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Characterization of portuguese sown rainfed grasslands using remote sensing and machine learning

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Abstract

Grasslands are crucial ecosystems that support and provide a diverse number of ecosystem services. Sown biodiverse pastures rich in legumes (SBP) were developed with the main goal of increasing grassland production while minimizing fertilizers inputs. In this paper, the main properties of SBP in Portugal were estimated using remote sensing and machine learning in six different farms and two production years (spring 2018 and 2019). Four pasture characteristics were considered: aboveground standing biomass, fraction of legumes, plant nitrogen (N) content and plant phosphorus (P) content. Remote sensing data were obtained from Sentinel-2. The spectral bands combined with 5 vegetation indices and 9 covariates were used. Multiple linear regression, LASSO, Ridge, random forests, XGBoost and LightGBM regression models were used. Two cross-validation approaches were used: (1) a random approach with random selection of the folds (RN-CV), and (2) a structured approach where each fold is a unique combination of farm and year, which is subsequently used to assess the performance of the model obtained with the 8 other folds (LLYO-CV). Results showed that the random forest method had the best estimation accuracy for all pasture characteristics. Regarding cross-validation approaches, the algorithms with RN-CV have higher estimation accuracy for all pasture characteristics (on average about 10% lower RMSE and an R2 85% higher), as compared to the algorithms with LLYO-CV. However, LLYO-CV should avoid overfitting and improve generalization of the models because in each fold the model is tested in a farm and year that was not used for training. The RMSE for all variables were significantly low, especially in RN-CV. Plant P is the variable where the choice of CV approach has the least influence (RMSE of test set with RN-CV: 0.71 g P kg− 1; LLYO-CV: 0.72 g P kg− 1). Standing biomass is the variable with the highest difference between CV approaches (RN-CV: 722 kg ha− 1; LLYO-CV: 825 kg ha− 1). The RMSE, of legumes and plant N were moderately affected by the CV approach (legume RN-CV: 0.11; LLYO-CV: 0.12 – plant N RN-CV: 3.96 g N kg− 1; LLYO-CV: 3.99 g N kg− 1). The algorithms developed here were applied for entire parcels in the two farms with the most different climate conditions as demonstration of their potential future use for precision farming.

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Availability of data and material

The raw input data is used in this work is available in the Supporting Information File S1.

Code Availability

The Python script used in this work is available on Github (https://github.com/tgmorais/SBPproperties).

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References

  • Adjorlolo, C., Mutanga, O., & Cho, M. A. (2014). Estimation of canopy nitrogen concentration across c3 and c4 grasslands using worldview-2 multispectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4385–4392. https://doi.org/10.1109/JSTARS.2014.2320601

    Article  Google Scholar 

  • Ali, I., Cawkwell, F., Dwyer, E., Barrett, B., & Green, S. (2016). Satellite remote sensing of grasslands: from observation to management. Journal of Plant Ecology, 9(6), 649–671. https://doi.org/10.1093/jpe/rtw005

    Article  Google Scholar 

  • APA. (2018). Portuguese National Inventory Report on Greenhouse Gases, 1990–2018. Amadora, Portugal: Portuguese Environmental Agency

    Google Scholar 

  • Askari, M. S., McCarthy, T., Magee, A., & Murphy, D. J. (2019). Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques. Remote Sensing, 11(15), 1835. https://doi.org/10.3390/rs11151835

    Article  Google Scholar 

  • Badreldin, N., Prieto, B., & Fisher, R. (2021). Remote sensing Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning. Remote Sensing, 13(24), 4972. https://doi.org/10.3390/rs13244972

    Article  Google Scholar 

  • Belsky, A. J. (1994). Influences of Trees on Savanna Productivity: Tests of Shade, Nutrients, and Tree-Grass Competition. Ecology, 75(4), 922–932. https://doi.org/10.2307/1939416

