Background and Aims Understanding how plant allometry, plant architecture and phenology contribute to fruit production can identify those plant traits that maximize fruit yield. In this study, we compared these variables and fruit yield for two shrub species, Vaccinium angustifolium and Vaccinium myrtilloides, to test the hypothesis that phenology is linked to the plants’ allometric traits, which are predictors of fruit production. Methods We measured leaf and flower phenology and the above-ground biomass of both Vaccinium species in a commercial wild lowbush blueberry field (Quebec, Canada) over a 2-year crop cycle; 1 year of pruning followed by 1 year of harvest. Leaf and flower phenology were measured, and the allometric traits of shoots and buds were monitored over the crop cycle. We hand-collected the fruits of each plant to determine fruit attributes and biomass. Key Results During the harvesting year, the leafing and flowering of V. angustifolium occurred earlier than that of V. myrtilloides. This difference was related to the allometric characteristics of the buds due to differences in carbon partitioning by the plants during the pruning year. Through structural equation modelling, we identified that the earlier leafing in V. angustifolium was related to a lower leaf bud number, while earlier flowering was linked to a lower number of flowers per bud. Despite differences in reproductive allometric traits, vegetative biomass still determined reproductive biomass in a log–log scale model. Conclusions Growing buds are competing sinks for non-structural carbohydrates. Their differences in both number and characteristics (e.g. number of flowers per bud) influence levels of fruit production and explain some of the phenological differences observed between the two Vaccinium species. For similar above-ground biomass, both Vaccinium species had similar reproductive outputs in terms of fruit biomass, despite differences in reproductive traits such as fruit size and number.

As the focus of soil science education in Canada and elsewhere has shifted towards nonsoil science majors, it is important to understand if and how this has affected the scope of introductory soil science courses. The objectives of this study were to inventory Canadian postsecondary units that offer introductory soil science courses and to document attributes of instructors, students, and teaching approaches in these courses. We surveyed 58% of the instructors of introductory soil science courses across Canada, and most of these courses were offered by geography and environmental science units. The majority of instructors followed a traditional lecture (86%) and laboratory (76%) delivery format, whereas 36% used online teaching resources. Introductory courses were delivered by primarily one instructor, who held a Ph.D. in a tenure track position and in most cases developed the course themselves. Over half of the instructors surveyed used either a required or a recommended textbook, pointing to the need for creation of a Canadian-authored soil science textbook. Several follow-up studies are needed to evaluate teaching methods used in the upper level soil science courses, students' perceptions of teaching in soil science, and instructors' knowledge of resources available for online and (or) blended learning.

Arctic soils store large amounts of labile soil organic matter (SOM) and several studies have suggested that SOM characteristics may explain variations in SOM cycling rates across Arctic landscapes and Arctic ecosystems. The objective of this study was to investigate the influence of routinely measured soil properties and SOM characteristics on soil gross N mineralization and soil GHG emissions at the landscape scale. This study was carried out in three Canadian Arctic ecosystems: Sub-Arctic (Churchill, MB), Low-Arctic (Daring Lake, NWT), and High-Arctic (Truelove Lowlands, NU). The landscapes were divided into five landform units: (1) upper slope, (2) back slope, (3) lower slope, (4) hummock, and (5) interhummock, which represented a great diversity of Static and Turbic Cryosolic soils including Brunisolic, Gleysolic, and Organic subgroups. Soil gross N mineralization was measured using the (15) N dilution technique, whereas soil GHG emissions (N2 O, CH4 , and CO2 ) were measured using a multicomponent Fourier transform infrared gas analyzer. Soil organic matter characteristics were determined by (1) water-extractable organic matter, (2) density fractionation of SOM, and (3) solid-state CPMAS (13) C nuclear magnetic resonance (NMR) spectroscopy. Results showed that gross N mineralization, N2 O, and CO2 emissions were affected by SOM quantity and SOM characteristics. Soil moisture, soil organic carbon (SOC), light fraction (LF) of SOM, and O-Alkyl-C to Aromatic-C ratio positively influenced gross N mineralization, N2 O and CO2 emissions, whereas the relative proportion of Aromatic-C negatively influenced those N and C cycling processes. Relationships between SOM characteristics and CH4 emissions were not significant throughout all Arctic ecosystems. Furthermore, results showed that lower slope and interhummock areas store relatively more labile C than upper and back slope locations. These results are particularly important because they can be used to produce better models that evaluate SOM stocks and dynamics under several climate scenarios and across Arctic landscapes and ecosystems.

