LEB5055 - Intelligent Decision Systems in Biosystems

Credit hours

Instructor
Andre Freitas Colaco

Objective
Present and discuss concepts and applications regarding the process of knowledge construction aimed at
decision-making in biosystems in the digital age. This course addresses the different decision
architectures employed in agricultural, livestook and forestry production, including those based on
empirical observations as well as mechanistic agronomic knowledge. It also covers current approaches
that combine field experimentation, digital technologies, and data science techniques to promote the optimization of highly complex production systems, either through human or machine (artificial) learning
about the local biophysical norms that define the performance of a production system.

Content
• Decision frameworks in biosystems aimed at the production of food, fibers, and energy.
• Performance metrics for the optimization of production systems from technical, economic, and environmental perspectives.
• Simulation models
• Decision support systems
• Digital tools and data-driven decision approaches
• On-farm experimentation
• Field validation and management
• Examples of intelligent decision systems in agricultural production, livestock, and mixed systems.

Bibliography
Conhecimento agronômico, modelos de simulação e sistemas de suporte à decisão
• LEMAIRE, G., TANG, L., BÉLANGER, G., ZHU, Y., JEUFFROY, M. H. (2021). Forward new paradigms for crop mineral nutrition and fertilization towards sustainable agriculture. European Journal of Agronomy, 125.
• HOCHMAN, Z., REES, HVAN, CARBERRY, P.S., HUNT, J.R., MCCOWN, R.L., GARTMANN, A., HOLZWORTH, D., REES, SVAN, DALGLIESH, N.P., LONG, W., PEAKE, A.S., POULTON, P.L., MCCLELLAND, T. (2009). Re-inventing model-based decision support with Australian dryland farmers. Crop Pasture Science. 60, 1057–1070. https://doi.org/10.1071/CP09020
• HOLZWORTH, D.P., HUTH, N.I., DEVOIL, P.G., ZURCHER, E.J., HERRMANN, N.I., MCLEAN, G., et al. (2014). APSIM– Evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw. 62, 327–350. https://doi.org/10.1016/j.envsoft.2014.07.009.
• ZHAI, Z., MARTÍNEZ, J.F., BELTRAN, V. AND MARTÍNEZ, N.L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, p.105256.
Métricas de desempenho
• COLAÇO, A. F., PAGLIUCA, L. G., ROMANELLI, T. L., MOLIN, J. P. (2020). Economic viability, energy and nutrient balances of site-specific fertilisation for citrus. Biosystems Engineering, 200, 138–156.
• HOCHMAN, Z., & HORAN, H. (2018). Causes of wheat yield gaps and opportunities to advance the water-limited yield frontier in Australia. Field Crops Research, 228, 20–30. https://doi.org/10.1016/j.fcr.2018.08.023.
Ferramentas digitais e abordagens de decisão data-driven
• COLAÇO, A. F., RICHETTI, J., BRAMLEY, R. G. V., LAWES, R. A. (2021). How will the next-generation of sensor-based decision systems look in the context of intelligent agriculture? A case-study. Field Crops Research, 270, 108205.
• COLAÇO, A. F., WHELAN, B., BRAMLEY, R. G. V., RICHETTI, J., FAJARDO, M., MCCARTHY, A., PERRY, E. M., BANDER, A., LEO, S, FITZGERALD, G. J., LAWES, R. (2024). Digital strategies for nitrogen management in grain production systems: lessons from multi-method assessment using on-farm experimentation. Precision Agriculture, online. https://doi.org/10.1007/s11119-023-10102-z
• LAWES, R. A., OLIVER, Y. M., HUTH, N. I. (2019). Optimal nitrogen rate can be predicted using average yield and estimates of soil water and leaf nitrogen with infield experimentation. Agronomy Journal, 111(3), 1155–1164.
• NILOOFAR P, FRANCIS, D. P, LAZAROVA-MOLNAR, S., VULPE, A., VOCHIN, M. C., SUCIU, G., BALANESCU, M., ANESTIS, V., BARTZANAS, T. (2021). Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Computers and Electronics in Agriculture, 190, 106406, https://doi.org/10.1016/j.compag.2021.106406
Experimentação on-farm
• BISHOP, T. F. A., LARK, R. M. (2006). The geostatistical analysis of experiments at the landscape-scale. Geoderma, 133(1–2), 87–106. https://doi.org/10.1016/j.geoderma.2006.03.039
• BRAMLEY, R. G. V., LAWES, R. A., COOK, S. E. (2013). Spatially distributed experimentation. In M. A. Oliver, T. Bishop, & B. Marchant (Eds.), Precision Agriculture for Sustainability and Environmental Protection (205–218). Routledge.
• BRAMLEY, R. G. V., SONG, X., COLAÇO, A. F., EVANS, K. J., COOK, S. E. (2022). Did someone say "farmer-centric"? Digital tools for spatially distributed on-farm experimentation. Agronomy for Sustainable Development, 42:105. https://doi.org/10.1007/s13593-022-00836-x
• LACOSTE, M., COOK, S., MCNEE, M., GALE, D., INGRAM, J., BELLON-MAUREL, V., et al. (2022). On-Farm Experimentation to transform global agriculture. Nature Food, 3(1), 11–18. https://doi.org/10.1038/s43016-021-00424-4.