Showing papers by "Nicolas Tremblay published in 2017"

Tools and Strategies for Sustainable Nitrogen Fertilisation of Vegetable Crops

Abstract: In intensive vegetable production, N fertiliser applications often contribute to a supply of N that appreciably exceeds crop N requirements resulting in the loss of N to the environment which can result in NO3− contamination of water bodies. There is a range of tools and strategies that can assist vegetable growers to improve N management. These include various methods based on soil analysis or estimation of the soil N supply, N balance calculations, methods based on plant analysis, methods based on monitoring crops with optical sensors, and the use of computerised decision support systems based on simulation models or data bases. Use of these tools has been demonstrated to appreciably reduce fertiliser N application and N losses while maintaining production. The selection of tools to be used by a grower will be influenced by factors such as availability, the grower’s technical level, and economic considerations. For fertigation systems with high frequency N application, a combination of a planning method such as a decision support system with a monitoring method is recommended. Additional tools that can assist in demonstrating to stakeholders the benefit of improved N management are simulation models that provide scenario analysis. Fundamental strategies for improving N fertiliser management are to consider all N sources such as root zone soil mineral N and N mineralised from organic materials, and to partition N application so that it coincides with crop N demand.

Model comparison of soil processes in eastern Canada using DayCent, DNDC and STICS

Abstract: Process-based models are useful tools for estimating the complex interactions between plant, soil and climate systems, assessments which are necessary for improving nutrient cycling and reducing trace gas emissions. Incorporation of knowledge gained through new research is ongoing, thus there is a need for evaluation of model processes and process interactions. In this study, three sites in eastern Canada (St. Bruno, St. Jean, and Ottawa) planted with spring wheat during the years 1993–2007 were used to evaluate and compare the water and N process simulations of the models DayCent, DNDC, and STICS. The simulated soil moisture by all models was generally well represented with low ARE ( 0.1. The unsaturated flow mechanism included in DayCent further improved soil moisture estimates compared to the other models. When sufficient replicate data was available measurement variability was considered, resulting in soil nitrogen being only slightly underestimated (ARE of −10, −1, and −22%, for DayCent, DNDC, and STICS, respectively). On average across the three sites, considering all statistics, the DNDC model proved to be most accurate for simulating mineral N, followed by DayCent and then STICS. Continued process model development is reliant on measurement datasets that can accurately represent carbon and nitrogen dynamics. Frequently, site specific biases convolute model mechanism evaluation and thus assessments have to be conducted across numerous sites to better benchmark model performance. On this premise a comprehensive multi-site inter-model mechanism evaluation was conducted and future model development needs were identified.

Using the CSM–CERES–Maize model to assess the gap between actual and potential yields of grain maize

Abstract: Maize in Canada is grown mainly in the south-eastern part of the country. No comprehensive studies on Canadian maize yield levels have been done so far to analyse the barriers of obtaining optimal yields associated with cultivar, environmental stress and agronomic management practices. The objective of the current study was to use a modelling approach to analyse the gaps between actual and potential (determined by cultivar, solar radiation and temperature without any other stresses) maize yields in Eastern Canada. The CSM–CERES–Maize model in DSSAT v4·6 was calibrated and evaluated with measured data of seven cultivars under different nitrogen (N) rates across four sites. The model was then used to simulate grain yield levels defined as: yield potential (YP), water-limited (YW, rainfed), and water- and N-limited yields with N rates 80 kg/ha (YW, N-80N) and 160 kg/ha (YW, N-160N). The options were assessed to further increase grain yield by analysing the yield gaps related to water and N deficiencies. The CSM–CERES–Maize model simulated the grain yields in the experiments well with normalized root-mean-squared errors <0·20. The model was able to capture yield variations associated with varying N rates, cultivar, soil type and inter-annual climate variability. The seven calibrated cultivars used in the experiments were divided into three grades according to their simulated YP: low, medium and high. The simulation results for the 30-year period from 1981 to 2010 showed that the average YP was 15 000 kg/ha for cultivars with high yield potential. The YP is generally about 6000 kg/ha greater than the actual yield (YA) at each experimental site in Eastern Canada. Two-thirds of this gap between YP and YA is probably associated with water stress, as a gap of approximately 4000 kg/ha between the YW and the YP was simulated. This gap may be reduced through crop management, such as introducing irrigation to improve the distribution of available water during the growing season. The simulated yields indicated a gap of about 3000 and 1000 kg/ha between YW and YW,N-80N for cultivars with high YP and low YP, respectively. The gap between YW and YW,N-160N decreased to <2000 kg/ha for high Yp cultivars with little difference for the low Yp cultivars. The different yield gaps among cultivars suggest that cultivars with high YP require high N rates but cultivars with low YP may need only low N rates.