en

Recent highlights

Next-generation agri-environment schemes: Integrating innovations in biodiversity monitoring, farming technologies and digital tools

Rapid developments in sensor technologies, digital tools, and artificial intelligence are transforming how biodiversity and ecosystems are monitored and managed. At the same time, novel farming technologies are reshaping food production, with substantial implications for agrobiodiversity. However, little research has examined how these technological developments can be integrated into agri-environment schemes (AES), the key policy instrument for conserving agrobiodiversity. This perspective paper explores how recent developments in digital technologies both require and enable us to reconsider today’s AES. 

paper_1.webp
© Cord

Cultivated lands: Blind spots in global biodiversity data

Biodiversity loss due to agricultural expansion and intensification is well documented. Accordingly, the Global Biodiversity Framework calls for transforming agroecosystems towards sustainable management. Yet, cultivated land remains a blind spot in global, open-access biodiversity data, creating knowledge gaps and reporting biases. Overcoming this requires tailored incentives, data integration, and farmer agency.

paper_2.webp
© Remelgado

Narrowing farmland biodiversity knowledge gaps with Digital Agriculture

Digital Agriculture – broadly defined as the use of digital technologies and data to manage and optimize agricultural production systems – holds significant but largely untapped potential for biodiversity monitoring. Both fields share many (semi-)automated data collection technologies, analytical methods and workflows, but remain largely disconnected — and are sometimes even perceived as incompatible — in research, education and practice. Here, we explore how existing data streams from Digital Agriculture can directly contribute with primary biodiversity data required by policy-relevant applications, linking them to the Essential Biodiversity Variables framework. 

