Many proteins depend on metals for proper functioning, yet there is little information on the distribution of low metal concentrations in freshwaters nor what constitutes ‘low’. Eight dissolved metals were surveyed in 2017 in 39 lakes and reservoirs across Canada. RDA analysis revealed Co, Cu, Ni and V in one group with higher concentrations on the Prairies and Fe and Mn in a second group with higher concentrations on the Prairies and the Boreal Shield in Ontario. Zn and Mo lacked geographic patterns. Concentrations ranged several fold. Monod growth kinetic parameters were explored for their potential to infer growth limitation. Metal concentrations at or below growth thresholds (Tm, < 1 nmol L-1) should severely limit growth while concentrations between Tm and twice the half-saturation constant (2Km) will limit growth when other nutrients are in excess. Published and inferred Tm suggest that Co, Mo and Ni could have been low enough to occasionally limit growth in several Canadian oligotrophic lakes. There are too few published Km values to infer limitation above Tm.

1. Impacts of three cobalt (Co) concentrations were examined on heterocyst frequency and growth rate in four diazotrophic cyanobacteria species in nitrogen (N)-depleted culture and growth rate in one non-diazotrophic species in N-replete culture.After 11 days in batch culture, heterocyst frequency (HF, % of all cells that are heterocysts) increased from 4.1-5.7% to 5.4-7.4% to 5.9-9.3% at 0.17, 17 and 170 nmol L -1 Co, implicating Co in heterocyst differentiation.Growth rate was not significantly affected by Co in any of the species suggesting that the impact of low Co on other metabolic pathways was minimized.2. Stoichiometric extrapolation of culture results to N-limited natural systems with lower nutrient concentrations infers that HF could be limited by sub-nanomolar Co concentrations.3. In experimentally fertilized N-limited Lake 227, mean summer HF in 2000-2020 was 3.4% (epilimnion) and 4.0% (metalimnion).However, in 2017 (the only year for which Co data are available) dissolved Co increased from 0.7 to 2.0 nmol L -1 during the bloom simultaneously with increasing HF and cyanobacteria biomass, hence, Co probably did not limit HF and biomass.HF was significantly higher after 2015 following a shift in dominant bloom species from Aphanizomenon schindlerii to smaller A. skujae.The smaller cell size may have required a higher HF in order to maintain a relatively constant supply rate of fixed N per unit biomass.4. Surveys of ambient Co in over 280 aquatic systems across Canada and elsewhere indicate that Co is sometimes low enough to theoretically limit HF in N-limited waters.However, numerous variables influence HF so a clear understanding of relationships between Co and HF in natural systems remains elusive.

Anthropogenic sources of total phosphorus (TP) and chloride (Cl−) to lakes and rivers have been issues of concern for many decades in the Great Lakes Basin with northern Boreal Shield headwater tributaries less well studied. In the Sturgeon River – Lake Nipissing – French River basin, a headwater basin of Georgian Bay, Lake Huron, water quality monitoring of major inflows to Lake Nipissing, the third largest inland lake located entirely within Ontario, is only available from the mid-1960s to the 1990s. During the period of 2015–2018, we conducted monthly water quality surveys of major and minor inflows for two water years and have generated the first chloride (Cl−) and total phosphorus (TP) elemental budgets for the lake. Review of available long-term concentration data indicate decreasing TP concentrations by decade in major inflows, but select inflows continue to exhibit concentrations above provincial objectives, including inflows from agricultural areas that are no longer part of provincial monitoring programs. Some inflows also show high average Cl− concentrations with potential influences (e.g., road salt, agricultural activities) to stream water quality throughout the year. Water and elemental budgets indicate that while specific runoff (l/s/km2) is quite similar across contributing catchments, yields of Cl− and TP (kg/ha/yr) are disproportionately higher in catchments with urban and agricultural activities. While uncertainties in the water balance and elemental yields remain, this first effort to quantify annual elemental budgets of Lake Nipissing highlights the need to develop community-based, spatially distributed water quality surveying for long-term ecosystem monitoring and future planning.