    Article  Google Scholar 

  • Catchpole, W. R., & Wheeler, C. J. (1992). Estimating plant biomass: A review of techniques. Australian Journal of Ecology, 17(2), 121–131. https://doi.org/10.1111/j.1442-9993.1992.tb00790.x

    Article  Google Scholar 

  • Chatterjee, S., & Hadi, A. S. (2015). Regression analysis by example. New York City, United States: John Wiley & Sons

    Google Scholar 

  • Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 13-17-August-2016). https://doi.org/10.1145/2939672.2939785

  • Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., & Jones, P. D. (2018). An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets. Journal of Geophysical Research: Atmospheres, 123(17), 9391–9409. https://doi.org/10.1029/2017JD028200

    Article  Google Scholar 

  • Davids, C., Karlsen, S. R., Ancin, M., & Jorgensen, M. (2018). UAV based mapping of grassland yields for forage production in northern Europe. In Sustainable meat and milk production from grasslands. Proceedings of the 27th General Meeting of the European Grassland Federation, (pp. 845–847). Wageningen, The Netherlands: Wageningen Academic Publishers

  • EC. (2003). European Soil Database (distribution version v2.0). European Commission Joint Research Centre

  • ESRI (2016). ArcGIS Desktop 10.5 ArcGIS Desktop. Redlands, CA, USA

  • Fangueiro, D., Surgy, S., Fraga, I., Cabral, F., & Coutinho, J. (2015). Band application of treated cattle slurry as an alternative to slurry injection: Implications for gaseous emissions, soil quality, and plant growth. Agriculture Ecosystems and Environment, 211, 102–111. https://doi.org/10.1016/j.agee.2015.06.003

    Article  CAS  Google Scholar 

  • Fangueiro, D., Ribeiro, H., Vasconcelos, E., Coutinho, J., & Cabral, F. (2009). Treatment by acidification followed by solid-liquid separation affects slurry and slurry fractions composition and their potential of N mineralization. Bioresource Technology, 100(20), 4914–4917. https://doi.org/10.1016/j.biortech.2009.04.032

    Article  CAS  PubMed  Google Scholar 

  • Flynn, K. C., Frazier, A. E., & Admas, S. (2020). Performance of chlorophyll prediction indices for Eragrostis tef at Sentinel-2 MSI and Landsat-8 OLI spectral resolutions. Precision Agriculture, 21(5), 1057–1071. https://doi.org/10.1007/s11119-020-09708-4

    Article  Google Scholar 

  • Gao, B. C. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3

    Article  Google Scholar 

  • Garroutte, E., Hansen, A., & Lawrence, R. (2016). Using NDVI and EVI to Map Spatiotemporal Variation in the Biomass and Quality of Forage for Migratory Elk in the Greater Yellowstone Ecosystem. Remote Sensing, 8(5), 404. https://doi.org/10.3390/rs8050404

    Article  Google Scholar 

  • Gillet, F., Murisier, B., Buttler, A., Gallandat, J. D., & Gobat, J. M. (1999). Influence of tree cover on the diversity of herbaceous communities in subalpine wooded pastures. Applied Vegetation Science, 2(1), 47–54. https://doi.org/10.2307/1478880

    Article  Google Scholar 

  • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7

    Article  Google Scholar 

  • Gitelson, A., & Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 22(3), 247–252. https://doi.org/10.1016/1011-1344(93)06963-4

    Article  CAS  Google Scholar 

  • Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., et al. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Head, T., Kumar, M., Nahrstaedt, H., Louppe, G., & Shcherbatyi, I. (2020). scikit-optimize/scikit-optimize. Zenodo. https://doi.org/10.5281/zenodo.4014775

  • IPMA (2018). Climate normals. http://www.ipma.pt/en/index.html. Accessed 9 May 2022

  • Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., et al. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 3146–3154

    Google Scholar 

  • Kleinebecker, T., Weber, H., & Hölzel, N. (2011). Effects of grazing on seasonal variation of aboveground biomass quality in calcareous grasslands. Plant Ecology, 212(9), 1563–1576. https://doi.org/10.1007/s11258-011-9931-1

