Agriculture And Agri-Food Canada Research Articles

Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images

Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP) and the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) satellite missions, but these data are based on passive microwave sensors, which have limited spatial resolution. Thus, detailed observations and analyses of the local distribution of SM are limited. The recent emergence of deep learning techniques, such as rectified linear unit (ReLU) and dropout, has produced effective solutions to complex problems. Deep neural networks (DNNs) have been used to accurately estimate hydrologic factors, such as SM and evapotranspiration, but studies of SM estimates derived from the joint use of DNN and high-resolution satellite data, such as Sentinel-1 and Sentinel-2, are lacking. In this study, we aim to estimate high-resolution SM at 30 m resolution, which is important for local-scale SM monitoring in croplands. We used a variety of input data, such as radar factors, optical factors, and vegetation indices, which can be extracted from Sentinel-1 and -2, terrain information (e.g., elevation), and crop information (e.g., cover type and month), and developed an integrated SM model across various crop surfaces by using these input data and DNN (which can learn the complexity and nonlinearity of the various data). The study was performed in the agricultural areas of Manitoba and Saskatchewan, Canada, and the in situ SM data for these areas were obtained from the Agriculture and Agri-Food Canada (AAFC) Real-time In Situ Soil Monitoring for Agriculture (RISMA) network. We conducted various experiments with several hyperparameters that affected the performance of the DNN-based model and ultimately obtained a high-performing SM model. The optimal SM model had a root-mean-square error (RMSE) of 0.0416 m3/m3 and a correlation coefficient (CC) of 0.9226. This model’s estimates showed better agreement with in situ SM than the SMAP 9 km SM. The accuracy of the model was high when the daily precipitation was zero or very low and also during the vegetation growth stage. However, its accuracy decreased when precipitation or the vitality of the vegetation were high. This suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. Nevertheless, the distribution of SM estimated by our model generally reflected the local soil characteristics. This work will aid in drought and flood prevention and mitigation, and serve as a tool for assessing the potential growth of crops according to SM conditions.

Surface biophysical features fusion in remote sensing for improving land crop/cover classification accuracy

Preparing up-to-date land crop/cover maps is important to study in order to achieve food security. Therefore, the aim of this study was to evaluate the impact of surface biophysical features in the land crop/cover classification accuracy and introduce a new fusion-based method with more accurate results for land crop/cover classification. For this purpose, multi-temporal images from Sentinel 1 and 2, and an actual land crop map prepared by Agriculture and Agri-Food Canada (AAFC) in 2019 were used for 3 test sites in Ontario, Canada. Firstly, surface biophysical features maps were prepared based on spectral indices from Sentinel 2 including Normalized Difference Vegetation Index (NDVI), Index-based Built-up Index (IBI), Wetness, Albedo, and Brightness and co-polarization (VV) and cross-polarization (VH) from Sentinel 1 for different dates. Then, different scenarios were generated; these included single surface biophysical features as well as a combination of several surface biophysical features. Secondly, land crop/cover maps were prepared for each scenario based on the Random Forest (RF). In the third step, based on the voting strategy, classification maps from different scenarios were combined. Finally, the accuracy of the land crop/cover maps obtained from each of the scenario was evaluated. The results showed that the average overall accuracy of land crop/cover maps obtained from individual scenario (one feature) including NDVI, IBI, Wetness, Albedo, Brightness, VV and VH were 66%, 68%, 63%, 60%, 57%, 62% and 58%, respectively, which by the surface biophysical features fusion, the overall accuracy of land crop/cover maps increased to 83%. Also, by combining the classification results obtained from different scenarios based on voting strategy, the overall accuracy increased to 89%. The results of this study indicate that the feature level-based fusion of surface biophysical features and decision level based fusion of land crop/cover maps obtained from various scenarios increases the accuracy of land crop/cover classification.

Priorities, Narratives, and Collaboration: Insights From Evolving Federal Mandates on Food Systems in Canada

Whether it is in a post-election period, a cabinet shuffle, or prorogation of parliament, the speech from the throne and mandate letters signal a government's priorities as they relate to emergent issues and long-standing public policy challenges. While the speech from the throne has been regularly available through parliamentary and government records, federal mandate letters have only been made publicly available more recently, and little research has been done on their role in shaping change. Using Critical Discourse Analysis (CDA), the authors explore how the overarching narratives presented by the current federal government have evolved across the period from 2015 to 2021. The authors then compare these narratives with the mandated commitments to the Minister of Agriculture and Agri-Food Canada (AAFC) during the same period. Through this comparative analysis, the authors highlight how the overarching narratives that emerged in later mandates, in particular the need to address systemic inequity, diverge with the commitments delivered to the Minister of AAFC. Part of the reason for identifying the divergence between central narratives and the current AAFC mandate is the hope that better alignment is possible. This includes making a new food policy environment in Canada; One that is equitable, prosperous for all, supports true reconciliation and Indigenous sovereignty, and ushers in a brighter future for the next generation and our planet. To conclude, the authors present alternative food systems frameworks that could help better achieve the more just and resilient world that the federal government narratives outlines.

