Vergleich der ökonomischen Auswirkungen der digitalen Grünlandertragsschätzung in kleinstrukturierten Bergbauernbetrieben

Autor/innen

  • Anna Kiefer
  • Christoph Stumpe
  • Christoph Hütt
  • Enno Bahrs

DOI:

https://doi.org/10.15150/lt.2024.3302

Abstract

Diese Studie vergleicht anhand einer Kosten-Nutzen-Analyse die wirtschaftlichen Auswirkungen des Einsatzes dreier digitaler Technologien in kleinbäuerlichen Betrieben in bergigen Regionen Süddeutschlands zur Grünlandertragschätzung:„Rising Plate Meter (RPM)“, Unmanned Aerial Vehicle mit „Structure from Motion (UAV SfM)“ und „Portable Light Detection and Ranging (UAV LiDAR)“. Die Ergebnisse zeigen, dass die digitale Grünlandertragsschätzung nach derzeitigem Stand der Technik zu vergleichsweisen hohen Kosten führt, die zu einem großen Teil aus Arbeits- und Abschreibungskosten bestehen. Dennoch konnten diese beim Einsatz des RPM in allen untersuchten Betriebstypen kompensiert werden, sofern dadurch eine Verbesserung der Weidenutzung um nur 5 % erzielt wird. Die Kosten für ein UAV-LiDAR konnten dagegen nach dem derzeitigen Stand der Technologie wirtschaftlich nicht kompensiert werden. Sobald jedoch die technischen Entwicklungen und positiven Veränderungen der rechtlichen Rahmenbedingungen umgesetzt sind, sollten die Kosten der untersuchten UAV-basierten Technologien deutlich sinken. Dies könnte zu einer weiteren Verbreitung in weidebasierten Produktionssystemen führen.

Literaturhinweise

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), pp. 649–671

Ali, A. M.; Abouelghar, M. A.; Belal, A. A.; Saleh, N.; Younes, M.; Selim, A.; Magignan, S. (2022): Crop yield prediction using multi sensors remote sensing. The Egyptian Journal of Remote Sensing and Space Science 25(3), pp. 711–716

Alckmin, G. T. de; Kooistra, L.; Rawnsley, R.; Lucieer, A. (2021): Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices. Precision Agriculture 22(1), pp. 205–225

Bareth, G.; Schellberg, J. (2018): Replacing manual rising plate meter measurements with low-cost UAV-derived sward height data in grasslands for spatial monitoring. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 86(3), pp. 157–168

Bazzo, C. O. G.; Kamali, B.; Hütt, C.; Bareth, G.; Gaiser, T. (2023): A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV. Remote Sensing 15(3), p. 639

Beukes, P. C.; McCarthy, S.; Wims, C. M.; Gregorini, P.; Romera, A. J. (2019): Regular estimates of herbage mass can improve profitability of pasture-based dairy systems. Animal Production Science 59(2), pp. 359–367

Bioland (2022): Bioland Richtlinien. https://www.bioland.de/richtlinien, accessed on 25 July 2023

Borra-Serrano, I.; Swaef, T. de; Muylle, H.; Nuyttens, D.; Vangeyte, J.; Mertens, K.; Saeys, W.; Somers, B.; Roldán‐Ruiz, I.; Lootens, P. (2019): Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery. Grass and Forage Science 74(3), pp. 356–369

Campbell, B. M.; Beare, D. J.; Bennett, E. M.; Hall-Spencer, J. M.; Ingram, J. S.; Jaramillo, F.; Ortiz, R.; Ramankutty, N.; Sayer, J.A.; Shindell, D. (2017): Agriculture production as a major driver of the Earth system exceeding planetary boundaries. Ecology and Society 22(4): 8

Chen, Y.; Guerschman, J.; Shendryk, Y.; Henry, D.; Harrison, M. T. (2021): Estimating pasture biomass using sentinel-2 imagery and machine learning. Remote Sensing 13(4), 603

Cunliffe, A.M.; Brazier, R.E.; Anderson, K. (2016): Ultra-Fine Grain Landscape-Scale Quantification of Dryland Vegetation Structure with Drone-Acquired Structure-from-Motion Photogrammetry. Remote Sensensingof Environment 183, pp. 129–143

