Progress 07/01/22 to 06/30/23
Outputs
Target Audience:Our target audience includes farmers, crop consultants, agro-industry representatives, undergraduateand graduate students, extension educators and specialists, data modelers and digital agriculture researchers andeducators, and corn commodity board members. Changes/Problems:Of all of the projects I lead, this project is the primary one most impacted by the effects of COVID-19. While we made excellent strides during the last project period, we are still behind our timeline. This was a function of delays in successfully onboarding key contributors to the project. We recognize that this project is being well-received, for example, the success of two Postdoctoral Scholars in securing academic and industry positions in epidemiology and data science. This is why we are requesting an additional no-cost extension to guarantee that the project's success over the past year can be taken to completion. What opportunities for training and professional development has the project provided?We integrated a wage payroll employee into the team to continue the development of the app tool. They successfully integrated into the team and have become the key collaborator for our efforts with the Institute of Computational and Data Science at Penn State. They are leading the efforts to finalize the app launch and incorporate the tool into the OpenCropManager platform we are developing. How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?We are requesting an additional no-cost extension, which will enable us to complete the following items for the project: Collect scouting data to verify app utility. This will include farmer locations, along with empirical research trial locations. Collaborate with ICDS on incorporating Corn Scout into the OpenCropManager. Obtain feedback on app utility from targeted stakeholders. Incorporate feedback and update Corn Scout via the Open Crop Manager. Launch Corn Scout via the Open Crop Manager for the 2024 growing season.
Impacts
What was accomplished under these goals?
Building from new knowledge generated in our previous reporting period focusing on a systematic review, combined with efforts on a sister project to develop new scouting and reporting software, we completed the template and EpiCollect5 app Corn Scouting.Our tool provides for capturing geographic location (latitude and longitude) and works on- or offline. The platform was developed to capture the growth stage and information about abiotic and biotic stressors. We created this tool and supporting guide information to enable scouts to assess the disease, give an estimate of the severity, and capture images that can be used for follow-up validation. The user can also provide estimates of plant population and other notes about the crop condition. The user can give multiple reports (i.e., different pests or diseases) at a single sampling location. All reports are synced to a Google Drive database (cropriskstatus@gmail.comis the host). We also successfully secured additional funding for a sister soybean project, which provided us the means to collaborate with Penn State's Institute for Computational and Data Science (ICDS) on developing an open-source scouting platform that improves our current system by providing a centralized data collection structure and incorporating additional tools for growers. We have established the collaboration that will enable the integration of theCorn Scout app into the platform. This provides the mechanism tobe well-positioned to expand on our efforts to give the farmers near real-time scouting data while developing the database structure for long-term modeling efforts to understand risk factors that drive corn production across the U.S. Lastly, this project supported the efforts of one Postdoctoral Scholar, who, given their actions, was contracted to work in data science and agriculture for a major multinational company.
Publications
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Progress 07/01/21 to 06/30/22
Outputs
Target Audience:Our target audence is multiple, including: farmers, crop consultants, agro-industry representatives, undergraduate andgraduate students, extension educators and specialists, datamodelers and digital agriculture researchers and educators,and corn commodity board members. Changes/Problems:Due to the impact and subsequent restrictions due to COVID-19, our project suffered substantial delays in finalizing contracts with scientific staff leading the project. Interviews were conducted during Fall 2019, with the expected starting date during Spring 2020. Due to restrictions, we could not have someone commence on the project until October 2020, 14-15 months after the original start date. A second person who was offered a position on the project during the Fall of 2019 after interviewing could not begin until March 2021. We have only recently been able to make progress on building the conceptual model for the project, including conducting an extensive systematic review of the literature related to disease forecasting systems for corn diseases. What opportunities for training and professional development has the project provided?Both postdoctoral scholars spent the last year network building, including with colleagues in the library, data, and computational sciences. Furthermore, they took lead roles in collaborating with an extensive network of team members for several similar projects. How have the results been disseminated to communities of interest?The conceptual model for the project was presented at a professional conference (virtual) in 2021. Additionally, we use the information generated in this project for building new networks that have parallel sets of questions and needs for improved data pipelines. What do you plan to do during the next reporting period to accomplish the goals?Our focus during the next reporting period includes efforts on objectives 1, 2, and 4. These will drive the data collection and reporting system to generate new data for objective 3.?
