InnoFarm
Reconciling innovative farming practices and networks to enable sustainable development of smart Swiss farming systems (InnoFarm)
Background
Agriculture needs to produce food while at the same time reducing environmental impacts and supporting the provision of ecosystem services. Innovations in information and communications technology (ICT) that lead to new farming practices such as precision agriculture, use of spatially explicit data, climate-smart agriculture or innovative crop rotations are applied in agriculture at an increasing rate. However, a sole focus on technological aspects of ICT in agriculture might create lock-ins that restrict the large potential to contribute to more sustainable agricultural systems, especially for small farm structures. Thus, new technologies must also allow for alternative forms of networks between these small-scale farms such as collectives, cooperatives or other forms of collaboration.
Research Questions
Our vision is to contribute to an increase of resource efficiency, economic viability and to strengthen cohesion of smart farming systems in Swiss agriculture by introducing innovative farming practices, new management and network systems as well as innovative policies.
Our research questions are:
- How can crop traits relevant to the improvement of innovative small-scale and smart farming systems be detected in an automated, reliable way from images taken by UAVs? (Crop Sciences Group & Grassland Sciences Group)
- How can high-resolution data on GHG fluxes be used to assess resilience to environmental disturbances and efficient use of scarce resources in agricultural production systems and contribute to climate change mitigation strategies? (Grassland Sciences Group & Crop Sciences Group)
- Which innovative networks (from parcel exchange, machine sharing, monitoring networks to cooperatives and contract farming) can facilitate adoption and diffusion of innovative farming practices? (Agricultural Economics and Policy Group)
- What are costs and benefits as well as barriers and success factors for the implementation of networks that support adoption of technology in the Swiss agricultural sector? (whole consortium)
For further info on the other work packages and outreach to the public, please see external page here.
Publications
2022
Maier R, Hörtnagl L, Buchmann N (2022) Greenhouse gas fluxes (CO2, N2O and CH4) of pea and maize during two cropping seasons: Drivers, budgets, and emission factors for nitrous oxide. Science of The Total Environment 849: 157541, doi: external page 10.1016/j.scitotenv.2022.157541
Paul-Limoges E, Revill A, Maier R, Buchmann N, Damm A (2022) Insights for the partitioning of ecosystem evaporation and transpiration in short-statured croplands. Journal of Geophysical Research: Biogeosciences 127: e2021JG006760, doi: external page 10.1029/2021JG006760
Merz QM, Walter A, Maier R, Hörtnagl L, Buchmann N, Kirchgessner N, Aasen H (2022) Relationship of leaf elongation rate of young wheat leaves, gross primary productivity and environmental variables in the field with hourly and daily temporal resolution. Agricultural and Forest Meteorology 320: 108902, doi: external page 10.1016/j.agrformet.2022.108902
Lembrechts JJ, …, Buchmann N, ..., Eugster W, …, Feigenwinter I, …, Gharun M, …, Hörtnagl L, ..., Maier R, …, Shekhar A, et al. (2022) Global maps of soil temperature. Global Change Biology 28: 3110-3144, doi: external page 10.1111/gcb.16060