44264_2025_118_Fig2_HTML.webp
© Remelgado

Publication list

  • Remelgado, R., Beckmann, M., Vítězslav, M. et al...Cord, A. F. (2026). Narrowing farmland biodiversity knowledge gaps with Digital Agriculture. npj Sustain. Agric. 4, 10. https://doi.org/10.1038/s44264-025-00118-5
  • Wuepper, D., Möhring, N., Cord, A. F., Meijide, A., Storm, H., Qaim, M., Heckelei, T., Börner, J., Hadi, H., Kuhlmann, H., Stachniss, C., & Ewert, F. (2026). From technological fixes to systemic change: Vision-led innovation for Europe's crop farming systems. Agricultural Systems, 233, 104593. https://doi.org/10.1016/j.agsy.2025.104593
  • Kernecker, M. L., Hagemann, N., Cord, A. F., Wendler, J., & Knierim, A. (2026). Cultivating biodiversity: When conservation in agricultural landscapes embodies farmers' values. People and Nature, 00, 1–18. https://doi.org/10.1002/pan3.70237
  • Moudrý, V., Remelgado, R., Forkel, M., Torresani, et al. (2026). Spaceborne Canopy Height Products Should Be Complemented With Airborne Laser Scanning Data: Toward a European Canopy Height Model,  Earth and Space Science, https://doi.org/10.1029/2025EA004544
  • Birkhofer, K., Wätzold, F., Remelgado, R., Addison, P., Aigbedion‐Atalor, P., Cord, A. F., ...(2026). A Framework for Multidimensional Management of Invasive Alien Insect Pests in Sub‐Saharan Africa, Journal of Applied Entomology, https://doi.org/10.1111/jen.70060
  • Cord, A., & Wätzold, F. (2026). Next-generation agri-environment schemes: Integrating innovations in biodiversity monitoring, farming technologies and digital tools, Biological Conservation, 317, 111761. https://doi.org/10.1016/j.biocon.2026.111761
  • Kernecker, M. L., Hagemann, N., Cord, A. F., Wendler, J., & Knierim, A. (2026). Cultivating biodiversity: When conservation in agricultural landscapes embodies farmers' values. People and Nature, 8, 706–723. https://doi.org/10.1002/pan3.70237
  • Hölting, L., Cord, A. F., Hänel, S., et al. (2025). Determining the Ecological Value and Farmers' Perceptions of Set-Aside Land. Ecosphere16, 10, e70433. https://doi.org/10.1002/ecs2.70433
  • Hagemann, N., Gerling, C., Hölting, L...Cord, A. F. Improving result-based schemes for nature conservation in agricultural landscapes—challenges and best practices from selected European countries. Reg Environ Change 25, 12 (2025). https://doi.org/10.1007/s10113-024-02324-2
  • Cord, A. F., Darras, K. F. A., Ogawa, R., et al. (2025). Leveraging passive acoustic monitoring for result-based agri-environmental schemes: Opportunities, challenges and next steps. Biological Conservation, 293, 110595. https://doi.org/10.1016/j.biocon.2025.111042
  • Anderle, M., Ceresa, F., Hilpold, A., Marsoner, T., Roilo, S., Scanferla, J., ... & Brambilla, M. (2025). Direct and indirect ecological drivers of bird communities in intensively cultivated landscapes. Journal of Environmental Management, 393, 127169 https://doi.org/10.1016/j.jenvman.2025.127169
  • Roilo, S., Hofmeester, T. R., Frauendorf, M., Widén, A., & Cord, A. F. (2025). The untapped potential of camera traps for farmland biodiversity monitoring: current practice and outstanding agroecological questions. Remote Sensing in Ecology and Conservation, 11(3). https://doi.org/10.1002/rse2.426
  • Ogawa, R., Gosselin, F., Darras, K. F., Roilo, S., & Cord, A. F. (2025). A classification-occupancy model based on automatically identified species data. Ecology, 106(5), e70086.https://doi.org/10.1002/ecy.70086
  • Moudrá, L., Barták, V., Moudrý, V., Remelgado, R., Roilo, S., Rocchini, D., ... & Šálek, M. (2025). Habitat heterogeneity from lidar and hyperspectral data: Implications for bird guilds and restoration management of coal mines. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.70071
  • Roilo, S., Engler, J. O., & Cord, A. F. (2025). Global impact of the COVID-19 lockdown on biodiversity data collection. Scientific Reports, 15(1), 8767. https://doi.org/10.1038/s41598-025-93275-z
  • Darras, K.F.A., Rountree, R.A., Van Wilgenburg, S.L., Cord, A.F., et al. (2025), Worldwide Soundscapes: A Synthesis of Passive Acoustic Monitoring Across Realms. Global Ecol Biogeogr, 34: e70021. https://doi.org/10.1111/geb.70021
  • Remelgado, R., Levers, C., Cord, A. F. (2025) Cultivated lands: blind spots in global biodiversity data, Trends in Ecology & Evolution, 40, 12, 1155-1158, https:/doi.org/10.1016/j.tree.2025.09.017
  • Roilo, S. (2025). Spatially-explicit biodiversity models help unpack the varying effectiveness of Agri-Environment Measures. Landscape Online, 100, 1144. https://doi.org/10.3097/LO.2025.1144
  • Anderle, M., Ceresa, F., Hilpold, A., Marsoner, T., Roilo, S., Scanferla, J., Dellavedova, R., Tappeiner, U., Brambilla, M. (2025). Direct and indirect ecological drivers of bird communities in intensively cultivated landscapes, Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2025.127169
  • Bonato, M., Burian, A., Equihua, J. A., Cord, A., F., Bartkowski, B. Strauch, M. (2025). Minimizing trade-offs in agricultural landscapes through optimal spatial allocation of agri-environmental practices, Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2025.126939
  • Ogawa, R.,  A. B. Butler,  G. Wang,  et al. (2025). Is wild turkey habitat selection spatially consistent? A three-decade meta-analysis in Mississippi. Wildlife Society Bulletin 49(S1):e70000, https://doi.org/10.1002/wsb.70000
  • Moudrý, V., Bazzichetto, M., Remelgado, R., Devillers, R., Lenoir, J., Mateo, R.G., Lembrechts, J.J., Sillero, N., Lecours, V., Cord, A.F., Barták, V., Balej, P., Rocchini, D., Torresani, M., Arenas-Castro, S., Man, M., Prajzlerová, D., Gdulová, K., Prošek, J., Marchetto, E., Zarzo-Arias, A., Gábor, L., Leroy, F., Martini, M., Malavasi, M., Cazzolla Gatti, R., Wild, J. and Šímová, P. (2024). Optimising occurrence data in species distribution models: sample size, positional uncertainty, and sampling bias matter. Ecography, 2024: e07294. https://doi.org/10.1111/ecog.07294
  • Barrasso, C., Krüger, R., Eltner, A., & Cord, A. F. (2024). Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning. Ecological Indicators, 169, https://doi.org/10.1016/j.ecolind.2024.112780
  • Schulz, D., Stetter, C., Muro, J., Spekker, J., Börner, J., Cord, A., F., Finger, R. (2024). Trade-offs between grassland plant biodiversity and yields are heterogenous across Germany. Commun Earth Environ 5, 514, https://doi.org/10.1038/s43247-024-01685-0
  • Ogawa, R., Engler, J. O., Cord, A. F. (2024). Functional responses in habitat selection as a management tool to evaluate agri-environment schemes for farmland birds. Ecological Modelling, 494, 111762, https://doi.org/10.1016/j.ecolmodel.2024.110778
  • Václavík, T., Beckmann, M., Bednář, M., Brdar, S., Breckenridge, G., Cord, A.F., ...(2024). Farming system archetypes help explain the uptake of agri-environment practices in Europe, Environ. Res. Lett. 19, 074004, https://doi.org/10.1088/1748-9326/ad4efa