Impacts
What was accomplished under these goals?
A systematic map to overview the available literature on the corn disease prediction and forecasting models, crop loss assessment, and general management were performed for the most important U.S. The disease selected to be included in the systemic map considered the frequency, prevalence, severity, and economic impact.In addition to the project members ' expertise, studies such as Savary et al. (2019) and Mueller et al. (2020)wereused to select 16 diseases. QueriesusingkeywordsandBoolean operators were used foreach disease and each database. Askeywords,we use the common name of the disease and itsvariationsand the scientific name of the pathogen, includingpastnamesand thecommon and scientific nameofmaize(maize,corn, andZeamays).When multiple pathogens cause the same disease, all its names were used in the query. Common and pathogen names are based on the list from theAmerican Phytopathological Societyand listed in Table 1(www.apsnet.org/edcenter/resources/commonnames/Pages/Corn.aspx). Anexamplefor gray leaf spotis shownbelow: ("Gray leaf spot" OR "Grey leaf spot" OR "Cercospora zeae-maydis" OR "Cercospora zeina") AND (corn OR maize OR "Zea mays") Operators "OR" indicate that both terms can be present, and operator "AND" indicate that both terms must be present. The names within parentheses and quotation marks are a single element level. The queries search for the presence of these words in the title, abstract, and keywords. Therefore,anystudiesthatcontain the name or disease or its pathogen andthe name of the crop in the title, abstract, or keywordsshould be included in thissearch.In addition, results were filtered to include only studies written in English and from peer-reviewsources.+ The queries were replicatedon fourpeer-review databases, Scopus, Web of Science, CABI, and PUBMED, and safe in a format that reference software could read(.risor .txt).Allqueries were done on December 17, 2021.Across 16 diseases, there were 37,705 potential articles identified (the range across diseases was from 92 to 8,718.) Results were then combinedper disease,andduplications were removed. First,we used the software MendeleyDesktop, then the platform Rayyan (www.rayyan.ai). Thefirst screeningwasbased only on the title and abstractof studies that could potentially be classified asstudies that can be used incorn disease prediction and forecasting models, crop loss assessment, and general management. The nextsteps consist ofmore rigorous screening by readingall studiespre-selectedand classifyingthem into the three categories. The final step will be to produce a report of these findings.The results of this systematic review will be helpful to identify understudy diseases or topics, agglutinatethe knowledge, and serve as a base forsimulations anda diseasesupport tool. Following a sister project funded by one of our commodity stakeholders where we developed a sampling and scouting platform for soybean pests, we are working to create a parallel platform for corn pests. This will providethe system for collecting long-term databases in (near) real-time data for dissemination to stakeholders. We aim to reduce the repetitive processes in such systems by merging the field data collection with cloud-based database pipelines that can link with other agronomic and pest management sources of information. This aligns closely with our objectives 1 and 2 and will feed objective 3 as more data are obtained. These research and outreach efforts will continue to move the platform to field application over the next year.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Cucak, M., F. Dalla Lana, O.M. Shittu, V.C. Garnica, P. Ojiambo, E. De Wolf, D. Shah, P. Paul, and P. Esker. 2021. Development and integration of the new-age decision support in crop disease protection. 2021 APS Annual Meeting Research On-Demand.
