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Conventional tile drainage is essential for crop production in Eastern Canada. It is of increasing interest for cereal and grain producers, especially in light of potential agronomic and environmental benefits. Based on data collected over a 13-year period at an intensive maize production site in southern Quebec, we found that controlled drainage has an overall positive effect on grain yield. In some years, depending on precipitation intensities and timing, nitrous oxide (N2O) fluxes under controlled drainage were greater than under conventional tile drainage. In other years, controlled drainage had 43% lower N2O emissions. Controlled drainage significantly reduced NO3-N contamination and tended to increase crop yield. It can be a beneficial management practice to be adopted on subsurface-drained croplands. [ABSTRACT FROM AUTHOR]

Late winter/early spring has been recognized as a critical period for subsurface drainage discharge and associated nutrient losses due to snowmelt and rainfall in croplands under cold, humid climates in North America and Europe. Although the detachment and transport processes of soil particles under snowmelt and rainfall are known to be different, studies quantifying the contribution of snowmelt to winter drainage discharge are limited. This study aims to investigate the contribution of snowmelt to subsurface drainage discharge at two winterized experimental sites in southern Quebec and Ontario, Canada, using observed data and the Root Zone Water Quality Model-Simultaneous Heat and Water model (RZ-SHAW model). The RZ-SHAW model was calibrated and validated against the measured snow depth and year-round subsurface drainage discharge data from 2021 to 2023 at St. Emmanuel, Quebec, and from 2000 to 2003 at Harrow, Ontario. RZ-SHAW demonstrated satisfactory Nash-Sutcliffe model efficiency values (NSE = 0.50) for simulating snow depth, soil temperature, and winter subsurface drainage discharge. The calibrated RZ-SHAW model was used to simulate the contribution of snowmelt to subsurface drainage for the two sites from 1990 to 2022. The snowmelt was predicted to contribute 55% and 44% of the winter and annual subsurface drainage discharge for the cropland in Southern Quebec. In contrast, for the southern Ontario site, the contribution of snowmelt to winter and annual subsurface drainage discharge was 29% and 18%, respectively. Considering that snowmelt in Southern Quebec contributed a significant fraction of winter and annual subsurface drainage discharge, more attention should be devoted to adapting and developing new winter management for nutrient loss in future research in the region. [ABSTRACT FROM AUTHOR]

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This paper presents a machine learning (ML) surrogate modeling for fast processing in meshless/ meshfree methods. The main idea is to leverage the universal approximation (UA) propriety of supervised ML models (shallow/ deep learning and other regression models) to surrogate the heavy shape function construction in meshless methods. The resulting ML metamodel preserves the same accuracy of the meshless interpolation while avoiding costly matrix inversion operations. The total computation time for solving 3D test simulation problems (using more than 20 k nodes) is reduced by a factor of 1 k in the case of the Gaussian process (GP) metamodel. [ABSTRACT FROM AUTHOR]

Methodologies to predict crop water requirements in arid and semi-arid regions are well known. Humid regions such as eastern Canada pose a challenge because irrigation is normally only required for short periods during the growing season to supplement rainfall. This study assessed the capability of the AquaCrop model to simulate the effects of different irrigation regimes on field-grown tomatoes (Solanum lycopersicum L.) in the humid region of eastern Canada. The experimental study was conducted at the Horticultural Research station of McGill University, Quebec, Canada. There were three irrigation treatments in 2017 and in 2019, that were based on the % depletion of available water content (AWC). The AquaCrop model was calibrated and verified with the 2017 and 2019 field data, respectively. The verified model was used to predict irrigation water requirements and fruit yield for the driest year (1993) and the average rainfall year (2001) of a 35-year historic weather dataset from 1986 to 2000, for three different soil types (silty clay, sandy loam, and heavy clay) under four irrigation regimes. Model performance was greatly improved when the seasonal harvest index (HI) measured from experimental data was used instead of the default model HI values. AquaCrop was suitable for estimating dry yield and total biomass with RMSE = 0.57 ton ha-1 and 0.89 ton ha-1, respectively, in the calibration phase, and RMSE = 0.28 ton ha-1 and 0.01 ton ha-1 for dry yield and total biomass, respectively, in the verification phase. These results indicate a very high accuracy of AquaCrop to estimate total above ground biomass and fruit yield in humid regions with seasonally adjusted HI values. The predictions showed that maintaining AWC depletion below 25% resulted in no significant decrease in crop yield and biomass, making it an optimum water management guideline for irrigated tomato production in Quebec. The findings of this study are useful to crop growers and water resource managers in eastern Canada, who seek better irrigation strategies to optimize productivity. [ABSTRACT FROM AUTHOR]