Majid Farhadloo https://lowinputturf.umn.edu/ en Fine-grain spatial information may enhance the value of turfgrass cultivar data https://lowinputturf.umn.edu/fine-grain-spatial-information-may-enhance-value-turfgrass-cultivar-data <span class="field field--name-title field--type-string field--label-hidden">Fine-grain spatial information may enhance the value of turfgrass cultivar data</span> <span class="field field--name-uid field--type-entity-reference field--label-hidden"><span lang="" about="/user/326" typeof="schema:Person" property="schema:name" datatype="">monc0003</span></span> <span class="field field--name-created field--type-created field--label-hidden"><time datetime="2022-05-24T10:55:17-05:00" title="Tuesday, May 24, 2022 - 10:55" class="datetime">Tue, 05/24/2022 - 10:55</time> </span> <div class="panel-display brenham-flipped clearfix"> <div class="container-fluid"> <div class="row"> <div class="col-md-12 radix-layouts-header panel-panel"> <div class="panel-panel-inner"> <div data-block-plugin-id="field_block:node:dl_news_blog:field_dl_publish_date" class="block block-layout-builder block-field-blocknodedl-news-blogfield-dl-publish-date"> <div class="field field--name-field-dl-publish-date field--type-datetime field--label-hidden field__item"><time datetime="2022-05-24T10:55:17Z" class="datetime">Tue, 05/24/2022 - 10:55</time> </div> </div> </div> </div> </div> <div class="row"> <div class="col-md-8 radix-layouts-content panel-panel"> <div class="panel-panel-inner"> <div data-block-plugin-id="field_block:node:dl_news_blog:field_dl_body" class="block block-layout-builder block-field-blocknodedl-news-blogfield-dl-body"> <div class="clearfix text-formatted field field--name-field-dl-body field--type-text-with-summary field--label-hidden field__item"><p><em>By Majid Farhadloo, and Shashi Shekhar, Department of Computer Science, University of Minnesota</em></p> <p><span><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>Thanks to NTEP-DB 1.0 [1], the relational database we created for the </span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><a href="https://ntep.org/"><span><span><span>National Turfgrass Evaluation Program</span></span></span></a></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span> (NTEP), obtaining turfgrass cultivars' data will soon be much easier. In addition, a user-friendly web application by </span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><a href="https://research.umn.edu/units/uspatial/"><span><span><span>U-Spatial</span></span></span></a></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span> allows homeowners, researchers, and other stakeholders to extract meaningful information from NTEP data in a variety of formats.  For example, retailers can learn the best cultivars to stock in different store locations around the country. </span></span></span></span></span></span></span></p> <p><span><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><a href="https://lowinputturf.umn.edu/new-tools-simplify-searching-suitable-turfgrass-using-ntep-data"><span><span><span>The current NTEP database</span></span></span></a></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span> represents location using state names and site IDs within states that can be used to infer high-level geographic regions. It lacks fine-grain location and terrain information such as latitude, longitude, and local elevation. Incorporating spatial information into the database promises to greatly expand its capabilities, making it possible, for example, to detect hotspots of turf disease or stress within a trial, identify co-occurrences between disease and environmental conditions, and leverage machine learning to predict cultivar quality ratings [2]. In collaboration with the turfgrass research group at the University of Minnesota, we have explored ways to test how spatial data might enhance the value of the NTEP database. For example, turfgrass researchers have observed winter damage to cultivars located at lower elevations, which they attribute to temperature fluctuations and ice water accumulation in those areas. We decided to use this observation to test whether cultivar performance can be explained by elevation data.</span></span></span></span></span></span></span></p> <figure role="group" class="caption caption-drupal-entity default-image"><div alt="Two graphs with color coding showing turfgrass quality in the first graph and the plot randomization in the second graph." data-embed-button="media_browser" data-entity-embed-display="media_image" data-entity-embed-display-settings="{&quot;link_url&quot;:&quot;&quot;,&quot;link_url_target&quot;:0,&quot;image_style&quot;:&quot;&quot;,&quot;image_link&quot;:&quot;&quot;}" data-entity-type="media" data-entity-uuid="b37773f8-93e9-4427-94e0-84d9579fafd4" data-langcode="en" title="Majid Fig 1" class="embedded-entity"> <img loading="lazy" src="/sites/lowinputturf.umn.edu/files/2022-05/Fig%201.png" alt="Two graphs with color coding showing turfgrass quality in the first graph and the plot randomization in the second graph." title="Majid Fig 1" typeof="foaf:Image" /></div> <figcaption>Figure 1. The original dataset. 1a shows turfgrass quality rating for May 2017, and 1b shows the experimental trial with block randomization.</figcaption></figure><p><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><span><span>The turfgrass research group provided us with a test dataset from 2016 NTEP perennial ryegrass trial in Minnesota consisting of turfgrass quality ratings using a 1-9 scale (9=best turfgrass quality) that were taken during a single growing season (Figure 1a).  The dataset is represented as a spreadsheet describing rows and columns for each cultivar, one cultivar to a cell. In field experiments, each site is partitioned into several replication blocks where each cultivar is evaluated in all the replications to minimize the random effect and account for spatial variability. Figure 1(b) shows the data divided into three blocks, representing the divisions of the test site locations. Cell values are the unique IDs of cultivars. Each cultivar is randomly assigned to a location in each block. For example, cultivar 91 can be found in the first block (row 1, column 2), the second block (row 10, column 2), and the third block (row 18, column 5). </span></span></span></span></span></p> <figure role="group" class="caption caption-drupal-entity default-image"><div alt="Graph of experimental plots and elevation" data-embed-button="media_browser" data-entity-embed-display="media_image" data-entity-embed-display-settings="{&quot;link_url&quot;:&quot;&quot;,&quot;link_url_target&quot;:0,&quot;image_style&quot;:&quot;&quot;,&quot;image_link&quot;:&quot;&quot;}" data-entity-type="media" data-entity-uuid="dc8aa2ad-7caf-492c-b88b-36fa44ce16d0" data-langcode="en" title="Majid Fig 2" class="embedded-entity"> <img loading="lazy" src="/sites/lowinputturf.umn.edu/files/2022-05/Fig%202.jpg" alt="Graph of experimental plots and elevation" title="Majid Fig 2" typeof="foaf:Image" /></div> <figcaption>Figure 2. Mapping elevation information to the existing dataset.</figcaption></figure><p><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><span><span>We supplemented the turfgrass dataset with spatial information, namely geo-coordinates from Google Maps and elevation information from LIDAR data collected by Minnesota Geospatial Commons [3]. Figure 2 maps the digital elevation for each cultivar at a given location, with darker and lighter colors indicating lower and higher elevations, respectively. Elevation data does not tell the complete story since it is difficult to identify a direct correlation between the elevation data and the turfgrass quality ratings. Therefore, we explored additional spatial data, including water flow direction and accumulation, to determine the correlation.</span></span></span></span></span></p> <figure role="group" class="caption caption-drupal-entity default-image"><div alt="Graph of experimental plots and water flow" data-embed-button="media_browser" data-entity-embed-display="media_image" data-entity-embed-display-settings="{&quot;link_url&quot;:&quot;&quot;,&quot;link_url_target&quot;:0,&quot;image_style&quot;:&quot;&quot;,&quot;image_link&quot;:&quot;&quot;}" data-entity-type="media" data-entity-uuid="a0fc35a5-3724-4dd5-906a-2f5bd31ca04d" data-langcode="en" title="Majid Fig 3" class="embedded-entity"> <img loading="lazy" src="/sites/lowinputturf.umn.edu/files/2022-05/Fig%203.jpg" alt="Graph of experimental plots and water flow" title="Majid Fig 3" typeof="foaf:Image" /></div> <figcaption>Figure 3. Water flow accumulation map across the study area.</figcaption></figure><p><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><span><span>Flow direction is a required input to construct a flow accumulation map. It shows the direction (e.g., east, west, south) where water would flow if there is water across the study area. We used water flow direction to calculate flow accumulation values for the study area. The cell values in Figure 3 indicate the number of cells that flow into and accumulate in a catchment area. Since water accumulates in depressions in the ground, the flow accumulation data can be viewed as a map of landscape depression in the study area. Using all of this information, that is, turf quality ratings, elevation data, and flow accumulation data, we can evaluate a cultivar’s sensitivity to different growing conditions. It is also worth noting that flow accumulation information can help researchers identify specific areas in the plot area that might not provide reliable results as indicated in the bounding box in Figure 3.</span></span></span></span></span></p> <figure role="group" class="caption caption-drupal-entity default-image"><div alt="The randomized location of one cultivar on the experimental plot plan and quality rating in relation to depression" data-embed-button="media_browser" data-entity-embed-display="media_image" data-entity-embed-display-settings="{&quot;link_url&quot;:&quot;&quot;,&quot;link_url_target&quot;:0,&quot;image_style&quot;:&quot;&quot;,&quot;image_link&quot;:&quot;&quot;}" data-entity-type="media" data-entity-uuid="34b096da-6e95-4639-8380-8e78658c8821" data-langcode="en" title="Majid Fig 4" class="embedded-entity"> <img loading="lazy" src="/sites/lowinputturf.umn.edu/files/2022-05/Fig%204.jpg" alt="The randomized location of one cultivar on the experimental plot plan and quality rating in relation to depression" title="Majid Fig 4" typeof="foaf:Image" /></div> <figcaption>Figure 4. Evaluation of sensitivity or robustness of a sample cultivar based on depression. 4a shows a sample cultivar replicated in three locations and 4b shows quality rating plot versus depression.</figcaption></figure><p><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>To test if a given cultivar's performance may be affected by elevation variation, we selected a cultivar grown in a depression, as indicated by its high cell value on the water flow accumulation map (row 10 and column 2 in Figure 3). This cultivar corresponds to cell 91, referred to in Figure 1(b) earlier. Figure 4 (a) shows the cultivar replicated in three test locations, L1, L2, and L3. We plotted the cultivar's ratings according to depression information (number of cells in the catchment area, Figure 3) for the three test locations to evaluate if depressions in the landscape influence the cultivar's performance. Figure 4(b) shows the cultivar ratings f</span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>rom August to October when the temperature progressively declines. In addition, with the advent of colder weather a region with a significant depression (i.e., L2) has a similar or relatively better quality rating compared to the flatter test regions (i.e., L1 or L3)<span>. This shows a simple illustrative example to help turfgrass researchers evaluate a cultivar's performance, where some cultivars may be less impacted by elevation variation in the landscape. We note supplementary data, including spatiotemporal and environmental information (e.g., more precise geo-coordinates, climatic data,) may be required to evaluate better and comprehend the cultivar's performance. </span></span></span></span></span></span></p> <p><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>All spatial maps used in this analysis were created with ArcGIS software [4].</span></span></span></span></span></span></p> <p><span><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>In conclusion, enhancing existing NTEP turfgrass data with fine-grain location and terrain information has the potential to provide spatial context (e.g., depressions) to improve the current understanding of turfgrass quality.</span></span></span></span></span></span></p> <h1><span><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>Resources for Further Exploration</span></span></span></span></span></span></h1> <ol><li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>Xie, Yiqun, Majid Farhadloo, Ning Guo, Shashi Shekhar, Eric Watkins, Len Kne, Han Bao, Aaron J. Patton, and Kevin Morris. "NTEP‐DB 1.0: A relational database for the national turfgrass evaluation program." <em>International Turfgrass Society Research Journal</em>, Accepted on May 5th, 2021, DOI: 10.1002/its2.76.</span></span></span></span></span></span></li> <li><span><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>S. Shekhar and P. Vold. <em>Chapter 5: Spatial Databases</em>. In: </span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><a href="https://mitpress.mit.edu/books/spatial-computing"><span><span><span><span>Spatial Computing</span></span></span></span></a></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>, The MIT Press Essential Knowledge series, MIT Press, Feb. 2020.</span></span></span></span></span></span></li> <li><span><span><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN">Minnesota Geospatial Commons, 2021,  </span><span lang="EN" xml:lang="EN" xml:lang="EN"><a href="https://www.mngeo.state.mn.us/chouse/elevation/lidar.html"><span><span>https://www.mngeo.state.mn.us/chouse/elevation/lidar.html</span></span></a></span></span></span></span></span></span></li> <li><span><span><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN">ArcGIS Pro, 2021, <a href="https://pro.arcgis.com/en/pro-app/2.8/help/layouts/create-a-map-series.htm">https://pro.arcgis.com/en/pro-app/2.8/help/layouts/create-a-map-series.htm</a></span></span></span></span></span></span></li> </ol></div> </div> <div data-block-plugin-id="field_block:node:dl_news_blog:field_dl_tags" class="block block-layout-builder block-field-blocknodedl-news-blogfield-dl-tags"> <div class="field field--name-field-dl-tags field--type-entity-reference field--label-inline"> <div class="field__label">Tags</div> <div class="field__items"> <div class="field__item"><a href="/taxonomy/term/211" hreflang="en">Majid Farhadloo</a></div> <div class="field__item"><a href="/taxonomy/term/216" hreflang="en">Shashi Shekhar</a></div> <div class="field__item"><a href="/taxonomy/term/51" hreflang="en">University of Minnesota</a></div> <div class="field__item"><a href="/taxonomy/term/226" hreflang="en">NTEP</a></div> </div> </div> </div> </div> </div> <div class="col-md-4 radix-layouts-sidebar panel-panel"> <div > </div> </div> </div> </div> </div><!-- /.brenham-flipped --> Tue, 24 May 2022 15:55:17 +0000 monc0003 376 at https://lowinputturf.umn.edu New tools simplify searching for suitable turfgrass using the NTEP data https://lowinputturf.umn.edu/new-tools-simplify-searching-suitable-turfgrass-using-ntep-data <span class="field field--name-title field--type-string field--label-hidden">New tools simplify searching for suitable turfgrass using the NTEP data</span> <span class="field field--name-uid field--type-entity-reference field--label-hidden"><span lang="" about="/user/326" typeof="schema:Person" property="schema:name" datatype="">monc0003</span></span> <span class="field field--name-created field--type-created field--label-hidden"><time datetime="2020-06-08T12:14:18-05:00" title="Monday, June 8, 2020 - 12:14" class="datetime">Mon, 06/08/2020 - 12:14</time> </span> <div class="panel-display brenham-flipped clearfix"> <div class="container-fluid"> <div class="row"> <div class="col-md-12 radix-layouts-header panel-panel"> <div class="panel-panel-inner"> <div data-block-plugin-id="field_block:node:dl_news_blog:field_dl_publish_date" class="block block-layout-builder block-field-blocknodedl-news-blogfield-dl-publish-date"> <div class="field field--name-field-dl-publish-date field--type-datetime field--label-hidden field__item"><time datetime="2020-06-08T12:14:18Z" class="datetime">Mon, 06/08/2020 - 12:14</time> </div> </div> </div> </div> </div> <div class="row"> <div class="col-md-8 radix-layouts-content panel-panel"> <div class="panel-panel-inner"> <div data-block-plugin-id="field_block:node:dl_news_blog:field_dl_body" class="block block-layout-builder block-field-blocknodedl-news-blogfield-dl-body"> <div class="clearfix text-formatted field field--name-field-dl-body field--type-text-with-summary field--label-hidden field__item"><hr /><p><em><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>By Yiqun Xie, Majid Farhadloo, Shashi Shekhar, and Len Kne; University of Minnesota</span></span></span></em></p> <p><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN">Suppose you have to choose a turfgrass for your lawn. How would you shop for grass seed suitable for your lawn? Would you like to have convenient search and recommendation facilities similar to those available for buying consumer products at e-commerce sites such as amazon.com? </span></span></span></span></p> <p><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN">Today, consumers get little information for deciding which grasses to purchase for their lawns despite the long existence of NTEP data [</span><span><span>1</span></span><span lang="EN" xml:lang="EN" xml:lang="EN">]. Since the 1980s, the </span><a href="http://www.ntep.org" target="_blank"><span><span>National Turfgrass Evaluation Program</span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN"> (NTEP) has collected large volumes of field-experiment data containing invaluable information about the performance and characteristics of turfgrass cultivars under a variety of conditions (e.g., climate). Searching for suitable cultivars through the current NTEP website (</span><a href="http://www.ntep.org" target="_blank"><span><span>www.ntep.org</span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN">) can be daunting even for simple “recommendation” queries (e.g., which cultivar has the highest average quality rating in Minnesota in the past 10 years?), which are of primary interest to consumers [</span><span><span>2</span></span><span lang="EN" xml:lang="EN" xml:lang="EN">]. Overall, finding answers to such simple queries is difficult with the current NTEP website requiring dozens of clicks to gather summary reports followed by manual merging and filtering. In addition, consumers may be overwhelmed with technical terms and jargon. </span></span></span></span></p> <figure role="group" class="caption caption-drupal-entity default-image"><div alt="Entity relationship model for NTEP database" data-embed-button="media_browser" data-entity-embed-display="media_image" data-entity-embed-display-settings="{&quot;image_style&quot;:&quot;&quot;,&quot;image_link&quot;:&quot;&quot;}" data-entity-type="media" data-entity-uuid="14eb3bdf-aca2-409a-a51a-3410b9f68a8a" data-langcode="en" title="entity relationship model" class="embedded-entity"> <img loading="lazy" src="/sites/lowinputturf.umn.edu/files/2020-06/entity%20relationship%20model.png" alt="Entity relationship model for NTEP database" title="entity relationship model" typeof="foaf:Image" /></div> <figcaption>Figure 1: An Entity-Relationship model of the new NTEP database [3,4].</figcaption></figure><p><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN">To address these limitations, our interdisciplinary team collaborated with NTEP to design and develop (1) an NTEP database for simplifying search and queries, as well as (2) a user-friendly web-application to make it easy and quick for users such as consumers and researchers to extract desired information in various formats from NTEP data [3, </span><span><span>4</span></span><span lang="EN" xml:lang="EN" xml:lang="EN">]. The conceptual design of the new NTEP database [3,</span><span><span>4</span></span><span lang="EN" xml:lang="EN" xml:lang="EN">] uses an </span><a href="https://en.wikipedia.org/wiki/Entity%E2%80%93relationship_model" target="_blank"><span><span>Entity-Relationship model</span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN"> as shown in Figure 1, which allows flexibility in answering a wide range of queries such as the following:</span></span></span></span></p> <ul><li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN">Homeowners: Which cultivar with low maintenance needs will work the best for my lawn?</span></span></span></span></li> <li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN">Retailers: Which cultivars should be stocked for sale in my local area?</span></span></span></span></li> <li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN">Turfgrass managers: Which cultivars have the best potential at my site (e.g., golf course)?</span></span></span></span></li> </ul><p><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>The </span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><a href="https://en.wikipedia.org/wiki/Entity%E2%80%93relationship_model" target="_blank"><span><span>Entity-Relationship model</span></span></a></span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span> also improves data quality with documentation and database-based enforcement of constraints (e.g., a cultivar can only belong to one species). Furthermore, it provides extensibility towards future database enrichment (e.g., adding new properties for field tests). The new database was implemented using a </span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><a href="https://en.wikipedia.org/wiki/Relational_database" target="_blank"><span><span>relational database management system</span></span></a></span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span> with the </span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><a href="https://en.wikipedia.org/wiki/SQL" target="_blank"><span><span>SQL query language</span></span></a></span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span> and </span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><a href="https://en.wikipedia.org/wiki/Simple_Features" target="_blank"><span><span>spatial libraries</span></span></a></span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span> [</span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><span><span>5</span></span></span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>,</span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><span><span>6</span></span></span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>]. It was tested via sanity checks against existing summary reports from the NTEP website, and inspection of new query results by turfgrass researchers. It showed that the new NTEP database is able to answer the queries correctly and efficiently (reducing dozens of clicks needed to answer a query to a few). Next, the database was demonstrated to project team members at the </span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><a href="https://portal.nifa.usda.gov/web/crisprojectpages/1013078-increasing-low-input-turfgrass-adoption-through-breeding-innovation-and-public-education.html" target="_blank"><em><span><span>Increasing Low-Input Turfgrass Adoption Through Breeding, Innovation, and Public Education</span></span></em></a></span></span></span><em> </em><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>2019 project meeting and made available to team members for alpha and beta testing.</span></span></span></p> <figure role="group" class="caption caption-drupal-entity default-image"><div alt="demo of web application for NTEP database" data-embed-button="media_browser" data-entity-embed-display="media_image" data-entity-embed-display-settings="{&quot;image_style&quot;:&quot;&quot;,&quot;image_link&quot;:&quot;&quot;}" data-entity-type="media" data-entity-uuid="a67b948a-8f14-467a-9f6e-df59e945266c" data-langcode="en" title="NTEP web app" class="embedded-entity"> <img loading="lazy" src="/sites/lowinputturf.umn.edu/files/2020-06/web%20app.png" alt="demo of web application for NTEP database" title="NTEP web app" typeof="foaf:Image" /></div> <figcaption>Figure 2. Web-application as a bridge between users and the database</figcaption></figure><p><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span>To improve the homeowner experience, our </span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span><a href="https://research.umn.edu/units/uspatial/" target="_blank"><span><span>U-Spatial</span></span></a></span></span></span><span lang="EN" xml:lang="EN" xml:lang="EN"><span><span> collaborators added a graphical web-application to reduce the jargon and user effort to get seed recommendations. For example, users can get turfgrass recommendations (e.g., a cultivar with the highest quality rating) by specifying a few simple criteria (e.g., location, amount of shade). To reduce user effort, the system can auto-fill many properties such as location and number of hours of sunshine based on smartphone location and existing solar maps as shown in Figure 2. The recommendations are based on the information contained in the NTEP database. Once a fully functional web app is complete, a homeowner could simply pull out a smartphone and get a turfgrass cultivar recommendation while standing on their lawn.</span></span></span></p> <h3><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>Resources for Further Exploration</span></span></span></span></span></h3> <ol><li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>C. Yue, J. Wang, E. Watkins, S. Bonos, K. Nelson, J. Murphy, W. Meyer, W. and B. Horgan, </span></span><a href="https://acsess.onlinelibrary.wiley.com/doi/abs/10.2134/agronj2016.05.0310" target="_blank"><span><span><span>Consumer Preferences for Information Sources of Turfgrass Products and Lawn Care</span></span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN"><span>. Agronomy Journal, 109: 1726-1733, 2017. doi:10.2134/agronj2016.05.0310</span></span></span></span></span></li> <li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>C. Yue, J. Wang, E. Watkins,Y.  Xie, S. Shekhar, S. Bonos, A. Patton, K. Morris, and K. Moncada. </span></span><a href="https://journals.ashs.org/horttech/view/journals/horttech/29/5/article-p599.xml" target="_blank"><span><span><span>User preferences for accessing publically available turfgrass cultivar performance data</span></span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN"><span>. HortTechnology, 29(5), pp.599-610, 2019.</span></span></span></span></span></li> <li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>Y. Xie, M. Farhadloo, N. Guo, S. Shekhar, E. Watkins, L. Kne, H. Bao, A. Patton and K. Morris. NTEP-DB 1.0: A Relational Database for the National Turfgrass Evaluation Program. To be submitted.</span></span></span></span></span></li> <li><span><span><span><a href="http://www.spatial.cs.umn.edu/NTEP/ntep_2019_summer_print.pdf" target="_blank"><span><span><span>Report on information delivery: An NTEP database</span></span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN"><span> (</span></span><a href="http://www.spatial.cs.umn.edu/NTEP/ntep_2019_summer_print.pdf" target="_blank"><span><span><span>Slide-set</span></span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN"><span>) at 2019 annual meeting of the </span></span><a href="https://portal.nifa.usda.gov/web/crisprojectpages/1013078-increasing-low-input-turfgrass-adoption-through-breeding-innovation-and-public-education.html" target="_blank"><em><span><span><span>Increasing Low-Input Turfgrass Adoption Through Breeding, Innovation, and Public Education</span></span></span></em></a><span lang="EN" xml:lang="EN" xml:lang="EN"><span> project, 2019.  </span></span><a href="http://www.spatial.cs.umn.edu/NTEP/ntep_2019_summer_print.pdf" target="_blank"><span><span><span>http://www.spatial.cs.umn.edu/NTEP/ntep_2019_summer_print.pdf</span></span></span></a></span></span></span></li> <li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>S. Shekhar and P. Vold. <em>Chapter 5: Spatial Databases</em>. In: </span></span><a href="https://mitpress.mit.edu/books/spatial-computing" target="_blank"><span><span><span>Spatial Computing</span></span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN"><span>, The MIT Press Essential Knowledge series, MIT Press, Feb. 2020.</span></span></span></span></span></li> <li><span><span><span><span lang="EN" xml:lang="EN" xml:lang="EN"><span>S. Shekhar and S. Chawla, </span></span><a href="https://www.amazon.com/Spatial-Databases-Tour-Shashi-Shekhar/dp/0130174807" target="_blank"><span><span><span>Spatial Databases: A Tour</span></span></span></a><span lang="EN" xml:lang="EN" xml:lang="EN"><span>, Prentice Hall, 2003.</span></span></span></span></span></li> </ol></div> </div> <div data-block-plugin-id="field_block:node:dl_news_blog:field_dl_tags" class="block block-layout-builder block-field-blocknodedl-news-blogfield-dl-tags"> <div class="field field--name-field-dl-tags field--type-entity-reference field--label-inline"> <div class="field__label">Tags</div> <div class="field__items"> <div class="field__item"><a href="/taxonomy/term/206" hreflang="en">Yiqun Xie</a></div> <div class="field__item"><a href="/taxonomy/term/211" hreflang="en">Majid Farhadloo</a></div> <div class="field__item"><a href="/taxonomy/term/216" hreflang="en">Shashi Shekhar</a></div> <div class="field__item"><a href="/taxonomy/term/221" hreflang="en">Len Kne</a></div> <div class="field__item"><a href="/taxonomy/term/226" hreflang="en">NTEP</a></div> <div class="field__item"><a href="/taxonomy/term/231" hreflang="en">National Turfgrass Evaluation Program</a></div> </div> </div> </div> </div> </div> <div class="col-md-4 radix-layouts-sidebar panel-panel"> <div > </div> </div> </div> </div> </div><!-- /.brenham-flipped --> Mon, 08 Jun 2020 17:14:18 +0000 monc0003 221 at https://lowinputturf.umn.edu