Numerous studies have speculated that lowbush blueberry ( Vaccinium angustifolium ) is less efficient than weed species at taking up inorganic nitrogen (N) derived from fertilizers, thus raising questions as to the effectiveness of N fertilization in commercial fields. However, competition for acquiring N as well as specific interactions between blueberry and companion weeds characterized by contrasted functional traits remain poorly documented. Here, we assessed fertilizer-derived N acquisition efficiency and biomass production in lowbush blueberry and two common weed species that have different functional traits—sweet fern ( Comptonia peregrina ), a N 2 -fixing shrub, and poverty oat grass ( Danthonia spicata ), a perennial grass—in a commercial blueberry field in Québec, Canada. In 2015, 15 N-labelled ammonium sulfate was applied at a rate of 45 kg ha -1 to 1 m 2 field plots containing lowbush blueberry and one of the two weeds present at several different density levels (0 to 25 plants m -2 ). In 2016, each plot was harvested to determine vegetative biomass and the percentage of fertilizer-derived N recovered (PFNR) in each species. The PFNR was higher in blueberry (24.4 ± 9.3%) than in sweet fern (13.4 ± 2.6%) and poverty oat grass (3.3 ± 2.9%). However, lowbush blueberry required about four times more root biomass than sweet fern and poverty oat grass to uptake an equivalent amount of N from ammonium sulfate. The PFNR in poverty oat grass increased with plant density (from 0.8% to 6.4% at 2–3 and >6 plants m -2 , respectively), which resulted in a decrease in blueberry’s PFNR (from 26.0 ± 1.4% to 8.6 ± 1.8%) and aboveground vegetative biomass production (from 152 ± 58 to 80 ± 28 g m -2 ). The increase in biomass production and N content in sweet fern with increasing plant density was not accompanied by an increase in PFNR (29.7 ± 8.4%), suggesting an increasing contribution of atmospherically-derived N. This mechanism (i.e., N sparing) likely explained blueberry’s higher biomass production and N concentration in association with sweet fern than with poverty oat grass. Overall, our study confirms lowbush blueberry low efficiency (on a mass basis) at taking up N derived from the fertilizer as compared to weeds and reveals contrasted and complex interactions between blueberry and both weed species. Our results also suggest that the use of herbicides may not be necessary when poverty oat grass is present at a low density (<15 plants of poverty oat grass m -2 ) and that adding inorganic N fertilizer is counterproductive when this species is present at a high density as it takes up as much fertilizer as lowbush blueberry.

Agroecosystem conditions limit the productivity of lowbush blueberry. Our objectives were to investigate the effects on berry yield of agroecosystem and crop management variables, then to develop a recommendation system to adjust nutrient and soil management of lowbush blueberry to given local meteorological conditions. We collected 1504 observations from N-P-K fertilizer trials conducted in Quebec, Canada. The data set, that comprised soil, tissue, and meteorological data, was processed by Bayesian mixed models, machine learning, compositional data analysis, and Markov chains. Our investigative statistical models showed that meteorological indices had the greatest impact on yield. High mean temperature at flower bud opening and after fruit maturation, and total precipitation at flowering stage showed positive effects. Low mean temperature and low total precipitation before bud opening, at flowering, and by fruit maturity, as well as number of freezing days (<−5 °C) before flower bud opening, showed negative effects. Soil and tissue tests, and N-P-K fertilization showed smaller effects. Gaussian processes predicted yields from historical weather data, soil test, fertilizer dosage, and tissue test with a root-mean-square-error of 1447 kg ha−1. An in-house Markov chain algorithm optimized yields modelled by Gaussian processes from tissue test, soil test, and fertilizer dosage as conditioned to specified historical meteorological features, potentially increasing yield by a median factor of 1.5. Machine learning, compositional data analysis, and Markov chains allowed customizing nutrient management of lowbush blueberry at local scale.