{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T02:34:26Z","timestamp":1791426866642,"version":"4.3.3"},"reference-count":60,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:00:00Z","timestamp":1787961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019370","name":"National Funds through FCT","doi-asserted-by":"publisher","award":["UIDB\/05183\/2025"],"award-info":[{"award-number":["UIDB\/05183\/2025"]}],"id":[{"id":"10.13039\/501100019370","id-type":"DOI","asserted-by":"publisher"}]},{"award":["UIDB\/05183\/2025"],"award-info":[{"award-number":["UIDB\/05183\/2025"]}],"id":[{"id":"https:\/\/ror.org\/00snfqn58","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pasture quality monitoring is essential for optimizing grazing management and reducing the incidence of nutritional and metabolic disorders in ruminants, yet conventional field-based measurements remain labor-intensive and limited in spatial coverage. This review examines how remote sensing (RS) technologies can support pasture-based livestock systems by providing timely, scalable assessments of biomass, botanical composition, and nutritive attributes. Data from multispectral, hyperspectral, radio detection and ranging (RADAR), and light detection and ranging (LiDAR) sensors, acquired via satellite, unmanned aerial vehicle (UAV), and proximal platforms, are combined with machine learning (ML) methods and radiative transfer models to derive pasture biophysical and quality indicators. The reviewed evidence shows that RS reliably estimates pasture biomass and structural traits, while advances in spectral unmixing, data fusion, and artificial intelligence (AI) improve the characterization of heterogeneous swards and support emerging indicators related to forage quality. Integrating these remotely sensed metrics into grassland decision-support frameworks can enhance grazing allocation, inform fertilization and irrigation decisions, and help detect conditions associated with nutritional imbalances. Overall, the synthesis demonstrates that RS, particularly when combined with advanced modelling and cloud-based processing, offers a robust pathway for improving pasture monitoring and strengthening the nutritional management of ruminants, thereby supporting more sustainable and animal welfare-focused grazing systems.<\/jats:p>","DOI":"10.3390\/s26175472","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:55:56Z","timestamp":1788155756000},"page":"5472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sensor-Based Pasture Quality Monitoring: Supporting Grazing Management and Preventing Nutritional and Metabolic Disorders in Ruminants"],"prefix":"10.3390","volume":"26","author":[{"given":"Henrique","family":"Pinto","sequence":"first","affiliation":[{"name":"MED\u2014Mediterranean Institute for Agriculture, Environment and Development and CHANGE\u2014Global Change and Sustainability Institute, University of \u00c9vora, Mitra, Ap. 94, 7006-554 \u00c9vora, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2955-8011","authenticated-orcid":false,"given":"Ricardo","family":"Santos","sequence":"additional","affiliation":[{"name":"College of Computing, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso Do Sul, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7175-9992","authenticated-orcid":false,"given":"Guilherme","family":"Defalque","sequence":"additional","affiliation":[{"name":"College of Computing, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso Do Sul, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8465-1318","authenticated-orcid":false,"given":"Francisco J.","family":"Moral","sequence":"additional","affiliation":[{"name":"Departamento de Expresi\u00f3n Gr\u00e1fica, Escuela de Ingenier\u00edas Industriales, Universidad de Extremadura, Avenida de Elvas s\/n, 06006 Badajoz, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5178-8158","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Serrano","sequence":"additional","affiliation":[{"name":"MED\u2014Mediterranean Institute for Agriculture, Environment and Development and CHANGE\u2014Global Change and Sustainability Institute, University of \u00c9vora, Mitra, Ap. 94, 7006-554 \u00c9vora, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,29]]},"reference":[{"key":"ref_1","first-page":"101459","article-title":"Pasture monitoring using remote sensing and machine learning: A review of methods and applications","volume":"37","author":"Shahi","year":"2025","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Murphy, D.J., Murphy, M.D., O\u2019Brien, B., and O\u2019Donovan, M. (2021). A review of precision technologies for optimising pasture measurement on Irish grassland. Agriculture, 11.","DOI":"10.3390\/agriculture11070600"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/1828051X.2025.2480821","article-title":"Precision livestock farming: An overview on the application in extensive systems","volume":"24","author":"Bernabucci","year":"2025","journal-title":"Ital. J. Anim. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lottering, R., Peerbhay, K., and Adelabu, S. (2025). Remote sensing applications in agricultural, earth and environmental sciences. Appl. Sci., 15.","DOI":"10.3390\/app15084537"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Asher, A., and Brosh, A. (2022). Decision support system (DSS) for managing a beef herd and its grazing habitat\u2019s sustainability: Biological\/agricultural basis of the technology and its validation. Agronomy, 12.","DOI":"10.3390\/agronomy12020288"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wu, G. (2019). Management of metabolic disorders (including metabolic diseases) in ruminant and nonruminant animals. Animal Agriculture: Sustainability, Challenges and Innovations, Elsevier. [1st ed.].","DOI":"10.1016\/B978-0-12-817052-6.00027-6"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kang, D., Lungu, S.E., Danso, F., Dzou, C.F., Chen, Y., Zheng, X., Nie, F., Lin, H., Chen, J., and Zhou, G. (2025). Animal health and nutrition: Metabolic disorders in cattle and improvement strategies. Front. Vet. Sci., 12.","DOI":"10.3389\/fvets.2025.1470391"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"skaf137","DOI":"10.1093\/jas\/skaf137","article-title":"ASAS\u2013NANP symposium: Mathematical modeling in animal nutrition\u2014Application of modeling innovations to support satellite remote sensing for sustainable grazing cattle management","volume":"103","author":"Fernandes","year":"2025","journal-title":"J. Anim. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108674","DOI":"10.1016\/j.agee.2023.108674","article-title":"Testing a novel pasture quality index using remote sensing tools in semiarid and Mediterranean grasslands","volume":"357","author":"Adar","year":"2023","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Serrano, J., Shahidian, S., Paix\u00e3o, L., Marques da Silva, J., and Pani\u00e1gua, L.L. (2024). Pasture quality assessment through NDVI obtained by remote sensing: A validation study in the Mediterranean silvo-pastoral ecosystem. Agriculture, 14.","DOI":"10.3390\/agriculture14081350"},{"key":"ref_11","unstructured":"Pulllanagari, R., Dynes, R.A., King, W.M., Yule, I.J., Thulin, S., Knox, N.M., and Ramoelo, A. (2013). Remote sensing of pasture quality. Assessment and Impact of Grass and Forage Quality Proceedings of the 22nd International Grassland Congress, Sydney, Australia, University of Kentucky."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Serrano, J., Shahidian, S., and Marques da Silva, J. (2019). Evaluation of normalized difference water index as a tool for monitoring pasture seasonal and inter-annual variability in a Mediterranean agro-silvo-pastoral system. Water, 11.","DOI":"10.3390\/w11010062"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100562","DOI":"10.1016\/j.animal.2022.100562","article-title":"Effects of dietary crude protein concentration on animal performance and nitrogen utilisation efficiency at different stages of lactation in Holstein-Friesian dairy cows","volume":"16","author":"Yang","year":"2022","journal-title":"Animal"},{"key":"ref_14","first-page":"143","article-title":"Determination of water content in corn stover silage using near-infrared spectroscopy","volume":"12","author":"Zhang","year":"2019","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1080\/05704928.2013.878720","article-title":"Use of infrared spectroscopy for in-field measurement and phenotyping of plant properties: Instrumentation, data analysis, and examples","volume":"49","author":"Cozzolino","year":"2014","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_16","unstructured":"Castro, P., Fern\u00e1ndez-lorenzo, B., and Valladares, J. (2005). An\u00e1lisis de pastos mediante NIRS. Producciones agroganaderas: Gesti\u00f3n eficiente y conservaci\u00f3n del medio natural (Vol. I). XLV Reuni\u00f3n Cient\u00edfica de la SEEP (Sesi\u00f3n: Producci\u00f3n Animal), Villaviciosa, Instituto Nacional de Investigaciones Forestales, Agr\u00edcolas y Pecuarias (INIFAP)."