AAC Excellence oat

AAC Excellence is a covered, spring oat (Avena sativa L.) cultivar developed by the Ottawa Research and Development Center (ORDC), Agriculture and Agri-Food Canada (AAFC). It was derived from a four-way cross, OA1250-1/MN07205//Rigodon/HiFi, made in 2009. It has been tested in the Quebec provincial Oat Registration and Recommendation (QCORR) trials since 2017. Based on orthogonal data from the 2018–2021 QCORR trials, AAC Excellence yielded 6% higher than the mean of official checks (AAC Dieter, Canmore, and CS Camden) and its yield was more stable across years than the checks and other cultivars. AAC Excellence had similar levels of test weight and kernel weight to those of the checks, a β-glucan level similar to Akina and better than AAC Nicolas and all checks, and a groat content level similar to AAC Nicolas and AC Dieter and better than Akina and other checks. It had a superior package of yield and quality and is most adapted to Quebec, the Maritimes, and northern Ontario.

AAC McRae soybean

AAC McRae is a high yielding food grade soybean [Glycine max (L.) Merr.] cultivar with yellow hilum, high protein concentration, and acceptable processing quality for foreign and domestic tofu, soymilk, and miso markets. It has excellent soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) and soybean sudden death syndrome (SDS) (Fusarium virguliforme O'Donnell & T. Aoki) resistance. AAC McRae was developed at the Agriculture and Agri-Food Canada (AAFC) Harrow Research and Development Centre (Harrow-RDC), Harrow, Ontario, and is adapted to areas of southwest Ontario with 3100 or more crop heat units and has a relative maturity group of 2.2 (MG 2.2).

AAC Richard soybean

AAC Richard is a food grade soybean [Glycine max (L.) Merr.] cultivar with yellow hilum, high protein concentration, and good processing quality for foreign and domestic soymilk, tofu, and miso markets. It has resistance to soybean cyst nematode (SCN) (Heterodera glycines Ichinohe). AAC Richard was developed at the Agriculture and Agri-Food Canada (AAFC) Harrow Research and Development Centre (Harrow-RDC), Harrow, Ontario, and is adapted to areas of southwest Ontario with 3100 or more crop heat units and has a relative maturity of 2.3 (MG 2.3).

How climatic and sociotechnical factors influence crop production: a case study of canola production

In most climate change research, agricultural yields are explained as a function of climatic and biophysical factors such as soil, rainfall and temperature. However, the increased use of integrated sensors, digital technologies and robotics within the agricultural sector has dramatically altered the way in which we produce food. Considering both the agriculture industry’s continuing widespread technological innovation and a rapidly changing biophysical environment, there is a need to explore how sociotechnical and climatic variables interact to determine yield. In this paper, we present a regression model derived from Agriculture and Agri-Food Canada (AAFC) yield data, Environment and Climate Change Canada (ECCC) climate and land capability data, and Statistics Canada Census of Agriculture databases that include sociotechnical variables such as farms that use GIS and GPS had access to high-speed internet alongside more traditional biophysical factors to predict canola (rape seed) yields in the southern prairies of Canada. We demonstrated that about 38% of canola yield variability could be explained by temperature and rainfall during the growing season (defined as 3 months of June, July and August) and access to high-speed internet, application of chemical fertilizer, fungicides and average age of farm operators. While of a preliminary nature, our results demonstrate that a better understanding of how climatic and sociotechnical factors interact is necessary to anticipate how climate change may affect the crop yield.

Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada

The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Developing and implementing novel methods for improving these products are an ongoing priority of AAFC. Consequently, it is beneficial to implement advanced machine learning and big data processing methods along with open-access satellite imagery to effectively produce accurate ACI maps. In this study, for the first time, the Google Earth Engine (GEE) cloud computing platform was used along with an Artificial Neural Networks (ANN) algorithm and Sentinel-1, -2 images to produce an object-based ACI map for 2018. Furthermore, different limitations of the proposed method were discussed, and several suggestions were provided for future studies. The Overall Accuracy (OA) and Kappa Coefficient (KC) of the final 2018 ACI map using the proposed GEE cloud method were 77% and 0.74, respectively. Moreover, the average Producer Accuracy (PA) and User Accuracy (UA) for the 17 cropland classes were 79% and 77%, respectively. Although these levels of accuracies were slightly lower than those of the AAFC’s ACI map, this study demonstrated that the proposed cloud computing method should be investigated further because it was more efficient in terms of cost, time, computation, and automation.

