Evaluation of the impact of various agricultural practices on nitrate leaching under the root zone of potato and sugar beet using the STICS soil–crop model

The quaternary aquifer of Vitoria-Gasteiz (Basque Country, Northern Spain) is characterised by a shallow water table mainly fed by drainage water, and thus constitutes a vulnerable zone in regards to nitrate pollution. Field studies were performed with a potato crop in 1993 and a sugar beet crop in 2002 to evaluate their impact on nitrate leaching. The overall predictive quality of the STICS soil–crop model was first evaluated using field data and then the model was used to analyze dynamically the impacts of different crop management practices on nitrate leaching. The model was evaluated (i) on soil nitrate concentrations at different depths and (ii) on crop yields. The simulated values proved to be in satisfactory agreement with measured values. Nitrate leaching was more pronounced with the potato crop thanwith the sugar beet experiment due to i) greater precipitation, ii) lower N uptake of the potato crop due to shallow root depth, and iii) a shorter period of growth. The potato experiment showed that excessive irrigation could significantly increase nitrate leaching by increasing both drainage and nitrate concentrations. The different levels of N-fertilization examined in the sugar beet study had no notable effects on nitrate leaching due to its high N uptake capacity. Complementary virtual experiments were carried out using the STICS model. Our study confirmed that in vulnerable zones agricultural practices must be adjusted, that is to say: 1) N-fertilizer should not be applied in autumn before winter crops; 2) crops with low N uptake capacity (e.g. potatoes) should be avoided or should be preceded and followed by nitrogen catch crops or cover crops; 3) the nitrate concentration of irrigation water should be taken into account in calculation of the N-fertilization rate, and 4) Nfertilization must be precisely adjusted in particular for potato crops

Modelling grass yields in northern climates – a comparison of three growth models for timothy

During the past few years, several studies have compared the performance of crop simulation models to assess the uncertainties in model-based climate change impact assessments and other modelling studies. Many of these studies have concentrated on cereal crops, while fewer model comparisons have been conducted for grasses. We compared the predictions for timothy grass (Phleum pratense L.) yields for first and second cuts along with the dynamics of above-ground biomass for the grass simulation models BASGRA and CATIMO, and the soil-crop model STICS. The models were calibrated and evaluated using field data from seven sites across Northern Europe and Canada with different climates, soil conditions and management practices. Altogether the models were compared using data on timothy grass from 33 combinations of sites, cultivars and management regimes. Model performances with two calibration approaches, cultivar-specific and generic calibrations, were compared. All the models studied estimated the dynamics of above-ground biomass and the leaf area index satisfactorily, but tended to underestimate the first cut yield. Cultivar-specific calibration resulted in more accurate first cut yield predictions than the generic calibration achieving root mean square errors approximately one third lower for the cultivar-specific calibration. For the second cut, the difference between the calibration methods was small. The results indicate that detailed soil process descriptions improved the overall model performance and the model responses to management, such as nitrogen applications. The results also suggest that taking the genetic variability into account between cultivars of timothy grass also improves the yield estimates. Calibrations using both spring and summer growth data simultaneously revealed that processes determining the growth in these two periods require further attention in model development

Simulating switchgrass aboveground biomass and production costs in eastern Canada with the Integrated Farm System Model

Switchgrass (Panicum virgatum L.) is a herbaceous perennial grass that can be used as bedding for livestock, planted in buffer strips, and used as biofuel but it is still not widely grown in eastern Canada. The objectives of this study were to verify the performance of the Integrated Farm System Model (IFSM) in simulating switchgrass growth and to estimate its yield potential and production cost in Eastern Canada. The performance of the IFSM was assessed with dry matter (DM) yield of switchgrass (cv. Cave-in-Rock) measured over three growing seasons (2015 to 2017) in southern Quebec, Canada. The model performed reasonably well with normalized root mean square errors of 19.5% for calibration and 27.9% for validation. Simulation results of potential yield and economic management over the long-term (1986 to 2015) for five switchgrass production sites in Eastern Canada indicated that average DM yields in Quebec City and Fredericton (9.6 and 9.7 t ha-1, respectively) were significantly lower than average DM yields in Saint-Hubert, Ottawa, and London (10.8, 10.4, and 11.0 t ha-1 respectively). Average annual production costs per tonne of DM for the spring harvest were higher at low-yield sites ($66.67 and$64.50 for Fredericton and Quebec City, respectively) than at high-yield sites ($60.10,$62.82, and $60.08 for Saint-Hubert, Ottawa, and London, respectively). The IFSM-estimated production costs were within the range of the calculated values reported in other agro-economic analyses conducted in Ontario and Quebec.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Les perturbations de la production fourragère au Canada face au changement climatique