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"139","DOI":"10.17138\/TGFT(4)139-145","article-title":"Ecuaciones de calibraci\u00f3n en espectroscop\u00eda de reflectancia en el infrarrojo cercano (NIRS) para predicci\u00f3n de par\u00e1metros nutritivos en forrajes tropicales","volume":"4","author":"Molano","year":"2016","journal-title":"Trop. Grassl.-Forrajes Trop."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.fcr.2017.02.003","article-title":"Influence of the critical phase of stem elongation on yield and forage quality of Perennial Ryegrass genotypes in the first reproductive growth","volume":"205","author":"Ullmann","year":"2017","journal-title":"Field Crops Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Baath, G.S., Baath, H.K., Gowda, P.H., Thomas, J.P., Northup, B.K., Rao, S.C., and Singh, H. (2020). Predicting forage quality of warm-season legumes by near infrared spectroscopy coupled with machine learning techniques. Sensors, 20.","DOI":"10.3390\/s20030867"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"138561","DOI":"10.1016\/j.scitotenv.2020.138561","article-title":"Modern Holstein-origin dairy cows within grassland-based systems partition more feed nitrogen into milk and excrete less in manure","volume":"727","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5434","DOI":"10.3168\/jds.2016-11917","article-title":"Effects of dietary crude protein concentration on late-lactation dairy cow performance and indicators of nitrogen utilization","volume":"100","author":"Barros","year":"2017","journal-title":"J. Dairy Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3967","DOI":"10.1093\/jas\/sky256","article-title":"Interaction between feed use efficiency and level of dietary crude protein on enteric methane emission and apparent nitrogen use efficiency with Norwegian red dairy cows","volume":"96","author":"Kidane","year":"2018","journal-title":"J. Anim. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"395","DOI":"10.3168\/jds.2018-15249","article-title":"Energy and nitrogen partitioning in dairy cows at low or high metabolizable protein levels is affected differently by postrumen glucogenic and lipogenic substrates","volume":"102","author":"Nichols","year":"2019","journal-title":"J. Dairy Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"697","DOI":"10.15232\/aas.2021-02179","article-title":"A survey of United States dairy cattle nutritionists\u2019 practices and perceptions of reducing crude protein in lactating dairy cow diets","volume":"37","author":"Daley","year":"2021","journal-title":"Appl. Anim. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101358","DOI":"10.1016\/j.animal.2024.101358","article-title":"Nitrogen excretion and utilisation of dairy cows grazing temperate semi-natural grasslands","volume":"18","author":"Velasco","year":"2024","journal-title":"Animal"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1071\/AN17166","article-title":"Regular estimates of herbage mass can improve profitability of pasture-based dairy systems","volume":"59","author":"Beukes","year":"2019","journal-title":"Anim. Prod. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Serrano, J., Mendes, S., Shahidian, S., and Marques da Silva, J. (2023). Pasture quality monitoring based on proximal and remote optical sensors: A case study in the Montado Mediterranean ecosystem. AgriEngineering, 5.","DOI":"10.3390\/agriengineering5010025"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/B978-0-12-386473-4.00005-1","article-title":"Proximal soil sensing: An effective approach for soil measurements in space and time","volume":"113","author":"Adamchuk","year":"2011","journal-title":"Adv. Agron."