    Article  Google Scholar 

  • Liu, K., Zhou, Q. B., Wu, W., Bin, Xia, T., & Tang, H. J. (2016, February 1). Estimating the crop leaf area index using hyperspectral remote sensing. Journal of Integrative Agriculture, 15(2), 475–491.https://doi.org/10.1016/S2095-3119(15)61073-5

  • Magalhães, M. R., Pena, S. B., Müller, A., Cunha, N. S., Silva, J. F., Cardoso, S., A., et al. (2018). de ordenamento do território (in English: “EPIC WebGIS- Knowledge sharing as a tool to integrate the landscape into land use planning policies”). Revista Cartográfica, (96), 159–176. https://doi.org/10.35424/rcar.v0i96.193. EPIC WebGIS-A partilha de conhecimento como ferramenta de integração da paisagem nas políticas

  • Magalhães, M. R. (2001). A arquitectura paisagista: morfologia e complexidade (in English: “Landscape architecture: morphology and complexity”). Editorial Estampa

  • Mariano, D. A., Santos, C. A. C., dos, Wardlow, B. D., Anderson, M. C., Schiltmeyer, A. V., Tadesse, T., et al. (2018). Use of remote sensing indicators to assess effects of drought and human-induced land degradation on ecosystem health in Northeastern Brazil. Remote Sensing of Environment, 213, 129–143. https://doi.org/10.1016/J.RSE.2018.04.048

    Article  Google Scholar 

  • McKinney, W. (2010). Data Structures for Statistical Computing in Python. In S. van der Walt & J. Millman (Eds.), Proceedings of the 9th Python in Science Conference (pp. 56–61). https://doi.org/10.25080/Majora-92bf1922-00a

  • Meyer, H., Lehnert, L. W., Wang, Y., Reudenbach, C., Nauss, T., & Bendix, J. (2017). From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information? International Journal of Applied Earth Observation and Geoinformation, 55, 21–31. https://doi.org/10.1016/j.jag.2016.10.001

    Article  Google Scholar 

  • Morais, T. G., Teixeira, R. F. M., & Domingos, T. (2018a). The Effects on Greenhouse Gas Emissions of Ecological Intensification of Meat Production with Rainfed Sown Biodiverse Pastures. Sustainability, 10(11), 4184. https://doi.org/10.3390/su10114184

    Article  CAS  Google Scholar 

  • Morais, T. G., Teixeira, R. F. M., & Domingos, T. (2019). Detailed global modelling of soil organic carbon in cropland, grassland and forest soils. PLOS ONE, 14(9), e0222604. https://doi.org/10.1371/journal.pone.0222604

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Morais, T. G., Teixeira, R. F. M., Rodrigues, N. R., & Domingos, T. (2018b). Characterizing livestock production in Portuguese sown rainfed grasslands: Applying the inverse approach to a process-based model. Sustainability, 10(12), 4437. https://doi.org/10.3390/su10124437

    Article  Google Scholar 

  • Morais, T. G., Teixeira, R. F. M., Figueiredo, M., & Domingos, T. (2021, November 1). The use of machine learning methods to estimate aboveground biomass of grasslands: A review. Ecological Indicators. 130, 108081. https://doi.org/10.1016/j.ecolind.2021.108081

  • Moreno-Opo, R., Pina, M., & Mogena, A. (2021). Sowing enriched pastures for extensive livestock enhances the abundance of birds and arthropods in Mediterranean grasslands. European Journal of Wildlife Research, 67(3), 1–12. https://doi.org/10.1007/s10344-021-01486-2

    Article  Google Scholar 

  • Murphy, D. J., Shine, P., Brien, B. O., Donovan, M. O., & Murphy, M. D. (2021). Utilising grassland management and climate data for more accurate prediction of herbage mass using the rising plate meter. Precision Agriculture, 22(4), 1189–1216. https://doi.org/10.1007/S11119-020-09778-4/TABLES/7