Synthetic aperture radar (SAR) is more sensitive to the dielectric properties and structure of the targets and less affected by weather conditions than optical sensors, making it more capable of detecting changes induced by management practices in agricultural fields. In this study, the capability of C-band SAR data for detecting crop seeding and harvest events was explored. The study was conducted for the 2019 growing season in Temiskaming Shores, an agricultural area in Northern Ontario, Canada. Time-series SAR data acquired by Sentinel-1 constellation with the interferometric wide (IW) mode with dual polarizations in VV (vertical transmit and vertical receive) and VH (vertical transmit and horizontal receive) were obtained. interferometric SAR (InSAR) processing was conducted to derive coherence between each pair of SAR images acquired consecutively in time throughout the year. Crop seeding and harvest dates were determined by analyzing the time-series InSAR coherence and SAR backscattering. Variation of SAR backscattering coefficients, particularly the VH polarization, revealed seasonal crop growth patterns. The change in InSAR coherence can be linked to change of surface structure induced by seeding or harvest operations. Using a set of physically based rules, a simple algorithm was developed to determine crop seeding and harvest dates, with an accuracy of 85% (n = 67) for seeding-date identification and 56% (n = 77) for harvest-date identification. The extra challenge in harvest detection could be attributed to the impacts of weather conditions, such as rain and its effects on soil moisture and crop dielectric properties during the harvest season. Other factors such as post-harvest residue removal and field ploughing could also complicate the identification of harvest event. Overall, given its mechanism to acquire images with InSAR capability at 12-day revisiting cycle with a single satellite for most part of the Earth, the Sentinel-1 constellation provides a great data source for detecting crop field management activities through coherent or incoherent change detection techniques. It is anticipated that this method could perform even better at a shorter six-day revisiting cycle with both satellites for Sentinel-1. With the successful launch (2019) of the Canadian RADARSAT Constellation Mission (RCM) with its tri-satellite system and four polarizations, we are likely to see improved system reliability and monitoring efficiency.

This study explores the potential of vegetation indices (VIs) for crop leaf area index (LAI) estimation, with a focus on comparing red-edge reflectance based (RE-based) and the visible reflectance based (VIS-based) VIs. Seven VIs were derived from multi-temporal RapidEye images to correlate with LAI of two crop species having contrasting leaf structures and canopy architectures: spring wheat (a monocot) and canola (a dicot) in northern Ontario, Canada. The relationship between LAI and the selected VIs (LAI-VI) was characterized using a semi-empirical model. The Markov Chain Monte Carlo (MCMC) sampling method was used to estimate the model parameters, including the extinction coefficient (KVI) and VI value for dense green canopy (VI∞). Results showed that crop-specific regression models were much closer to a generic regression model using the RE-based VIs than using the VIS-based VIs. Furthermore, the joint posterior probability distribution of the KVI and VI∞ of the RE-based VIs tended to converge for the two crops. This suggests that the RE-based VIs are not as sensitive to canopy structure, e.g., the average leaf angle (ALA), as the VIS-based VIs. This is also demonstrated by the sensitivity analyses using both PROSAIL simulations and field measurements. Hence, the RE-based VIs can be used to develop a more generic LAI estimation algorithm for different crops. Further studies are required to assess the impact of soil reflectance and other factors, such as illumination-target-viewing geometries and atmospheric conditions, on LAI retrieval.

While governments in Canada have a duty to act honourably in the development of legislative actions that may affect Aboriginal or treaty rights, Indigenous peoples' input and knowledge have largely been excluded from the process. The Ontario provincial government recently sought to remedy this failure by engaging with Indigenous groups in the development and implementation of the Great Lakes Protection Act. Using qualitative data, this article explores the successes, challenges and lessons learned during Crown-Indigenous engagement in the development of this Act. The article concludes with recommendations on ways to strengthen processes of engagement between government and Indigenous groups.

Remote sensing has been recognized as a cost-effective way to detect the spatial and temporal variability of crop growth and productivity. In this study, multispectral RapidEye images were used to delineate homogeneous zones of soil and crop development in two fields in Ontario, Canada, one planted with canola ( Brassica napus L. ) and the other with spring wheat ( Triticum aestivum L.). The two fields received different levels of nitrogen (N) treatments during the pre-planting land preparation phase. Soil textures, mineral nitrogen content and crop yield were used to interpret the results of zone delineation. The analysis of variance (ANOVA) tests revealed that the high-resolution RapidEye data, particularly the imagery acquired at peak crop growth stages (i.e. when leaf area index (LAI) is high), provided valuable information for delineating within-field variability of crop growth and yield. Further analysis showed that for both crops, the spatial patterns of crop growth condition varied throughout the growth cycle, revealing different impacts of soil properties and N fertilization on the crops. In particular, during peak growth stage, the within-field variability was most strongly affected by the pre-planting N application and had the strongest correlation with crop yield. These results suggest that high-resolution satellite data (e.g., RapidEye) could assist in making decisions on optimal N fertilization for enhanced crop productivity.