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ali, A., and Kaul, H.P. (2025). Monitoring yield and quality of forages and grassland in the view of Precision Agriculture applications\u2014A review. Remote Sens., 17.","DOI":"10.3390\/rs17020279"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Serrano, J., Franco, J., Shahidian, S., and Moral, F.J. (2024). Estimation of dry matter yield in Mediterranean pastures: Comparative study between Rising Plate Meter and Grassmaster II probe. Agriculture, 14.","DOI":"10.3390\/agriculture14101737"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Petrovski, K.R., Kirkwood, R.N., Teixeira Rodrigues de Almeida, S., and Caetano, M. (2025). A Proposed framework for nutritional assessment in compromised ruminants. Ruminants, 5.","DOI":"10.3390\/ruminants5040057"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sudhakara Reddy, B., Sivajothi, S., Narmada Raghavi, D., and Karthik, P. (2025). Clinical physiology of digestive disorders in ruminants. Fundamentals of Veterinary Pathophysiology, CRC Press. [1st ed.]. Chapter 11.","DOI":"10.1201\/9781003426837-11"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1007\/s00203-019-01706-z","article-title":"Understanding the alteration in rumen microbiome and CAZymes profile with diet and host through comparative metagenomic approach","volume":"201","author":"Bohra","year":"2019","journal-title":"Arch. Microbiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"s151","DOI":"10.1017\/S1751731119000752","article-title":"Invited review: Application of meta-omics to understand the dynamic nature of the rumen microbiome and how it responds to diet in ruminants","volume":"13","author":"Gruninger","year":"2019","journal-title":"Animal"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hao, Y., Gong, Y., Huang, S., Ji, S., Wang, W., Wang, Y., Yang, H., Cao, Z., and Li, S. (2021). Effects of age, diet CP, NDF, EE, and starch on the rumen bacteria community and function in dairy cattle. Microorganisms, 9.","DOI":"10.21203\/rs.3.rs-537889\/v1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hynd, P. (2019). Animal Nutrition: From Theory to Practice, CSIRO Publishing. [1st ed.].","DOI":"10.1079\/9781789242911.0000"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s12549-025-00684-z","article-title":"Grass and browse\u2014How forage type affects digestive physiology in ruminants","volume":"105","author":"Hummel","year":"2025","journal-title":"Palaeobiodivers. Palaeoenviron."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Serrano, J., Shahidian, S., and Moral, F.J. (2024). Crude protein as an indicator of pasture availability and quality: A validation of two complementary sensors. Agronomy, 14.","DOI":"10.3390\/agronomy14102310"},{"key":"ref_39","first-page":"559","article-title":"A review on beef cattle supplementation technologies","volume":"11","author":"Defalque","year":"2024","journal-title":"Inf. Process. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e06826","DOI":"10.1590\/1678-5150-pvb-6826","article-title":"Hypomagnesemia in beef cattle","volume":"41","author":"Doncel","year":"2021","journal-title":"Pesqui. Vet. Bras."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20420","DOI":"10.1038\/s41598-021-98735-w","article-title":"A Spatial analysis of lime resources and their potential for improving soil magnesium concentrations and pH in grassland areas of England and Wales","volume":"11","author":"Bide","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"354","DOI":"10.15414\/afz.2023.26.04.354-359","article-title":"Alimentary metabolic disorders in high-producing dairy cows: A review","volume":"26","year":"2023","journal-title":"Acta Fytotech. Zootech."},{"key":"ref_43","first-page":"3329","article-title":"An IoT-driven smart agriculture framework for precision farming, resource optimization, and crop health monitoring","volume":"4","author":"Irfan","year":"2025","journal-title":"Acad. Int. J. Soc. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"123901","DOI":"10.1016\/j.renene.2025.123901","article-title":"Novel machine learning unlocks high lipid productivity and resolves trade-offs in algal biofuel production","volume":"256","author":"Lopez","year":"2026","journal-title":"Renew. Energy"},{"key":"ref_45","first-page":"101282","article-title":"Pixels to pasture: Using machine learning and multispectral remote sensing to predict biomass and nutrient quality in tropical grasslands","volume":"36","author":"Zwick","year":"2024","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"233","DOI":"10.9734\/jeai\/2025\/v47i33331","article-title":"Integration of AI and IoT for yield optimization in precision farming","volume":"47","author":"Singh","year":"2025","journal-title":"J. Exp. Agric. Int."