Reduced within-field potato (Solanum tuberosum L.) yield variation may lead to increased productivity and reduced environmental impact. Using soil samples collected from 88 site-years in commercial fields in New Brunswick, Canada from 2013-2017, this study examined how within-field variation in potato tuber yield was related to soil properties and topographic features. At each of 774 sampling locations, a wide range of soil physical and chemical properties was measured in the lab and topographic features were assessed using a regional digital elevation model. Principal component (PC) analysis identified three PCs, which accounted for 79.1% of the total variation. The PC1 (41.3% of total variance) was dominated by soil texture (i.e., sand, silt) and the quantity and quality of soil organic matter (i.e., soil organic C, particulate organic matter C, and soil C/N ratio). Under rain-fed potato production in New Brunswick, finer soil texture and increased soil organic matter pools are expected to enhance soil water availability and thereby improve yield. The PC2 (22.7% of total variance) was related primarily to parameters associated with soil fertility, and PC3 (15.1% of total variance) was related primarily with concave or convex landforms, which may influence yield through drought or excess water. This study demonstrated the value in using multivariate approaches to identify the factors that control within-field yield variability in the presence of significant regional variation in soil properties and environmental conditions. The findings point to the value of enhancing the quantity and quality of soil organic matter as a key strategy to overcome yield limitations under rain-fed production.

Estimating biophysical parameters of native grassland enables management changes that affect ecological processes and economic benefits. Although multiple hyperspectral studies were focused on native grasslands, just a few compare data at different scales and among ecoregions. In this study, we compared data collected at different spectral and spatial scales and among Canadian Prairie ecoregions. Field observations indicate that the Fescue Ecoregion grasslands has specific dominant species, while the Moist-Mixed and Mixed Ecoregions share similar dominant species, which is important in determining parameters such as leaf area index (LAI) and canopy height. Hyperspectral measurements showed a specific signature for the Fescue Ecoregion, due to denser canopies, while the Moist-Mixed and Mixed Ecoregions showed similar spectral characteristics to each other. The correlation between biophysical parameters and spectral indices reveals the importance of LAI, since it was significantly correlated with all spectral indices analyzed. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and the Plant Senescence Reflectance Index (PSRI) showed significant correlations with biophysical parameters. The comparison results indicated the PSRI being overestimated at all sites (satellite data) and NDVI underestimated at all sites. Finally, the satellite-derived LAI showed a significant positive relationship with the field-measured LAI.

Differentiation of grassland/forage types and accurate estimates of their location and extent are important for understanding their ecological processes and for applying appropriate management practices. We are aiming to reveal the different spectral characteristics of six grassland/forage land covers in three ecoregions located in the Canadian Prairies, based on field data and satellite images. Three spectral indices representing productivity (Normalized Difference Vegetation Index (NDVI)), moisture content (Normalized Difference Moisture Index (NDMI)), and plant photosynthetic activity (Plant Senescence Reflectance Index (PSRI)) were used for comparison of means, comparison of coefficient of variation (CV), and analysis of variance (ANOVA). The results indicated that different grassland types show distinguishable spectral characteristics in the Moist-Mixed and Mixed Ecoregions, while it was not possible to differentiate the classes in the Fescue Ecoregion. To further investigate the within-sites and between-sites heterogeneity, we calculated the CV in a 3 × 3 window and placed them in comparative triangles to demonstrate their potential separability. Results indicated that the triangles based on the CV offered greater class separability in the Fescue Ecoregion and in the Mixed Ecoregion.