    Article  CAS  Google Scholar 

  • Naidoo, R., Balmford, A., Costanza, R., Fisher, B., Green, R. E., Lehner, B., et al. (2008). Global mapping of ecosystem services and conservation priorities. Proceedings of the National Academy of Sciences, 105(28), 9495–9500. https://doi.org/10.1073/PNAS.0707823105

  • Nelson, D. W., & Sommers, L. E. (1973). Determination of Total Nitrogen in Plant Material 1. Agronomy Journal, 65(1), 109–112. https://doi.org/10.2134/agronj1973.00021962006500010033x

    Article  CAS  Google Scholar 

  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830

    Google Scholar 

  • Pereira, H. M., Domingos, T., Marta-Pedroso, C., Proença, V., Rodrigues, P., Ferreira, M., et al. (2009). Uma avaliação dos serviços dos ecossistemas em Portugal. Ecossistemas e Bem-Estar Humano Avaliação para Portugal do Millennium Ecosystem Assessment (in English: “Portuguese Millennium Ecosystem Assessment: State of the Assessment Report”) (pp. 687–716). Lisboa, Portugal: Escolar Editora

    Google Scholar 

  • Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020, July 1). Sentinel-2 data for land cover/use mapping: A review. Remote Sensing. 12(14), 2291 https://doi.org/10.3390/rs12142291

  • Prado, A., del, Brown, L., Schulte, R., Ryan, M., & Scholefield, D. (2006). Principles of Development of a Mass Balance N Cycle Model for Temperate Grasslands: An Irish Case Study. Nutrient Cycling in Agroecosystems, 74(2), 115–131. https://doi.org/10.1007/s10705-005-5769-z

    Article  Google Scholar 

  • Pullanagari, R. R., Yule, I. J., Tuohy, M. P., Hedley, M. J., Dynes, R. A., & King, W. M. (2012). In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture. Precision Agriculture, 13(3), 351–369. https://doi.org/10.1007/s11119-011-9251-4

    Article  Google Scholar 

  • Ramoelo, A., Cho, M. A., Mathieu, R., Madonsela, S., van de Kerchove, R., Kaszta, Z., et al. (2015). Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. International Journal of Applied Earth Observation and Geoinformation, 43, 43–54. https://doi.org/10.1016/j.jag.2014.12.010

    Article  Google Scholar 

  • Ren, H., Zhou, G., & Zhang, X. (2011). Estimation of green aboveground biomass of desert steppe in Inner Mongolia based on red-edge reflectance curve area method. Biosystems Engineering, 109(4), 385–395. https://doi.org/10.1016/J.BIOSYSTEMSENG.2011.05.004

    Article  Google Scholar 

  • Ribeiro, I., Proença, V., Serra, P., Palma, J., Domingo-Marimon, C., Pons, X., et al. (2019). Remotely sensed indicators and open-access biodiversity data to assess bird diversity patterns in Mediterranean rural landscapes. Scientific Reports, 9(1), 6826. https://doi.org/10.1038/s41598-019-43330-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rouse, J. W., Haas, R. H., Schell, J. A., & Deeering, D. (1973). Monitoring vegetation systems in the Great Plains with ERTS (Earth Resources Technology Satellite). In Third Earth Resources Technology Satellite-1 Symposium (Vol. 1, pp. 309–317)

  • Rubel, F., & Kottek, M. (2010). Observed and projected climate shifts 1901–2100 depicted by world maps of the Köppen-Geiger climate classification. Meteorologische Zeitschrift, 19(2), 135–141. https://doi.org/10.1127/0941-2948/2010/0430

    Article  Google Scholar 

  • Saleem, M. H., Potgieter, J., & Mahmood Arif, K. (2021). & Mahmood Arif karif, K. Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments. Precision Agriculture, 22, 2053–2091. https://doi.org/10.1007/s11119-021-09806-x

  • Serrano, J., Shahidian, S., da Silva, J. M., & Carvalho, M. (2016). Monitoring of soil organic carbon over 10 years in a Mediterranean silvo-pastoral system: potential evaluation for differential management. Precision Agriculture, 17(3), 274–295. https://doi.org/10.1007/s11119-015-9419-4

