K2O, Mg, P2O5, Ca and all studied micronutrients except for Fe. The findings demonstrate that the observed within-field variability, alongside agronomic expertise and economic and environmental sustainability goals, justifies the implementation of Precision Agriculture in Mediterranean agro-silvo-pastoral systems. Sun, 31 May 2026 23:00:00 GMT http://hdl.handle.net/10174/42294 2026-05-31T23:00:00Z http://hdl.handle.net/10174/42289 Title: Impact of Sheep Stocking Rates in Soil Compaction: Case Study in Montado Mediterranean Ecosystem Authors: Serrano, João; Carreira, Emanuel; Shahidian, S.; Paniagua, L.; Moral, F.; Pereira, A. Abstract: The economic return on extensive sheep farming (pasture-based systems) of the Southern of Portugal is low, as it occupies degraded soils. Therefore, the environmental perspective prevails, with the recovery of soil functionality and the preservation of the Montado as an ecosystem of high nature value. This study assesses the impact of two grazing management systems and stocking rates (SR), continuous grazing (CG) with low SR versus deferred grazing (DG) with high SR, on soil compaction. The study was carried out between December 2023 and June 2025 on a 4-ha pasture. This area of natu-ral grassland was divided into four grazing parks of 1 ha each, two under DG management and two under CG management. During the study, the cone index (CI, in kPa) was measured in the topsoil layer (0–30 cm) on eight dates, with an electronic cone penetrometer at 48 georeferenced areas. The results of CI measurement showed no significant differences between treat-ments in all depths measured (0–10, 10–20, and 20–30 cm). These findings are encouraging from the point of view of soil conservation and sustainabil-ity, revealing good prospects for the possible intensification of extensive livestock production. Sun, 31 May 2026 23:00:00 GMT http://hdl.handle.net/10174/42289 2026-05-31T23:00:00Z http://hdl.handle.net/10174/42281 Title: Rising Plate Meter as a Tool for Pasture Dry Matter Estimation: Case Study in Mediterranean Dryland Pastures Authors: Serrano, João; Shahidian, S.; Charneca, R.; Paniagua, L.; Moral, F. Abstract: In ruminant-based extensive livestock systems, pastures are one of the main components and the main feeding source given that they are the most eco-nomical resource. Accurate information about pasture dry matter (DM, in kg ha-1) availability is a key parameter in the manager’s decision, particularly when calculating stocking rates and supplementation needs. This study evaluates an electronic sensor (rising plate meter, RPM) to estimate DM in biodiverse dryland pastures. The study was carried out between December 2023 and December 2025 on three pasture fields, two located in Portuguese Alentejo region (Southern Portugal), “Mitra” and “Tapada dos Números”, and one located in Spanish Extremadura region (Cubillos). The experimental work consisted of sensor measurements, followed by the collection of more than 200 pasture samples, distributed between different dates of the pasture vegetative cycles of 2023/2024 and 2024/2025. The best estimation models for DM were obtained based on measurements carried out in Autumn and Winter (R2 > 0.70) and decreased significantly when based on measurements carried out in Spring. These results show the potential for the research and development of proximal and remote sensing tools to support pasture moni-toring and animal production management. Sun, 31 May 2026 23:00:00 GMT http://hdl.handle.net/10174/42281 2026-05-31T23:00:00Z http://hdl.handle.net/10174/41874 Title: Technological development for monitoring pasture quality and supporting grazing management decisions – a review Authors: Pinto, Henrique; Santos, Ricardo; Moral, Francisco; Amaral, Alexandre; Escribano, Carlos; Almeida, António; Serrano, João Abstract: Effective grazing management depends on accurate and timely information on pasture biomass and nutritive value. This review examines recent advances in sensing technologies for estimating forage quality parameters, particularly crude protein (CP) and neutral detergent fiber (NDF), in pasture-based systems. Emphasis is placed on two complementary indicators: Dry Matter (DM) and Crude Protein (CP). Proximal tools such as rising plate meters (RPM) have evolved to incorporate optical components capable of estimating vegetation indices like the Normalized Difference Vegetation Index (NDVI), which may correlate with forage quality. Meanwhile, satellite-based remote sensing (Sentinel-2) offers broader spatial coverage and access to multiple spectral bands. These allow for the computation of several indices that can be explored using statistical and machine learning (ML) models to predict CP and NDF content. The integration of sensor-derived data with artificial intelligence (AI) represents a promising avenue for developing predictive models and decision-support systems (DSS), improving rotational grazing, supplementation planning, and reducing the occurrence of metabolic and nutritional disorders. This review highlights key findings in the literature and identifies knowledge gaps, particularly in the validation of new technologies across different pasture types and seasons. Future research should focus on combining multi-source data for real-time, on farm applications. Tue, 31 Mar 2026 23:00:00 GMT http://hdl.handle.net/10174/41874 2026-03-31T23:00:00Z