Abstract The geographic distribution of the Rocky Mountain wood tick, Dermacentor andersoni Stiles, was determined in Alberta, Canada, by drag sampling at 86 and 89 sites during 2011 and 2012, respectively. Tick density and prevalence varied between years, averaging (range) 1.0 (0–26.2) and 5.9 (0–110) ticks/1,000 m2 in 2011 and 2012, respectively. Ticks were detected at 24.4% and 42.7% of the sites sampled in each respective year. Tick density and presence declined in a northerly direction to 51.6°N and in a westerly direction to ca. 113°W, except for a small area of high density at the edge of the Rocky Mountains in the southeastern portion of the province. Ticks were most abundant in the Dry Mixedgrass and Montane natural subregions and in areas with Brown Chernozemic, Regosol, and Solodized Solonetzic great soil groups. A logistic regression model indicated that tick presence was increased in the Dry Mixedgrass natural subregion and in regions with greater temperatures during the previous summer and normal winter precipitation but was reduced in areas with Dark Brown Chernozemic soils. The model will be useful for predicting tick presence and the associated risk of tick-borne diseases in the province.

Potato tuber shape is an important quality trait for breeding and variety development. Length to width (L/W) ratio is a commonly used method to score potato tubers for suitability for different markets and is relatively easy to measure, though labor intensive when done manually. L/W also does not adequately capture secondary growth and other tuber malformations that contribute to tuber shape. Tuber shape has a genetic component and is a prime target for early breeding selection. In the current study we developed an image analysis pipeline to extract tuber shape statistics from images taken using inexpensive, commercially available cameras. The image processing pipeline was used to evaluate greenhouse grown tubers from 32 unique crosses. Tubers from greenhouse grown plants were then grown in a field located in Vauxhall, AB, Canada, and evaluated for tuber shape. Randomly selected tuber images were also shown to industry agronomists and potato growers located in Southern Alberta and their shape scored for suitability for processing (French fry and chipping) markets. Based on measurements taken from greenhouse grown tubers we were able to classify whether mean tuber shape from field grown plants were within ideal shape parameters for processing markets with ~76–86% accuracy. Based on performance of progeny we identified parents which show higher breeding value for tuber shape.

Leafy spurge, a noxious perennial weed, is a major threat to the prairie ecosystem in North America. Strategic planning to control leafy spurge requires monitoring its spatial distribution and spread. The ability to detect flowering leafy spurge at two biological control sites in southern Saskatchewan, Canada, was investigated using an unmanned aerial vehicle (UAV) system. Three flight missions were conducted on June 30, 2016, during the leafy spurge flowering period. Imagery was acquired at four flight heights and one or two acquisition times, depending on the site. The sites were reflown on June 28, 2017, to evaluate the change in flowering leafy spurge over time. Mixture tuned matched filtering (MTMF) and hue, intensity, and saturation (HIS) threshold analyses were used to determine flowering leafy spurge cover. Flight height of 30 m was optimal; the strongest relationships between UAV and ground estimates of leafy spurge cover (r(2)= 0.76 to 0.90; normalized root mean square error [NRMSE] = 0.10 to 0.13) and stem density (r(2)= 0.72 to 0.75) were observed. Detection was not significantly affected by the image analysis method (P > 0.05). Flowering leafy spurge cover estimates were similar using HIS (1.9% to 14.8%) and MTMF (2.1% to 10.3%) and agreed with the ground estimates (using HIS: r(2) = 0.64 to 0.93, NRMSE = 0.08 to 0.25; using MTMF: r(2) = 0.64 to 0.90, NRMSE = 0.10 to 0.27). The reduction in flowering leafy spurge cover between 2016 and 2017 detected using UAV images and HIS (8.1% at site 1 and 2.7% at site 2) was consistent with that based on ground digital photographs (10% at site 1 and 1.8% at site 2). UAV imagery is a useful tool for accurately detecting flowering leafy spurge and could be used for routine monitoring purposes in a biological control program.

The recent popularization of unmanned aerial vehicles (UAVs) for use in terrestrial remote-sensing science has brought a class of inexpensive and largely unmeasured set of sensors into the scientific domain. Remote-sensing science demands high-quality data for information production and requires that the radiometric and spectral characteristics of imaging systems are known. This study compared the radiometric and spectral characteristics of 10 imaging systems commonly used in UAV research. From very inexpensive board-level cameras, consumer-grade cameras and purpose built remote-sensing systems were tested. The results show that sensor non-linearity with respect to radiance is a major limitation in producing reliable results. Spectral results demonstrated a degree of similarity between sensors with broad overlapping spectral bands being the norm. Careful attention to radiometric correction and spectral characterization will enhance the quality of the data produced from these systems.