Dandois, J. P.; Ellis, E. C. (2013): High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sensing of Environment 136, pp. 259–276

De Rosa, D.; Basso, B.; Fasiolo, M.; Friedl, J.; Fulkerson, B.; Grace, P. R.; Rowlings, D. W. (2021): Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery. Computers and Electronics in Agriculture 180, 105880

Dewhurst, R. J.; Moloney, A. P. (2013): Modification of animal diets for the enrichment of dairy and meat products with omega-3 fatty acids. In: Food enrichment with omega-3 fatty acids, pp. 257–287 Woodhead Publishing

EASA (2023): Easy Access Rules for Unmanned Aircraft Systems (Regulations (EU) 2019/947 and 2019/945). https://www.easa.europa.eu/en/document-library/easy-access-rules/easy-access-rules-unmanned-aircraft-systems-regulations-eu, accessed on 25 July 2023

FAO (2023): GLEAM 3.0 Assessment of greenhouse gas emissions and mitigation potential. Food and Agricultural Organisation of the United Nations. https://www.fao.org/gleam/dashboard-old/en/, accessed on 1 April 2023

Ghajar, S.; Tracy, B. (2021): Proximal Sensing in Grasslands and Pastures. Agriculture 11(8), p. 740

Gamage, A.; Gangahagedara, R.; Gamage, J.; Jayasinghe, N.; Kodikara, N.; Suraweera, P.; Merah, O. (2023): Role of organic farming for achieving sustainability in agriculture. Farming System 1(1), 100005

Ganz, S.; Kaber, Y. and Adler, P. (2019): Measuring tree height with remote sensing—a comparison of photogrammetric and LiDAR data with different field measurements. Forests 10(8), 694

Garbach, K.; Milder, J. C.; DeClerck, F. A.; Montenegro de Wit, M.; Driscoll, L. and Gemmill-Herren, B. (2017): Examining multi-functionality for crop yield and ecosystem services in five systems of agroecological intensification. International Journal of Agricultural Sustainability 15(1), pp. 11–28

Garnett, T.; Appleby, M. C.; Balmford, A.; Bateman, I. J.; Benton, T. G.; Bloomer, P.; Godfray, H. C. J. (2013): Sustainable intensification in agriculture: premises and policies. Science 341(6141), pp. 33–34

Geipel, J.; Bakken, A. K.; Jørgensen, M.; Korsaeth, A. (2021): Forage yield and quality estimation by means of UAV and hyperspectral imaging. Precision Agriculture 22, pp. 1437–1463

Grüner, E.; Wachendorf, M.; Astor, T. (2020): The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures. PLOS ONE 15( 6), https://doi.org/10.1371/journal.pone.0234703

Hanrahan, L.; Geoghegan, A.; O’Donovan, M.; Griffith, V.; Ruelle, E.; Wallace, M.; Shalloo, L. (2017): PastureBase Ireland: A grassland decision support system and national database. Comp Electron Agric 1, pp. 193–201

Harder, P.; Pomeroy, J. W.; Helgason, W. D. (2020): Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques. The Cryosphere 14(6), pp. 1919–1935

Hart, L.; Oudshoorn, F.; Latsch, R.; Umstätter, C. (2019): How accurate is the Grasshopper® system in measuring dry matter quantity of Swiss and Danish grassland? Precision Livestock Farming 9, pp. 188–193

Hart, L.; Werner, J.; Velasco, E.; Perdana-Decker, S.; Weber, J.; Dickhoefer, U.; Umstaetter, C. (2020): Reliable biomass estimates of multispecies grassland using the rising plate meter. Grassland Science in Europe 25, pp. 641–643

Hennessy, D.; Delaby, L.; van den Pol-van Dasselaar, A.; Shalloo, L. (2020): Increasing grazing in dairy cow milk production systems in Europe. Sustainability 12(6), 2443

Higgins, S.; Schellberg, J.; Bailey, J. S. (2019): Improving productivity and increasing the efficiency of soil nutrient management on grassland farms in the UK and Ireland using precision agriculture technology. European Journal of Agronomy 106, pp. 67–74

