Accounting for minimum data required to train a machine learning model to accurately monitor Australian dairy pastures using remote sensing

Machine Learning


  • Roche, J. R. et al. A 100-year review: A century of change in temperate grazing dairy systems. J. Dairy Sci. 100, 10189–10233. https://doi.org/10.3168/jds.2017-13182 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Roche, J. R. et al. Review: New considerations to refine breeding objectives of dairy cows for increasing robustness and sustainability of grass-based milk production systems. Animal 12, 350–362. https://doi.org/10.1017/S1751731118002471 (2018).

    Article 

    Google Scholar 

  • Moscovici Joubran, A., Pierce, K. M., Garvey, N., Shalloo, L. & O’Callaghan, T. F. Invited review: A 2020 perspective on pasture-based dairy systems and products. J. Dairy Sci. 104, 7364–7382. https://doi.org/10.3168/jds.2020-19776 (2020).

    Article 
    CAS 

    Google Scholar 

  • Daley, C. A., Abbott, A., Doyle, P. S., Nader, G. A. & Larson, S. A review of fatty acid profiles and antioxidant content in grass-fed and grain-fed beef. Nutr. J. 9, 1–12. https://doi.org/10.1186/1475-2891-9-10 (2010).

    Article 
    CAS 

    Google Scholar 

  • Elgersma, A. Grazing increases the unsaturated fatty acid concentration of milk from grass-fed cows: A review of the contributing factors, challenges and future perspectives. Eur. J. Lipid Sci. Technol. 117, 1345–1369. https://doi.org/10.1002/ejlt.201400469 (2015).

    Article 
    CAS 

    Google Scholar 

  • van Zanten, H. H. E., Mollenhorst, H., Klootwijk, C. W., van Middelaar, C. E. & de Boer, I. J. M. Global food supply: Land use efficiency of livestock systems. Int. J. Life Cycle Assess. 21, 747–758. https://doi.org/10.1007/s11367-015-0944-1 (2016).

    Article 
    CAS 

    Google Scholar 

  • García, S. C. & Fulkerson, W. Opportunities for future Australian dairy systems: a review. Aust. J. Exp. Res. 45, 1041–1055. https://doi.org/10.1071/EA04143 (2005).

    Article 

    Google Scholar 

  • Hanrahan, L. et al. Factors associated with profitability in pasture-based systems of milk production. J. Dairy Sci. 101, 5474–5485. https://doi.org/10.3168/jds.2017-13223 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Shalloo, L. et al. Review: Grass-based dairy systems, data and precision technologies. Animal 12, s262–s271. https://doi.org/10.1017/S175173111800246X (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wales, W. J. & Kolver, E. S. Challenges of feeding dairy cows in Australia and New Zealand. Anim. Prod. Sci. 57, 1366–1383. https://doi.org/10.1071/AN16828 (2017).

    Article 

    Google Scholar 

  • García, S. C., Islam, M. R., Clark, C. E. F. & Martin, P. M. Kikuyu-based pasture for dairy production: A review. Crop Pasture Sci. 65, 787–797. https://doi.org/10.1071/cp13414 (2014).

    Article 

    Google Scholar 

  • Chapman, D. Using ecophysiology to improve farm efficiency: Application in temperate dairy grazing systems. Agriculture 6, 17–36. https://doi.org/10.3390/agriculture6020017 (2016).

    Article 

    Google Scholar 

  • Macdonald, K. A., Glassey, C. B. & Rawnsley, R. P. in 4th Australasian Dairy Science Symposium. p. 199–209 (2010).

  • García, S. C. & Holmes, C. Seasonality of calving in pasture-based dairy systems: its effects on herbage production, utilisation and dry matter intake. Aust. J. Exp. Res. 45, 1–9. https://doi.org/10.1071/EA00110 (2005).

    Article 
    ADS 

    Google Scholar 

  • Fariña, S., Garcia, S. C. & Fulkerson, W. J. A complementary forage system whole-farm study: forage utilisation and milk production. Anim. Prod. Sci. 51, 460–470. https://doi.org/10.1071/AN10242 (2011).