Almost concurrent imagery from Landsat-5 and Radarsat-2 are examined separately and in combination to maximize the accuracy of a simple classification of a typical multi-use grassland region in western Canada. Almost all classifications were of sufficient accuracy to be used in an operational sense. Landsat seven band classification was the most accurate, but was deemed less likely to be useful as an operational tool due to its relatively long revisit period and its weather sensitivity. The Radarsat Constellation Mission satellites, when fully deployed, will provide sufficiently accurate classifications with a very short revisit period.

The Grassland Vegetation Inventory (GVI), which represents a comprehensive biophysical, anthropogenic, and land-use inventory of grasslands in Alberta, is widely used as a baseline for grassland conditions. An up-to-date GVI is essential for understanding grassland changes and for planning management or conservation actions on grasslands. In this study, a hybrid change detection method is proposed that incorporates change vector analysis and a set of vegetation indices (VIs) measuring different vegetation attributes for mapping the conversion of native grassland to cultivated agriculture, and ultimately to update the GVI based on multiseasonal and multiyear Landsat images. Vegetation indices that contribute significantly to differentiation between existing native grassland and land recently converted from native grassland to cultivated cropland were identified by using stepwise regression analyses and were used as inputs for mapping the conversion between 2006 and 2011 or 2015. The results showed that land conversion can be detected using a single image acquired during the growing season, but that the accuracy of identification is affected by the date of image collection and the nature of the VIs used. The greatest accuracy in detecting land conversion between 2006 and 2011 was achieved using the difference in VI between years (dVI) for the Shortwave Infrared Reflectance 3/2 Ratio (SWIR32) and the Enhanced Vegetation Difference Index (EVI) derived from July imagery (accuracy = 95.2 %; Kappa = 0.86). The same combination of SWIR32 and EVI was also effective, although with lower accuracy (accuracy = 86.0 %; Kappa = 0.64) when tested on a larger geographical area and for detecting land use change between 2006 and 2015. The method proposed here could be applied to detect the land cover conversion in other grassland regions, although the optimal VIs and image acquisition date may need to be modified depending on the type of land use activities implemented in each region.

Abstract. Native grasslands are an important forage resource for the cattle industry and play a vital role in hydrological, carbon, and nutrient cycles; energy flow; faunal and floral biodiversity; and recreational services. Despite their importance, information on the health of Canada's native grasslands is extremely limited. This study investigated the use of functional relationships, namely, differences in the Normalized Difference Vegetation Index (NDVI) and a shortwave infrared index (SWIR75 or CAI) related to cellulose and lignin content, along with spectral mixture analysis (SMA), to derive estimates of broad categories of grassland ground cover at 4 test sites established in the mixed prairie grassland in southern Alberta, Canada. Field campaigns were carried out in 2009 and 2010, at peak grass production, to collect fractional ground cover of photosynthetic vegetation (fPV), nonphotosynthetic vegetation (fNPV), and background (fB), as well as ground spectra of various grassland components. The ability to separate PV, NPV, and B using NDVI and the SWIR75 or CAI indices derived from field spectroradiometer data and Landsat-5 TM data was investigated. Although reasonable estimates of fPV were derived using the NDVI and SWIR75 indices with SMA (r = 0.82) from Landsat imagery, the similarity in the spectral characteristics of soil, lichens, litter, and standing senescent vegetation confounded the ability to estimate fNPV and fB. In the absence of a hyperspectral satellite system offering the ability to derive CAI, the ability to evaluate grassland health in the mixed grass prairie is limited.Resume. Les paturages naturels sont une ressource fourragere importante pour l'industrie du betail et jouent un role vital dans les cycles hydrologique, du carbone et des elements nutritifs, ainsi que dans les flux d'energie, la biodiversite de la faune et de la flore, et la fourniture des services recreatifs. Malgre leur importance, les informations sur la sante des paturages naturels du Canada sont extremement limitees. Cette etude a examine l'utilisation des relations fonctionnelles, a savoir les differences de l'indice de vegetation par difference normalisee (NDVI) et un indice d'ondes courtes (SWIR75 ou CAI) lie a la cellulose et la teneur en lignine, avec l'analyse des spectres mixtes (SMA) pour obtenir des estimations de grandes categories de couverture vegetale. Quatre sites d'essai ont ete etablis dans la prairie mixte du sud de l'Alberta, Canada. Les campagnes de terrain ont ete realisees en 2009 et 2010, au pic de production de l'herbe, afin de recueillir la couverture fractionnaire du sol de la vegetation photosynthetique (fPV), la vegetation non photosynthetique (fNPV) et le fond (fB), ainsi que des spectres du sol des differentes composantes de prairies. La capacite de separer PV, NPV, et B en utilisant les indices NDVI et SWIR75 (ou CAI) derives des donnees spectroradiometriques de terrain et des donnees Landsat-5 TM a ete etudiee.Bien que des estimations raisonnables de fPV ont ete calculees en utilisant les indices NDVI et SWIR75 en utilisant SMA (r = 0,82) a partir d'images Landsat, la similitude des caracteristiques spectrales du sol, des lichens, de la couche de vegetation morte et de la vegetation senescente debout a reduite la capacite d'estimer la fNPV et le fB. En l'absence d'un capteur satellitaire hyperspectral offrant la possibilite d'obtenir le CAI, la capacite a evaluer la sante des paturages naturels dans la prairie mixte est limitee.