Hütt, C.; Bareth, G. (2022): Investigation of UAV-LiDAR penetration depth in meadows for monitoring forage mass. In: Grassland Science in Europe, Vol. 27 – Grassland at the Heart of Circular and Sustainable Food Systems. Caen, pp. 617–619

Hütt, C.; Bolten, A.; Hüging, H.; Bareth, G. (2022): UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 91, pp. 65–76

Kelly, P. (2019): The EU cereals sector: Main features, challenges and prospects. European Union, European Parliamentary Research Service

Kiefer, L.; Menzel, F.; Bahrs, E. (2014): The effect of feed demand on greenhouse gas emissions and farm profitability for organic and conventional dairy farms. Journal of Dairy Science 97(12), pp. 7564–7574

Kleen, J.L.; Guatteo, R. (2023): Precision Livestock Farming: What Does It Contain and What Are the Perspectives? Animals 13(5), 779

Klingler, A.; Schaumberger, A.; Vuolo, F.; Poetsch, E. M. (2020): Suitability of non-destructive yield and quality measurements on permanent grassland. 28th General Meeting of the European Grassland Federation, 19.–22.10.2020, Helsinki. In: Virkajärvi, P.; Hakala, K.; Hakojärvi, M.; Helin, J.; Herzon, I.; Jokela, V.; Peltonen, S.; Rinne, M.; Seppänen, M.; Uusi-Kämppä, J. (Hrsg.): Meeting the future demands for grassland production – Proceedings of the 28th General Meeting of the European Grassland Federation, Grassland Science in Europe, Volume 25, Wageningen, Wageningen Academic Publishers, pp. 602–604

Libran-Embid, F.; Klaus, F.; Tscharntke, T.; Grass, I. (2020): Unmanned aerial vehicles for biodiversity-friendly agricultural landscapes-A systematic review. Science of The Total Environment 732, Article 139204

Lyu, X.; Li, X., Dang, D.; Dou, H.; Wang, K.; Lou, A. (2022): Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review. Remote Sensing 14(5), 1096

Lussem, U.; Schellberg, J.; Bareth, G. (2020): Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment. PFG—J. Photogramm. Remote Sens. Geoinf. Sci. 88, pp 407–422

Mäder, P.; Fliessbach, A.; Dubois, D.; Gunst, L.; Fried, P.; Niggli, U. (2002): Soil fertility and biodiversity in organic farming. Science 296(5573), pp. 1694–1697

Makkar, H. P. S. (2018): Feed demand landscape and implications of food-not feed strategy for food security and climate change. Animal 12(8), pp. 1744–1754

Maretto, L.; Faccio, M.; Battini, D. (2023): The adoption of digital technologies in the manufacturing world and their evaluation: A systematic review of real-life case studies and future research agenda. Journal of Manufacturing Systems 68, pp. 576–600

McSweeney D.; Coughlan N.E.; Cuthbert R.N.; Halton P.; Ivanov S. (2019): Micro-sonic sensor technology enables enhanced grass height measurement by Rising Plate Meter. Information Processing in Agriculture 6, pp. 279–284

McSweeney, D.; Foley, C.; Halton, P.; O’Brien, B. (2015): Calibration of an automated grass height measurement tool equipped with global positioning system to enhance the precision of grass measurement in pasture-based farming systems. Pages 265–267 in Proc. Grassland and forages in high output dairy farming systems, Proceedings of the 18th Symposium of the European Grassland Federation, Wageningen, The Netherlands, 15–17 June 2015, Wageningen Academic Publishers

Mondelaers, K.; Aertsens, J.; Van Huylenbroeck, G. (2009): A meta‐analysis of the differences in environmental impacts between organic and conventional farming. British food journal 111(10), pp. 1098–1119

Muller, A.; Schader, C.; El-Hage Scialabba, N.; Brüggemann, J.; Isensee, A.; Erb, K. H.; Niggli, U. (2017): Strategies for feeding the world more sustainably with organic agriculture. Nature communications 8(1), pp. 1–13

