OVERVIEW OF OUR PROJECT
Development of decision-support components for optimal N fertilizer applications
at the field scale
Agriculture and Agri-Food Canada Science and Technology Branch Research Project (2013-2016)
Objective (partial)
• To establish the key factors (soil, management, weather) and relationships driving optimal N recommendations through meta-analyses and modeling based on extensive databases from past and new experiments.
Meta-analyses
• Soil properties, weather conditions, previous crop, soil organic matter status, among others, are known to affect soil N availability and plant N uptake. Meta-analysis is suitable for agronomic research in which several investigators have examined similar problems and generated substantial information sometimes characterized by heterogeneity and contradictions. It is a recognized statistical method for estimating treatment effects in a series of experiments to explain the sources of heterogeneity that has been used in many fields of research such as medicine, social science, ecology and agricultural sciences. If the treatment effect varies from one study to the next (which is often the case for N fertilization studies), meta-analysis can be applied to assess the levels of effects for subgroups and thus identifies factors associated with the magnitude of the effect sizes. Meta-analysis is a systematic method for combining the results from a series of studies and addressing apparently conflicting findings by identifying potential explanatory variables. Example of paper: Tremblay, N., M.Y. Bouroubi, C. Bélec, R. Mullen, N. Kitchen, W. Thomason, S. Ebelhar, D. Mengel, B. Raun, D. Francis, E.D. Vories, and I. Ortiz-Monasterio. 2012. Corn Response to Nitrogen is Influenced by Soil Texture and Weather. Agronomy Journal 104(6): 1658-1671. https://www.agronomy.org/publications/aj/pdfs/104/6/1658
Modeling
• Soil-crop models are essential tools for optimizing agriculture production with regard to environmental forcing conditions while facing these growing challenges. Soil-crop models predict yield potential and N and water use under given pedoclimatic conditions and account for growth-limiting factors such as drought, heat, and frost (Gonzalez-Dugo et al., 2010). Soil-crop models can be used to refine management practices, especially for fertilizer usage and timing, by simulating crop productivity in response to regionally observed climatic variations and soil properties (Singh et al., 2008). To predict changes in agroecosystems, process-based models must be dynamic and integrate the impact of climate, soil properties, management practices on the growth and development of various crops. STICS is such of a soil–crop model that simulates crop development and environmental impacts. It was initially parameterized and evaluated for bare soil, winter wheat, and maize (Brisson et al., 1998) and then adapted for other crops such as winter rapeseed, sunflower, soybean, flax, sugar beet, and potato (Brisson et al., 2003). Recently STICS was adapted to several short season field crops of eastern Canada and verified for biomass and yield (Jego et al., 2010, 2011). Verification of the N dynamics predictions in soil and plant was initiated in wheat and corn grown in eastern Canada (Sansoulet et al., 2012). Soil-crop models can be useful tools in illustrating and predicting the long-term effects of agricultural practices on the N cycle in various conditions as illustrated in the catch crop study of Constantin et al., (2012). Example of paper: Jégo, G., Pattey, E., Bourgeois, G., Morrison, M.J., Drury, C.F., Tremblay, N. and Tremblay, G. 2010. Calibration and performance evaluation of soybean and spring wheat cultivars using the STICS crop model in Eastern Canada. Field Crops Res. 117: 183-196.
For more information
• Nicolas.Tremblay@agr.gc.ca