Accurate and efficient weed detection in crop fields is a key requirement for directed herbicide application in real-time Site-Specific Weed Management (SSWM). Using very high spatial resolution (1.25 mm) hyperspectral (HS) image data (61 bands, 400–1000 nm at 10 nm spectral resolution), this study determined that reduced HS bandsets are feasible for discriminating weeds (wild oats, redroot pigweed) from crops (field pea, spring wheat, canola) using Artificial Neural Network (ANN) classification. A 7-band set identified through principal component analysis and stepwise discriminant analysis yielded ANN classification accuracies (88% to 94%) that were nearly equivalent to the full 61-band HS results (89% to 95%) at replicate field plots in southern Alberta, Canada. Therefore, low dimensional narrowband sensors or similar bandsets derived from HS data warrant consideration for SSWM. The computational savings possible from this substantial level of data reduction are potentially critical for enabling optimal use of HS data in real-time ground-based SSWM systems. Recommendations made based on these results have potentially broader implications to SSWM with respect to on-board processing efficiency, weed–crop discrimination method, and sensor and algorithm design.

Growing evidence of global climate change has led to global concerns over the vulnerability of agriculture to drought. Located in a semiarid environment, southern Alberta has suffered significant losses of agricultural productivity due to drought hazards in recent decades. Understanding the relationship between crop production and drought conditions is essential for coping with increasingly uncertain climate conditions. This study attempts to quantify the magnitude of crop production vulnerability to drought in southern Alberta. The standard precipitation index is used to measure drought stress in the region. The empirical results provide a detailed picture of the spatial variation in crop production vulnerability to varying drought conditions. Vulnerability maps from this study reveal that pockets in the study area may experience significant productivity loss given the existing level of adaptive capacity. While the irrigation districts have been associated with a lower level of vulnerability than dryland outside the irrigated region, uncertain water supply under varying climatic conditions coupled with increasing water allocation for non-agricultural uses may increase the vulnerability in these districts.