dos Reis Martins M, Necpálová M, Ammann C, Buchmann N, Calanca P, Flechard CR, Hartman MD, Krauss M, Le Roy P, Mäder P, Maier R, Morvan T, Nicolardot B, Skinner C, Six J, Keel SG (2022) Modeling N2O emissions of complex cropland management in Western Europe using DayCent: performance and scope for improvement. European Journal of Agronomy 141: 126613, doi: external page 10.1016/j.eja.2022.126613
2021
Delwiche KB, Knox SH, Malhotra A, Fluet-Chouinard E, McNicol G, Feron S, Ouyang Z, Papale D, Trotta C, Canfora E, Cheah Y-W, Christianson D, Alberto MCR, Alekseychik P, Aurela M, Baldocchi D, Bansal S, Billesbach DP, Bohrer G, Bracho R, Buchmann N, Campbell DI, Celis G, Chen J, Chen W, Chu H, Dalmagro HJ, Dengel S, Desai AR, Detto M, Dolman H, Eichelmann E, Euskirchen E, Famulari D, Friborg T, Fuchs K, Goeckede M, Gogo S, Gondwe MJ, Goodrich JP, Gottschalk P, Graham SL, Heimann M, Helbig M, Helfter C, Hemes KS, Hirano T, Hollinger D, Hörtnagl L, Iwata H, Jacotot A, Jansen J, Jurasinski G, Kang M, Kasak K, King J, Klatt J, Koebsch F, Krauss KW, Lai DYF, Mammarella I, Manca G, Marchesini LB, Matthes JH, Maximon T, Merbold L, Mitra B, Morin TH, Nemitz E, Nilsson MB, Niu S, Oechel WC, Oikawa PY, Ono K, Peichl M, Peltola O, Reba ML, Richardson AD, Riley W, Runkle BRK, Ryu Y, Sachs T, Sakabe A, Sanchez CR, Schuur EA, Schäfer KVR, Sonnentag O, Sparks JP, Stuart-Haëntjens E, Sturtevant C, Sullivan RC, Szutu DJ, Thom JE, Torn MS, Tuittila E-S, Turner J, Ueyama M, Valach AC, Vargas R, Varlagin A, Vazquez-Lule A, Verfaillie JG, Vesala T, Vourlitis GL, Ward EJ, Wille C, Wohlfahrt G, Wong GX, Zhang Z, Zona D, Windham-Myers L, Poulter B, Jackson RB (2021) FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands. Earth System Science Data 13: 3607-3689, doi: external page 10.5194/essd-13-3607-2021
Irvin J, …, Hörtnagl L, …, Maier R, …, et al. (2021) Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands. Agricultural and Forest Meteorology 308-309: 108528, doi: external page 10.1016/j.agrformet.2021.108528
2020
Graf A, Klosterhalfen A, Arriga N, Bernhofer C, Bogena H, Bornet F, Brüggemann N, Brümmer C, Buchmann N, Chi J, Chipeaux C, Cremonese E, Cuntz M, Dušek J, El-Madany TS, Fares S, Fischer M, Foltynova L, Gielen B, Gottschalk P, Gharun M, Ghiasi S, Grünwald T, Heinemann G, Heinesch B, Heliasz M, Holst J, Hörtnagl L, Ibrom A, Ingwersen J, Jurasinski G, Klatt J, Knohl A, Koebsch F, Konopka J, Korkiakoski M, Kowalska N, Kremer P, Kruijt B, Lafont S, Léonard J, De Ligne A, Longdoz B, Loustau D, Magliulo V, Mammarella I, Manca G, Mauder M, Migliavacca M, Mölder M, Ney P, Nilsson M, Neirynck J, Paul-Limoges E, Peichl M, Pitacco A, Poyda A, Rebmann C, Roland M, Sachs T, Schmidt M, Siebicke L, Schrader F, Šigut L, Tuittila ES, Varlagin A, Vendrame N, Vincke C, Völksch I, Wille C, Weber S, Wizemann HD, Zeeman M, Vereecken H (2020) Altered energy partitioning across terrestrial ecosystems in the European drought year 2018. Philosophical Transactions of the Royal Society B 375: 20190524, doi: external page 10.1098/rstb.2019.0524
Lembrechts JJ, …, Gharun M, Buchmann N, et al. (2020) SoilTemp: a global database of near-surface temperature. Global Change Biology 26: 6616-6629, doi: external page 10.1111/gcb.15123
Pastorello GZ, … Buchmann N, … Eugster W, … Feigenwinter I, … Gharun M, … Hörtnagl L, …, Maier R, … Merbold L, …, Paul-Limoges E, … in total 286 coauthors (2020) The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data 7: 225, doi: external page 10.1038/s41597-020-0534-3
2017
Walter A, Finger R, Huber R, Buchmann N. 2017. Smart farming is key to developing sustainable agriculture. PNAS 114: 6148-6150, doi: external page 10.1073/pnas.1707462114