In recognizing the cumulative effects of multiple stressors on altering aquatic ecosystem function, scientists have become increasingly interested in capturing high-frequency response variables using a variety of sensors. This practice has led to a demand for novel ways to visualize and analyze the wealth of data in order to meet policy and management goals. Time series data collected as part of these monitoring activities are not easily analyzed with traditional methods. In this paper, a visual analytics system is described that leverages humans’ innate capability for pattern recognition and feature detection. High-frequency monitoring of weather and water conditions in Lake Nipissing, a large, shallow, inland lake in northeastern Ontario, Canada, is used as a case study. These visualizations are presented as Web-based tools to facilitate community-based participatory research among scientists, government agencies, and community stakeholders. These analytics techniques contribute to collaborative research endeavors and to the understanding of the response of lake conditions to environmental change.

Information on crop phenological development stages such as emergence, flowering, fruiting, maturing and senescence is essential for crop production surveillance and yield prediction. It has long been related to optical spectral signatures such as the Normalized Difference Vegetation Index (NDVI) or spectral shifts in the red-edge range. In recent years, more efforts have been made to explore the sensitivity of Synthetic Aperture Radar (SAR), particularly polarimetric SAR signatures, to crop biophysical parameters or phenological stages. In this study, phenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data. Both crops are very common in north eastern Ontario, Canada, but have very anatomically different development processes. From multi-temporal RADARSAT-2 data acquired in three consecutive years (2012–2014), significant correlations were observed between a number of SAR polarimetric parameters and the growth parameters of both crops. Strong correlation was observed between plant height and the Alpha angle of the Cloude-Pottier decomposition, with the R2 of 0.91 and 0.66 for canola and wheat, respectively. The R2 increased when the polarimetric parameters were smoothed in the time domain (R2 of 0.98 for canola and 0.88 for wheat). Strong correlation was also observed for the two crops between the effective leaf area index (LAIe) and the Beta angle, and between days-after-seeding (DAS) and a combination of the Alpha and the Beta angles. These findings show that multi-temporal C-band polarimetric SAR parameters could be used for tracking crop phenological development stages.

This paper reports on the findings of a multi-site qualitative case study research project designed to document the utility and perceived usefulness of weather station and imagery data associated with the online resource GeoVisage among northeastern Ontario farmers. Interviews were conducted onsite at five participating farms (three dairy, one cash crop, and one public access fruit/vegetable) in 2014–2016, and these conversations were transcribed and returned to participants for member checking. Interview data was then entered into Atlas.ti software for the purpose of qualitative thematic analysis. Fifteen codes emerged from the data and findings center around three overarching themes: common uses of weather station data (e.g., air/soil temperature, rainfall); the use of GeoVisage Imagery data/tools (e.g., acreage calculations, remotely sensed imagery); and future recommendations for the online resource (e.g., communication, secure crop imagery, mobile access). Overall, weather station data and tools freely accessible through the GeoVisage site were viewed as representing a timely, positive, and important addition to contemporary agricultural decision-making in northeastern Ontario farming.

Core Ideas Wheat yield at both field and regional scales was successfully simulated using CSM–CERES–Wheat. There is a considerable room to improve spring wheat yield in eastern Ontario. Average yield in eastern Ontario can reach 3600 kg ha −1 with fertilization at 100 kg N ha −1 . Crop models may need to include lodging—often related to high N rates in eastern Canada. Wheat ( Triticum aestivum L.) yield is relatively low in eastern Canada. This study aimed to assess fertilizer N management options to improve the regional yield of wheat using the CSM–CERES–Wheat model. The model was adapted to simulate winter wheat by replacing air temperatures with estimated temperatures under snow cover, and then the model was evaluated for simulating winter wheat using experimental data collected at two sites and spring wheat at three sites in eastern Canada. Across all the experimental years and sites, the normalized root mean squared error (nRMSE) between simulated and measured yields was 14%. Regional yield under rainfed conditions in the Eastern Ontario Region (a Census of Agriculture unit as a case study) was simulated with 0, 1, 1.5, and 2 times the recommended N rate (around 50 kg N ha −1 ) and unlimited N for the calibrated cultivars of spring wheat from 1981 to 1999. The simulated average regional yield (in dry matter) with the recommended N rate ranged from 2180 kg ha −1 for cultivar Hoffman to 2502 kg ha −1 for AC Brio. Both were close to the reported yield of 2440 kg ha −1 , with nRMSE values ranging between 20.3 and 16.6%. The simulated regional yields with unlimited N were two times that with the recommended N rate, showing a considerable yield gap. Our simulations indicate that regional yield could increase to 3600 kg ha −1 in the Eastern Ontario Region if the N rate was increased to around 100 kg N ha −1 , although a slight decrease in N use efficiency would occur. In addition, with such increases in the N fertilization rate, other abiotic factors such as lodging should be evaluated.