Biophysical parameters, such as leaf area index (LAI) and leaf chlorophyll content, play crucial roles in precision agricultural management, forest ecology monitoring, and global climate change studies. Accurate and robust retrieval of these parameters from remote sensing data still remains a challenge. One of the commonly used methods is through the inversion of a physical canopy model. However, it is often an ill-posed problem, mainly due to the model complexity and observation uncertainties. In this study, a contribution index (CI) was derived to quantify the effect of a given observation on the retrieval of model parameters of interest that accounted for both the uncertainty of this observation and its sensitivity to the model parameters. The CI was used in the merit function to weight each observation to improve the physical model inversion. To evaluate the CI based merit function, the look-up table (LUT) model inversion was conducted using the coupled PROSPECT and SAIL model to retrieve LAI and leaf chlorophyll content. The results using both simulated and real hyperspectral data showed that employing CI significantly improved the retrieval accuracy by reducing the prediction errors by at least 10 % compared with the traditional LUT method.

In this study, the fractional cover (f) of three grassland components (photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and background (B)) were estimated using Landsat 5 and CHRIS/Proba sensors. In 2009, a field campaign was carried out at three sites on the mixed prairie of southern Alberta, Canada to collect in situ measurements of fractional cover. Landsat 5 and CHRIS/Proba images were acquired near the same time as the ground measurements. The fPV was found to be closely related to the Modified Transformed Vegetation Indexes 1 and 2 (MTVI1, MTVI2; R 0.72 and 0.76) calculated from Landsat imagery. Narrow band versions of these and two other narrow band indices, the Red-edge Index (RE) and the Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI)), derived from nadir CHRIS imagery were also reasonable predictors of fPV. The estimates of non-photosynthetic vegetation were poor using these indices. A soil adjusted vegetation index, the Normalized Difference Senescent Vegetation Index derived from Landsat 5 produced a reasonable relationship with NPV ground cover (R 0.70; RMSE 3.52%). Estimation of fB from 100-(fPV+fNPV) consequently gave a similar reasonable relationship (R of 0.71~ 0.82 and RMSE of 5.57~7.06%). The results showed that fPV and fNPV of mixed prairie rangeland could be estimated with an RMSE of 4-6% using Landsat-derived vegetation indices. Such estimates of f could become a critical input to more comprehensive estimation of grassland biomass and growth rates in Alberta rangelands.

Quad-pol imagery from both Terrasar-X and RADARSAT- 2 were acquired over the grasslands of southern Alberta, Canada, in the early spring of 2010. Dates and incidence angles were as closely matched as was possible. Both sets of imagery showed qualitative agreement in spatial structure, but the Terrasar-X imagery was significantly less well defined with a much higher noise floor. The range of pixel values due to speckle noise, even after modest filtering, was large enough to mask any significant relationship between the two sources of imagery on a pixel-by-pixel basis.

Native grasslands play an important role in ecosystem function, biodiversity, climate change, and economics, yet quantifiable estimates of the rate and location of native grassland change in western Canada are not readily available. To date, optical remote sensing has been explored for grassland mapping, but cloud cover limits the availability of timely data for the discrimination of improved, as opposed to native, grassland. In this study we investigated the utility of RADARSAT-2 polarimetric imagery to map native grassland, improved grassland, and agricultural crops. Fine quad-polarisation mode RADARSAT-2 data were acquired at two incidence angles over a test site in southern Alberta every 24 days from 1 April to 31 October 2009 and were processed using the Freeman-Durden decomposition. Double-bounce, volumetric, and surface scattering properties suggest that native grasslands can be distinguished from cultivated cropping, especially using a mid-to late-season image. However, discriminating native grasslands from improved grasslands was more difficult. Land cover classification of a single RADARSAT-2 image from July 2009 provided reasonable but slightly less accurate results compared with a single Landsat-5 Thematic Mapper image (Kappa value of 0.65 compared with 0.81).