    Article  Google Scholar 

  • Sinha, S., Jeganathan, C., Sharma, L. K., & Nathawat, M. S. (2015). A review of radar remote sensing for biomass estimation. International Journal of Environmental Science and Technology, 12(5), 1779–1792. https://doi.org/10.1007/s13762-015-0750-0

    Article  Google Scholar 

  • Sloat, L. L., Gerber, J. S., Samberg, L. H., Smith, W. K., Herrero, M., Ferreira, L. G., et al. (2018). Increasing importance of precipitation variability on global livestock grazing lands. Nature Climate Change, 8(3), 214–218. https://doi.org/10.1038/s41558-018-0081-5

    Article  Google Scholar 

  • Teixeira, R. F. M., Barão, L., Morais, T. G., & Domingos, T. (2019). “BalSim”: A carbon, nitrogen and greenhouse gas mass balance model for pastures. Sustainability, 11(1), 53. https://doi.org/10.3390/su11010053

    Article  CAS  Google Scholar 

  • Teixeira, R. F. M., Proença, V., Crespo, D., Valada, T., & Domingos, T. (2015). A conceptual framework for the analysis of engineered biodiverse pastures. Ecological Engineering, 77, 85–97. https://doi.org/10.1016/j.ecoleng.2015.01.002

    Article  Google Scholar 

  • Tong, X., Duan, L., Liu, T., & Singh, V. P. (2019). Combined use of in situ hyperspectral vegetation indices for estimating pasture biomass at peak productive period for harvest decision. Precision Agriculture, 20(3), 477–495. https://doi.org/10.1007/S11119-018-9592-3

    Article  Google Scholar 

  • Ullah, S., Si, Y., Schlerf, M., Skidmore, A. K., Shafique, M., & Iqbal, I. A. (2012). Estimation of grassland biomass and nitrogen using MERIS data. International Journal of Applied Earth Observation and Geoinformation, 19, 196–204. https://doi.org/10.1016/J.JAG.2012.05.008

    Article  Google Scholar 

  • UNFCCC. (2015). Measurements for Estimation of Carbon Stocks in Afforestation and Reforestation Project Activities under the Clean Development Mechanism: A Field Manual. Bonn, Germany: United Nations Climate Change Secretariat (UNFCCC)

    Google Scholar 

  • Vallentin, C., Harfenmeister, K., Itzerott, S., Kleinschmit, B., Conrad, C., & Spengler, D. (2021). Suitability of satellite remote sensing data for yield estimation in northeast Germany. Precision Agriculture, 2021, 1–31. https://doi.org/10.1007/S11119-021-09827-6

    Article  Google Scholar 

  • Vilar, P., Morais, T. G., Rodrigues, N. R., Gama, I., Monteiro, M. L., Domingos, T., et al. (2020). Object-Based Classification Approaches for Multitemporal Identification and Monitoring of Pastures in Agroforestry Regions using Multispectral Unmanned Aerial Vehicle Products. Remote Sensing, 12(5), 814. https://doi.org/10.3390/rs12050814

    Article  Google Scholar 

  • Xia, J., Ma, M., Liang, T., Wu, C., Yang, Y., Zhang, L., et al. (2018). Estimates of grassland biomass and turnover time on the Tibetan Plateau. Environmental Research Letters, 13(1), https://doi.org/10.1088/1748-9326/aa9997

  • Xu, B., Yang, X. C., Tao, W. G., Qin, Z. H., Liu, H. Q., Miao, J. M., et al. (2008). MODIS-based remote sensing monitoring of grass production in China. International Journal of Remote Sensing, 29(17–18), 5313–5327. https://doi.org/10.1080/01431160802036276

    Article  Google Scholar 

  • Yan, F., Wu, B., & Wang, Y. (2013). Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years. Journal of Arid Land, 5(4), 521–530. https://doi.org/10.1007/s40333-013-0180-0