    Article 
    CAS 

    Google Scholar 

  • Fariña, S., Garcia, S. C., Fulkerson, W. J. & Barchia, I. M. Pasture-based dairy farm systems increasing milk production through stocking rate or milk yield per cow: Pasture and animal responses. Grass Forage Sci. 66, 316–332. https://doi.org/10.1111/j.1365-2494.2011.00795.x (2011).

    Article 

    Google Scholar 

  • Fulkerson, W. J. & Donaghy, D. J. Plant-soluble carbohydrate reserves and senescence – key criteria for developing an effective grazing management system for ryegrass-based pastures: a review. Aust. J. Exp. Agric. 41, 261–275 (2001).

    Article 
    CAS 

    Google Scholar 

  • García, S. C. et al. in 22nd International Grassland Congress. p. 1709–1716 (2013).

  • Heins, B. J., Pereira, G. M. & Sharpe, K. T. Precision technologies to improve dairy grazing systems. JDS Commun. 4, 308–315. https://doi.org/10.3168/jdsc.2022-0308 (2023).

    Article 

    Google Scholar 

  • Ortega, G. et al. Monitoring herbage mass and pasture growth rate of large grazing areas: A comparison of the correspondence, cost and reliability of indirect methods. J. Agric. Sci. 161, 502–511. https://doi.org/10.1017/s0021859623000333 (2023).

    Article 

    Google Scholar 

  • Fulkerson, W. J. & Slack, K. Estimating mass of temperate and tropical pastures in the subtropics. Aust. J. Exp. Agric. 33, 865–869 (1993).

    Article 

    Google Scholar 

  • Reeves, M., Fulkerson, W. J. & Kellaway, R. C. Forage quality of kikuyu (Penisetum clandestinum): Time of defoliation and nitrogen fertiliser application and in comparison with perennail ryegrass (Lolium perenne). Aust. J. Agric. Res. 47, 1349–1359 (1996).

    Article 

    Google Scholar 

  • López-Díaz, J. E., Roca-Fernández, A. I. & González-Rodríguez, A. Measuring herbage mass by Non-destructive methods: A review. J. Agric. Sci. Technol. 1, 303–314 (2011).

    Google Scholar 

  • Reinermann, S., Asam, S. & Kuenzer, C. Remote sensing of grassland production and management—A review. Remote Sens. 12, 1949–1981. https://doi.org/10.3390/rs12121949 (2020).

    Article 
    ADS 

    Google Scholar 

  • French, P., O’Brien, B. & Shalloo, L. Development and adoption of new technologies to increase the efficiency and sustainability of pasture-based systems. Anim. Prod. Sci. 55, 931–935. https://doi.org/10.1071/an14896 (2015).

    Article 

    Google Scholar 

  • McSweeney, D., Coughlan, N. E., Cuthbert, R. N., Halton, P. & Ivanov, S. Micro-sonic sensor technology enables enhanced grass height measurement by a rising plate meter. Inf. Process. Agric. 6, 279–284. https://doi.org/10.1016/j.inpa.2018.08.009 (2019).

    Article 

    Google Scholar 

  • Doonan, B. M. & Irvine, L. D. Pasture management for Tasmanian dairy farmers. 1–61 (Tasmania, 2006).

  • Nickmilder, C. et al. Development of machine learning models to predict compressed sward height in walloon pastures based on sentinel-1, sentinel-2 and meteorological data using multiple data transformations. Remote Sens. 13, 408–437. https://doi.org/10.3390/rs13030408 (2021).

    Article 
    ADS 

    Google Scholar 

  • Edirisinghe, A., Clark, D. & Waugh, D. Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing. Int. J. Appl. Earth Obs. Geoinf. 16, 5–16. https://doi.org/10.1016/j.jag.2011.11.006 (2012).