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"514","DOI":"10.32996\/jcsts.2025.7.2.54","article-title":"The technological revolution in agriculture: Cloud computing as the backbone of smart farming","volume":"7","author":"Alex","year":"2025","journal-title":"J. Comput. Sci. Technol. Stud."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s40725-025-00251-x","article-title":"Close-range remote sensing of forest structure for biodiversity assessments: A systematic literature review","volume":"11","author":"Feigl","year":"2025","journal-title":"Curr. For. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"107342","DOI":"10.1016\/j.rineng.2025.107342","article-title":"Edge-enabled smart agriculture framework: Integrating IoT, lightweight deep learning, and agentic AI for context-aware farming","volume":"28","author":"Tariq","year":"2025","journal-title":"Results Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"101611","DOI":"10.1016\/j.iot.2025.101611","article-title":"Virtual sensors for smart farming: An IoT- and AI-enabled approach","volume":"32","author":"Chourlias","year":"2025","journal-title":"Internet Things"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., \u0141obodzi\u0144ska, A., and \u015anieg, M. (2025). The IoT and AI in agriculture: The time is now\u2014A systematic review of smart sensing technologies. Sensors, 25.","DOI":"10.3390\/s25123583"},{"key":"ref_52","first-page":"279","article-title":"A review of precision technologies in pasture-based dairying systems","volume":"59","author":"Shalloo","year":"2021","journal-title":"Int. J. Agric. Resour. Gov. Ecol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"111029","DOI":"10.1016\/j.ecolind.2023.111029","article-title":"A conceptual framework for holistic assessment of decision support systems for sustainable livestock farming","volume":"155","author":"Niloofar","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Panda, S.S., Terril, T.T., Siddique, A., Mahapatra, A.K., Morgan, E.R., Pech-Cervantes, A.A., and Van Wyk, J.A. (2024). Development of a decision support system for animal health management using geo-information technology: A novel approach to precision livestock management. Agriculture, 14.","DOI":"10.20944\/preprints202402.0944.v1"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Reeves, M.C., Swisher, J., Krebs, M., Warnke, K., Hanberry, B.B., Hudson, T., and Hall, S.A. (2025). Data-driven decision support to guide sustainable grazing management. Land, 14.","DOI":"10.3390\/land14010140"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e07564","DOI":"10.1002\/advs.202507564","article-title":"Big data and AI-powered modeling: A pathway to sustainable precision animal nutrition","volume":"41","author":"Zhang","year":"2025","journal-title":"Adv. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Patera, V., Di Fazio, S., Messina, G., and Pratic\u00f2, S. (2026). The application of remote sensing technologies in pastures monitoring: A review for the Mediterranean region. Sustainability, 18.","DOI":"10.3390\/su18031642"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Wr\u00f3bel, B., Zielewicz, W., and Paszkiewicz-Jasi\u0144ska, A. (2025). Improving forage quality from permanent grasslands to enhance ruminant productivity. Agriculture, 15.","DOI":"10.3390\/agriculture15131438"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Susruthan, V., Donaghy, D.J., Kenyon, P.R., Sneddon, N.W., and Cartmill, A.D. (2025). Measuring herbage mass: A review. Agronomy, 15.","DOI":"10.3390\/agronomy15102264"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3390\/agriengineering2020015","article-title":"Estimation of productivity in dryland Mediterranean pastures: Long-term field tests to calibration and validation of the Grassmaster II probe","volume":"2","author":"Serrano","year":"2020","journal-title":"AgriEngineering"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5472\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:58:52Z","timestamp":1788155932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,29]]},"references-count":60,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175472"],"URL":"https:\/\/doi.org\/10.3390\/s26175472","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,29]]}}}