Murphy, D. J.; O’Brien, B.; Hennessy, D.; Hurley, M.; Murphy, M. D. (2021): Evaluation of the precision of the rising plate meter for measuring compressed sward height on heterogeneous grassland swards. Precision Agriculture 22, pp. 922–946

Naturland (2022): Naturland Richtlinien Erzeugung. https://www.naturland.de/images/01_naturland/documents/Naturland-Richtlinien_Erzeugung.pdf, accessed on 30 Dec 2022

O’Donovan, M. (2000): The relationship between the performance of dairy cows and grassland management practice on intensive dairy farms in Ireland. PhD Thesis, National University of Ireland

O'Donovan, M.; Dillon, P.; Rath, M.; Stakelum, G. (2002): A comparison of four methods of herbage mass estimation. Irish Journal of Agricultural and Food Research 41(1), pp. 17–27

O’Brien B.; Murphy D.; Askari M.S.; Burke R.; Magee A.; Umstätter, C.; McCarthy T. (2019): Modelling precision grass measurements for a web-based decision platform to aid grassland management. Precision Livestock Farming 9, pp. 858–863

Obanawa, H.; Yoshitoshi, R.; Watanabe, N.; Sakanoue, S. (2020): Portable LiDAR-based method for improvement of grass height measurement accuracy: comparison with SfM methods. Sensors 20(17), 4809

Olesen, J. E.; Schelde, K.; Weiske, A.; Weisbjerg, M. R.; Asman, W. A. H.; Djurhuus, J. (2006): Modelling greenhouse gas emissions from European conventional and organic dairy farms. Agriculture ecosystems and environment 112(2-3), pp. 207–220

Oliveira, R. A.; Näsi, R.; Niemeläinen, O.; Nyholm, L.; Alhonoja, K.; Kaivosoja, J.; Honkavaara, E: (2019): Assessment of RGB and hyperspectral UAV remote sensing for grass quantity and quality estimation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, pp 489–494

Picek, L.; Šulc, M.; Patel, Y.; Matas, J. (2022): Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings. Frontiers in Plant Science 13, p. 787527

Ploll, U.; Petritz, H.; Stern, T. (2020): A social innovation perspective on dietary transitions: Diffusion of vegetarianism and veganism in Austria. Environmental Innovation and Societal Transitions 36, pp. 164–176

Riegl (2023): Miniaturized LiDAR sensor for unmanned laser scanning RIEGL miniVUX-1 UAV®. www.ricopter.com, accessed on 15 March 2023

Rinehart, L. (2008): Ruminant nutrition for graziers. National Sustainable Agriculture Information Service (ATTRA)

Rotz, C. A. (2018): Modeling greenhouse gas emissions from dairy farms. Journal of Dairy science 101(7), pp. 6675–6690

Sanderson, M. A.; Skinner, R. H.; Barker, D. J.; Edwards, G. R.; Tracy, B. F.; Wedin, D. A. (2004): Plant species diversity and management of temperate forage and grazing land ecosystems. Crop Science 44(4), pp. 1132–1144

Schader, C.; Muller, A.; Scialabba, N. E. H.; Hecht, J.; Isensee, A.; Erb, K. H.; Niggli, U. (2015): Impacts of feeding less food-competing feedstuffs to livestock on global food system sustainability. Journal of the Royal Society Interface 12(113), 2015089

Schellberg, J.; Hill, M. J.; Gerhards, R.; Rothmund, M. and Braun, M. (2008): Precision agriculture on grassland: Applications, perspectives and constraints. European Journal of Agronomy 29(2–3), pp. 59–71

Schwieder, M.; Buddeberg, M.; Kowalski, K.; Pfoch, K.; Bartsch, J.; Bach, H.; Pickert, J.; Hostert, P. (2020): Estimating grassland parameters from Sentinel-2: A model comparison study. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 88, pp. 379–390

Steffen, W.; Richardson, K.; Rockstrom, J.; Cornell, S.E.; Fetzer, I.; Bennett, E.M.; Biggs. R.; Carpenter, S.R.; de Vries, W.; de Wit, C.A.; Folke, C. (2015): Planetary boundaries: Guiding human development on a changing planet. Science 347(6223)