Applying polymer-coated urea (PCU) instead of uncoated urea may benefit winter wheat (Triticum aestivum L.) production by reducing weed growth or increasing grain yield and protein concentration. Field trials were conducted for 3 yr under rainfed and irrigated conditions in Lethbridge and 2 yr under rainfed conditions in Lacombe to determine potential benefits of substituting urea with PCU for typical winter wheat production practices in Alberta. Four factors were included in each experiment: (1) urea type (urea, PCU and a 50:50 blend of urea and PCU), (2) application method (fall side-band vs. spring broadcast), (3) N rate (1× and 1.5× recommended N rate), and (4) herbicide application (none vs. full). Herbicide application substantially reduced weed biomass at all site-years, but only increased average grain yield by 9%. Dicot weed biomass was not affected by fertilizer treatment, but monocot weed biomass was less for fall banded than spring broadcast application and less for urea than PCU or blend at the 1× N rate. Over all site-years, substitution of 50 or 100% of urea with PCU increased grain yield by an average of 4.3% and reduced protein concentration by 1.3%. Substitution of urea with PCU had the largest impact at the site-year with most severe drought stress, indicating that PCU benefits were probably due to factors other than reduced N loss. Further study is required to evaluate potential benefits from substitution of urea with PCU over a wide range of environmental conditions.Key words: Triticum aestivum, nitrogen fertilizer, placement, timing, split N, grain protein concentration, polymer-coated urea

This paper reports on an investigation of the suitability of rangeland terrain as a terrestrial benchmark site for monitoring the radiometric performance of satellite sensors after launch. The test site considered is in Newell County rangeland in Alberta (NCRA). Seventy-two Landsat and 34 Satellite Pour l'Observation de la Terre (SPOT) images spanning 1985–2008 were used in the retrospective analysis. Coefficient of variation was used to assess the spatial uniformity and temporal stability of the surface radiometry. Mean top-of-atmosphere (TOA) reflectances were also examined. Image statistics were acquired for various window sizes within a region of 13 km × 13 km, the largest area common to almost all images of the NCRA region. Results are presented for a refined area of 3 km × 3 km and the most uniform 1 km × 1 km area within the refined area. In particular, the results indicate that the spatial radiometric uniformity of the 1 km × 1 km NCRA site has been consistently within 5% since 1985 but subject to considerable temporal variation within that 5%. Hence, the NCRA site is not deemed to be a strong candidate as a primary benchmark site for routine calibration monitoring purposes, although it could serve at times as a secondary site in the absence of or in addition to other possibilities.

Radarsat 2 quad polarization imagery has been used to study the effectiveness of polarimetric radar to monitor the extent and health of prairie grasslands in southern Alberta, Canada. In this report of preliminary findings, the imagery is shown to be effective in the separation of cropped lands from rangelands, and in the separation of native grasslands and improved pastures. Classification was more accurate using Freeman-Durden decomposition parameters than using Cloude-Pottier parameters. Incidence angle differeces were noted and use of multiple angles in classification improved accuracy. In a second part of the study, it was shown that polarimetric imagery was capable of identifying weeds and brush growing in native rangland, and in separating different kinds of brush and weeds. Validated sample sets were too small to allow proper accuracy assessment, but a ‘performance metric’ showed that accuracy would be improved by use of multiple incidence angles, and by the use of Freeman-Durden or coherency matrix parameters.

The effect of soil properties and weather on herbicide persistence and injury to following crops were studied at a site near Lethbridge, Alberta, Canada, with undulating topography that included no-tillage and conventional tillage systems on adjacent fields. Soil pH ranged from 5.2 (lower slope no-tillage) to 7.8 (upper slope conventional tillage) and soil organic matter content ranged from 2.3% (upper slope conventional tillage) to 4.4% (lower slope no-tillage). During the years when the experiments were conducted rainfall ranged from < 50% of normal to > 150% of normal. During dry years atrazine and metsulfuron severely injured wheat and lentil crops, seeded 1 yr after herbicide application, on upper slope locations. The most severe injury occurred on the upper slope conventional tillage location. In years with high rainfall, no crop injury occurred 1 yr after atrazine and metsulfuron application on either upper or lower slope locations in both tillage systems. Imazamox plus imazethapyr caused almost 100% injury in the lower slope position in the no-tillage system (pH 5.2) in the driest year. Following-crop injury due to the imidazolinone herbicides decreased with increasing rainfall and increasing soil pH. The most severe injury to following crops seemed to occur when herbicide dissipation was dependent on microbial activity and rainfall was below normal.