Water agencies from 7 of the 10 Canadian provinces shared their experiences regarding history, successes, challenges and lessons learned with integrated watershed management. Based on these contributions, it is clear that an integrated approach does not mean ‘all-encompassing’. Rather, it proposes desirable and feasible solutions through a systems approach based on sound technical information (e.g. biophysical and socio-economic), public engagement and monitoring. The roles of all participants must be clearly defined in order to promote success and facilitate implementation. Enduring and emerging challenges, such as adequate capacity and financing, engagement with Aboriginal communities and other stakeholders, and successful implementation, are identified.

Understanding the spatial variability of soil mineral nitrogen (SMN) and crop growth is an important step for implementing precision nitrogen (N) management technologies for canola production. A 3 yr field experiment in Ontario investigated the within-field spatial variability of SMN in relation to growth parameters and yield. Each year, large strips in a commercial field were randomly assigned a preplant N treatment (0, 50, 100, and 150 kg ha(-1)), with three replicates of each. Our data showed that SMN varied widely among field-strips receiving different treatments and also within strips receiving the same N rate, indicating significant spatial variability in N availability at the field- and strip-scale. Some crop measurements exhibited wide variations in parallel with the SMN dynamics. At the early flowering stage, SMN contents displayed a strong relationship with plant height and branch numbers. Although grain yield showed a positive response to N, the inconsistent yield increase with increasing N supply was likely due to the inherent variations in soil N supply among years and fields, indicating an inefficient use of the uniformly applied preplant fertilizer N by the crop. The strong associations between SMN and crop parameters or yield provided a substantial evidence for implementing in-season variable rate N application.

Securing safe and adequate drinking water is an ongoing issue for many Canadian First Nations communities despite nearly 15 years of reports, studies, policy changes, financial commitments, and regulations. The federal drinking water evaluation scheme is narrowly scoped, ignoring community level social factors, which may play a role in access to safe water in First Nations. This research used the 2006 Aboriginal Affairs and Northern Development Canada First Nations Drinking Water System Risk Survey data and the Community Well-Being Index, including labour force, education, housing, and income, from the 2006 Census. Bivariate analysis was conducted using the Spearman’s correlation, Kendall’s tau correlation, and Pearson’s correlation. Multivariable analysis was conducted using an ordinal (proportional or cumulative odds) regression model. Results showed that the regression model was significant. Community socioeconomic indicators had no relationship with drinking water risk characterization in both the bivariate and multivariable models, with the sole exception of labour force, which had a significantly positive effect on drinking water risk rankings. Socioeconomic factors were not important in explaining access to safe drinking water in First Nations communities. Improvements in the quality of safe water data as well as an examination of other community processes are required to address this pressing policy issue.

This case study explores the North Bay-Mattawa Conservation Authority's experience in implementing IWRM. Successes include protecting life and property by mitigating flood and erosion hazards; building capacity through multi-stakeholder collaborations; and fostering community stewardship. Ongoing challenges include limited resources and narrow mandate for addressing broader watershed and natural resources issues; and a need to enhance relationships with First Nations. The NBMCA has learned numerous lessons on how to apply IWRM, including collaborating early and often and fostering community stewardship.