Canadian Regional Agriculture Model: PMP Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use by a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves were then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Canadian Regional Agriculture Model: Positive Mathematical Programming Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use using a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves are then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

Canadian Regional Agriculture Model: Positive Mathematical Programming Applied to the Beef Sector in Canada

This paper describes the Positive Mathematical Programming (PMP), the method for calibrating models of agricultural livestock production and resource use using a nonlinear total cost function. The PMP method is applied to agricultural sectoral models to study changes in policy and market signals. The Canadian Regional Agriculture Model (CRAM) is a regional, multi-sectoral, comparative static, partial equilibrium, mathematical programming model developed and maintained by Agriculture and Agri-Food Canada (AAFC) since mid-eighties. The PMP process converts a linear model using flexibility constraints into a nonlinear model in the absence of the flexibility constraints. A component of CRAM is the beef sector. The elements of the set of total cost curves are defined as quadratic function in terms of the number of cows and calves in the beef production activities. The marginal cost curves are then approximated using the shadow values from linear programming solution with linear curves. Once the flexibility constraints were removed, the model automatically calibrates to the base year production levels. The results from four scenarios indicated the beef sector of CRAM could predict the impact of the scenarios on the size of beef herd. In Scenario 1 where cash costs were increased by 10 percent, the breeding herd size decreased from 3.73 percent in New Brunswick to 0.0 percent in Ontario and Quebec. In Scenario 2 where barley costs were decreased by 10 percent, the breeding herd size increased from 0 percent for British Columbia, Alberta, Ontario, Quebec, Prince Edward Island and Nova Scotia to 1.93 percent for New Brunswick. In Scenario 3 where carcass weight per beef cow could be increased by 10 percent, the increase in beef herd size ranged from 0 percent for Ontario and Quebec to 2.56 percent for New Brunswick. In Scenario 4 where world beef prices were increased by 10 percent increase in beef herd size ranged from 4.48 percent for Manitoba to 25.78 percent for New Brunswick.

AAC Wigle soybean

AAC Wigle is a high yielding, high-protein, large-seeded, soybean cyst nematode resistant food-grade soybean [Glycine max (L.) Merr.] cultivar with yellow hilum and acceptable processing quality for foreign and domestic tofu, soymilk, and miso markets. It was developed at the Agriculture and Agri-Food Canada (AAFC) Harrow Research and Development Centre (Harrow-RDC), Harrow, ON. AAC Wigle is adapted to areas of southwestern Ontario with 3200 or more crop heat units and has a relative maturity of 2.2 (MG 2.2) and protein content of 45.8%.

AAC Big Ben soybean

AAC Big Ben is a high yielding, soybean cyst nematode resistant food-grade soybean [Glycine max (L.) Merr.] cultivar with yellow hilum and acceptable processing quality for foreign and domestic tofu, soymilk, and miso markets. AAC Big Ben was developed at the Agriculture and Agri-Food Canada (AAFC) Harrow Research and Development Centre (Harrow-RDC), Harrow, ON. AAC Big Ben is adapted to areas of southwestern Ontario with 3300 or more crop heat units and has a relative maturity group of 2.3 (MG 2.3).

Effects of Field Selection Parameters and Specific Gravity on Culinary Evaluation Traits in a Potato Breeding Programme

Potato culinary evaluation is an integral component of a breeding programme to determine suitability of new cultivars to specific end-uses. In most breeding programmes, quality assessment is conducted after a few generations of field selection on tubers collected from the field or from storage conditions. In order to optimize selection procedures, we analysed the relationship between specific gravity of potato tubers and quality evaluation traits as well as the effects of field selection parameters in clones that are intended for fresh market and processing uses. A total number of 237 advanced breeding clones were analysed over three consecutive years in the potato breeding programme at Agriculture and Agri-Food Canada (AAFC). Principal component analysis (PCA) and linear mixed-effects model fit by maximum likelihood indicated that parameters used in field selection significantly contributed to specific gravity and culinary components. Specific gravity also contributed significantly to culinary traits related to acceptable quality of baked, boiled and French-fried products. However, such relationship was not observed for chip quality. Significant associations were found between field selection parameters, such as maturity, early vigour, top vigour, early harvest, tuber size and tuber appearance and culinary components related to French fry and chip quality, but not for quality of baked and boiled products. Broad-sense heritability estimates for various culinary evaluation traits ranged from 57 to 92%. The results suggest that specific gravity and culinary traits should be incorporated in early or intermediate generation selection parameters in order to optimize selection gains for specific end-uses.