    Article  Google Scholar 

  • Yang, J., Guo, X., Li, Y., Marinello, F., Ercisli, S., & Zhang, Z. (2022). A survey of few-shot learning in smart agriculture: developments, applications, and challenges. Plant Methods, 2022 18:1(1), 1–12. https://doi.org/10.1186/S13007-022-00866-2. 18

    Article  Google Scholar 

  • Yang, S., Feng, Q., Liang, T., Liu, B., Zhang, W., & Xie, H. (2018). Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region. Remote Sensing of Environment, 204, 448–455. https://doi.org/10.1016/J.RSE.2017.10.011

    Article  Google Scholar 

  • Yu, H., Wu, Y., Niu, L., Chai, Y., Feng, Q., Wang, W., et al. (2021). A method to avoid spatial overfitting in estimation of grassland above-ground biomass on the Tibetan Plateau. Ecological Indicators, 125, 107450. https://doi.org/10.1016/j.ecolind.2021.107450

    Article  Google Scholar 

  • Zhang, C., Kovacs, J. M., Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13, 693–712. https://doi.org/10.1007/s11119-012-9274-5

    Article  CAS  Google Scholar 

  • Zhao, F., Xu, B., Yang, X., Jin, Y., Li, J., Xia, L., et al. (2014). Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China. Remote Sensing, 6(6), 5368–5386. https://doi.org/10.3390/rs6065368

    Article  Google Scholar 

  • Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., et al. (2004). Estimating aboveground biomass using Landsat 7 ETM + data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, 93(3), 402–411. https://doi.org/10.1016/J.RSE.2004.08.008

    Article  Google Scholar 

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Funding

This work was supported by Fundação para a Ciência e Tecnologia through projects “LEAnMeat - Lifecycle-based Environmental Assessment and impact reduction of Meat production with a novel multi-level tool” (PTDC/EAM-AMB/30809/2017) and “GrassData - Development of algorithms for identification, monitoring, compliance checks and quantification of carbon sequestration in pastures” (DSAIPA/DS/0074/2019), projects UIDB/04129/2020, UIDP/04129/2020 and UIDB/05183/2020, and by grants SFRH/BD/115407/2016 (T. Morais) and CEECIND/00365/2018 (R. Teixeira). The work was also supported by FCT/MCTES (PIDDAC) through project UID/EEA/50009/2019 and by Programa de desenvolvimento rural (PDR2020) through “Viabilização de pastagens semeadas biodiversas através da otimização da fertilização fosfatada” (PDR2020-101-030690) and “GO SOLO: Avaliação da dinâmica da matéria orgânica em solos de pastagens semeadas biodiversas através do desenvolvimento de um método de monitorização expedito e a baixo custo” (PDR2020-101-031243).

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Authors and Affiliations

  1. MARETEC – Marine, Environment and Technology Centre, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisboa, Portugal

    Tiago G. Morais, Marjan Jongen, Tiago Domingos & Ricardo F.M. Teixeira

  2. Centro de Estudos Florestais (CEF), Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisboa, Portugal

    Marjan Jongen

  3. Linking Landscape, Environment, Agriculture and Food (LEAF), Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisboa, Portugal

    Camila Tufik & David Fangueiro

  4. Terraprima – Serviços Ambientais, Sociedade Unipessoal, Lda, 2135-199, Samora Correia, Portugal

    Nuno R. Rodrigues & Ivo Gama

  5. Mediterranean Institute for Agriculture, Environment and Development (MED), Universidade de Évora, P.O. Box 94, 7002-554, Évora, Portugal

    João Serrano

  6. IDMEC - Mechanical Engineering Institute, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Susana Vieira

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  1. Tiago G. Morais
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Morais, T.G., Jongen, M., Tufik, C. et al. Characterization of portuguese sown rainfed grasslands using remote sensing and machine learning. Precision Agric 24, 161–186 (2023). https://doi.org/10.1007/s11119-022-09937-9

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