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Progress 07/01/20 to 06/30/21
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 2, our major limiting factor was the impact of the COVID-19 pandemic, which caused delays in the starting dates for the two postdoctoral fellows contributing to the project. We are now better positioned to continue with the proposed research and are moving forward with several new ideas. Nonetheless, the project will most likely require a no-cost extension to achieve the proposed goals of the project. What opportunities for training and professional development has the project provided?Postdoctoral scholars have participated in training at Penn State, including the essentials for online teaching and supporting accommodations for online learners. They have also participated in 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 for the next reportingperiod: Continue development of the decision support system through integration of different data sources. Design the template for the decision support system. Multiple papers are in development focused on the current state of decision support systems and the challenges with their implementation. Complete the online training program in plant disease epidemiology and integrate this tool into the Plant Disease Epidemiology course at Penn State. Reach out to cooperators around the U.S. regarding additional data sources for consideration for the decision support system. Present new research at least at two professional meetings.
Impacts
What was accomplished under these goals?
Database development: Our team met with the iPiPE team to discuss the current status of the modeling platform and weather data availability and format. We also commenced with the digitization ofPlant Disease Management Reports, integrated with fungicide efficacy charts, to determine the effect of specific fungicide active ingredients on the risk and need for such compounds in corn. Furthermore, we have examined corn performance trial data, including public and private sector data. This data will be imperative to establish yield potential due to genetics and disease resistance incorporated into commercial material. We have also examined environmental data for the period 1990-current to establish seasonal trends for different growing years and how they impacted corn production. Educational development: As part of a comprehensive training plan in plant disease epidemiology, we started a project to develop 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.?
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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Progress 07/01/19 to 06/30/20
Outputs
Target Audience:Our target audence 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:As indicated in other sections, we did an active recruitment to fill the primary postdoctoral research fellow position. We had two excellent candidates, as such we made both offers as we felt this would provide the best avenue for successful completion of this project, along with sister projects recently funded. Nonetheless, in both situations, the candidates were in the latter stages of finishing their doctoral dissertations. Both will join the program during the first six months of 2020. We felt this was acceptable since we were most interested in having the best candidates that could commence with the project. What opportunities for training and professional development has the project provided?
Nothing Reported
How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?With two new researchers starting on the project, we expect an exponential increase in activities in the next reporting period. We will focus principally on objectives (1) and (2) in the next reporting period.
Impacts
What was accomplished under these goals?
We successfully recruited two postdoctoral researchers to the project. In both cases, they are currently finishing their doctoral disseratations, as such, both will commence in the program during the first six months of 2020. We felt that it was more important to recruit the best researchers to the project, especially those who matched to the project goals and objectives. We also have been working with the iPiPE team (another USDA-funded project) to establish the proper network for the arrival of the new postdocs to integrate with this group as we build the new decision support platforms.We have agreements for them to spend time with the iPiPE team to develop the necessary collaborations as outlined in the original proposal.
Publications
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Progress 05/01/19 to 04/30/20
Outputs
Target Audience:Our target audence 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:As indicated in other sections, we did an active recruitment to fill the primary postdoctoral research fellow position. We had two excellent candidates, as such we made both offers as we felt this would provide the best avenue for successful completion of this project, along with sister projects recently funded. Nonetheless, in both situations, the candidates were in the latter stages of finishing their doctoral dissertations. Both will join the program during the first six months of 2020. We felt this was acceptable since we were most interested in having the best candidates that could commence with the project. What opportunities for training and professional development has the project provided?
Nothing Reported
How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?With two new researchers starting on the project, we expect an exponential increase in activities in the next reporting period. We will focus principally on objectives (1) and (2) in the next reporting period.
Impacts
What was accomplished under these goals?
We successfully recruited two postdoctoral researchers to the project. In both cases, they are currently finishing their doctoral disseratations, as such, both will commence in the program during the first six months of 2020. We felt that it was more important to recruit the best researchers to the project, especially those who matched to the project goals and objectives. We also have been working with the iPiPE team (another USDA-funded project) to establish the proper network for the arrival of the new postdocs to integrate with this group as we build the new decision support platforms.We have agreements for them to spend time with the iPiPE team to develop the necessary collaborations as outlined in the original proposal.
Publications
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