    Article 
    ADS 

    Google Scholar 

  • Edirisinghe, A., Hill, M. J., Donald, G. E. & Hyder, M. Quantitative mapping of pasture biomass using satellite imagery. Int. J. Remote Sens. 32, 2699–2724. https://doi.org/10.1080/01431161003743181 (2011).

    Article 

    Google Scholar 

  • Piñeiro, G., Oesterheld, M. & Paruelo, J. M. Seasonal variation in aboveground production and radiation-use efficiency of temperate rangelands estimated through remote sensing. Ecosystem 9, 357–373. https://doi.org/10.1007/s10021-005-0013-x (2006).

    Article 

    Google Scholar 

  • Sellers, P. J. Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens. 6, 1335–1372. https://doi.org/10.1080/01431168508948283 (1985).

    Article 

    Google Scholar 

  • LIC. SPACETM, https://www.lic.co.nz/products-and-services/space/. Accessed 30 May 2023.

  • Pasture.io. Pasture.io-pasture measurement on autopilot, https://pasture.io/. Accessed 12 May 2023.

  • Stumpe, C., Leukel, J. & Zimpel, T. Prediction of pasture yield using machine learning-based optical sensing: A systematic review. Precis. Agric. 25, 430–459. https://doi.org/10.1007/s11119-023-10079-9 (2023).

    Article 

    Google Scholar 

  • Ara, I. et al. Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review. Agric. Water Manag. 257, 107161–107177. https://doi.org/10.1016/j.agwat.2021.107161 (2021).

    Article 

    Google Scholar 

  • Ogungbuyi, M. G., Mohammed, C., Ara, I., Fischer, A. M. & Harrison, M. T. Advancing skyborne technologies and high-resolution satellites for pasture monitoring and improved management: A review. Remote Sens. 15, 4866. https://doi.org/10.3390/rs15194866 (2023).

    Article 
    ADS 

    Google Scholar 

  • Belgiu, M. & Drăguţ, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011 (2016).

    Article 
    ADS 

    Google Scholar 

  • de Togeiro Alckmin, G., Kooistra, L., Rawnsley, R. & Lucieer, A. Comparing methods to estimate perennial ryegrass biomass: Canopy height and spectral vegetation indices. Precis. Agric. 22, 205–225. https://doi.org/10.1007/s11119-020-09737-z (2020).

    Article 

    Google Scholar 

  • Kallenbach, R. L. Describing the dynamic: Measuring and assessing the value of plants in the pasture. Crop Sci. 55, 2531–2539. https://doi.org/10.2135/cropsci2015.01.0065 (2015).

    Article 
    CAS 

    Google Scholar 

  • L’Huillier, P. J. & Thomson, N. A. in Proceedings of New Zealand Grassland Association. p. 117–122 (1988).

  • King, W. M. G., Rennie, G. M., Dalley, D. E., Dynes, R. A. & Upsdell, M. P. in 4th Australasian Dairy Science Symposium. p. 233–238 (2010).

  • Gargiulo, J. et al. Spatial and temporal pasture biomass estimation integrating electronic plate meter, planet cubesats and sentinel-2 satellite data. Remote Sens. 12, 3222–3238. https://doi.org/10.3390/rs12193222 (2020).

    Article 
    ADS 

    Google Scholar 

  • Botha, P. R., Meeske, R. & Snyman, H. A. Kikuyu over-sown with ryegrass and clover; dry matter production, botanical composition and nutritional value. Afr. J. Range Forage Sci. 25, 93–101. https://doi.org/10.2989/AJRF.2008.25.3.1.598 (2008).

    Article 

    Google Scholar 

  • Alvarez-Mendoza, C. I. et al. Predictive modeling of above-ground biomass in brachiaria pastures from satellite and UAV imagery using machine learning approaches. Remote Sens. 14, 5870–5891. https://doi.org/10.3390/rs14225870 (2022).

    Article 
    ADS 

    Google Scholar 

  • Morse-McNabb, E. M., Hasan, M. F. & Karunaratne, S. A multi-variable sentinel-2 random forest machine learning model approach to predicting perennial ryegrass biomass in commercial dairy farms in southeast Australia. Remote Sens. 15, 2915–2946. https://doi.org/10.3390/rs15112915 (2023).