With increasing demands for renewable energy and dietary vegetable oils, the production of canola has become widespread in recent years. Modeling canola growth and yield is a helpful approach to predict canola responses to various environments, especially under climate change. However, few studies have been performed for predicting growth and yield of canola in Canada. In this study, we evaluated the CSM‐CROPGRO‐Canola model in Decision Support System for Agrotechnology Transfer v4.6 for simulating spring canola at West Nipissing in Eastern Canada. The model was evaluated using plant and soil data collected from field experiments over three growing seasons (2012–2014). The model could predict the observed crop development and successfully mimic the characteristics of canola regarding light absorption and utilization using combinations of leaves and pods. The accumulations of aboveground biomass were satisfactorily simulated in the life cycle under different nitrogen (N) fertilizer application rates, with a normalized RMSE of 19%. The seed yields were successfully predicted with different N application rates except for an underestimation under zero N application. The underestimation of yield under low N rates was possibly related to the deficiency in the simulated N mineralization that could also be associated with inaccurate input soil data. A better simulation of seed yields under low N application was achieved when the soil organic matter module based on the CENTURY model was used in DSSAT v4.6. The calibrated model simulated soil moisture and inorganic N contents satisfactorily, showing a good performance of the CSM‐CROPGRO‐Canola model for the study region.

Information on spatial and temporal variability of crop growth status is important for understanding the interaction between plants and environmental conditions. In particular, information at the sub-field level is critical for detecting and combating within-field yield-limiting factors for implementation of precision agriculture. High-resolution optical satellite sensors are useful tools to acquire the necessary information in this regard. This study used multi-temporal RapidEye data to estimate the green effective plant area index (PAI) and leaf chlorophyll content of two field crops, spring wheat (Triticum aestivum L.) and canola (Brassica napus L.). The objective was to evaluate the capability of RapidEye imagery for detecting within-field variability of crop growth conditions. Field experiments were carried out in northern Ontario over two consecutive years, 2012 and 2013. Several vegetation indices were selected and derived from the RapidEye data to correlate with crop PAI measured using digital hemispherical photography and leaf chlorophyll content index (CCI) measured using a CCM-200 chlorophyll meter. Results showed that the effective PAI and CCI of the two crops were best correlated with different indices. For effective PAI, the modified triangular vegetation index 2 (MTVI2) was the best for spring wheat (R2=0.74, RMSE=0.72 and n=200) whereas the red-edge normalized difference vegetation index (NDVIRE) was the best for canola (R2=0.83, RMSE=0.49 and n=147). For CCI, the chlorophyll vegetation index (CVI) was the best for spring wheat (R2=0.78, RMSE=4.1 and n=128) whereas the red-edge chlorophyll index (CIRE) was the best for canola (R2=0.33, RMSE=6.4 and n=88). The study also confirmed that the combined transformed chlorophyll absorption reflectance index/optimized soil adjusted vegetation index (TCARI/OSAVI) was satisfactory for spring wheat leaf chlorophyll content estimation when the crop canopy is not sparse. Using selected vegetation indices, RapidEye data can be used to map the within-field variability of crop growth conditions, which is useful for applications in precision agriculture. The reflectance in the red-edge or the green bands is useful in mapping crop leaf chlorophyll variability.

With the growth of the low altitude remote sensing (LARS) industry in recent years, their practical application in precision agriculture seems all the more possible. However, only a few scientists have reported using LARS to monitor crop conditions. Moreover, there have been concerns regarding the feasibility of such systems for producers given the issues related to the post-processing of images, technical expertise, and timely delivery of information. The purpose of this study is to showcase actual requests by farmers to monitor crop conditions in their fields using an unmanned aerial vehicle (UAV). Working in collaboration with farmers in northeastern Ontario, we use optical and near-infrared imagery to monitor fertilizer trials, conduct crop scouting and map field tile drainage. We demonstrate that LARS imagery has many practical applications. However, several obstacles remain, including the costs associated with both the LARS system and the image processing software, the extent of professional training required to operate the LARS and to process the imagery, and the influence from local weather conditions (e. g. clouds, wind) on image acquisition all need to be considered. Consequently, at present a feasible solution for producers might be the use of LARS service provided by private consultants or in collaboration with LARS scientific research teams.

The lessons and opportunities of integrated water resource management in Ontario are described by focusing attention on conservation authorities: watershed-based agencies formed between 1946 and 1979. Six foundational principles of the programme are explained: the watershed as the management unit; local initiative; provincial–municipal partnership; a healthy environment for a healthy economy; a comprehensive approach; and cooperation and coordination. Illustrative examples from the Grand River and Halton Region conservation authorities provide the basis for conclusions. The six principles have served the integrated water resource management programme well. In addition, the ability to make difficult budgetary decisions and adapt to changing public need has contributed to the conservation authorities' success.