Comparative Analysis of High-Resolution Soil
 Moisture Simulations from the Soil, Vegetation, and
 Snow (SVS) Land Surface Model Using SAR Imagery
 Over Bare Soil

Soil moisture is a key variable in Earth systems, controlling the exchange of water andenergy between land and atmosphere. Thus, understanding its spatiotemporal distribution andvariability is important. Environment and Climate Change Canada (ECCC) has developed a newland surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS landsurface scheme features sophisticated parameterizations of hydrological processes, including watertransport through the soil. It has been shown to provide more accurate simulations of the temporaland spatial distribution of soil moisture compared to the current operational land surface scheme.Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, wesimulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme overan experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps wereproduced between May and November 2015. Simulated soil moisture values were compared withestimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture andAgri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic ApertureRadar (SAR) imagery. Statistical analysis of the results showed an overall promising performanceof the SVS land surface scheme in simulating soil moisture values at high resolution scale.Investigation of the SVS output was conducted both independently of the soil texture, and as afunction of the soil texture. The SVS model tends to perform slightly better over coarser texturedsoils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulatedSVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. TheRoot Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. Theunbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore,results show an Index of Agreement (IA) between the simulated and the derived soil moisturealways higher than 0.90.

Drought weakens the positive effects of defoliation on native rhizomatous grasses but enhances the drought-tolerance traits of native caespitose grasses.

The objective of this study was to evaluate the drought tolerance, compensatory growth, and different plant traits between two native perennial caespitose grasses and two native rhizomatous grasses in response to drought and defoliation. A randomized complete block design at the Swift Current Research and Development Centre (SCRDC) of Agriculture and Agri‐Food Canada (AAFC) examined the effects of water stress and clipping on the plant biomass, plant morphological traits, and relative leaf chlorophyll content (SPAD value) of four native grasses (caespitose grass: Hesperostipa comata and H. curtiseta; rhizomatous grass: Pascopyrum smithii and Elymus lanceolatus). Drought drastically decreased the shoot and root biomass, plant height, number of tillers and leaf growth of P. smithii and E. lanceolatus, as well as the rhizome biomass and R/S ratio of P. smithii. Defoliation had a positive effect on the shoot biomass of P. smithii and E. lanceolatus under well water treatments (100% and 85% of field capacity). However, the compensatory growth of P. smithii and E. lanceolatus significantly declined with increased water stress. In addition, there are no significant changes in plant biomass, plant height, number of tillers and leaves, and SPAD value of H. comata and H. curtiseta under relative dry condition (70% of field capacity). Consequently, these results demonstrated that the rhizomatous grasses possessed a stronger compensation in response to defoliation under wet conditions, but the positive effects of defoliation can be weakened by drought. The caespitose grasses (Hesperostipa species) exhibited a greater drought tolerance than rhizomatous grasses due to the relatively stable plant traits in response to water stress.

Integrating cloud-based workflows in continental-scale cropland extent classification

Accurate information on cropland spatial distribution is required for global-scale assessments and agricultural land use policies. Cloud computing platforms such as Google Earth Engine (GEE) provide unprecedented opportunities for large-scale classifications of Landsat data. We developed a novel method to fuse pixel-based random forest classification of continental-scale Landsat data on GEE and an object-based segmentation approach known as recursive hierarchical segmentation (RHSeg). Using our fusion method, we produced a continental-scale cropland extent map for North America at 30 m spatial resolution for the nominal year 2010. The total cropland area for North America was estimated at 275.18 million hectares (Mha). The overall accuracies of the map are >90% across the continent. This map also compares well with the United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-food Canada (AAFC) annual crop inventory (ACI), and the Mexican government agency Servicio de Información Agroalimentaria y Pesquera (SIAP)'s agricultural boundaries. Furthermore, our map compared well with sub-country statistics including state-wise and county-wise cropland statistics in regression models resulting in R2 > 0.84. This key contribution paves the way for more detailed products such as crop intensity, crop type, and crop irrigation, and provides a method for creating high-resolution cropland extent maps for other countries where spatial information about croplands are not as prevalent.

AAC Glenview sainfoin (Onobrychis viciifoila subsp. viciifolia)

AAC Glenview sainfoin, tested as LRC 4342, was developed by the Agriculture and Agri-Food Canada (AAFC) Lethbridge Research and Development Centre, Lethbridge, Alberta. This cultivar was derived from parental clones selected for improved forage yield in mixed stands with alfalfa under grazing and regrowth after grazing. When grown under irrigated and rainfed conditions in western Canada, Glenview out yielded Nova (check) by 21% in pure stands over 37 location–years and 19% in mixed stands with alfalfa over 17 location–years. This cultivar flowers and matures about 8 d earlier than Nova and has slightly larger seed than Nova (24.7 g 1000 seeds−1 vs 20 g 1000 seeds−1). It can be grown for hay and pasture in pure stands but Glenview is well suited for mixed sainfoin–alfalfa stands. Breeder seed for the cultivar will be produced by AAFC and the multiplication and distribution rights were awarded to Monarch Feeders Ltd., Monarch, AB, Canada.

Management of Molecular Data in DINA with SeqDB

Agriculture and Agri-Food Canada (AAFC) is home to numerous specimen and environmental collections generating highly relational data sets that are analyzed using molecular methods (Sanger and NGS). The need to have a system to properly manage these data sets and to capture accurate, standardized metadata over entire laboratory workflows has been a long-term strategic vision of the Biodiversity group at AAFC. Without robust tracking, many difficulties arise when trying to publish or submit data to external repositories. To even know what work has been carried out on individual collection records over a researchers career becomes a demanding task if the information is retrievable at all. SeqDB was built to resolve these issues by centralizing, standardizing and improving the availability and data quality of source specimen collection data that is being studied using molecular methods. SeqDB also facilitates integration with tools and external repositories in order to take the burden off researchers and technicians having to create adequate systems to track and mobilize their data sets, allowing them to focus on research and collection management. The development of SeqDB aligns with agile development methodologies and attempts to fulfill rapidly emerging needs from genetics and genomics research, which can evolve and fade quickly at times or be without clear requirements. The success of SeqDB as an application supporting DNA sequencing workflows has put it in the same space as other monolithic architectures before it. As the feature set to support the application continues to increase, the number of software developers vs operations and maintenance staff is difficult to rebalance in our organisation. In an effort to manage the scope for the project and ensure we are able to continue to deliver on our mandate, the sequence tracking workflows of the application will become part of the DINA ecosystem (“DIgital information system for NAtural history data”, https://dina-project.net). Other functions of SeqDB such as collections management and taxonomy tree curation, will be replaced with the DINA modules implementing these functions. In order to allow SeqDB to become a module of DINA, it has been decided to refactor the application to base it on a Service Oriented Architecture. By doing so, all molecular data of SeqDB will be exposed as JSON API Web Services (JavaScript object notation application programming interface) allowing other modules, user interfaces and the current SeqDB application to communicate in a standardised way. The new architecture will also bring an important technology upgrade for SeqDB where the front end will eventually become a project in itself.

Mixtures of native perennial forage species produce higher yields than monocultures in a long-term study

To evaluate the forage yield and quality of seven perennial native species in monoculture and binary mixtures under a range of climate conditions, a 6-yr field experiment was conducted at the Swift Current Research and Development Centre (SCRDC), Agriculture and Agri-Food Canada (AAFC), in Swift Current, SK. Seven native perennial forage species from three functional groups (C3, C4grasses, and legumes) were seeded in 2010 in monocultures and binary mixtures. Forage yield and quality [crude protein, acid detergent fiber (ADF), neutral detergent fiber (NDF), phosphorus (P), calcium (Ca), and copper (Cu)] were measured during the first week of July and last week of August in 2011–2016. Mixtures that included western wheatgrass [Pascopyrum smithii (Rydb.) Löve] (WWG) tended to produce a greater yield when 90% of the composition within these mixtures was WWG. Adding bluebunch wheatgrass [Pseudoroegneria spicata (Pursh) Löve] (BBW), little blue stem [Schizachyrium scoparium (Michx.) Nash] (LBS), and prairie clovers (Dalea spp.) to the binary mixtures can increase the positive aspects of species diversity on stability and productivity in seeded pastures. Among the grasses, WWG contained higher crude protein and lower ADF and NDF concentration. Mixtures of forage species produced higher forage yield compared with monocultures. Native forage species can produce stable forage yield across very different climate situations. In mixtures, WWG showed promising results in forage productivity and quality and can be a suitable option for seeded pastures.