The predicted climate warming in the agricultural regions of Canada for the period 2040-2069 (2.4 – 3.8°C) and the predicted variations in precipitations will have for effect: 1/ to increase annual yield because of an additional harvest, 2/ to increase the risks of winter damages to winter-sensitive forage species, and 3/ to decrease slightly the forage nutritive value. These effects will vary with the forage species and the region. The increase in CO2 concentration will benefit alfalfa over forage grasses. The projected yield increases in several crops grown on dairy farms along with the northward movement of some crops (e.g. corn and soybean) presents new opportunities and challenges for perennial forage crops in Canadian forage-ruminant systems.Le réchauffement climatique prévu dans les régions agricoles du Canada pour la période 2040-2069 (2,4 – 3,8°C) et les variations prévues des précipitations auront pour effet : 1/ d’augmenter le rendement annuel grâce à une récolte additionnelle, 2/ d’augmenter les risques de dommages hivernaux des espèces fourragères sensibles à l’hiver, et 3/ de diminuer faiblement la valeur nutritive des fourrages. Ces effets varieront selon l’espèce fourragère et la région. L’augmentation de la concentration en CO2 favorisera davantage la luzerne que les graminées fourragères. Les projections d’augmentation de rendements pour plusieurs espèces typiquement cultivées sur les fermes laitières ainsi que le déplacement vers le nord de certaines cultures (p. ex. maïs et soja) laissent présager de nouvelles opportunités et défis pour les cultures fourragères pérennes dans les systèmes fourrages-ruminants du Canada

Estimating the yield potential of short-rotation willow in Canada using the 3PG model

The ability to predict short-rotation coppice (SRC) willow productivity for a given region would be very helpful for large-scale deployment of this crop. The objectives of this study were to calibrate and validate the 3PG model for two commonly used clones (SX64 and SX67) and to provide yield potential estimates for 16 sites across Canada. One dataset for each clone, including leaf area index (LAI) and stem biomass was used for calibrating parameters controlling leaf and stem growth. All other datasets, coming from 8 different willow plantations, were used for model validation. Model performance was good in predicting stem biomass for the SX64 (normalized mean error [NME] = -8%, normalized root mean square error [NRMSE] = 22%) and SX67 (NME = -3%, NRMSE = 16%) clones. Predictions were more scattered for LAI, with NRMSE close to 35% and 33% and NME of 1% and 8% for SX64 and SX67, respectively. The simulation results show that the highest yields were obtained with the three-year rotation for the SX67 clone, whereas a two-year rotation seemed to be more appropriate for the SX64 clone. The simulation results also show that growing degree-days had a significant impact on yield potential which varied from 10.5 to 16.5 t DM haâ 1 for SX64 and from 7.5 to 11.5 t DM haâ 1 for SX67.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

STICS Soil–Crop Model Performance for Predicting Biomass and Nitrogen Status of Spring Barley Cropped for 31 Years in a Gleysolic Soil from Northeastern Quebec (Canada)

Spring barley (Hordeum vulgare L.) is an increasingly important cash crop in the province of Quebec (Canada). Soil–crop models are powerful tools for analyzing and supporting sustainable crop production. STICS model has not yet been tested for spring barley grown over several decades. This study was conducted to calibrate and evaluate the STICS model, without annual reinitialization, for predicting aboveground biomass and N nutrition attributes at harvest during 31 years of successive cropping of spring barley grown in soil (silty clay, Humic Gleysol) from the Saguenay–Lac-Saint-Jean region (northeastern Quebec, Canada). There is a good agreement between observed and predicted variables during the 31 successive barley cropping years. STICS predicted well biomass accumulation and plant N content with a low relative bias (|normalized mean error| = 0–13%) and small prediction error (normalized root mean square error = 6–25%). Overall, the STICS outputs reproduced the same trends as the field-observed data with various tillage systems and N sources. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. These ‘newly calibrated’ parameters in STICS for spring barley cropped under continental cold and humid climates require validation using independent observation datasets from other sites

New model-based insights for strategic nitrogen recommendations adapted to given soil and climate

International audienceAbstractManaging nitrogen (N) fertilizer applied in agricultural fields is important for increasing crop productivity while limiting the environmental contamination caused by release of reactive N, especially for crops with high N demand (e.g., corn, Zea mays L.). However, for given soil properties, the optimum amount of N applied depends on climatic conditions. The central question to N management is then what should be the recommended N rate for given soil and climate that would minimize the release of reactive N while maintaining the crop productivity. To address this central challenge of N management, we used a recently developed model-based methodology (called “Identifying NEMO”), which was proved to be effective in identifying ecophysiological optimum N rate and optimum nitrogen use efficiency (NUEopt). We performed modeling for dominant soils and various agroclimatic conditions in five regions along the Mixedwood Plains ecozone, where more than 90% of Canadian corn production takes place. Here, we analyzed for the first time the effect of soil and climate on ecophysiological optimum N rate in an ecozone where there exists a significant agroclimatic gradient. Our results indicated that there were some commonalities among all soils and regions, which we could classify them into two groups with NUEopt ranging from 10 to 17 kg dry yield kg−1 N. For cases with low NUEopt, the recommended N for an expected dry yield of 8 t ha−1 varied from 115 to 199 kg ha−1, whereas they were much lower (79–154 kg ha−1) for cases with high NUEopt. These recommendations were 20–40 kg ha−1 lower than provincial recommendations. Moreover, we found that the different behavior of the two groups was due to soil textures and soils available water holding capacity. For most locations, soils with intermediate available water holding capacity (i.e., 12–15%v) had relatively higher expected yield and lower recommended N

Reproducibility of digital arterial obstructive disease diagnosis using laser Doppler flowmetry in systemic sclerosis

International audienc