Stumpe, C.; Werner, J.; Böttinger, S. (2021): Accuracy improvement of Rising Plate Meter measurements to support management decisions in the Black Forest region. Sensing–New Insights into Grassland Science and Practice, pp. 217–219

Sozzi, M.; Marinello, F.; Pezzuolo, A.; Sartori, L. (2018): Benchmark of satellites image services for precision agricultural use. In: Proceedings of the AgEng Conference, Wageningen, The Netherlands, pp. 8–11

Sun, Z.; Wang, X.; Wang, Z.; Yang, L.; Xie, Y.; Huang, Y. (2021): UAVs as remote sensing platforms in plant ecology: review of applications and challenges. Journal of Plant Ecology 14(6), pp. 1003–1023

ten Harkel, J.; Bartholomeus, H.; Kooistra, L. (2019): Biomass and crop height estimation of different crops using UAV-based lidar. Remote Sens 12(1), 17

Tilman, D.; Balzer, C.; Hill, J.; Befort, B. L. (2011): Global food demand and the sustainable intensification of agriculture. Proceedings of the national academy of sciences 108(50), pp. 20260–20264

Tuomisto, H. L.; Hodge, I. D.; Riordan, P.; Macdonald, D. W. (2012): Does organic farming reduce environmental impacts? – A meta-analysis of European research. Journal of environmental management 112, pp. 309–320

Wang, D.; Xin, X.; Shao, Q.; Brolly, M.; Zhu, Z.; Chen, J. (2017): Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar. Sensors 17(1), 180

Wang, J.; Xiao, X.; Bajgain, R.; Starks, P.; Steiner, J.; Doughty, R. B.; Chang, Q. (2019): Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing 154, pp 189–201

Whitcraft, A.K.; Vermote, E.F.; Becker-Reshef, I.; Justice, C.O. (2015): Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations. Remote Sensing of Environment 156, pp 438–447

Wijesingha, J.; Astor, T.; Schulze-Brüninghoff, D.; Wengert, M.; Wachendorf, M. (2020): Predicting forage quality of grasslands using UAV-borne imaging spectroscopy. Remote Sensing 12(1), 126

Willett, W.; Rockström, J.; Loken, B.; Springmann, M.; Lang, T.; Vermeulen, S.; Murray, C. J. (2019): Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. The Lancet 393(10170), pp. 447–492

Zhang, C.; Kovacs, J.M. (2012): The application of small unmanned aerial systems for precision agriculture: a review. Precis Agric 13, pp. 693–712

Zhang, F.; Hassanzadeh, A.; Kikkert, J.; Pethybridge, S.J.; van Aardt, J.; Ientilucci, E.; Renschler, C.S.; Spacher, P.J.; Chowdhury, S. (2021): Comparison of UAS-based structure-from-motion and LiDAR for structural characterization of short Broadacre crops. Remote Sens 13(19), 3975

Zhang, Z.; Hua, T.; Zhao, Y.; Li, Y.; Wang, Y.; Wang, F.; Sun, J. (2023): Divergent effects of moderate grazing duration on carbon sequestration between temperate and alpine grasslands in China. Science of The Total Environment 858, 159621

Zhao, X.; Su, Y.; Hu, T.; Cao, M.; Liu, X.; Yang, Q.; Guan, H.; Liu, L.; Guo, Q. (2022): Analysis of UAV lidar information loss and its influence on the estimation accuracy of structural and functional traits in a meadow steppe. Ecological Indicators 135, 108515

Zheng, H.; Zhou, X.; He, J.; Yao, X.; Cheng, T.; Zhu, Y.; Cao, W.; Tian, Y. (2020): Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV). Comput. Electron. Agric. 169, 105223

Downloads

Veröffentlicht

2024-01-30

Zitationsvorschlag

Kiefer, A., Stumpe, C., Hütt, C., & Bahrs, E. (2024). Vergleich der ökonomischen Auswirkungen der digitalen Grünlandertragsschätzung in kleinstrukturierten Bergbauernbetrieben. Agricultural-engineering.Eu, 79(1). https://doi.org/10.15150/lt.2024.3302

Ausgabe

Rubrik

Fachartikel

Am häufigsten gelesenen Artikel dieser/dieses Autor/in