    Article 
    ADS 

    Google Scholar 

  • Punalekar, S. M. et al. Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model. Remote Sens. Environ. 218, 207–220. https://doi.org/10.1016/j.rse.2018.09.028 (2018).

    Article 
    ADS 

    Google Scholar 

  • Anderson, G. & McNaughton, L. in 8th Australasian Dairy Symposium. p. 191–195 (2018).

  • Woodward, S. J. R., Neal, M. B. & Cross, P. S. Preliminary investigation into the feasibility of combining satellite and GPS data to identify pasture growth and grazing. J. N. Z. Grassl. 81, 47–54. https://doi.org/10.33584/jnzg.2019.81.404 (2019).

    Article 

    Google Scholar 

  • Harrison, M. T., Roggero, P. P. & Zavattaro, L. Simple, efficient and robust techniques for automatic multi-objective function parameterisation: Case studies of local and global optimisation using APSIM. Environ. Model. Softw. 117, 109–133. https://doi.org/10.1016/j.envsoft.2019.03.010 (2019).

    Article 

    Google Scholar 

  • Handcock, R. N. et al. in Innovations in remote sensing and photogrammetry lecture notes in geoinformation and cartography (eds Simon Jones & Karin Reinke) Ch. Chapter 24, 309–321 (Springer, 2009).

  • Mata, G. et al. in Proceedings of New Zealand Grassland Association. p. 109–114 (2011).

  • Ogungbuyi, M. G. et al. Enabling regenerative agriculture using remote sensing and machine learning. Land 12, 1142. https://doi.org/10.3390/land12061142 (2023).

    Article 

    Google Scholar 

  • Fulkerson, W. J., Slack, K. & Havilah, E. The effect of defoliation interval and height on growth and herbage quality of kikuyu grass (Pennisetum clandestinum). Trop. Grassl. 33, 138–145 (1999).

    Google Scholar 

  • Anderson, G. et al. Use of pasture botanical composition data on the accuracy of satellite pasture biomass estimates. J. N. Z. Grassl. 81, 249–254. https://doi.org/10.33584/jnzg.2019.81.367 (2019).

    Article 

    Google Scholar 

  • Reeves, M., Fulkerson, W. J., Kellaway, R. C. & Dove, H. A comparison of three techniques to determine the herbage intake of dairy cows grazing kikuyu (Pennisetum clandestinum) pasture. Aust. J. Agric. Res. 36, 23–30 (1996).

    Article 

    Google Scholar 

  • Numata, I. et al. Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data. Remote Sens. Environ. 109, 314–327. https://doi.org/10.1016/j.rse.2007.01.013 (2007).

    Article 
    ADS 

    Google Scholar 

  • Australian Bureau of Meteorology. Historical weather data, http://www.bom.gov.au/climate/data-services/data-requests.shtml/. Accessed 9 April 2022.

  • Chang-Fung-Martel, J., Harrison, M. T., Rawnsley, R., Smith, A. P. & Meinke, H. The impact of extreme climatic events on pasture-based dairy systems: A review. Crop Pasture Sci. 68, 1158–1169. https://doi.org/10.1071/cp16394 (2017).

    Article 

    Google Scholar 

  • R Development Core Team. R: A language and environment for statistical computing. (2009).

  • Planet Team. Planet Imagery Product Specifications, https://www.planet.com/products/planet-imagery/. Accessed 15 Nov 2022.

  • European Space Agency. Sentinel-2, https://sentinel.esa.int/web/sentinel/missions/sentinel-2/. Accessed 10 Oct 2022.

  • Bibby, J. & Toutenburg, H. Improved estimation and prediction. J. Appl. Math. Mech 58, 45–49. https://doi.org/10.1002/zamm.19780580108 (1978).

    Article 
    MathSciNet 

    Google Scholar 



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *