Progress 06/15/22 to 06/14/23
Outputs
Target Audience:Our target audience includes farmers, crop consultants, agro-industry representatives, undergraduate and graduate students, extension educators and specialists, data modelers and digital agriculture researchers and educators, and corn commodity board members. Changes/Problems:In 2022, we encountered challenges during our field trials in North Carolina, including the presence of Hessian fly and freezing temperatures that caused injury to early-season wheat varieties at some sites. In addition, a few plots had to be excluded from the analysis due to harvesting errors. In 2023, our field trials in North Carolina faced the challenge of variable stand caused by the presence of Hessian fly in some experimental sites. What opportunities for training and professional development has the project provided?The two Ph.D. students continued participating in the Rockey FFAR Fellows program, which offers a 3-year professional training program for graduates in the United States and Canada. They attended two residential sessions in Raleigh, North Carolina, in August 2022 and April 2023, with training focused on scientific communication and networking opportunities. Additionally, both students have been meeting regularly with their industry mentors to explore potential career paths in the industry. Furthermore, the graduate student at North Carolina State presented research information at two grower meetings (Piedmont Soybean Field Day on September 28th in Salisbury, NC, and the Hoke/Robeson/Scotland Corn and Soybean Meeting on February 23rd in Lumberton, NC.) How have the results been disseminated to communities of interest?The research focused on the systematic review for modeling Fusarium head blight epidemics was accepted as a poster presentation at Plant Health 2023 (American Phytopathological Society) and will be presented in Denver, Colorado, in August 2023. Field research in North Carolina will be presented as a poster at the 2023 ASA, CSSA, SSSA International Annual Meeting in St. Louis in November 2023. What do you plan to do during the next reporting period to accomplish the goals? Complete data wrangling for the PDMR reports and develop a statistical framework for the meta-analyses. Conduct a third year of field studies in North Carolina, collect all necessary data, and begin the analysis as part of the research aims forStagonospora nodorumblight prediction. Analyze data from research stations and on-farm trials to validate the Fusarium head blight risk tool. Initiate a second on-farm trial with current collaborating growers exploring the impact of late-season wheat diseases. Present results from different research trials at two professional meetings. Develop a platform template for incorporation in the OpenCropManager.
Impacts
What was accomplished under these goals?
The North Carolina project is embarking on its second field season, during which wheat trials were planted between late October and early November of 2022. In contrast to the previous season, weather conditions have provided sufficient moisture and temperature for wheat growth and disease initiation in our experiments. The evaluation protocol for a disease prediction model has been adjusted slightly, using four wheat cultivars instead of five, and the trials taking place across six locations as last year's. This change is due to a cultivar that experienced severe freeze damage during the previous season, which resulted in its removal from the experiment. All field activities for the 2022/2023 season were carried out on schedule, following the revised protocol developed by expert research advisors, as in the previous season. Inoculated areas showed a slightly earlier onset and higher disease levels, which will likely enhance our ability to evaluate the disease prediction model and make future model adjustments. Variation in disease incidence is desired for data modeling. If weather conditions permit, we aim to harvest all North Carolina trials by the end of July, and seeds for the 2023 season will be treated, packed, and planted between September and November of the same year. In addition to monitoring the field trials and collecting data, we have conducted preliminary data analysis and coding to investigate underlying climatic patterns linked to disease onset and development, as described in the previous report. In the upcoming phase, we plan to continue refining the statistical analyses as data from the 2023 season comes in. With all data gathered from these field trials, we aim to answer these questions: (1) How consistent and stable are cultivars in resistingStagonospora nodorumblotch (SNB) infection across multiple environments? (2) Are there any potential valuable climatic predictors at critical crop stage timings that explain SNB epidemics? In other words, what are critical timing and variables associated with disease initiation and progress? (3) Can we further improve SNB prediction by incorporating cultivar disease resistance information into the disease model? Here we can see how objective (1) may contribute to other objectives. (4) Can we reduce uncertainty in disease prediction and improve model performance using Bayesian inference? Describing biological processes using statistical models is a challenge. We would like to see if expert knowledge incorporated in the framework via prior specification would help us address that question. In conclusion, subsequent steps will involve collecting the remaining data for the 2023 season, continuing existing statistical analysis, and moving into later objectives as time allows. Apart from field trials, we have digitized the Plant Disease Management Reports (PDMR) and begun preliminary data analysis. Compiling data from multiple locations and diseases was challenging due to the non-standardized format of scientific reports. However, we have successfully digitized + 400 PDMRs representing fungicide efficacy trials conducted between 1999 and 2022. These reports contain quantitative information on fungicide effectiveness and cultivar tolerance for regionally prevalent wheat diseases in the United States, including states such as Kansas, Michigan, New York, Virginia, Kentucky, Alabama, Arkansas, Oklahoma, Illinois, Nebraska, North Dakota, Wisconsin, and Indiana. With this information, we can provide stakeholders with insights into fungicide efficacy over time and space and advance the development of a decision-support platform for wheat disease management at a national level. After completing other projects, we plan to analyze these data. Nine small grain trials were planted in Fall 2022 at two Penn State agricultural research stations in Pennsylvania. These trials are focused on the integrated management of Fusarium Head blight disease with genetic resistance and timely fungicide application. In a few weeks, disease ratings, harvesting, and postharvest seed processing will be carried out. These trials align with PhD research validating the Fusarium head blight risk tool. We are also examining how microclimates at the canopy and soil level affect FHB disease development. We have deployed iButtons to take temperature and humidity reading at soil and canopy levels. In collaboration with four wheat growers in Pennsylvania, we are conducting on-farm trials to see how fungicide application decisions for FHB influence late-season foliar diseases like rust and powdery mildew. We aim to understand how this decision affects wheat yield and quality. The wheat fields from these on-farm trials will also be used to validate the Fusarium risk tool. In our previous report, we mentioned that during our efforts to digitize the PDMRs, we discovered that many reports needed more crucial information, such as measurement errors or equivalent measurements of the evaluated treatments. This information is essential for conducting modern meta-analyses and accurately estimating treatment effects. To address this issue, one of the students on our project developed R codes that use available information in the reports, such as means and post-hoc tests, to estimate the missing variance from trials. To make these estimations reproducible and accessible to the scientific community, the codes were tested on simulated data and converted into an R shiny app publicly available athttps://garnica.shinyapps.io/MSE_FindR/. This research was presented at the Plant Health 2022 Conference in Pittsburgh, Pennsylvania, and the 2022 National Fusarium Head Blight Forum in Tampa, Florida. We are currently in the final stages of writing a manuscript introducing MSE FindR to the scientific community. With this tool, researchers from different disciplines may conveniently estimate variability from published reports with missing data, allowing for the inclusion of these reports in quantitative reviews. The systematic review is currently focused on Fusarium Head Blight prediction and forecasting models in wheat. The initial collection of FHB-related scientific papers from electronic databases such as Web of Science, Scopus, CABI, and PUBMED yielded 6769 articles. First stage screening for articles focused on predicting or forecasting FHB disease or DON accumulation is completed, with 40 articles selected. These articles are currently undergoing a detailed review of the models and their applications. Most of the articles chosen utilized weather variables for their predictions. Temperature and relative humidity were the most used predictors, with some models also incorporating rainfall data. This systematic review will provide valuable insight into the available FHB models. The findings of this study can help develop more accurate and reliable models for predicting FHB in wheat, which can aid in the effective management of this disease. Graduate student progress: One graduate student presented his doctoral proposal seminar in December 2022 and is expected to take his written and oral exams in June and July, respectively, after the completion of data collection. He has also fulfilled his coursework and teaching obligations, including the required hours for his Statistics minor as part of his North Carolina State University program. The second PhD student passed his qualifying exam in December 2022. He has completed all coursework required for his primary program - Plant Pathology, and will meet course requirements in International Agriculture and Development in Fall 2023.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Garnica, V.C., Shah, D.A., Esker, P., Ojiambo, P.S. (2022) Got Fisher's LSD or Tukey's HSD?: a R Shiny app tool for recovering variance in designed experiments when only mean and post-hoc tests are reported. APS Meeting, August 6-10, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Garnica, V.C., Shah, D.A., Esker, P., Ojiambo, P.S. (2022) MSE FindR: a R Shiny app tool for recovering variance in designed experiments using treatment means and post-hoc test results. 2022 National Fusarium Head Blight Forum, December 4-6, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Shittu, O.M., Cucak, M., Dalla Lana, F., Bucker Moraes, W.B., Paul, P.A., Shah, D.A., De Wolf, E.D., and Esker, P.D. (2022). Validation of the Fusarium head blight risk tool and its application in Pennsylvania. APS Meeting, 6-10 August 2022.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2023
Citation:
Shittu, O.M., Dalla Lana, F., Esker, P.D. (2023). Systematic review of predictive models for fusarium head blight in wheat. APS Meeting, 12-16 August 2023.
- Type:
Journal Articles
Status:
Other
Year Published:
2024
Citation:
Garnica, V.C., Shah, D.A., Esker, P., Ojiambo, P.S. (202x) MSE FindR: An R Shiny-based application to estimate mean square error using treatment means and post-hoc results. To be submitted to Methods in Ecology and Evolution.
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Progress 06/15/21 to 06/14/22
Outputs
Target Audience:Our target audience is multiple, including farmers, crop consultants, agro-industry representatives, undergraduate andgraduate students, extension educators and specialists, data modelers and digital agriculture researchers and educators,and corn commodity board members. Changes/Problems:During field trials in North Carolina in 2022, our challenges included the occurrence of Hessian fly and the impact of freezing temperatures on wheat growth, which caused injury to early season varieties in some experimental sites. With the departure of one postdoctoral team member for a faculty position, their efforts on the project were incorporated into the two PhD projects. Otherwise, there were no additional changes or problems with the project. What opportunities for training and professional development has the project provided?In addition to technical development, the two graduate students in the project have been selected to participate in the Rockey FFAR (Foundation for Food & Agricultural Research) Fellows program, a 3-year program aimed at providing professional training for graduate students in the United States and Canada. As part of the program's requirements, Ph.D. students will participate in multiple meetings and training sessions. Both will have a co-mentor with an industry professional, allowing them to learn about industry career options and develop professional connections during their Ph.D. program. To be selected for such a prestigious program, students had to network and secure industry support. How have the results been disseminated to communities of interest?One of the graduate students on the project presented their first seminar in the graduate colloquium during Spring 2022. Another of the students on the project is preparing their first seminar for presentation during Fall 2022. Two poster presentations will be delivered at Plant Health 22 (organized by the American Phytopathological Society.) What do you plan to do during the next reporting period to accomplish the goals? Carry out data analysis from field trials and write codes required for developing a decision support system. Initiate activities in the FFAR professional development program. Present new research in at least two professional meetings. Create a preliminary mechanistic model for testing that can be integrated into the development of the decision support system. Summarize information obtained from the systematic review and prioritize this information for potential integration in the decision support system.
Impacts
What was accomplished under these goals?
In North Carolina, wheat trials were established between the last week of October and the first two weeks of November 2021. Because of the relatively warmer temperatures in December, wheat development was slightly ahead of normal. Still, all field activities were carried out on time based on the protocol devised by researchers in the group. Despite low disease levels in non-inoculated areas, three to four disease assessments have been collected, and at the present moment, half of the trials have been harvested. We expect all wheat to be harvested by the end of this month, as the weather cooperates. In Pennsylvania, nine trials were established during Fall 2022 and focused on the integrated management of Fusarium head blight based on genetic resistance and fungicide (including biological) timing. All field activities were carried out appropriately, with an expected harvest in July 2022. For trials in both locations, preliminary data analysis will commence after harvest to explore the underlying climatic patterns associated with disease onset and development. Plans are also underway for 2022-2023. The combination of trials across the locations will be used to develop the decision support system. In addition to the field trials, the graduate students have finalized part of the digitalization of the Plant Disease Management Reports (PDMR) and initiated preliminary data analysis.This is a critical step before the implementation of more sophisticated statistical analyses. In total, 77 PDMRs representing 118 trials conducted between 2009 and 2020 have been digitalized. Reports provide quantitative information about fungicide efficacy and cultivar tolerance for many important diseases across major wheat-producing areas of the United States, including Kansas, Michigan, New York, Virginia, Kentucky, Alabama, Arkansas, Oklahoma, Illinois, Nebraska, North Dakota, Wisconsin, and Indiana. With this information, we will be able to provide stakeholders with insights into the efficacy of fungicides and progress with developing a decision support platform for disease management at a national level. During the digitalization of the PDMRs, it was noted that many of these reports failed to indicate standard information about treatment efficacy, such as mean error (or equivalent measurement). These parameters are regularly required to implement data synthesis methods and correctly estimate treatment effects. To solve this issue, one of the graduate students developed R code to estimate the missing variance from trials using other information available in the reports, such as the mean and post-hoc tests. As part of our goal to make these estimations reproducible and available to the scientific community, codes have been tested on simulated data and converted into an R shiny app that will become publicly accessible soon. The resulting product of this effort will be presented as a poster entitled "Got Fisher's LSD or Tukey's HSD?: an R Shiny app tool for recovering variance in designed experiments when only mean and post-hoc tests are reported." at Plant Health 2022 in Pittsburgh, Pennsylvania. With this tool, plant pathologists and researchers from other disciplines can conveniently estimate variability from published reports with missing error variance, enabling the inclusion of reports with missing data in quantitative reviews. A systematic map to explore the available literature on wheat disease prediction and forecasting models, crop loss assessment, and general management was performed for the most important diseases. Diseases selected for inclusion in the systemic map considered the frequency, prevalence, severity, and economic impact. Specific keywords and phrases were defined for each disease. To date, nine diseases have been selected for inclusion. Searches across multiple four search engines yielded 62,077 articles (range across diseases: 1,953 to 14,337). These articles are currently being screened for final selection for further analysis. In addition, one of the graduate students was selected to participate in a 10-day Bayesian course offered by the Natural Resource Ecology Laboratory at Colorado State University in June 2022. This training will be critical to equip the student with tools for complex statistical analysis, such as the PDMR reports, which contain complex underlying structures that can be informative from a disease management perspective and may be ignored using frequentist inference. Bayesian methods provide flexibility and enhance our ability to capture the uncertainty arising from ecological and epidemiological processes, which could be potentially helpful for disease prediction at diverse scales. We will be discussing opportunities to dissipate the knowledge among other students and post-doctorates in the group. A second graduate student participated in a week-long Decision Support System for Agrotechnology Transfer Workshop in Griffin, Georgia. This intensive workshop provided a base for exploring and developing mechanistic models, which will help explore and integrate some of the features that this modeling approach offers as part of developing the Decision Support System for wheat diseases. One postdoctoral member of the team was successful in securing a tenure-track faculty position. Their role in the project will be integrated into the research being conducted by the graduate students.
Publications
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2022
Citation:
Shittu, O.M., Cucak, M., Dalla Lana, F., Bucker Moraes, W.B., Paul, P.A., Shah, D.A., De Wolf, E.D., and Esker, P.D. (2022). Validation of the Fusarium head blight risk tool and its application in Pennsylvania. APS Meeting, 6-10 August 2022.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2022
Citation:
Garnica, V.C., Shah, D.A., Esker, P., Ojiambo, P.S. (2022) Got Fisher's LSD or Tukey's HSD?: a R Shiny app tool for recovering variance in designed experiments when only mean and post-hoc tests are reported. APS Meeting, 6-10 August 2022.
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Progress 06/15/20 to 06/14/21
Outputs
Target Audience:Our target audience is multiple, including farmers, crop consultants, agro-industry representatives, undergraduate and graduate students, extension educators and specialists, data modelers and digital agriculture researchers and educators, and corn commodity board members. Changes/Problems:During year 1, our major limiting factor was the impact of the COVID-19 pandemic, which caused delays in the starting dates for the two graduate students contributing to the project. Both were able to commence their studies, but one semester later than initially expected. Furthermore, one postdoctoral fellow on the project will begin later than expected as they continue to finish their Ph.D. thesis due to the pandemic's effects. What opportunities for training and professional development has the project provided?Graduates received training on Human Subjects Research and Responsible Conduct of Research. How have the results been disseminated to communities of interest?Two virtual conference presentations were made where the target audience in both cases were researchers in agronomy and crop science, and plant pathology. What do you plan to do during the next reporting period to accomplish the goals?Key plans moving into the next period: Focus on objectives 1 and 3, which are the continued development of the decision-support platform through integration of different data sources and the training of the next generation of epidemiologists. Both graduate students successfullly complete development of their thesis proposals. Test the updated plant disease epidemiology tools with graduate students at Penn State. Meet with the advisory committee and provide updates on the status of the project. Present new research at least at two professional meetings.
Impacts
What was accomplished under these goals?
Our team met with members of the advisory committee to discuss the project objectives and solicit their advice and comments about the project. This session was beneficial given that their questions, comments, and suggestions about different data sources and things to consider as we build the alpha version of the model. This session helped the graduate students begin meeting and knowing the different researchers that this project will reach from a networking perspective. Additionally, we commenced with the digitization ofPlant Disease Management Reportsfocused on wheat fungicide efficacy trials around the United States. The two graduate student projects focus on different timing windows for disease management decisions. Each has taken the lead on digitizing reports by the year and by target window based on wheat growth and development. They are also receiving training this summer on wheat and barley disease identification and quantification in the field using our existing trials. This provides preliminary data to define new research trials (University research farm and on-farm with grower cooperators) that will help provide new data for the development of the decision support system. Educational development: As part of a comprehensive training plan in plant disease epidemiology, we started developing open resource teaching materials to integrate this into an online web portal. We beta-tested this material in a course offered at the University of Costa Rica during Spring 2021. Based on the results of this course, materials will be updated for use in the epidemiology course offered at Penn State and through training workshops. This component links with a sister project, also funded by USDA.?
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Cucak, M., Dalla Lana, F., Ojiambo, P., De Wolf, D., Shah, E., Paul, P., Esker, P. (2020) Using Advanced Statistical Methods, Big Data and Open Science to Upgrade Current Crop Disease Management Decision Support Approaches. ASA, CSSA and SSSA International Annual Meeting, 9-13 November 2020.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Cucak, M., Dalla Lana, F., Ojiambo, P., De Wolf, E., Paul, P., Esker, P. (2020) Into the new era of decision support in crop protection: Multifaceted disease management advisors based on machine learning and open science. APS Meeting, 10-14 August 2020.
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